CN116976821B - Enterprise problem feedback information processing method, device, equipment and medium - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence, and provides an enterprise problem feedback information processing method, device, equipment and medium, which can call a corresponding feedback information processing model according to a feedback mode of problem feedback information, input the problem feedback information into the feedback information processing model for recognition to obtain a target problem, further rapidly recognize and extract the target problem based on the artificial intelligence model, and query in a strategy library based on the target problem so as to automatically acquire a coping strategy corresponding to the target problem, further process the problem feedback information based on the queried coping strategy without human participation, thereby ensuring the high efficiency and accuracy of the problem feedback information processing.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an enterprise problem feedback information processing method, device, equipment and medium.
Background
With the rapid development of enterprises, the number of staff is increasing, so that better collection and systemization of recording problems are required.
However, in the prior art, the processing speed of the problem feedback is slow for enterprises, and the problem feedback is often not effectively processed due to the insufficient experience of staff, so that the processing process has high requirements for staff individuals.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a method, an apparatus, a device, and a medium for processing feedback information of an enterprise problem, which are aimed at solving the problems of slow processing speed and unreasonable processing of feedback information of an enterprise problem.
An enterprise problem feedback information processing method, the enterprise problem feedback information processing method comprising:
when problem feedback information is received, detecting a feedback mode of the problem feedback information as a target type;
invoking a feedback information processing model corresponding to the target type as a target model;
inputting the problem feedback information into the target model for recognition to obtain a target problem;
calling a pre-constructed strategy library;
inquiring in the strategy library based on the target problem to obtain a coping strategy corresponding to the target problem as a target strategy;
and processing the problem feedback information based on the target strategy.
According to a preferred embodiment of the present invention, before the feedback information processing model corresponding to the target type is called as the target model, the method further includes:
acquiring historical problem feedback information;
acquiring text information from the historical problem feedback information, and training a first feedback information processing model by taking the text information as a training sample; the first feedback information processing model comprises a text recognition model and a keyword extraction model;
Acquiring voice information from the historical problem feedback information, and training a second feedback information processing model by taking the voice information as a training sample; the second feedback information processing model comprises a voice recognition model and the keyword extraction model;
acquiring video information from the historical problem feedback information, and training a third feedback information processing model by taking the video information as a training sample; the third feedback information processing model comprises an image feature extraction model, the voice recognition model and the keyword extraction model.
According to a preferred embodiment of the present invention, after the objective problem is obtained, the method further includes:
when the target type is a video type, acquiring micro-expression characteristics of a user in the video process in real time;
invoking a pre-trained risk level prediction model;
inputting the user micro-expression characteristics and the target problems into the risk level prediction model to obtain a target risk level;
when the target risk level is a preset risk level, the problem feedback information is processed preferentially according to the priority corresponding to the preset risk level.
According to a preferred embodiment of the invention, the method further comprises:
And configuring the mapping relation between each risk level and the processing priority.
According to a preferred embodiment of the present invention, before the invoking the pre-built policy repository, the method further includes:
acquiring response data in a preset time range;
clustering the coping data to obtain each problem and coping strategies corresponding to each problem;
establishing a mapping relation between each problem and a corresponding coping strategy;
and constructing the strategy library based on the mapping relation.
According to a preferred embodiment of the present invention, after the policy repository is constructed based on the mapping relationship, the method further includes:
storing the strategy library to a blockchain node;
and updating the strategy library at preset time intervals.
According to a preferred embodiment of the present invention, the processing the problem feedback information based on the target policy includes:
when the target type is online feedback, displaying a response surgery on a designated interface;
and when the target type is a complaint type, sending the processing mode to terminal equipment of the appointed contact person.
An enterprise problem feedback information processing apparatus, the enterprise problem feedback information processing apparatus comprising:
the detection unit is used for detecting a feedback mode of the problem feedback information as a target type when the problem feedback information is received;
The calling unit is used for calling the feedback information processing model corresponding to the target type as a target model;
the recognition unit is used for inputting the problem feedback information into the target model for recognition to obtain a target problem;
the calling unit is used for calling a policy library constructed in advance;
the query unit is used for querying in the strategy library based on the target problem to obtain a coping strategy corresponding to the target problem as a target strategy;
and the processing unit is used for processing the problem feedback information based on the target strategy.
