Business and financial integration platform for cross-border full-link collaborative operation

Abstract

The invention belongs to the technical field of business and financial integration, and discloses a business and financial integration platform for cross-border full-link collaborative operation. The platform comprises a compliance risk pre-judgment module, a risk decision response module, a toughness supply chain control module, an autonomous decision agent module and an immersive decision auxiliary module, and is used for analyzing feature vectors, outputting to obtain a business risk prediction value, processing the business risk prediction value and outputting to obtain a risk decision report according to a processing result. The method comprises the following steps: performing extreme environment simulation based on a business risk prediction value in combination with influence factors, outputting a supply chain reconstruction report according to a simulation result, analyzing the supply chain reconstruction report, generating an autonomous decision agent report according to an analysis result, and obtaining a comprehensive information reference report. The method has the remarkable advantages of being high in compliance risk prediction capacity, good in auxiliary decision making effect and large in extreme scene coping effect.

Description

Technical Field

[0001] This invention relates to the field of business-finance integration technology, and more specifically, to a business-finance integration platform for cross-border end-to-end collaborative operations. Background Technology

[0002] Business-finance integration refers to the deep integration of business processes and financial risk control through technological means, realizing an intelligent management system that enables real-time data exchange, dynamic risk quantification, and automated decision execution. Its core value lies in risk prevention, collaborative decision-making, and cost control. For the currently popular cross-border trade industry, the ability to quickly and effectively achieve business-finance integration will bring numerous benefits to enterprise development.

[0003] However, existing integrated business and finance platforms generally suffer from problems such as data silos, delayed responses, and fragmented decision-making. Traditional integrated business and finance platforms heavily rely on manual data acquisition and suffer from the drawback of collecting only a single type of data, making it difficult to support risk calculations regarding product compliance. Furthermore, the risk response mechanism in traditional integrated business and finance platforms is often mechanical, merely indicating the existence of risks without providing corresponding countermeasures, thus requiring manual root cause analysis and risk management. Additionally, they lack consideration for extreme scenarios. In today's rapidly changing environment, cross-border trade needs to account for more extreme scenarios to enable companies to more accurately address and resolve related issues. Moreover, traditional integrated business and finance platforms are inefficient in human-computer interaction, directly increasing the time cost for staff and consequently impacting the company's profitability.

[0004] In view of this, the present invention proposes an integrated business and finance platform for cross-border end-to-end collaborative operations to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution, including:

[0006] The business data acquisition module is used to collect business datasets, which include payee location data, currency preference data, historical success rate data, and product category data.

[0007] The risk data acquisition module is used to collect risk datasets, which include real-time exchange rate fluctuation data, tariff threshold data, and declaration deadline data.

[0008] The basic data interaction module is used to preprocess the business dataset and the basic dataset, and to extract data interaction features to obtain feature vectors.

[0009] Furthermore, the steps of preprocessing the business dataset and the basic dataset, and extracting data interaction features include:

[0010] Q1: Clean the data by removing outliers and normalize all sub-data items in the basic dataset to the range of [0, 1] according to the normalization formula;

[0011] Q2: Based on the recipient's location data S a And based on the geographical risk level, it is matched to obtain geographical risk characteristic data A. a ;

[0012] Q3: Based on currency preference data S b Calculate the historical volatility of the currency to obtain currency stability characteristic data A. b ;

[0013] Q4: Based on product category data S d And based on the product risk level, match them to obtain product risk characteristic data A. c ;

[0014] Q5: Calculate real-time exchange rate fluctuation data S e The absolute value of the fluctuation influence characteristic data A is obtained. d ;

[0015] Q6: Based on tariff threshold data S f And based on the tariff risk level, it is matched to obtain tariff risk characteristic data A. e ;

[0016] Q7: Calculate the data S minus the reporting deadline. g Obtain the urgent characteristic data A of the application. f ;

[0017] Q8: Combine geographical risk characteristic data, currency stability characteristic data, product risk characteristic data, volatility impact characteristic data, tariff risk characteristic data, declaration urgency characteristic data, and historical success rate data to obtain a feature vector;

[0018] The compliance risk prediction module is used to analyze feature vectors and output predicted business risks.

