Multi-channel bank customer demand intelligent investigation and feedback optimization method and system

Abstract

The invention relates to a multi-channel bank customer demand intelligent investigation and feedback optimization method and system, and the method specifically comprises the steps: recognizing and obtaining demand data from feedback and complaint suggestions collected from all channels through a big data technology, and carrying out the structural processing of the demand data through a natural language processing technology, and forming entry data; weight values are distributed based on customer types, attention is calculated in combination with the number of times that the demand items are put forward, the demand items are sorted, and a list to be selected is formed; by dynamically monitoring the accumulated attention of the demand items in the preset time and combining manual analysis, the demands with high attention are added into the credible demand list, the demands in the credible demand list are optimized, and demand implementation is further optimized based on a customer feedback result. And a normalized and intelligent customer demand investigation and feedback optimization process is formed, so that the investigation efficiency and the data accuracy can be improved, the customer investigation cost can be reduced, and more accurate and personalized service experience can be provided.

Description

Technical Field

[0001] The present application relates to the fields of data management and banking financial technology, and specifically discloses a multi-channel bank customer demand intelligent survey and feedback optimization method and system. Background Art

[0002] In banking operations, customer demand research is a crucial part. Traditional customer demand research methods usually collect online and offline customer needs at one time by formulating a special demand survey form, or use big data technology to mine customer behavior data to analyze potential needs. However, these methods have many shortcomings.

[0003] On the one hand, the cost of a one-time survey is high, and the survey method is often invasive, which can easily cause dissatisfaction and resistance from customers. On the other hand, under the current background of stricter personal information protection, big data mining faces the problems of data collection difficulties and infringement risks. These problems make it difficult for traditional survey methods to effectively and accurately obtain the real needs of customers.

[0004] In order to solve the above problems, the present invention proposes a multi-channel bank customer demand intelligent survey and feedback optimization method and system. This technical solution uses the trust relationship established between the bank and the customer to collect customer feedback through all channels online and offline, and combines natural language processing and big data analysis technology to perform structured processing and quantitative analysis of customer needs, thereby achieving continuous and accurate grasp of customer needs, reducing customer survey costs, and improving data accuracy and reliability. Summary of the invention

[0005] In order to solve the deficiencies of the prior art, the present invention proposes a multi-channel bank customer demand intelligent survey and feedback optimization method and system.

[0006] To achieve the above objectives, the technical solutions adopted by the present invention include:

[0007] A multi-channel bank customer demand intelligent survey and feedback optimization method, characterized by comprising:

[0008] S1. Collect feedback and complaints from banking service channels and obtain feedback and suggestions through identification;

[0009] S2. Obtaining demand data from the feedback suggestion information based on big data;

[0010] S3. Using natural language processing technology, the demand data is structured to form demand item data;

[0011] S4. Statistically analyzing the demand item data, calculating the attention of the demand item, and sorting the demand items according to the attention to form a list of demand items to be selected;

[0012] S5. Dynamically monitor the cumulative attention of each demand item within the preset time, and add the demand with increasing attention or high ranking to the list of trusted demands in combination with manual analysis;

[0013] S6. Implement the requirements in the trusted requirements list, obtain feedback results, and optimize the implementation of the requirements based on the feedback results.

[0014] Furthermore, banking service channels specifically include:

[0015] Online electronic banking channels, offline branch channels, and remote customer service channels.

[0016] Furthermore, the identification and feedback suggestion information specifically includes:

[0017] Using big data, we can mine potential customer needs from customer transaction records and browsing behaviors in online electronic banking channels;

[0018] Through the customer feedback mechanism, suggestions and complaints are recorded in real time for offline outlets;

[0019] Using speech recognition technology, voice complaints and suggestions from customers in remote customer service channels are converted into text, and semantic analysis is performed to extract demand information.

[0020] Further, the structural processing of the demand data specifically includes:

[0021] The demand data is structured according to the business field, model information, demand type, function name, promotion channel, problem-raising customer portrait, and problem-raising time.

[0022] Furthermore, before the demand data is structured, the following steps are included:

[0023] The demand data is formatted to form demand data in a unified format.

