Management system and method for business and financial difference condition of scrap steel enterprise

Abstract

The invention discloses a system and a method for managing industry and financial difference conditions of a scrap steel enterprise, and belongs to the technical field of scrap steel. The system comprises a financial operation condition filling subsystem, a business data summarization subsystem, a financial data summarization subsystem and a comparison subsystem. The system solves the problems that an existing management mode is difficult to track business and financial data differences in real time and lacks a dynamic early warning mechanism, and helps an enterprise management layer to quickly position business and financial deviation roots by automatically collecting business and financial data and comparing and analyzing the business and financial data in real time to generate a visual difference report; by establishing a hierarchical authority management system, it is ensured that a business department and a financial department achieve collaborative analysis on the premise of data isolation, and sensitive operation data leakage is effectively prevented; business flow and financial vouchers are bidirectionally stored by adopting a block chain technology, so that the non-tampering property of a comparison traceability process is ensured, and the credibility of business and financial difference analysis and the auditing tracking efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of scrap steel technology, specifically to a management system and method for managing the discrepancies between business and financial conditions of scrap steel enterprises. Background Technology

[0002] In the operation of scrap steel enterprises, discrepancies commonly exist between business data and financial data due to factors such as the timing of entry, measurement standards (e.g., weight differences, deductions), and manual adjustments. Currently, the main reliance is on traditional manual reconciliation, which has significant drawbacks: First, the data is fragmented, with business and financial data not being interconnected; second, the analysis is superficial, only allowing for total quantity comparisons and failing to trace and quantify the specific causes of the discrepancies; and third, it lacks timeliness, failing to achieve real-time monitoring and early warning, resulting in serious delays in management decision-making.

[0003] Currently, there is no system that can automatically collect data from existing business and financial systems in the scrap steel industry, conduct in-depth analysis, and intuitively display the root causes of differences in business and financial data, which restricts the improvement of the level of refinement in enterprise operation and management. Summary of the Invention

[0004] The purpose of this invention is to provide a management system and method for the discrepancy between business and financial status in scrap steel enterprises. By constructing a multi-source data automatic collection and algorithm calculation system, it can achieve accurate quantification, root cause tracing, and visual monitoring of the discrepancy between business and financial status, thereby solving the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A management system for discrepancies between business and financial statements of scrap steel enterprises, comprising:

[0007] The financial and operational data entry subsystem is used for users to manually enter some financial and operational data, and records the amount of manually entered data.

[0008] The business data aggregation subsystem is used to automatically collect and aggregate outbound, weighing, and deduction data from the sales outbound table, base order table, and payment and receipt table of the business end to form business accounting benchmark data; the business accounting benchmark data includes: business sales volume, weight difference, and deduction difference;

[0009] The financial data aggregation subsystem is used to automatically collect and aggregate invoice, provisional and revenue recognition data from the monthly financial closing data to form financial accounting benchmark data. The financial accounting benchmark data includes: invoice revenue, provisional revenue for the current month, and provisional revenue reversal for the previous month.

[0010] The comparison subsystem is used to receive the business accounting benchmark data and the financial accounting benchmark data, perform business-financial difference calculation, and generate a visualized business-financial difference analysis report.

[0011] Furthermore, the comparison subsystem includes:

[0012] The core difference calculation module is used to calculate the core business and financial difference amount according to the preset first algorithm. The calculation formula is: Core difference amount = ∑A - ∑B - (∑C + ∑D - ∑E + ∑F);

[0013] Where A refers to the sales volume of business documents, B refers to the weight of collaborative payment and receipt in the pending confirmation status, C refers to the revenue volume of invoices, D refers to the estimated revenue volume for this month, E refers to the estimated reversal volume for last month, and F refers to the amount of manually entered data.

[0014] The difference composition analysis module is used to decompose and quantify the specific sources of difference according to the preset second algorithm. Its calculation formula is: Difference composition quantity = ∑G + ∑H + ∑I + ∑J - ∑F;

[0015] Wherein, G refers to the weight difference recorded on a single weighing basis, H refers to the difference in the deduction of miscellaneous items, I refers to the outbound quantity that has not yet been provisionally estimated for the current month, J refers to the impact of historical payment terms, and F refers to the amount of data manually entered.

