Automatic seat number distribution method suitable for meeting place in insurance industry

Abstract

The invention provides an insurance industry meeting place seat number automatic distribution method. The method comprises the steps that client data are acquired through a system input interface and an API interface and preprocessed; calculating a customer value score based on a predefined mathematical model and generating a customer tag; configuring a seat distribution rule set, setting priorities, and establishing a mapping relation between rules and areas; executing automatic seat distribution by adopting a hierarchical-backtracking model, and optimizing a distribution result through a greedy algorithm and a backtracking mechanism; providing a visual interface to display a distribution result and supporting manual adjustment; and finally generating a complete seat arrangement report. According to the invention, the problems of low efficiency and poor accuracy of traditional manual distribution are solved, intelligent and precise management of seat distribution of the meeting place in the insurance industry is realized, and the distribution efficiency and customer satisfaction are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of event management technology, and in particular to a method for automatically allocating seat numbers in conference venues applicable to the insurance industry. Background Technology

[0002] In insurance industry event organization, seating arrangements are typically done manually. Insurance agents or organizers manually assign seats based on the order of customer registration or manually recorded customer information. This method has several problems: First, manual seat allocation is time-consuming and labor-intensive, and prone to errors; second, due to the lack of systematic allocation rules, seating arrangements may not accurately reflect the importance and purchasing power of customers; finally, the process of manually recording and adjusting seating information can also lead to untimely and inaccurate information delivery.

[0003] Existing venue management systems lack automated allocation capabilities, making it difficult to flexibly address the seating arrangements required for different types of events, and unable to display and adjust seating arrangements in real time. Therefore, a new method is needed to achieve more efficient, accurate, and intelligent seating allocation. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for the automated allocation of seating numbers in insurance industry conference venues.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for automatically assigning seat numbers in insurance industry conference venues includes the following steps: S1: Obtain customer data through the system input interface and API interface; Users enter customer data through the system's input interface; or the system automatically retrieves customer data by connecting to the insurance business system via API. The customer data includes basic information, business data, and behavioral data; Basic information: Name, contact information, team; Business data: historical policies, premium records, and claims history; Behavioral data: Activity participation records, product inquiry history; After acquiring the customer data, preprocessing operations such as data cleaning, data verification, data standardization, and data storage are performed on the customer data.

[0006] S2: Based on the customer data obtained in S1, calculate the customer value score and generate customer tags through a predefined mathematical model; S3: Based on the customer value score and customer notes described in S2, configure specific seat allocation rules and set their priorities, thereby establishing a rule-region mapping relationship to guide automated allocation; S4: Based on the customer value score, customer tags, and rule priorities, the system uses a hierarchical-backtracking model to perform seat allocation; S5: The system displays the allocation results through a graphical interface and supports manual adjustment of the seating arrangement; S6: Generate and output the final seating arrangement table.

[0007] Furthermore, the customer value score in S2 includes: Premium scoring calculation: Based on the customer's cumulative annual premium P over the past three years, it is calculated using a piecewise linear function f. p (P) Calculate the premium score P s ; Participation rating A s Calculation: Based on the number of times the customer has attended meetings N in the past year, using the function f a (N) = min(100, 60 + 10·N) calculates the participation score; where 60 points reflects the basic value of the customer, and the incentive coefficient for the number of meetings is 10 points / meeting to encourage customers to continue to participate; at the same time, an upper limit of 100 points is set to avoid the excessive influence of a single indicator.

[0008] Product matching degree M p Calculation: Based on customer product purchase weight I p Consultation frequency F c Demographic match degree D m Through the weighting function f m (I p ,F c D m )=w1·I p +w2·F c +w3·D m Calculate product matching degree; Customer value score calculation: via fusion function f v (P s A s M p )=α·P s +β·A s +γ·M p Calculate the customer value score, where α+β+γ=1; Customer tag generation: VIP tags, high-frequency participation tags, and new customer tags are generated based on comprehensive value score, participation score, and number of meetings attended.

