CN114286343A - Multi-path outbound system, risk identification method, equipment, medium and product - Google Patents
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- CN114286343A CN114286343A CN202111666585.7A CN202111666585A CN114286343A CN 114286343 A CN114286343 A CN 114286343A CN 202111666585 A CN202111666585 A CN 202111666585A CN 114286343 A CN114286343 A CN 114286343A
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Abstract
The disclosure provides a multi-path outbound system, a risk identification method, equipment, a medium and a product, which relate to the technical field of communication, wherein the multi-path outbound system comprises a multi-path outbound module, a data transmission module and a control module; the multi-path outbound module is used for calling each number to be detected in the number set to be detected to obtain calling data of each number to be detected; sending the calling data of each number to be detected to a data transmission module; the data transmission module is used for transmitting the call data of each number to be detected to the control module; and the control module is used for determining the number risk state of each number to be detected based on the call data of each number to be detected. The risk identification method and the risk identification device can improve the accuracy of risk identification.
Description
Technical Field
The disclosure relates to the technical field of artificial intelligence, and further relates to the technical field of intelligent finance, in particular to a multi-path outbound system, a risk identification method, equipment, a medium and a product.
Background
Currently, the automatic outbound service has been widely used in various fields, for example, in generating offer information for online marketing. The automatic outbound service means automatic dialing and playing the pre-recorded voice to the user.
In practice, it is found that when online activities generate benefit information, it is often difficult to identify fraudulent accounts registered by using virtual devices, so that the benefit information is maliciously acquired.
Disclosure of Invention
The present disclosure provides a multi-path outbound system, risk identification method, apparatus, medium, and product.
According to an aspect of the present disclosure, a multi-path outbound system is provided, which includes a multi-path outbound module, a data transmission module, and a control module; the multi-path outbound module is used for calling each number to be detected in the number set to be detected to obtain calling data of each number to be detected; sending the calling data of each number to be detected to a data transmission module; the data transmission module is used for transmitting the call data of each number to be detected to the control module; and the control module is used for determining the number risk state of each number to be detected based on the call data of each number to be detected.
According to another aspect of the present disclosure, there is provided a risk identification method including: acquiring a number set to be detected; determining the call data of each number to be detected in the number set to be detected; and determining the number risk state of each number to be detected based on the calling data of each number to be detected.
According to another aspect of the present disclosure, there is provided an electronic device including: one or more processors; a memory for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement any of the risk identification methods described above.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the risk identification method as any one of the above.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements a method for risk identification as any of the above.
According to the technology disclosed by the invention, the multi-path outbound system and the risk identification method can be used for identifying the number with the risk, so that a fraud account registered by using the virtual equipment can be determined based on the number with the risk, the risk identification can be carried out based on the fraud account, and the accuracy of the risk identification can be improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a system architecture diagram of one embodiment of a multi-way outbound system according to the present disclosure;
FIG. 2 is a schematic diagram of one application scenario of a multi-way outbound method according to the present disclosure;
FIG. 3 is a flow diagram for one embodiment of a risk identification method according to the present disclosure;
FIG. 4 is a flow diagram of another embodiment of a risk identification method according to the present disclosure;
fig. 5 is a block diagram of an electronic device for implementing a risk identification method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
As shown in fig. 1, the multi-way outbound system 100 may include a multi-way outbound module 101, a data transmission module, and a control module 103; and
the multi-path outbound module 101 is configured to call each to-be-detected number in the to-be-detected number set to obtain call data of each to-be-detected number; sending the call data of each number to be detected to the data transmission module 102;
the data transmission module 102 is configured to transmit call data of each number to be detected to the control module 103;
and the control module 103 is configured to determine a number risk state of each number to be detected based on the call data of each number to be detected.
The multi-path outbound module 101 can implement parallel calling of multiple communication lines, thereby implementing simultaneous calling of multiple numbers and improving calling efficiency. And, for each communication line in the multi-path outbound module 101, call data corresponding to the communication line may be acquired. Wherein the call data may include call audio data, call state information. The call audio data may include preset audio data selected from an audio library based on a preset man-machine conversation policy, and conversation audio data generated by a man-machine conversation during an outgoing call. The call state information is a number dial-through state returned by an operator before the number is dialed through, such as a normal dial-through state, a shutdown state, and the like.
When the multi-path outbound module 101 performs multi-path outbound, each communication line may be used to call one to-be-detected number in the number set to be detected, so as to obtain call data of each to-be-detected number, and transmit the call data of each to-be-detected number to the data transmission module 102. The number set to be detected may be a number whose number risk state needs to be detected, and may be obtained by pre-judging a risk based on number information, or may be preset, which is not limited in this embodiment.