[0016] Furthermore, the formula for calculating the estimated amount of goods not yet received for delivery this month is: Estimated amount of goods not yet received for delivery this month (∑I) = ∑A - ∑B - (∑C - ∑K) - (∑D - ∑L) - ∑G - ∑H;

[0017] Where A refers to sales volume, B refers to the weight of collaborative payment and receipt orders in the pending confirmation status, C refers to invoice revenue, K refers to the invoice volume of historical payment period outbound orders in this month, D refers to the estimated revenue volume in this month, L refers to the estimated volume of historical payment period outbound orders in this month, G refers to the weight difference recorded in the invoice, and H refers to the deduction difference in the deduction operation.

[0018] Furthermore, the impact of the historical payment period data on the current month's data is a comprehensive adjustment item, calculated using the formula: Historical payment period impact (∑J) = - (∑E + ∑K + ∑L);

[0019] Where E refers to the estimated reversal amount from the previous month, K refers to the invoicing amount of historical payment period delivery orders in this month, and L refers to the estimated amount of historical payment period delivery orders in this month.

[0020] Furthermore, it also includes:

[0021] The access control subsystem is used to establish and maintain user access systems at the head office, subsidiary, and department levels, ensuring that business departments and finance departments can conduct collaborative analysis and access under the premise of data isolation.

[0022] The data security subsystem uses blockchain technology to perform one-way hash encryption and notarization on the source data collected by the business data aggregation subsystem and the financial data aggregation subsystem, ensuring the immutability and audit traceability of the business and financial comparison and traceability process.

[0023] Furthermore, the business-financial discrepancy analysis report generated by the comparison subsystem allows the head office to query data from all subsidiaries in the business-financial discrepancy module, and allows subsidiaries to view their own historical business-financial discrepancy data as a basis for business management decisions, while also allowing the head office to monitor the business compliance of each subsidiary.

[0024] A method for managing discrepancies between business and financial statements in scrap steel enterprises includes the following steps:

[0025] Step 1: Automatically collect outbound and measurement data from the business side through the business data aggregation subsystem, automatically collect invoice and provisional data from the financial side through the financial data aggregation subsystem, and receive manually entered data from users through the financial operation status reporting subsystem.

[0026] Step 2: The comparison subsystem calculates the total sales volume based on the collected business data and financial data, respectively.

[0027] Step 3: The comparison subsystem calls the core difference calculation module, executes the first algorithm, and calculates the total core business and financial difference.

[0028] Step 4: The comparison subsystem calls the difference composition analysis module and executes the second algorithm to decompose the total core business and financial difference into several specific difference components, such as weight difference, miscellaneous difference, estimated amount of goods not yet received this month, and the impact of historical payment terms.

[0029] Step 5: The system generates a visual analysis report containing the total amount and composition of the discrepancies. Access to the report is controlled through the access management subsystem, allowing authorized personnel from the head office and subsidiaries to view the report and use it to pinpoint the root causes of business and financial deviations and optimize business management.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] 1. This invention, by constructing a complete algorithm system, transforms ambiguous business and financial differences into precisely quantifiable indicators, and can clearly trace back to specific business links such as weight difference, time series difference, and pending quantities in transit, providing unprecedented accuracy for management decisions.

[0032] 2. Through innovative permission design and the application of blockchain technology, this invention ensures the flexibility of data reporting by subsidiaries while guaranteeing the transparency, trustworthiness, and security of global data to headquarters, achieving an effective balance between centralization and decentralization, and constructing a trustworthy business and financial data governance system. Attached Figure Description

[0033] Figure 1 Screenshot of the financial and operational status reporting submodule page of this invention;

[0034] Figure 2 Screenshot of the newly added page for the financial and operational status reporting submodule of this invention;

[0035] Figure 3 This is a screenshot of the business and financial difference report page of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] To address the current lack of a system capable of automatically aggregating, deeply analyzing, and intuitively displaying the root causes of discrepancies in existing business and financial systems within the scrap steel industry—a technical challenge that hinders the improvement of refined business management—please refer to [link / reference needed]. Figure 1-3 This embodiment provides the following technical solution:

[0038] A management system for discrepancies between business and financial statements of scrap steel enterprises, comprising:

[0039] The financial and operational data entry subsystem is used for users to manually enter some financial and operational data, and records the amount of manually entered data.

