Real estate lease management system

Abstract

The invention relates to the technical field of lease management, in particular to a real estate lease management system, which comprises a contract regulation checking module, a resource allocation optimization module, a credit risk analysis module and an event management decision module, and identifies the rent amount and the payment period based on lease contract text information. According to the method, key financial terms in a contract are automatically examined, and regulation standard comparison is performed, so that the inspection speed and accuracy of contract compliance are improved, the necessity of dependence on manual operation is reduced in the automatic identification and comparison process, management errors and cost are reduced, and through optimized resource and time management, the efficiency is improved. Key links of a lease period are effectively arranged and adjusted, so that the operation efficiency is remarkably improved, risk management is more accurate through analysis of tenant credit data and credit risk prediction, the possibility of overdue and default is reduced, lease key events are automatically responded, management decisions are adjusted according to the lease key events, and the lease efficiency is improved. And the management flexibility and response are greatly enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of leasing management, and in particular to a real estate leasing management system. Background Art

[0002] Lease management technology involves the application of information technology to enhance the management efficiency of leased assets, including real estate, vehicles, equipment and other assets. This field combines database management, online transaction processing and automated contract management to optimize each link of the lease cycle, such as lease approval, asset status tracking, payment processing and contract renewal. The development of technology supports multi-platform access, including mobile devices and cloud services, thus realizing real-time data update and remote management.

[0003] A real estate rental management system is a system used to manage real estate leases. The system is mainly used to handle leasing operations of real estate such as houses, apartments, and commercial spaces, including tenant screening, lease contract management, payment collection, maintenance log records, and financial management. The main purpose is to simplify the rental management process, reduce errors and costs, and improve management efficiency. By automating routine tasks, the system can effectively manage real estate assets.

[0004] When dealing with high data volumes and real-time data updates, existing technologies still rely on manual processing in some links, such as the review of lease contracts and manual allocation of resources, which not only prolongs the operation time but also increases the risk of errors. Insufficient information integration leads to delayed and inaccurate decision support. For example, in the absence of credit assessment, it is difficult to effectively screen potential high-risk tenants, increasing the risk of financial losses. In terms of remote management and multi-platform support, existing technologies do not provide sufficient support, which limits the flexibility and expansion capabilities of leasing and affects the timeliness and accuracy of leasing management. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a real estate rental management system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a real estate leasing management system, the system comprising: The contract and regulation verification module identifies the rental amount and payment cycle based on the text information of the lease contract, compares the preliminary review results with regulatory standards, automatically detects abnormal or non-compliant clauses in the contract, and generates a contract compliance record; The resource allocation optimization module analyzes resource allocation and time arrangement based on the contract compliance records, automatically formulates resource reallocation processes according to resource and time reallocation needs, adjusts key management links of real estate leasing, and generates optimized configuration of leasing processes; The credit risk analysis module is based on the optimized configuration of the leasing process, combines the tenant's credit data and behavior patterns, compares historical data, evaluates credit risk characteristics, simulates the tenant's future behavior and credit changes, and generates tenant credit data; The event management decision module responds to key events in leasing activities, including payment and contract renewal, based on the tenant credit data, records the response to the leasing activities, and makes leasing decisions and management process adjustments based on the impact of the leasing activities to obtain leasing decision support results.

[0007] The present invention is improved in that the steps of comparing regulatory standards are specifically as follows: Based on the text information of the lease contract, natural language processing is performed to identify key rent amount and payment cycle data, extract and format the information into actionable numerical form, and obtain key information extraction results; Based on the key information extraction results, the degree of deviation between the information items and regulatory standards is calculated using the formula: ; Obtain the regulatory standards comparison results, among which, represents the total deviation score, is the weight coefficient, is the extracted data item, It is the corresponding regulatory standard. Represents the number of terms that need to be compared.

[0008] The present invention is improved in that the steps of obtaining the contract compliance record are specifically as follows: Based on the comparison results of the regulatory standards, verify whether each clause is compliant, record the compliant and non-compliant clauses, and obtain a list of non-compliant clauses; Using the list of non-compliant terms, the formula is used: ; Calculate the total improvement need score for the contract , obtain the contract compliance record, where is the number of noncompliant terms, is based on the weight of the regulatory criticality, is the improvement score for the non-compliant clause.

