CN111950641A - Business processing method, model training method, device and equipment - Google Patents
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Abstract
The embodiment of the specification provides a business processing method, a model training method, a device and equipment, which can be used in the field of artificial intelligence. The method comprises the following steps: receiving a service processing request; the service processing request comprises service application information; the service application information corresponds to at least two modal characteristics; respectively extracting modal characteristic data corresponding to the modal characteristics from the service application information; fusing each modal characteristic data to obtain fused characteristic data; judging whether the service processing request is an abnormal request or not according to the fusion characteristic data; and processing the service corresponding to the service processing request under the condition that the service processing request is not an abnormal request. By the method, the user identity can be verified by using the data with various modal characteristics when the service of the user is processed, and the accuracy of identity verification is improved.
Description
Technical Field
The embodiment of the specification relates to the technical field of artificial intelligence, in particular to a business processing method, a model training method, a device and equipment.
Background
With the development of internet technology, more and more organizations and enterprises use their own service systems to implement online business processing. When the online service processing is carried out, the system verifies the identity of the user and processes the service application submitted by the user after the identity verification is passed.
However, currently, when processing services, the received user identity information and service application information have a single data form, for example, the user himself is described only by text. Such application information in the form of a single datum is often more susceptible to counterfeiting. When a lawbreaker counterfeits the application information, the counterfeited application information often cannot reflect more comprehensive characteristic information, and the identification of the counterfeit information is generally difficult, so that data or fund leakage is caused. Therefore, a technical solution capable of comprehensively verifying the user identity when processing the service is needed.
Disclosure of Invention
An embodiment of the present specification aims to provide a service processing method, a model training method, a device, and an apparatus, so as to solve a technical problem of how to accurately verify a user identity when processing a service.
In order to solve the above technical problem, an embodiment of the present specification provides a service processing method, including:
receiving a service processing request; the service processing request comprises service application information; the service application information corresponds to at least two modal characteristics;
respectively extracting modal characteristic data corresponding to the modal characteristics from the service application information;
fusing each modal characteristic data to obtain fused characteristic data;
judging whether the service processing request is an abnormal request or not according to the fusion characteristic data;
and processing the service corresponding to the service processing request under the condition that the service processing request is not an abnormal request.
An embodiment of this specification further provides a service processing apparatus, including:
the request receiving module is used for receiving a service processing request; the service processing request comprises service application information; the service application information corresponds to at least two modal characteristics;
a modal characteristic data extraction module, configured to respectively extract modal characteristic data corresponding to the modal characteristics from the service application information;
the modal characteristic data fusion module is used for fusing each modal characteristic data to obtain fused characteristic data;
the judging module is used for judging whether the service processing request is an abnormal request or not according to the fusion characteristic data;
and the service processing module is used for processing the service corresponding to the service processing request under the condition that the service processing request is not an abnormal request.
The embodiment of the present specification further provides a service processing device, which includes a memory and a processor; the memory to store computer program instructions; the processor to execute the computer program instructions to implement the steps of: receiving a service processing request; the service processing request comprises service application information; the service application information corresponds to at least two modal characteristics; respectively extracting modal characteristic data corresponding to the modal characteristics from the service application information; fusing each modal characteristic data to obtain fused characteristic data; judging whether the service processing request is an abnormal request or not according to the fusion characteristic data; and processing the service corresponding to the service processing request under the condition that the service processing request is not an abnormal request.
In order to solve the above technical problem, an embodiment of the present specification further provides a model training method, including:
acquiring service application sample data; the service application sample data corresponds to at least two modal characteristics;
respectively extracting modal feature sample data corresponding to the modal features from the service application sample data;
fusing the modal characteristic sample data to obtain a fused sample vector;
and training a pre-constructed abnormal service identification model according to the fusion sample vector.
An embodiment of this specification further provides a model training device, including:
the sample data acquisition module is used for acquiring the sample data of the service application; the sample data of the service application is marked with abnormal conditions; the service application sample data corresponds to at least two modal characteristics;
a modal characteristic sample data extraction module, configured to extract modal characteristic sample data corresponding to the modal characteristics from the service application sample data, respectively;
the sample vector fusion module is used for fusing the modal characteristic sample data to obtain a fusion sample vector;
and the model training module is used for training an abnormal business recognition model according to the fusion sample vector and the corresponding abnormal condition.
