Integrated office method, system and equipment based on artificial intelligence and storage medium

Abstract

The invention provides an integrated office method, system and device based on artificial intelligence and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining first business information handled by a client within a preset time period, and associated business information associated with the first business information; predicting a first to-do service of the customer in combination with the first service information and the associated service information; acquiring group characteristics of a group to which the customer belongs, and generating a second to-do service of the customer according to the group characteristics; identifying second service information handled by other users in the relative relationship network corresponding to the customer within a preset duration, and determining a third to-be-handled service of the customer according to the second service information; and displaying the first to-be-handled service, the second to-be-handled service and the third to-be-handled service on a display screen, so that a client selects a target to-be-handled service from the display screen. The technical effect of the invention is that services required to be handled by special groups such as old people and the like can be accurately positioned.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, specifically to an integrated office method, system, device, and storage medium based on artificial intelligence. Background Technology

[0002] With the intelligent transformation of government services and enterprise offices, self-service terminals have been widely used in various business processing scenarios. However, for special groups such as the elderly, due to their unfamiliarity with the operation of smart devices and difficulty in accurately expressing their business needs, they often need repeated guidance from staff to complete the process. This not only reduces efficiency but also increases the labor costs of service organizations.

[0003] Currently, existing self-service systems mainly use simple menu navigation or keyword search to assist users in finding and processing business. While these methods reduce the manual workload at service windows and improve the automation of business processing, they require users to have a clear understanding of the business types and processing procedures. For groups such as the elderly who are unfamiliar with the business system, it is difficult to accurately locate the business that the elderly and other special groups need to process. Summary of the Invention

[0004] This application provides an integrated office method, system, device, and storage medium based on artificial intelligence, for accurately locating the business that special groups such as the elderly need to handle.

[0005] In a first aspect, this application provides an integrated office method based on artificial intelligence. The method includes: responding to a customer's business processing request, obtaining first business information processed by the customer within a preset time period, and related business information associated with the first business information; combining the first business information and the related business information to predict the customer's first pending business; obtaining the group characteristics of the customer's group, and generating the customer's second pending business based on the group characteristics; identifying second business information processed by other users in the customer's corresponding kinship network within the preset time period, and determining the customer's third pending business based on the second business information; and displaying the first pending business, the second pending business, and the third pending business on a display screen, so that the customer can select a target pending business from the display screen.

[0006] By employing the aforementioned technical solution, the system predicts the first pending task based on the customer's primary business information and its related business information. It then generates the second pending task by combining the characteristics of the customer's group, and determines the third pending task by analyzing the second business information of other users in the kinship network. This constructs a multi-layered business recommendation system encompassing individual, group, and social relationship dimensions. This multi-dimensional recommendation mechanism not only provides accurate recommendations based on the customer's individual business handling patterns but also leverages group experience to offer references for customers lacking relevant experience. Furthermore, by analyzing business information within the kinship network, it captures key business nodes in the family lifecycle, making the recommendation results more comprehensive and forward-looking. By centrally displaying these three types of pending tasks on a screen, the system provides customers with clear business guidance, accurately identifying the business needs of special groups such as the elderly.

[0007] Optionally, predicting the customer's first pending business by combining the first business information and the related business information includes: determining the customer's business processing pattern based on the first business information, the business processing pattern including business type, processing time, and processing cycle; determining a first recommended business based on the business processing pattern; determining a second recommended business that is related to the first recommended business based on the related business information; and using the first recommended business and the second recommended business as the customer's first pending business.

[0008] By employing the aforementioned technical solution and analyzing customers' initial business information to extract patterns in business processing, it can not only generate primary recommended services based on business type, processing time, and processing cycle, but also identify related secondary recommended services by combining associated business information, thus forming a complete business chain recommendation. This two-tiered recommendation mechanism based on historical data ensures both the timeliness and regularity of recommendations while avoiding business omissions, enabling customers to systematically complete related business transactions.

[0009] Optionally, generating the customer's second pending task based on the group characteristics includes: determining the common pending tasks of the customer's group based on the group characteristics; filtering out the customer's unfinished tasks from the common pending tasks; sorting the unfinished tasks in descending order of priority to generate a task sequence; and taking the unfinished tasks in the task sequence that are preceding a preset position as the customer's second pending task.

[0010] By employing the aforementioned technical solution, common pending tasks are identified through analysis of group characteristics. Unfinished tasks are then prioritized, and those preceding a pre-defined task are selected as the second pending task. This achieves accurate recommendations based on group experience. This screening and prioritization mechanism, based on collective intelligence, ensures the universal applicability of the recommended tasks while highlighting important tasks through prioritization, effectively preventing customers from missing crucial tasks due to information asymmetry.

[0011] Optionally, determining the customer's third pending task based on the second business information includes: determining the business processing characteristics of other users in the kinship network based on the second business information; selecting candidate businesses that match the business processing characteristics from a preset business database based on the business processing characteristics; calculating the correlation index between the candidate businesses and the customer, and sorting the candidate businesses in descending order of the correlation index to generate a correlation degree sequence; determining the weight coefficients corresponding to the other users in the kinship network based on their closeness to the customer; and determining the customer's third pending task by combining the correlation degree sequence and the weight coefficients.