Then, the data transmission module 102 may send the call data of each number to be detected, which is sent by the multi-path outbound module 101, to the control module 103, so that the control module 103 determines the number risk state of each number to be detected based on the call data of each number to be detected. Wherein the number risk status may be risk or non-risk. Alternatively, the number risk status may be a probability or level that the number may be at risk.
In some optional implementation manners of this embodiment, the control module 103 is further configured to determine a risk number from each number to be detected based on a number risk state of each number to be detected; determining a risk account corresponding to the risk number; and adding the risk account number to a preset blacklist.
In this implementation, the control module 103 may determine the risk number based on the number risk status of each number to be detected. For example, the control module 103 may determine a number to be detected for which the number risk status indicates that there is a risk as a risk number. In addition, the control module 103 may further determine a risk account corresponding to the risk number, and add the risk account to a preset blacklist. The risk account number can be an account number registered by the online marketing platform of the risk number, and the risk account number and the risk number have a corresponding relation. The preset blacklist may be a blacklist set not to issue a preference.
Optionally, determining the risk number from each number to be detected based on the number risk status of each number to be detected may include: and for each number to be detected, determining the number to be detected as a risk number in response to determining that the number risk state of the number to be detected indicates that the probability of risk is greater than a preset probability threshold.
Alternatively, the following steps may also be performed: and configuring the preferential information issuing authority of the preset blacklist as prohibition of issuing.
Alternatively, the following steps may also be performed: determining a risk level corresponding to each risk account in a preset blacklist; and configuring preferential information issuing permission corresponding to each risk account based on the risk level. The privilege information issue authority may include, but is not limited to, prohibiting issuing completely, prohibiting issuing within a preset time period, and the like, which is not limited in this embodiment.
In some optional implementation manners of this embodiment, the control module 103 is further configured to, for each number to be detected, in response to determining that the call data of the number to be detected includes call state information, determine, based on the call state information of the number to be detected, a number risk state of the number to be detected.
In this implementation manner, the call state information may be a number dialing state returned by the operator of the number to be detected, such as a normal dialing state, a shutdown state, a blank number state, and the like. The control module 103 may determine the number risk state of the number to be detected directly based on the call state information of the number to be detected, in a case where the call state information of the number to be detected is received. For example, if the call state information of the number to be detected indicates that the number to be detected is in a normal dial-up state, it may be determined that the number risk state of the number to be detected is risk-free. Or, if the call state information of the number to be detected indicates that the number to be detected is in an abnormal dialing state, the number risk state of the number to be detected can be determined to be the existence risk. The abnormal dialing status may include, but is not limited to, a shutdown, a blank number, and the like, which is not limited in this embodiment.
Optionally, the following steps may also be performed: and stopping calling the number to be detected under the condition of receiving the calling state information of the number to be detected. In this way, the call can be stopped before the user receives the call, thereby reducing the impact on the calling user.
In some optional implementation manners of this embodiment, the control module 103 is further configured to, for each number to be detected, in response to determining that the call data of the number to be detected does not include call state information, obtain a call audio of the number to be detected; and determining the number risk state of the number to be detected based on the calling audio and the pre-trained voice recognition model.
In this implementation manner, the control module 103 may obtain the call audio of the number to be detected when it is determined that the call data of the number to be detected does not include the call state information. The call audio may include the man-machine conversation voice of the number to be detected during the outgoing call. The control module 103 may determine the number risk status of the number to be detected based on the call audio and the pre-trained speech recognition model. The pre-trained voice recognition model is used for carrying out voice classification based on voice recognition, and the number risk state of the number to be detected is determined based on the classification result. In addition, the pre-trained speech recognition model may be modeled by using machine learning models such as a recurrent neural network model and a long-short term memory model, which is not limited in this embodiment.
Optionally, the control module 103 may further sample and slice the call audio, and gradually input the pre-trained speech recognition model in segments, so as to use the speech recognition model for the sequence modeling task, perform speech recognition on each audio slice, obtain audio classification, and determine the number risk state of the number to be detected based on the classification result.
In some optional implementations of this embodiment, the control module 103 is further configured to determine an audio category corresponding to the call audio based on the call audio and a pre-trained speech recognition model; and determining the number risk state of the number to be detected based on the audio category.
In this implementation, the control module 103 may input the call audio into a pre-trained speech recognition model, and determine an audio category corresponding to the call audio, where the audio category may include, but is not limited to, a normal dial category, a blank number category, a stop category, and the like. Specifically, the control module 103 may determine the number to be detected in the normal dialing category as a number without risk, and determine the number to be detected in the blank number category or the stop category as a number with risk.
