Real estate lease cash flow forecasting and automated reconciliation management method and system

Abstract

The present application relates to the field of real estate financial management information technology, in particular to a real estate lease cash flow prediction and automatic reconciliation management method and system, comprising the following steps: searching for lease contract electronic files and quantifying overdue risk scores, establishing tenant repayment performance attribute parameters in combination with rental space and rent-free period, performing axial offset using average delay days, comparing receivables with actual collection items to lock offset scale to generate state data table, and generating automatic reconciliation correction instructions. In the present application, by quantifying overdue risk, the accuracy of default situation judgment is improved, the financial risk warning capability is strengthened, the multi-dimensional performance characteristics are used to realize dynamic prediction of cash inflow, the timeliness of cash flow prediction table is enhanced, through full-range write-off state mapping and abnormal point labeling, manual accounting error is reduced, the reliability of write-off state is ensured, the reconciliation correction efficiency is improved, the management and control precision of bulk asset operation benefit is guaranteed, and the financial compliance risk is reduced.

Description

Technical Field

[0001] This invention relates to the field of information technology for real estate financial management, and in particular to a method and system for real estate leasing cash flow forecasting and automatic reconciliation management. Background Technology

[0002] The field of information technology in real estate financial management involves the digital integrated management of real estate lease assets, financial accounting, and fund dynamics. This field encompasses asset file digitization, full lifecycle contract monitoring, rental invoice generation and disbursement, fund recovery and write-off, and automated financial statement generation. It achieves consistency between real estate operation and financial accounting processes by establishing unified data logic standards. Traditional real estate lease cash flow forecasting and automatic reconciliation management methods involve financial personnel manually entering planned receivables at different future time points into spreadsheets in office software, based on clauses in the lease contract such as rent increases, rent-free periods, and property miscellaneous fees, to generate a cash flow forecast table. During the fund recovery process, they manually retrieve account transaction records, compare the remitter's name, transfer amount, and outstanding invoices, manually enter the write-off status in ledgers or spreadsheets, and fill in the corresponding receipt information.

[0003] Traditional real estate leasing financial management relies heavily on manual entry of contract terms, resulting in low data collection efficiency and recording errors. When faced with complex price increases and rent-free period constraints, static prediction models struggle to capture the dynamic impact of tenant behavior fluctuations on cash flow. Asset holders are unable to accurately assess overdue risks, leading to inaccurate decision-making. The fund recovery phase employs a manual bank statement reconciliation model, which has a lengthy write-off cycle and is prone to overlooking minor discrepancies. It also lacks the ability to conduct closed-loop monitoring of abnormal reconciliation points, causing the risk of discrepancies between financial records and actual assets to accumulate continuously. This weakens the company's depth of control over the operating efficiency of large assets and limits the expansion of management scale. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a method for real estate rental cash flow forecasting and automatic reconciliation management, including the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a method for real estate leasing cash flow forecasting and automatic reconciliation management, comprising the following steps: S1: Retrieve the electronic lease contract associated with the unit number, align the rent due date with the current date to lock the lag span, extract the unresolved balance records from the relational database, execute the logical combination of the book balance and the overdue penalty level, determine the asset default deviation trend, and obtain the overdue risk quantitative score; S2: Call the overdue risk quantification score, extract the rent calculation space scale and rent-free period range of the shop and perform a spread mapping, verify the timeliness and amount integrity of the funds collected in the past payment period, integrate the original delay time of the tenant and the reliability index of the performance behavior to perform feature collection, and establish the tenant payment performance attribute parameters. S3: Based on the tenant's repayment performance attribute parameters, benchmark the remaining accounts payable expected performance account index, extract the starting point of the default penalty associated with the unwritten balance to perform debt compensation review, apply the average delay days to drive the time axis offset mapping of the prediction node, integrate the time change trajectory of fund inflow, and generate a dynamic cash flow shift prediction table. S4: Based on the dynamic cash flow shift forecast table, extract the actual receipts of the continuous payment period forecast accounts receivable matching and verification of the deviation scale between the time phase and the amount of cash inflow, extract the unclosed link components to connect the full-caliber reconciliation status mapping, mark the abnormal points of accounting flow, and generate an account reconciliation offset status data table.

[0005] As a further aspect of the present invention, the overdue risk quantification score includes loss expectation level, default deviation coefficient, and asset risk rating; the tenant repayment performance attribute parameters include capital occupation index, performance inertia weight, and recovery efficiency index; the dynamic cash flow shift forecast table includes a corrected inflow window, expected cash inflow, and periodic repayment capacity; and the account reconciliation offset status data table includes settlement gap distribution, time series difference identifier, and logical collision anomaly component.

[0006] As a further aspect of the present invention, the specific steps for quantifying the overdue risk score are as follows: S101: Retrieve electronic copies of shop rental contracts, extract the agreed due date, align the rent collection date with the current date scale, determine the date phase lag span, anchor the contract performance time constraints, and establish the overdue days value; S102: Call the overdue days value, map the preset risk level matrix to lock the penalty level, extract the unrecovered balance records in the database, execute the logical combination of the book-standing assets and the penalty level, and generate the balance mapping coefficient. S103: Based on the balance mapping coefficient, integrate the credit default benchmark weight, determine the default deviation trend of the berth assets, integrate the quantitative conversion of the credit constraint clause status, lock the financial overdue coordinates, and obtain the quantitative score of overdue risk.

[0007] As a further aspect of the present invention, the specific steps for the tenant payment fulfillment attribute parameters are as follows: S201: Call the overdue risk quantification score, extract the geometric scale of the rent calculation space and the rent-free period range of the stall, perform the space occupancy ratio and rent-free division logic benchmarking, spread the target entity risk density, and obtain the area and lease term correction coefficient. S202: Based on the area lease term correction coefficient, extract the real-time arrival coordinates of past payment terms and the planned benchmark, verify the coupling status of fund recovery timeliness and amount integrity, aggregate the original payment term deviation, and obtain the quantitative value of the recovery target rate. S203: Call the quantitative value of the payment collection rate, integrate the average delay time of the tenant in the past, extract the reliable metric index of the performance behavior to execute the logic coupling, collect multi-dimensional performance feature components, and establish tenant payment collection performance attribute parameters.

