CN112200671A - Method and device for detecting transaction fluctuation, storage medium and electronic equipment - Google Patents
Method and device for detecting transaction fluctuation, storage medium and electronic equipment Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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Abstract
The embodiment of the application discloses a method and a device for detecting transaction fluctuation, a storage medium and electronic equipment. The method comprises the following steps: receiving an asset transaction request of a financial product; wherein the asset transaction request includes asset information; loading the asset information into an information chain; the information chain is generated by adopting at least two information storage units according to a safety storage rule; executing response operation of the transaction request to obtain transaction information; and converting the transaction information to obtain transaction data; and dividing the training set and the test set of the transaction data, and inputting the training set and the test set to a preset model to obtain output error data so as to perform transaction fluctuation detection. According to the technical scheme, a new technical means for detecting transaction fluctuation is adopted, and the financial management system transaction can be detected no matter whether the model is changed or fails.
Description
Technical Field
The embodiment of the application relates to the technical field of transaction fluctuation detection, in particular to a method and a device for detecting transaction fluctuation, a storage medium and electronic equipment.
Background
With the development of economy and the advancement of science and technology, financial consciousness of people is gradually enhanced, and more users begin to invest idle funds into purchasing financial products without being limited to the traditional regular or current savings.
In order to ensure the safety of assets in financial products, banks have models which are specially used for detecting financial system transactions.
Over time, there are models that are no longer satisfactory for the current transaction and that need to be optimized or reconstructed to continue to detect transaction fluctuations for the financial system.
Disclosure of Invention
The embodiment of the application provides a method, a device, a storage medium and electronic equipment for detecting transaction fluctuation, and the financial system transaction can be detected no matter whether a model changes or fails by adopting a new technical means for detecting transaction fluctuation.
In a first aspect, an embodiment of the present application provides a method for detecting transaction fluctuation, where the method includes:
receiving an asset transaction request of a financial product; wherein the asset transaction request includes asset information;
loading the asset information into an information chain; the information chain is generated by adopting at least two information storage units according to a safety storage rule;
executing response operation of the transaction request to obtain transaction information; and converting the transaction information to obtain transaction data;
and dividing the training set and the test set of the transaction data, and inputting the training set and the test set to a preset model to obtain output error data so as to perform transaction fluctuation detection.
In a second aspect, an embodiment of the present application provides an apparatus for detecting transaction fluctuation, the apparatus including:
the asset transaction request receiving module is used for receiving an asset transaction request of a financial product; wherein the asset transaction request includes asset information;
the asset information loading module is used for loading the asset information into an information chain; the information chain is generated by adopting at least two information storage units according to a safety storage rule;
the transaction data acquisition module is used for executing response operation of the transaction request to obtain transaction information; and converting the transaction information to obtain transaction data;
and the error data acquisition module is used for dividing the transaction data into a training set and a test set and inputting the training set and the test set into a preset model to obtain output error data so as to detect transaction fluctuation.
In a third aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for detecting transaction fluctuations as described in embodiments of the present application.
In a fourth aspect, embodiments of the present application provide an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method for detecting transaction fluctuation according to embodiments of the present application.
According to the technical scheme provided by the embodiment of the application, an asset transaction request of a financial product is received, asset information is loaded into an information chain, the information chain is at least two information storage units generated by adopting a safety storage rule, response operation of the transaction request is executed to obtain transaction information, the transaction information is converted to obtain transaction data, after the transaction data is obtained, the transaction data is divided into a training set and a testing set, and the training set and the testing set are input into a preset model to obtain output error data, so that transaction fluctuation detection is carried out. By executing the technical scheme provided by the application, a new technical means for detecting transaction fluctuation is adopted, and the financial system transaction can be detected no matter whether the model is changed or invalid.
Drawings
FIG. 1 is a flow chart of a method for detecting transaction fluctuations as provided in an embodiment of the present application;
FIG. 2 is a schematic diagram of a process for detecting transaction fluctuation provided in the second embodiment of the present application;
FIG. 3 is a schematic structural diagram of an apparatus for detecting transaction fluctuation according to a third embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example one
Fig. 1 is a flowchart of a method for detecting transaction fluctuation according to an embodiment of the present application, where the present embodiment is applicable to a case of detecting transaction fluctuation, and the method may be executed by an apparatus for detecting transaction fluctuation according to an embodiment of the present application, where the apparatus may be implemented by software and/or hardware, and may be integrated in an intelligent terminal or the like for detecting transaction fluctuation.