[0040] Operating entities: Headquarters and subsidiaries; such as Figure 1-2 When closing their financial statements, each subsidiary can enter the financial and operational data that needs to be manually entered on the financial and operational status reporting subsystem page. The specific information to be entered includes: subsidiary name, customer name, business type, material type, revenue volume (revenue quantity), revenue amount (revenue amount), operating cost volume (operating quantity), and operating cost amount (operating amount). The revenue volume will be included in the calculation of subsequent report data.

[0041] like Figure 3After all subsidiaries and branches have completed their monthly financial closing and submitted all manually entered data, the head office can view the monthly and all historical monthly business-financial difference reports. This report includes a business data summary subsystem, a financial data summary subsystem, and a comparison subsystem. The business data subsystem includes fields such as weight difference, the difference between the current month's outbound orders and the current month's invoicing, the current month's outbound provisional quantity, and pending confirmation of collaborative (within the Chain Finance) receipts and payments. The financial data subsystem includes the current month's invoicing quantity, the current month's outbound quantity for the current month's payment period, the current month's total provisional quantity, the impact of historical payment period data on the current month's data, the provisional quantity of historical payment period outbound orders in the current month, the current month's invoicing quantity of historical payment period outbound orders, and the current month's provisional reversal fields. The comparison subsystem includes business sales volume, financial sales volume, business-financial difference, difference summary, and manually entered data from the financial operations details table.

[0042] The business data aggregation subsystem is used to automatically collect and process data from business systems at various bases, and calculate core business indicators using preset algorithms. Specifically, the system automatically obtains business data tables such as the sales outbound theme table and outbound order table through data interfaces, and executes the following calculation logic:

[0043] 1. Sales Volume: Sum the weights on the sales outbound orders in the sales outbound subject table whose status is "confirmed receipt by the other party".

[0044] 2. Weight difference: Calculate the difference between the weight weighed at our factory and the weight weighed at the supplier's factory in the outbound order form, and sum the differences of all outbound orders that meet the conditions; the conditions are that the outbound date and the steel mill's receipt date are in the same month, and there is a corresponding sales invoice.

[0045] 3. Deduction of impurities: Calculate the difference between the amount of impurities deducted upon the other party's entry into the factory and the amount of impurities deducted upon our exit from the factory, and sum the differences of all eligible outbound orders, with the same conditions as for the weight difference calculation;

[0046] 4. Weight of pending collaborative (within ChainGold) payment and receipt orders: This counts the weight of orders in the payment and receipt order table that are pending confirmation, have non-empty contract numbers, and whose customers belong to the ChainGold system.

[0047] The financial data aggregation subsystem is used to automatically collect invoice, provisional, and revenue data from the financial system, and form a financial accounting benchmark through multi-dimensional calculations. The system automatically obtains financial data sources such as cost accounting data tables and sales revenue tables through data connections, and performs the following calculations:

[0048] 1. Invoice Revenue Quantity: The voucher description in the summary cost accounting data table is of the month-end closing type, and the weight data in the source table is not related to the outbound order, and the payment period is within the query period;

[0049] 2. Estimated Revenue for the Month: This summarizes the weight data in the sales revenue report that are within the query period and whose total amount is not zero.

[0050] 3. Reversal of estimated revenue from the previous month: Take the sum of weight data from the previous month with the payment period in the sales revenue statement, and take a negative value;

[0051] 4. Invoice volume of historical outbound orders in this month: Differentiate between base business and trade business, and summarize the invoice volume of non-red-ink invoice lines in the sales invoice table separately, with the condition that the month of creation of the outbound order is earlier than the month of invoice issuance.

[0052] The comparison subsystem receives business and financial data and performs difference calculations and analysis using multiple algorithm models. Specifically, this system includes the following core computing modules:

[0053] 1. Core difference calculation module, execute the first algorithm: Core difference amount = ∑A - ∑B - (∑C + ∑D -∑E + ∑F);

[0054] Where A refers to the sales volume of the business document.

[0055] B refers to the weight of the collaborative payment / receipt in the pending confirmation status.

[0056] C refers to the invoice revenue amount.