The embodiment of the present specification further provides a model training device, which includes a memory and a processor;
the memory to store computer program instructions;
the processor to execute the computer program instructions to implement the steps of: acquiring service application sample data; the sample data of the service application is marked with abnormal conditions; the service application sample data corresponds to at least two modal characteristics; respectively extracting modal feature sample data corresponding to the modal features from the service application sample data; fusing the modal characteristic sample data to obtain a fused sample vector; and training an abnormal business recognition model according to the fusion sample vector and the corresponding abnormal condition.
As can be seen from the technical solutions provided in the embodiments of the present specification, by acquiring service application information corresponding to at least two modal features, after respectively extracting modal feature data corresponding to the modal features, the modal feature data are fused, and whether a service processing request is an abnormal request is determined by using the fused modal feature data. By the method, when the service application information is judged, the plurality of modal characteristics can be comprehensively considered, and the condition that the data type is single can be avoided by identifying the data fused with the plurality of modal characteristics, so that the fused data has abundant characteristics, the difficulty of counterfeiting the data is increased, and the accuracy of verifying the user application is improved.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the specification, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a model training method according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a service processing method according to an embodiment of the present disclosure;
FIG. 3 is a block diagram of a model training apparatus according to an embodiment of the present disclosure;
fig. 4 is a block diagram of a service processing apparatus according to an embodiment of the present disclosure;
FIG. 5 is a block diagram of a model training apparatus according to an embodiment of the present disclosure;
fig. 6 is a block diagram of a service processing device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort shall fall within the protection scope of the present specification.
In order to solve the above technical problem, an embodiment of a model training method according to an embodiment of the present disclosure is first described with reference to fig. 1. The execution subject of the method can be model training equipment, and the model training equipment comprises but is not limited to a server, an industrial personal computer, a PC (personal computer) and the like. The method comprises the following specific implementation steps.
S110: acquiring service application sample data; the service application sample data corresponds to at least two modal characteristics.
The service application sample data is sample data obtained by training the abnormal service identification model. The service application sample data may be sample data provided for a service to be identified, for example, when the service is a credit card application service, service application information submitted by a user in a history record may be collected, and the service application information may be used as the service application sample data.
The service application sample data can correspond to an abnormal condition label, and the abnormal condition label is used for labeling whether the service application corresponding to the service application sample data is an abnormal application. Correspondingly, based on the abnormal condition marking and the service application sample data, a supervised learning method can be adopted for subsequent model training. Corresponding labels do not exist in the service application sample data, and a preset classifier is trained by directly using an unsupervised learning method during subsequent training. The specific way of performing machine learning can be selected according to the requirements of the actual situation, and is not described herein.
The modal characteristics are used for representing the type corresponding to the data contained in the sample data of the service application. In some implementations, the modal features can include at least two of image modal features, fingerprint modal features, audio modal features, and text modal features. Image modality features can correspond to image data, such as applicants' photographs, document images, and the like; the modality features may correspond to fingerprint data provided by the applicant, and may be, for example, fingerprints captured by the terminal device; the audio modality features may correspond to audio sent by the applicant, such as voice commands preset by the applicant or voice information sent by the applicant itself; the text modality features may correspond to text submitted by the applicant, such as application material information, personal identification information, and the like. The above example is only for introducing the modal characteristics, and other information types may be selected as the modal characteristics in practical applications, which is not described herein again.
S120: and respectively extracting modal feature sample data corresponding to the modal features from the service application sample data.
The modal feature sample data is data corresponding to the modal feature. For example, when the modal feature is an image modal feature, an image in the modal feature sample data may be extracted as modal feature sample data; when the modal characteristics are audio modal characteristics, an audio file in the modal characteristic sample data can be extracted as modal characteristic sample data. The specific method for extracting modal feature sample data can be adjusted according to the requirements of practical application, and details are not repeated.
