Seat answering method and device, equipment and medium

Abstract

The invention relates to the technical field of computers, and discloses a seat answering method and device, equipment and a medium, and the method comprises the steps: receiving a call request from a user, analyzing the call request through a call type recognition model, and obtaining the type information of the call request; wherein the type information comprises a voice call request and a video call request; determining a corresponding target call channel according to the type information of the call request information, and inputting the target call channel to a channel matching model to determine a seat state pool corresponding to the target call channel; scoring seats in an idle state in the seat state pool by using a seat evaluation model to obtain an optimal target seat; and distributing the call request to the optimal target seat so as to respond to the call request through the optimal target seat, and updating the seat state of the optimal target seat to a busy state. The method can be applied to a medical business management program system, and the response efficiency and the response accuracy of the seat can be improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an agent answering method, device, equipment, and medium. Background Art

[0002] With the development of the "Internet + Healthcare" model, telemedicine services, health consultations, and chronic disease management are becoming widely used in medical institutions and health management platforms. Patients communicate with doctors or health advisors in real time through various means, including phone calls, video calls, and smart devices. This is especially true for scenarios such as chronic disease follow-up, psychological counseling, and home rehabilitation guidance, placing higher demands on the communication system's responsiveness and service continuity.

[0003] However, existing medical agent systems mostly rely on traditional single-channel access methods, lack a unified scheduling mechanism, and are unable to coordinate and handle concurrent requests from multiple channels. They also struggle to synchronize agent status in real time, leading to long waiting times for patients, high transfer failure rates, and even the inability to quickly connect with professionals in emergencies, posing serious safety risks. The timeliness and accuracy of agent scheduling are crucial, especially in the areas of sudden illness monitoring and remote emergency warning. Therefore, a medical service-oriented agent response method with multi-channel identification, intelligent agent matching, and real-time status control capabilities is urgently needed to improve the service efficiency, response accuracy, and overall system stability of telemedicine platforms. Summary of the Invention

[0004] The present invention provides an agent answering method, device, computer equipment and medium to solve the technical problems of low agent answering efficiency and low accuracy in related technologies.

[0005] In a first aspect, an agent answering method is provided, comprising:

[0006] Receiving a call request from a user, and analyzing the call request using a call type recognition model to obtain type information of the call request; wherein the type information includes a voice call request and a video call request;

[0007] Determine a corresponding target call channel according to the type information of the call request information, and input the target call channel into a channel matching model to determine an agent status pool corresponding to the target call channel;

[0008] Using a seat evaluation model to score the idle seats in the seat status pool to obtain the optimal target seat;

[0009] The call request is dispatched to the optimal target agent so that the call request is answered by the optimal target agent, and the agent state of the optimal target agent is updated to a busy state.

[0010] In a second aspect, an agent answering device is provided, comprising:

[0011] a receiving module, configured to receive a call request from a user and analyze the call request using a call type recognition model to obtain type information of the call request; wherein the type information includes a voice call request and a video call request;

[0012] a determination module, configured to determine a corresponding target call channel according to the type information of the call request information, and input the target call channel into a channel matching model to determine an agent status pool corresponding to the target call channel;

[0013] A scoring module is used to score the idle seats in the seat status pool using a seat evaluation model to obtain the optimal target seat;

[0014] The answering module is configured to dispatch the call request to the optimal target agent so that the optimal target agent answers the call request and updates the agent status of the optimal target agent to a busy state.

[0015] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned agent answering method are implemented.

[0016] In a fourth aspect, a computer-readable storage medium is provided, storing a computer program that, when executed by a processor, implements the steps of the agent answering method described above. In the solution implemented by the agent answering method, apparatus, computer device, and storage medium, a call request from a user can be received and analyzed using a call type identification model to obtain call request type information; the type information includes voice call requests and video call requests. Furthermore, a target call channel corresponding to the call request type information can be determined based on the call request type information. The target call channel is input into a channel matching model to determine an agent status pool corresponding to the target call channel. An agent evaluation model is then used to score idle agents in the agent status pool to determine the optimal target agent. Consequently, the call request can be dispatched to the optimal target agent, who can answer the call request, and the optimal target agent's status is updated to busy. In this embodiment, the call type can be accurately identified and the appropriate target call channel and agent resources can be quickly matched. By intelligently scoring agent status through an agent evaluation model, requests can be precisely assigned to the optimal target agent, shortening response times and improving the patient service experience. This solution also dynamically updates agent status, reflecting agent load in real time and improving resource utilization efficiency. For diverse medical request scenarios, this invention ensures an intelligent and efficient response process, enhancing the service capabilities and intelligence level of the medical system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 This is a schematic diagram of an application environment of an agent answering method according to an embodiment of the present invention;

