Method, device and electronic equipment for generating auxiliary information of a business system

Abstract

This application discloses a method, apparatus, and electronic device for generating auxiliary information for a business system. Relating to the field of computers, the method includes: acquiring behavioral data of a target user performing business processing operations using a business system; acquiring target indicators for evaluating the business processing operations, obtaining a set of M target indicators, and determining the indicator values ​​of each target indicator based on the behavioral data, obtaining a set of M indicator values; determining whether there is an anomaly in the business processing operation based on the set of M indicator values, and if an anomaly exists, determining the cause of the anomaly based on the set of M indicator values; determining auxiliary information for the business processing operation based on the cause of the anomaly, and sending the auxiliary information to the business system. This application solves the problem in related technologies where the high update frequency of business systems leads to low accuracy and efficiency in performing business processing operations.

Description

Technical Field

[0001] This application relates to the field of computers, and more specifically, to a method, apparatus, and electronic device for generating auxiliary information for a business system. Background Technology

[0002] In the modern civil aviation industry, updating business systems is a crucial means of improving service quality and enhancing operational efficiency. From flight scheduling, passenger check-in, and baggage handling to customer service, almost all key business processes are constantly being updated and iterated to adapt to increasing flight density, passenger traffic, and complex safety regulatory requirements. However, rapid system iteration also brings challenges, especially for frontline operators. Frequent system upgrades mean that operators may not be able to quickly learn and master the use of the new system, thus affecting the accuracy and efficiency of business processing.

[0003] To improve operators' proficiency and accuracy in using business systems, civil aviation companies typically organize large-scale offline training sessions regularly. However, this approach has significant limitations: on the one hand, due to limited training resources, it is difficult to cover all employees frequently; on the other hand, offline training is often costly, time-consuming, and labor-intensive, and for widely distributed branches and remote sites, the difficulty of organization and implementation efficiency are greatly reduced.

[0004] Furthermore, if employees cannot quickly master the new functions after the business system is updated, they may make mistakes in actual operation, affecting flight punctuality, passenger satisfaction, and even operational safety.

[0005] Currently, there is no effective solution to the problem that the accuracy and efficiency of business processing operations are low due to the high frequency of updates to business systems in related technologies. Summary of the Invention

[0006] The main objective of this application is to provide a method, apparatus, and electronic device for generating auxiliary information for a business system, in order to solve the problem in the related art that the accuracy and efficiency of using the business system to perform business processing operations are low due to the high update frequency of the business system.

[0007] To achieve the above objectives, according to one aspect of this application, a method for generating auxiliary information for a business system is provided. The method includes: acquiring behavioral data of a target user performing a business processing operation within the business system, wherein the business processing operation is for processing a target business; acquiring target indicators for evaluating the business processing operation, obtaining a set of M target indicators, and determining the indicator values ​​of each target indicator based on the behavioral data, obtaining a set of M indicator values, where M is a positive integer; determining whether there is an anomaly in the business processing operation based on the set of M indicator values, and if an anomaly exists, determining the cause of the anomaly based on the set of M indicator values; determining auxiliary information for the business processing operation based on the cause of the anomaly, and sending the auxiliary information to the business system.

[0008] Optionally, the target indicators for evaluating business processing operations are obtained, resulting in a set of M target indicators, including: obtaining the business type of the target business, determining the business level of the target business based on the business type; obtaining the business detection dimensions under the business level, resulting in M ​​business detection dimensions, and obtaining the indicators under each business detection dimension, resulting in a set of M target indicators.

[0009] Optionally, determining the indicator values ​​of each target indicator based on behavioral data to obtain M sets of indicator values ​​includes: for any target indicator set, determining the initial parameters required to calculate the indicator values ​​of each target indicator in the target indicator set, obtaining N sets of initial parameters, where the target indicator set includes N target indicators, and N is a positive integer; for any target initial parameter set, determining the calculation method of each initial parameter in the target initial parameter set, and calculating each initial parameter according to the calculation method and behavioral data, obtaining a set of parameter values ​​for the target initial parameter set; determining the set of parameter values ​​for each of the N initial parameter sets, obtaining N sets of parameter values; calculating the indicator values ​​of the corresponding target indicators using each set of parameter values, obtaining N indicator values, and determining the N indicator values ​​as the set of indicator values ​​for the target indicator set.

[0010] Optionally, determining whether a business processing operation is abnormal based on the set of M indicator values ​​includes: calculating the score of the business detection dimension corresponding to each set of indicator values ​​to obtain M first scores; obtaining the preset weight of each business detection dimension, and performing a weighted summation operation on the M first scores using the preset weight to obtain a second score for the business processing operation; comparing the second score with a first threshold, and determining that the business processing operation is not abnormal if the second score is greater than or equal to the first threshold, and determining that the business processing operation is abnormal if the second score is less than the first threshold.

[0011] Optionally, obtaining the preset weights for each business detection dimension includes: obtaining the importance score for each business detection dimension to obtain M importance scores; determining the scenario coefficients for each business detection dimension based on the target business to obtain M scenario coefficients; and determining the preset weights for each business detection dimension based on the scenario coefficients and importance scores for each business detection dimension.