In some embodiments, when the modal feature sample data is extracted, data extraction may be performed for a feature dimension with a strong business meaning, for example, when text modal feature sample data is extracted, text information corresponding to some specific content may be selected as the text modal feature sample data; when data corresponding to image modality features are selected, a picture with a strong business meaning, such as a certificate image, can be identified from the data. By combining the service meaning of the modal characteristic sample data for screening, the effectiveness of the selected modal characteristic sample data is improved, and the identification accuracy of the trained model is improved.
S130: and fusing the modal characteristic sample data to obtain a fused sample vector.
After obtaining modal feature sample data corresponding to at least two modal features, the modal feature sample data may be fused. The specific fusion method may be to fuse these modal feature sample data into one feature vector, and then map the fused feature vector into a preset vector subspace, thereby obtaining a fusion sample vector. The method is only an exemplary introduction to the process of fusing modal feature sample data, and other methods for obtaining a fusion sample vector may be selected according to requirements in practical application, which is not described herein again.
In some embodiments, before fusing the modal feature sample data, the modal feature sample data may be preprocessed to reduce noise in the modal feature sample data and improve accuracy of a training result. The preprocessing may include denoising processing and normalization processing.
And denoising, namely removing the noise sample data according to the modal characteristic sample data. Specifically, the noise sample data may be determined in the modal characteristic sample data according to a preset noise data template so as to be removed, or the noise sample data may be directly screened out from the modal characteristic sample data based on the characteristics of continuity and the like of the modal characteristic sample data. The specific way of determining the noise sample data may be adjusted according to the requirements of practical applications, and is not limited to the above example, and is not described herein again.
The standardized processing can adjust the modal characteristic sample data to the same data distribution range, so that the sample data is consistent in distribution, and the modal characteristic sample data can be processed in the subsequent process. The normalization process may be, for example, a normalization process, which divides the data into a distribution range of [0,1], so as to facilitate the application of the data.
S140: and training a pre-constructed abnormal service identification model according to the fusion sample vector.
The abnormal traffic recognition model may be a model for recognizing abnormal traffic. The abnormal service identification model can be a mathematical model and is used for identifying whether the corresponding service is an abnormal service or not by combining different modal characteristics of the service application data. The abnormal service identification model may be a bayesian classification model, a Support Vector Machine (SVM) classification model, a Convolutional Neural Network (CNN) classification model, or the like, which is not limited herein.
After the fusion sample vector is obtained, the abnormal business recognition model can be trained according to the fusion sample vector, so that the trained model can recognize the abnormal conditions of the business based on the training sample.
In some embodiments, a plurality of abnormal service identification models may be pre-constructed, and the abnormal service identification models may be trained based on the same service application sample data. And aiming at the abnormal business identification models, respectively inputting verification data into the abnormal business identification models to obtain verification results corresponding to the models. And judging the accuracy of each abnormal business identification model according to the verification result, and determining the final abnormal business identification model applied to business processing according to the accuracy of different models.
The model training method comprises the steps of acquiring service application information corresponding to at least two modal characteristics, respectively extracting modal characteristic data corresponding to the modal characteristics, fusing the modal characteristic data, and training an abnormal service identification model by utilizing the fused fusion characteristic data, so that the abnormal service identification model can identify abnormal conditions of services. By the method, when the service application information is judged, the plurality of modal characteristics can be comprehensively considered, and the condition that the data type is single can be avoided by identifying the data fused with the plurality of modal characteristics, so that the fused data has abundant characteristics, the difficulty of counterfeiting the data is increased, and the accuracy of verifying the user application is improved.
Based on the embodiment of the model training method, the embodiment of the present specification further provides a service processing method. The execution main body of the service processing method is service processing equipment, and the service processing equipment comprises but is not limited to a server, an industrial personal computer, a PC (personal computer) and the like. As shown in fig. 2, the method includes the following specific implementation steps.
S210: receiving a service processing request; the service processing request comprises service application information; the service application information corresponds to at least two modality features.
The service processing request is a request provided by the user for processing a specific service, for example, when the user needs to apply for a credit card, the service processing request may be a request for applying for a credit card.
The service processing request includes service application information, which may be data provided by a service to be processed, and may include, for example, personal identity information, certificate information, biometric information, and the like of a user.