[0019] Furthermore, the steps for analyzing the eigenvectors include:

[0020] Step 1: Obtain a set of historical feature vectors stored in the database, and compare them with the current time based on the timestamp. Group the comparison results from smallest to largest and label them accordingly. The labeling results are L1, L2, L3, ..., Ln. Use the labeling results as the sample set.

[0021] Step 2: Divide the sample set into 70% training set, 15% test set and 15% validation set, and build a business risk prediction model based on the sample set.

[0022] Step 3: Based on the historical feature vectors in the training set, select the historical feature vectors of the past three months as the first reference dataset D1, select the historical feature vectors of the past twelve months as the second reference dataset D2, and select the historical feature vectors of similar products of the past three months as the third reference dataset D3.

[0023] Step 4: Based on the reference dataset and the base learner, calculate the first predicted value. The specific formula for the calculation is as follows:

[0024]

[0025] Obtain the first predicted value of the k-th reference dataset. Among them, T k η is the number of decision trees generated for the k-th reference dataset. t Let φ be the weight factor of the t-th tree. t (D p (referencing dataset D) p The output value of the t-th tree, where λ is the regularization coefficient and ||Ω|| is the complexity penalty term of the tree;

[0026] Step 5: Perform multiple evaluation and validation based on the first reference dataset, the second reference dataset, the third reference dataset, and the first predicted value. The specific formula set for multiple evaluation and validation is as follows:

[0027]

[0028] The feature-weighted verification value C was obtained respectively. a Time decay verification value of the k-th reference dataset The residual correction validation value of the k-th reference dataset Where, ω i Let i be the global weight factor for the i-th feature data. Let f be the partial derivative of the i-th feature data with respect to the first predicted value of the k-th reference dataset, max be the maximization function, ε be the minimum value, γ be the decay coefficient, and Δm be the partial derivative of the i-th feature data with respect to the first predicted value of the k-th reference dataset. k Let N be the latest data time difference of the k-th reference dataset, and let ROC-AUC be the area under the curve of the reference dataset on the test set. k The number of samples in the reference dataset is given, and tanh is the hyperbolic tangent function. Let σ be the prediction residual for the j-th sample. k Let k be the standard deviations of the reference datasets;

[0029] Step Six: Calculate the business risk prediction value A based on the first predicted value, feature-weighted verification value, time-decay verification value, and residual-corrected verification value of the k-th reference dataset. h The specific formula for calculation is:

[0030]

[0031] Where Sigmoid is the Sigmoid activation function;

[0032] Step 7: Output the business risk forecast values ​​to the risk decision-making and response module and the resilient supply chain control module;

[0033] The risk decision-making and response module is used to process the predicted values ​​of business risks and output a risk decision report based on the processing results.

[0034] Furthermore, the methods for processing business risk forecasts and outputting risk decision reports based on the processing results include:

[0035] A low-risk report is generated when the predicted business risk value is less than 0.3; a medium-risk report is generated when the predicted business risk value is greater than or equal to 0.3 or less than 0.6; and a high-risk report is generated when the predicted business risk value is greater than or equal to 0.6.

[0036] The low-risk report indicates that the predicted compliance risk for commodity exports is low, and staff should proceed with their work according to the pre-arranged schedule.