[0012] By employing the aforementioned technical solution, candidate services are screened by analyzing the business handling characteristics of other users in the kinship network. The third pending service is determined by combining correlation index ranking and kinship weighting, thus achieving intelligent recommendation based on social relationship networks. This recommendation mechanism, which comprehensively considers business relevance and kinship weighting, ensures the relevance of recommended services to customers while improving the accuracy of recommendations through differentiated weighting based on kinship, effectively capturing key business opportunities throughout the family lifecycle.

[0013] Optionally, calculating the correlation index between the candidate service and the customer includes: selecting target candidate services that are related to the customer from the candidate services, obtaining the time interval between the processing time of each target candidate service and the current time; determining the correlation index between each target candidate service and the customer based on the time interval, wherein the correlation index is inversely proportional to the time interval.

[0014] By employing the aforementioned technical solution, a correlation index is calculated based on the time interval between the processing time of candidate services and the current time. Furthermore, the inverse relationship between the correlation index and the time interval allows for a more dynamic reflection of the relevance between candidate services and customers. This approach fully considers the time sensitivity of service processing, prioritizing services with higher recent relevance, thereby improving the timeliness and accuracy of recommendations. In addition, this time-interval-based correlation calculation method avoids the limitations of static recommendations, making service recommendations more flexible and intelligent, and better suited to customers' actual needs.

[0015] Optionally, displaying the first, second, and third pending tasks on the display screen includes: obtaining the priorities corresponding to the first, second, and third pending tasks; sorting the first, second, and third pending tasks according to the priorities; and displaying the sorted pending tasks on the display screen in different display modes.

[0016] By adopting the above technical solution, and identifying the relationship between customers and target candidate services, and calculating the correlation index based on the time interval between the processing time and the current time, a calculation model was established in which the correlation index is inversely proportional to the time interval. This correlation calculation method based on time decay not only highlights the influence of recently occurring services, making the recommendation results more consistent with the actual business development patterns, but also objectively evaluates the degree of correlation between different services through a quantitative calculation model, avoiding bias caused by subjective judgment, and improving the timeliness and accuracy of service recommendations.

[0017] Optionally, after displaying the first, second, and third pending tasks on the display screen, the method further includes: if the customer does not select a target pending task from the first, second, and third pending tasks, generating a supplementary task request interface, responding to the customer's input of a custom task, extracting key business features from the custom task; matching similar tasks from a preset task library or generating a new task template based on the key business features; and displaying the similar task or the new task template as supplementary pending tasks on the display screen.

[0018] By adopting the above technical solution and providing a business supplement request interface, customers can input custom business requirements. A flexible feedback mechanism is established by extracting key business features to match similar business requirements or generate new business templates. This dynamically supplementary recommendation method not only provides customers with a channel to express personalized needs but also expands the coverage of business recommendations through intelligent feature matching. This effectively solves the problem that preset recommendations cannot meet specific customer needs, improving the system's service completeness and adaptability.

[0019] Secondly, this application provides an integrated office system based on artificial intelligence, the system comprising: a response module, a combination module, an acquisition module, a recognition module, and a display module; wherein, The response module is used to respond to a customer's business processing request, obtain first business information processed by the customer within a preset time period, and related business information associated with the first business information; the combination module is used to combine the first business information and the related business information to predict the customer's first pending business; the acquisition module is used to acquire the group characteristics of the customer's group, and generate the customer's second pending business based on the group characteristics; the identification module is used to identify the second business information processed by other users in the customer's corresponding kinship network within the preset time period, and determine the customer's third pending business based on the second business information; the display module is used to display the first pending business, the second pending business, and the third pending business on the display screen, so that the customer can select a target pending business from the display screen.

[0020] Thirdly, this application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program of any of the above-mentioned integrated office methods based on artificial intelligence.

[0021] Fourthly, this application provides a computer-readable storage medium that stores a computer program capable of being loaded by a processor and executing any of the aforementioned integrated office methods based on artificial intelligence.

[0022] In summary, this application includes at least one of the following beneficial technical effects: Based on a customer's primary business information and related business information, the system predicts the first pending task. Combining this with the characteristics of the customer's group, it generates the second pending task. Furthermore, by analyzing the second business information of other users in the kinship network, it determines the third pending task, thus constructing a multi-layered business recommendation system encompassing individual, group, and social relationship dimensions. This multi-dimensional recommendation mechanism not only provides accurate recommendations based on individual customer business handling patterns but also leverages group experience to offer guidance to customers lacking relevant experience. Simultaneously, by analyzing business information within the kinship network, it captures key business nodes in the family lifecycle, making the recommendation results more comprehensive and forward-looking. By centrally displaying these three types of pending tasks on a screen, the system provides customers with clear business guidance, accurately identifying the necessary tasks for special groups such as the elderly. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating an integrated office method based on artificial intelligence provided in an embodiment of this application; Figure 2This is a schematic diagram of the structure of an integrated office system based on artificial intelligence provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0024] Explanation of reference numerals in the attached figures: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0026] In the description of the embodiments in this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0027] Figure 1 This is a flowchart illustrating an integrated office method based on artificial intelligence, provided in an embodiment of this application. Figure 1 As shown, the method includes S101-S105: S101, in response to a customer's business processing request, obtains the first business information processed by the customer within a preset time period, as well as the related business information associated with the first business information.