[0024] Furthermore, the calculation of the attention of the demand item specifically includes:

[0025] Assign corresponding weight values ​​based on customer types, and calculate demand attention based on the number of times the demand items are raised.

[0026] In addition, the present invention also relates to a multi-channel bank customer demand intelligent survey and feedback optimization system, which is characterized by comprising:

[0027] Feedback collection module, used to collect feedback and complaints from banking service channels, and obtain feedback and suggestion information through recognition technology;

[0028] A demand analysis module, based on big data processing technology, extracts demand data from the feedback suggestion information obtained by the feedback collection module;

[0029] A structured processing module, using natural language processing technology, performs structured processing on the demand data extracted by the demand analysis module to form demand itemized data;

[0030] A statistical analysis module performs statistical analysis on the demand item data generated by the structured processing module, calculates the attention of the demand items, and sorts the demand items according to the attention, and generates a list of demand items to be selected;

[0031] The monitoring and screening module dynamically monitors the cumulative attention of each demand item within a preset time, and screens the demands with increasing attention or high rankings into the list of credible demands through manual analysis;

[0032] The implementation and optimization module implements the requirements in the trusted requirement list determined by the monitoring and screening module, collects feedback results, and optimizes the implementation of the requirements based on the feedback results.

[0033] Furthermore, the statistical analysis module is also used for:

[0034] Assign corresponding weight values ​​based on customer types, and calculate demand attention based on the number of times the demand items are raised.

[0035] In addition, the present invention also relates to an electronic device, characterized in that it comprises a processor and a memory;

[0036] The memory is used to store operation instructions;

[0037] The processor is used to execute the above method by calling the operation instruction.

[0038] In addition, the present invention also relates to a computer-readable storage medium, characterized in that a computer program is stored on the storage medium, and the computer program implements the above method when executed by a processor.

[0039] The present invention discloses a multi-channel bank customer demand intelligent survey and feedback optimization method and system, which aims to realize comprehensive and intelligent survey of customer demand by integrating multiple bank service channels of online electronic banking, offline outlets, and remote customer service. The technical solution first identifies and obtains demand data from feedback and complaint suggestions collected from various channels through big data technology, and uses natural language processing technology to structure the demand data to form itemized data. Weight values ​​are assigned based on customer types, and the attention is calculated in combination with the number of times the demand items are proposed, and the demand items are sorted to form a list of candidates. By dynamically monitoring the cumulative attention of the demand items within a preset time, combined with manual analysis, the demands with high attention are added to the credible demand list, the demands in the credible demand list are optimized, and the demand implementation is further optimized based on the customer feedback results, forming a normalized and intelligent customer demand survey and feedback optimization process. Therefore, the technical solution improves the survey efficiency and data accuracy, reduces the customer survey cost, and provides a more accurate and personalized service experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flowchart of a multi-channel bank customer demand intelligent survey and feedback optimization method provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of the structure of a multi-channel bank customer demand intelligent survey and feedback optimization system provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present invention.

[0044] Those skilled in the art can understand that, unless otherwise stated, the singular forms "one", "an", "said" and "the" used herein may also include plural forms, and the "first", "second", etc., are only used to distinguish and define objects for the purpose of introducing the scheme clearly, and do not limit the objects themselves. Of course, the objects defined by "first" and "second" may be the same terminal, device, user, etc., or the same type of terminal, device and user. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0045] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0046] Therefore, this embodiment is applied to a large commercial bank, which has a complete online electronic banking system, a wide offline branch network and an efficient remote customer service center. Based on this, a multi-channel bank customer demand intelligent survey and feedback optimization method is proposed. The process is as follows: Figure 1 As shown, including:

[0047] S1. In online electronic banking channels, through online platforms such as bank apps and online banking, collect customers’ transaction records and browsing behavior data, and use big data technology to explore potential customer needs, such as financial products that customers frequently inquire about and types of transactions that have not been successfully completed.

[0048] In offline outlets, deploy customer feedback systems such as suggestion boxes, feedback forms or applications on mobile devices to record customer suggestions and complaints in real time.