[0008] As a further aspect of the present invention, the specific steps of the dynamic cash flow shift forecast table are as follows: S301: Based on the tenant's repayment performance attribute parameters, benchmark the remaining accounts receivable of the real estate lease contract, classify the revenue and expenditure account code attributes, collect the index of accounts to be performed under the lease status, and establish the expected account period capital scale. S302: Call the expected amount of funds for the payment period, extract the starting point for calculating default penalties related to the unwritten balance, match the daily default penalty ratio to perform inflow scale review, determine the overlap status of debt compensation gain and principal, and obtain the expected inflow amount of debt. S303: Based on the expected inflow of the receivables, apply the average delay days to perform the time axis offset mapping of the original payment and receipt nodes, anchor the time change trajectory of the fund inflow, integrate the fund distribution of the offset axis, and generate a dynamic cash flow shift forecast table.

[0009] As a further aspect of the present invention, the specific steps for the account reconciliation offset status data table are as follows: S401: Based on the dynamic cash flow shift forecast table, extract the continuous payment period forecast receivables, match the VAT invoice header with the actual receipt of cash flow, anchor the relationship between asset number and fund account, and establish a cash and expenditure matching matrix. S402: Call the income and expenditure matching matrix to verify the time phase difference between real-time arrival and predicted value, anchor the deviation scale of fund inflow and time node, map the difference in the dimension of account reconciliation status, and generate the logical offset of amount. S403: Based on the aforementioned logical offset level, extract the unclosed link components, perform abnormal transaction attribution attribute determination, cluster reconciliation difference data, connect the full-caliber reconciliation status mapping and mark abnormal points, and generate an account reconciliation offset status data table.

[0010] As a further aspect of the present invention, the method further includes step S5: S5: Based on the account reconciliation offset status data table, extract the budget deviation variance benchmark and mark the control status, retrieve the average actual amount received by the deviating entity to replace the original contractually agreed portion, encapsulate the financial reconciliation control rule execution instruction field, and generate an automated reconciliation correction instruction.

[0011] As a further aspect of the present invention, the automated reconciliation correction instruction includes an entry balancing coefficient, an error correction weight, and a reconciliation logic control variable.

[0012] As a further aspect of the present invention, the specific steps of the automated reconciliation correction instruction are as follows: S501: Based on the account reconciliation offset status data table, extract the budget deviation variance benchmark, retrieve the asset coverage of the corresponding berth deposit balance, mark the offset abnormal points to determine the control status, and establish an abnormal offset trigger judgment value. S502: Call the abnormal offset trigger judgment value, extract the average actual amount received within the deviation of the entity cycle, replace the original contractual amount component, verify and correct the convergence of the flow and the integrity of the payment period sequence, and generate a reconciliation amount correction mapping matrix. S503: Based on the reconciliation amount correction mapping matrix, associate the financial reconciliation control rule execution instruction field encapsulation, combine the trigger conditions and correction action path, identify financial management dimension control parameters, and obtain automated reconciliation correction instructions.

[0013] Real estate leasing cash flow forecasting and automated reconciliation management system, including: The rental risk assessment module retrieves electronic copies of shop rental contracts, aligns the rent collection date with the current date scale, determines the date phase lag span to establish the overdue days, maps a preset risk matrix to lock the penalty level, extracts unwritten balance records to execute the logical composite of book-standing assets, integrates credit default benchmark weights to determine the asset default deviation trend, and establishes a quantitative score for overdue risk. The tenant performance analysis module calls the overdue risk quantification score, extracts the geometric scale of the shop space and the rent-free period range to perform space occupancy and rent-free point benchmarking, extracts the real-time arrival coordinates of past payment periods to verify the coupling status of fund recovery timeliness and amount integrity, integrates the average delay time of the past period and the reliability index to collect multi-dimensional performance characteristics, and obtains the tenant's payment repayment performance attribute parameters. The cash flow shift prediction module, based on the tenant's repayment and performance attribute parameters, divides the remaining payment period revenue and expenditure account codes and the expected performance account index, extracts the unwritten balance associated with the default penalty starting point to perform debt compensation compounding, applies the average delay days to perform the time axis offset mapping of the original payment and receipt nodes, anchors the fund inflow change trajectory and offset axis fund distribution, and generates a dynamic cash flow shift prediction table. The account reconciliation offset identification module calls the dynamic cash flow shift prediction table, matches the continuous payment period prediction of accounts receivable with the invoice-related cash flow actual receipts, verifies the deviation scale of the cash inflow level and time node, extracts the reconciliation difference data of the unclosed link component clusters, connects the full-caliber reconciliation status mapping, and establishes an account reconciliation offset status data table. The financial instruction correction module, based on the account reconciliation offset status data table, extracts the budget deviation variance benchmark to determine the control status, extracts the average actual amount received by the deviating entity to replace the original contractually agreed component, and associates the financial reconciliation rule execution instruction field encapsulation with the action path to obtain automated reconciliation correction instructions.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: This invention improves the accuracy of asset default assessment and strengthens financial risk early warning capabilities by locking the execution date phase and quantifying risks of electronic contract execution dates. It integrates rent calculation scales with historical performance behavior to construct parameters, optimizes the tenant payment profile extraction logic, and applies average delay days to drive the time axis offset of payment and receipt nodes. This enables dynamic prediction of the trajectory of fund inflow changes, enhances the timeliness of cash flow forecasts, extracts actual receipts from accounts receivable matching data for consecutive payment periods, verifies the deviation between time phase and fund volume, marks abnormal points in accounting flow, connects the mapping of full-caliber write-off status, reduces manual reconciliation errors, ensures the reliability of account reconciliation offset status, and encapsulates correction instructions related to financial write-off rules to improve the efficiency of automated reconciliation correction and ensure the accuracy of large-scale asset management. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] Please see Figure 1 This invention provides a method for real estate rental cash flow forecasting and automatic reconciliation management, including the following steps: S1: Retrieve the electronic lease contract associated with the unit number, align the rent due date with the current date to lock the lag span, extract the unresolved balance records from the relational database, execute the logical combination of the book balance and the overdue penalty level, determine the asset default deviation trend, and obtain the overdue risk quantitative score; S2: Call the overdue risk quantification score, extract the rent calculation space scale and rent-free period range of the shop to perform a spread mapping, verify the timeliness and amount integrity of the funds collected in the past payment period, integrate the original delay time of the tenant and the reliability index of the performance behavior to perform feature collection, and establish tenant payment performance attribute parameters. S3: Based on the tenant's repayment performance attribute parameters, benchmark the remaining accounts payable expected performance account index, extract the starting point of the default penalty associated with the unwritten balance to execute the debt compensation review, apply the average delay days to drive the time axis offset mapping of the prediction node, integrate the time change trajectory of fund inflow, and generate a dynamic cash flow shift prediction table. S4: Based on the dynamic cash flow shift forecast table, extract the actual receipts of the continuous account period forecast accounts receivable matching and collection flow, verify the deviation scale between the time phase and the amount of funds inflow, extract the unclosed link components to connect the full-caliber reconciliation status mapping, mark the abnormal points of accounting flow, and generate an account reconciliation offset status data table. S5: Based on the account reconciliation offset status data table, extract the budget deviation variance benchmark and mark the control status, retrieve the average actual amount received by the deviating entity to replace the original contractually agreed portion, associate the financial reconciliation control rule execution instruction field encapsulation, and generate automated reconciliation correction instructions.