[0028] When a customer initiates a service request via a terminal device, the system first needs to obtain the customer's historical business data as the basis for recommendations. Specifically, the system will query all business records processed by the customer on the platform within a preset time period, and use these records as the primary business information. The preset time period can be set according to actual needs. These business records include information such as business type (e.g., social security payment, housing provident fund withdrawal, tax declaration), processing time, and processing status. For example, a customer may have processed "social security and housing provident fund payment" and "personal income tax declaration" within the past three months.

[0029] While acquiring the initial business information, the system also acquires other business information related to these completed transactions, i.e., related business information. Related businesses refer to those that are interdependent or related in terms of business processes, processing conditions, or the application of results. For example, there is a relationship between the payment of social insurance and housing provident fund and the adjustment of social security base, because changes in the social security base directly affect the amount of social insurance and housing provident fund contributions; similarly, there is a relationship between the individual income tax declaration and the maintenance of special additional deductions information, because the information on special additional deductions affects the calculation of individual income tax.

[0030] The purpose of obtaining this information is to understand a customer's business handling habits and potential business needs by analyzing their historical business transactions and related transactions. This historical data-based analysis method helps the system more accurately predict the business a customer may need to handle in the future, thereby providing more targeted business recommendations.

[0031] S102, combining primary business information and related business information, predicts the customer's primary pending business.

[0032] To accurately predict the business a customer might need to handle, the system first conducts an in-depth analysis of the initial business information to extract patterns in customer transactions. These patterns include three dimensions: business type, processing time, and processing cycle. Business type reflects the types of business a customer frequently handles; processing time indicates the time of day a customer tends to process transactions; and the processing cycle describes the regular time intervals at which transactions occur. For example, the system might discover that a customer habitually processes social security payments on the morning of the 15th of each month and files individual income tax returns at the end of each quarter.

[0033] Based on the aforementioned business processing patterns, the system will first generate a top recommended service. Specifically, the system will predict services that may need to be processed in the near future based on the current time and the customer's processing cycle. For example, if it finds that the customer usually processes social security payments on the 15th of each month, and that date is approaching, the system will prioritize social security payments as the top recommended service. This time-based recommendation method reminds customers to complete recurring tasks on time, avoiding overdue situations.

[0034] Based on the initial recommended service, the system further analyzes previously acquired related service information to identify services associated with the initial recommended service and designates them as the second recommended service. For example, when the initial recommended service is social security payment, the system will check for related service requests such as social security base adjustment. Similarly, if the system detects that a customer may need to file an individual income tax return, it will recommend updating special additional deductions information accordingly.

[0035] Ultimately, the system integrates the first and second recommended services as the customer's top priority task. This recommendation method not only considers the customer's historical processing habits but also expands the scope of recommendations through correlation analysis, enabling a more comprehensive coverage of the customer's business needs. For example, for a customer who needs to make social security contributions, the system will not only remind them to pay on time but also proactively suggest whether they need to adjust their social security contribution base, thus helping the customer complete the relevant business more systematically.

[0036] Based on the above embodiments, as an optional implementation, in S102, combining the first business information and related business information, predicting the customer's first pending business specifically includes S21-S24: S21. Based on the first business information, determine the customer's business processing pattern, which includes business type, processing time, and processing cycle.

[0037] To achieve more accurate business forecasting and recommendations, the system needs to conduct in-depth mining and analysis of customers' historical business data. This analysis begins with primary business information, using data mining techniques to extract patterns in customer business transactions. Specifically, the system identifies patterns across three dimensions: the business type dimension records the types of business customers frequently handle and their frequency, such as monthly social security payments and quarterly individual income tax declarations; the processing time dimension analyzes the time of day customers tend to process their transactions, such as between 9:00 AM and 11:00 AM; and the processing cycle dimension identifies the time intervals between recurring transactions, such as monthly payroll and individual income tax declarations on the 15th of each month and quarterly updates of special additional deductions.

[0038] S22. Based on the business processing patterns, determine the first recommended business.

[0039] Based on identified patterns in business transactions, the system predicts the services a customer might need to complete in the near future, considering the current timeframe, and prioritizes these services. For example, if the system detects that a customer makes social security contributions on the 15th of each month, and the 15th is approaching, it will prioritize social security contributions. Similarly, if the system detects that a customer conducts individual income tax reconciliation at the end of each quarter, and the quarter is currently nearing its end, it will prioritize individual income tax reconciliation. This time-based recommendation method provides precise reminders for business transactions, preventing customers from missing important deadlines.

[0040] S23, Based on the related business information, determine the second recommended business that is related to the first recommended business.