[0049] In remote customer service channels, voice recognition technology is used to convert voice complaints and suggestions made by customers over the phone into text, and key demand information is extracted through semantic analysis;

[0050] S2. Use big data analysis tools, such as Hadoop or Spark, to process the large amount of collected data and extract key information that reflects customer needs.

[0051] For structured data (such as transaction records), SQL queries are used to extract and clean data; for unstructured data (such as text after speech recognition), text mining technology is used to extract useful information;

[0052] S3. After formatting the demand data to form the demand data in a unified format, the extracted demand data is then structured according to dimensions such as business field (such as financial management, loan, payment, etc.), model information (such as customer behavior model, risk preference model, etc.), demand type (such as functional demand, service demand, etc.), function name, improvement channel (such as online optimization, offline improvement, etc.), problem-raising customer portrait (such as age, gender, occupation, etc.), problem-raising time, etc., to form clear and organized demand itemized data;

[0053] S4. Assign different weight values ​​according to the number of times each demand item is raised and the customer type (such as long-tail customers, private banking customers). The weight of long-tail customers is 0.2, and that of private banking customers is 0.8.

[0054] Calculate the weighted attention of each requirement item, such as using the formula:

[0055] Attention = 0.2*sum (number of times long-tail customers raise the issue) + 0.8 (number of times private banking customers raise the issue)

[0056] Specifically, within a certain statistical period, a demand item A is proposed by 5 long-tail customers and 6 private banking customers, so the attention of this demand item A is 0.2*5+0.8*6=5.8;

[0057] The demand items are sorted according to the calculated attention levels to form a candidate list of demand items.

[0058] S5. Set a preset time window (such as 30 days) and dynamically monitor the changes in the attention of each demand item during this time. For demand items with continuously increasing attention or high ranking, combine manual analysis to evaluate their impact on banking business and feasibility of implementation, and add them to the list of credible demands.

[0059] S6. The bank has formulated specific implementation plans for the requirements in the trusted requirements list and implemented them one by one. During the implementation process, the bank pays close attention to customer feedback and adjusts the implementation strategy in a timely manner to ensure that the requirements are effectively met. At the same time, the bank optimizes the implementation of requirements based on the feedback results to continuously improve service quality and customer satisfaction.

[0060] Therefore, this technical solution first uses big data technology to identify and obtain demand data from feedback and complaints collected from various channels, and uses natural language processing technology to structure the demand data to form itemized data. Weight values ​​are assigned based on customer types, and the attention is calculated based on the number of times the demand items are raised. The demand items are sorted to form a list of candidates. By dynamically monitoring the cumulative attention of demand items within a preset time, combined with manual analysis, high-attention demands are added to the list of trusted demands, and the demands in the list of trusted demands are optimized. Based on customer feedback results, the implementation of demands is further optimized to form a normalized and intelligent customer demand survey and feedback optimization process. Therefore, this technical solution improves survey efficiency and data accuracy, reduces customer survey costs, and provides a more accurate and personalized service experience.

[0061] In addition, it also involves a multi-channel bank customer demand intelligent survey and feedback optimization system, the structure of which is as follows: Figure 2 As shown, including:

[0062] The feedback collection module 201 is used to collect feedback and complaint suggestions from banking service channels, and obtain feedback suggestion information through identification technology, wherein banking service channels specifically include: online electronic banking channels, offline branch channels, and remote customer service channels; identification and obtaining feedback suggestion information specifically includes:

[0063] Using big data, we can mine potential customer needs from customer transaction records and browsing behaviors in online electronic banking channels;

[0064] Through the customer feedback mechanism, suggestions and complaints are recorded in real time for offline outlets;

[0065] Using speech recognition technology, voice complaints and suggestions from customers in remote customer service channels are converted into text, and semantic analysis is performed to extract demand information.