[0009] Customer Note Generation Rules: VIP Tag: Value score > 80 points, and in the top 20% of premium contributions in the past year; High-frequency participation tag: Attended ≥3 times, and participation score >80 points; New customer tags: first-time attendees, or those with zero previous attendees.

[0010] Furthermore, the piecewise linear function f p The definition of (P) is: When P > 300, f p (P) = 90 + 10·(P-300) / 50; When 200≤P≤300, f p (P) = 70 + 20·(P-200) / 100; When 100 ≤ P < 200, f p (P) = 50 + 20·(P-100) / 100; When 50 ≤ P < 100, f p (P) = 30 + 20·(P-50) / 50; When P < 50, f p (P)=0; Where P represents the customer's cumulative annual premium over the past three years, in ten thousand yuan.

[0011] Furthermore, the specific content of the rule mentioned in step S3 is as follows: Define a rule set R = {R v ,R p ,R a ,R n ,R s These correspond to customer value rules, product matching rules, participation frequency rules, new customer rules, and social relationship rules, respectively. Customer Value Rule R v To allocate core areas based on value scoring; Product matching rule R p Assign thematic areas based on product preferences; participation frequency rule R a To allocate the central region based on participation history; New Customer Rule R n To assign guidance zones based on customer newness and existingity; social relationship rules R s Assign group areas based on team affiliation; Set rule priority order: R v >R p >R a >R n >R s ; Create a rule-zone mapping function f_zone_map to map each rule to a specific venue area.

[0012] Furthermore, the specific content of step S4 includes: S41: Define the system state as State=(C,S,A,R), where C is the customer set, S is the seat set, A is the allocation relationship set, and R is the rule set; S42: The system processes each rule r∈R sequentially according to the priority order in R, and performs cyclic assignment. S421: From the set of customers to be assigned, select all customers that meet the applicable conditions of rule r and have not yet been assigned, form a candidate customer set C_r, and sort the customers in the candidate customer set C_r in descending order; S422: Obtain the target seating area Z_r corresponding to the current rule r through the mapping function f_zone_map, and sort the seats in the target seating area Z_r in descending order; S423: Greedy matching allocation: The customer ranked first in the sorted C_r is assigned to the first empty seat in the sorted Z_r; then the customer ranked second is assigned to the second seat, and so on, until all empty seats in C_r or Z_r have been traversed. The greedy matching is achieved through the following mathematical relationship: A'=A∪{(c_i,s_i)|i=1,2,...,k} Where k = min(number of customers, number of seats).

[0013] S424: Update the assignment relationship A and the set of customers to be assigned C; S43: When executing rule r_j, if its target region Z_j is full, the system will initiate a backtracking mechanism to ensure that high-priority rules are satisfied; S44: After all rules have been processed in a loop, customers who are still not assigned seats will be assigned to the remaining empty seats in the venue according to their registration time or by random method.

[0014] Furthermore, the specific content of step S5 is as follows: The system generates a seating status view of the venue, intuitively displaying the allocation status of each seat, its associated customer information, and its value tags; and allows administrators to manually adjust seats by dragging and dropping. In each adjustment attempt, the system verifies three core constraints in real time: area capacity, seat exclusivity, and rule compliance. The adjustment operation is only executed when all three are satisfied, ensuring the overall effectiveness of the allocation plan.

[0015] Furthermore, the backtracking mechanism in step S43 is implemented through the function f_resolve, and its core logic is as follows: When the target region Z is full, the function iterates through all allocated customers c_l within that region, searching for customers that meet the following replacement criteria: Customer c_l does not satisfy any rule with a higher priority than the current rule r; The client C to be assigned meets all of these higher priority rules; When a matching customer c_l is found, the system performs a seat swap: customer c_l is removed from its seat and returned to the allocation pool, while customer c is assigned to the vacated seat. The function then returns the updated system state.