The pre-trained speech recognition model can be obtained by training based on the following steps: obtaining a sample audio slice and a sample audio category; inputting the sample audio slice into a model to be trained to obtain a predicted audio category output by the model to be trained; and training the model to be trained based on the predicted audio class, the sample audio class and the preset loss function until a preset convergence condition is met, and obtaining the pre-trained voice recognition model.
In some optional implementation manners of this embodiment, the data transmission module 102 is further configured to convert the call data of each number to be detected into a digital signal for transmission; and converts the digital signal into an analog signal before transmitting call data of each number to be detected to the control module 103 to transmit the analog signal to the control module 103.
In this implementation manner, the call data output by the multi-path outbound module 101 may be an analog signal, and the data transmission module 102 may convert the analog signal into a digital signal for transmission, so as to reduce noise interference caused by parallel multi-path transmission. And before the data transmission module 102 transmits the digital signal to the control module 103, the digital signal may be converted into an analog signal, so as to transmit the analog signal to the control module 103 for performing multi-channel outbound control.
The multi-path outbound system provided by the embodiment of the disclosure can identify the number with risk, so that a fraud account registered by using the virtual device can be determined based on the number with risk, risk identification is performed based on the fraud account, and accuracy of risk identification can be improved.
With continued reference to fig. 2, a schematic diagram of one application scenario of a multi-way outbound system in accordance with the present disclosure is shown. In the application scenario of fig. 2, the multi-path outbound module may perform multi-path parallel outbound on each number to be detected in the number set 301 to be detected, specifically, each communication module in the multi-path outbound module may call one number to be detected, for example, a first communication module is used to call the number a to be detected to obtain call data a, a second communication module is used to call the number B to be detected to obtain call data B, and a third communication module is used to call the number C to be detected to obtain call data C. Then, the call data 302 obtained by performing the multi-path parallel outbound call may be sent to the control module through the data transmission module, so that the control module determines the number risk state 303 corresponding to the call data 302. For example, a risk state a corresponding to the call data a is determined, a risk state B corresponding to the call data B is determined, and a risk state C corresponding to the call data C is determined. Thereafter, the control module may determine the risk number 304 from the number set 301 to be detected based on the number risk status, and add the risk number 304 to the preset blacklist 305. For example, if it is determined from the number set 301 to be detected that the number at risk is the number a to be detected, the number a to be detected is added to the preset blacklist 305. Optionally, the risk account corresponding to the risk number 304 may be further analyzed, and the risk account is added to the preset blacklist.
With continued reference to FIG. 3, a flow 300 of one embodiment of a risk identification method according to the present disclosure is shown. The risk identification method of the embodiment comprises the following steps:
In this embodiment, the execution main body (including the terminal or the server of the control module 103) may obtain a number to be detected in a number risk state from other devices that are locally stored or connected in advance, so as to obtain a number set to be detected. The number set to be detected may be obtained by pre-judging the risk based on the number information, or may be preset, which is not limited in this embodiment.
In this embodiment, the multi-path call module may perform multi-path parallel call on each number to be detected in the number set to be detected. And for each communication line in the multi-path outbound module, call data corresponding to the communication line can be acquired, and the call data corresponding to the communication line is sent to the control module.
The call data may include call audio data and call state information. The call audio data may include preset audio data selected from an audio library based on a preset man-machine conversation policy, and conversation audio data generated by a man-machine conversation during an outgoing call. The call state information is a number dial-through state returned by an operator before the number is dialed through, such as a normal dial-through state, a shutdown state, and the like.
In this embodiment, the number risk status may be risk or non-risk. Alternatively, the number risk status may be a probability or level that the number may be at risk.
In some optional implementation manners of this embodiment, the control module 103 is further configured to determine a risk number from each number to be detected based on a number risk state of each number to be detected; determining a risk account corresponding to the risk number; and adding the risk account number to a preset blacklist.
In this implementation, the execution subject may determine the risk number based on the number risk status of each number to be detected. For example, the executive agent may determine as a risk number the number to be detected whose number risk status indicates that there is a risk. And the executive body can also determine a risk account corresponding to the risk number and add the risk account to a preset blacklist. The risk account number can be an account number registered by the online marketing platform of the risk number, and the risk account number and the risk number have a corresponding relation. The preset blacklist may be a blacklist set not to issue a preference.