[0057] D refers to the estimated revenue for this month.

[0058] E refers to the estimated amount reversed last month.

[0059] F refers to the amount of data manually entered;

[0060] 2. The difference composition analysis module executes the second algorithm: Difference composition quantity = ∑G + ∑H + ∑I + ∑J -∑F,

[0061] Where G refers to the weight difference recorded on a single weighing record.

[0062] H refers to the impurity difference in the impurity removal operation.

[0063] I refers to the estimated outbound volume that has not yet been included in this month's inventory.

[0064] J refers to the impact of historical payment terms.

[0065] F refers to the amount of data manually entered;

[0066] The estimated outbound volume for this month that has not yet been received is calculated using the third algorithm: Estimated outbound volume for this month that has not yet been received (∑I) = ∑A - ∑B - (∑C - ∑K) - (∑D - ∑L) - ∑G - ∑H

[0067] Where A refers to sales volume.

[0068] B refers to the weight of the collaborative payment / receipt in the pending confirmation status.

[0069] C refers to the amount of invoices received.

[0070] K refers to the invoiced quantity of historical payment period delivery orders in this month.

[0071] D refers to the estimated revenue for this month.

[0072] L refers to the provisional estimate of the outbound shipment quantity for this month based on historical payment terms.

[0073] G refers to the weight difference recorded on a single weighing scale.

[0074] H refers to the difference in impurities removed during the impurities removal operation;

[0075] The impact of historical payment period data on this month's data is calculated using the fourth algorithm: Impact of historical payment period (∑J) = - (∑E + ∑K + ∑L)

[0076] Here, E refers to the estimated reversal amount from the previous month.

[0077] K refers to the invoiced quantity of historical payment period delivery orders in this month.

[0078] L refers to the provisional estimate of the current month's outbound shipments from historical accounts.

[0079] The access control subsystem is used to implement hierarchical data access control; specifically, the access settings are as follows:

[0080] 1. The financial operations reporting subsystem grants data entry permissions to all subsidiaries;

[0081] 2. The business data aggregation subsystem, financial data aggregation subsystem, and comparison subsystem primarily grant headquarters supervisory personnel full access to view and analyze the data.

[0082] 3. Implement data isolation and security control through differentiated permission settings.

[0083] The data security subsystem uses blockchain technology to encrypt and store core source data. Specifically, it generates unique digital fingerprints for business and financial source data through a hash function and stores them in a distributed ledger to ensure that the data is tamper-proof and traceable.

[0084] In summary, this invention addresses the problems of existing management methods, such as the difficulty in real-time tracking of discrepancies between business and financial data and the lack of dynamic early warning mechanisms. By automatically collecting and comparing business and financial data in real time, it generates visualized discrepancy reports to help enterprise management quickly pinpoint the root causes of business-financial deviations. Furthermore, by establishing a hierarchical access control system, it ensures collaborative analysis between business and finance departments under data isolation, effectively preventing the leakage of sensitive operational data. Finally, by employing blockchain technology for two-way notarization of business transaction records and financial vouchers, it ensures the immutability of the comparison and traceability process, enhancing the credibility of business-financial discrepancy analysis and improving audit tracking efficiency.

[0085] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A management system for discrepancies between business and financial statements in scrap steel enterprises, characterized in that: include: The financial and operational data entry subsystem is used for users to manually enter some financial and operational data, and records the amount of manually entered data. The business data aggregation subsystem is used to automatically collect and aggregate outbound, weighing, and deduction data from the sales outbound table, base order table, and payment and receipt table of the business end to form business accounting benchmark data; The business accounting benchmark data includes: business sales volume, weight difference, and miscellaneous loss difference; The financial data aggregation subsystem is used to automatically collect and aggregate invoice, provisional and revenue recognition data from the monthly financial closing data to form financial accounting benchmark data. The financial accounting benchmark data includes: invoice revenue, provisional revenue for the current month, and provisional revenue reversal for the previous month. The comparison subsystem is used to receive the business accounting benchmark data and the financial accounting benchmark data, perform business-financial difference calculation, and generate a visualized business-financial difference analysis report.