[0037] The medium-risk report includes an explanation of the predicted compliance risks for commodity exports, requesting staff to strengthen monitoring of the commodity process and to manually intervene in the commodity review process;

[0038] The high-risk report includes a description of the high predicted compliance risk for the export of the goods, requesting staff to immediately suspend the goods transactions and activate the emergency mechanism;

[0039] When a medium-risk or high-risk report is received, the global weight factor ω of the i-th feature data with the largest contribution value is detected. i And generate a feature data contribution report;

[0040] The feature data contribution report includes an explanation of the most problematic underlying data represented by the i-th feature data item;

[0041] Package low-risk reports, medium-risk reports, high-risk reports, and feature data contribution reports to obtain a risk decision report;

[0042] The resilient supply chain control module is used to simulate extreme environments based on business risk forecasts and influencing factors, and outputs a supply chain restructuring report based on the simulation results.

[0043] Furthermore, methods for simulating extreme environments based on business risk forecasts and influencing factors, and then outputting a supply chain restructuring report based on the simulation results, include:

[0044] The comprehensive score is obtained by weighted summation of the predicted business risk values ​​and the various sub-data points of the impact dataset.

[0045] When the comprehensive evaluation value is greater than R1 or less than R2, a partial reconstruction report is generated; when the comprehensive evaluation value is greater than or equal to R2, a full reconstruction report is generated.

[0046] The partial restructuring report includes an explanation that the supply chain risk assessment level is medium, and the system recommends selecting other ports as backup ports for docking;

[0047] The complete restructuring report includes an explanation that the supply chain risk assessment level is high, and the system recommends abandoning the existing routes and selecting alternative routes.

[0048] By packaging the partial and full restructuring reports, a supply chain restructuring report is obtained.

[0049] The autonomous decision-making agent module is used to analyze the supply chain restructuring report and generate an autonomous decision-making agent report based on the analysis results.

[0050] Furthermore, the methods for analyzing supply chain restructuring reports include:

[0051] Identify the content of the supply chain restructuring report;

[0052] When the supply chain restructuring report is a partial restructuring report, partial operation instructions are generated; when the supply chain restructuring report is a full restructuring report, all operation instructions are generated.

[0053] Some operation instructions contain a set of characters representing the execution of a partial adjustment strategy;

[0054] All operation instructions contain a set of characters representing the execution of all adjustment strategies;

[0055] Package some operation instructions and all operation instructions to obtain an autonomous decision-making agent report;

[0056] An immersive decision support module is used to support manual information retrieval and, in conjunction with real-time logistics trajectory heatmaps, to obtain comprehensive information reference reports;

[0057] Furthermore, methods for supporting manual information retrieval and combining it with real-time logistics trajectory heatmaps to obtain comprehensive information reference reports include:

[0058] Generate a query report based on manually queried information.

[0059] Based on real-time logistics trajectories and rendered using WebGL, a heatmap of the logistics trajectory is obtained. When the customs clearance status is delayed, the real-time logistics trajectory is red; when the quality inspection status is in progress, the real-time material trajectory is yellow; otherwise, the real-time logistics trajectory is green.

[0060] By combining query information reports and logistics trajectory heatmaps, a comprehensive information reference report is obtained;

[0061] The system data management module is used to store system datasets, display risk decision reports, supply chain restructuring reports, and comprehensive information reference reports through a visualization panel, and process autonomous decision-making agency reports;

[0062] Furthermore, the methods for processing reports on autonomous decision-making delegation include:

[0063] Obtain autonomous decision-making agency reports;

[0064] For some or all operation instructions, use a template engine to parse the instruction text, split the content of the instruction text, and send the split content to the corresponding operation department's email address.

[0065] The system dataset includes business datasets, basic datasets, business risk forecasts, risk decision reports, supply chain restructuring reports, autonomous decision-making agency reports, and comprehensive information reference reports;

[0066] Furthermore, S1: Collect business datasets, which include payee location data, currency preference data, historical success rate data, and product category data;

[0067] S2: Collect risk datasets, which include real-time exchange rate fluctuation data, tariff threshold data, and declaration deadline data;

[0068] S3: Preprocess the business dataset and the basic dataset, and extract data interaction features to obtain feature vectors;

[0069] S4: Analyze the feature vectors and output the predicted business risk values;