[0012] Optionally, determining the cause of anomalies in business processing operations based on the M sets of indicator values ​​includes: calculating the score of the business detection dimension corresponding to each set of indicator values ​​to obtain M first scores; obtaining the second threshold of each business detection dimension and comparing the first score of each business detection dimension with the second threshold to obtain M comparison results; obtaining the target comparison result representing the anomaly from the M comparison results and determining the business detection dimension to which the target comparison result belongs as the anomaly detection dimension; obtaining the set of abnormal indicator values ​​under the anomaly detection dimension and determining the abnormal indicator value from the set of abnormal indicator values ​​according to the preset requirements of each indicator value in the set of abnormal indicator values; determining the cause of the anomaly of the abnormal indicator value and determining the cause of the anomaly as the cause of the anomaly in the business processing operation.

[0013] Optionally, determining auxiliary information for business processing based on the cause of the anomaly includes: obtaining keywords for the cause of the anomaly and searching the auxiliary information database based on the keywords to obtain multiple initial auxiliary information; selecting auxiliary information related to the target business from the multiple initial auxiliary information to obtain auxiliary information for business processing.

[0014] To achieve the above objectives, according to another aspect of this application, an apparatus for generating auxiliary information for a business system is provided. The apparatus includes: a first acquisition unit, configured to acquire behavioral data of a target user performing a business processing operation within the business system, wherein the business processing operation is for processing a target business; a second acquisition unit, configured to acquire target indicators for evaluating the business processing operation, obtaining M sets of target indicators, and determining the indicator values ​​of each target indicator based on the behavioral data, obtaining M sets of indicator values, where M is a positive integer; a judgment unit, configured to judge whether there is an anomaly in the business processing operation based on the M sets of indicator values, and, if an anomaly exists, determine the cause of the anomaly based on the M sets of indicator values; and a determination unit, configured to determine auxiliary information for the business processing operation based on the cause of the anomaly, and send the auxiliary information to the business system.

[0015] To achieve the above objectives, according to another aspect of this application, an electronic device is provided, comprising a memory storing an executable program; and a processor for running the program, wherein the program executes the above-described method for generating auxiliary information of a business system during runtime.

[0016] To achieve the above objectives, according to another aspect of this application, a computer program product is provided, including computer instructions, which, when executed by a processor, implement the steps of the above-described method for generating auxiliary information for a business system.

[0017] In this embodiment, the method involves acquiring behavioral data of a target user during a business processing operation within a business system. This business processing operation involves handling a target business. The method includes: acquiring target indicators to evaluate the business processing operation, obtaining a set of M target indicators, and determining the indicator values ​​of each target indicator based on the behavioral data, resulting in a set of M indicator values ​​(M is a positive integer); determining whether the business processing operation is abnormal based on the set of M indicator values, and if so, determining the cause of the abnormality based on the set of M indicator values; determining auxiliary information for the business processing operation based on the cause of the abnormality, and sending the auxiliary information to the business system. By acquiring behavioral data, the method determines whether the business processing operation is abnormal and generates corresponding auxiliary information if an abnormality exists. This generates auxiliary information to provide intelligent business operation support and assistance to the user, helping them improve their operational proficiency. This allows users to improve their proficiency in using the business system based on the auxiliary information, achieving the goal of automated and personalized business system training for users. This improves the accuracy and efficiency of using the business system, thus solving the technical problem in related technologies where the high update frequency of the business system leads to low accuracy and efficiency in performing business processing operations. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 A hardware structure block diagram of a computer terminal for implementing a method for generating auxiliary information in a business system is shown.

[0020] Figure 2 This is a flowchart of a method for generating auxiliary information for a business system according to Embodiment 1 of this application;

[0021] Figure 3 This is a schematic diagram of an auxiliary information generation device for a business system according to Embodiment 2 of this application;

[0022] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] It should be noted that the methods, apparatuses, and electronic devices for generating auxiliary information of business systems as defined in this disclosure can be used in the computer field or any field other than the computer field, and the application fields of the methods, apparatuses, and electronic devices for generating auxiliary information of business systems as defined in this disclosure are not limited.

[0027] It should be noted that all information, user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, and displayed data) used in this application are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with the relevant regulations and standards of the relevant regions, have adopted necessary management measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse use. If the user chooses to refuse, the process proceeds to the expert decision-making process. For example, this system has an interface with relevant users or institutions. Before obtaining relevant information, a request to obtain the information needs to be sent to the aforementioned user or institution through the interface. After receiving consent from the aforementioned user or institution, the relevant information is obtained. Users can view the purpose of data use in real time through the authorization interface and have the right to withdraw authorization or delete data at any time. After authorization is withdrawn, the system will terminate the relevant data processing within 24 hours.

[0028] The embodiments or examples disclosed herein are not exhaustive, but merely illustrative of some embodiments or examples, and are not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment or example can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment or example can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment or example can be arbitrarily interchanged. Furthermore, optional methods or examples in a particular embodiment or example can be arbitrarily combined; moreover, embodiments or examples can be arbitrarily combined. For example, some or all steps of different embodiments or examples can be arbitrarily combined, and a particular embodiment or example can be arbitrarily combined with optional methods or examples of other embodiments or examples.