[0020] The quantitative score for overdue risk includes expected loss level, default deviation coefficient, and asset risk rating. Tenant repayment performance attribute parameters include capital occupation index, performance inertia weight, and recovery efficiency indicator. The dynamic cash flow shift forecast table includes the corrected entry window, expected cash inflow, and periodic repayment capacity. The account reconciliation offset status data table includes settlement gap distribution, time series difference identifier, and logical collision anomaly component. The automated reconciliation correction instructions include entry reconciliation coefficient, error correction weight, and reconciliation logic control variables.

[0021] Please see Figure 2 The specific steps for quantifying the overdue risk score are as follows: S101: Retrieve electronic copies of shop rental contracts, extract the agreed due date, align the rent collection date with the current date scale, determine the date phase lag span, anchor the contract performance time constraints, and establish the overdue days value; The electronic lease agreement for the berth is accessed via a distributed file management interface. Binary stream data is read, and the unstructured document is converted into text by an optical character recognition (OCR) engine for hierarchical text parsing. The coordinates of the "due date" keyword are located, the date string is extracted, and converted into a standard timestamp format. After obtaining the agreed due date (May 5th), the current calendar date (May 20th) is synchronized via a clock interface, and the two are aligned in a unified time vector space. The difference between the current timestamp and the due date timestamp is calculated to be 1,296,000 seconds. Dividing this value by the daily second base of 86,400 seconds, the date phase lag is determined to be 15 days. Subsequently, the associated performance time constraint field in the contract logic library is retrieved, revealing a 3-day grace period for payment for this specific berth. A logical subtraction operation is performed, subtracting the grace period of 3 days from the phase lag span of 15 days, and triggering a Boolean judgment operation. If the result is greater than 0, the difference is determined as the actual default period, thus establishing the overdue days value as 12 days. During this process, date data is standardized and cleaned to remove non-numeric characters, ensuring that the calculated time axis offset does not contain redundant offsets, thereby achieving timely anchoring of the contract performance status.

[0022] S102: Call the overdue days value, map the preset risk level matrix to lock the penalty level, extract the unrecovered balance records in the database, execute the logical combination of the book-standing assets and the penalty level, and generate the balance mapping coefficient. The system uses the overdue days value of 12 days as the index key to match a pre-loaded risk level matrix cached in memory. This matrix is ​​divided into three levels—low, medium, and high—based on interval thresholds, with the 8-15 day interval corresponding to the medium risk level. The locked penalty level weight is 1.5. Simultaneously, a database query thread is started to access the accounts receivable / payable details table in the financial ledger, extracting the unresolved balance records corresponding to the asset numbers, obtaining a real-time outstanding balance of 50,000 yuan. A logical compound operation is performed on the outstanding assets and penalty level, using the unresolved balance of 50,000 yuan as the multiplicand and the penalty level of 1.5 as the multiplier, calculating a risk component of 75,000. To eliminate differences between different units, a baseline quantification denominator of 10,000 is introduced to perform a quotient operation, yielding a value of 7.5. Subsequently, this value is assigned to the corresponding risk feature vector through mapping logic to generate a balance mapping coefficient. At this stage, zero-value verification and anomaly detection are performed on the extracted unreconciled balance. If negative or empty values ​​are found in the accounting records, an error interception mechanism is triggered, and the data is forcibly traced back to the previous financial transfer node to perform data repair.

