CN114286343B - Multi-way outbound system, risk identification method, equipment, medium and product - Google Patents
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- CN114286343B CN114286343B CN202111666585.7A CN202111666585A CN114286343B CN 114286343 B CN114286343 B CN 114286343B CN 202111666585 A CN202111666585 A CN 202111666585A CN 114286343 B CN114286343 B CN 114286343B
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Abstract
The disclosure provides a multi-path outbound system, a risk identification method, equipment, a medium and a product, and relates 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-way outbound module is used for calling each number to be detected in the number set to be detected to obtain call data of each number to be detected; and sending the call data of each number to be detected to a data transmission module; the data transmission module is used for transmitting 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 device can improve 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 multipath outbound system, a risk identification method, equipment, a medium and a product.
Background
Currently, automatic outbound services have been widely used in various fields, for example, in online marketing to generate preference information. The automatic outbound service refers to automatic call making and playing pre-recorded voice to a user.
In practice, it has been found that online activities often have difficulty identifying fraudulent account numbers registered with virtual devices when generating the offer information, resulting in malicious acquisition of the offer information.
Disclosure of Invention
The disclosure provides a multi-way outbound system, a risk identification method, equipment, a medium and a product.
According to one aspect of the present disclosure, there is provided a multi-way outbound system including a multi-way outbound module, a data transmission module, and a control module; the multi-way outbound module is used for calling each number to be detected in the number set to be detected to obtain call data of each number to be detected; and sending the call data of each number to be detected to a data transmission module; the data transmission module is used for transmitting 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; each number to be detected in the number set to be detected, and determining call data of the number to be detected; and 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 an electronic device including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the risk identification method as in any of the above.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform any one of the risk identification methods above.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements any of the above for a risk identification method.
According to the technology disclosed by the disclosure, a multi-path outbound system and a risk identification method are provided, and numbers with risks can be identified, so that a fraud account registered by using virtual equipment can be determined based on the numbers with risks, and further risk identification is performed based on the fraud account, and the accuracy of risk identification can be improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for 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 multiple outbound system according to the present disclosure;
FIG. 2 is a schematic diagram of one application scenario of the multi-way outbound method according to the present disclosure;
FIG. 3 is a flow chart of one embodiment of a risk identification method according to the present disclosure;
FIG. 4 is a flow chart of another embodiment of a risk identification method according to the present disclosure;
fig. 5 is a block diagram of an electronic device used to implement a risk identification method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one 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, without conflict, the embodiments of the present disclosure and features of the embodiments may be combined with each other. 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
a multi-way outbound module 101, configured to call each number to be detected in the set of numbers to be detected, so as to obtain call data of each number to be detected; and transmits call data of each number to be detected to the data transmission module 102;
a data transmission module 102, configured to transmit call data of each number to be detected to the control module 103;
and the control module 103 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 multi-way outbound module 101 can realize parallel calling of a plurality of communication lines, so that simultaneous calling of a plurality of numbers is realized, and calling efficiency can be improved. Also, for each communication line in the multi-way 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 human-machine conversation policy, and conversation audio data generated by a human-machine conversation during an outbound call. The call state information is a dialing state returned by an operator before dialing the number, such as a normal dialing state, a stopping state, and the like.
When the multi-way outbound module 101 performs multi-way outbound, each communication line may be used to call one number to be detected in the set of numbers to be detected, so as to obtain call data of each number to be detected, and transmit the call data of each number to be detected to the data transmission module 102. The number set to be detected may be a number in which a number risk state needs to be detected, may be obtained by pre-judging a risk based on number information, or may be preset, which is not limited in this embodiment.
After that, the data transmission module 102 may send the call data of each number to be detected sent by the multi-way outbound module 101 to the control module 103, so that the control module 103 determines the number risk status of each number to be detected based on the call data of each number to be detected. The number risk status may be risk or no risk. Alternatively, the number risk status may be a probability or a level that the number may be at risk.