[0009] The present invention is improved in that the steps of automatically formulating the resource reconfiguration process are specifically as follows: Based on the contract compliance records, extract non-compliant resource allocation and time scheduling data, determine the resources and time points that need to be optimized, and obtain reallocation demand analysis results; Based on the reallocation demand analysis results, reconfigure resources and time using the formula: ; Optimize the utilization efficiency and time allocation of each resource to obtain the resource reconfiguration process ,in, Indicates The current configuration of the resource. Indicates The time requirement of a resource, is the maximum time that the resource can be used, is the total number of resource items.

[0010] The present invention is improved in that the steps of obtaining the optimized configuration of the leasing process are specifically as follows: Analyze the existing leasing management process, identify efficiency bottlenecks and non-compliance issues in contract signing, payment time and resource allocation, and obtain key problem analysis records; Based on the analysis records of the key issues, the contract signing and payment process was redesigned using the formula: ; Optimize the time point of each process and obtain the process time configuration ,in, Representation process The efficiency coefficient, is the current process time, is the optimal time point for the target. is the sensitivity coefficient of the adjusted response, is the number of processes involved in the optimization; According to the process time configuration, the current leasing operation process is adjusted, including contract signing, payment schedule and resource allocation, to obtain an optimized configuration of the leasing process.

[0011] The present invention is improved in that the steps of comparing historical data are specifically as follows: Based on the optimized configuration of the leasing process, collect tenant credit data and behavior patterns, integrate payment history, credit score changes and contract execution data, and obtain preliminary data collation information; The preliminary data collation information is analyzed using the formula: ; Compare historical data to obtain data comparison and analysis results, among which, Representative The change in credit of each tenant, It is Tenants in Performance on behavioral indicators, Behavioral indicators The influence coefficient of The intercept term.

[0012] The present invention is improved in that the steps of obtaining the tenant credit data are specifically as follows: Extract key credit risk characteristics, analyze the impact of characteristics on future credit behavior, and generate a risk characteristic summary; The risk profile is predicted to estimate future credit scores using the formula: ; Get the predicted credit behavior results, where Indicates tenant Predicted credit score, Indicates tenant In Features Performance on is the impact coefficient of each credit risk characteristic, is the intercept term, representing the basic credit score, is the total number of credit risk characteristics; Based on the predicted credit behavior results, each tenant's future credit risk and management process are evaluated to obtain the tenant's credit data.

[0013] The present invention is improved in that the steps of obtaining the leasing decision support result are specifically as follows: Based on the tenant credit data, collect key event records, including payment and contract renewal status, identify rental behavior patterns and trends, and obtain activity record information; Based on the activity record information, the formula is used: ; Assess the impact of key events on the leasing business and obtain impact scores ,in, represents the payment delay frequency, represents the contract renewal rate, Represents the tenant response rate, and is the weight coefficient; Based on the impact score, the leasing decision and management process are adjusted to obtain a leasing decision support result.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by automatically reviewing the key financial terms in the contract and comparing them with regulatory standards, the speed and accuracy of contract compliance inspection are improved. The process of automatic identification and comparison reduces the necessity of relying on manual operations, thereby reducing management errors and costs. Through optimized resource and time management, the key links of the lease cycle are effectively arranged and adjusted, thereby significantly improving operational efficiency. The analysis of tenant credit data and credit risk prediction make risk management more accurate and reduce the possibility of overdue and default. Automatic response to key lease events and adjustment of management decisions accordingly greatly enhances management flexibility and response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart for comparing regulatory standards in the present invention; Figure 3 A flowchart for obtaining contract compliance records in the present invention; Figure 4 A flowchart of a resource reallocation process automatically formulated in the present invention; Figure 5 A flowchart for obtaining the optimized configuration of the leasing process in the present invention; Figure 6 A flowchart for comparing historical data in the present invention; Figure 7 This is a flowchart for obtaining tenant credit data in the present invention; Figure 8 The figure is a flow chart for obtaining leasing decision support results in the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0018] Example:

[0019] See also Figure 1 The present invention provides a technical solution: a real estate leasing management system comprising: The contract and regulation verification module identifies the rental amount and payment cycle based on the text information of the lease contract, compares the preliminary review results with regulatory standards, automatically detects abnormal or non-compliant clauses in the contract, and generates a contract compliance record; The resource allocation optimization module analyzes resource allocation and time arrangements based on contract compliance records, automatically formulates resource reallocation processes based on resource and time reallocation needs, adjusts key management links of real estate leasing, including contract signing, payment time and resource allocation, and generates optimized configurations for leasing processes; The credit risk analysis module is based on the optimization configuration of the leasing process, combines the tenant's credit data and behavior patterns, compares historical data, evaluates credit risk characteristics, simulates the tenant's future behavior and credit changes, and generates tenant credit data; The event management decision module responds to key events in leasing activities, including payment and contract renewal, based on tenant credit data, records the response to leasing activities, and adjusts leasing decisions and management processes based on the impact of leasing activities to obtain leasing decision support results.