As shown in fig. 1, the method for detecting transaction fluctuation includes:
s110, receiving an asset transaction request of a financial product; wherein the asset transaction request includes asset information.
The asset information may be various product information of the bank. For example, the fixed deposit information, the lease information, and the loan information. The fixed-term deposit refers to a deposit in which a bank and a depositor agree a term and an interest rate in advance during deposit and draw out the original information after the term is reached. Lease refers to the act of a lessor giving away the asset usage rights to a lessee for a contracted period of time to obtain a lease amount. Loans are a form of credit activity in which a bank or other financial institution borrows monetary funds at a rate and must return.
In this embodiment, the asset transaction request of the financial product may be a click operation, or an off-line operation of a bank.
S120, loading the asset information into an information chain; the information chain is at least two information storage units generated by adopting a safety storage rule.
In this embodiment, the information chain may be a serial transaction record that is cryptographically connected in series and protects the content, and is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
It will be appreciated that asset information may be distributed in tokenized form by loading the asset information into the chain. Tokenization is a process of converting rights and benefits into specific units of value, such as converting assets into digitized tokens that run on a bitcoin blockchain.
In this embodiment, the information chain includes at least two information storage units, which are used to record all asset transaction information of each node in the information chain within a certain time. For example, it can be used to record interest, principal, etc. that needs to be paid when they expire within a certain time.
S130, executing response operation of the transaction request to obtain transaction information; and converting the transaction information to obtain transaction data.
In this embodiment, the transaction information may be information generated when assets in the chain of information are transacted. For example, the transaction information may be information of interest due for payment.
In this embodiment, the transaction data may be data obtained by performing rule processing on the transaction information and may be used for model verification.
In this embodiment, the response operation of the transaction request is executed, and the transaction of the asset information in the information chain may be recorded to obtain the transaction information. After the transaction information is obtained, the transaction information is converted, and transaction data can be obtained.
In this technical solution, optionally, the converting the transaction information into transaction data includes:
determining target transaction information in the information chain within preset time;
and converting the target transaction information to obtain transaction data.
The preset time may be 1 day, 3 days, or 5 days. Preferably, the preset time may be 1 day. The target transaction information of 1 day in the information chain can meet the requirement of transaction fluctuation detection.
By setting the preset time, the target transaction information in the preset time is determined, and the target transaction information is converted into transaction data, so that the transaction fluctuation detection requirement can be met, and data support can be timely provided for transaction detection.
In this technical solution, optionally, the converting the target transaction information into transaction data includes:
and converting the target transaction information in the information chain into transaction data according to a modeling rule.
In this embodiment, the modeling rules may be the types of data required for different verification models. For example, some models require data in the form of column vectors, while some models require data in the form of row vectors.
In this embodiment, the target transaction information may be information generated when the financial asset is transacted, and the format of the information may not meet the transaction detection requirement, and the target transaction information needs to be converted into transaction data capable of meeting the requirement according to the modeling rule. For example, the target transaction information may be converted to transaction data in the form of a column vector according to a modeling rule.
The target transaction data is converted into transaction data according to the modeling rule, and data support is provided for subsequent transaction detection.
And S140, dividing the transaction data into a training set and a testing set, and inputting the training set and the testing set into a preset model to obtain output error data so as to perform transaction fluctuation detection.
In this embodiment, the pre-set model may be a model used to detect asset transaction changes. For example, the preset model may be a regression model, a linear model, or the like. The regression model is a mathematical model that quantitatively describes the statistical relationship. The linear model includes a linear regression model and an analysis of variance model, and can be used for economy, management and the like.
In this embodiment, the error data may be data generated when the asset transaction fluctuates.
Where the test set is a data set used to verify the performance of the finally selected model.
Where the training set is the data set used to estimate the model.
In this embodiment, the transaction data is divided into a training set and a test set, and the data is input into the preset model, where the training set can train the preset model, and the test set can be used to estimate the result of the preset model, so as to obtain the output error data.
In this technical solution, optionally, the dividing of the training set and the test set is performed on the transaction data, and the transaction data is input to a preset model to obtain output error data, so as to perform transaction fluctuation detection, including:
and dividing a training set and a test set by adopting K-fold cross validation, and inputting the training set and the test set to a preset model to obtain output error data so as to detect transaction fluctuation.