[0066] The demand analysis module 202 extracts demand data from the feedback suggestion information obtained by the feedback collection module based on big data processing technology;

[0067] The structured processing module 203 formats the demand data to form demand data in a unified format;

[0068] Using natural language processing technology, the demand data extracted by the demand analysis module is structured to form demand itemized data, wherein the structured processing specifically includes:

[0069] Structuring the demand data according to business fields, model information, demand types, function names, promotion channels, problem-raising customer portraits, and problem-raising time;

[0070] The statistical analysis module 204 performs statistical analysis on the demand item data generated by the structured processing module, calculates the attention of the demand items, and sorts the demand items according to the attention to generate a list of demand items to be selected, wherein calculating the attention of the demand items specifically includes:

[0071] Assign corresponding weight values ​​based on customer types, and calculate demand attention based on the number of times demand items are raised;

[0072] The monitoring and screening module 205 dynamically monitors the cumulative attention of each demand item within a preset time, and screens the demands with continuously increasing attention or high ranking into the credible demand list in combination with manual analysis;

[0073] The implementation and optimization module 206 implements the requirements in the trusted requirement list determined by the monitoring and screening module, collects feedback results, and optimizes the implementation of the requirements based on the feedback results.

[0074] By using this system, the above method can be executed and the corresponding technical effects can be achieved.

[0075] An embodiment of the present invention further provides an electronic device for executing the above method, as an implementation device of the method, comprising a processor and a memory;

[0076] A memory, used for storing operation instructions;

[0077] The processor is used to execute the multi-channel bank customer demand intelligent investigation and feedback optimization method provided in any embodiment of the present application by calling operation instructions.

[0078] As an example, Figure 3 A schematic diagram of the structure of an electronic device applicable to an embodiment of the present application is shown, and the electronic device 300 includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one. It is understandable that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the specific structure of the electronic device 300. In other embodiments of the present application, the electronic device 300 may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware. Optionally, the electronic device may also include a display screen 305 for displaying images, or receiving user operation instructions when necessary.

[0079] The processor 301 is applied in the embodiment of the present application to implement the method shown in the above method embodiment. The transceiver 304 may include a receiver and a transmitter. The transceiver 304 is applied in the embodiment of the present application to implement the function of the electronic device of the embodiment of the present application communicating with other devices when executed.

[0080] Processor 301 may be a CPU (Central Processing Unit), a general purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0081] The processor 301 may also include one or more processing units, for example: the processor 301 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), a controller, a memory, a video codec, a digital signal processor (digital signal processor, DSP), a baseband processor, and / or a neural network processor (Neural-network Processing Unit, NPU), etc. Different processing units can be independent devices or integrated into one or more processors. Among them, the controller can be the nerve center and command center of the electronic device 300. The controller can generate an operation control signal according to the instruction opcode and the timing signal to complete the control of fetching and executing instructions. A memory can also be set in the processor 301 for storing instructions and data. In some embodiments, the memory in the processor 301 is a cache memory. The memory can save instructions or data that the processor 301 has just used or circulated.

[0082] The processor 301 can run the multi-channel bank customer demand intelligent survey and feedback optimization method provided in the embodiment of the present application. The processor 301 may include different devices. For example, when the CPU and GPU are integrated, the CPU and the GPU can cooperate to execute the multi-channel bank customer demand intelligent survey and feedback optimization method provided in the embodiment of the present application. Some algorithms are executed by the CPU, and other algorithms are executed by the GPU to obtain faster processing efficiency.

[0083] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0084] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or may include a high-speed random access memory, or may include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0085] Optionally, the memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the multi-channel bank customer demand intelligent survey and feedback optimization method provided in any embodiment of the present application.

[0086] The memory 303 can be used to store computer executable program codes, which include instructions. The processor 301 executes various functional applications and data processing of the electronic device 300 by running the instructions stored in the memory 303. The memory 303 may include a program storage area and a data storage area. Among them, the program storage area may store the code of the operating system, application program, etc. The data storage area may store data created during the use of the electronic device 300 (such as images, videos, etc. collected by the camera application), etc.

[0087] The memory 303 may also store one or more computer programs corresponding to the multi-channel bank customer demand intelligent survey and feedback optimization method provided in the embodiment of the present application. The one or more computer programs are stored in the above-mentioned memory 303 and are configured to be executed by the one or more processors 301. The one or more computer programs include instructions, and the above-mentioned instructions can be used to execute each step in the above-mentioned corresponding embodiment.