[0023] S103: Based on the balance mapping coefficient, integrate the credit default benchmark weight, determine the default deviation trend of the store assets, integrate the quantitative conversion of the status of credit constraint clauses, lock in the financial overdue coordinates, and obtain the quantitative score of overdue risk. Based on the balance mapping coefficient, and integrating the credit default benchmark weight retrieved from the credit management module, the average default frequency of the tenant over the past 24 calendar months was obtained, and the credit default benchmark weight was calculated to be 0.8. The balance mapping coefficient of 7.5 and the credit default benchmark weight of 0.8 were arithmetically multiplied to obtain a deviation baseline value of 6.0. Next, the status of credit constraint clauses in the contract database was retrieved, the performance bond amount of 20,000 yuan was extracted, and a ratio was calculated with the unwritten balance of 50,000 yuan to obtain a credit coverage ratio component of 0.4. A transformation function was applied to perform an inverse proportional transformation on the credit coverage ratio component to obtain a credit weakening coefficient. The deviation baseline value of 6.0 and the credit weakening coefficient were weighted and aggregated, and a base risk constant of 20.5 was superimposed. By performing multi-dimensional feature vector magnitude calculation within the financial evaluation space, the financial delinquency coordinates were locked, and finally, a normalized mapping was used to obtain a delinquency risk quantification score of 86.5. Comparative test data shows that when processing 5,000 overdue samples, the risk level classification of this calculation logic achieved a 94.2% match with the actual asset loss results. Compared with the traditional single-logic judgment method, the identification error rate decreased from 15.6% to 3.8%. This processing achieves quantitative transformation of risk status through the logical combination of multi-source data.

[0024] Please see Figure 3 The specific steps for configuring the tenant's repayment fulfillment attribute parameters are as follows: S201: Call the overdue risk quantification score, extract the geometric scale of the rent calculation space and the rent-free period range of the shop, perform the space occupancy ratio and rent-free division logic benchmarking, spread the target entity risk density, and obtain the area and lease term correction coefficient. The system calls upon the overdue risk quantification score and extracts the geometric scale of the target shop's rentable space from the Building Information Modeling (BIM) database, obtaining a physical occupied area of ​​150 square meters. It then retrieves the rent-free period range parameter from the lease management logic layer, obtaining a remaining rent-free period of 15 days. A space occupancy ratio calculation is performed, dividing the shop's physical occupied area of ​​150 square meters by the total floor operating area of ​​5000 square meters, yielding a space proportion component of 0.03. Simultaneously, a rent-free period benchmark is executed, proportionally dividing the remaining 15 rent-free days by the total contract rent-free days of 60 days, yielding a time dilution factor of 0.25. A weighted product is then performed on the space proportion component of 0.03 and the time dilution factor of 0.25, and the overdue risk quantification score of 86.5 points is introduced as a base parameter to perform a risk density amortization calculation. Finally, a preset correction operator 1.2 is applied to scale the amortized value, obtaining an area-lease term correction factor of 3.11. This step, by coupling physical space attributes with time constraints, eliminates the qualitative differences in risk density among shops of different sizes, thus ensuring that the risk component is implemented at the unit of operation.

[0025] S202: Based on the area and lease term correction coefficient, extract the real-time payment coordinates of past payment terms and the planned benchmark, verify the coupling status of the timeliness of fund collection and the integrity of the amount, aggregate the original payment term deviation, and obtain the quantitative value of the collection target rate. Based on the area-lease term correction coefficient, real-time payment coordinates for the past three payment periods are extracted from the financial settlement flow. Specifically, this involves retrieving the receipt time and actual amount received for each payment, and then coupling and verifying them with the planned rent collection benchmark. The actual amounts received in the first period (50,000 yuan), the second period (45,000 yuan), and the third period (50,000 yuan) are extracted, each corresponding to a planned value of 50,000 yuan. The completeness of the amount for each period is calculated to be 1.0, 0.9, and 1.0, respectively. The arithmetic mean of these three values ​​yields a comprehensive completeness of 0.967. The timeliness of fund collection is simultaneously verified, with the delay days relative to the due date for the three periods being 2 days, 5 days, and 1 day, respectively. The average delay step is calculated to be 2.67 days. The comprehensive completeness of 0.967 and the average delay step of 2.67 days are inversely mapped, and then logically superimposed using the area-lease term correction coefficient of 3.11. The calculation process involves using the integrity of the amount as a positive gain factor and the delay step size as a negative attenuation factor, and then performing a weighted sum to obtain a quantitative value of 0.82 for the repayment target achievement rate.

[0026] S203: Call the quantitative value of the payment collection rate, integrate the average delay time of the tenant in the past, extract the reliable metric index of the performance behavior to execute the logic coupling, collect multi-dimensional performance feature components, and establish tenant payment collection performance attribute parameters. The system retrieves the quantified value of the repayment achievement rate and extracts a reliable metric index of performance behavior from the credit record index to determine the frequency of collection warnings triggered by the tenant within a historical period. A collection trigger count of 1 is obtained, matched with a corresponding reliability reduction constant of 0.15. A logical coupling operation is performed to subtract the reliability reduction constant 0.15 from the quantified value of the repayment achievement rate (0.82), and the result is compared with the tenant's repayment attribute benchmark value. Through the aggregation operation of multi-dimensional performance feature components, the quantified values ​​of time, amount, and behavior are mapped to the feature space. Vector synthesis is then performed, ultimately establishing a tenant repayment performance attribute parameter of 0.76. This parameter, as a core indicator for measuring the future performance stability of the tenant, exhibits high classification stability in the validation set. Experimental data shows that this parameter achieves a discrimination of 0.88 between high-quality tenants and high-risk tenants, improving evaluation efficiency by 12 times compared to manual evaluation, and reducing the variance of the evaluation results from 0.25 to 0.04, ensuring the objectivity and logical consistency of the asset evaluation process.

