Collection method and device based on voice recognition, storage medium and computer equipment

Abstract

The invention relates to the technical field of debt collection equipment, and discloses a collection method and device based on voice recognition, a storage medium and computer equipment, and the method comprises the steps: S1, obtaining overdue customer information, S2, carrying out data classification and data evaluation, S3, carrying out the initial communication and contact, carrying out the voice recognition of the telephone communication content between a debtor and a collection worker through a collection AI model program, and carrying out the collection of the debt. The method comprises the following steps: S1, carrying out real-time pairing according to communication contents to generate communication verbal skills required by workers, and assisting the workers to communicate with the communication verbal skills, S4, negotiating repayment, S5, carrying out secondary communication and follow-up, and S6, carrying out final communication. According to the voice recognition-based collection method and device, the storage medium and the computer equipment, an added collection AI model program performs voice recognition on telephone communication content between a debtor and a collection worker, and a proper debt collection talk skill or debt repayment scheme is generated according to the communication content, so that the worker is assisted to communicate with the collection method and device, and the collection efficiency is improved. And the debt listing efficiency is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of debt collection equipment, and in particular to a debt collection method, device, storage medium and computer equipment based on speech recognition. Background Art

[0002] Bank debt collection refers to the act of pursuing the repayment of debts from borrowers who have overdue payments, that is, the bank pursues the principal and interest of the overdue loan from the borrower. Bank debt collection in the prior art usually includes telephone collection, SMS collection, letter collection, on-site collection and legal litigation; For the above debt collection methods, staff need to spend a lot of energy collecting and arranging the list of debtors, and at the same time, they need to cooperate with communication equipment to make multiple call records with the debtors. At the same time, due to the large number of debtors, when the staff finally communicate with the debtors, they cannot accurately select appropriate debt collection words or debt repayment plans according to the actual situation of the debtors, resulting in low debt collection efficiency. At the same time, it will also increase the workload and work pressure of the staff. Summary of the Invention

[0003] (I) Technical Problems to be Solved In view of the deficiencies of the prior art, the present invention provides a debt collection method, device, storage medium and computer equipment based on speech recognition to solve the technical problems that when the staff communicate with the debtors, they cannot accurately select appropriate debt collection words or debt repayment plans according to the actual situation of the debtors, resulting in low debt collection efficiency, and at the same time, it will also increase the workload and work pressure of the staff.