[0041] After determining the primary recommended service, the system further analyzes related service information to identify services that are related to the primary recommended service and designate them as secondary recommended services. This relationship may manifest in preconditions, subsequent processes, or synergistic effects. For example, when the primary recommended service is housing provident fund withdrawal, the system will register the housing loan contract as the secondary recommended service; when the primary recommended service is individual income tax settlement, the system will also recommend the verification of special additional deductions. This associated recommendation mechanism helps customers complete the relevant service chain more systematically and avoid missing important steps.

[0042] S24, designate the first and second recommended services as the customer's primary agency services.

[0043] Ultimately, the system will combine the first and second recommended services to form the customer's top pending tasks. This recommendation mechanism, through a combination of time-series and correlation analysis, ensures both the timeliness of recommendations and the completeness of service processing. For example, for customers who are about to apply for housing provident fund withdrawal, the system will not only remind them to prepare the withdrawal application but also proactively suggest that they complete related procedures such as housing contract registration, thereby achieving integrated processing of services.

[0044] S103: Obtain the group characteristics of the customer's group and generate the customer's second pending task based on the group characteristics.

[0045] In practice, the system first needs to obtain the group characteristics of the customer's group. These group characteristics refer to tag attributes that reflect the common features of a user group, including but not limited to dimensions such as age group, occupation type, industry, and work location. For example, a customer might simultaneously belong to the 25-35 age group, an IT professional, an employee of a high-tech company, and work in Chaoyang District, Beijing. Based on these group characteristic tags, the system will find user groups with similar characteristics.

[0046] After identifying the target customer group, the system analyzes the business transactions of users within that group to extract common pending transactions. Common pending transactions refer to types of transactions that are frequently or commonly processed within a specific group. For example, employees of high-tech enterprises may commonly have needs such as applying for personal income tax incentives and technology innovation subsidies; while newly hired employees may have needs related to initial social security enrollment and opening a housing provident fund account.

[0047] To improve the relevance of recommendations, the system filters out unfinished tasks from the identified common pending tasks. This step avoids recommending completed duplicate tasks, improving the practicality of the recommendations. Next, the system prioritizes these unfinished tasks, generating a task sequence. Prioritization is based on factors such as the timeliness of the task, the proportion of tasks completed within the group, and policy requirements. For example, policy-related tasks with deadlines are given higher priority, while non-mandatory tasks with greater flexibility are given relatively lower priority.

[0048] Ultimately, the system will recommend unfinished transactions that are preceding a preset position in the transaction sequence as the customer's second pending transaction. The preset position refers to a threshold set by the system to control the number of pending transactions displayed to the customer, preventing information redundancy due to excessive recommendations. For example, if the preset position is set to the first three, the system will recommend the three highest-priority unfinished transactions as the customer's second pending transaction.

[0049] Based on the above embodiments, as an optional implementation method, in S103, generating a second pending task for the customer according to group characteristics specifically includes S31-S34: S31, based on group characteristics, determine the common pending tasks of the customer's group.

[0050] To fully leverage group data and provide customers with more comprehensive business recommendations, the system conducts in-depth analysis based on previously acquired group characteristics. These characteristics include multi-dimensional tags such as age group, occupation type, industry, and work location, for example, employees under 35 in the technology industry or employees of high-tech enterprises. The system first analyzes the business processing records of users with the same group characteristic tags, extracting the types of business that these groups commonly need to handle and identifying them as common pending tasks. For example, for employees of high-tech enterprises, common pending tasks might include applications for science and technology innovation subsidies, talent allowances, and special tax incentives; for newly hired employees, it might include initial social security enrollment and opening a housing provident fund account.

[0051] S32, filter out customers' unfinished business from the shared pending business list.

[0052] After identifying common pending tasks within a group, the system compares these tasks with the target customer's historical transaction records to filter out tasks that the customer has not yet completed. This step aims to avoid repeatedly recommending tasks already completed by the customer, ensuring the effectiveness of the recommendations. For example, if a customer has already completed their initial social security enrollment, even if this is a common pending task for newly hired employees, the system will not include it in the recommendations. This filtering mechanism ensures the relevance of the recommendations and improves the efficiency of system resource utilization.

[0053] S33: Sort the unprocessed transactions in descending order of priority to generate a transaction sequence.

[0054] For the selected unprocessed applications, the system prioritizes them based on multiple evaluation metrics. These metrics include the timeliness of the application (e.g., policy expiration date), accessibility (the percentage of applicants who have completed the application within the group), and importance (the degree of impact on individual rights). For example, applications for policy subsidies that are about to expire will be given higher priority; applications with a high percentage of applicants will also receive a corresponding increase in priority; and applications directly related to important individual rights will also be given priority. The system calculates the weighted scores of these metrics and sorts the unprocessed applications in descending order, generating a priority-marked application sequence.

[0055] S34, designates unprocessed business items that are prior to a preset position in the business sequence as the customer's second pending business item.

