CN116109318B - Interactive financial payment and big data compression storage method and system based on blockchain - Google Patents

Interactive financial payment and big data compression storage method and system based on blockchain Download PDF

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CN116109318B
CN116109318B CN202310308238.XA CN202310308238A CN116109318B CN 116109318 B CN116109318 B CN 116109318B CN 202310308238 A CN202310308238 A CN 202310308238A CN 116109318 B CN116109318 B CN 116109318B
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CN116109318A (en
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杨芳
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Beijing Haisheng Technology Co ltd
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Abstract

The invention discloses a blockchain-based interactive financial payment and big data compression storage method and system, and relates to the technical field of voice analysis. The method comprises the following steps: in the financial payment process, a certain user collects face images of the user and performs user identification by utilizing a face identification technology; collecting voice signals of the user, and carrying out identity recognition confirmation; after the confirmation is completed, extracting and displaying the information to be paid of the user, and sending out prompt voice; collecting a target voice signal of a user and a facial image of the user, and identifying and judging the effectiveness of the target voice signal; if the voice recognition result is agreeing to pay and the voice recognition result is judged to be effective, performing compression coding on all voice signals sent by the user in the payment process; relevant data is stored in a uplink. The invention combines a plurality of mathematical models, thereby greatly improving the safety and efficiency in the financial payment process; and the core information is stored in a uplink manner by using a block chain technology.

Description

Interactive financial payment and big data compression storage method and system based on blockchain
Technical Field
The invention relates to the technical field of voice analysis, in particular to an interactive financial payment and big data compression storage method and system based on a blockchain.
Background
In recent years, with the increasing popularity of internet financial services, more and more users can conveniently complete financial payment services. However, in the financial payment process, many financial payment systems have no better interaction function, so that not only can better payment experience be brought to users, but also obvious potential safety hazards exist; meanwhile, the storage of important data in the financial payment process often occupies huge storage resources, and the storage process has no higher security.
With the continuous updating of modern information technology, the technology in the fields of artificial intelligence, blockchain and the like can effectively improve the interactivity and the safety of a financial payment system and effectively reduce the resource consumption in the process of storing important data. Therefore, the method and the system for interactive financial payment and big data compression storage based on the blockchain are very important in value and significance.
Disclosure of Invention
In order to overcome the problems or at least partially solve the problems, the embodiment of the invention provides a blockchain-based interactive financial payment and big data compression storage method and system, which are combined with a multi-decision plane interactive voice print recognition model based on sample multi-pass high-quality replacement, a voice recognition model based on expression analysis posterior, and a back end comparison type voice compression coding model based on multi-module parallel connection, so that the safety and efficiency in the financial payment process are greatly improved; and the core information is stored in a uplink manner by using a block chain technology, so that the data security is ensured.
The invention adopts the technical scheme that:
in a first aspect, an embodiment of the present invention provides a blockchain-based interactive financial payment and big data compression storage method, including the steps of:
in the financial payment process, a certain user collects face images of the user and performs user identification by utilizing a face identification technology so as to obtain initial identification information;
collecting a voice signal of the user, and identifying and confirming the identity of the user by using a multi-decision plane mutual inspection voiceprint identification model based on sample multi-round high-quality replacement;
after the identification confirmation of the user is completed, extracting and displaying the information to be paid of the user, and sending prompt voice to the corresponding user;
collecting a target voice signal sent by a user according to prompt voice and a facial image of the user, and identifying and judging the effectiveness of the target voice signal by utilizing a voice identification model based on an expression analysis posterior;
if the voice recognition result is agreeing to pay and the voice recognition result is judged to be effective, performing compression coding on all voice signals sent by the user in the payment process by using a rear-end comparison type voice compression coding model based on multi-module parallel connection so as to obtain a voice signal compression coding result;
and acquiring and uploading user identity information, payment transaction information and a voice signal compression coding result to a blockchain to realize uplink storage.