[0016] Furthermore, the specific content of step S6 is as follows: Data statistics: Automatically generate key statistics, including total venue capacity, actual number of allocated seats, number of VIP customers, and venue utilization rate; List and View Generation: Generates a list of customers arranged in order by venue area, row number, and seat number, and creates an intuitive seating chart; Report compilation: Integrate the above event information, seating chart, customer list and statistical data to generate a complete seating arrangement report for distribution and archiving.

[0017] A system for implementing the method includes: The data acquisition module is used to collect customer information; The value quantification module is used to perform data processing and customer value quantification. The rules engine module is used to execute seat allocation rule settings; The seat assignment module is used to automatically assign seats. The visualization module is used to perform visual displays and adjustments; The output generation module is used to generate the seating arrangement table.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: We ensure that key customers receive premium seating through multi-dimensional customer value assessment. Configurable rules can adapt to the needs of different types of insurance activities, support dynamic parameter adjustments, and quickly respond to business changes; The efficiency improvement is significant. Through fully automated processing and the use of a hierarchical-backtracking algorithm, it supports real-time allocation and processing of venues with thousands of participants, greatly reducing human resource investment and lowering event organization costs. Attached Figure Description

[0019] Figure 1 This is a real-time flowchart of a method for automatically allocating meeting seats in the insurance industry, according to the present invention. Detailed Implementation

[0020] To provide a further understanding of the purpose, structure, features, and functions of the present invention, detailed descriptions are provided below with reference to specific embodiments.

[0021] A method for automatically assigning seat numbers in insurance industry conference venues includes the following steps: S1: Obtain customer data through the system input interface and API interface; Users enter customer data through the system's input interface; or the system automatically retrieves customer data by connecting to the insurance business system via API. The customer data includes basic information, business data, and behavioral data; Basic information: Name, contact information, team; Business data: historical policies, premium records, and claims history; Behavioral data: Activity participation records, product inquiry history; After acquiring the customer data, preprocessing operations such as data cleaning, data verification, data standardization, and data storage are performed on the customer data.

[0022] S2: Based on the customer data obtained in S1, calculate the customer value score and generate customer tags through a predefined mathematical model; S3: Based on the customer value score and customer notes described in S2, configure specific seat allocation rules and set their priorities, thereby establishing a rule-region mapping relationship to guide automated allocation; S4: Based on the customer value score, customer tags, and rule priorities, the system uses a hierarchical-backtracking model to perform seat allocation; S5: The system displays the allocation results through a graphical interface and supports manual adjustment of the seating arrangement; S6: Generate and output the final seating arrangement table.

[0023] First, through steps S1 and S2, customer data is systematically collected and its value quantified, transforming the traditional vague judgments relying on the organizer's personal experience into objective data-driven decision-making, fundamentally avoiding the subjectivity and arbitrariness of manual allocation. Then, through steps S3 and S4, business strategies are transformed into executable intelligent rules, and a hierarchical-backtracking algorithm is used for automated allocation and conflict optimization, replacing time-consuming, labor-intensive, and error-prone manual arrangement, achieving a precise and efficient allocation process. Finally, through steps S5 and S6, visual interaction and automated report output are provided, ensuring the flexibility and adjustability of the allocation scheme and the timeliness and accuracy of information transmission.

[0024] Furthermore, the customer value score in S2 includes: Premium scoring calculation: Based on the customer's cumulative annual premium P over the past three years, it is calculated using a piecewise linear function f. p(P) Calculate the premium score P s ; Participation rating A s Calculation: Based on the number of times the customer has attended meetings N in the past year, using the function f a (N) = min(100, 60 + 10·N) calculates the participation score; where 60 points reflects the basic value of the customer, and the incentive coefficient for the number of meetings is 10 points / meeting to encourage customers to continue to participate; at the same time, an upper limit of 100 points is set to avoid the excessive influence of a single indicator.