[0056] Ultimately, the system selects unfinished transactions from the business sequence that precede a preset position as the customer's second pending task. This preset position refers to a system-defined threshold for the number of recommended transactions, controlling the number of pending tasks displayed to the customer. For example, if the preset position is set to the top three, the system will recommend the three highest-priority unfinished transactions as the second pending task. This quantity control mechanism avoids information overload caused by excessive recommendations, helping customers focus their attention on the most important tasks.

[0057] S104, identify the second business information handled by other users in the customer's corresponding kinship network within a preset time period, and determine the customer's third pending business based on the second business information.

[0058] The system first identifies the customer's family network, which refers to user groups with direct family relationships to the customer, such as spouse, children, and parents. For example, a customer's family network might include their spouse, one minor child, and two elderly parents. The system then retrieves all transaction records processed by these family members within a preset timeframe and uses these records as secondary business information.

[0059] Based on the acquired second business information, the system analyzes the business transaction characteristics of other users in the kinship network. These characteristics include dimensions such as business type distribution, transaction sequence, and related business chains. For example, the system might discover that a customer's spouse recently applied for a housing loan, or that their parents applied for senior citizen subsidies; these constitute specific business transaction characteristics.

[0060] Based on the identified business processing characteristics, the system will filter candidate businesses that match these characteristics from a pre-set business database. The pre-set business database refers to the complete set of all processable businesses compiled by the system in advance. For example, when it is found that a relative has applied for a housing loan, the system will include related businesses such as housing provident fund withdrawal and deed tax payment as candidate businesses; when it identifies an application for elderly subsidies, it may associate it with candidate businesses such as tax incentives for supporting the elderly.

[0061] To assess the relevance of candidate services to target customers, the system calculates a correlation index for each candidate service. The correlation index calculation takes into account the timeliness of the service, i.e., its relationship to the current point in time. Specifically, if a service was recently processed by a relative, its correlation index will be relatively high; conversely, if the service was processed earlier, its correlation index will be relatively low. The system then sorts the candidate services in descending order based on their correlation indices, forming a correlation sequence.

[0062] Simultaneously, the system determines the weight coefficient for each user based on the closeness of their relationship with other users in the kinship network. For example, a spouse's weight coefficient may be higher than that of other relatives because business needs between spouses are usually more closely related. Ultimately, the system combines the relationship sequence and weight coefficients to select the most suitable business to recommend to the customer as the third pending business.

[0063] Based on the above embodiments, as an optional implementation, in S104, determining the customer's third pending task according to the second business information specifically includes S41-S45: S41, Based on the second business information, determine the business processing characteristics of other users in the kinship network.

[0064] To fully explore the business connections within kinship networks, the system first needs to analyze a second set of business information: the business transaction records of family members within a preset timeframe. By analyzing these records, the system can identify the business transaction characteristics of other users within the kinship network. These characteristics include the distribution of business types (e.g., a predominance of financial or social security transactions), the sequence of transactions (e.g., applying for a housing loan before withdrawing housing provident funds), and related business chains (e.g., a series of transactions related to children's education). For example, the system might discover that a customer's spouse has recently been frequently processing real estate-related transactions, or that elderly parents have been processing a concentrated number of pension-related transactions.

[0065] S42, Based on the characteristics of business processing, select candidate businesses that match the characteristics of business processing from the preset business database.

[0066] Based on the identified business processing characteristics, the system filters candidate businesses that match these characteristics from a pre-set business database. The pre-set business database is a complete set of processable businesses maintained by the system, containing attribute tags and association rules for various businesses. The filtering process considers factors such as the degree of attribute matching and scenario relevance. For example, when it is detected that a relative has applied for a housing loan, the system will include related businesses such as housing provident fund withdrawal and deed tax payment in the candidate set; when it is detected that a relative has completed retirement procedures, the system will include businesses such as pension application and medical insurance benefit change as candidate businesses.

[0067] S43, calculate the correlation index between candidate services and customers, and sort the candidate services in descending order of correlation index to generate a correlation sequence.

[0068] To assess the relevance of candidate services to target customers, the system calculates a correlation index for each candidate service. The correlation index calculation comprehensively considers factors such as the timeliness of the service (interval with the current time), the strength of the correlation (the degree of dependence between services), and the support of historical data (the probability of processing in similar scenarios). For example, if a candidate service is highly relevant to a recent service processed by a relative and shows strong transmissibility in historical data, it will receive a high correlation index. The system then sorts the candidate services in descending order based on the calculated correlation indices, forming a correlation sequence.

[0069] Based on the above embodiments, as an optional implementation, in S43, calculating the correlation index between candidate services and customers specifically includes S431-S432: S431, Select target candidate services that are related to the customer from the candidate services, and obtain the time interval between the processing time of each target candidate service and the current time.

[0070] To more accurately assess the relevance of candidate services to customers, the system needs to conduct in-depth analysis over time. This analysis first requires filtering target candidate services from the candidate service set that have a substantial relationship with the customer. This relationship may manifest in several ways, such as prerequisite dependencies (completing service A requires first completing service B), synergistic effects (completing services A and B simultaneously yields better results), or transmission effects (completing service A usually triggers a demand for service B). For example, when a relative applies for a housing loan, the system will identify housing provident fund withdrawal as a target candidate service because these two services have a clear business chain relationship.