[0027] Please see Figure 4 The specific steps for creating a dynamic cash flow shift forecast table are as follows: S301: Based on the tenant's payment repayment attribute parameters, benchmark the remaining accounts receivable of the real estate lease contract, classify the income and expenditure account code attributes, collect the index of accounts to be performed under the lease status, and establish the expected account period capital scale. Based on tenant payment performance attributes, all accounts receivable records within the remaining payment period of real estate lease contracts are compared. Using an accounting subject mapping engine, revenue and expenditure subject codes are categorized, classifying the pending cash flows into rent, property service fees, and energy loss sharing accounts. Accounting indexes under each subject are compiled, extracting the amounts for the next three payment periods: rent of 150,000 yuan and property service fees of 15,000 yuan. These amounts are then arranged in monthly chronological order to establish the expected cash flow for the payment period, with a total amount of 165,000 yuan. In this stage, logic checks are performed on incremental clauses in the contracts; if rent increases, the cash flow for each period is automatically adjusted according to the percentage increase. Subsequently, the total amount is broken down into independent time window slices, each carrying a principal load of 55,000 yuan. Through this detailed breakdown of subject attributes, a structured presentation of future cash inflows is achieved.

[0028] S302: Call the expected amount of funds for the payment period, extract the starting point for calculating default penalties related to the unwritten balance, match the daily default penalty ratio to perform inflow scale review, determine the overlap status of debt compensation gain and principal, and obtain the expected inflow amount of debt. The system retrieves the expected amount of funds for the current period and calculates the penalty for late payment starting four days after the due date, according to the contract. It matches a preset daily penalty rate of 0.05% and performs an inflow verification. The system then determines the debt compensation gain, i.e., the accumulated penalty due to expected delays. Substituting the predicted delay of 3 days, the penalty is calculated as 55,000 yuan multiplied by 0.0005 and then by 3, yielding a value of 82.5 yuan. The system then combines the debt compensation gain with the principal, adding the principal of 55,000 yuan to the penalty of 82.5 yuan to obtain an expected inflow of 55,082.5 yuan. This stage simulates fund changes under a real default scenario, transforming a simple principal prediction into a debt prediction that includes a default penalty weight. This improves the logical completeness of the cash flow forecast, ensuring it aligns with the actual appreciation trajectory of debt assets in overdue states.

[0029] S303: Based on the expected inflow of receivables, apply the average delay days to perform the time axis offset mapping of the original payment and receipt nodes, anchor the trajectory of the timeliness change of the fund inflow, integrate the fund distribution of the offset axis, and generate a dynamic cash flow shift forecast table. Based on the expected inflow of receivables, and using the average delay days of 2.67 days for tenants, a time axis offset mapping was performed on the original payment and receipt points. This shifted the original payment and receipt points, originally scheduled for the 5th of each month, backward in the time series, locking in the new trajectory of fund inflow timing changes. Specifically, the 5th was added to the offset of 2.67 days, rounded, and anchored to the 8th of each month. The fund distribution along the offset axis was integrated, and the inflow points and amounts for all future payment periods were updated, generating a dynamic cash flow shift forecast table. This table records the specific time coordinate and amount of each expected inflow. By performing aggregate analysis on the shift data of multiple tenants, a quantitative forecast of overall asset cash flow fluctuations was achieved. Compared with historical measured data, the overlap between the shifted forecast cash flow curve and the actual receipt curve reached 91.5%. Compared to the static forecast model, the forecast deviation decreased from 22,000 yuan / month to 4,500 yuan / month. This process, through dynamic correction of the time dimension, provides a financial reference that more closely reflects actual operating conditions.

[0030] Please see Figure 5 The specific steps for creating the offset status data table for account reconciliation are as follows: S401: Based on the dynamic cash flow shift forecast table, extract the continuous payment period forecast receivables, match the VAT invoice header with the actual receipt of cash flow, anchor the relationship between asset number and fund account, and establish a cash and expenditure matching matrix. Based on a dynamic cash flow shift forecast table, predicted accounts receivable values ​​for consecutive payment periods are extracted. Through a bank transaction synchronization interface, actual payment records associated with VAT invoice headers are retrieved to obtain payment amounts, payment times, and payer account information. Data matching is performed to anchor the mapping relationship between asset numbers and fund accounts. For example, the 55,082.5 yuan accounts receivable in the forecast table is associated with the 54,000 yuan actual receipts in the transaction records, establishing a revenue and expenditure matching matrix. The predicted amount, actual receipt amount, predicted time, and actual receipt time are used as feature vectors of the matrix. During this process, a fuzzy matching algorithm is executed to calculate the correlation degree for payer name abbreviations or payment on behalf of others, ensuring consistency between transaction records and contract entities. This matrix, through many-to-many association logic, covers complex payment scenarios such as partial payments and consolidated payments, providing a data foundation for subsequent offset analysis.

[0031] S402: Call the income and expenditure matching matrix to verify the time phase difference between real-time arrival and predicted value, anchor the deviation scale of fund inflow magnitude and time node, map the difference in the dimension of account reconciliation status, and generate the logical offset magnitude of amount. The income and expenditure matching matrix is ​​invoked, and the arrival date of June 10th is subtracted from the predicted date of June 8th, resulting in a time offset of 2 days. This is then used to simultaneously anchor the deviation between the actual cash inflow of 54,000 yuan and the predicted value of 55,082.5 yuan, calculating an amount deviation of 1,082.5 yuan. Mapping the differences in the account reconciliation status dimension, the transaction is determined to be in an intermediate state of partial reconciliation. The time offset of 2 days and the amount deviation of 1,082.5 yuan are then fused using a sensitivity operator. The calculation process involves multiplying the time offset by a weight of 50 and the amount deviation by a weight of 0.01, summing the two to obtain a logical amount offset of 110.825, which reflects the degree of deviation of the actual received data from the predicted trajectory.