[0029] Example 1

[0030] According to an embodiment of this application, an embodiment of a method for generating auxiliary information for a business system is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for generating auxiliary information in a business system is shown. Figure 1As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, processing devices such as microprocessors or programmable logic devices), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface, a universal serial bus port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0032] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0033] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the auxiliary information generation method of the business system in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the above-mentioned auxiliary information generation method of the business system. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0034] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0035] The display may be, for example, a touchscreen LCD display that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0036] Under the aforementioned operating environment, this application provides the following: Figure 2 The method for generating auxiliary information in the business system is shown. Figure 2 This is a flowchart of a method for generating auxiliary information for a business system according to Embodiment 1 of this application, such as... Figure 2 As shown, the method includes:

[0037] Step S201: Obtain behavioral data of the target user during the process of performing business processing operations in the business system, wherein the business processing operations are used to process the target business.

[0038] It should be noted that the execution entity in this embodiment can be the auxiliary information generation system of the business system. This system can be configured in the business system, and by acquiring the behavioral data generated when the user uses the business system, it can determine the user's proficiency in business operations, and then provide the user with different auxiliary information to improve the user's proficiency in using the business system.

[0039] It should be noted that the target user can be an employee who uses the business system to handle business. The business handling operation can be the specific operation related to the target business that the target user performs in the business system, such as passenger information entry, baggage weight recording, fast boarding process, etc. The behavioral data can be a detailed operation record of the target user in the process of performing the business handling operation, including operation time, instruction type, operation path, emergency response time, etc.

[0040] For example, when a target user logs into the business system and begins performing business transactions, the system automatically starts a behavior data collection program. This program will record every step of the target user's business transactions in real time and comprehensively through methods such as log recording, operation event tracking, and video recording. This includes, but is not limited to, operation trigger time, instruction type, operation path, erroneous operations, and emergency response. The behavior data collection frequency must reach at least once per second to improve the completeness and accuracy of the data, so as to facilitate subsequent in-depth behavior analysis.

[0041] For example, when a user is detected to be performing a flight information query, the system will record the start and end timestamps of the operation, as well as the query parameters selected by the user and the query results.

[0042] Step S202: Obtain the target indicators for evaluating the business processing operations, resulting in a set of M target indicators. Determine the indicator values ​​of each target indicator based on the behavioral data, resulting in a set of M indicator values, where M is a positive integer.

[0043] It should be noted that the target indicators are key metrics used to quantitatively evaluate various operational behaviors in business processing, and are used to measure the operator's operational capabilities and compliance. The indicator values ​​are specific scores or numerical values ​​derived from the quantitative analysis of the target indicators.

[0044] For example, after the behavioral data collection is completed, the system enters the multi-dimensional behavioral analysis phase. First, based on the collected behavioral data, noise and outliers need to be removed through a preprocessing module, and the data needs to be standardized. Next, it is necessary to determine M target indicator sets under each evaluation dimension. Each target indicator set corresponds to one dimension, which may include core evaluation dimensions such as operational proficiency, process compliance, emergency response capability, and data entry accuracy. For example, the target indicators for the operational proficiency dimension may include indicators such as average response time, number of repeated operations, and total process completion time; the target indicators for the process compliance dimension may include the degree of deviation between the operation path and the system's preset standard process, including the number of deviation nodes, frequency of violations, and number of backtracking attempts.

[0045] Furthermore, through the data analysis and evaluation module, the indicator values ​​for each preset target indicator are calculated based on behavioral data to obtain the corresponding indicator values. For example, for operational proficiency, the average response time of all actions performed by the user when conducting business operations can be calculated, as well as the total time consumed based on action information, or the number of repeated operations can be determined based on action information, etc., thus obtaining a set of M indicator values. These indicator value sets are then used as reference data to determine the target user's usage of the business system and to judge whether there are any abnormalities in business operations.

[0046] Step S203: Determine whether there is an anomaly in the business processing operation based on the set of M indicator values, and if there is an anomaly in the business processing operation, determine the cause of the anomaly based on the set of M indicator values.

[0047] It should be noted that the cause of the anomaly can be determined by analyzing the set of indicator values, which indicates that there is an abnormality in the business processing operation, such as an excessively long operation response time.

[0048] For example, after obtaining a set of M indicator values, the system can use an anomaly detection algorithm to evaluate operational behavior. The anomaly detection algorithm can compare indicator values ​​with preset thresholds; if an indicator value fails to meet the threshold requirements, it is considered an anomaly in the business processing operation. The anomaly detection process will cover all M target indicators, improving the comprehensiveness of the evaluation. Once an anomaly in the business processing operation is confirmed, the system will conduct in-depth analysis of the indicator value set to identify the specific indicator causing the anomaly, thereby determining the cause of the anomaly. For example, an operation response time exceeding the normal range may indicate insufficient operational proficiency, thus determining the cause of the anomaly and its position within the operational process.

[0049] Step S204: Determine the auxiliary information for business processing based on the cause of the anomaly, and send the auxiliary information to the business system.

[0050] For example, based on the determined cause of the anomaly, the system will retrieve auxiliary information related to the target business from the auxiliary information database. The auxiliary information database should contain abundant training resources, operation guidelines, case studies, and emergency manuals, thereby providing targeted solutions for anomaly detection results.