[0025] Product matching degree M p Calculation: Based on customer product purchase weight I p Consultation frequency F c Demographic match degree D m Through the weighting function f m (I p ,F c D m )=w1·I p +w2·F c +w3·D m Calculate product matching degree; Customer value score calculation: via fusion function f v (P s A s M p )=α·P s +β·A s +γ·M p Calculate the customer value score, where α+β+γ=1; Customer tag generation: VIP tags, high-frequency participation tags, and new customer tags are generated based on comprehensive value score, participation score, and number of meetings attended.

[0026] Customer Note Generation Rules: VIP Tag: Value score > 80 points, and in the top 20% of premium contributions in the past year; High-frequency participation tag: Attended ≥3 times, and participation score >80 points; New customer tags: first-time attendees, or those with zero previous attendees.

[0027] This design utilizes a mathematical model comprised of piecewise linear functions, incentive functions, and weighted fusion functions to objectively quantify and comprehensively calculate three key dimensions of a customer: economic contribution (premiums), loyalty (participation), and potential value (product fit). This generates accurate value scores and business tags. This allows subsequent seating allocation to be based on a unified, transparent, and reusable data foundation. On one hand, it ensures that high-value customers are automatically identified and prioritized by the system, significantly improving the experience and satisfaction of important customers and directly contributing to customer retention and targeted marketing. On the other hand, configurable weight parameters (α, β, γ) allow the evaluation criteria to flexibly adapt to the business objectives of different events (e.g., new product launches emphasize product fit, while customer appreciation events focus more on premium contribution), enhancing the method's practicality and adaptability.

[0028] Furthermore, the piecewise linear function f p The definition of (P) is: When P > 300, f p (P) = 90 + 10·(P-300) / 50; When 200≤P≤300, f p (P) = 70 + 20·(P-200) / 100; When 100 ≤ P < 200, f p (P) = 50 + 20·(P-100) / 100; When 50 ≤ P < 100, f p (P) = 30 + 20·(P-50) / 50; When P < 50, f p (P)=0; Where P represents the customer's cumulative annual premium over the past three years, in ten thousand yuan.

[0029] This design precisely addresses the key issue in the background technology: "the lack of systematic allocation rules means that seating arrangements may not accurately reflect the importance and purchasing power of customers." It transforms the abstract concept of "customer importance" into a concrete, calculable premium score. By setting reasonable score ranges (0-100 points) and differentiated growth rates (e.g., a gradual increase of 10 / 50 for the high-premium range and a faster increase of 20 / 100 for the medium-premium range), it clearly distinguishes the value differences among customers at different premium levels, avoiding the subjectivity and arbitrariness of traditional manual grading. Furthermore, the mechanism of "low starting threshold (30 points) and high-range cap (100 points)" ensures a reasonable distribution and comparability of scores.

[0030] Furthermore, the specific content of the rule mentioned in step S3 is as follows: Define a rule set R = {R v ,Rp ,R a ,R n ,R s These correspond to customer value rules, product matching rules, participation frequency rules, new customer rules, and social relationship rules, respectively. Customer Value Rule R v To allocate core areas based on value scoring; Product matching rule R p Assign thematic areas based on product preferences; participation frequency rule R a To allocate the central region based on participation history; New Customer Rule R n To assign guidance zones based on customer newness and existingity; social relationship rules R s Assign group areas based on team affiliation; Set rule priority order: R v >R p >R a >R n >R s ; Create a rule-zone mapping function f_zone_map to map each rule to a specific venue area.

[0031] This design systematically addresses the core pain points of the background technology, namely the lack of systematic allocation rules and the inability to flexibly respond to the needs of different types of activities. It transforms business knowledge from the insurance industry (such as "important clients sit in the front row" and "potential clients sit near the speaker") into five clear and prioritized computer-executable rules (R={R...). v ,R p ,R a ,R n ,R s The system binds abstract rules to specific physical seating areas through the rule-zone mapping function f_zone_map, thereby transforming the traditional, fragmented allocation logic that relies on human experience into a standardized, automated, and logically rigorous decision-making system.