[0020] Contract compliance records include clause compliance, payment terms identification results, and contract term verification results. Leasing process optimization configuration includes signing process, payment process automation, and resource allocation efficiency. Tenant credit data includes credit rating, overdue records, and payment habits. Leasing decision support results include process adjustment basis and impact assessment results.

[0021] See also Figure 2 , the specific steps for regulatory standards comparison are: Based on the text information of the lease contract, natural language processing is performed to identify key rent amount and payment cycle data, extract and format the information into actionable numerical form, and obtain key information extraction results; When executing natural language processing technology to identify key terms in the contract, such as the rental amount and payment cycle, the contract text is first imported into the text parsing system to extract the amount and cycle data. For the rental amount, it is located by searching for keywords such as "amount" and "rent", and the numbers following it are identified as the amount data. Similarly, for the payment cycle, time period expressions such as "monthly" and "quarterly" are identified. Then, the identified data is formatted, and the standardized amount unit is RMB yuan and the time unit is month. Through detailed steps, the accuracy and consistency of the data extracted from the contract text are ensured. The preliminary review results generated will provide accurate basic data for the regulatory comparison process.

[0022] Based on the key information extraction results, the degree of deviation between the information items and regulatory standards is calculated using the formula: ; Obtain the regulatory standards comparison results, among which, represents the total deviation score, is the weight coefficient, is the extracted data item, It is the corresponding regulatory standard. Represents the number of terms that need to be compared; There is a dataset of contract terms, including rental amounts and payment cycles, regulatory standards 1000 yuan and once a month respectively, if the weight 0.6 and 0.4 respectively, the rental amount is extracted from the contract The rent is 950 yuan, and the payment cycle is once every two months. First, the rent deviation is calculated as |950-1000|=50, and the payment cycle deviation is |1-2|=1. Apply the formula to calculate the deviation score: ; The result shows that the deviation score in the contract is 30.4, indicating that the contract has high deviations in certain key terms and needs further review or adjustment to meet regulatory requirements.

[0023] See also Figure 3 , the specific steps for obtaining contract compliance records are: Based on the results of the regulatory standards comparison, verify whether each clause is compliant, record the compliant and non-compliant clauses, and obtain a list of non-compliant clauses. The process involves a comprehensive review of the contract. First, scan the contract text through the regulatory database and the latest legal standards to identify keywords and phrases such as "rent", "payment cycle", "termination conditions", etc., and match the information with the regulations in the database to verify the compliance of each item. For each non-compliant clause found, it will be recorded and listed in the review report. The records include the content of the clause, non-compliant regulations and risk warnings. Through a systematic process, ensure that the compliance of the contract is fully reviewed.

[0024] Using the list of non-compliant terms, use the formula: ; Calculate the total improvement need score for the contract , obtain the contract compliance record, where is the number of noncompliant terms, is based on the weight of the regulatory criticality, is the improvement score of the non-compliant clause; If there are three non-compliant clauses in the contract, the non-compliant severity score of each clause The relevant regulatory criticality weights are 5, 3 and 7 respectively. They are 0.5, 0.3 and 0.2 respectively. Substitute them into the formula to calculate the total improvement requirement score: ; The results show that the total improvement needs score is 4.8, which reflects that the contract has a moderate level of non-compliance and requires moderate adjustments and improvements to meet regulatory requirements. The higher the value, the more serious the contract non-compliance and the greater the urgency of improvement, which guides the formulation and implementation of subsequent compliance improvement suggestions.

[0025] See also Figure 4 ,The steps for automatically formulating the resource reallocation process are as follows: Based on the contract compliance records, extract the non-compliant resource allocation and time scheduling data, determine the resources and time points that need to be optimized, and obtain the reallocation demand analysis results; The non-compliant data extracted from the contract compliance records were screened to identify key resource allocation and time scheduling issues. The resources involved include but are not limited to office facilities, equipment leasing, and human resources. Time issues include inconsistencies in the start and end dates of leases, thereby generating reallocation demand analysis results. The analysis at this stage helps identify the highest priority adjustment items and prepares for resource reallocation for high-priority items to ensure effective use of resources and time.