The cross validation can be that the original data is grouped under a certain meaning, one part is used as a training set, the other part is used as a validation set, firstly, the training set is used for training the classifier, and then, the model obtained by training is tested by using the test set, so that the model is used as a performance index for evaluating the classifier.
In the embodiment, the K-fold cross validation is to divide the data set into K parts in equal proportion, one of the K parts is used as test data, and the other K-1 parts are used as training data.
Through adopting K-fold cross validation to divide data into training set and test set to input and predetermine the error data that the model obtained the output, carry out transaction fluctuation and detect, not only the data bulk requirement is few, and each data can all be regarded as test set test model moreover, avoids because data cause causes the error, has improved the degree of accuracy that the transaction detected.
In this technical scheme, optionally, adopt K-fold cross validation to carry out the partition of training set and test set to input to preset model and obtain the error data of output, in order to carry out transaction fluctuation detection, include:
performing at least one type of division processing of a training set and a test set on the transaction data by adopting K-fold cross validation, and inputting the result of the at least one type of division processing into a preset model to obtain at least one output error data;
averaging the at least one error data to obtain a composite error data for detecting transaction fluctuations.
The transaction data is divided into the training set and the test set by adopting at least one dividing method, and the transaction data can be used as the training set and the test set, so that the accuracy of transaction fluctuation detection is improved.
In this technical solution, optionally, performing at least one type of partition processing on the transaction data by using K-fold cross validation, and inputting a result of the at least one type of partition processing to a preset model to obtain at least one output error data includes:
dividing the transaction data into K parts, taking K-1 parts of data as a training set, and taking the other 1 part of data as a test set; wherein K is a positive integer greater than or equal to 2;
and inputting the result of the at least one type of division processing into a preset model to obtain at least one output error data.
Dividing the transaction data into K parts, wherein K-1 part of the data is used as a training set, and the other 1 part of the data is used as a testing set, so that at least one error data can be obtained. Different transaction data are divided, a plurality of error data can be obtained, and errors caused by data reasons can be avoided.
In this technical solution, optionally, after dividing the transaction data into K shares, the method further includes:
and carrying out random division on the K transaction data for K times, so that K-1 training set and 1 test set exist in the division result.
And each data is randomly divided, so that each data can be possibly used as a test set, and the accuracy of transaction fluctuation detection is improved.
In this technical solution, optionally, after the transaction fluctuation detection, the method further includes:
it is determined whether there is an abnormal operation.
The abnormal operation may be that the user with high authority gives the user with low authority to use the abnormal operation, or that the abnormal operation is frequently transacted.
In this embodiment, when it is determined that there is an abnormal operation, the transaction may be closed to protect the security of the asset.
User assets may be protected by processing asset transactions by determining whether an abnormal operation exists.
In this technical solution, optionally, after determining whether there is an abnormal operation, the method further includes:
and if the abnormal operation exists, searching the abnormal operation information from the information chain so as to ensure the safety of the assets.
In the present embodiment, when it is determined that there is an abnormal operation, the information of the abnormal operation is found from the information chain, and the cause of the abnormal operation can be determined.
By searching the abnormal operation information, which abnormal operations can be determined, the abnormal operations are mastered, and the asset safety can be protected.
According to the technical scheme provided by the embodiment of the application, an asset transaction request of a financial product is received, asset information is loaded into an information chain, the information chain is at least two information storage units generated by adopting a safety storage rule, response operation of the transaction request is executed to obtain transaction information, the transaction information is converted to obtain transaction data, after the transaction data is obtained, the transaction data is divided into a training set and a testing set, and the training set and the testing set are input into a preset model to obtain output error data, so that transaction fluctuation detection is carried out. By executing the technical scheme provided by the application, a new technical means for detecting transaction fluctuation is adopted, and the financial system transaction can be detected no matter whether the model is changed or invalid.
Example two
Fig. 2 is a schematic diagram of a process of detecting transaction fluctuation in the second embodiment of the present invention, and the second embodiment is further optimized based on the first embodiment. The concrete optimization is as follows: loading the asset information into an information chain, comprising: the asset information is loaded into at least two chains of information using STO technology. The details which are not described in detail in this embodiment are shown in the first embodiment. As shown in fig. 2, the method comprises the steps of:
s210, receiving an asset transaction request of a financial product; wherein the asset transaction request includes asset information.
And S220, loading the asset information into at least two information chains by adopting an STO technology.