In some optional implementations of this embodiment, the control module 103 is further configured to determine a risk number from the numbers to be detected based on a number risk status of the numbers 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 the number to be detected whose number risk status indicates that there is a risk as the risk number. Moreover, 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 may be an account number registered by the risk number on-line marketing platform, and the risk account number and the risk number have a corresponding relationship. The preset blacklist may be a blacklist set to not 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: for each number to be detected, determining the number to be detected as a risk number in response to determining that the number risk status of the number to be detected indicates that the probability of risk exists 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 to prohibit issuing.
Alternatively, the following steps may also be performed: determining a risk grade corresponding to each risk account of a preset blacklist; and configuring preferential information issuing authorities corresponding to each risk account based on the risk level. The privilege for issuing the preferential information may include, but is not limited to, completely prohibiting the issuing, prohibiting the issuing within a preset period of time, and the like, which is not limited in this embodiment.
In some optional implementations of this embodiment, the control module 103 is further configured to, for each number to be detected, determine, in response to determining that the call data of the number to be detected includes call state information, a number risk status of the number to be detected based on the call state information of the number to be detected.
In this implementation, 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 stop state, a null state, and the like. The control module 103 may determine the number risk status of the number to be detected directly based on the call status information of the number to be detected, in case call status 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-through state, it may be determined that the number risk state of the number to be detected is no risk. 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, determining that the number risk state of the number to be detected is a risk. The abnormal dialing state may include, but is not limited to, a stop, a null symbol, etc., 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 that the call state information of the number to be detected is received. 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 implementations of this embodiment, the control module 103 is further configured to, for each number to be detected, obtain, in response to determining that call data of the number to be detected does not include call state information, call audio of the number to be detected; and determining the number risk status of the number to be detected based on the calling audio and the pre-trained voice recognition model.
In this implementation, the control module 103 may obtain the call audio of the number to be detected in the case that it is determined that the call data of the number to be detected does not include call state information. The call audio here may include a human-machine conversation voice of the number to be detected during the outbound call. The control module 103 may determine the number risk status of the number to be detected based on the call audio and a 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 a machine learning model such as a cyclic 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 the call audio, segment the call audio into a pre-trained speech recognition model step by step, so that the speech recognition model for the sequence modeling task performs speech recognition on each audio slice to obtain audio classification, and determine the number risk status 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 class 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.
In this implementation, the control module 103 may input the call audio into a pre-trained speech recognition model, and determine an audio class corresponding to the call audio, where the audio class may include, but is not limited to, a normal dial-through class, a null number class, a stop class, and the like. Specifically, the control module 103 may determine the number to be detected of the normal dial-through type as the number without risk, and determine the number to be detected of the null type or the stop type as the number with risk.
The pre-trained speech recognition model may be obtained based on training: acquiring 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 class output by the model to be trained; training a model to be trained based on the predicted audio category, the sample audio category and a preset loss function until a preset convergence condition is met, and obtaining a pre-trained voice recognition model.
In some optional implementations of the present embodiment, the data transmission module 102 is further configured to convert call data of each number to be detected into a digital signal for transmission; and converts the digital signals into analog signals to transmit the analog signals to the control module 103 before transmitting call data of the respective numbers to be detected 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 be capable of converting the analog signal into a digital signal for transmission, so as to reduce noise interference caused by parallel multiplexing. 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-way outbound control.
According to the multi-way outbound system provided by the embodiment of the disclosure, the number with risk can be identified, so that the fraud account registered by using the virtual equipment can be determined based on the number with risk, and further, risk identification is performed based on the fraud account, and the accuracy of risk identification can be improved.