[0004] (II) Technical Solutions To achieve the above object, the present invention provides the following technical solutions: A debt collection method based on speech recognition, including: S1: Obtain overdue customer information First, the debt collector collects the information of overdue customers to ensure that the contacted object is indeed the debtor and the contact information is accurate, and then enters the overdue customer information into the computer equipment used by the debt collection device; S2: Data classification and data evaluation An AI model program for debt collection is built into the debt collection device. Through the AI model program for debt collection, the overdue customer information is classified and sorted, and the specific situation of the debt is deeply understood through the model, including the amount of arrears, the number of overdue days and interest calculation. At the same time, the historical repayment records of the debtors are classified and displayed, and finally their repayment ability and repayment willingness are evaluated, and the list of debtors after evaluation is classified and saved; S3: Initial communication and contact The computer device for the collection device transmits the classified debtor information to the inside of the collection device through the communication device. The staff cooperates with the collection device through the debtor list, contacts the debtor, clearly expresses the purpose of debt collection, and politely reminds the debtor to fulfill the repayment obligation. At this stage, the computer device for the collection device will cooperate with the collection device at the same time, save the recordings of the debtor and the staff, classify the debtor according to the communication between the debtor and the staff and the AI model, and at the same time, the collection AI model program performs speech recognition on the phone communication content between the debtor and the collection staff, generates appropriate collection words according to the communication content, assists the staff to communicate with the debtor, and can generate the communication words needed by the staff in real time for pairing; S4: Negotiate repayment Record and classify the debtor's information through the collection device to assist the staff to enter the stage of negotiating repayment. At the same time, the collection device formulates a flexible repayment plan according to the debtor's income status, family burden and living environment. Through formulating the plan, the staff can communicate with the debtor quickly, which is in line with the bank's interests and can be accepted by the debtor. And the computer device for the collection device can, according to the communication situation between the staff and the debtor, cooperate with the communication device to transmit the written information to the debtor's email, and at the same time urge the debtor to make a written confirmation and record it for filing; S5: Secondary communication and follow-up The computer device for the collection device classifies and compares the debtor list according to the debtor's repayment information combined with the debtor list, classifies the debtor list into three categories: repaid, continuous repayment and refused repayment lists, and conducts secondary communication with the debtors in the continuous repayment list and the refused repayment list. The computer device for the collection device understands the reasons according to the calls between the staff and the debtor, and adjusts the collection strategy according to the actual situation. At the same time, the collection AI model program performs speech recognition on the phone communication content between the debtor and the collection staff again, generates the communication words needed by the staff in real time for pairing again according to the communication content, exerts secondary pressure on the debtor, and records the communication situation, as well as the debtor's repayment attitude and subsequent commitments. At the same time, record, classify and update the debtor list again; S6: Final communication Based on the three-classification lists, the list of debtors who refuse to perform their debts is determined. The computer equipment used in the collection device assists the staff in drafting the complaint, and writes down the basic information of the plaintiff and the defendant, the litigation request, the facts and reasons. Then, based on the size of the debt, the nature of the debt and the legal provisions, the court with jurisdiction is determined and the complaint is submitted to the court through the system. The litigation fees are then automatically paid according to the court's notice. At the same time, a copy of the complaint is integrated and sent to the staff's email address. At the same time, the staff is urged to attend the trial, and the debtor's arrears record and the record of secondary communication information are automatically integrated into simple and clear evidence. Finally, the evidence is printed and distributed to the staff to assist in presenting evidence and conducting debate at the trial.

[0005] Preferably, the debt collection AI model program is a Caffe framework, and the debt collection AI model program is stored through a storage device. The Caffe framework provides a concise and clear Caffe framework. ++ The code structure and intuitive Python interface reduce the difficulty of deep learning tasks. The Caffe framework performs well in computing performance. It uses efficient C ++ It can quickly perform model training and inference. When processing large-scale debtor list data sets, Caffe can make full use of hardware resources to improve training speed and efficiency. The Caffe framework supports the training of large-scale deep learning models and can process large amounts of data and parameters. Caffe performs well in complex tasks such as image classification, target detection, and face recognition, and can train high-precision models. At the same time, the Caffe framework has active community support, and staff can get help and exchange experiences in the community. The Caffe framework supports many different types of neural network architectures, including convolutional neural networks, recurrent neural networks, etc. Users can flexibly build and train neural networks according to their needs. Caffe also provides a wealth of plug-ins and extension interfaces.

[0006] Preferably, in step S1, after the debtor's data is collected, the data is uploaded to the collection AI model program for training by the collection AI model program. By training the AI ​​model, it can quickly process and analyze massive data, significantly improving work efficiency. Through continuous learning and optimization, the AI ​​model has high accuracy in prediction and decision-making, improving the accuracy and efficiency of communications between staff and debtors, and at the same time enabling the AI ​​model to adapt to a variety of complex environments, solve practical problems, and enable the AI ​​model to significantly improve the quality of service for staff.

[0007] Debt collection device, including a communication device and a call module, and the call module is connected to the communication device through a twisted pair. A control module and a display screen are respectively assembled on the upper end of the communication device. Storage mechanisms are embedded on both sides of the upper end of the communication device, and disinfectant is filled at the inner bottom of the storage mechanism. A disinfection mechanism is assembled inside the storage mechanism. The communication device and the call module are common telephone devices in the prior art. Among them, the call module includes a microphone, as well as a protruding call end and a sound-emitting end. The control module and the display screen enable the staff to control the communication device to work. The disinfectant inside the storage mechanism can be alcohol disinfectant, chlorine-containing disinfectant, peroxide disinfectant, etc. The storage mechanism can support and protect the disinfection mechanism, and at the same time can store disinfectant to disinfect and clean the call module. And the disinfectant should use a type with a relatively fast volatilization rate. At the same time, the call module fits perfectly with the inner cavity of the storage mechanism, and then can seal the storage mechanism, thereby avoiding the volatilization of the disinfectant, and a filter membrane is assembled inside the call module, and the filter membrane can prevent the vaporization of the disinfectant from entering the inside of the call module and damaging the internal components thereof.