A computer device, the computer device comprising:
a memory storing at least one instruction; and
And the processor executes the instructions stored in the memory to realize the enterprise problem feedback information processing method.
A computer readable storage medium having stored therein at least one instruction for execution by a processor in a computer device to implement the enterprise problem feedback information processing method.
According to the technical scheme, the corresponding feedback information processing model can be called according to the feedback mode of the problem feedback information, the problem feedback information is input into the feedback information processing model to be identified, the target problem is further rapidly identified and extracted based on the artificial intelligent model, the target problem is queried in the strategy library based on the target problem, the coping strategy corresponding to the target problem is automatically acquired, the problem feedback information is further processed based on the queried coping strategy, human participation is not needed, and the high efficiency and the accuracy of the problem feedback information processing are ensured.
Drawings
FIG. 1 is a flow chart of a method for processing enterprise issue feedback information according to a preferred embodiment of the present invention.
FIG. 2 is a functional block diagram of a preferred embodiment of the enterprise problem feedback information processing apparatus of the present invention.
FIG. 3 is a schematic diagram of a computer device for implementing a method for processing feedback information of enterprise problems according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart of a method for processing feedback information of enterprise problems according to a preferred embodiment of the present invention. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs.
The enterprise problem feedback information processing method is applied to one or more computer devices, wherein the computer device is a device capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware of the computer device comprises, but is not limited to, a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, an ASIC), a programmable gate array (Field-Programmable Gate Array, FPGA), a digital processor (Digital Signal Processor, DSP), an embedded device and the like.
The computer device may be any electronic product that can interact with a user in a human-computer manner, such as a personal computer, tablet computer, smart phone, personal digital assistant (Personal Digital Assistant, PDA), game console, interactive internet protocol television (Internet Protocol Television, IPTV), smart wearable device, etc.
The computer device may also include a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network server, a server group composed of a plurality of network servers, or a Cloud based Cloud Computing (Cloud Computing) composed of a large number of hosts or network servers.
The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (Virtual Private Network, VPN), and the like.
S10, when problem feedback information is received, detecting a feedback mode of the problem feedback information as a target type.
In this embodiment, the problem feedback information may include, but is not limited to: online problem consultation or complaint, telephone consultation or complaint, video consultation or complaint, use case feedback, and the like.
In this embodiment, the feedback manner may include, but is not limited to: the specific division modes of the types can be configured according to actual service requirements, and the method is not limited.
S11, calling a feedback information processing model corresponding to the target type as a target model.
In this embodiment, before the invoking the feedback information processing model corresponding to the target type as the target model, the method further includes:
acquiring historical problem feedback information;
acquiring text information from the historical problem feedback information, and training a first feedback information processing model by taking the text information as a training sample; the first feedback information processing model comprises a text recognition model and a keyword extraction model;
acquiring voice information from the historical problem feedback information, and training a second feedback information processing model by taking the voice information as a training sample; the second feedback information processing model comprises a voice recognition model and the keyword extraction model;
acquiring video information from the historical problem feedback information, and training a third feedback information processing model by taking the video information as a training sample; the third feedback information processing model comprises an image feature extraction model, the voice recognition model and the keyword extraction model.
The text recognition model, the keyword extraction model, the voice recognition model and the image feature extraction model can be neural network models with corresponding functions, deep learning models and the like, and the invention does not limit specific model types.
The text recognition model can be used for recognizing specific text content from text information.
The keyword extraction model is used for extracting keywords in the data to be processed.
The specific voice content can be extracted from the input voice through the voice recognition model.
The image feature extraction model can be used for extracting corresponding image content from each frame of image.
Through the above embodiments, each type of corresponding artificial intelligence model can be trained.
Specifically, the model obtained by training can be deployed at the blockchain node, and then the corresponding model can be directly obtained from the blockchain node when the model needs to be called, for example, the model identification can be recorded when the model is stored, and then the model can be directly matched in each model on the blockchain node according to the model identification when the model is called, so as to obtain the corresponding model.