The modal characteristics are used for representing the type corresponding to the data contained in the sample data of the service application. In some implementations, the modal features can include at least two of image modal features, fingerprint modal features, audio modal features, and text modal features. Image modality features can correspond to image data, such as applicants' photographs, document images, and the like; the modality features may correspond to fingerprint data provided by the applicant, and may be, for example, fingerprints captured by the terminal device; the audio modality features may correspond to audio sent by the applicant, such as voice commands preset by the applicant or voice information sent by the applicant itself; the text modality features may correspond to text submitted by the applicant, such as application material information, personal identification information, and the like. The above example is only for introducing the modal characteristics, and other information types may be selected as the modal characteristics in practical applications, which is not described herein again.
S220: and respectively extracting modal characteristic data corresponding to the modal characteristics from the service application information.
The modal characteristic data is data corresponding to the modal characteristic. For example, when the modality feature is an image modality feature, an image in the modality feature data may be extracted as modality feature data; when the modal characteristics are audio modal characteristics, an audio file in the modal characteristic data can be extracted as modal characteristic data. The specific method for extracting modal feature data may be adjusted according to the requirements of practical applications, and details thereof are not repeated.
In some embodiments, when the modal feature data is extracted, data extraction may be performed for a feature dimension with a strong business meaning, for example, when text modal feature data is extracted, text information corresponding to some specific content may be selected as the text modal feature data; when data corresponding to image modality features are selected, a picture with a strong business meaning, such as a certificate image, can be identified from the data. By combining the service meaning of the modal characteristic data for screening, the effectiveness of the selected modal characteristic data is improved, and the identification accuracy of the trained model is improved.
S230: and fusing the modal characteristic data to obtain fused characteristic data.
After obtaining modal feature sample data corresponding to at least two modal features, the modal feature sample data may be fused. The specific fusion method may be to fuse these modal feature sample data into one feature vector, and then map the fused feature vector into a preset vector subspace, thereby obtaining a fusion sample vector. The method is only an exemplary introduction to the process of fusing modal feature sample data, and other methods for obtaining a fusion sample vector may be selected according to requirements in practical application, which is not described herein again.
In some embodiments, before fusing the modal feature sample data, the modal feature sample data may be preprocessed to reduce noise in the modal feature sample data and improve accuracy of a training result. The preprocessing may include denoising processing and normalization processing.
And denoising, namely removing the noise sample data according to the modal characteristic sample data. Specifically, the noise sample data may be determined in the modal characteristic sample data according to a preset noise data template so as to be removed, or the noise sample data may be directly screened out from the modal characteristic sample data based on the characteristics of continuity and the like of the modal characteristic sample data. The specific way of determining the noise sample data may be adjusted according to the requirements of practical applications, and is not limited to the above example, and is not described herein again.
The standardized processing can adjust the modal characteristic sample data to the same data distribution range, so that the sample data is consistent in distribution, and the modal characteristic sample data can be processed in the subsequent process. The normalization process may be, for example, a normalization process, which divides the data into a distribution range of [0,1], so as to facilitate the application of the data.
S240: and judging whether the service processing request is an abnormal request or not according to the fusion characteristic data.
In some embodiments, the determining whether the service processing request is an abnormal request according to the fusion feature data may be identifying whether the service processing request corresponding to the fusion feature data is an abnormal request by using an abnormal service identification model. The abnormal service identification model may be a model trained based on the embodiment corresponding to fig. 1. For the introduction of the abnormal service identification model, reference may be made to the description in the corresponding embodiment of fig. 1, and details are not described here.
S250: and processing the service corresponding to the service processing request under the condition that the service processing request is not an abnormal request.
If the service processing request is identified to be not an abnormal request based on the fusion characteristic data, the service processing request is indicated to be a normal request, higher risk does not exist when the service processing request is processed, and the service can be processed based on a normal processing flow. The specific processing manner may be to process the service by combining the characteristics of the service in the actual application, which is not described herein again.
If the service processing request is identified to be an abnormal request based on the fusion characteristic data, it indicates that there may be a certain risk in processing the service corresponding to the service processing request, which is easy to cause data or fund leakage, and may refuse to process the service processing request and feed back corresponding rejection information. The specific processing mode may also be set according to the requirements in practical applications, which is not described herein.