[0026] Based on the results of the reallocation demand analysis, reconfigure resources and time using the formula: ; Optimize the utilization efficiency and time allocation of each resource to obtain the resource reconfiguration process ,in, Indicates The current configuration of the resource. Indicates The time requirement of a resource, is the maximum time that the resource can be used, is the total number of resource items; There are three resources that need to be reconfigured. Resource configuration 10, 20 and 30 respectively, time requirements 3, 6 and 9 hours respectively, the maximum available time For 24 hours, substitute it into the formula and get: ; ; ; The results show that the new resource allocation score is 26.1. A higher score indicates that the allocation of resources and time is reasonable relative to the urgency of the improvement required, demonstrating the effectiveness of responding to non-compliance issues in the contract by adjusting the existing resource and time allocation.

[0027] See also Figure 5 , the specific steps for obtaining the optimized configuration of the leasing process are: Analyze the existing leasing management process, identify efficiency bottlenecks and non-compliance issues in contract signing, payment time and resource allocation, and obtain key problem analysis records; Conduct in-depth analysis of the existing leasing management process, identify efficiency bottlenecks and compliance issues in key management links such as contract signing, payment scheduling and resource allocation through data mining, use data analysis technology to conduct a comprehensive assessment of various processes and operation points, find out the key factors that lead to delays and non-compliance with industry standards, and generate key issue analysis records. The records provide a basis and clear direction for further optimization measures, ensuring that the subsequent process redesign can solve specific problems in a targeted manner.

[0028] Based on the key issues analysis records, redesign the contract signing and payment process, using the formula: ; Optimize the time point of each process and obtain the process time configuration ,in, Representation process The efficiency coefficient, is the current process time, is the optimal time point for the target. is the sensitivity coefficient of the adjusted response, is the number of processes involved in the optimization; There are three key processes that need to be adjusted, and their respective efficiency coefficients For 1.5, 2.0 and 2.5, the current time 3, 4 and 5 hours respectively, target time Set to 4 hours, adjust the coefficient Set to 0.3 and calculate the new time point configuration for each process: ; ; ; The results show that by adjusting the time configuration of each process, the new process time configuration score is 4.09, which is a significant improvement over the original one, thereby enhancing the efficiency and response speed of the overall operation.

[0029] According to the process time configuration, adjust the current leasing operation process, including contract signing, payment schedule and resource allocation, to obtain the optimal configuration of the leasing process; Based on the optimized process time configuration, we immediately began to adjust the current leasing operation process, including making specific changes to contract signing, payment schedule and resource allocation. By introducing new process settings, the contract signing process was simplified, and electronic signatures and automated approval processes were integrated to reduce manual processing time; the payment schedule was adjusted to be synchronized with project milestones to ensure that the cash flow was consistent with the project progress; and resource allocation was optimized by introducing resource management software to automatically match resources with project requirements. The adjustment ensured that the entire leasing process was more efficient and adapted to the current business environment. The resulting optimized leasing process configuration improved operational efficiency and customer satisfaction.

[0030] See also Figure 6 , the specific steps for historical data comparison are: Based on the optimization configuration of the leasing process, collect tenants' credit data and behavior patterns, integrate payment history, credit score changes and contract execution data, and obtain preliminary data collation information; Through the optimization configuration of the leasing process, the credit data and behavior patterns of tenants are collected and organized, including payment history, changes in credit scores and contract performance. The compilation of this data set is intended to provide a basis for subsequent in-depth analysis. During the data sorting process, various types of information are systematically classified and indexed to ensure that all relevant historical information can be effectively tracked and utilized. The integrated preliminary data sorting information provides a comprehensive view, including not only the tenant's past behavior patterns, but also every detail of his credit changes; The preliminary data collation information was analyzed using the formula: ; Compare historical data to obtain data comparison and analysis results, among which, Representative The change in credit of each tenant, It is Tenants in Performance on behavioral indicators, Behavioral indicators The influence coefficient of The intercept term represents the baseline value of overall credit change without considering any behavioral indicators; Three behavioral indicators are taken into account. For a particular tenant, the indicator is the number of late payments. , number of contract breaches , and the amount of overdue payment , the weights of the behavioral indicators are set to 0.3, 0.5, and 0.2, respectively, and the intercept term Assuming the baseline credit change is set to 2, the tenant's credit change can be calculated as follows: ; ; The results show that the change in the tenant's credit is significant, indicating that his credit situation is deteriorating, which is critical for decision makers when conducting credit management and risk control.