[0008] Preferably, the disinfection mechanism includes a frame. A sliding frame is slidably arranged inside the frame. A sponge block is embedded inside the sliding frame. Sponge columns are assembled on both sides of the bottom of the sponge block. The bottom of the sponge column extends into the disinfectant. Connecting rods are connected to both sides of the bottom of the sliding frame through hinges, and the other end of the connecting rod is connected to the storage mechanism through a hinge. Springs are evenly arranged at the bottom of the frame and are connected to the storage mechanism. The frame can move up and down inside the storage mechanism. The spring can drive the frame to return and rise after being released from pressure. The sliding frame can move left and right reciprocally inside the frame. The sponge block and the sponge column can absorb the disinfectant inside the storage mechanism. At the same time, the sponge block and the sponge column are connected to the sliding frame by traditional clamping methods, which is convenient for the staff to replace them. The connecting rod can push the sliding frame when the frame descends, so that it slides and disinfects at the bottom of the call module.

[0009] Preferably, guide blocks are embedded on both sides of the sliding frame, and track grooves adapted to the guide blocks are opened at both ends of the inner side of the frame. The track grooves are slidably connected to the guide blocks. The guide blocks can cooperate with the springs to guide the sponge block so that it can only move in a horizontal straight line inside the frame, greatly improving the overall stability of the disinfection mechanism during operation.

[0010] A computer device for a collection device, including a display module and a host, and the host is embedded in the inner bottom of the display module. A bracket is assembled at the bottom of the host. A signal line is assembled on the back of the display module, and the signal line is connected to a communication device. The display module consists of a camera, a microphone and a display component. At the same time, the display module and the host are connected by a line. The host consists of a processing chip, a memory, a GPU and a memory module. And a heat dissipation system is also equipped inside the host to dissipate heat when the host is working at high frequency. At the same time, an assembly socket is provided outside the host, and a keyboard, a mouse and a stylus can be externally connected to provide diversified operation methods for the staff.

[0011] The inside of the host includes a processing chip, and a collection AI model program is stored inside the host. When the collection AI model program is executed by the processing chip, the processing chip executes the collection method based on voice recognition as described above.

[0012] Preferably, the display module includes a liquid crystal display screen, and a printing device is assembled on the back of the liquid crystal display screen. The printing device is directly connected to the liquid crystal display screen by a line. A communication device is assembled on the back of the host. The combination of the display module and the host can display the list of debtors and integrate data, and can also print and display the aggregated data through an external printing device.

[0013] A computer storage medium, including a storage module with a collection AI model program built in. The storage module is inserted into the data interface of the host, and the storage module is connected to the host through the interface to provide a debt collection AI model for the host to assist the staff in working. The storage module is connected to the host by plugging, which is convenient for the staff to disassemble it to maintain and collect the collection AI model program inside.

[0014] The collection AI model program is built in the computer storage medium. When the collection AI model program is executed by the processing chip, the processing chip executes the collection method based on voice recognition as described above.