[0071] For each identified target candidate service, the system calculates the time interval between its processing time within the kinship network and the current time. This time interval reflects the timeliness of the service and is an important indicator for assessing the strength of the association. For example, if a relative applied for a child's education subsidy a week ago, the system will record this 7-day time interval; if it was a social security transfer application processed a month ago, it will record a 30-day time interval. This time-based measurement method can reflect the decay pattern of service influence.

[0072] S432, determine the correlation index between each target candidate task and the customer based on the time interval, wherein the correlation index is inversely proportional to the time interval.

[0073] After acquiring the time interval data, the system determines the correlation index between each target candidate service and the customer based on a preset calculation model. This calculation model follows the principle that the correlation index is inversely proportional to the time interval; that is, the shorter the time interval, the higher the correlation index; and the longer the time interval, the lower the correlation index. The specific calculation can be as follows: Correlation Index = Base Score × (1 / Time Interval) × Weighting Factor. Here, the base score is an initial score set based on the importance of the service itself; the weighting factor can be dynamically adjusted according to factors such as service type and seasonal characteristics. For example, a recently occurred service (shorter time interval) may obtain a higher correlation index, indicating that this type of service is more likely to trigger related customer needs; while an earlier-occurring service (longer time interval) will have a correspondingly lower correlation index, reflecting the natural decay of service relevance.

[0074] S44. Determine the weight coefficients of other users based on their closeness to the customer in the kinship network.

[0075] Simultaneously, the system assigns a weight coefficient to each user based on the closeness of their relationship with other users in the kinship network. This closeness is determined by the degree of kinship and overlap in their social circles. For example, spouses typically receive the highest weight coefficient because business needs between spouses are often highly related and synchronized; immediate family members (such as parents and children) may receive the second highest weight; other relatives are weighted according to the frequency of actual interaction and the degree of social connection.

[0076] S45, combining the correlation sequence and weight coefficients, determines the customer's third pending task.

[0077] Finally, the system combines the relevance sequence with weight coefficients to calculate the final recommendation priority. Specifically, the final score for each candidate service takes into account both its position in the relevance sequence and the weight coefficient of its corresponding family member. For example, a candidate service highly related to a spouse's services is likely to be prioritized because the spouse has a higher weight coefficient. The system selects the most suitable service as the customer's third pending service based on the final score.

[0078] S105 displays the first, second, and third pending tasks on the screen so that the customer can select the target pending task from the screen.

[0079] In its implementation, the system first obtains the priority of each type of pending task. The priority criteria include multiple dimensions such as the timeliness, mandatory nature, and relevance of the task. For example, recurring tasks that are about to expire (such as social security payments) in the first category of pending tasks will be given a higher priority; tasks involving policy deadlines (such as special subsidy applications) in the second category will also receive a higher priority; and matters in the third category that are highly related to recent business transactions handled by relatives will also have their priority increased.

[0080] Based on the acquired priority information, the system will uniformly sort these three categories of pending tasks, forming a comprehensive list of tasks. The sorting process considers not only the priority of the tasks themselves but also assigns appropriate weights based on the characteristics of tasks from different sources. For example, the first pending task derived from personal historical data analysis may receive a higher base weight because this type of task has the strongest direct relevance to the customer.

[0081] In terms of interface design, the system employs differentiated display methods to highlight the importance and characteristics of different pending tasks. Specifically, high-priority pending tasks are marked with eye-catching colors (such as red) or special symbols (such as stars); time-sensitive tasks display countdown prompts; and related tasks are presented logically through connections or grouping. For example, the system might mark social security payment tasks in red and display that there are 2 days left until the deadline, while displaying its related social security base adjustment task in a similar position.

[0082] To enhance user experience, the system uses a layered display approach when showing pending tasks. The top layer displays the most urgent and important tasks, allowing customers to focus on them immediately; the next layer displays important but not urgent tasks; and the last layer shows general, suggestive tasks. Each task card includes key information such as the task name, deadline, and required materials. Customers can click on the card to access more detailed instructions.

[0083] Meanwhile, the system allows customers to filter and sort the displayed tasks according to their needs. For example, customers can choose to view only specific types of tasks or rearrange the tasks in chronological order. When a customer selects a task on the screen, the system will respond immediately and guide the customer through the corresponding task processing flow.

[0084] Based on the above embodiments, as an optional implementation, in S105, displaying the first pending task, the second pending task, and the third pending task on the display screen specifically includes S51-S53: S51, obtain the priority of the first, second and third pending tasks.

[0085] To help customers identify and process pending tasks more intuitively and efficiently, the system needs to display various pending tasks scientifically and reasonably. First, the system will obtain the priority of each of the first, second, and third pending tasks. This priority is a comprehensive score that considers multiple factors: for the first pending task, its timeliness (e.g., the number of days remaining until the deadline) and mandatory nature (e.g., whether it is a legally required service) are mainly considered; for the second pending task, its prevalence among the group (processing rate) and policy timeliness are emphasized; for the third pending task, it is mainly based on its relevance to family members' services and its time-related nature. For example, social security payment services that are about to expire will receive a higher priority, while non-mandatory services with more flexibility will be assigned a relatively lower priority.