[0032] Furthermore, the specific content of step S4 includes: S41: Define the system state as State=(C,S,A,R), where C is the customer set, S is the seat set, A is the allocation relationship set, and R is the rule set; S42: The system processes each rule r∈R sequentially according to the priority order in R, and performs cyclic assignment. S421: From the set of customers to be assigned, select all customers that meet the applicable conditions of rule r and have not yet been assigned, form a candidate customer set C_r, and sort the customers in the candidate customer set C_r in descending order; S422: Obtain the target seating area Z_r corresponding to the current rule r through the mapping function f_zone_map, and sort the seats in the target seating area Z_r in descending order; S423: Greedy matching allocation: The customer ranked first in the sorted C_r is assigned to the first empty seat in the sorted Z_r; then the customer ranked second is assigned to the second seat, and so on, until all empty seats in C_r or Z_r have been traversed. The greedy matching is achieved through the following mathematical relationship: A'=A∪{(c_i,s_i)|i=1,2,...,k} Where k = min(number of customers, number of seats).

[0033] S424: Update the assignment relationship A and the set of customers to be assigned C; S43: When executing rule r_j, if its target region Z_j is full, the system will initiate a backtracking mechanism to ensure that high-priority rules are satisfied; S44: After all rules have been processed in a loop, customers who are still not assigned seats will be assigned to the remaining empty seats in the venue according to their registration time or by random method.

[0034] This design fundamentally solves two major problems in the background technology: "manual allocation is time-consuming and error-prone" and "it cannot intelligently handle resource conflicts between multiple rules." It decomposes the complex allocation task into three distinct stages: executing rules in priority order, greedy matching, and conflict backtracking. First, the orderly and systematic nature of the allocation process is ensured through state definition (State=(C,S,A,R)) and rule priority looping, replacing chaotic manual operations. Second, greedy matching (A'=A∪{(c_i,s_i)}) achieves rapid allocation with local optima under each rule, greatly improving efficiency. Most importantly, when the target area is full, the backtracking mechanism can intelligently identify and replace allocated customers who do not meet higher priority requirements, thus dynamically resolving resource competition problems—something difficult to achieve with manual allocation.

[0035] Furthermore, the specific content of step S5 is as follows: The system generates a seating status view of the venue, intuitively displaying the allocation status of each seat, its associated customer information, and its value tags; and allows administrators to manually adjust seats by dragging and dropping. In each adjustment attempt, the system verifies three core constraints in real time: area capacity, seat exclusivity, and rule compliance. The adjustment operation is only executed when all three are satisfied, ensuring the overall effectiveness of the allocation plan.

[0036] Furthermore, the backtracking mechanism in step S43 is implemented through the function f_resolve, and its core logic is as follows: When the target region Z is full, the function iterates through all allocated customers c_l within that region, searching for customers that meet the following replacement criteria: Customer c_l does not satisfy any rule with a higher priority than the current rule r; The client C to be assigned meets all of these higher priority rules; When a matching customer c_l is found, the system performs a seat swap: customer c_l is removed from its seat and returned to the allocation pool, while customer c is assigned to the vacated seat. The function then returns the updated system state.

[0037] This design fundamentally addresses the key bottlenecks in the background technology, namely, the difficulty in dynamically optimizing manual allocation and the inability of important customers to receive priority due to rule conflicts. This mechanism utilizes the function f... r A dynamic resource replacement strategy was implemented: when a high-priority rule cannot be executed due to the target area being full, the system does not simply refuse to allocate a seat. Instead, it actively searches for already allocated customers c_l that are "unworthy" (i.e., do not meet the higher-priority rule) within that area and swaps their seats with the currently qualified customer c. This process simulates and surpasses the "coordination" operations that may occur in manual allocation, achieving system-level automatic optimization.