The invention provides a multi-decision plane mutual inspection voiceprint recognition model based on sample multi-pass high-quality replacement, which is used for recognizing and confirming user identities; the model fully utilizes the thought of mutual inspection of multiple decision planes, and the accuracy of user identification confirmation is obviously improved. On the basis, the invention provides a speech recognition model based on expression analysis posterior, which is used for recognizing and judging the validity of a speech signal; the model utilizes the coding similarity detection method based on the image super-resolution to complete the recognition of the user expression, and judges the voice recognition effectiveness of the user through the user expression recognition result, so that the safety and effectiveness of the voice recognition result are further improved. Furthermore, the invention also provides a back-end comparison type voice compression coding model based on multi-module parallel connection, which is used for carrying out compression coding on all voice signals sent by a user; the model obtains the compression coding result of the voice signal with the highest compression ratio by utilizing the intelligent connection and the back-end comparison modes of a plurality of modules. Meanwhile, the invention uses the blockchain technology to store the user identity information, transaction information and voice signal compression coding result in a uplink way, thereby ensuring the safety of the system.
Based on the first aspect, in some embodiments of the present invention, the method for identifying and confirming the identity of the user by using the multi-decision-plane cross-test voiceprint identification model based on sample multi-round high-quality substitution includes the following steps:
extracting a plurality of sections of voice signals of a corresponding user from a preset voice database to form a positive sample data set according to the initial identity identification information, and extracting a plurality of sections of voice signals of a non-user to form a plurality of negative sample data sets;
training the SVM model by utilizing the positive sample data set and each negative sample data set respectively to obtain a plurality of voiceprint recognition decision planes;
and respectively utilizing the plurality of voiceprint recognition decision planes to carry out voiceprint recognition on the voice signal of the user so as to obtain and determine the identity of the user according to the plurality of voiceprint recognition results.
Based on the first aspect, in some embodiments of the present invention, the method for identifying and determining the validity of the target voice signal by using the voice recognition model based on the expression analysis posterior includes the following steps:
recognizing the target voice signal by utilizing the voice recognition model to generate a voice recognition result;
if the voice recognition result is agreement payment, carrying out expression analysis on the facial image of the user, generating and judging whether the voice recognition result is effective according to the analysis result.
Based on the first aspect, in some embodiments of the present invention, the method for performing expression analysis on a facial image of a user, generating and determining whether a voice recognition result is valid according to the analysis result includes the following steps:
respectively carrying out super-resolution reconstruction on the facial image of the user and a plurality of preset expression template images;
image coding is carried out on the reconstructed user face image and each expression template image, and the similarity between the user face image and each expression template image is calculated so as to determine the expression recognition result of the end user;
if the expression recognition result of the end user is a negative expression, judging that the voice recognition result is invalid, and prohibiting payment; otherwise, the voice recognition result is judged to be valid.
Based on the first aspect, in some embodiments of the present invention, the method for compression coding all voice signals sent by the user in the payment process by using the back-end comparison type voice compression coding model based on multi-module parallel connection includes the following steps:
the voice compression coding modules adopting different voice compression methods are connected in parallel, and the tail ends of the voice compression modules are connected with a compression result comparison module together to obtain a rear end comparison type voice compression coding model based on multi-module parallel connection; the compression result comparison module is used for judging whether the compression result of the voice signal of each voice compression coding module is minimum, and sending out a signal to take the corresponding minimum compression result of the voice signal as a final output result;
and performing compression coding on all voice signals sent by the user in the payment process by using a back-end comparison type voice compression coding model based on multi-module parallel connection.
In a second aspect, an embodiment of the present invention provides a blockchain-based interactive financial payment and big data compression storage system, including an initial identification module, an identity confirmation module, a payment prompting module, an identification determination module, a voice compression module, and a uplink storage module, where:
the initial identification module is used for acquiring a face image of a user in the financial payment process of the user, and carrying out user identification by utilizing a face identification technology so as to obtain initial identification information;
the identity confirmation module is used for collecting the voice signal of the user and utilizing a multi-decision plane mutual inspection voiceprint recognition model based on sample multi-turn high-quality replacement to recognize and confirm the identity of the user;
the payment prompt module is used for extracting and displaying information to be paid of the user after the identification confirmation of the user is completed, and sending prompt voice to the corresponding user;
the recognition judging module is used for collecting a target voice signal sent by a user according to the prompt voice and a facial image of the user, and recognizing and judging the effectiveness of the target voice signal by utilizing a voice recognition model based on an expression analysis posterior;
the voice compression module is used for carrying out compression coding on all voice signals sent by the user in the payment process by utilizing a back-end comparison type voice compression coding model based on multi-module parallel connection if the voice recognition result is agreeing to payment and the voice recognition result is judged to be effective, so as to obtain a voice signal compression coding result;
and the uplink storage module is used for acquiring and uploading user identity information, payment transaction information and a voice signal compression coding result to the blockchain to realize uplink storage.