[0086] S52, sort the first, second, and third pending tasks according to priority.

[0087] Based on the acquired priority information, the system will uniformly rank these three types of pending tasks. The ranking process not only considers the priority score of each task itself but also assigns appropriate weight coefficients based on the task's source. For example, the first type of pending task from personal historical data analysis may receive a higher base weight because this type of task has the strongest direct relevance to the customer; the second type of pending task from group characteristic analysis may receive a medium weight; and the third type of pending task from kinship networks has its weight determined based on the strength of the association. Through this weighted ranking method, the system can more accurately reflect the importance of tasks from different sources.

[0088] S53 displays the sorted pending tasks on the screen in different ways.

[0089] After sorting, the system will display these pending tasks on the screen using differentiated display methods. Specific display strategies include: for high-priority tasks, using eye-catching colors (such as red) or special identifiers (such as stars, flashing effects, etc.) to mark them and placing them in the most prominent position in the display area; for time-sensitive tasks, adding countdown prompts to intuitively display the remaining processing time; and for related tasks, displaying their logical relationship through connections or grouping. For example, the system might mark the "Social Security Payment" task in red and display "2 days left until deadline," while presenting its related "Social Security Base Adjustment" task in a similar position.

[0090] In addition, the system uses a tiered display approach based on different priorities: the top layer displays the most urgent and important tasks with a prominent visual effect; the next layer displays important but not urgent tasks with a softer visual effect; and finally, there are general tasks with reminders, displayed in a standard way. Each task card includes key information such as the task name, processing deadline, and required materials. Customers can click on the card to get more detailed instructions.

[0091] After displaying the first, second, and third to-do items on the screen, the specific tasks also include: If the customer does not select a target pending task from the first, second, and third pending tasks, a business supplement request interface is generated. In response to the customer's input of custom business requirements, the key business features in the custom business requirements are extracted. Based on the key business features, similar businesses are matched from the preset business library or a new business template is generated. The similar business or the new business template is displayed on the screen as a supplementary pending task.

[0092] To better meet customers' personalized business needs, the system provides a business supplement mechanism in addition to recommending regular pending tasks. When the system detects that a customer has not selected a target pending task from the recommended first, second, and third pending tasks, it automatically generates a business supplement request interface. This interface features an intuitive and user-friendly design, including multiple input methods such as text input boxes and voice input buttons, allowing customers to describe their specific business needs in natural language. For example, a customer might enter personalized descriptions such as "I want to apply for a kindergarten education subsidy for my child" or "I need to apply for social security for flexible employment personnel."

[0093] When a customer enters their custom business requirements on the supplementary request interface, the system activates its intelligent analysis engine to extract key business features from the input text. These features include dimensions such as the business subject (e.g., "children," "flexible employment personnel"), business type (e.g., "education subsidies," "social security"), and business attributes (e.g., subsidy type, social security type). The system uses natural language processing technology, employing keyword recognition and semantic analysis to transform unstructured requirement descriptions into structured feature labels. For example, for the requirement "wanting to apply for a kindergarten education subsidy for my child," the system will extract key features such as "minor," "education," and "subsidy."

[0094] Based on the extracted key business features, the system first attempts to match similar businesses from a pre-set business database. This database stores various standardized business templates, each labeled with corresponding feature tags. The system calculates the feature matching degree to identify the business type most closely related to the customer's needs. For example, the system might match a "kindergarten education subsidy" request with a "preschool education subsidy application" request, or a "social security for flexible employment personnel" request with a "personal social security registration" request. For businesses with a high matching degree, the system will directly recommend them to the customer as supplementary pending services.

[0095] If no highly matching existing business is found in the preset business database, the system will automatically generate a new business template based on the extracted features. The generation process of the new business template will refer to existing businesses with similar features and combine them with the business rule engine to build a customized business process that meets the customer's needs. For example, for special subsidy applications under certain new policies, the system may generate a new business template that is adapted to the regular subsidy application process and combined with the special requirements of the new policy.

[0096] The system will prominently display similar or newly generated service templates on the screen. The displayed content includes key information such as the service name, eligibility requirements, required materials, and processing procedures, and uses special visual effects (such as a color scheme different from regular pending services) to distinguish these supplementary pending services. Simultaneously, the system will provide detailed service descriptions and operation instructions on the display interface to help customers understand and confirm whether these supplementary services meet their actual needs.