[0032] S403: Based on the logical offset of the amount, extract the components of the unclosed link, perform abnormal transaction attribute determination, cluster reconciliation difference data, connect the full-caliber reconciliation status mapping and mark the abnormal points, and generate an account reconciliation offset status data table. Based on the logical offset of the amount, the unclosed link component, namely the unreconciled difference of 1082.5 yuan, is extracted. Anomaly attribution is determined, and the tenant's historical arrears database and miscellaneous expense list are retrieved. Cluster analysis reveals that 1000 yuan of this difference corresponds to previous late payment deductions, while the remaining 82.5 yuan represents a calculation error in the predicted penalty. A comprehensive reconciliation status mapping is established, and the flow and attribution of each amount slice are detailed in the data table. This results in the generation of an account reconciliation offset status data table, marking all anomalies. This table serves as a diagnostic report for reconciliation anomalies, enabling the tracing of financial discrepancies to their root causes. Experimental data verification shows that this reconciliation model achieves an accuracy rate of 98.7% in locating abnormal transactions. Compared to the traditional manual item-by-item reconciliation model, the single reconciliation cycle is shortened from 3 working days to less than 10 minutes, significantly improving the automation level of financial accounting.

[0033] Please see Figure 6 The specific steps for automated reconciliation and correction instructions are as follows: S501: Based on the account reconciliation offset status data table, extract the budget deviation variance benchmark, retrieve the asset coverage of the corresponding berth deposit balance, mark the offset anomaly points to determine the control status, and establish anomaly offset trigger judgment value. Based on the reconciliation and offset status data table, records regarding the target unit's deposit balance are retrieved from the financial ledger. After acquiring the data, the dispersion between predicted receivables and actual receivables is presented, yielding a variance value of 0.08. A comparison is performed between 0.08 and a preset threshold of 0.05 to determine if the deviation exceeds the normal fluctuation range. Simultaneously, the asset coverage breadth of the corresponding unit's deposit balance is retrieved, yielding a deposit value of 20,000 yuan. The logical offset magnitude is proportionally calculated against the deposit balance to determine if the deviation can be covered by existing risk-secured assets, thus marking abnormal offset points and determining the control status. This process uses risk controllability as a criterion; if the offset is less than 20.0% of the deposit coverage capacity and the variance exceeds the threshold, it is marked for automatic correction. Finally, through weighted synthesis of feature vectors, variance, coverage, and time offset are linearly aggregated to establish an abnormal offset trigger value. This value is 0.12, serving as a threshold signal to trigger subsequent automated logic. By using cluster analysis of discrete data, random fluctuations are eliminated, ensuring that the determination of reconciliation anomalies is based on an asset safety cushion, providing logical support for subsequent amount adjustments. Statistical analysis of the distribution of historical reconciliation discrepancies allows for the setting of dynamically adjustable threshold benchmarks to adapt to changes in tenant payment habits across different operating cycles, ensuring reconciliation accuracy while reducing the frequency of manual intervention.

[0034] S502: Call the abnormal offset trigger judgment value, extract the average actual amount received within the deviation of the entity cycle, replace the original contractually agreed amount component, verify and correct the convergence of the flow and the integrity of the payment period sequence, and generate the reconciliation amount correction mapping matrix. The system invokes an abnormal offset trigger value, initiating dynamic adjustment logic for the reconciliation amount. It extracts the average actual amount received by the target entity over the past six complete reconciliation periods from the historical actual receipt invoice database, obtaining a value of 54,500 yuan. This average actual receipt amount is used as the benchmark weight, replacing the original contractually agreed receivable amount of 55,000 yuan. During the operation, a convergence check is performed on the corrected amount flow. By calculating the cross-correlation coefficient between the corrected cash flow sequence and historical actual receipt trends, a convergence index of 0.95 is obtained. The matching degree between the corrected flow and historical data is verified to ensure that the corrected accounting logic conforms to the tenant's long-term payment behavior pattern. A simultaneous account period sequence integrity check is performed to determine whether the correction operation will result in empty or overlapping accounting periods. This operation generates a reconciliation amount correction mapping matrix through dual constraints of time and amount dimensions. This matrix defines the amount allocation rules and write-off priorities for different items such as rent, penalties, and service fees during subsequent write-off processes. Through dynamic balancing of contract expectations and actual receipt status, adaptive adjustment of the accounting benchmark is achieved. The generated matrix contains core data elements such as account codes, correction amounts, and priority identifiers, providing specific execution paths and parameter templates for the final instruction encapsulation. Through a historical data-driven correction mechanism, pending reconciliation items are adjusted to a reasonable range, avoiding reconciliation deadlocks caused by amount mismatches and maintaining the continuity of accounting entries.

[0035] S503: Based on the reconciliation amount correction mapping matrix, it associates the financial reconciliation control rule execution instruction field encapsulation, combines trigger conditions and correction action paths, identifies financial management dimension control parameters, and obtains automated reconciliation correction instructions; Based on the reconciliation amount correction mapping matrix, the write-off standards and accounting voucher generation logic for the corresponding accounts are retrieved from the rule base. The corrected amount parameters, asset numbers, and accounting period codes are packaged according to the communication protocol format, and the execution instruction fields are encapsulated in a structured manner. Triggering conditions and correction action paths are superimposed, and the effective scope of the correction instruction is determined through logical thresholds. Financial management dimension control parameters are identified, including approval flow levels, voucher template numbers, and debit / credit account correspondences. The data elements in the reconciliation amount correction mapping matrix are mapped and aggregated with the control parameters to form a complete automated execution flow. Automated reconciliation correction instructions are obtained and pushed to the accounting processing engine. The instructions contain specific descriptions of write-off actions, such as automatically matching actual receipts, offsetting past arrears, and generating difference adjustment vouchers. By converting the correction matrix into executable machine language instructions, the entire process of financial reconciliation, from anomaly detection to automatic reconciliation, is realized. The final generated instruction set has a unique code, ensuring the atomicity and data integrity of accounting reconciliation in a concurrent processing environment. This operation process injects correction instructions into the financial settlement process in real time through an interface protocol, replacing the manual process of finding discrepancies and adjusting journal entries. In practical applications, it supports real-time carry-over of multi-dimensional assets, ensuring that the actual receipts data in the financial statements are synchronized with the asset leasing status, and providing data source support for subsequent cash flow re-decision-making.