The system greatly improves the safety and efficiency in the financial payment process by combining a plurality of modules such as the initial identification module, the identity confirmation module, the payment prompt module, the identification judgment module, the voice compression module, the uplink storage module and the like. The system adopts a multi-decision plane mutual inspection voiceprint recognition model based on sample multi-pass high-quality replacement to recognize and confirm the identity of a user; the model fully utilizes the thought of mutual inspection of multiple decision planes, and the accuracy of user identification confirmation is obviously improved. On the basis, the system adopts a speech recognition model based on expression analysis posterior to recognize and judge the validity of the speech signal; the model utilizes the coding similarity detection method based on the image super-resolution to complete the recognition of the user expression, and judges the voice recognition effectiveness of the user through the user expression recognition result, so that the safety and effectiveness of the voice recognition result are further improved. Furthermore, the system also adopts a back-end comparison type voice compression coding model based on multi-module parallel connection to perform compression coding on all voice signals sent by a user; the model obtains the compression coding result of the voice signal with the highest compression ratio by utilizing the intelligent connection and the back-end comparison modes of a plurality of modules. Meanwhile, the system utilizes the blockchain technology to store the user identity information, transaction information and voice signal compression coding results in a uplink manner, so that the safety of the system is ensured.
In a third aspect, embodiments of the present application provide an electronic device comprising a memory for storing one or more programs; a processor. The method of any of the first aspects described above is implemented when one or more programs are executed by a processor.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as in any of the first aspects described above.
The embodiment of the invention has at least the following advantages or beneficial effects:
the embodiment of the invention provides a blockchain-based interactive financial payment and big data compression storage method and a blockchain-based interactive financial payment and big data compression storage system, which provide a multi-decision-plane interactive voiceprint recognition model based on sample multi-round high-quality replacement, and carry out recognition and confirmation on user identities; the model fully utilizes the thought of mutual inspection of multiple decision planes, and the accuracy of user identification confirmation is obviously improved. On the basis, the invention provides a speech recognition model based on expression analysis posterior, which is used for recognizing and judging the validity of a speech signal; the model utilizes the coding similarity detection method based on the image super-resolution to complete the recognition of the user expression, and judges the voice recognition effectiveness of the user through the user expression recognition result, so that the safety and effectiveness of the voice recognition result are further improved. Furthermore, the invention also provides a back-end comparison type voice compression coding model based on multi-module parallel connection, which is used for carrying out compression coding on all voice signals sent by a user; the model obtains the compression coding result of the voice signal with the highest compression ratio by utilizing the intelligent connection and the back-end comparison modes of a plurality of modules. Meanwhile, the invention uses the blockchain technology to store the user identity information, transaction information and voice signal compression coding result in a uplink way, thereby ensuring the safety of the system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a blockchain-based interactive financial payment and big data compression storage method in accordance with an embodiment of the present invention;
FIG. 2 is a flowchart of identification confirmation in a blockchain-based interactive financial payment and big data compression storage method according to an embodiment of the present invention;
FIG. 3 is a flowchart of performing voice recognition and effectiveness determination in a blockchain-based interactive financial payment and big data compression storage method according to an embodiment of the present invention;
FIG. 4 is a schematic block diagram of an interactive financial payment and big data compression storage system based on a blockchain in accordance with an embodiment of the present invention;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention.
Reference numerals illustrate: 100. an initial identification module; 200. an identity confirmation module; 300. a payment prompting module; 400. an identification judgment module; 500. a voice compression module; 600. a ul storage module; 101. a memory; 102. a processor; 103. a communication interface.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the description of the embodiments of the present invention, "plurality" means at least 2.