[0051] It should be noted that the system employs technologies such as keyword matching, content analysis, and intelligent recommendation to filter out the most relevant auxiliary information from multiple initial auxiliary information sources, forming a set of auxiliary information for business processing. This auxiliary information includes not only text materials but may also include multimedia formats such as interactive simulation exercises, video tutorials, and expert guidance meetings, aiming to provide intuitive and practical supplementary training resources. Once the auxiliary information is determined, the system will immediately send it to the business system through pop-up prompts, email notifications, or push notifications to personal training platforms, enabling target users to promptly access and utilize the auxiliary information for learning and training. This will improve the efficiency and accuracy of business processing when users subsequently use the business system.

[0052] It should be noted that the usage time of this embodiment can be when the user completes a business operation, or when the user is in the process of performing a business operation. For example, when it is detected that the target user stays for a long time in a certain operation step, the action information of the current operation can be collected, and the relevant auxiliary information of the stopped operation step can be determined. In this way, by providing timely auxiliary guidance to the user on the business operation process, the efficiency and accuracy of the business operation can be improved.

[0053] The method for generating auxiliary information for a business system provided in this application embodiment involves: acquiring behavioral data of a target user during a business processing operation in the business system, wherein the business processing operation is used to process a target business; acquiring target indicators for evaluating the business processing operation, obtaining a set of M target indicators, and determining the indicator value of each target indicator based on the behavioral data, obtaining a set of M indicator values, where M is a positive integer; determining whether there is an anomaly in the business processing operation based on the set of M indicator values, and if there is an anomaly, determining the cause of the anomaly based on the set of M indicator values; determining auxiliary information for the business processing operation based on the cause of the anomaly, and then... The method of sending information to the business system involves acquiring behavioral data to determine whether there are any anomalies in the business processing operations. If anomalies are found, corresponding auxiliary information is generated. This auxiliary information provides intelligent business operation support and assistance to users, helping them improve their operational proficiency. Users can enhance their familiarity with the business system based on the auxiliary information, achieving the goal of automated and personalized business system training. This improves the accuracy and efficiency of the business system and solves the technical problem of low accuracy and efficiency in performing business processing operations due to the high update frequency of the business system.

[0054] To accurately determine the target indicators, optionally, in the method for generating auxiliary information of the business system provided in this application embodiment, obtaining the target indicators for evaluating the business processing operation and obtaining a set of M target indicators includes: obtaining the business type of the target business, determining the business level of the target business according to the business type; obtaining the business detection dimensions under the business level, obtaining M business detection dimensions, and obtaining the indicators under each business detection dimension, thus obtaining a set of M target indicators.

[0055] It should be noted that "business type" refers to the basic classification of the target business, such as flight check-in, baggage check-in, and emergency handling. "Business level" can be based on the complexity, operational difficulty, and impact on system performance of the business type. "Business evaluation dimensions" are sub-areas of operational evaluation, which may include operational proficiency, process compliance, emergency handling capabilities, and data entry accuracy. "Indicators" can be specific and measurable evaluation criteria, such as average operation response time, number of repeated operations, process deviation, and emergency response time, used to quantitatively assess user operations.

[0056] For example, when determining the target indicator set, the system first obtains the business type code of the target business, such as domestic flight check-in (BZJ-CN), international flight check-in (BZJ-INTL), and baggage check-in (BAG-HDL). Next, the system maps the business type to the corresponding business level according to a preset business level mapping table. For example, domestic flight check-in corresponds to basic business, international flight check-in corresponds to intermediate business, and back-end system fault emergency handling corresponds to advanced business.

[0057] Furthermore, after obtaining the business level, the system calls the business inspection dimension acquisition module to read the preset list of business inspection dimensions under the business level and generate an M-dimensional vector. Each dimension corresponds to a business inspection dimension. For example, M=[D1, D2, D3] represents the set of inspection dimensions for basic business, where D1 is operational proficiency, D2 is process compliance, and D3 is data entry accuracy. The generation of the M-dimensional vector is based on the business level mapping table, making the evaluation system more targeted and differentiated under different business levels.

[0058] For each business detection dimension determined by the system, the indicator configuration module reads the preset indicator list under that dimension and generates a target indicator set G_i corresponding to dimension D_i. For example, for operation proficiency D1, the indicator configuration module extracts the average operation response time O_avg, the number of repeated operations O_rep, and the total process completion time O_tot from the preset indicator library as indicators, generating a target indicator set G1=[O_avg, O_rep, O_tot]. The same method is used to generate indicator sets for other dimensions, ultimately forming M target indicator sets.

[0059] This embodiment determines the business level of the target business, determines the detection dimensions for operation detection of the business based on the business level, and obtains the indicators under each business detection dimension. This achieves the technical effect of accurately obtaining the detection indicators for operation detection of the target business and improves the accuracy of anomaly detection results.

[0060] To accurately determine indicator values, optionally, in the auxiliary information generation method of the business system provided in this application embodiment, determining the indicator values ​​of each target indicator based on behavioral data to obtain M indicator value sets includes: for any target indicator set, determining the initial parameters required to calculate the indicator values ​​of each target indicator in the target indicator set, obtaining N initial parameter sets, wherein the target indicator set includes N target indicators, and N is a positive integer; for any target initial parameter set, determining the calculation method of each initial parameter in the target initial parameter set, and calculating each initial parameter according to the calculation method and behavioral data, obtaining the parameter value set of the target initial parameter set; determining the parameter value set of each initial parameter set in the N initial parameter sets, obtaining N parameter value sets; calculating the indicator value of the corresponding target indicator using each parameter value set, obtaining N indicator values, and determining the N indicator values ​​as the indicator value set of the target indicator set.