[0088] Of course, the code of the multi-channel bank customer demand intelligent survey and feedback optimization method provided in the embodiment of the present application can also be stored in an external memory.

[0089] The display screen 305 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 300 may include 1 or N display screens 305, where N is a positive integer greater than 1. The display screen 305 can be used to display information input by a user or information provided to a user and various graphical user interfaces (GUI). For example, the display screen 305 can display photos, videos, web pages, or files.

[0090] The electronic device provided in the embodiment of the present application is applicable to any embodiment of the above method. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, which will not be repeated here.

[0091] An embodiment of the present invention also provides a computer-readable storage medium capable of implementing all the steps in the method in the above embodiment, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps in the method in the above embodiment are implemented.

[0092] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0094] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0096] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A multi-channel bank customer demand intelligent survey and feedback optimization method, characterized in that: include: S1. Collect feedback and complaints from banking service channels and obtain feedback and suggestions through identification; S2. Obtaining demand data from the feedback suggestion information based on big data; S3. Using natural language processing technology, the demand data is structured to form demand item data; S4. Statistically analyzing the demand item data, calculating the attention of the demand item, and sorting the demand items according to the attention to form a list of demand items to be selected; S5. Dynamically monitor the cumulative attention of each demand item within the preset time, and add the demand with increasing attention or high ranking to the list of trusted demands in combination with manual analysis; S6. Implement the requirements in the trusted requirements list, obtain feedback results, and optimize the implementation of the requirements based on the feedback results.

2. The method according to claim 1, characterized in that The banking service channels in step S1 specifically include: Online electronic banking channels, offline branch channels, and remote customer service channels.

3. The method according to claim 2, characterized in that The step S1 of identifying and obtaining feedback suggestion information specifically includes: Using big data, we can mine potential customer needs from customer transaction records and browsing behaviors in online electronic banking channels; Through the customer feedback mechanism, suggestions and complaints are recorded in real time for offline outlets; Using speech recognition technology, voice complaints and suggestions from customers in remote customer service channels are converted into text, and semantic analysis is performed to extract demand information.

4. The method according to claim 1, characterized in that: The structural processing of the demand data in step S2 specifically includes: The demand data is structured according to the business field, model information, demand type, function name, promotion channel, problem-raising customer portrait, and problem-raising time.

5. The method according to claim 4, characterized in that Before the demand data is structured, the following steps are included: The demand data is formatted to form demand data in a unified format.

6. The method according to claim 1, characterized in that The step S4 of calculating the attention degree of the demand item specifically includes: Assign corresponding weight values ​​based on customer types, and calculate demand attention based on the number of times the demand items are raised.

7. A multi-channel bank customer demand intelligent survey and feedback optimization system, characterized by: include: Feedback collection module, used to collect feedback and complaints from banking service channels, and obtain feedback and suggestion information through recognition technology; A demand analysis module, based on big data processing technology, extracts demand data from the feedback suggestion information obtained by the feedback collection module; A structured processing module, using natural language processing technology, performs structured processing on the demand data extracted by the demand analysis module to form demand itemized data; A statistical analysis module performs statistical analysis on the demand item data generated by the structured processing module, calculates the attention of the demand items, and sorts the demand items according to the attention, and generates a list of demand items to be selected; The monitoring and screening module dynamically monitors the cumulative attention of each demand item within a preset time, and screens the demands with continuously increasing attention or high rankings into the list of credible demands through manual analysis; The implementation and optimization module implements the requirements in the trusted requirement list determined by the monitoring and screening module, collects feedback results, and optimizes the implementation of the requirements based on the feedback results.

8. The system according to claim 7, characterized in that The statistical analysis module is also used for: Assign corresponding weight values ​​based on customer types, and calculate demand attention based on the number of times the demand items are raised.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store operation instructions; The processor is used to execute the method according to any one of claims 1 to 6 by calling the operation instruction.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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