[0019] Figure 2 This is a flow chart of an agent answering method according to an embodiment of the present invention;

[0020] Figure 3 yes Figure 1 A schematic flow chart of a specific implementation of step S10;

[0021] Figure 4 This is a structural diagram of an agent answering device according to one embodiment of the present invention;

[0022] Figure 5 is a structural diagram of a computer device in one embodiment of the present invention;

[0023] Figure 6 FIG. 2 is another structural diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] The agent answering method provided in the embodiment of the present invention can be applied in the following situations: Figure 1 In an application environment, a client communicates with a server via a network. The server receives call requests from users through the client and analyzes the call requests using a call type identification model to obtain call request type information, including voice and video call requests. Based on the call request type information, the server determines the corresponding target call channel and inputs the target call channel into a channel matching model to determine the corresponding agent status pool. The server uses an agent evaluation model to score idle agents in the agent status pool to determine the optimal target agent. The server then dispatches the call request to the optimal target agent, allowing the optimal target agent to respond to the call request and update the optimal target agent's status to busy. Finally, the optimal target agent is fed back to the client. This invention accurately identifies call types and quickly matches the appropriate target call channel with agent resources. The agent evaluation model intelligently scores agent status, enabling precise allocation of requests to the optimal target agent, thereby shortening response time and improving patient service experience. This solution also dynamically updates agent status, reflecting agent load in real time and improving resource utilization efficiency. For multiple types of medical request scenarios, the present invention can ensure the intelligence and efficiency of the response process and improve the service capabilities and intelligence level of the medical system.

[0026] The client can be, but is not limited to, various personal computers, laptops, smartphones, tablet computers, and portable wearable devices. The server can be implemented as an independent server or a server cluster consisting of multiple servers. The present invention is described in detail below through specific embodiments.

[0027] See also Figure 2 As shown, Figure 2 A flowchart of an agent response method provided in an embodiment of the present invention includes the following steps:

[0028] S10: Receive a call request from a user, and analyze the call request using a call type recognition model to obtain type information of the call request.

[0029] The type information includes a voice call request and a video call request.

[0030] It should be understood that within the medical service platform, patients can choose different communication methods to connect with agents based on their needs, such as voice calls for simple consultations, chronic disease follow-up, and medication consultations, or video calls for face-to-face diagnosis and treatment, rehabilitation guidance, and psychological counseling. Because different communication methods have different requirements for access channels, agent configurations, and service capabilities, it is necessary to accurately identify the type of user call request before establishing a call.

[0031] For example, a call type recognition model can intelligently analyze user-initiated call requests. Based on deep learning training of call signaling structure, audio and video encoding features, and media protocol information, this model can automatically determine whether the request is a voice or video call and output the corresponding type label. This type information not only serves as the core basis for subsequent channel scheduling but can also be used to pre-configure agent resources, such as dispatching medical personnel with video consultation equipment and permissions, or matching remote agents with high-load voice processing capabilities.

[0032] By introducing a type recognition model at the initial stage of call access, it is possible to quickly classify and accurately identify call methods, effectively reduce problems with human judgment and resource misallocation, and improve the platform's operating efficiency and service reliability in multiple scenarios and multiple businesses. It is especially suitable for scenarios with high real-time demands, such as telemedicine, family doctor services, and smart health follow-up.

[0033] Among them, such as Figure 3 As shown, in step S10, that is, analyzing the call request by using the call type recognition model to obtain the type information of the call request, the following steps are included:

[0034] S11: Extract key information from the call request.

[0035] The key information includes key signaling fields, media protocol information and communication request structure.

[0036] S12: Input the key information as an input vector to the call type recognition model, and output the type information of the call request.