[0061] For example, when calculating the indicator value, for each target indicator set, the system identifies and extracts N initial parameter sets, each of which is used to calculate one target indicator. Then, based on the calculation method of each initial parameter in the initial parameter set, the parameter values ​​of each initial parameter are calculated using action information to obtain the set of initial parameter values ​​corresponding to the initial parameter set.

[0062] Furthermore, after obtaining the initial parameter value set, the corresponding target indicators can be calculated based on the parameter values ​​in the initial parameter value set. For example, when calculating the average response time of an operation, the system will summarize the response time in all operation records, sum them up and divide by the total number of operations to obtain the average value, thereby calculating the indicator value of each target indicator based on the action information, and obtaining the indicator value set under each dimension, that is, M indicator value sets.

[0063] This embodiment determines the initial parameter set for each target indicator and calculates the target indicator based on the parameter values ​​of the initial parameter set, thereby obtaining the set of indicator values ​​corresponding to each target indicator set, achieving the technical effect of accurately determining the indicator values ​​of each target indicator.

[0064] To accurately determine whether a business processing operation is abnormal, optionally, in the auxiliary information generation method of the business system provided in this application embodiment, determining whether a business processing operation is abnormal based on a set of M indicator values ​​includes: calculating the score of the business detection dimension corresponding to each set of indicator values ​​to obtain M first scores; obtaining the preset weight of each business detection dimension, and performing a weighted summation operation on the M first scores using the preset weight to obtain a second score for the business processing operation; comparing the second score with a first threshold, and determining that the business processing operation is not abnormal if the second score is greater than or equal to the first threshold, and determining that the business processing operation is abnormal if the second score is less than the first threshold.

[0065] It should be noted that the business inspection dimensions are assessment aspects related to the operation of the business system, such as operational proficiency, process compliance, emergency handling capabilities, and data entry accuracy. The first score is a single-dimensional score calculated from the set of indicator values ​​for each business inspection dimension. The preset weights are pre-set weight ratios based on the importance of different business inspection dimensions.

[0066] For example, when determining whether a business processing operation is abnormal, it is necessary to perform a multi-dimensional comprehensive score on the user's operation based on the action information, and then determine whether the business processing operation is abnormal based on the quantitative score. First, it is necessary to calculate the score of each business detection dimension. This score can be determined based on the indicator value of the target indicator under each business detection dimension. For example, the first score of the operation proficiency dimension can be calculated by weighted average of the average operation response time, the number of repeated operations, and the total process completion time.

[0067] Furthermore, after obtaining the first score under each business detection dimension, the first score is multiplied by the preset weight of each dimension, and then the products are accumulated to obtain the second score that fully reflects the user's business handling capabilities. The preset weights are determined by the analytic hierarchy process combined with business priorities, reflecting the relative importance of each business detection dimension in the overall evaluation.

[0068] Furthermore, after obtaining the comprehensive score, i.e., the second score, it can be compared with the first threshold to identify whether the operator's overall performance meets the basic requirements for operating the business system. If the second score is greater than or equal to the first threshold, it indicates that the operation is smooth, compliant, and effective, and the user can skillfully handle daily and emergency business. Conversely, if it is lower than the first threshold, it suggests that there may be abnormalities such as non-standard operation, insufficient skills, or improper emergency handling. Further analysis of the causes and remedial measures are required to accurately identify and judge whether there are abnormalities in the business processing operation.

[0069] This embodiment performs a multi-dimensional comprehensive score on the user's operation based on action information, obtains the first score under each dimension, and then sums them by weight to obtain a comprehensive score. Based on the comprehensive score, it determines whether the user's operation is abnormal, thus achieving the technical effect of accurately judging whether there is anomaly in the business processing operation.

[0070] To obtain more accurate preset weights, optionally, in the method for generating auxiliary information of a business system provided in this application embodiment, obtaining the preset weight of each business detection dimension includes: obtaining the importance score of each business detection dimension to obtain M importance scores; determining the scenario coefficient of each business detection dimension according to the target business to obtain M scenario coefficients; and determining the preset weight of each business detection dimension according to the scenario coefficient and importance score of each business detection dimension.

[0071] For example, when determining the preset weight of each business detection dimension, it is first necessary to determine the importance score of each business detection dimension. The importance score can be the average of the importance of each business detection dimension relative to other business detection dimensions, rather than its own importance. For example, compared with dimension B, dimension A has an importance score of 2 points; compared with dimension C, dimension A has an importance score of 9 points; and compared with dimension D, dimension A has an importance score of 7 points. The average of the above three scores is calculated to obtain the importance score of dimension A as 6 points.

[0072] Furthermore, the scenario coefficients for each business detection dimension can be determined, and the scenario coefficients can be multiplied by the importance score to obtain the preset weights for each business detection dimension.

[0073] This embodiment determines the preset weights by determining the importance scores and scenario coefficients of each dimension, thus achieving the technical effect of accurately determining the preset weights of each dimension.