With continued reference to fig. 2, a schematic diagram of one application scenario of the multi-way outbound system according to the present disclosure is shown. In the application scenario of fig. 2, the multiple outbound modules may perform multiple parallel outbound for each number to be detected in the set of numbers to be detected 301, and specifically, each communication module in the multiple outbound modules may call one number to be detected, for example, call the number to be detected a with a first communication module to obtain call data a, call the number to be detected B with a second communication module to obtain call data B, and call the number to be detected C with a third communication module to obtain call data C. Then, for the call data 302 obtained by performing multiple parallel outbound calls, the call data may be sent to the control module through the data transmission module, so that the control module determines the number risk status 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. The control module may then determine the risk number 304 from the set of numbers to be detected 301 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 set of numbers to be detected 301 that the number at risk is the number to be detected a, the number to be detected a 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 a 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:
step 301, a number set to be detected is obtained.
In this embodiment, the executing body (including the terminal or the server of the control module 103) may obtain the number of the risk state of the number to be detected from other devices that are stored locally or are connected in advance, so as to obtain the 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.
Step 302, determining call data of each number to be detected in the set of numbers to be detected.
In this embodiment, the multiple call module may perform multiple parallel calls on each number to be detected in the set of numbers to be detected. And, for each communication line in the multi-way outbound module, call data corresponding to the communication line can be obtained and sent to the control module.
The call data may include call audio data, call state information, among others. The call audio data may include preset audio data selected from an audio library based on a preset human-machine conversation policy, and conversation audio data generated by a human-machine conversation during an outbound call. The call state information is a dialing state returned by an operator before dialing the number, such as a normal dialing state, a stopping state, and the like.
Step 303, determining the number risk status of each number to be detected based on the call data of each number to be detected.
In this embodiment, the number risk status may be risk-present or risk-free. Alternatively, the number risk status may be a probability or a level that the number may be at risk.
In some optional implementations of this embodiment, the control module 103 is further configured to determine a risk number from the numbers to be detected based on a number risk status of the numbers 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 manner, the executing body may determine the risk number based on the number risk status of each number to be detected. For example, the executing body may determine a number to be detected whose number risk status indicates that there is a risk as a risk number. And the executing 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 may be an account number registered by the risk number on-line marketing platform, and the risk account number and the risk number have a corresponding relationship. The preset blacklist may be a blacklist set to not 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: for each number to be detected, determining the number to be detected as a risk number in response to determining that the number risk status of the number to be detected indicates that the probability of risk exists is greater than a preset probability threshold.
According to the risk identification method provided by the embodiment of the disclosure, the number with risk can be identified, so that the fraudulent account registered by using the virtual equipment can be determined based on the number with risk, and further 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:
step 401, a number set to be detected is obtained.
In this embodiment, for the detailed description of step 401, please refer to the detailed description of step 201, and the detailed description is omitted here.
Step 402, determining call data of each number to be detected in the number set to be detected based on an analog signal corresponding to the number to be detected; the analog signals are obtained by digital-to-analog conversion of the digital signals transmitted by the data transmission module.
In this embodiment, the call data of each number to be detected output by the multi-way 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 based on the digital signal. The execution body can obtain the call data of each number to be detected based on the analysis of the analog signal.
Step 403, for each number to be detected, determining a number risk status of the number to be detected based on the call status information of the number to be detected in response to determining that the call data of the number to be detected includes call status information.
In this embodiment, 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 stop state, a null state, and the like. The executing body may determine the number risk status of the number to be detected directly based on the call status information of the number to be detected, under the condition that the call status 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-through state, it may be determined that the number risk state of the number to be detected is no risk. 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, determining that the number risk state of the number to be detected is a risk. The abnormal dialing state may include, but is not limited to, a stop, a null symbol, etc., 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 that the call state information of the number to be detected is received. In this way, the call can be stopped before the user receives the call, thereby reducing the impact on the calling user.
Step 404, 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, obtaining call audio of the number to be detected.
In this embodiment, in the case where it is determined that the call data of the number to be detected does not include call state information, the execution subject may acquire call audio of the number to be detected. The call audio here may include a human-machine conversation voice of the number to be detected during the outbound call.
Step 405, determining the number risk status of the number to be detected based on the call audio and the pre-trained speech recognition model.