[0015] (III) Beneficial effects Compared with the prior art, the present invention provides a collection method, device, storage medium and computer device based on voice recognition, and has the following beneficial effects: 1. The debt collection method, device, storage medium and computer equipment based on speech recognition, when collecting debts from debtors, through the added display module, host and storage module and the built-in debt collection AI model program, classify the list of debtors three times. At the same time, the debt collection AI model program performs speech recognition on the phone communication content between the debtors and the debt collection staff, generates appropriate debt collection words or debt repayment plans according to the communication content, assists the staff in communicating with them, greatly improves the debt collection efficiency, reduces the thinking frequency of the staff, and at the same time improves the debt collection success rate. It can also assist the staff in integrating and processing the information of debtors who refuse to repay, and automatically submit the processed list to the court, assist the staff in paying fees and arguing in the online court, thus greatly reducing the workload of the staff; 2. The debt collection method, device, storage medium and computer equipment based on speech recognition, when the communication device is in use, through the added disinfection mechanism, avoids the saliva droplets splashing on the outside of the communication device when the staff communicates with the debtors, thus causing peculiar smell on the outside of the communication device and increasing the risk of germ transmission, and improves the comfort of the staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is the flowchart of the debt collection method.

[0017] Figure 2 is the external schematic diagram of the display module of the present invention; Figure 3 is the external schematic diagram of the communication device of the present invention; Figure 4 is the external schematic diagram of the storage mechanism of the present invention; Figure 5 is the external schematic diagram of the disinfection mechanism of the present invention; Figure 6 is the external schematic diagram of the sliding frame of the present invention.

[0018] DESCRIPTION OF THE REFERENCE NUMERALS 1. Display module; 2. Host; 21. Bracket; 3. Storage module; 4. Communication device; 41. Control module; 42. Display screen; 43. Call module; 5. Storage mechanism; 6. Disinfection mechanism; 61. Frame; 62. Spring; 63. Sliding frame; 64. Sponge block; 65. Sponge column; 66. Connecting rod; 67. Guide block; 7. Signal wire. DETAILED DESCRIPTION OF THE INVENTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] The present invention provides a technical solution. Please refer to Figure 1 and Figure 2 , a collection method based on speech recognition, including S1: Obtain overdue customer information The collector first collects the information of the overdue customers to ensure that the contacted object is indeed the debtor and the contact information is accurate, and then enters the overdue customer information into the computer device used by the collection device; S2: Data classification and data evaluation The collection device is built with a collection AI model program. Through the collection AI model program, the overdue customer information is classified and sorted, and the specific situation of the debt is deeply understood through the model, including the amount of arrears, the number of overdue days, and interest calculation. At the same time, the historical repayment records of the debtor are classified and displayed, and finally their repayment ability and repayment willingness are evaluated, and the list of debtors after evaluation is classified and saved; S3: Initial communication and contact The computer device used by the collection device transmits the classified debtor information to the inside of the collection device through the communication device. The staff cooperates with the collection device through the debtor list to contact the debtor, clearly express the purpose of debt collection, and politely remind the debtor to fulfill the repayment obligation. At this stage, the computer device used by the collection device will cooperate with the collection device to save the recording of the debtor and the staff, and classify the debtor according to the communication between the debtor and the staff with the cooperation of the AI model. At the same time, the collection AI model program performs speech recognition on the phone communication content between the debtor and the collection staff, generates appropriate collection words according to the communication content, and assists the staff to communicate with them, and can generate the communication words required by the staff in real time; S4: Negotiate repayment The collection device records and classifies the information of the debtor to assist the staff to enter the stage of negotiating repayment. At the same time, the collection device formulates a flexible repayment plan according to the debtor's income status, family burden and living environment. Through formulating the plan, the staff can communicate with the debtor quickly, which is in line with the interests of the bank and can be accepted by the debtor. And the computer device used by the collection device can, according to the communication situation between the staff and the debtor, cooperate with the communication device to transmit the written information to the debtor's email, and at the same time urge the debtor to make a written confirmation and record it for filing; S5: Second communication and follow-up The computer equipment used in the collection device classifies and compares the debtor's repayment information in combination with the debtor list, and classifies the debtor list three times into the list of repaid, continuous repayment and refusal to repay, and conducts secondary communication with the debtors on the continuous repayment list and the refusal to repay list. The computer equipment used in the collection device understands the reasons based on the conversations between the staff and the debtor, and adjusts the collection strategy according to the actual situation. At the same time, the collection AI model program again performs voice recognition on the telephone communication content between the debtor and the collection staff, and generates the communication words required by the staff in real time based on the communication content, puts secondary pressure on the debtor, and records the communication situation, as well as the debtor's repayment attitude and subsequent commitments, and records, classifies and updates the debtor list again; S6: Final Communication Based on the three-classification lists, the list of debtors who refuse to perform their debts is determined. The computer equipment used in the collection device assists the staff in drafting the complaint, and writes down the basic information of the plaintiff and the defendant, the litigation request, the facts and reasons. Then, based on the size of the debt, the nature of the debt and the legal provisions, the court with jurisdiction is determined and the complaint is submitted to the court through the system. The litigation fees are then automatically paid according to the court's notice. At the same time, a copy of the complaint is integrated and sent to the staff's email address. At the same time, the staff is urged to attend the trial, and the debtor's arrears record and the record of secondary communication information are automatically integrated into simple and clear evidence. Finally, the evidence is printed and distributed to the staff to assist in presenting evidence and conducting debate at the trial.