[0074] To accurately determine the cause of anomalies, optionally, in the method for generating auxiliary information for a business system provided in this application embodiment, determining the cause of anomalies in a business processing operation based on M sets of indicator values ​​includes: calculating the score of the business detection dimension corresponding to each set of indicator values ​​to obtain M first scores; obtaining the second threshold of each business detection dimension and comparing the first score of each business detection dimension with the second threshold to obtain M comparison results; obtaining the target comparison result representing the anomaly from the M comparison results and determining the business detection dimension to which the target comparison result belongs as the anomaly detection dimension; obtaining the set of abnormal indicator values ​​under the anomaly detection dimension and determining the abnormal indicator value from the set of abnormal indicator values ​​according to the preset requirements of each indicator value in the set of abnormal indicator values; determining the cause of the anomaly of the abnormal indicator value and determining the cause of the anomaly as the cause of the anomaly in the business processing operation.

[0075] For example, when determining the cause of an anomaly in a business operation, it is first necessary to calculate the first score of each business detection dimension. This score can be determined based on the indicator values ​​of the target indicators under each business detection dimension. For example, the first score of the operation proficiency dimension can be calculated by weighted average of the average response time of the operation, the number of repeated operations, and the total time to complete the process.

[0076] Furthermore, in order to detect which dimension the user's operation is abnormal, it is necessary to obtain the second threshold of each business detection dimension, compare the first score of each business detection dimension with the second threshold, obtain multiple comparison results, determine the target comparison result representing the abnormality, and then determine the abnormality detection dimension.

[0077] Furthermore, after determining the anomaly detection dimension, we can obtain the set of anomaly indicator values ​​under the anomaly detection dimension, determine the anomaly indicator values ​​in the set, and then determine the cause of the anomaly based on the anomaly indicator values.

[0078] For example, if the abnormal indicator value is 5 errors in step A, the reason for the abnormality is that the user is not familiar with the operation process of step A and needs to receive auxiliary training for step A.

[0079] This embodiment determines the abnormal dimension based on the first score under each dimension, determines the abnormal indicator value according to the preset requirements of each indicator value, and then determines the cause of the abnormality based on the abnormal indicator value, thus achieving the technical effect of accurately determining the cause of the abnormality.

[0080] To accurately obtain auxiliary information, optionally, in the auxiliary information generation method of the business system provided in this application embodiment, determining the auxiliary information for business processing operation based on the cause of the anomaly includes: obtaining keywords of the cause of the anomaly, and searching in the auxiliary information database based on the keywords to obtain multiple initial auxiliary information; selecting auxiliary information related to the target business from the multiple initial auxiliary information to obtain auxiliary information for business processing operation.

[0081] For example, after determining the cause of the operational anomaly, the system will automatically extract the keywords most relevant to the cause. These keywords can come from descriptions of anomaly detection dimensions, explanations of specific indicators, or detailed records of the operational process, such as "operation response time," "process deviation," and "data entry error." After extracting the keywords, the system will use these keywords as search criteria to query the auxiliary information database using exact matching or fuzzy matching algorithms to find all initial auxiliary information related to the cause of the anomaly.

[0082] Furthermore, after retrieving multiple initial pieces of auxiliary information, it is necessary to filter out the auxiliary information that best meets the current target business needs. This can be done by combining information such as business scenarios, operator training records, and learning preferences, using content analysis algorithms (such as natural language processing) or manual rules to further filter the auxiliary information, retaining only information that can directly resolve the cause of the anomaly or improve specific business processing capabilities. The filtered auxiliary information will be compiled by the system into personalized feedback reports, providing operators with targeted guidance and suggestions to improve business processing efficiency.

[0083] This embodiment achieves the technical effect of accurately determining auxiliary information by using keyword retrieval of abnormal causes and personalized filtering of auxiliary information.

[0084] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0085] Example 2

[0086] This application also provides an apparatus for generating auxiliary information for a business system. It should be noted that this apparatus can be used to execute the method for generating auxiliary information for a business system provided in the above embodiments. The apparatus for generating auxiliary information for a business system provided in this application will be described below.

[0087] According to an embodiment of this application, an apparatus for generating auxiliary information for implementing the above-described business system is also provided. Figure 3 This is a schematic diagram of an auxiliary information generation device for a business system according to Embodiment 2 of this application, such as... Figure 3 As shown, the device includes:

[0088] The first acquisition unit 31 is used to acquire behavioral data of the target user during the process of performing business processing operations in the business system, wherein the business processing operations are used to process the target business.

[0089] The second acquisition unit 32 is used to acquire target indicators for evaluating business processing operations, obtain a set of M target indicators, and determine the indicator values ​​of each target indicator based on behavioral data, thereby obtaining a set of M indicator values, where M is a positive integer.

[0090] Judgment unit 33 is used to determine whether there is an abnormality in the business processing operation based on the set of M indicator values, and if there is an abnormality in the business processing operation, to determine the cause of the abnormality in the business processing operation based on the set of M indicator values.

[0091] The determination unit 34 is used to determine the auxiliary information for business processing operations based on the cause of the anomaly, and send the auxiliary information to the business system.