[0037] For example, for step S11, the call request initiated by the user can come from various sources, including traditional telephone calls and Internet-based video calls. In order to ensure that the user's intention can be accurately understood and the appropriate reception channel can be matched, the call request can first be structured and extracted to extract the key information. This key information usually includes the signaling field in the call request (such as the call initiation method, target address, protocol type), media protocol information (such as whether it contains audio and video data streams, the codec protocol used), and the communication request structure (such as the data packet arrangement format, transmission mode, etc.). This information constitutes an important basis for identifying the call method.

[0038] In step S12, the extracted key information can be organized into a feature vector and fed into a call type recognition model as input. This model, pre-trained on a large amount of medical call data, can identify the pattern characteristics of different call types and output corresponding call type labels, such as "voice call request" or "video call request." This intelligent recognition mechanism enables accurate classification before a call is connected, effectively supporting subsequent channel allocation and agent resource scheduling, ensuring call quality and consultation efficiency during the medical service process. It is particularly suitable for high-concurrency medical scenarios such as emergency pre-screening, chronic disease follow-up, and remote consultations.

[0039] S20: Determine a corresponding target call channel according to the type information of the call request information, and input the target call channel into a channel matching model to determine an agent status pool corresponding to the target call channel.

[0040] For example, in a telemedicine service system, different types of call requests should be connected to different communication channels to ensure communication quality and service adaptability. For example, voice calls are suitable for services such as quick consultations, chronic disease follow-up, and health consultations, requiring lower bandwidth and minimal system resource usage. Video calls, on the other hand, are suitable for highly interactive scenarios such as remote consultations, postoperative recovery guidance, and psychological counseling, and have higher requirements for channel bandwidth, terminal equipment, and agent expertise. Therefore, in step S20, based on the call type information identified in the previous step, the target call channel that matches that type can be prioritized.

[0041] Specifically, preset channel selection logic can be used to map call types to channels. For example, voice calls can be mapped to public network voice channels or IP phone channels, while video calls can be mapped to video access channels based on WebRTC, SIP, or proprietary protocols. After determining the target channel, this channel information can be input into a channel matching model. The model calculates and outputs the corresponding agent status pool based on channel attributes, current network load, service scenario tags (such as emergency, psychological, and rehabilitation), and platform resource distribution.

[0042] It's important to note that the agent status pool is a dynamic collection of qualified medical service personnel or virtual agent resources, representing currently available response resources. In healthcare scenarios, this precise matching mechanism ensures that each call request is routed to the most appropriate service link and connected by the most appropriate agent based on actual needs, thereby improving response efficiency and ensuring service quality. It is particularly suitable for smart healthcare platforms that integrate multiple channels and intelligently dispatch calls.

[0043] In some embodiments, inputting the target call channel into a channel matching model to determine the agent status pool corresponding to the target call channel includes: generating a corresponding channel vector representation based on the target call channel; wherein the channel vector representation includes user identity level information and business scenario labels; and analyzing the channel vector representation through the channel matching model to obtain the agent status pool corresponding to the target call channel.

[0044] For example, to achieve more accurate and intelligent matching of channels and agent resources, not only can channels be selected based on the call type itself, but call channels can also be further vectorized. This channel vector representation comprehensively considers information from multiple dimensions, including the user's identity level (such as ordinary patients, chronic disease management users, high-risk patients, etc.) and the current business scenario label (such as daily consultation, remote consultation, emergency warning, psychological counseling, etc.). Through this structured vector construction method, the system can abstract complex channel requirements into a unified input form, adapting to subsequent model processing.

[0045] Next, the channel vector is passed as input to the channel matching model. Based on a pre-trained classification or regression mechanism, the model automatically analyzes the most suitable agent status pool for the current channel, combined with historical scheduling data and resource status information. For example, for a video call request from a high-level user with a service label of "emergency," the model will prioritize matching with a pool of available and emergency-handling video medical agents, ensuring rapid access and priority response to the call request. This model-driven dynamic matching mechanism enables optimal resource allocation in a multi-channel, multi-service concurrent environment, significantly improving the intelligence and response efficiency of medical services.

[0046] S30: Using the seat evaluation model, score the idle seats in the seat status pool to obtain the optimal target seat.