S12, inputting the problem feedback information into the target model for recognition to obtain a target problem.
According to the embodiment, the main characteristics of the data are automatically extracted based on the artificial intelligent model, the target problem is obtained, manual participation is not needed, and the problem determination efficiency is improved.
In this embodiment, after the objective problem is obtained, the method further includes:
when the target type is a video type, acquiring micro-expression characteristics of a user in the video process in real time;
invoking a pre-trained risk level prediction model;
inputting the user micro-expression characteristics and the target problems into the risk level prediction model to obtain a target risk level;
when the target risk level is a preset risk level, the problem feedback information is processed preferentially according to the priority corresponding to the preset risk level.
The risk level prediction model may be a pre-trained deep learning model with a prediction function.
In the above embodiment, the risk level is predicted according to the user microexpressive features and the target problem, so as to determine the sequence of processing each problem according to different risk levels.
In this embodiment, the method further includes:
and configuring the mapping relation between each risk level and the processing priority.
Through the embodiment, the mapping relation between each risk level and the processing priority is pre-configured, so that the processing sequence of the problems is determined according to different risk levels, the problems with high risk levels are guaranteed to be processed preferentially, the processing timeliness of the problems is prevented from being delayed, and more customer complaints are brought.
S13, calling a pre-constructed strategy library.
The policy library is used for storing mapping relation between each problem and corresponding coping policies.
Specifically, before the invoking the pre-built policy repository, the method further includes:
acquiring response data in a preset time range;
clustering the coping data to obtain each problem and coping strategies corresponding to each problem;
establishing a mapping relation between each problem and a corresponding coping strategy;
and constructing the strategy library based on the mapping relation.
The preset time range may be within 3 months from the current time.
By establishing the strategy library, the coping strategy can be determined in a follow-up auxiliary manner, so that no matter how the experience of staff for solving the problem is, the strategy in the strategy library can be utilized to assist in decision making, the response efficiency is improved, and the problem can be solved better.
In this embodiment, after the policy repository is constructed based on the mapping relationship, the method further includes:
storing the strategy library to a blockchain node;
and updating the strategy library at preset time intervals.
Wherein the preset time interval may be each month, etc.
In the embodiment, the policy library is stored to the blockchain node, so that the data can be prevented from being tampered maliciously, and the safety of the data is ensured. Moreover, by periodically updating the policy repository, the policies provided in the policy repository can be kept valid and practical.
S14, inquiring in the strategy library based on the target problem to obtain a coping strategy corresponding to the target problem as a target strategy.
Specifically, the target problem may be traversed in the policy repository, and a coping policy corresponding to the traversed problem may be used as the target policy.
S15, processing the problem feedback information based on the target strategy.
In this embodiment, the processing the problem feedback information based on the target policy includes:
when the target type is online feedback, displaying a response surgery on a designated interface;
and when the target type is a complaint type, sending the processing mode to terminal equipment of the appointed contact person.
For example: when an online feedback channel is provided, if a user carries out online feedback, the user needs to answer the questions of the client in time, and at the moment, a answering operation can be displayed on a chat interface so as to assist the staff to carry out quick response; if the user performs telephone feedback, at the moment, a response technology is displayed on a visual interface of the staff, so that the staff can deal with the user according to the prompt of the response technology; if the user performs offline feedback, if only a problem is submitted in the deliberate feedback field, the processing mode can be sent to the terminal equipment of the staff to assist the staff in quickly solving the problem.
The opinion feedback column can include a newly added question button, a filling information interface and a submit button. The filling information interface can include question types, dates, question details, feedback comments and the like. Further, after the problem is submitted, the corresponding problem can be sent to the terminal equipment of the staff, and the staff can check various submitted problems on the appointed problem processing interface and perform corresponding operation by triggering a processing button or a checking button. After a problem is resolved, the resolved problem may be marked, such as by marking the problem that has been processed as "resolved," etc., to avoid the problem being repeatedly processed.
And, for example: the customer service system can record the problem by customer service and feed back the problem to corresponding personnel, feed back the processing result to customer service after processing, feed back the processing result to customer by customer service, record the problem and corresponding solution after the process is finished, and be convenient for use when the strategy library is updated later or used when the problem is traced back.