In this embodiment, STO is a technology of internally issued tokens, and directly hooks actual financial securities, and assets such as stocks, bonds, and periodic securities in a financial system. And accepts the administration of an internal regulatory body. In the formal transaction, the asset objects are loaded into the information chain in the form of tokens and issued by taking the certificates as carriers. Where a pass is a digital form of a rights voucher representing a right, an inherent and inherent value. The certificate can represent all rights and interests which can be digitalized.
In the embodiment, the asset information is loaded into at least two information chains by adopting the STO technology, the asset information can be loaded into the information chains in a tokenized form, and the asset information can be issued by taking a certificate as a carrier.
In this technical solution, optionally, the loading the asset information into at least two information chains by using the STO technology includes:
verifying whether the asset information is valid;
if yes, the financial asset information is loaded into at least two information chains in the form of tokens by adopting an STO technology.
In this embodiment, it is first necessary to verify if the asset information is valid to load the financing asset information in token form into at least two information chains using the STO technique. Wherein, the asset information verification may be the verification of the asset information by an information storage unit in the information chain. Asset information may only be added to the information chain when it is valid.
By verifying the asset information, the asset information can be loaded into the information chain only when the asset information is valid, thereby improving the efficiency of asset verification.
In this technical solution, optionally, whether the asset information is valid is verified through an information storage unit signature in an information chain.
In this embodiment, the signature may be a digital signature, mainly to prevent the information and data of the sending node from being maliciously forged and tampered. The process of digital signature is to use a certain algorithm to automatically generate a pair of public key and private key, the public key can be given to all people, and the private key is reserved. The information sender encrypts the information by using a private key and sends the information out. The receiver decrypts the received information with the public key to obtain the real information.
The information chain comprises a plurality of information storage units, and the asset information is signed through the information storage units to verify whether the asset information is valid, so that the asset verification efficiency is improved.
S230, executing response operation of the transaction request to obtain transaction information; and converting the transaction information to obtain transaction data.
And S240, dividing the transaction data into a training set and a testing set, and inputting the training set and the testing set into a preset model to obtain output error data so as to perform transaction fluctuation detection.
According to the technical scheme provided by the embodiment of the application, firstly, an asset trading request of a financial product is received, asset information is loaded into at least two information chains by adopting an STO technology, response operation of the trading request is executed to obtain trading information, the trading information is converted to obtain trading data, after the trading data is obtained, the trading data is divided into a training set and a testing set, and the training set and the testing set are input into a preset model to obtain output error data, so that trading fluctuation detection is carried out. By executing the technical scheme provided by the application, a new technical means for detecting transaction fluctuation is adopted, and the financial system transaction can be detected no matter whether the model is changed or invalid.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an apparatus for detecting transaction fluctuation according to a third embodiment of the present application, and as shown in fig. 3, the apparatus includes:
an asset transaction request receiving module 310, configured to receive an asset transaction request of a financial product; wherein the asset transaction request includes asset information;
an asset information loading module 320 for loading the asset information into an information chain; the information chain is generated by adopting at least two information storage units according to a safety storage rule;
the transaction data acquisition module 330 is configured to execute a response operation of the transaction request to obtain transaction information; and converting the transaction information to obtain transaction data;
and the error data acquisition module 340 is configured to perform training set and test set division on the transaction data, and input the transaction data to a preset model to obtain output error data, so as to perform transaction fluctuation detection.
In this embodiment, optionally, the asset information loading module 320 includes:
and the asset information loading submodule is used for loading the asset information into at least two information chains by adopting an STO technology.
In this technical solution, optionally, the asset information loading submodule includes:
an asset information verification subunit, configured to verify whether the asset information is valid;
and the asset information loading subunit is used for loading the financial asset information into at least two information chains in a token form by adopting an STO technology if the asset information loading subunit is used for loading the financial asset information into at least two information chains in the token form.
In this technical solution, optionally, the asset information verification subunit is specifically configured to:
and verifying whether the asset information is valid or not through the information storage unit signature in the information chain.
In this technical solution, optionally, the transaction data obtaining module 330 includes:
the target transaction information determining submodule is used for determining target transaction information in the information chain within preset time;
and the transaction data acquisition submodule is used for converting the target transaction information to obtain transaction data.
In this technical solution, optionally, the transaction data obtaining sub-module is specifically configured to:
and converting the target transaction information in the information chain into transaction data according to a modeling rule.