[0047] In some embodiments, using an agent evaluation model to score idle agents in the agent status pool to obtain an optimal target agent includes: extracting key indicator information of several agents currently in the idle state in the agent status pool; wherein the key indicator information includes service records, historical call satisfaction scores, average response time, and current workload; constructing a feature input vector based on the key quality assurance information, and inputting the feature input vector into the agent evaluation model for scoring to obtain a scoring result; and determining the agent with the highest scoring result as the target agent.

[0048] For example, to achieve more accurate and personalized agent scheduling, an agent evaluation model can be introduced to intelligently score currently idle agent resources. Specifically, all idle agents are first filtered out from the target agent status pool, and then key indicator information for each candidate agent is extracted. These indicators include their past service records (such as service time and task types), historical call satisfaction scores (reflecting user evaluations), average response time (measurement of answering efficiency), and current workload (such as whether they have just completed a high-intensity service or are currently lightly loaded). This data can comprehensively reflect the comprehensive service capabilities and current availability of each agent.

[0049] These key indicators can then be constructed into feature input vectors and fed into a trained agent evaluation model for scoring. This model, which can be constructed using a weighted algorithm, a machine learning regression network, or a deep neural network, outputs a comprehensive agent score based on the input feature dimensions. The system then sorts the scores and selects the agent with the highest score as the optimal target agent for the current call request. This modeled scoring and decision-making mechanism not only achieves dynamic optimization of medical service personnel resources but also improves patient experience and satisfaction with service access, making it particularly suitable for the high-concurrency and demanding service environments of telemedicine.

[0050] S40: dispatching the call request to the optimal target agent, so that the call request is answered by the optimal target agent, and updating the agent status of the optimal target agent to a busy state.

[0051] In some embodiments, dispatching the call request to the optimal target agent so that the optimal target agent answers the call request includes: sending a call allocation instruction to the target agent, so that the target agent reserves resources and enters a waiting state after receiving the call allocation instruction; in response to the target agent entering the waiting state, recording the agent's answering completion status.

[0052] For example, to ensure that call requests are accurately and efficiently received and processed by the optimal target agent, the call request can be first dispatched to the corresponding agent. Specifically, a call allocation instruction can be sent to the target agent, containing key information such as user identity, call type, and request priority. Upon receiving this instruction, the agent terminal automatically performs resource reservation operations, including locking audio and video channels, activating the call interface or headset, and switching to the "waiting for answering" state to prepare for the incoming user request. This operation ensures that a channel is allocated to the call request in a timely manner before resources are occupied, preventing conflicts or loss of calls.

[0053] Once the target agent officially enters the waiting state, you can monitor their responses in real time. If the agent successfully connects or completes preparations for the call, their "answer completed" status is recorded and updated to the scheduling log and agent status pool, marking the agent as currently occupied. This status is also synchronized to all scheduling nodes to ensure the agent is not reassigned during an active call.

[0054] This step effectively improves the real-time and accuracy of call request processing, and is particularly suitable for scenarios in medical services that require high access response time, such as remote consultations, emergency warnings, and psychological interventions.

[0055] In some embodiments, the process of updating the agent status of the optimal target agent to a busy state includes the following steps: calling a state management service to mark the agent status of the target agent as busy; writing the busy state into the state change timestamp of the target agent to obtain the state update result of the optimal target agent.

[0056] In some embodiments, the method further includes: monitoring a release signal of a call channel corresponding to the optimal target agent; and upon detecting the release signal, restoring the agent state of the optimal target agent from the busy state to the idle state.

[0057] For example, to ensure that agent resources are not called repeatedly after allocation, the status of the optimal target agent can be updated in real time. Specifically, by calling the status management service, the current status of the agent can be changed from idle to busy, indicating that the agent is handling a call task and cannot be assigned again. Subsequently, the time of this status change operation can be recorded as the status change timestamp and written into the status database together with the agent ID, thereby generating a complete status update record. This record is not only used for subsequent status recovery judgment, but also serves as an important basis for scheduling logs, service monitoring and resource optimization analysis, ensuring the accuracy of agent status synchronization and the reliability of system scheduling in multi-channel, high-concurrency environments.