In this embodiment, the whole flow data of each enterprise problem feedback information processing is recorded, so as to assist in subsequent reference and tracing.
According to the technical scheme, the corresponding feedback information processing model can be called according to the feedback mode of the problem feedback information, the problem feedback information is input into the feedback information processing model to be identified, the target problem is further rapidly identified and extracted based on the artificial intelligent model, the target problem is queried in the strategy library based on the target problem, the coping strategy corresponding to the target problem is automatically acquired, the problem feedback information is further processed based on the queried coping strategy, human participation is not needed, and the high efficiency and the accuracy of the problem feedback information processing are ensured.
FIG. 2 is a functional block diagram of a preferred embodiment of the enterprise problem feedback information processing apparatus of the present invention. The enterprise problem feedback information processing apparatus 11 includes a detection unit 110, a calling unit 111, an identification unit 112, a calling unit 113, a query unit 114, and a processing unit 115. The module/unit referred to in the present invention refers to a series of computer program segments, which are stored in a memory, capable of being executed by a processor and of performing a fixed function. In the present embodiment, the functions of the respective modules/units will be described in detail in the following embodiments.
The detecting unit 110 is configured to detect, when the problem feedback information is received, a feedback manner of the problem feedback information as a target type.
In this embodiment, the problem feedback information may include, but is not limited to: online problem consultation or complaint, telephone consultation or complaint, video consultation or complaint, use case feedback, and the like.
In this embodiment, the feedback manner may include, but is not limited to: the specific division modes of the types can be configured according to actual service requirements, and the method is not limited.
The calling unit 111 is configured to call, as a target model, a feedback information processing model corresponding to the target type.
In this embodiment, before the feedback information processing model corresponding to the target type is called as a target model, historical problem feedback information is obtained;
acquiring text information from the historical problem feedback information, and training a first feedback information processing model by taking the text information as a training sample; the first feedback information processing model comprises a text recognition model and a keyword extraction model;
acquiring voice information from the historical problem feedback information, and training a second feedback information processing model by taking the voice information as a training sample; the second feedback information processing model comprises a voice recognition model and the keyword extraction model;
Acquiring video information from the historical problem feedback information, and training a third feedback information processing model by taking the video information as a training sample; the third feedback information processing model comprises an image feature extraction model, the voice recognition model and the keyword extraction model.
The text recognition model, the keyword extraction model, the voice recognition model and the image feature extraction model can be neural network models with corresponding functions, deep learning models and the like, and the invention does not limit specific model types.
The text recognition model can be used for recognizing specific text content from text information.
The keyword extraction model is used for extracting keywords in the data to be processed.
The specific voice content can be extracted from the input voice through the voice recognition model.
The image feature extraction model can be used for extracting corresponding image content from each frame of image.
Through the above embodiments, each type of corresponding artificial intelligence model can be trained.
Specifically, the model obtained by training can be deployed at the blockchain node, and then the corresponding model can be directly obtained from the blockchain node when the model needs to be called, for example, the model identification can be recorded when the model is stored, and then the model can be directly matched in each model on the blockchain node according to the model identification when the model is called, so as to obtain the corresponding model.
The identifying unit 112 is configured to input the problem feedback information to the target model for identifying, so as to obtain a target problem.
According to the embodiment, the main characteristics of the data are automatically extracted based on the artificial intelligent model, the target problem is obtained, manual participation is not needed, and the problem determination efficiency is improved.
In this embodiment, after the target problem is obtained, when the target type is a video type, user micro-expression features in the video process are collected in real time;
invoking a pre-trained risk level prediction model;
inputting the user micro-expression characteristics and the target problems into the risk level prediction model to obtain a target risk level;
when the target risk level is a preset risk level, the problem feedback information is processed preferentially according to the priority corresponding to the preset risk level.
The risk level prediction model may be a pre-trained deep learning model with a prediction function.
In the above embodiment, the risk level is predicted according to the user microexpressive features and the target problem, so as to determine the sequence of processing each problem according to different risk levels.