Optionally, determining the risk number from each number to be detected based on the number risk status of each number to be detected may include: and for each number to be detected, determining the number to be detected as a risk number in response to determining that the number risk state of the number to be detected indicates that the probability of risk is greater than a preset probability threshold.
According to the risk identification method provided by the embodiment of the disclosure, the number with the risk can be identified, so that the fraud account registered by using the virtual equipment can be determined based on the number with the risk, and the accuracy of risk identification can be improved based on risk identification.
With continued reference to FIG. 4, a flow 400 of another embodiment of a risk identification method according to the present disclosure is shown. As shown in fig. 4, the risk identification method of the present embodiment may include the following steps:
In this embodiment, for the detailed description of step 401, please refer to the detailed description of step 201, which is not described herein again.
In this embodiment, the call data of each number to be detected output by the multi-path outbound module may be an analog signal, the data transmission module may convert the analog signal into a digital signal for transmission, and the control module may perform digital-to-analog conversion on the digital signal to obtain the analog signal. The execution body can obtain the calling data of each number to be detected based on the analysis of the analog signal.
In this embodiment, the call state information may be a number dial-up state returned by the operator of the number to be detected, such as a normal dial-up state, a shutdown state, a blank number state, and the like. The execution main body can directly determine the number risk state of the number to be detected based on the call state information of the number to be detected under the condition that the call state information of the number to be detected is received. For example, if the call state information of the number to be detected indicates that the number to be detected is in a normal dial-up state, it may be determined that the number risk state of the number to be detected is risk-free. Or, if the call state information of the number to be detected indicates that the number to be detected is in an abnormal dialing state, the number risk state of the number to be detected can be determined to be the existence risk. The abnormal dialing status may include, but is not limited to, a shutdown, a blank number, and the like, which is not limited in this embodiment.
Optionally, the following steps may also be performed: and stopping calling the number to be detected under the condition of receiving the calling state information of the number to be detected. In this way, the call can be stopped before the user receives the call, thereby reducing the impact on the calling user.
In this embodiment, in the case where it is determined that the call data of the number to be detected does not include the call state information, the execution main body may acquire the call audio of the number to be detected. The call audio may include the man-machine conversation voice of the number to be detected during the outgoing call.
In this embodiment, the executive body may determine the number risk status of the number to be detected based on the call audio and the pre-trained speech recognition model. The pre-trained voice recognition model is used for carrying out voice classification based on voice recognition, and the number risk state of the number to be detected is determined based on the classification result. In addition, the pre-trained speech recognition model may be modeled by using machine learning models such as a recurrent neural network model and a long-short term memory model, which is not limited in this embodiment.
In some optional implementations of this embodiment, determining the number risk status of the number to be detected based on the call audio and the pre-trained speech recognition model includes: determining an audio category corresponding to the call audio based on the call audio and a pre-trained voice recognition model; and determining the number risk state of the number to be detected based on the audio category.
Optionally, the execution main body may further sample the call audio into slices, and gradually input the pre-trained speech recognition models in segments, so as to use the speech recognition models for the sequence modeling task, perform speech recognition on each audio slice, obtain audio classification, and determine the number risk state of the number to be detected based on the classification result.
The pre-trained speech recognition model can be obtained by training based on the following steps: obtaining a sample audio slice and a sample audio category; inputting the sample audio slice into a model to be trained to obtain a predicted audio category output by the model to be trained; and training the model to be trained based on the predicted audio class, the sample audio class and the preset loss function until a preset convergence condition is met, and obtaining the pre-trained voice recognition model.
And step 406, determining a risk number from the numbers to be detected based on the number risk state of each number to be detected.
In this embodiment, the call state information may be a number dial-up state returned by the operator of the number to be detected, such as a normal dial-up state, a shutdown state, a blank number state, and the like. The execution main body can directly determine the number risk state of the number to be detected based on the call state information of the number to be detected under the condition that the call state information of the number to be detected is received. For example, if the call state information of the number to be detected indicates that the number to be detected is in a normal dial-up state, it may be determined that the number risk state of the number to be detected is risk-free. Or, if the call state information of the number to be detected indicates that the number to be detected is in an abnormal dialing state, the number risk state of the number to be detected can be determined to be the existence risk. The abnormal dialing status may include, but is not limited to, a shutdown, a blank number, and the like, which is not limited in this embodiment.
In this embodiment, the executing agent may determine the risk number based on the number risk status of each number to be detected. For example, the executive agent may determine as a risk number the number to be detected whose number risk status indicates that there is a risk. And the executive body can also determine a risk account corresponding to the risk number and add the risk account to a preset blacklist. The risk account number can be an account number registered by the online marketing platform of the risk number, and the risk account number and the risk number have a corresponding relation.