[0058] As can be seen, the above solution accurately identifies call types and quickly matches the appropriate target call channel with agent resources. The agent evaluation model intelligently scores agent status, enabling precise allocation of requests to the optimal target agent, thereby shortening response time and improving the patient service experience. This solution also dynamically updates agent status, reflecting agent load in real time and improving resource utilization efficiency. For multi-type medical request scenarios, this invention ensures the intelligence and efficiency of the response process, enhancing the service capabilities and intelligence level of the medical system.

[0059] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0060] In one embodiment, a seat response device is provided, which corresponds one-to-one with the seat response method in the above embodiment. Figure 4 As shown, the agent response device includes a receiving module 101, a determining module 102, a scoring module 103, and a response module 104. The functional modules are described in detail as follows:

[0061] The receiving module 101 is configured to receive a call request from a user and analyze the call request using a call type recognition model to obtain type information of the call request; wherein the type information includes a voice call request and a video call request;

[0062] A determination module 102 is configured to determine a corresponding target call channel according to the type information of the call request information, and input the target call channel into a channel matching model to determine an agent status pool corresponding to the target call channel;

[0063] A scoring module 103 is configured to score the idle seats in the seat status pool using a seat evaluation model to obtain an optimal target seat;

[0064] The answering module 104 is configured to dispatch the call request to the optimal target agent so that the optimal target agent answers the call request and updates the agent status of the optimal target agent to a busy state.

[0065] In one embodiment, the receiving module 101 is specifically configured to:

[0066] Extracting key information from the call request, wherein the key information includes key signaling fields, media protocol information, and a communication request structure;

[0067] The key information is input as an input vector to the call type recognition model, and the type information of the call request is output.

[0068] In one embodiment, the determination module 102 is specifically configured to:

[0069] Generating a corresponding channel vector representation according to the target call channel; wherein the channel vector representation includes user identity level information and a service scenario label;

[0070] The channel vector representation is analyzed by the channel matching model to obtain an agent status pool corresponding to the target call channel.

[0071] In one embodiment, the scoring module 103 is further configured to:

[0072] Extracting key indicator information of several agents currently in the idle state from the agent status pool; wherein the key indicator information includes service records, historical call satisfaction scores, average response time, and current workload;

[0073] Constructing a feature input vector based on the key quality assurance information, and inputting the feature input vector into the agent evaluation model for scoring to obtain a scoring result;

[0074] The seat with the highest score is determined as the target seat.

[0075] In one embodiment, the response module 104 is specifically configured to:

[0076] Sending a call allocation instruction to the target agent, so that the target agent reserves resources and enters a waiting state after receiving the call allocation instruction;

[0077] In response to the target agent entering the waiting state, the agent's answering completion state is recorded.

[0078] In one embodiment, the response module 104 is specifically configured to:

[0079] Calling a status management service to mark the target agent's agent status as busy;

[0080] The busy state is written into the state change timestamp of the target agent to obtain the state update result of the optimal target agent.

[0081] In one embodiment, the response module 104 is specifically configured to:

[0082] Monitoring the release signal of the call channel corresponding to the optimal target seat;

[0083] After detecting the release signal, the seat state of the optimal target agent is restored from the busy state to the idle state.

[0084] The present invention provides an agent response device that accurately identifies call types and quickly matches target call channels with agent resources. An agent evaluation model intelligently scores agent status, enabling precise allocation of requests to the optimal target agent, thereby shortening response time and improving the patient service experience. This solution also dynamically updates agent status, reflecting agent load in real time and improving resource utilization efficiency. For multi-type medical request scenarios, the present invention ensures the intelligence and efficiency of the response process, enhancing the service capabilities and intelligence level of the medical system.

[0085] The specific definition of the agent response device can be found in the definition of the agent response method above and will not be repeated here. Each module in the agent response device described above may be implemented in whole or in part via software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor within a computer device in hardware form, or may be stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0086] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, memory, network interface and database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it implements the functions or steps on the server side of a seat answering method.

[0087] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When executed by the processor, the computer program implements the functions or steps on the client side of an agent answering method.