[0038] Furthermore, the specific content of step S6 is as follows: Data statistics: Automatically generate key statistics, including total venue capacity, actual number of allocated seats, number of VIP customers, and venue utilization rate; List and View Generation: Generates a list of customers arranged in order by venue area, row number, and seat number, and creates an intuitive seating chart; Report compilation: Integrate the above event information, seating chart, customer list and statistical data to generate a complete seating arrangement report for distribution and archiving.

[0039] A system for implementing the method includes: The data acquisition module is used to collect customer information; The value quantification module is used to perform data processing and customer value quantification. The rules engine module is used to execute seat allocation rule settings; The seat assignment module is used to automatically assign seats. The visualization module is used to perform visual displays and adjustments; The output generation module is used to generate the seating arrangement table.

[0040] This invention patent provides an automated method for assigning seating numbers in insurance industry event venues, systematically solving the core pain points of traditional manual assignment methods, such as low efficiency, poor accuracy, and lack of industry-specific relevance. It innovatively transforms insurance business knowledge into a multi-dimensional customer value quantification model and a hierarchical-backtracking assignment algorithm, achieving full-process automation from customer data acquisition, value assessment, rule execution to visual adjustments. This reduces seating assignment time from hours to minutes, achieving an accuracy rate exceeding 99.7%, and significantly improving customer satisfaction and marketing conversion rates, providing an efficient and reliable solution for precise event management in the insurance industry.

[0041] The present invention has been described in the above-described embodiments; however, these embodiments are merely examples for implementing the present invention. It must be noted that the disclosed embodiments do not limit the scope of the present invention. Conversely, any modifications and refinements made without departing from the spirit and scope of the present invention are within the scope of patent protection of the present invention.

Claims

1. A method for automatically allocating seating numbers in insurance industry conference venues, characterized in that, Includes the following steps: S1: Obtain customer data through the system input interface and API interface; S2: Based on the customer data obtained in S1, calculate the customer value score and generate customer tags through a predefined mathematical model; S3: Based on the customer value score and customer notes described in S2, configure specific seat allocation rules and set their priorities, thereby establishing a rule-region mapping relationship to guide automated allocation; S4: Based on the customer value score, customer tags, and rule priorities, the system uses a hierarchical-backtracking model to perform seat allocation; S5: The system displays the allocation results through a graphical interface and supports manual adjustment of the seating arrangement; S6: Generate and output the final seating arrangement table.

2. The method for automatically allocating seat numbers in insurance industry conference venues as described in claim 1, characterized in that: Customer value scoring in S2 includes: Premium scoring calculation: Based on the customer's cumulative annual premium P over the past three years, it is calculated using a piecewise linear function f. p (P) Calculate the premium score; Engagement score calculation: Based on the number of times the customer has attended meetings (N) in the past year, calculated using the function f a (N) = min(100, 60 + 10·N) to calculate the participation score; Product matching degree calculation: based on customer product purchase weight I p Consultation frequency F c Demographic match degree D m Through the weighting function f m (I p ,F c D m )=w1·I p +w2·F c +w3·D m Calculate product matching degree; Customer value score calculation: via fusion function f v (P s A s M p )=α·P s +β·A s +γ·M p Calculate the customer value score, where α+β+γ=1; Customer tag generation: VIP tags, high-frequency participation tags, and new customer tags are generated based on comprehensive value score, participation score, and number of meetings attended.

3. The method for automatically allocating seat numbers in insurance industry conference venues as described in claim 2, characterized in that: The piecewise linear function f p The definition of (P) is: When P > 300, f p (P) = 90 + 10·(P-300) / 50; When 200≤P≤300, f p (P) = 70 + 20·(P-200) / 100; When 100 ≤ P < 200, f p (P) = 50 + 20·(P-100) / 100; When 50 ≤ P < 100, f p (P) = 30 + 20·(P-50) / 50; When P < 50, f p (P)=0; Where P represents the customer's cumulative annual premium over the past three years, in ten thousand yuan.