[0021] See also Figure 3 and Figure 4 The debt collection AI model program is a Caffe framework, and the debt collection AI model program is stored through a storage device. The Caffe framework provides a concise and clear C ++ The code structure and intuitive Python interface reduce the difficulty of deep learning tasks. The Caffe framework performs well in computing performance. It uses efficient C ++Implementation, capable of quickly performing model training and inference. When dealing with large-scale debtor list datasets, Caffe can make full use of hardware resources to improve training speed and efficiency. The Caffe framework supports the training of large-scale deep learning models, can handle a large amount of data and parameters, and performs excellently in complex tasks such as image classification, object detection, and face recognition, capable of training high-precision models. At the same time, the Caffe framework has active community support, and staff can obtain help and exchange experiences in the community. In addition, the Caffe framework supports a variety of different types of neural network architectures, including convolutional neural networks, recurrent neural networks, etc., and users can flexibly build and train neural networks according to their own needs. Caffe also provides rich plug-ins and extension interfaces.

[0022] Please refer to Figure 5 and Figure 6 In step S1, after collecting the debtor's data, the data is uploaded to the collection AI model program for training. By training the AI model, it can quickly process and analyze massive data, significantly improving work efficiency. Through continuous learning and optimization, the AI model has high accuracy in prediction and decision-making, improving the accuracy and efficiency of the staff's calls with debtors. At the same time, the AI model can adapt to various complex environments, solve practical problems, and significantly improve the service quality for the staff.

[0023] The collection device includes a communication device 4 and a call module 43, and the call module 43 is connected to the communication device 4 through a twisted pair. The upper end of the communication device 4 is respectively equipped with a control module 41 and a display screen 42. Storage mechanisms 5 are embedded on both sides of the upper end of the communication device 4, and disinfectant is filled at the inner bottom of the storage mechanism 5. A disinfection mechanism 6 is assembled inside the storage mechanism 5. The communication device 4 and the call module 43 are common telephone devices in the prior art. The call module 43 includes a microphone, as well as a protruding call end and a sound-emitting end. The control module 41 and the display screen 42 enable the staff to control the communication device 4 to work. The disinfectant inside the storage mechanism 5 can be alcohol disinfectant, chlorine-containing disinfectant, peroxide disinfectant, etc. The storage mechanism 5 can support and protect the disinfection mechanism 6, and at the same time can store disinfectant to disinfect and clean the call module 43. The disinfectant should be of a type with relatively fast volatilization. At the same time, the call module 43 fits perfectly with the inner cavity of the storage mechanism 5, and then can seal the storage mechanism 5 to avoid the volatilization of the disinfectant. In addition, a filter membrane is assembled inside the call module 43, and the filter membrane can prevent the vaporization of the disinfectant from entering the inside of the call module 43 and damaging the internal components.