In this embodiment, the executing body may determine the number risk status of the number to be detected based on the call audio and the pre-trained voice 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 a machine learning model such as a cyclic neural network model and a long-short-term memory model, which is not limited in this embodiment.
In some optional implementations of the present 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 class corresponding to the calling audio based on the calling 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 executing body may sample the call audio, stepwise input the call audio into a pre-trained speech recognition model, so that the speech recognition model for the sequence modeling task performs speech recognition on each audio slice to obtain audio classification, and determines the number risk status of the number to be detected based on the classification result.
The pre-trained speech recognition model may be obtained based on training: acquiring 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 class output by the model to be trained; training a model to be trained based on the predicted audio category, the sample audio category and a preset loss function until a preset convergence condition is met, and obtaining a pre-trained voice recognition model.
Step 406, determining the risk number from the numbers to be detected based on the number risk status of each number to be detected.
In this embodiment, 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 stop state, a null state, and the like. The executing body may determine the number risk status of the number to be detected directly based on the call status information of the number to be detected, under the condition that the call status 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-through state, it may be determined that the number risk state of the number to be detected is no risk. 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, determining that the number risk state of the number to be detected is a risk. The abnormal dialing state may include, but is not limited to, a stop, a null symbol, etc., which is not limited in this embodiment.
Step 407, determining a risk account corresponding to the risk number.
In this embodiment, the executing body may determine the risk number based on the number risk status of each number to be detected. For example, the executing body may determine a number to be detected whose number risk status indicates that there is a risk as a risk number. And the executing 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 may be an account number registered by the risk number on-line marketing platform, and the risk account number and the risk number have a corresponding relationship.
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 to prohibit issuing.
Alternatively, the following steps may also be performed: determining a risk grade corresponding to each risk account of a preset blacklist; and configuring preferential information issuing authorities corresponding to each risk account based on the risk level. The privilege for issuing the preferential information may include, but is not limited to, completely prohibiting the issuing, prohibiting the issuing within a preset period of time, and the like, which is not limited in this embodiment.
According to the risk identification method provided by the embodiment of the disclosure, different number risk state determination modes can be executed based on whether the call state information is contained, so that the number risk state determination efficiency and accuracy are improved. And adding the account corresponding to the number with risk to a preset blacklist, and performing risk management based on the preset blacklist, so that the risk control effect can be improved.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 5 illustrates a schematic block diagram of an example electronic device 500 that may 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 telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 500 includes a computing unit 501 that can perform various suitable actions and processes according to 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 computing unit 501, ROM 502, and RAM503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Various components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, etc.; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508 such as a magnetic disk, an 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 via a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as a risk identification method. For example, in some embodiments, the risk identification method may be implemented as a computer software program tangibly embodied on 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 by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On 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, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code 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 code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. 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. The 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 pointing device (e.g., a mouse or 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 may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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 a client and a server. The client and server are typically 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 incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (12)
1. A multipath outbound system comprises a multipath 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 call data of each number to be detected; and sending the call data of each number to be detected to the data transmission module;
the data transmission module is used for transmitting call data of each number to be detected to the control module;
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; 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; adding the risk account number to a preset blacklist; determining a risk level corresponding to each risk account of the preset blacklist; based on the risk level, configuring the privilege of issuing the preferential information corresponding to each risk account, wherein the privilege of issuing the preferential information comprises the following steps: and completely prohibiting the release and prohibiting the release within a preset time period.
2. The system of claim 1, wherein the control module is further to: for each number to be detected, determining a number risk status of the number to be detected based on the call status information of the number to be detected in response to determining that the call data of the number to be detected includes call status information.
3. The system of claim 1 or 2, wherein the control module is further to: for each number to be detected, acquiring call audio of the number to be detected in response to determining that call data of the number to be detected does not include call state information; and determining the number risk state of the number to be detected based on the calling audio and a pre-trained voice recognition model.