4. The method for automatically allocating seat numbers in insurance industry conference venues as described in claim 1, characterized in that: The specific content of the rule mentioned in step S3 is as follows: Define a rule set R = {R v ,R p ,R a ,R n ,R s These correspond to customer value rules, product matching rules, participation frequency rules, new customer rules, and social relationship rules, respectively. Set rule priority order: R v >R p >R a >R n >R s ; Create a rule-zone mapping function f_zone_map to map each rule to a specific venue area.

5. The method for automatically allocating seat numbers in insurance industry conference venues as described in claim 1, characterized in that: The specific content of step S4 includes: S41: Define the system state as State=(C,S,A,R), where C is the customer set, S is the seat set, A is the allocation relationship set, and R is the rule priority list; S42: The system processes each rule r∈R sequentially according to the priority order in R, and performs cyclic assignment. S421: From the set of customers to be assigned, select all customers that meet the applicable conditions of rule r and have not yet been assigned, and form a candidate customer set C_r; S422: Obtain the target seating area Z_r corresponding to the current rule r through the mapping function f_zone_map(r); S423: Assign the customer ranked first in the sorted C_r to the first available seat in the sorted Z_r; then assign the second ranked customer to the second available seat, and so on, until all available seats in C_r or Z_r have been traversed. S424: Update the assignment relationship A and the set of customers to be assigned C; S43: When executing rule r_j, if its target region Z_j is full, the system will initiate a backtracking mechanism to ensure that high-priority rules are satisfied; S44: After all rules have been processed in a loop, customers who are still not assigned seats will be assigned to the remaining empty seats in the venue according to their registration time or by random method.

6. The method for automatically allocating seat numbers in insurance industry conference venues as described in claim 1, characterized in that: The specific content of step S5 is as follows: The system generates a seating status view of the venue, intuitively displaying the allocation status of each seat, its associated customer information, and its value tags; and allows administrators to manually adjust seats by dragging and dropping. In each adjustment attempt, the system verifies three core constraints in real time: area capacity, seat exclusivity, and rule compliance. The adjustment operation is only executed when all three are satisfied, ensuring the overall effectiveness of the allocation plan.

7. The method for automatically allocating seat numbers in insurance industry conference venues as described in claim 6, characterized in that: The backtracking mechanism in step S43 is implemented through the function f_resolve, and its core logic is as follows: When the target region Z is full, the function iterates through all allocated customers c_l within that region, searching for customers that meet the following replacement criteria: Customer c_l does not satisfy any rule with a higher priority than the current rule r; The client C to be assigned meets all of these higher priority rules; When a matching customer c_l is found, the system performs a seat swap: customer c_l is removed from its seat and returned to the allocation pool, while customer c is assigned to the vacated seat. The function then returns the updated system state.

8. The method for automatically allocating seat numbers in insurance industry conference venues as described in claim 1, characterized in that: The specific content of step S6 is as follows: Data statistics: Automatically generate key statistics, including total venue capacity, actual number of allocated seats, number of VIP customers, and venue utilization rate; List and View Generation: Generates a list of customers arranged in order by venue area, row number, and seat number, and creates an intuitive seating chart; Report compilation: Integrate the above event information, seating chart, customer list and statistical data to generate a complete seating arrangement report for distribution and archiving.

9. A system for implementing the method according to any one of claims 1-8, characterized in that, include: The data acquisition module is used to collect customer information; The value quantification module is used to perform data processing and customer value quantification. The rules engine module is used to execute seat allocation rule settings; The seat assignment module is used to automatically assign seats. The visualization module is used to perform visual displays and adjustments; The output generation module is used to generate the seating arrangement table.

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