Examples
As shown in fig. 1, in a first aspect, an embodiment of the present invention provides a blockchain-based interactive financial payment and big data compression storage method, including the following steps:
s1, a user acquires a face image of the user in a financial payment process, and performs user identification by utilizing a face identification technology to obtain initial identification information; the face image of the user is automatically extracted by the smart device (mobile phone, etc.), and then the identity of the user is primarily recognized (for example, the identity of the user is primarily recognized as 'Wang Wei') by using the face recognition technology.
S2, collecting voice signals of the user, and identifying and confirming the identity of the user by using a multi-decision plane mutual-verification voiceprint identification model based on sample multi-round high-quality replacement;
further, as shown in fig. 2, includes:
s21, extracting a plurality of sections of voice signals corresponding to a user from a preset voice database according to initial identity identification information to form a positive sample data set, and extracting a plurality of sections of voice signals not corresponding to the user to form a plurality of negative sample data sets;
s22, training the SVM model by utilizing the positive sample data set and each negative sample data set respectively to obtain a plurality of voiceprint recognition decision planes;
s23, voice print recognition is carried out on the voice signals of the user by utilizing the voice print recognition decision planes respectively, so that the identity of the user is obtained and determined according to the voice print recognition results.
In some embodiments of the invention, the system sounds a prompt to the user (please say a short period of time not less than 3 seconds). The user speaks a section of speech as required and the device automatically extracts the speech signal. The method for identifying and confirming the identity of the user by using a multi-decision-plane mutual-verification voiceprint identification model based on sample multi-pass high-quality replacement specifically comprises the following steps: for the user (who has primarily identified his identity as 'Wang Wei' in the face recognition process), the system automatically extracts multiple segments Wang Wei of speech signals previously recorded in the system to form a positive sample data set, and the system automatically extracts the same number of speech signals previously recorded in the system by other people to form a negative sample data set. Training the SVM model by utilizing the positive sample data set and the negative sample data set to obtain a voiceprint recognition decision plane A; the positive sample data set is kept unchanged, the system automatically extracts the same number of other voice signals to form a negative sample data set (the similarity between the negative sample data set and the last negative sample data set is calculated by using a similarity calculation method based on voice coding; the positive sample data set remains unchanged, the system randomly and automatically extracts the same number of other voice signals to form a negative sample data set (the similarity between the negative sample data set and the last negative sample data set is calculated by using a similarity calculation method based on voice coding), if the similarity is too high, the system randomly and automatically extracts the same number of other voice signals again to form the negative sample data set, and the positive sample data set and the negative sample data set are utilized to train the SVM model to obtain the voiceprint recognition decision plane C. Voice signals are respectively identified by voice print identification by utilizing a plurality of voice print identification decision planes, and if most voice print identification decision planes judge the user identity as 'Wang Wei', the user is finally judged as 'Wang Wei'.
S3, after the identification confirmation of the user is completed, extracting and displaying the information to be paid of the user, and sending prompt voice to the corresponding user, for example: a voice prompt is issued "if the transaction is agreed to answer 'agree to pay'.
S4, collecting a target voice signal sent by a user according to the prompt voice and a facial image of the user, and identifying and judging the effectiveness of the target voice signal by utilizing a voice identification model based on an expression analysis posterior;
further, as shown in fig. 3, includes:
s41, recognizing a target voice signal by utilizing a voice recognition model to generate a voice recognition result;
s42, if the voice recognition result is that payment is agreed, carrying out expression analysis on the facial image of the user, generating and judging whether the voice recognition result is effective according to the analysis result.
Further, the method comprises the steps of: respectively carrying out super-resolution reconstruction on the facial image of the user and a plurality of preset expression template images; image coding is carried out on the reconstructed user face image and each expression template image, and the similarity between the user face image and each expression template image is calculated so as to determine the expression recognition result of the end user; if the expression recognition result of the end user is a negative expression, judging that the voice recognition result is invalid, and prohibiting payment; otherwise, the voice recognition result is judged to be valid.