4. The system of claim 3, wherein the control module is further to: determining an audio class 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 class.
5. The system of claim 1, wherein the data transmission module is further to: converting call data of each number to be detected into digital signals for transmission; and converting the digital signals into analog signals before transmitting call data of each number to be detected to the control module, so as to transmit the analog signals to the control module.
6. A risk identification method, comprising:
acquiring a number set to be detected;
determining call data of each number to be detected in the number set to be detected;
determining the number risk state of each number to be detected based on the call data of each number to be detected;
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; adding the risk account number to a preset blacklist; determining a risk level corresponding to each risk account of the preset blacklist; based on the risk level, configuring the privilege of issuing the preferential information corresponding to each risk account, wherein the privilege of issuing the preferential information comprises the following steps: and completely prohibiting the release and prohibiting the release within a preset time period.
7. The method of claim 6, wherein the determining the number risk status of each number to be detected based on call data of each number to be detected comprises:
for each number to be detected, determining a number risk status of the number to be detected based on the call status information of the number to be detected in response to determining that the call data of the number to be detected includes call status information.
8. The method according to claim 6 or 7, 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, acquiring call audio of the number to be detected in response to determining that call data of the number to be detected does not include call state information;
and determining the number risk state of the number to be detected based on the calling audio and a pre-trained voice recognition model.
9. The method of claim 8, wherein the 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 class 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 class.
10. The method of claim 6, wherein the determining call data for each number to be detected in the set of numbers to be detected comprises:
determining call data of each number to be detected in the number set to be detected based on an analog signal corresponding to the number to be detected; the analog signals are obtained by digital-to-analog conversion of the digital signals transmitted by the data transmission module.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
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 6-10.
12. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 6-10.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105827787A (en) * | 2015-01-04 | 2016-08-03 | 中国移动通信集团公司 | Number marking method and number marking device |
| CN111274589A (en) * | 2020-01-15 | 2020-06-12 | 北京小米移动软件有限公司 | Authority control method, authority control device and computer storage medium |
| CN111654866A (en) * | 2020-05-29 | 2020-09-11 | 北京合力思腾科技股份有限公司 | Method, device and computer storage medium for preventing mobile communication from fraud |
| CN112380556A (en) * | 2020-11-30 | 2021-02-19 | 南京云悦欣自动化工程有限公司 | Account authority management distribution method |
| CN113067949A (en) * | 2021-03-16 | 2021-07-02 | 浙江百应科技有限公司 | Batch number detection method and device for outgoing calls of telephones and electronic equipment |
| CN113722433A (en) * | 2021-08-30 | 2021-11-30 | 中国建设银行股份有限公司 | Information pushing method and device, electronic equipment and computer readable medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150004965A1 (en) * | 2013-06-30 | 2015-01-01 | Avaya Inc. | System and method for separation of call origination and call delivery techniques |
-
2021
- 2021-12-31 CN CN202111666585.7A patent/CN114286343B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105827787A (en) * | 2015-01-04 | 2016-08-03 | 中国移动通信集团公司 | Number marking method and number marking device |
| CN111274589A (en) * | 2020-01-15 | 2020-06-12 | 北京小米移动软件有限公司 | Authority control method, authority control device and computer storage medium |
| CN111654866A (en) * | 2020-05-29 | 2020-09-11 | 北京合力思腾科技股份有限公司 | Method, device and computer storage medium for preventing mobile communication from fraud |
| CN112380556A (en) * | 2020-11-30 | 2021-02-19 | 南京云悦欣自动化工程有限公司 | Account authority management distribution method |
| CN113067949A (en) * | 2021-03-16 | 2021-07-02 | 浙江百应科技有限公司 | Batch number detection method and device for outgoing calls of telephones and electronic equipment |
| CN113722433A (en) * | 2021-08-30 | 2021-11-30 | 中国建设银行股份有限公司 | Information pushing method and device, electronic equipment and computer readable medium |
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