The service processing method comprises the steps of acquiring service application information corresponding to at least two modal characteristics, respectively extracting modal characteristic data corresponding to the modal characteristics, fusing the modal characteristic data, and judging whether a service processing request is an abnormal request or not by utilizing the fused fusion characteristic data. By the method, when the service application information is judged, the plurality of modal characteristics can be comprehensively considered, and the condition that the data type is single can be avoided by identifying the data fused with the plurality of modal characteristics, so that the fused data has abundant characteristics, the difficulty of counterfeiting the data is increased, and the accuracy of verifying the user application is improved.
Based on the above embodiment of the model training method, an embodiment of the present specification further provides a model training apparatus, which may be integrated in the model training device, as shown in fig. 3, and the model training apparatus may include the following modules.
A sample data obtaining module 310, configured to obtain sample data of a service application; the sample data of the service application is marked with abnormal conditions; the service application sample data corresponds to at least two modal characteristics;
a modal feature sample data extracting module 320, configured to extract modal feature sample data corresponding to the modal features from the service application sample data, respectively;
a sample vector fusion module 330, configured to fuse the modal feature sample data to obtain a fusion sample vector;
and the model training module 340 is configured to train an abnormal service identification model according to the fusion sample vector and the corresponding abnormal condition.
Based on the foregoing embodiment of the service processing method, an embodiment of the present specification further provides a service processing apparatus, where the service processing apparatus may be integrated in the service processing device, as shown in fig. 4, and the service processing apparatus may include the following modules.
A request receiving module 410, configured to receive a service processing request; the service processing request comprises service application information; the service application information corresponds to at least two modal characteristics;
a modal feature data extraction module 420, configured to respectively extract modal feature data corresponding to the modal features from the service application information;
a modal feature data fusion module 430, configured to fuse the modal feature data to obtain fused feature data;
a determining module 440, configured to determine whether the service processing request is an abnormal request according to the fused feature data;
the service processing module 450 is configured to process a service corresponding to the service processing request when the service processing request is not an abnormal request.
Based on the above model training method, as shown in fig. 5, an embodiment of the present specification further provides a model training device. The model training device may include a memory and a processor.
In this embodiment, the memory may be implemented in any suitable manner. For example, the memory may be a read-only memory, a mechanical hard disk, a solid state disk, a U disk, or the like. The memory may be used to store computer program instructions.
In this embodiment, the processor may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth.
The processor may execute the computer program instructions to perform the steps of: acquiring service application sample data; the sample data of the service application is marked with abnormal conditions; the service application sample data corresponds to at least two modal characteristics; respectively extracting modal feature sample data corresponding to the modal features from the service application sample data; fusing the modal characteristic sample data to obtain a fused sample vector; and training an abnormal business recognition model according to the fusion sample vector and the corresponding abnormal condition.
Based on the service processing method, as shown in fig. 6, an embodiment of the present specification further provides a service processing device. The traffic processing device may include a memory and a processor.
In this embodiment, the memory may be implemented in any suitable manner. For example, the memory may be a read-only memory, a mechanical hard disk, a solid state disk, a U disk, or the like. The memory may be used to store computer program instructions.
In this embodiment, the processor may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth.
The processor may execute the computer program instructions to perform the steps of: receiving a service processing request; the service processing request comprises service application information; the service application information corresponds to at least two modal characteristics; respectively extracting modal characteristic data corresponding to the modal characteristics from the service application information; fusing each modal characteristic data to obtain fused characteristic data; judging whether the service processing request is an abnormal request or not according to the fusion characteristic data; and processing the service corresponding to the service processing request under the condition that the service processing request is not an abnormal request.
It should be noted that the service processing method, the model training method, the device and the apparatus disclosed in the embodiments of the present specification may be used in the technical field of artificial intelligence to verify and process a service application, and of course, the service processing method, the model training method, the device and the apparatus may also be applied in other fields, which is not limited to this.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate a dedicated integrated circuit chip 2. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardbyscript Description Language (vhr Description Language), and the like, which are currently used by Hardware compiler-software (Hardware Description Language-software). It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
From the above description of the embodiments, it is clear to those skilled in the art that the present specification can be implemented by software plus a necessary general hardware platform. Based on such understanding, the technical solutions of the present specification may be essentially or partially implemented in the form of software products, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and include instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments of the present specification.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The description is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
While the specification has been described with examples, those skilled in the art will appreciate that there are numerous variations and permutations of the specification that do not depart from the spirit of the specification, and it is intended that the appended claims include such variations and modifications that do not depart from the spirit of the specification.