In this embodiment, a mapping relationship between each risk level and processing priority is configured.
Through the embodiment, the mapping relation between each risk level and the processing priority is pre-configured, so that the processing sequence of the problems is determined according to different risk levels, the problems with high risk levels are guaranteed to be processed preferentially, the processing timeliness of the problems is prevented from being delayed, and more customer complaints are brought.
The retrieving unit 113 is configured to retrieve a policy repository that is built in advance.
The policy library is used for storing mapping relation between each problem and corresponding coping policies.
Specifically, before the policy library constructed in advance is called, response data in a preset time range is obtained;
clustering the coping data to obtain each problem and coping strategies corresponding to each problem;
establishing a mapping relation between each problem and a corresponding coping strategy;
and constructing the strategy library based on the mapping relation.
The preset time range may be within 3 months from the current time.
By establishing the strategy library, the coping strategy can be determined in a follow-up auxiliary manner, so that no matter how the experience of staff for solving the problem is, the strategy in the strategy library can be utilized to assist in decision making, the response efficiency is improved, and the problem can be solved better.
In this embodiment, after the policy repository is constructed based on the mapping relationship, the policy repository is saved to a blockchain node;
and updating the strategy library at preset time intervals.
Wherein the preset time interval may be each month, etc.
In the embodiment, the policy library is stored to the blockchain node, so that the data can be prevented from being tampered maliciously, and the safety of the data is ensured. Moreover, by periodically updating the policy repository, the policies provided in the policy repository can be kept valid and practical.
The query unit 114 is configured to query in the policy repository based on the target problem, and obtain a coping policy corresponding to the target problem as a target policy.
Specifically, the target problem may be traversed in the policy repository, and a coping policy corresponding to the traversed problem may be used as the target policy.
The processing unit 115 is configured to process the problem feedback information based on the target policy.
In this embodiment, the processing unit 115 processing the problem feedback information based on the target policy includes:
when the target type is online feedback, displaying a response surgery on a designated interface;
And when the target type is a complaint type, sending the processing mode to terminal equipment of the appointed contact person.
For example: when an online feedback channel is provided, if a user carries out online feedback, the user needs to answer the questions of the client in time, and at the moment, a answering operation can be displayed on a chat interface so as to assist the staff to carry out quick response; if the user performs telephone feedback, at the moment, a response technology is displayed on a visual interface of the staff, so that the staff can deal with the user according to the prompt of the response technology; if the user performs offline feedback, if only a problem is submitted in the deliberate feedback field, the processing mode can be sent to the terminal equipment of the staff to assist the staff in quickly solving the problem.
The opinion feedback column can include a newly added question button, a filling information interface and a submit button. The filling information interface can include question types, dates, question details, feedback comments and the like. Further, after the problem is submitted, the corresponding problem can be sent to the terminal equipment of the staff, and the staff can check various submitted problems on the appointed problem processing interface and perform corresponding operation by triggering a processing button or a checking button. After a problem is resolved, the resolved problem may be marked, such as by marking the problem that has been processed as "resolved," etc., to avoid the problem being repeatedly processed.
And, for example: the customer service system can record the problem by customer service and feed back the problem to corresponding personnel, feed back the processing result to customer service after processing, feed back the processing result to customer by customer service, record the problem and corresponding solution after the process is finished, and be convenient for use when the strategy library is updated later or used when the problem is traced back.
In this embodiment, the whole flow data of each enterprise problem feedback information processing is recorded, so as to assist in subsequent reference and tracing.
According to the technical scheme, the corresponding feedback information processing model can be called according to the feedback mode of the problem feedback information, the problem feedback information is input into the feedback information processing model to be identified, the target problem is further rapidly identified and extracted based on the artificial intelligent model, the target problem is queried in the strategy library based on the target problem, the coping strategy corresponding to the target problem is automatically acquired, the problem feedback information is further processed based on the queried coping strategy, human participation is not needed, and the high efficiency and the accuracy of the problem feedback information processing are ensured.
FIG. 3 is a schematic diagram of a computer device for implementing a method for processing feedback information of enterprise problems according to a preferred embodiment of the present invention.