[0031] See also Figure 7 ,The specific steps for obtaining tenant credit data are as follows: Extract key credit risk characteristics, including payment delay frequency, credit score decline, etc., analyze the impact of characteristics on future credit behavior, and generate a risk characteristic summary; Extract key credit risk features, including the frequency of payment delays and the decline in credit scores. Integrated analysis of the data reveals which factors are most frequently associated with credit problems. Analysis is crucial for assessing the impact of future credit behavior. Through this step, an overview of all key credit risk features is generated, providing a basis for further predictive analysis. Each risk feature is analyzed to determine its potential impact on credit scores.

[0032] Predict the risk profile and estimate the future credit score using the formula: ; Get the predicted credit behavior results, where Indicates tenant Predicted credit score, Indicates tenant In Features Performance on is the impact coefficient of each credit risk characteristic, is the intercept term, representing the basic credit score, is the total number of credit risk characteristics; There are three credit risk characteristics. For a tenant, the characteristics are: the number of payment delays , credit score drops , major breach of contract , the corresponding influence coefficient is set as and , the intercept term Set to 600, the tenant's predicted credit score is calculated as follows: ; ; The results show that based on the current credit risk characteristics and its performance, the tenant's predicted credit score is 607.5, which provides a quantitative basis for assessing its future credit risk, making credit management more precise and effective.

[0033] Based on the predicted credit behavior results, evaluate each tenant's future credit risk and management process to obtain tenant credit data; Based on the prediction results, the predicted credit score and risk level of each tenant are evaluated and converted into specific management suggestions, including recommended credit strategies and necessary intervention measures, which not only provides a credit risk profile but also provides an operational basis for leasing management, thereby effectively managing and reducing potential credit risks.

[0034] See also Figure 8 , the specific steps for obtaining leasing decision support results are: Based on tenant credit data, collect key event records, including payment and contract renewal status, identify rental behavior patterns and trends, and obtain activity record information; By analyzing tenant credit data, we record the behavior of each tenant in key leasing activities, which mainly include payment and contract renewal. The collected data include but are not limited to the frequency of payment delays, renewal willingness and timeliness. Through the data, we can conduct a preliminary trend analysis of the tenant's behavior patterns, lay the foundation for the next in-depth analysis, and obtain activity record information.

[0035] Based on the activity record information, the formula is used: ; Assess the impact of key events on the leasing business and obtain impact scores ,in, represents the payment delay frequency, represents the contract renewal rate, Represents the tenant response rate, and is the weight coefficient; Frequency of late payment by a tenant , renewal rate , response rate The weight coefficients are and , substitute into the formula to calculate the impact score: ; ; The result shows that the tenant's overall impact score on the leasing business is 0.52, indicating a medium level of impact on the business. It quantifies the specific impact of each factor on the business and provides a basis for further decision-making.

[0036] Adjust leasing decisions and management processes based on impact scores to obtain leasing decision support results; Based on the impact scores collected, quantitative data is converted into specific management decisions, including adjusting lease contract terms, optimizing payment processes and renewal strategies. Each decision is based on previous scoring results to ensure its targeting and maximum effectiveness. The scoring results guide every adjustment of the lease management process to ensure that all changes reflect the behavioral trends and potential risks of tenants, thereby enhancing the security of leasing operations. The adjusted processes are integrated and implemented to form a comprehensive leasing decision support result.

[0037] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A real estate leasing management system, characterized in that: The system comprises: The contract and regulation verification module identifies the rental amount and payment cycle based on the text information of the lease contract, compares the preliminary review results with regulatory standards, automatically detects abnormal or non-compliant clauses in the contract, and generates a contract compliance record; The resource allocation optimization module analyzes resource allocation and time arrangement based on the contract compliance records, automatically formulates resource reallocation processes according to resource and time reallocation needs, adjusts key management links of real estate leasing, and generates optimized configuration of leasing processes; The credit risk analysis module is based on the optimized configuration of the leasing process, combines the tenant's credit data and behavior patterns, compares historical data, evaluates credit risk characteristics, simulates the tenant's future behavior and credit changes, and generates tenant credit data; The event management decision module responds to key events in leasing activities, including payment and contract renewal, based on the tenant credit data, records the response to the leasing activities, and makes leasing decisions and management process adjustments based on the impact of the leasing activities to obtain leasing decision support results.