[0097] Based on the above method, this application also discloses an integrated office system based on artificial intelligence, such as... Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of an integrated office system based on artificial intelligence, provided in an embodiment of this application. The system includes: a response module, a combination module, an acquisition module, a recognition module, and a display module; wherein, The response module is used to respond to customer service requests, obtain the first service information processed by the customer within a preset time period, and related service information associated with the first service information; the combination module is used to combine the first service information and related service information to predict the customer's first pending service; the acquisition module is used to acquire the group characteristics of the customer's group and generate the customer's second pending service based on the group characteristics; the identification module is used to identify the second service information processed by other users in the customer's corresponding kinship network within a preset time period, and determine the customer's third pending service based on the second service information; the display module is used to display the first, second, and third pending services on the display screen so that the customer can select the target pending service from the display screen.

[0098] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0099] Please see Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0100] The communication bus 1002 is used to realize the connection and communication between these components.

[0101] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0102] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0103] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 1001 and may be implemented as a separate chip.

[0104] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 3 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application for an integrated office method based on artificial intelligence.

[0105] exist Figure 3 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 1001 can be used to call an application program stored in the memory 1005 that is an integrated office method based on artificial intelligence. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0106] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more of the methods described in the above embodiments.

[0107] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0108] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, 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 couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0110] 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.

[0111] 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.

[0112] 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 device (CMD). 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 memory 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 memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0113] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. An integrated office method based on artificial intelligence, characterized in that, The method includes: In response to a customer's business processing request, obtain the first business information processed by the customer within a preset time period, as well as the associated business information related to the first business information; By combining the first business information and the related business information, the customer's first pending business is predicted; Obtain the group characteristics of the customer's group, and generate a second pending task for the customer based on the group characteristics; Identify the second business information processed by other users in the customer's kinship network within the preset time period, and determine the customer's third pending business based on the second business information; The first, second, and third pending tasks are displayed on the screen so that the customer can select a target pending task from the screen.

2. The integrated office method based on artificial intelligence according to claim 1, characterized in that, The step of combining the first business information and the associated business information to predict the customer's first pending business includes: Based on the first business information, the business processing pattern of the customer is determined, and the business processing pattern includes business type, processing time and processing cycle; Based on the aforementioned business processing patterns, the first recommended business is determined; Based on the associated business information, a second recommended business that is associated with the first recommended business is determined; The first recommended service and the second recommended service are designated as the customer's first agency service.

3. The integrated office method based on artificial intelligence according to claim 1, characterized in that, The step of generating the customer's second pending task based on the group characteristics includes: Based on the group characteristics, determine the common pending tasks of the customer group; Filter out the customer's unfinished business from the shared pending business items; The unprocessed transactions are sorted in descending order of priority to generate a transaction sequence; The unprocessed business that is preceding a preset position in the business sequence is designated as the customer's second pending business.

4. The integrated office method based on artificial intelligence according to claim 1, characterized in that, The step of determining the customer's third pending task based on the second business information includes: Based on the second business information, determine the business processing characteristics of other users in the kinship network; Based on the business processing characteristics, candidate businesses that match the business processing characteristics are selected from a preset business database; Calculate the correlation index between the candidate service and the customer, and sort the candidate services in descending order of the correlation index to generate a correlation degree sequence; The weight coefficients for the other users are determined based on their degree of closeness to the customer within the kinship network. By combining the correlation sequence and the weight coefficient, the customer's third pending task is determined.

5. The integrated office method based on artificial intelligence according to claim 4, characterized in that, The calculation of the correlation index between the candidate service and the customer includes: Select target candidate services that are related to the customer from the candidate services, and obtain the time interval between the processing time of each target candidate service and the current time; Based on the time interval, a correlation index between each target candidate task and the customer is determined, wherein the correlation index is inversely proportional to the time interval.

6. The integrated office method based on artificial intelligence according to claim 1, characterized in that, The step of displaying the first pending task, the second pending task, and the third pending task on the display screen includes: Obtain the priorities corresponding to the first pending task, the second pending task, and the third pending task; The first pending task, the second pending task, and the third pending task are sorted according to the priority. The sorted tasks will be displayed on the screen in different ways.

7. The integrated office method based on artificial intelligence according to claim 1, characterized in that, After displaying the first pending task, the second pending task, and the third pending task on the display screen, the method further includes: If the customer does not select a target pending task from the first pending task, the second pending task, and the third pending task, a business supplement request interface is generated to respond to the customer's input of custom business requirements and extract the key business features from the custom business requirements. Based on the key business characteristics, match similar businesses from the preset business library or generate new business templates; The similar business or the new business template will be displayed on the screen as a supplementary pending business.

8. An integrated office system based on artificial intelligence, characterized in that, The system includes: a response module, a combination module, an acquisition module, an identification module, and a display module; wherein, The response module is used to respond to a customer's business processing request, obtain the first business information processed by the customer within a preset time period, and the associated business information associated with the first business information; The combining module is used to combine the first business information and the associated business information to predict the customer's first pending business. The acquisition module is used to acquire the group characteristics of the customer's group and generate a second pending task for the customer based on the group characteristics. The identification module is used to identify the second business information handled by other users in the customer's kinship network within the preset time period, and to determine the customer's third pending business based on the second business information. The display module is used to display the first pending task, the second pending task, and the third pending task on the display screen, so that the customer can select the target pending task from the display screen.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1-7.

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