The computer device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program, such as an enterprise problem feedback information processing program, stored in the memory 12 and executable on the processor 13.
It will be appreciated by those skilled in the art that the schematic diagram is merely an example of the computer device 1 and does not constitute a limitation of the computer device 1, the computer device 1 may be a bus type structure, a star type structure, the computer device 1 may further comprise more or less other hardware or software than illustrated, or a different arrangement of components, for example, the computer device 1 may further comprise an input-output device, a network access device, etc.
It should be noted that the computer device 1 is only used as an example, and other electronic products that may be present in the present invention or may be present in the future are also included in the scope of the present invention by way of reference.
The memory 12 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 12 may in some embodiments be an internal storage unit of the computer device 1, such as a removable hard disk of the computer device 1. The memory 12 may in other embodiments also be an external storage device of the computer device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the computer device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the computer device 1. The memory 12 may be used not only for storing application software installed in the computer device 1 and various types of data, such as codes of an enterprise problem feedback information processing program, but also for temporarily storing data that has been output or is to be output.
The processor 13 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, a combination of various control chips, and the like. The processor 13 is a Control Unit (Control Unit) of the computer apparatus 1, connects the respective components of the entire computer apparatus 1 using various interfaces and lines, executes various functions of the computer apparatus 1 and processes data by running or executing programs or modules stored in the memory 12 (for example, executing an enterprise problem feedback information processing program or the like), and calls data stored in the memory 12.
The processor 13 executes the operating system of the computer device 1 and various types of applications installed. The processor 13 executes the application program to implement the steps of the embodiments of the enterprise problem feedback information processing method described above, such as the steps shown in fig. 1.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules/units may be a series of computer readable instruction segments capable of performing the specified functions, which instruction segments describe the execution of the computer program in the computer device 1. For example, the computer program may be divided into a detection unit 110, a calling unit 111, an identification unit 112, a calling unit 113, a querying unit 114, a processing unit 115.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor (processor) to perform portions of the enterprise problem feedback information processing methods according to various embodiments of the present invention.
The modules/units integrated in the computer device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may also be implemented by a computer program for instructing a relevant hardware device to implement all or part of the procedures of the above-mentioned embodiment method, where the computer program may be stored in a computer readable storage medium and the computer program may be executed by a processor to implement the steps of each of the above-mentioned method embodiments.
Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory, or the like.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one straight line is shown in fig. 3, but not only one bus or one type of bus. The bus is arranged to enable a connection communication between the memory 12 and at least one processor 13 or the like.
Although not shown, the computer device 1 may further comprise a power source (such as a battery) for powering the various components, preferably the power source may be logically connected to the at least one processor 13 via a power management means, whereby the functions of charge management, discharge management, and power consumption management are achieved by the power management means. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The computer device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described in detail herein.
Further, the computer device 1 may also comprise a network interface, optionally comprising a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the computer device 1 and other computer devices.
The computer device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the computer device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
Fig. 3 shows only a computer device 1 with components 12-13, it being understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the computer device 1 and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
In connection with fig. 1, the memory 12 in the computer device 1 stores a plurality of instructions to implement an enterprise problem feedback information processing method, the processor 13 may execute the plurality of instructions to implement:
when problem feedback information is received, detecting a feedback mode of the problem feedback information as a target type;
invoking a feedback information processing model corresponding to the target type as a target model;
inputting the problem feedback information into the target model for recognition to obtain a target problem;
calling a pre-constructed strategy library;
inquiring in the strategy library based on the target problem to obtain a coping strategy corresponding to the target problem as a target strategy;
and processing the problem feedback information based on the target strategy.
Specifically, the specific implementation method of the above instructions by the processor 13 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
The data in this case were obtained legally.