[0024] The call end and the sound - emitting end are respectively the microphone and the receiver inside the telephone in the prior art. Their main functions are as follows: Through the internal sound - sensing sensor, the sound vibration can be converted into an electrical signal. When the sound wave vibrates, it will cause the vibration of the carbon grains in the metal box, thereby changing the resistance in the circuit, and then generating a current with varying strength. After the sound is transmitted through the device, the sound - emitting end can convert the electrical signal into sound through the internal sound - emitting diaphragm and electromagnetic device. When the current with varying strength passes through the receiver, it will cause the change of the electromagnetic force of the electromagnet, and then the thin iron diaphragm will vibrate under the action of the changing magnetic force, thus emitting sound.

[0025] The disinfection mechanism 6 includes a frame 61. A sliding frame 63 is slidably arranged inside the frame 61. A sponge block 64 is embedded inside the sliding frame 63. Sponge columns 65 are assembled on both sides of the bottom of the sponge block 64. The bottom of the sponge columns 65 extends into the disinfectant solution. Both sides of the bottom of the sliding frame 63 are connected by hinges to a connecting rod 66, and the other end of the connecting rod 66 is connected to the storage mechanism 5 by a hinge. Springs 62 are evenly distributed at the bottom of the frame 61, and the springs 62 are connected to the storage mechanism 5. The frame 61 can move up and down inside the storage mechanism 5. The springs 62 can drive the frame 61 to return and rise after being released from being pressed. The sliding frame 63 can reciprocate left and right inside the frame 61. The sponge block 64 and the sponge columns 65 can absorb the disinfectant solution inside the storage mechanism 5. At the same time, the sponge block 64 and the sponge columns 65 are connected to the sliding frame 63 by traditional clamping methods, which is convenient for the staff to replace them. The connecting rod 66 can push the sliding frame 63 when the frame 61 descends, so that it slides and disinfects at the bottom of the call module 43.

[0026] Guide blocks 67 are embedded on both sides of the sliding frame 63. Track grooves adapted to the guide blocks 67 are opened at both ends of the inner side of the frame 61. The track grooves are slidably connected to the guide blocks 67. The guide blocks 67 can cooperate with the springs 62 to guide the sponge block 64, so that it can only move in a straight line horizontally inside the frame 61, greatly improving the overall stability of the disinfection mechanism 6 during operation.

[0027] A computer device for a collection device, including a display module 1 and a host 2, and the host 2 is embedded in the inner bottom of the display module 1. A bracket 21 is assembled at the bottom of the host 2. A signal line 7 is assembled on the back of the display module 1, and the signal line 7 is connected to a communication device 4. The display module 1 is composed of a camera, a microphone and a display component. At the same time, the display module 1 and the host 2 are connected by a line. The host 2 is composed of a processing chip, a memory, a GPU and a memory module. And a heat dissipation system is also equipped inside the host 2 to dissipate heat from the host 2 when it is working at high frequency. At the same time, an assembly socket is provided outside the host 2, and a keyboard, a mouse and a stylus can be externally connected to it, providing diversified operation methods for the staff.

[0028] The inside of the host 2 includes a processing chip, and a collection AI model program is stored inside the host 2. When the collection AI model program is executed by the processing chip, the processing chip executes the collection method based on voice recognition as described above.

[0029] The display module 1 includes a liquid crystal display screen, and a printing device is assembled on the back of the liquid crystal display screen. The printing device is directly connected to the liquid crystal display screen by a line. A communication device is assembled on the back of the host 2. The combination of the display module 1 and the host 2 can display the list of debtors and integrate data, and can also print and display the collected data through an externally connected printing device.

[0030] A computer storage medium, including a storage module 3 with a collection AI model program built in. The storage module 3 is inserted into the data interface of the host 2, and the storage module 3 is connected to the host 2 through the interface. Then it is convenient for the staff to disassemble it and maintain and collect the collection AI model program inside.