[0088] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed:

[0089] Receiving a call request from a user, and analyzing the call request using a call type recognition model to obtain type information of the call request; wherein the type information includes a voice call request and a video call request;

[0090] Determine a corresponding target call channel according to the type information of the call request information, and input the target call channel into a channel matching model to determine an agent status pool corresponding to the target call channel;

[0091] Using a seat evaluation model to score the idle seats in the seat status pool to obtain the optimal target seat;

[0092] The call request is dispatched to the optimal target agent so that the call request is answered by the optimal target agent, and the agent state of the optimal target agent is updated to a busy state.

[0093] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0094] Receiving a call request from a user, and analyzing the call request using a call type recognition model to obtain type information of the call request; wherein the type information includes a voice call request and a video call request;

[0095] Determine a corresponding target call channel according to the type information of the call request information, and input the target call channel into a channel matching model to determine an agent status pool corresponding to the target call channel;

[0096] Using a seat evaluation model to score the idle seats in the seat status pool to obtain the optimal target seat;

[0097] The call request is dispatched to the optimal target agent so that the call request is answered by the optimal target agent, and the agent state of the optimal target agent is updated to a busy state.

[0098] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can be found in the relevant descriptions of the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0099] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchl ink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0100] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0101] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A seat answering method, characterized in that: The method comprises: Receiving a call request from a user, and analyzing the call request using a call type recognition model to obtain type information of the call request; wherein the type information includes a voice call request and a video call request; Determine a corresponding target call channel according to the type information of the call request information, and input the target call channel into a channel matching model to determine an agent status pool corresponding to the target call channel; Using a seat evaluation model to score the idle seats in the seat status pool to obtain the optimal target seat; The call request is dispatched to the optimal target agent so that the call request is answered by the optimal target agent, and the agent state of the optimal target agent is updated to a busy state.

2. The method according to claim 1, characterized in that The analyzing the call request by using a call type identification model to obtain type information of the call request includes: Extracting key information from the call request, wherein the key information includes key signaling fields, media protocol information, and a communication request structure; The key information is input as an input vector to the call type recognition model, and the type information of the call request is output.

3. The method according to claim 1, characterized in that Inputting the target call channel into a channel matching model to determine an agent status pool corresponding to the target call channel includes: Generating a corresponding channel vector representation according to the target call channel; wherein the channel vector representation includes user identity level information and a service scenario label; The channel vector representation is analyzed by the channel matching model to obtain an agent status pool corresponding to the target call channel.

4. The method according to claim 1, wherein The method of using the seat evaluation model to score the idle seats in the seat status pool to obtain the optimal target seat includes: Extracting key indicator information of several agents currently in the idle state from the agent status pool; wherein the key indicator information includes service records, historical call satisfaction scores, average response time, and current workload; Constructing a feature input vector based on the key quality assurance information, and inputting the feature input vector into the agent evaluation model for scoring to obtain a scoring result; The seat with the highest score is determined as the target seat.

5. The method according to claim 1, wherein The dispatching of the call request to the optimal target agent so that the optimal target agent answers the call request includes: Sending a call allocation instruction to the target agent, so that the target agent reserves resources and enters a waiting state after receiving the call allocation instruction; In response to the target agent entering the waiting state, the agent's answering completion state is recorded.

6. The method according to claim 1, characterized in that The process of updating the seat status of the optimal target seat to the busy state includes the following steps: Calling a status management service to mark the target agent's agent status as busy; The busy state is written into the state change timestamp of the target agent to obtain the state update result of the optimal target agent.

7. The method according to claim 1, characterized in that The method further comprises: Monitoring the release signal of the call channel corresponding to the optimal target seat; After detecting the release signal, the seat state of the optimal target agent is restored from the busy state to the idle state.

8. An agent answering device, characterized in that: include: a receiving module, configured to receive a call request from a user and analyze the call request using a call type recognition model to obtain type information of the call request; wherein the type information includes a voice call request and a video call request; a determination module, configured to determine a corresponding target call channel according to the type information of the call request information, and input the target call channel into a channel matching model to determine an agent status pool corresponding to the target call channel; A scoring module is used to score the idle seats in the seat status pool using a seat evaluation model to obtain the optimal target seat; The answering module is configured to dispatch the call request to the optimal target agent so that the optimal target agent answers the call request and updates the agent status of the optimal target agent to a busy state.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the agent answering method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the agent answering method according to any one of claims 1 to 7 are implemented.

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