In the several embodiments provided in the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The invention is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. The units or means stated in the invention may also be implemented by one unit or means, either by software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (8)
1. The enterprise problem feedback information processing method is characterized by comprising the following steps of:
when problem feedback information is received, detecting a feedback mode of the problem feedback information as a target type;
invoking a feedback information processing model corresponding to the target type as a target model;
inputting the problem feedback information into the target model for recognition to obtain a target problem;
calling a pre-constructed strategy library;
Inquiring in the strategy library based on the target problem to obtain a coping strategy corresponding to the target problem as a target strategy;
processing the problem feedback information based on the target strategy;
before the feedback information processing model corresponding to the target type is called as a target model, historical problem feedback information is acquired; acquiring text information from the historical problem feedback information, and training a first feedback information processing model by taking the text information as a training sample; the first feedback information processing model comprises a text recognition model and a keyword extraction model; acquiring voice information from the historical problem feedback information, and training a second feedback information processing model by taking the voice information as a training sample; the second feedback information processing model comprises a voice recognition model and the keyword extraction model; acquiring video information from the historical problem feedback information, and training a third feedback information processing model by taking the video information as a training sample; the third feedback information processing model comprises an image feature extraction model, the voice recognition model and the keyword extraction model;
Before the policy library constructed in advance is called, response data in a preset time range are obtained; clustering the coping data to obtain each problem and coping strategies corresponding to each problem; establishing a mapping relation between each problem and a corresponding coping strategy; and constructing the strategy library based on the mapping relation.
2. The method for processing feedback information of an enterprise problem as claimed in claim 1, wherein after the target problem is obtained, the method further comprises:
when the target type is a video type, acquiring micro-expression characteristics of a user in the video process in real time;
invoking a pre-trained risk level prediction model;
inputting the user micro-expression characteristics and the target problems into the risk level prediction model to obtain a target risk level;
when the target risk level is a preset risk level, the problem feedback information is processed preferentially according to the priority corresponding to the preset risk level.
3. The enterprise problem feedback information processing method of claim 2, wherein the method further comprises:
and configuring the mapping relation between each risk level and the processing priority.
4. The method for processing feedback information of enterprise problems as claimed in claim 1, wherein after the policy repository is constructed based on the mapping relationship, the method further comprises:
storing the strategy library to a blockchain node;
and updating the strategy library at preset time intervals.
5. The enterprise issue feedback information processing method of claim 1, wherein the processing the issue feedback information based on the target policy comprises:
when the target type is online feedback, displaying a response surgery on a designated interface;
and when the target type is a complaint type, sending the processing mode to terminal equipment of the appointed contact person.
6. An enterprise problem feedback information processing apparatus, characterized in that the enterprise problem feedback information processing apparatus includes:
the detection unit is used for detecting a feedback mode of the problem feedback information as a target type when the problem feedback information is received;
the calling unit is used for calling the feedback information processing model corresponding to the target type as a target model;
the recognition unit is used for inputting the problem feedback information into the target model for recognition to obtain a target problem;
The calling unit is used for calling a policy library constructed in advance;
the query unit is used for querying in the strategy library based on the target problem to obtain a coping strategy corresponding to the target problem as a target strategy;
the processing unit is used for processing the problem feedback information based on the target strategy;
before the feedback information processing model corresponding to the target type is called as a target model, historical problem feedback information is acquired; acquiring text information from the historical problem feedback information, and training a first feedback information processing model by taking the text information as a training sample; the first feedback information processing model comprises a text recognition model and a keyword extraction model; acquiring voice information from the historical problem feedback information, and training a second feedback information processing model by taking the voice information as a training sample; the second feedback information processing model comprises a voice recognition model and the keyword extraction model; acquiring video information from the historical problem feedback information, and training a third feedback information processing model by taking the video information as a training sample; the third feedback information processing model comprises an image feature extraction model, the voice recognition model and the keyword extraction model;
Before the policy library constructed in advance is called, response data in a preset time range are obtained; clustering the coping data to obtain each problem and coping strategies corresponding to each problem; establishing a mapping relation between each problem and a corresponding coping strategy; and constructing the strategy library based on the mapping relation.
7. A computer device, the computer device comprising:
a memory storing at least one instruction; and
A processor executing instructions stored in the memory to implement the enterprise problem feedback information processing method as claimed in any one of claims 1 to 5.
8. A computer-readable storage medium, characterized by: the computer-readable storage medium has stored therein at least one instruction that is executed by a processor in a computer device to implement the enterprise problem feedback information processing method of any of claims 1 to 5.
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