[0031] In this solution, the debt list is first input into the inside of the display module 1, and the debt list is classified by the AI model inside the display module 1. Then the classified debt list is transmitted into the inside of the communication device 4, and the debtor is contacted through the communication device 4. At the same time, the display module 1 can record and record the call records, and generate appropriate words according to the call content. The staff can communicate with the debtor according to the words and record the contact results. Then, according to the contact results, they are recorded and classified again. And the call end and the voice end of the call module 43 are placed inside the storage mechanism 5. The disinfection mechanism 6 is driven to descend by pressure. At this time, the connecting rod 66 will push the sliding frame 63 to move. The sliding frame 63 absorbs the disinfectant through the sponge block 64 and the sponge column 65 to wipe and disinfect the call end of the call module 43, avoiding the saliva of the staff splashing outside the call module 43 and causing peculiar smell and bacterial transmission; When collecting debts from debtors, through the added display module 1, host 2, storage module 3 and the built-in debt collection AI model program, the list of debtors is classified three times. At the same time, it can assist the staff to communicate with them according to the debtors' words, greatly improving the debt collection efficiency, reducing the thinking frequency of the staff, and at the same time increasing the success rate of debt collection. It can also assist the staff to integrate the information of debtors who refuse to repay, and automatically submit the processed list to the court, assist the staff in paying fees and arguing in the online court, thus greatly reducing the workload of the staff; When the communication device 4 is in use, through the added disinfection mechanism 6, it can prevent the staff's saliva droplets from splashing on the outside of the communication device 4 when communicating with debtors, thus preventing the outside of the communication device 4 from generating strange smells and increasing the risk of germ transmission, and improving the comfort of the staff.

[0032] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0033] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A debt collection method based on speech recognition, characterized in that: include S1: Obtain overdue customer information The debt collector first collects the overdue customer's information to ensure that the person being contacted is indeed the debtor and that the contact information is accurate, and then enters the overdue customer information into the computer equipment used by the debt collection device; S2: Data classification and data evaluation The collection device has a built-in collection AI model program, which can classify and sort overdue customer information, and use the model to gain an in-depth understanding of the specific debt situation, including the amount of debt, overdue days, and interest calculation. It can also display the debtor's historical repayment records in categories, ultimately assess their repayment ability and willingness, and save the assessed debtor list in categories; S3: Initial communication and contact The computer equipment used in the collection device transmits the classified debtor information to the inside of the collection device through the communication equipment. The staff cooperates with the collection device through the debtor list to contact the debtor, clearly express the purpose of debt collection, and politely remind the debtor to fulfill the repayment obligation. At this stage, the computer equipment used in the collection device will cooperate with the collection device at the same time to save the recording of the debtor and the staff, and classify the debtor according to the communication between the debtor and the staff in cooperation with the AI ​​model. At the same time, the collection AI model program performs voice recognition on the telephone communication content between the debtor and the collection staff, and generates the communication words required by the staff in real time according to the communication content, to assist the staff in communicating with them; S4: Negotiate repayment The debtor's information is recorded and classified through the collection device, which assists the staff to enter the negotiation repayment stage. At the same time, the collection device formulates a flexible repayment plan based on the debtor's income status, family burden and living environment. Through the formulation of the plan, the staff and the debtor can communicate quickly, which is in line with the interests of the bank and can be accepted by the debtor. The computer equipment used in the collection device transmits written information to the debtor's mailbox according to the communication status between the staff and the debtor, and cooperates with the communication equipment, and urges the debtor to make written confirmation and record it for filing; S5: Second communication and follow-up The computer equipment used in the collection device classifies and compares the debtor's repayment information in combination with the debtor list, and classifies the debtor list three times into the list of repaid, continuous repayment and refusal to repay, and conducts secondary communication with the debtors on the continuous repayment list and the refusal to repay list. The computer equipment used in the collection device understands the reasons based on the conversations between the staff and the debtor, and adjusts the collection strategy according to the actual situation. At the same time, the collection AI model program again performs voice recognition on the telephone communication content between the debtor and the collection staff, and generates the communication words required by the staff in real time based on the communication content, puts secondary pressure on the debtor, and records the communication situation, as well as the debtor's repayment attitude and subsequent commitments, and records, classifies and updates the debtor list again; S6: Final Communication Based on the three-classification lists, the list of debtors who refuse to perform their debts is determined. The computer equipment used in the collection device assists the staff in drafting the complaint, and writes down the basic information of the plaintiff and the defendant, the litigation request, the facts and reasons. Then, based on the size of the debt, the nature of the debt and the legal provisions, the court with jurisdiction is determined and the complaint is submitted to the court through the system. The litigation fees are then automatically paid according to the court's notice. At the same time, a copy of the complaint is integrated and sent to the staff's email address. At the same time, the staff is urged to attend the trial, and the debtor's arrears record and the record of secondary communication information are automatically integrated into simple and clear evidence. Finally, the evidence is printed and distributed to the staff to assist in presenting evidence and conducting debate at the trial.