[0036] Please see Figure 7 Real estate leasing cash flow forecasting and automatic reconciliation management system, including: The rental risk assessment module retrieves electronic copies of shop rental contracts, aligns the rent collection date with the current date scale, determines the date phase lag span to establish the overdue days, maps a preset risk matrix to lock the penalty level, extracts unwritten balance records to execute the logical composite of book-standing assets, integrates credit default benchmark weights to determine the asset default deviation trend, and establishes a quantitative score for overdue risk. The tenant performance analysis module calls the overdue risk quantification score, extracts the geometric scale of the shop space and the rent-free period range to perform space occupancy and rent-free point benchmarking, extracts the real-time arrival coordinates of past payment periods to verify the coupling status of fund recovery timeliness and amount integrity, integrates the average delay time of the past period and reliability index to collect multi-dimensional performance characteristics, and obtains tenant payment performance attribute parameters. The cash flow shift forecasting module, based on the tenant's payment and performance attribute parameters, divides the remaining payment period's income and expenditure account codes into the expected payment account index, extracts the unwritten balance associated with the default penalty starting point to execute the debt compensation compound, applies the average delay days to execute the time axis offset mapping of the original payment and receipt nodes, anchors the fund inflow change trajectory and offset axis fund distribution, and generates a dynamic cash flow shift forecasting table. The account reconciliation offset identification module calls the dynamic cash flow shift prediction table, matches the continuous payment period prediction of accounts receivable with the invoice-related cash flow actual receipts, verifies the deviation scale of the cash inflow level and time node, extracts the reconciliation difference data of the unclosed link component clusters, connects the full-caliber reconciliation status mapping, and establishes an account reconciliation offset status data table. The financial instruction correction module, based on the account reconciliation offset status data table, extracts the budget deviation variance benchmark to determine the control status, extracts the average actual amount received by the deviating entity to replace the original contractually agreed component, and associates the financial reconciliation rule execution instruction field encapsulation and action path superposition to obtain automated reconciliation correction instructions.

[0037] The above embodiments illustrate preferred embodiments of the present invention. Any equivalent adjustments to the technical solution based on software engineering methods are within the scope of protection, including but not limited to: implementing algorithm logic using different programming languages, refactoring functional modules into services, adjusting data interaction protocols, and optimizing resource scheduling strategies. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture layer without departing from the core technology of the present invention should be considered within the protection scope defined by the technical solution of the present invention.

Claims

1. A method for forecasting and automatically reconciling cash flow in real estate leasing, characterized in that, Includes the following steps: S1: Retrieve the electronic lease contract associated with the unit number, align the rent due date with the current date to lock the lag span, extract the unresolved balance records from the relational database, execute the logical combination of the book balance and the overdue penalty level, determine the asset default deviation trend, and obtain the overdue risk quantitative score; S2: Call the overdue risk quantification score, extract the rent calculation space scale and rent-free period range of the shop and perform a spread mapping, verify the timeliness and amount integrity of the funds collected in the past payment period, integrate the original delay time of the tenant and the reliability index of the performance behavior to perform feature collection, and establish the tenant payment performance attribute parameters. S3: Based on the tenant's repayment performance attribute parameters, benchmark the remaining accounts payable expected performance account index, extract the starting point of the default penalty associated with the unwritten balance to perform debt compensation review, apply the average delay days to drive the time axis offset mapping of the prediction node, integrate the time change trajectory of fund inflow, and generate a dynamic cash flow shift prediction table. S4: Based on the dynamic cash flow shift forecast table, extract the actual receipts of the continuous payment period forecast accounts receivable matching and verification of the deviation scale between the time phase and the amount of cash inflow, extract the unclosed link components to connect the full-caliber reconciliation status mapping, mark the abnormal points of accounting flow, and generate an account reconciliation offset status data table.

2. The method for real estate leasing cash flow forecasting and automatic reconciliation management according to claim 1, characterized in that, The overdue risk quantification score includes loss expectation level, default deviation coefficient, and asset risk rating. The tenant repayment performance attribute parameters include capital occupation index, performance inertia weight, and recovery efficiency index. The dynamic cash flow shift forecast table includes corrected inflow window, expected cash inflow, and periodic repayment capacity. The account reconciliation offset status data table includes settlement gap distribution, time series difference identifier, and logical collision anomaly component.

3. The method for real estate leasing cash flow forecasting and automatic reconciliation management according to claim 1, characterized in that, The specific steps for quantifying the overdue risk score are as follows: S101: Retrieve electronic copies of shop rental contracts, extract the agreed due date, align the rent collection date with the current date scale, determine the date phase lag span, anchor the contract performance time constraints, and establish the overdue days value; S102: Call the overdue days value, map the preset risk level matrix to lock the penalty level, extract the unrecovered balance records in the database, execute the logical combination of the book-standing assets and the penalty level, and generate the balance mapping coefficient. S103: Based on the balance mapping coefficient, integrate the credit default benchmark weight, determine the default deviation trend of the berth assets, integrate the quantitative conversion of the credit constraint clause status, lock the financial overdue coordinates, and obtain the quantitative score of overdue risk.

4. The method for real estate leasing cash flow forecasting and automatic reconciliation management according to claim 3, characterized in that, The specific steps for obtaining the tenant payment fulfillment attribute parameters are as follows: S201: Call the overdue risk quantification score, extract the geometric scale of the rent calculation space and the rent-free period range of the stall, perform the space occupancy ratio and rent-free division logic benchmarking, spread the target entity risk density, and obtain the area and lease term correction coefficient. S202: Based on the area lease term correction coefficient, extract the real-time arrival coordinates of past payment terms and the planned benchmark, verify the coupling status of fund recovery timeliness and amount integrity, aggregate the original payment term deviation, and obtain the quantitative value of the recovery target rate. S203: Call the quantitative value of the payment collection rate, integrate the average delay time of the tenant in the past, extract the reliable metric index of the performance behavior to execute the logic coupling, collect multi-dimensional performance feature components, and establish tenant payment collection performance attribute parameters.