[0092] The auxiliary information generation device for a business system provided in this application embodiment acquires behavioral data of a target user during a business processing operation using the business system through a first acquisition unit 31, wherein the business processing operation is used to process a target business; a second acquisition unit 32 acquires target indicators for evaluating the business processing operation, obtaining a set of M target indicators, and determines the indicator value of each target indicator based on the behavioral data, obtaining a set of M indicator values, where M is a positive integer; a judgment unit 33 judges whether there is an anomaly in the business processing operation based on the set of M indicator values, and if there is an anomaly in the business processing operation, determines the cause of the anomaly based on the set of M indicator values; and a determination unit 34 determines the auxiliary information for the business processing operation based on the cause of the anomaly and sends the auxiliary information to the business system. By acquiring behavioral data, the system determines whether there are any anomalies in business processing operations. If anomalies are found, corresponding auxiliary information is generated. This auxiliary information provides users with intelligent business operation support and assistance, helping them improve their operational proficiency. Users can enhance their familiarity with the business system based on the auxiliary information, achieving the goal of providing automated and personalized business system training. This improves the accuracy and efficiency of the business system and solves the technical problem of low accuracy and efficiency in performing business processing operations due to the high frequency of business system updates.

[0093] Optionally, in the auxiliary information generation device for the business system provided in the embodiments of this application, the second acquisition unit 32 includes: a first acquisition module, used to acquire the business type of the target business and determine the business level of the target business according to the business type; and a second acquisition module, used to acquire the business detection dimensions under the business level, obtain M business detection dimensions, and acquire the indicators under each business detection dimension to obtain a set of M target indicators.

[0094] Optionally, in the auxiliary information generation device for the business system provided in this application embodiment, the second acquisition unit 32 includes: a first determining module, configured to determine, for any set of target indicators, the initial parameters required to calculate the indicator values ​​of each target indicator in the set of target indicators, to obtain N initial parameter sets, wherein the set of target indicators includes N target indicators, and N is a positive integer; a second determining module, configured to determine, for any set of target initial parameters, the calculation method of each initial parameter in the set of target initial parameters, and calculate each initial parameter according to the calculation method and behavioral data, to obtain a set of parameter values ​​for the set of target initial parameters; a third determining module, configured to determine the set of parameter values ​​for each of the N initial parameter sets, to obtain N set of parameter values; and a first calculation module, configured to calculate the indicator values ​​of the corresponding target indicators using each set of parameter values, to obtain N indicator values, and to determine the N indicator values ​​as the set of indicator values ​​for the set of target indicators.

[0095] Optionally, in the auxiliary information generation device for the business system provided in this application embodiment, the judgment unit 33 includes: a second calculation module, used to calculate the score of the business detection dimension corresponding to each set of indicator values ​​to obtain M first scores; a third acquisition module, used to acquire the preset weight of each business detection dimension, and perform a weighted summation operation on the M first scores through the preset weight to obtain a second score of the business processing operation; and a first comparison module, used to compare the second score with a first threshold, and if the second score is greater than or equal to the first threshold, determine that there is no abnormality in the business processing operation, and if the second score is less than the first threshold, determine that there is an abnormality in the business processing operation.

[0096] Optionally, in the auxiliary information generation device for the business system provided in the embodiments of this application, the third acquisition module includes: an acquisition submodule, used to acquire the importance score of each business detection dimension to obtain M importance scores; a first determination submodule, used to determine the scenario coefficient of each business detection dimension according to the target business to obtain M scenario coefficients; and a second determination submodule, used to determine the preset weight of each business detection dimension according to the scenario coefficient and importance score of each business detection dimension.

[0097] Optionally, in the auxiliary information generation device for the business system provided in this application embodiment, the judgment unit 33 includes: a third calculation module, used to calculate the score of the business detection dimension corresponding to each set of indicator values, and obtain M first scores; a second comparison module, used to obtain the second threshold of each business detection dimension respectively, and compare the first score of each business detection dimension with the second threshold to obtain M comparison results; a fourth acquisition module, used to obtain the target comparison result representing the abnormality from the M comparison results, and determine the business detection dimension to which the target comparison result belongs as the abnormal detection dimension; a fifth acquisition module, used to obtain the abnormal indicator value set under the abnormal detection dimension, and determine the abnormal indicator value from the abnormal indicator value set according to the preset requirements of each indicator value in the abnormal indicator value set; and a fourth determination module, used to determine the abnormal cause of the abnormal indicator value, and determine the abnormal cause as the abnormal cause of the business processing operation.

[0098] Optionally, in the auxiliary information generation device for the business system provided in this application embodiment, the determining unit 34 includes: a retrieval module, used to obtain keywords of the cause of the anomaly, and to retrieve multiple initial auxiliary information from the auxiliary information database based on the keywords; and a selection module, used to select auxiliary information related to the target business from the multiple initial auxiliary information to obtain auxiliary information for business processing.

[0099] It should be noted that the first acquisition unit 31, the second acquisition unit 32, the judgment unit 33, and the determination unit 34 mentioned above correspond to steps S201 to S204 in Embodiment 1. The instances and application scenarios implemented by each of the above units and the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware components or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules can also be part of a device and can run in the computer terminal 10 provided in Embodiment 1.

[0100] Example 3

[0101] Embodiments of this application may provide an electronic device. Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4 (Only one is shown) processor 1002, memory 1004, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0102] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0103] Those skilled in the art will understand that Figure 4 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 4 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 4 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0104] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0105] Example 4

[0106] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the method for generating auxiliary information of the business system provided in Embodiment 1.