In some embodiments of the present invention, after browsing the information to be paid, the user sends out a voice signal according to the prompt, and the device acquires the voice signal and the facial image of the user at the same time. And recognizing the voice signal and judging the effectiveness by using a voice recognition model based on expression analysis posterior. If the voice signal is recognized as 'pay consent' and the recognition result is judged to be valid, the next step is continued. The step of identifying and judging the validity of the voice signal comprises the following steps: and recognizing the voice signal of the user by using a common voice recognition model. If the voice signal is recognized as 'agreeing to pay', the facial image of the user is continuously subjected to expression analysis. Specifically, super-resolution reconstruction is performed on both a user face image and a plurality of expression template images (a template image corresponding to a plurality of expressions such as happiness and fear). And respectively calculating the similarity of the facial image of the user and each expression template image after the image is encoded, finding the expression template image with the highest similarity with the facial image of the user, and identifying the expression of the user as the expression corresponding to the expression template image. If the recognition result of the facial image of the user is a negative expression such as fear, the voice recognition result is invalid, the system is directly locked, and the system automatically alarms when necessary; if the recognition result of the facial image of the user is a positive expression such as happiness, the voice recognition result is judged to be effective.
S5, if the voice recognition result is agreeing to pay and the voice recognition result is judged to be effective, performing compression coding on all voice signals sent by the user in the payment process by using a rear-end comparison type voice compression coding model based on multi-module parallel connection so as to obtain a voice signal compression coding result;
further, the method comprises the steps of: the voice compression coding modules adopting different voice compression methods are connected in parallel, and the tail ends of the voice compression modules are connected with a compression result comparison module together to obtain a rear end comparison type voice compression coding model based on multi-module parallel connection; the compression result comparison module is used for judging whether the compression result of the voice signal of each voice compression coding module is minimum, and sending out a signal to take the corresponding minimum compression result of the voice signal as a final output result; and performing compression coding on all voice signals sent by the user in the payment process by using a back-end comparison type voice compression coding model based on multi-module parallel connection.
In some embodiments of the present invention, the speech compression coding modules a, B, C are connected in parallel (different speech compression methods are used in different speech compression modules respectively), and a compression result comparison module is connected together at their ends. For the compression result comparison module, it can calculate which module gets the least compression result of the voice signal, and send out the signal to make the compression result of the voice signal of the module as the final output result.
S6, acquiring and uploading user identity information, payment transaction information and a voice signal compression coding result to a blockchain to realize uplink storage.
The invention provides a multi-decision plane mutual inspection voiceprint recognition model based on sample multi-pass high-quality replacement, which is used for recognizing and confirming user identities; the model fully utilizes the thought of mutual inspection of multiple decision planes, and the accuracy of user identification confirmation is obviously improved. On the basis, the invention provides a speech recognition model based on expression analysis posterior, which is used for recognizing and judging the validity of a speech signal; the model utilizes the coding similarity detection method based on the image super-resolution to complete the recognition of the user expression, and judges the voice recognition effectiveness of the user through the user expression recognition result, so that the safety and effectiveness of the voice recognition result are further improved. Furthermore, the invention also provides a back-end comparison type voice compression coding model based on multi-module parallel connection, which is used for carrying out compression coding on all voice signals sent by a user; the model obtains the compression coding result of the voice signal with the highest compression ratio by utilizing the intelligent connection and the back-end comparison modes of a plurality of modules. Meanwhile, the invention uses the blockchain technology to store the user identity information, transaction information and voice signal compression coding result in a uplink way, thereby ensuring the safety of the system.
As shown in fig. 4, in a second aspect, an embodiment of the present invention provides a blockchain-based interactive financial payment and big data compression storage system, which includes an initial identification module 100, an identity confirmation module 200, a payment prompting module 300, an identification decision module 400, a voice compression module 500, and a uplink storage module 600, wherein:
the initial identification module 100 is configured to collect a face image of a user during a financial payment process, and perform user identification by using a face identification technology to obtain initial identification information;
the identity confirmation module 200 is used for collecting the voice signal of the user and utilizing a multi-decision plane mutual inspection voiceprint recognition model based on sample multi-turn high-quality replacement to recognize and confirm the identity of the user;
the payment prompting module 300 is used for extracting and displaying information to be paid of the user after the identification confirmation of the user is completed, and sending prompting voice to the corresponding user;
the recognition and judgment module 400 is used for collecting a target voice signal sent by a user according to the prompt voice and a facial image of the user, and recognizing and judging the effectiveness of the target voice signal by utilizing a voice recognition model based on an expression analysis posterior;
the voice compression module 500 is configured to perform compression encoding on all voice signals sent by the user in the payment process by using a back-end comparison type voice compression encoding model based on multi-module parallel connection if the voice recognition result is agreement payment and the voice recognition result is determined to be valid, so as to obtain a voice signal compression encoding result;
the uplink storage module 600 is configured to acquire and upload the user identity information, payment transaction information, and voice signal compression encoding result to the blockchain, so as to implement uplink storage.