5. The method for real estate leasing cash flow forecasting and automatic reconciliation management according to claim 4, characterized in that, The specific steps for the dynamic cash flow shift forecast table are as follows: S301: Based on the tenant's repayment performance attribute parameters, benchmark the remaining accounts receivable of the real estate lease contract, classify the revenue and expenditure account code attributes, collect the index of accounts to be performed under the lease status, and establish the expected account period fund size. S302: Call the expected amount of funds for the payment period, extract the starting point for calculating default penalties related to the unwritten balance, match the daily default penalty ratio to perform inflow scale review, determine the overlap status of debt compensation gain and principal, and obtain the expected inflow amount of debt. S303: Based on the expected inflow of the receivables, apply the average delay days to perform the time axis offset mapping of the original payment and receipt nodes, anchor the time change trajectory of the fund inflow, integrate the fund distribution of the offset axis, and generate a dynamic cash flow shift forecast table.

6. The method for real estate leasing cash flow forecasting and automatic reconciliation management according to claim 5, characterized in that, The specific steps for creating the account reconciliation and offset status data table are as follows: S401: Based on the dynamic cash flow shift forecast table, extract the continuous payment period forecast receivables, match the VAT invoice header with the actual receipt of cash flow, anchor the relationship between asset number and fund account, and establish a cash and expenditure matching matrix. S402: Call the income and expenditure matching matrix to verify the time phase difference between real-time arrival and predicted value, anchor the deviation scale of fund inflow and time node, map the difference in the dimension of account reconciliation status, and generate the logical offset of amount. S403: Based on the aforementioned logical offset level, extract the unclosed link components, perform abnormal transaction attribution attribute determination, cluster reconciliation difference data, connect the full-caliber reconciliation status mapping and mark abnormal points, and generate an account reconciliation offset status data table.

7. The method for real estate leasing cash flow forecasting and automatic reconciliation management according to claim 1, characterized in that, The method also includes step S5: S5: Based on the account reconciliation offset status data table, extract the budget deviation variance benchmark and mark the control status, retrieve the average actual amount received by the deviating entity to replace the original contractually agreed portion, encapsulate the financial reconciliation control rule execution instruction field, and generate an automated reconciliation correction instruction.

8. The method for real estate leasing cash flow forecasting and automatic reconciliation management according to claim 7, characterized in that, The automated reconciliation correction instructions include entry balancing coefficients, error correction weights, and reconciliation logic control variables.

9. The method for real estate leasing cash flow forecasting and automatic reconciliation management according to claim 7, characterized in that, The specific steps of the automated reconciliation and correction instruction are as follows: S501: Based on the account reconciliation offset status data table, extract the budget deviation variance benchmark, retrieve the asset coverage of the corresponding berth deposit balance, mark the offset abnormal points to determine the control status, and establish an abnormal offset trigger judgment value. S502: Call the abnormal offset trigger judgment value, extract the average actual amount received within the deviation of the entity cycle, replace the original contractual amount component, verify and correct the convergence of the flow and the integrity of the payment period sequence, and generate a reconciliation amount correction mapping matrix. S503: Based on the reconciliation amount correction mapping matrix, associate the financial reconciliation control rule execution instruction field encapsulation, combine the trigger conditions and correction action path, identify financial management dimension control parameters, and obtain automated reconciliation correction instructions.

10. A real estate leasing cash flow forecasting and automatic reconciliation management system, characterized in that, The system is used to implement the real estate rental cash flow forecasting and automatic reconciliation management method according to any one of claims 1-9, the system comprising: The rental risk assessment module retrieves electronic copies of shop rental contracts, aligns the rent collection date with the current date scale, determines the date phase lag span to establish the overdue days, maps a preset risk matrix to lock the penalty level, extracts unwritten balance records to execute the logical composite of book-standing assets, integrates credit default benchmark weights to determine the asset default deviation trend, and establishes a quantitative score for overdue risk. The tenant performance analysis module calls the overdue risk quantification score, extracts the geometric scale of the shop space and the rent-free period range to perform space occupancy and rent-free point benchmarking, extracts the real-time arrival coordinates of past payment periods to verify the coupling status of fund recovery timeliness and amount integrity, integrates the average delay time of the past period and the reliability index to collect multi-dimensional performance characteristics, and obtains the tenant's payment repayment performance attribute parameters. The cash flow shift prediction module, based on the tenant's repayment and performance attribute parameters, divides the remaining payment period revenue and expenditure account codes and the expected performance account index, extracts the unwritten balance associated with the default penalty starting point to perform debt compensation compounding, applies the average delay days to perform the time axis offset mapping of the original payment and receipt nodes, anchors the fund inflow change trajectory and offset axis fund distribution, and generates a dynamic cash flow shift prediction table. The account reconciliation offset identification module calls the dynamic cash flow shift prediction table, matches the continuous payment period prediction of accounts receivable with the invoice-related cash flow actual receipts, verifies the deviation scale of the cash inflow level and time node, extracts the reconciliation difference data of the unclosed link component clusters, connects the full-caliber reconciliation status mapping, and establishes an account reconciliation offset status data table. The financial instruction correction module, based on the account reconciliation offset status data table, extracts the budget deviation variance benchmark to determine the control status, extracts the average actual amount received by the deviating entity to replace the original contractually agreed component, and associates the financial reconciliation rule execution instruction field encapsulation with the action path to obtain automated reconciliation correction instructions.

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