[0107] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0108] Embodiments of this application also provide a computer program product, which, when executed on a data processing device, is a program adapted to perform the steps of a method for generating auxiliary information for a business system.

[0109] Embodiments of this application also provide a computer-readable storage medium, which includes a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to execute the above-described method for generating auxiliary information of the business system.

[0110] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0111] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0112] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0115] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0116] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for generating auxiliary information in a business system, characterized in that, include: Acquire behavioral data of the target user during the process of performing business processing operations in the business system, wherein the business processing operations are used to process the target business; Obtain target indicators for evaluating the business processing operations, resulting in a set of M target indicators. Determine the indicator values ​​of each target indicator based on the behavioral data, resulting in a set of M indicator values, where M is a positive integer. The system determines whether there is an abnormality in the business processing operation based on the set of M indicator values, and if there is an abnormality in the business processing operation, it determines the cause of the abnormality based on the set of M indicator values. Based on the cause of the anomaly, determine the auxiliary information for the business processing operation, and send the auxiliary information to the business system.

2. The method according to claim 1, characterized in that, Obtain the target indicators for evaluating the business processing operations, resulting in a set of M target indicators, including: Obtain the business type of the target business, and determine the business level of the target business based on the business type; Obtain the business detection dimensions under the business level to get M business detection dimensions, and obtain the indicators under each business detection dimension to get the set of M target indicators.

3. The method according to claim 1, characterized in that, Based on the behavioral data, the indicator values ​​for each target indicator are determined, resulting in a set of M indicator values, including: For any set of target indicators, determine the initial parameters required to calculate the indicator values ​​of each target indicator in the set of target indicators, and obtain N sets of initial parameters, wherein the set of target indicators includes N target indicators, and N is a positive integer; For any target initial parameter set, determine the calculation method of each initial parameter in the target initial parameter set, and calculate each initial parameter according to the calculation method and the behavior data to obtain the parameter value set of the target initial parameter set; Determine the set of parameter values ​​for each of the N initial parameter sets to obtain N parameter value sets; The corresponding target indicator value is calculated using each parameter value set to obtain N indicator values, and the N indicator values ​​are determined as the indicator value set of the target indicator set.

4. The method according to claim 1, characterized in that, Determining whether the business processing operation is abnormal based on the set of M indicator values ​​includes: Calculate the score for each business detection dimension corresponding to each set of indicator values ​​to obtain M first scores; Obtain the preset weight for each business detection dimension, and perform a weighted summation operation on the M first scores using the preset weights to obtain the second score for the business processing operation; The second score is compared with the first threshold. If the second score is greater than or equal to the first threshold, it is determined that the business processing operation is not abnormal. If the second score is less than the first threshold, it is determined that the business processing operation is abnormal.

5. The method according to claim 4, characterized in that, The preset weights for each business detection dimension include: Obtain the importance score for each business detection dimension, resulting in M ​​importance scores; Based on the target business, the scene coefficients for each business detection dimension are determined, resulting in M ​​scene coefficients; The preset weight of each business detection dimension is determined based on the scenario coefficient and importance score of each business detection dimension.

6. The method according to claim 1, characterized in that, The reasons for the abnormality in the business processing operation determined based on the set of M indicator values ​​include: Calculate the score for each business detection dimension corresponding to each set of indicator values ​​to obtain M first scores; The second threshold of each business detection dimension is obtained, and the first score of each business detection dimension is compared with the second threshold to obtain M comparison results; From the M comparison results, obtain the target comparison result that represents the anomaly, and determine the business detection dimension to which the target comparison result belongs as the anomaly detection dimension; Obtain the set of abnormal indicator values ​​under the anomaly detection dimension, and determine the abnormal indicator values ​​from the set of abnormal indicator values ​​according to the preset requirements of each indicator value in the set of abnormal indicator values; Determine the cause of the abnormal indicator value and identify the cause of the abnormality as the cause of the abnormality in the business processing operation.

7. The method according to claim 1, characterized in that, The auxiliary information for determining the business processing operation based on the stated cause of the anomaly includes: Obtain keywords for the cause of the anomaly, and search the auxiliary information database based on the keywords to obtain multiple initial auxiliary information; Auxiliary information associated with the target business is selected from the plurality of initial auxiliary information to obtain auxiliary information for the business processing operation.

8. A device for generating auxiliary information for a business system, characterized in that, include: The first acquisition unit is used to acquire behavioral data of the target user during the process of performing business processing operations in the business system, wherein the business processing operations are used to process the target business. The second acquisition unit is used to acquire target indicators for evaluating the business processing operation, obtain a set of M target indicators, and determine the indicator value of each target indicator based on the behavioral data, thereby obtaining a set of M indicator values, where M is a positive integer; The judgment unit is used to determine whether there is an abnormality in the business processing operation based on the set of M indicator values, and if there is an abnormality in the business processing operation, to determine the cause of the abnormality in the business processing operation based on the set of M indicator values. The determining unit is used to determine auxiliary information for the business processing operation based on the cause of the anomaly, and send the auxiliary information to the business system.

9. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method for generating auxiliary information of the business system according to any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, executes the method for generating auxiliary information of the business system according to any one of claims 1 to 7.

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