The system greatly improves the safety and efficiency in the financial payment process through the combination of a plurality of modules such as the initial recognition module 100, the identity confirmation module 200, the payment prompt module 300, the recognition judgment module 400, the voice compression module 500, the uplink storage module 600 and the like. Adopting a multi-decision plane mutual inspection voiceprint recognition model based on sample multi-pass high-quality replacement to recognize and confirm the user identity; the model fully utilizes the thought of mutual inspection of multiple decision planes, and the accuracy of user identification confirmation is obviously improved. On the basis, the system adopts a speech recognition model based on expression analysis posterior to recognize and judge the validity of the speech signal; the model utilizes the coding similarity detection method based on the image super-resolution to complete the recognition of the user expression, and judges the voice recognition effectiveness of the user through the user expression recognition result, so that the safety and effectiveness of the voice recognition result are further improved. Furthermore, the system also adopts a back-end comparison type voice compression coding model based on multi-module parallel connection to perform compression coding on all voice signals sent by a user; the model obtains the compression coding result of the voice signal with the highest compression ratio by utilizing the intelligent connection and the back-end comparison modes of a plurality of modules. Meanwhile, the system utilizes the blockchain technology to store the user identity information, transaction information and voice signal compression coding results in a uplink manner, so that the safety of the system is ensured.
As shown in fig. 5, in a third aspect, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs; a processor 102. The method of any of the first aspects described above is implemented when one or more programs are executed by the processor 102.
And a communication interface 103, where the memory 101, the processor 102 and the communication interface 103 are electrically connected directly or indirectly to each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules that are stored within the memory 101 for execution by the processor 102 to perform various functional applications and data processing. The communication interface 103 may be used for communication of signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor 102 may be an integrated circuit chip with signal processing capabilities. The processor 102 may be a general purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In the embodiments provided in the present application, it should be understood that the disclosed method, system and method may be implemented in other manners. The above-described method and system embodiments are merely illustrative, for example, flow charts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems, methods and computer program products according to various embodiments of the present application. 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/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.
In addition, the functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by the processor 102, implements a method as in any of the first aspects described above. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (5)

1. The interactive financial payment and big data compression storage method based on the blockchain is characterized by comprising the following steps of:
in the financial payment process, a certain user collects face images of the user and performs user identification by utilizing a face identification technology so as to obtain initial identification information;
collecting the voice signal of the user, and identifying and confirming the identity of the user by using a multi-decision-plane mutual-verification voiceprint identification model based on sample multi-round high-quality replacement, comprising the following steps: extracting a plurality of sections of voice signals of a corresponding user from a preset voice database to form a positive sample data set according to the initial identity identification information, and extracting a plurality of sections of voice signals of a non-user to form a plurality of negative sample data sets; training the SVM model by utilizing the positive sample data set and each negative sample data set respectively to obtain a plurality of voiceprint recognition decision planes; voice print recognition is carried out on the voice signals of the user by utilizing a plurality of voice print recognition decision planes respectively so as to obtain and determine the identity of the user according to a plurality of voice print recognition results;
after the identification confirmation of the user is completed, extracting and displaying the information to be paid of the user, and sending prompt voice to the corresponding user;
the method for recognizing the target voice signal and judging the effectiveness of the target voice signal comprises the steps of: recognizing the target voice signal by utilizing the voice recognition model to generate a voice recognition result; if the voice recognition result is that payment is agreed, carrying out expression analysis on the facial image of the user, generating and judging whether the voice recognition result is effective according to the analysis result;
if the voice recognition result is agreeing to pay and the voice recognition result is judged to be effective, performing compression coding on all voice signals sent by the user in the payment process by using a rear-end comparison type voice compression coding model based on multi-module parallel connection to obtain a voice signal compression coding result, wherein the method comprises the following steps: the voice compression coding modules adopting different voice compression methods are connected in parallel, and the tail ends of the voice compression modules are connected with a compression result comparison module together to obtain a rear end comparison type voice compression coding model based on multi-module parallel connection; the compression result comparison module is used for judging whether the compression result of the voice signal of each voice compression coding module is minimum, and sending out a signal to take the corresponding minimum compression result of the voice signal as a final output result; performing compression coding on all voice signals sent by the user in the payment process by using a rear-end comparison type voice compression coding model based on multi-module parallel connection;
and acquiring and uploading user identity information, payment transaction information and a voice signal compression coding result to a blockchain to realize uplink storage.