[0070] S5: Process the predicted business risks and output a risk decision report based on the processing results;

[0071] S6: Simulate extreme environments based on business risk forecasts and influencing factors, and output a supply chain restructuring report based on the simulation results;

[0072] S7: Analyze the supply chain restructuring report and generate an autonomous decision-making agency report based on the analysis results;

[0073] S8: Supports manual information query and combines it with real-time logistics trajectory heat map to obtain comprehensive information reference report;

[0074] S9: Stores system datasets, displays risk decision reports, supply chain restructuring reports, and comprehensive information reference reports through a visualization panel, and processes autonomous decision-making agency reports.

[0075] The technical effects and advantages of this invention's cross-border end-to-end collaborative operation integrated business and finance platform are as follows:

[0076] This invention collects a business dataset, including payee location data, currency preference data, historical success rate data, and product category data, and a risk dataset, including real-time exchange rate fluctuation data, tariff threshold data, and declaration deadline data. The business and basic datasets are preprocessed, and data interaction features are extracted to obtain feature vectors. These feature vectors are analyzed to output business risk prediction values. The business risk prediction values ​​are processed, and a risk decision report is output based on the processing results. Based on the business risk prediction values ​​and influencing factors, extreme environment simulations are performed, and a supply chain restructuring report is output based on the simulation results. The supply chain restructuring report is analyzed, and an autonomous decision-making agency report is generated based on the analysis results, supporting manual information retrieval. Combined with real-time logistics trajectory heatmaps, a comprehensive information reference report is obtained. The system dataset is stored, and the risk decision report, supply chain restructuring report, and comprehensive information reference report are stored together. By displaying information through a visual panel and processing autonomous decision-making reports, the system can accurately quantify and predict product compliance risks under the influence of multiple dynamic data, thereby giving enterprises the opportunity to make proactive adjustments. This significantly reduces the risk of losses caused by the passive response of traditional business and finance integrated platforms. In addition, through in-depth analysis of business risk prediction values, the system can transform data into intuitive and actionable response decisions, greatly reducing the time cost of manual decision-making while ensuring decision reliability. Furthermore, by considering extreme factors, the system can effectively cope with and handle the impact of extreme scenarios on enterprises, greatly improving the system's applicability. Finally, by combining manual query information with real-time logistics trajectory heatmaps, the system can further improve operational efficiency. Overall, the present invention has significant advantages such as strong compliance risk prediction capabilities, good decision support effects, and a strong ability to cope with extreme scenarios. Attached Figure Description

[0077] Figure 1 This is a schematic diagram of the cross-border end-to-end collaborative operation platform integrating business and finance according to the present invention;

[0078] Figure 2 This is a schematic diagram of the business and finance integration method for cross-border end-to-end collaborative operations according to the present invention. Detailed Implementation

[0079] 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.

[0080] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in the embodiments of this invention are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0081] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0082] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0083] In practice, the server-side equipment deployed in a cross-border end-to-end collaborative operation platform may consist of one or more devices. This platform can be implemented as: a business instance, a virtual machine, or hardware devices. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node, providing a platform for cross-border end-to-end collaborative operations to various user terminals. Alternatively, it can be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various user terminals. Or, it can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide a platform for cross-border end-to-end collaborative operations to various user terminals.

[0084] In terms of implementation, the business and finance integration platform and the user terminal for cross-border end-to-end collaborative operations are mutually compatible. That is, if the business and finance integration platform for cross-border end-to-end collaborative operations is an application installed on a cloud service platform, then the user terminal is a client that establishes a communication connection with the application; or if the business and finance integration platform for cross-border end-to-end collaborative operations is implemented as a website, then the user terminal is implemented as a webpage; or if the business and finance integration platform for cross-border end-to-end collaborative operations is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0085] like Figure 1 The diagram shown is a system architecture diagram of a cross-border end-to-end collaborative operation platform for integrated business and finance provided in an embodiment of the present invention.