2. The management system for discrepancies between business and financial statements of scrap steel enterprises according to claim 1, characterized in that: The comparison subsystem includes: The core difference calculation module is used to calculate the core business and financial difference amount according to the preset first algorithm. The calculation formula is: Core difference amount = ∑A - ∑B - (∑C + ∑D - ∑E + ∑F); Where A refers to the sales volume of business documents, B refers to the weight of collaborative payment and receipt in the pending confirmation status, C refers to the revenue volume of invoices, D refers to the estimated revenue volume for this month, E refers to the estimated reversal volume for last month, and F refers to the amount of manually entered data. The difference composition analysis module is used to decompose and quantify the specific sources of difference according to the preset second algorithm. Its calculation formula is: Difference composition quantity = ∑G + ∑H + ∑I + ∑J - ∑F; Wherein, G refers to the weight difference recorded on a single weighing basis, H refers to the difference in the deduction of miscellaneous items, I refers to the outbound quantity that has not yet been provisionally estimated for the current month, J refers to the impact of historical payment terms, and F refers to the amount of data manually entered.

3. The management system for discrepancies between business and financial statements of scrap steel enterprises according to claim 2, characterized in that: The formula for calculating the estimated amount of goods not yet received this month is: Estimated amount of goods not yet received this month (∑I) = ∑A -∑B - (∑C -∑K) -(∑D -∑L) -∑G - ∑H; Where A refers to sales volume, B refers to the weight of collaborative payment and receipt orders in the pending confirmation status, C refers to invoice revenue, K refers to the invoice volume of historical payment period outbound orders in this month, D refers to the estimated revenue volume in this month, L refers to the estimated volume of historical payment period outbound orders in this month, G refers to the weight difference recorded in the invoice, and H refers to the deduction difference in the deduction operation.

4. The management system for discrepancies between business and financial statements of scrap steel enterprises according to claim 2, characterized in that: The impact of historical payment period data on this month's data is a comprehensive adjustment item, calculated as follows: Impact of historical payment period (∑J) = - (∑E + ∑K + ∑L); Where E refers to the estimated reversal amount from the previous month, K refers to the invoicing amount of historical payment period delivery orders in this month, and L refers to the estimated amount of historical payment period delivery orders in this month.

5. The management system for discrepancies between business and financial statements of scrap steel enterprises according to claim 1, characterized in that: Also includes: The access control subsystem is used to establish and maintain user access systems at the head office, subsidiary, and department levels, ensuring that business departments and finance departments can conduct collaborative analysis and access under the premise of data isolation. The data security subsystem uses blockchain technology to perform one-way hash encryption and notarization on the source data collected by the business data aggregation subsystem and the financial data aggregation subsystem, ensuring the immutability and audit traceability of the business and financial comparison and traceability process.

6. The management system for discrepancies between business and financial statements of scrap steel enterprises according to claim 1, characterized in that: The business and financial discrepancy analysis report generated by the comparison subsystem allows the head office to query data from all subsidiaries in the business and financial discrepancy module, and allows subsidiaries to view their own historical business and financial discrepancy data as a basis for business management decisions, while also allowing the head office to monitor the business compliance of each subsidiary.

7. A method for managing discrepancies between business and financial statements in scrap steel enterprises, implemented based on the management system for discrepancies between business and financial statements in scrap steel enterprises as described in any one of claims 1-6, characterized in that: Includes the following steps: Step 1: Automatically collect outbound and measurement data from the business side through the business data aggregation subsystem, automatically collect invoice and provisional data from the financial side through the financial data aggregation subsystem, and receive manually entered data from users through the financial operation status reporting subsystem. Step 2: The comparison subsystem calculates the total sales volume based on the collected business data and financial data, respectively. Step 3: The comparison subsystem calls the core difference calculation module, executes the first algorithm, and calculates the total core business and financial difference. Step 4: The comparison subsystem calls the difference composition analysis module and executes the second algorithm to decompose the total core business and financial difference into several specific difference components, such as weight difference, miscellaneous difference, estimated amount of goods not yet received this month, and the impact of historical payment terms. Step 5: The system generates a visual analysis report containing the total amount and composition of the discrepancies. Access to the report is controlled through the access management subsystem, allowing authorized personnel from the head office and subsidiaries to view the report and use it to pinpoint the root causes of business and financial deviations and optimize business management.

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