2. The blockchain-based interactive financial payment and big data compression storage method of claim 1, wherein the method for performing expression analysis on the facial image of the user, generating and determining whether the voice recognition result is valid according to the analysis result comprises the following steps:
respectively carrying out super-resolution reconstruction on the facial image of the user and a plurality of preset expression template images;
image coding is carried out on the reconstructed user face image and each expression template image, and the similarity between the user face image and each expression template image is calculated so as to determine the expression recognition result of the end user;
if the expression recognition result of the end user is a negative expression, judging that the voice recognition result is invalid, and prohibiting payment; otherwise, the voice recognition result is judged to be valid.
3. The interactive financial payment and big data compression storage system based on the blockchain is characterized by comprising an initial identification module, an identity confirmation module, a payment prompt module, an identification judgment module, a voice compression module and a uplink storage module, wherein:
the initial identification module is used for acquiring a face image of a user in the financial payment process of the user, and carrying out user identification by utilizing a face identification technology so as to obtain initial identification information;
the identity confirmation module is used for collecting the voice signal of the user and utilizing a multi-decision plane mutual inspection voiceprint recognition model based on sample multi-turn high-quality replacement to recognize and confirm the identity of the user, and comprises the following steps: extracting a plurality of sections of voice signals of a corresponding user from a preset voice database to form a positive sample data set according to the initial identity identification information, and extracting a plurality of sections of voice signals of a non-user to form a plurality of negative sample data sets; training the SVM model by utilizing the positive sample data set and each negative sample data set respectively to obtain a plurality of voiceprint recognition decision planes; voice print recognition is carried out on the voice signals of the user by utilizing a plurality of voice print recognition decision planes respectively so as to obtain and determine the identity of the user according to a plurality of voice print recognition results;
the payment prompt module is used for extracting and displaying information to be paid of the user after the identification confirmation of the user is completed, and sending prompt voice to the corresponding user;
the recognition judging module is used for collecting a target voice signal sent by a user according to the prompt voice and a user facial image, and recognizing and judging the effectiveness of the target voice signal by utilizing a voice recognition model based on an expression analysis posterior, and comprises the following steps: recognizing the target voice signal by utilizing the voice recognition model to generate a voice recognition result; if the voice recognition result is that payment is agreed, carrying out expression analysis on the facial image of the user, generating and judging whether the voice recognition result is effective according to the analysis result;
the voice compression module is configured to, if the voice recognition result is agreement payment and the voice recognition result is determined to be valid, perform compression encoding on all voice signals sent by the user in the payment process by using a back-end comparison type voice compression encoding model based on multi-module parallel connection, so as to obtain a voice signal compression encoding result, where the voice signal compression encoding module includes: the voice compression coding modules adopting different voice compression methods are connected in parallel, and the tail ends of the voice compression modules are connected with a compression result comparison module together to obtain a rear end comparison type voice compression coding model based on multi-module parallel connection; the compression result comparison module is used for judging whether the compression result of the voice signal of each voice compression coding module is minimum, and sending out a signal to take the corresponding minimum compression result of the voice signal as a final output result; performing compression coding on all voice signals sent by the user in the payment process by using a rear-end comparison type voice compression coding model based on multi-module parallel connection;
and the uplink storage module is used for acquiring and uploading user identity information, payment transaction information and a voice signal compression coding result to the blockchain to realize uplink storage.
4. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the method of any of claims 1-2 is implemented when the one or more programs are executed by the processor.
5. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-2.
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