And step 408, adding the risk account number to a preset blacklist.
In this embodiment, the preset blacklist may be a blacklist set to not issue a preference.
Alternatively, the following steps may also be performed: and configuring the preferential information issuing authority of the preset blacklist as prohibition of issuing.
Alternatively, the following steps may also be performed: determining a risk level corresponding to each risk account in a preset blacklist; and configuring preferential information issuing permission corresponding to each risk account based on the risk level. The privilege information issue authority may include, but is not limited to, prohibiting issuing completely, prohibiting issuing within a preset time period, and the like, which is not limited in this embodiment.
The risk identification method provided by the above embodiment of the present disclosure may further execute different number risk state determination manners based on whether the call state information is included, so as to improve the number risk state determination efficiency and accuracy. And adding the account corresponding to the number with the risk to a preset blacklist, and performing risk management based on the preset blacklist, so that the risk control effect can be improved.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 5 illustrates a schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 500 comprises a computing unit 501 which may perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data required for the operation of the device 500 can also be stored. The calculation unit 501, the ROM 502, and the RAM503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 501 performs the various methods and processes described above, such as the risk identification method. For example, in some embodiments, the risk identification method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into RAM503 and executed by the computing unit 501, one or more steps of the risk identification method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the risk identification method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (15)
1. A multi-path outbound system comprises a multi-path outbound module, a data transmission module and a control module; and
the multi-path outbound module is used for calling each number to be detected in the number set to be detected to obtain calling data of each number to be detected; sending the calling data of each number to be detected to the data transmission module;
the data transmission module is used for transmitting the call data of each number to be detected to the control module;
and the control module is used for determining the number risk state of each number to be detected based on the call data of each number to be detected.
2. The system of claim 1, wherein the control module is further to: determining a risk number from each number to be detected based on the number risk state of each number to be detected; determining a risk account corresponding to the risk number; and adding the risk account number to a preset blacklist.
3. The system of claim 1, wherein the control module is further to: for each number to be detected, in response to determining that the call data of the number to be detected includes call state information, determining a number risk state of the number to be detected based on the call state information of the number to be detected.
4. The system of claim 1 or 3, wherein the control module is further to: for each number to be detected, in response to determining that the call data of the number to be detected does not include call state information, acquiring call audio of the number to be detected; and determining the number risk state of the number to be detected based on the call audio and the pre-trained voice recognition model.
5. The system of claim 4, wherein the control module is further to: determining an audio category corresponding to the call audio based on the call audio and the pre-trained speech recognition model; and determining the number risk state of the number to be detected based on the audio category.
6. The system of claim 1, wherein the data transmission module is further configured to: converting the call data of each number to be detected into digital signals for transmission; and before transmitting the call data of each number to be detected to the control module, converting the digital signals into analog signals so as to transmit the analog signals to the control module.
7. A risk identification method, comprising:
acquiring a number set to be detected;
determining the call data of each number to be detected in the number set to be detected;
and determining the number risk state of each number to be detected based on the calling data of each number to be detected.
8. The method of claim 7, further comprising:
determining a risk number from the numbers to be detected based on the number risk state of each number to be detected;
determining a risk account corresponding to the risk number;
and adding the risk account number to a preset blacklist.
9. The method of claim 7, wherein determining the number risk status of each number to be detected based on the call data of each number to be detected comprises:
for each number to be detected, in response to determining that the call data of the number to be detected includes call state information, determining a number risk state of the number to be detected based on the call state information of the number to be detected.
10. The method according to claim 7 or 9, wherein the determining the number risk status of each number to be detected based on the call data of each number to be detected comprises:
for each number to be detected, in response to determining that the call data of the number to be detected does not include call state information, acquiring call audio of the number to be detected;
and determining the number risk state of the number to be detected based on the call audio and the pre-trained voice recognition model.
11. The method of claim 10, wherein determining the number risk status of the number to be detected based on the call audio and a pre-trained speech recognition model comprises:
determining an audio category corresponding to the call audio based on the call audio and the pre-trained speech recognition model;
and determining the number risk state of the number to be detected based on the audio category.
12. The method according to claim 7, wherein the determining, for each number to be detected in the set of numbers to be detected, call data for the number to be detected comprises:
for each number to be detected in the number set to be detected, determining the call data of the number to be detected based on the analog signal corresponding to the number to be detected; the analog signal is obtained by performing digital-to-analog conversion on a digital signal transmitted by the data transmission module.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 7-12.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 7-12.
15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 7-12.
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