In this technical solution, optionally, the error data obtaining module 340 includes:
and the error data acquisition submodule is used for dividing the training set and the test set by adopting K-fold cross validation, inputting the training set and the test set into a preset model to obtain output error data and carrying out transaction fluctuation detection.
In this technical solution, optionally, the error data obtaining sub-module includes:
the error data acquisition subunit is used for carrying out at least one type of division processing on the transaction data by adopting K-fold cross validation and inputting the result of the at least one type of division processing into a preset model to obtain at least one output error data;
and the comprehensive error data acquisition subunit is used for averaging the at least one error data to obtain comprehensive error data so as to detect transaction fluctuation.
In this technical solution, optionally, the error data obtaining subunit is specifically configured to:
dividing the transaction data into K parts, taking K-1 parts of data as a training set, and taking the other 1 part of data as a test set; wherein K is a positive integer greater than or equal to 2;
and inputting the result of the at least one type of division processing into a preset model to obtain at least one output error data.
In this technical solution, optionally, the error data obtaining subunit further includes:
and carrying out random division on the K transaction data for K times, so that K-1 training set and 1 test set exist in the division result.
In this technical solution, optionally, the apparatus further includes:
and the abnormal operation determining module is used for determining whether abnormal operation exists.
In this technical solution, optionally, the apparatus further includes:
and the abnormal operation information determining module is used for searching the abnormal operation information from the information chain if the abnormal operation exists so as to ensure the safety of the assets.
The product can execute the method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.
Example four
Embodiments of the present application also provide a storage medium containing computer-executable instructions that, when executed by a computer processor, perform a method of detecting transaction fluctuations, the method comprising:
receiving an asset transaction request of a financial product; wherein the asset transaction request includes asset information;
loading the asset information into an information chain; the information chain is generated by adopting at least two information storage units according to a safety storage rule;
executing response operation of the transaction request to obtain transaction information; and converting the transaction information to obtain transaction data;
and dividing the training set and the test set of the transaction data, and inputting the training set and the test set to a preset model to obtain output error data so as to perform transaction fluctuation detection.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the computer system in which the program is executed, or may be located in a different second computer system connected to the computer system through a network (such as the internet). The second computer system may provide the program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application contains computer-executable instructions, and the computer-executable instructions are not limited to the operation of detecting transaction fluctuation as described above, and may also perform related operations in the method of detecting transaction fluctuation provided in any embodiment of the present application.
EXAMPLE five
The embodiment of the application provides electronic equipment, and the device for detecting transaction fluctuation provided by the embodiment of the application can be integrated into the electronic equipment. Fig. 4 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present application. As shown in fig. 4, the present embodiment provides an electronic device 400, which includes: one or more processors 420; a storage device 410 for storing one or more programs that, when executed by the one or more processors 420, cause the one or more processors 420 to implement a method for detecting transaction fluctuations as provided by embodiments of the present application, the method comprising:
receiving an asset transaction request of a financial product; wherein the asset transaction request includes asset information;
loading the asset information into an information chain; the information chain is generated by adopting at least two information storage units according to a safety storage rule;
executing response operation of the transaction request to obtain transaction information; and converting the transaction information to obtain transaction data;
and dividing the training set and the test set of the transaction data, and inputting the training set and the test set to a preset model to obtain output error data so as to perform transaction fluctuation detection.
Of course, those skilled in the art will appreciate that the processor 420 may also implement aspects of the method for detecting transaction fluctuations provided in any of the embodiments of the present application.
The electronic device 400 shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 4, the electronic device 400 includes a processor 420, a storage device 410, an input device 430, and an output device 440; the number of the processors 420 in the electronic device may be one or more, and one processor 420 is taken as an example in fig. 4; the processor 420, the storage device 410, the input device 430, and the output device 440 in the electronic apparatus may be connected by a bus or other means, and are exemplified by a bus 450 in fig. 4.
The storage device 410 is a computer-readable storage medium for storing software programs, computer-executable programs, and module units, such as program instructions corresponding to the method for detecting transaction fluctuation in the embodiments of the present application.
The storage device 410 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the storage 410 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, storage 410 may further include memory located remotely from processor 420, which may be connected via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 430 may be used to receive input numbers, character information, or voice information, and to generate key signal inputs related to user settings and function control of the electronic device. The output device 440 may include a display screen, speakers, or other electronic equipment.
The electronic equipment provided by the embodiment of the application can achieve the purposes of improving the speed of detecting transaction fluctuation and improving the processing effect.