Claims (10)
1. A method for processing a service, comprising:
receiving a service processing request; the service processing request comprises service application information; the service application information corresponds to at least two modal characteristics;
respectively extracting modal characteristic data corresponding to the modal characteristics from the service application information;
fusing each modal characteristic data to obtain fused characteristic data;
judging whether the service processing request is an abnormal request or not according to the fusion characteristic data;
and processing the service corresponding to the service processing request under the condition that the service processing request is not an abnormal request.
2. The method of claim 1, wherein the modal characteristics include at least two of:
image modality features, fingerprint modality features, audio modality features and text modality features.
3. A method according to claim 1, wherein the fusing of the respective modality feature data to obtain fused feature data comprises:
fusing each modal feature data into a fused feature vector;
and mapping the fusion feature vector to a preset vector subspace to obtain fusion feature data.
4. The method of claim 1, wherein said determining whether the service processing request is an exception request according to the fused feature data comprises:
and identifying whether the service processing request corresponding to the fusion characteristic data is an abnormal request or not by using an abnormal service identification model.
5. A traffic processing apparatus, comprising:
the request receiving module is used for receiving a service processing request; the service processing request comprises service application information; the service application information corresponds to at least two modal characteristics;
a modal characteristic data extraction module, configured to respectively extract modal characteristic data corresponding to the modal characteristics from the service application information;
the modal characteristic data fusion module is used for fusing each modal characteristic data to obtain fused characteristic data;
the judging module is used for judging whether the service processing request is an abnormal request or not according to the fusion characteristic data;
and the service processing module is used for processing the service corresponding to the service processing request under the condition that the service processing request is not an abnormal request.
6. A traffic processing device comprising a memory and a processor;
the memory to store computer program instructions;
the processor to execute the computer program instructions to implement the steps of: receiving a service processing request; the service processing request comprises service application information; the service application information corresponds to at least two modal characteristics; respectively extracting modal characteristic data corresponding to the modal characteristics from the service application information; fusing each modal characteristic data to obtain fused characteristic data; judging whether the service processing request is an abnormal request or not according to the fusion characteristic data; and processing the service corresponding to the service processing request under the condition that the service processing request is not an abnormal request.
7. A method of model training, comprising:
acquiring service application sample data; the service application sample data corresponds to at least two modal characteristics;
respectively extracting modal feature sample data corresponding to the modal features from the service application sample data;
fusing the modal characteristic sample data to obtain a fused sample vector;
and training a pre-constructed abnormal service identification model according to the fusion sample vector.
8. The method according to claim 7, wherein before fusing the respective modality feature data to obtain fused feature data, further comprising:
respectively preprocessing the modal characteristic data; the preprocessing comprises denoising processing and standardization processing.
9. A model training apparatus, comprising:
the sample data acquisition module is used for acquiring the sample data of the service application; the sample data of the service application is marked with abnormal conditions; the service application sample data corresponds to at least two modal characteristics;
a modal characteristic sample data extraction module, configured to extract modal characteristic sample data corresponding to the modal characteristics from the service application sample data, respectively;
the sample vector fusion module is used for fusing the modal characteristic sample data to obtain a fusion sample vector;
and the model training module is used for training an abnormal business recognition model according to the fusion sample vector and the corresponding abnormal condition.
10. A model training apparatus comprising a memory and a processor;
the memory to store computer program instructions;
the processor to execute the computer program instructions to implement the steps of: acquiring service application sample data; the sample data of the service application is marked with abnormal conditions; the service application sample data corresponds to at least two modal characteristics; respectively extracting modal feature sample data corresponding to the modal features from the service application sample data; fusing the modal characteristic sample data to obtain a fused sample vector; and training an abnormal business recognition model according to the fusion sample vector and the corresponding abnormal condition.
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