2. The real estate rental management system according to claim 1, characterized in that: The steps for comparing regulatory standards are as follows: Based on the text information of the lease contract, natural language processing is performed to identify key rent amount and payment cycle data, extract and format the information into actionable numerical form, and obtain key information extraction results; Based on the key information extraction results, the degree of deviation between the information items and regulatory standards is calculated using the formula: ； Obtain the regulatory standards comparison results, among which, represents the total deviation score, is the weight coefficient, is the extracted data item, It is the corresponding regulatory standard. Represents the number of terms that need to be compared.

3. The real estate rental management system according to claim 2, characterized in that: The specific steps for obtaining the contract compliance record are: Based on the comparison results of the regulatory standards, verify whether each clause is compliant, record the compliant and non-compliant clauses, and obtain a list of non-compliant clauses; Using the list of non-compliant terms, the formula is used: ； Calculate the total improvement need score for the contract , obtain the contract compliance record, where is the number of noncompliant terms, is based on the weight of regulatory criticality, is the improvement score for the non-compliant clause.

4. The real estate rental management system according to claim 1, characterized in that: The steps of automatically formulating the resource reconfiguration process are specifically as follows: Based on the contract compliance records, extract non-compliant resource allocation and time scheduling data, determine the resources and time points that need to be optimized, and obtain reallocation demand analysis results; Based on the reallocation demand analysis results, reconfigure resources and time using the formula: ； Optimize the utilization efficiency and time allocation of each resource to obtain the resource reconfiguration process ,in, Indicates The current configuration of the resource. Indicates The time requirement of a resource, is the maximum time that the resource can be used, is the total number of resource items.

5. The real estate rental management system according to claim 1, characterized in that: The steps for obtaining the optimized configuration of the leasing process are specifically as follows: Analyze the existing leasing management process, identify efficiency bottlenecks and non-compliance issues in contract signing, payment time and resource allocation, and obtain key problem analysis records; Based on the analysis records of the key issues, the contract signing and payment process was redesigned using the formula: ； Optimize the time point of each process and obtain the process time configuration ,in, Representation process The efficiency coefficient, is the current process time, is the optimal time point for the target. is the sensitivity coefficient of the adjusted response, is the number of processes involved in the optimization; According to the process time configuration, the current leasing operation process is adjusted, including contract signing, payment schedule and resource allocation, to obtain an optimized configuration of the leasing process.

6. The real estate rental management system according to claim 1, characterized in that: The steps for comparing historical data are specifically as follows: Based on the optimized configuration of the leasing process, collect tenant credit data and behavior patterns, integrate payment history, credit score changes and contract execution data, and obtain preliminary data collation information; The preliminary data collation information is analyzed using the formula: ； Compare historical data to obtain data comparison and analysis results, among which, Representative The change in credit of each tenant, It is Tenants in Performance on behavioral indicators, Behavioral indicators The influence coefficient of The intercept term.

7. The real estate rental management system according to claim 1, characterized in that: The steps for obtaining the tenant credit data are specifically as follows: Extract key credit risk characteristics, analyze the impact of characteristics on future credit behavior, and generate a risk characteristic summary; The risk profile is predicted to estimate future credit scores using the formula: ； Get the predicted credit behavior results, where Indicates tenant Predicted credit score, Indicates tenant In Features Performance on is the impact coefficient of each credit risk characteristic, is the intercept term, representing the basic credit score, is the total number of credit risk characteristics; Based on the predicted credit behavior results, each tenant's future credit risk and management process are evaluated to obtain the tenant's credit data.

8. The real estate rental management system according to claim 1, characterized in that: The steps for obtaining the leasing decision support results are specifically as follows: Based on the tenant credit data, collect key event records, including payment and contract renewal status, identify rental behavior patterns and trends, and obtain activity record information; Based on the activity record information, the formula is used: ； Assess the impact of key events on the leasing business and obtain impact scores ,in, represents the payment delay frequency, represents the contract renewal rate, Represents the tenant response rate, and is the weight coefficient; Based on the impact score, the leasing decision and management process are adjusted to obtain a leasing decision support result.

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