The device for detecting transaction fluctuation, the storage medium and the electronic device provided in the above embodiments can execute the method for detecting transaction fluctuation provided in any embodiment of the present application, and have corresponding functional modules and beneficial effects for executing the method. The technical details not described in detail in the above embodiments may be referred to a method for detecting transaction fluctuation provided in any of the embodiments of the present application.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.
Claims (15)
1. A method of detecting transaction fluctuations, comprising:
receiving an asset transaction request of a financial product; wherein the asset transaction request includes asset information;
loading the asset information into an information chain; the information chain is generated by adopting at least two information storage units according to a safety storage rule;
executing response operation of the transaction request to obtain transaction information; and converting the transaction information to obtain transaction data;
and dividing the training set and the test set of the transaction data, and inputting the training set and the test set to a preset model to obtain output error data so as to perform transaction fluctuation detection.
2. The method of claim 1, wherein loading the asset information into an information chain comprises:
the asset information is loaded into at least two chains of information using STO technology.
3. The method of claim 2, wherein loading the asset information into at least two chains of information using STO techniques comprises:
verifying whether the asset information is valid;
if yes, the financial asset information is loaded into at least two information chains in the form of tokens by adopting an STO technology.
4. The method of claim 3, wherein verifying that the asset information is valid comprises:
and verifying whether the asset information is valid or not through the information storage unit signature in the information chain.
5. The method of claim 1, wherein converting the transaction information into transaction data comprises:
determining target transaction information in the information chain within preset time;
and converting the target transaction information to obtain transaction data.
6. The method of claim 5, wherein converting the target transaction information into transaction data comprises:
and converting the target transaction information in the information chain into transaction data according to a modeling rule.
7. The method of claim 1, wherein the dividing of the training set and the testing set is performed on the transaction data, and the input of the training set and the testing set into the preset model results in output error data for transaction fluctuation detection, comprising:
and dividing a training set and a test set by adopting K-fold cross validation, and inputting the training set and the test set to a preset model to obtain output error data so as to detect transaction fluctuation.
8. The method of claim 7, wherein the dividing of the training set and the testing set is performed by using K-fold cross validation, and the dividing is input into a preset model to obtain output error data for transaction fluctuation detection, comprising:
performing at least one type of division processing of a training set and a test set on the transaction data by adopting K-fold cross validation, and inputting the result of the at least one type of division processing into a preset model to obtain at least one output error data;
averaging the at least one error data to obtain a composite error data for detecting transaction fluctuations.
9. The method of claim 8, wherein performing at least one partitioning process of a training set and a testing set on the transaction data by using K-fold cross validation, and inputting the result of the at least one partitioning process to a preset model to obtain at least one error data output, comprises:
dividing the transaction data into K parts, taking K-1 parts of data as a training set, and taking the other 1 part of data as a test set; wherein K is a positive integer greater than or equal to 2;
and inputting the result of the at least one type of division processing into a preset model to obtain at least one output error data.
10. The method of claim 9, wherein after dividing the transaction data into K shares, the method further comprises:
and carrying out random division on the K transaction data for K times, so that K-1 training set and 1 test set exist in the division result.
11. The method of claim 1, wherein after transaction fluctuation detection, the method further comprises:
it is determined whether there is an abnormal operation.
12. The method of claim 11, wherein after determining whether an abnormal operation exists, the method further comprises:
and if the abnormal operation exists, searching the abnormal operation information from the information chain so as to ensure the safety of the assets.
13. An apparatus for detecting transaction fluctuations, comprising:
the asset transaction request receiving module is used for receiving an asset transaction request of a financial product; wherein the asset transaction request includes asset information;
the asset information loading module is used for loading the asset information into an information chain; the information chain is generated by adopting at least two information storage units according to a safety storage rule;
the transaction data acquisition module is used for executing response operation of the transaction request to obtain transaction information; and converting the transaction information to obtain transaction data;
and the error data acquisition module is used for dividing the transaction data into a training set and a test set and inputting the training set and the test set into a preset model to obtain output error data so as to detect transaction fluctuation.
14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method of detecting transaction fluctuations as claimed in any one of claims 1 to 12.
15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements a method of detecting transaction fluctuations as claimed in any of claims 1-12.
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| CN202011036101.6A CN112200671A (en) | 2020-09-27 | 2020-09-27 | Method and device for detecting transaction fluctuation, storage medium and electronic equipment |
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