2. The debt collection method based on voice recognition according to claim 1, characterized in that: The collection AI model program is a Caffe framework, and the collection AI model program is stored through a storage device.

3. The debt collection method based on voice recognition according to claim 2 is characterized in that: In step S1, after the debtor's data is collected, the data is uploaded to the collection AI model program for training by the collection AI model program.

4. A debt collection device, comprising a communication device (4) and a call module (43), wherein the call module (43) is connected to the communication device (4) via a twisted pair cable, and the upper end of the communication device (4) is respectively equipped with a control module (41) and a display screen (42), characterized in that: Storage mechanisms (5) are embedded on both sides of the upper end of the communication device (4), and the inner bottom of the storage mechanism (5) is filled with disinfectant. A disinfection mechanism (6) is installed inside the storage mechanism (5).

5. The debt collection device according to claim 4, characterized in that: The disinfection mechanism (6) comprises a frame (61), a sliding frame (63) is slidably arranged inside the frame (61), a sponge block (64) is embedded inside the sliding frame (63), both sides of the bottom of the sponge block (64) are equipped with sponge columns (65), the bottom of the sponge column (65) extends into the interior of the disinfectant, both sides of the bottom of the sliding frame (63) are connected to connecting rods (66) through hinges, and the other end of the connecting rod (66) is connected to the storage mechanism (5) through a hinge, and springs (62) are evenly distributed at the bottom of the frame (61), and the springs (62) are connected to the storage mechanism (5).

6. The debt collection device according to claim 5, characterized in that: Guide blocks (67) are embedded on both sides of the sliding frame (63), and track grooves matching the guide blocks (67) are opened at both ends of the inner side of the frame (61), and the track grooves are slidably connected to the guide blocks (67).

7. A computer device for a debt collection device, comprising the debt collection device according to any one of claims 4 to 6, further comprising a display module (1) and a host (2), wherein the host (2) is embedded in the inner bottom of the display module (1), characterized in that: The bottom of the host (2) is equipped with a bracket (21), the back of the display module (1) is equipped with a signal line (7), and the signal line (7) is connected to the communication device (4), the host (2) includes a processing chip, and the host (2) stores a debt collection AI model program, and when the debt collection AI model program is executed by the processing chip, the processing chip executes the debt collection method based on voice recognition as described in claims 1-3.

8. The computer device for the debt collection device according to claim 7, characterized in that: The display module (1) comprises a liquid crystal display screen, and a printing device is mounted on the back of the liquid crystal display screen, the printing device and the liquid crystal display screen are directly connected via a line, and a communication device is mounted on the back of the host (2).

9. A computer storage medium, arranged inside a computer device for a debt collection device according to any one of claims 7 to 8, characterized in that: The computer storage medium has a built-in debt collection AI model program, and when the debt collection AI model program is executed by the processing chip, the processing chip executes the debt collection method based on voice recognition as described in claims 1-3.

10. The computer storage medium according to claim 9, wherein: It comprises a storage module (3) with a built-in debt collection AI model program, wherein the storage module (3) is inserted into a data interface of a host (2), and the storage module (3) is connected to the host (2) via the interface.

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