[0086] The cross-border end-to-end collaborative business and finance integrated platform described in this invention can be set up on a cloud server. In terms of implementation, it can be implemented as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed as a website. Depending on the functions implemented, the cross-border end-to-end collaborative business and finance integrated platform can include a business data acquisition module, a risk data acquisition module, a basic data interaction module, a compliance risk prediction module, a risk decision-making and response module, a resilient supply chain control module, an autonomous decision-making agent module, an immersive decision support module, and a system data management module. The modules described in this invention can also be called units, referring to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0087] In this embodiment of the invention, in the cross-border end-to-end collaborative operation integrated business and finance platform, each of the above-mentioned modules can be implemented independently and can call other modules. Here, "calling" can be understood as a module connecting to multiple modules of another type and providing corresponding services to those connected modules. For example, the sharing and evaluation module can call the same information collection module to obtain the information collected by that module. Based on the above characteristics, in the cross-border end-to-end collaborative operation integrated business and finance platform provided by this embodiment of the invention, without modifying the program code, the applicable scope of the cross-border end-to-end collaborative operation integrated business and finance platform architecture can be adjusted by adding modules and directly calling them, achieving cluster-style horizontal expansion to achieve the purpose of quickly and flexibly expanding the cross-border end-to-end collaborative operation integrated business and finance platform. In practical applications, the above-mentioned modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in a cloud server.

[0088] Example 1

[0089] Please see Figure 1As shown in this embodiment, the cross-border end-to-end collaborative operation integrated business and finance platform includes:

[0090] The business data acquisition module is used to collect business datasets, which include payee location data, currency preference data, historical success rate data, and product category data.

[0091] It needs to be explained that, through IoT GPS positioning terminals, the latitude and longitude coordinates of designated containers are collected and converted into country codes to obtain the payee's location data; through the enterprise ERP system database interface, the most frequently used currencies of designated payees in the past preset period are collected to obtain currency preference data; through blockchain transaction audit nodes, the on-chain transaction status codes of designated payees are collected, and the successful transaction volume in the past 30 days is divided by the total transaction volume to obtain historical success rate data; through RFID goods scanners, the HS codes in designated goods tags are collected to obtain product category data;

[0092] The risk data acquisition module is used to collect risk datasets, which include real-time exchange rate fluctuation data, tariff threshold data, and declaration deadline data.

[0093] It needs to be explained that, through the financial data API terminal, the exchange rate time series of the designated payee is collected, and the standard deviation of the exchange rate fluctuation is calculated to obtain real-time exchange rate fluctuation data; through the customs database interface, the tariff policy table of the designated payee is collected, and product category data is input for matching to obtain tariff threshold data; through the logistics system clock synchronization sensor, the declaration deadline timestamp of the designated payee is collected, and the declaration deadline timestamp is subtracted from the current timestamp and then divided by the total declaration duration to obtain the declaration time limit dataset;

[0094] The basic data interaction module is used to preprocess the business dataset and the basic dataset, and to extract data interaction features to obtain feature vectors.

[0095] Furthermore, the steps of preprocessing the business dataset and the basic dataset, and extracting data interaction features include:

[0096] Q1: Clean the data by removing outliers and normalize all sub-data items in the basic dataset to the range of [0, 1] according to the normalization formula;

[0097] It needs to be explained that removing outliers refers to, for example, negative numbers in the reporting deadline data; the basic dataset includes the business dataset and the basic dataset; the specific expression of the normalization formula is: Where X new For normalized values, X is any sub-data item of the basic data. maxX represents the historical maximum value of this arbitrary sub-data item. min This represents the historical minimum value of any given sub-data item; normalization is used to eliminate the dimensions of all sub-data items in the base dataset.

[0098] Q2: Based on the recipient's location data S a And based on the geographical risk level, it is matched to obtain geographical risk characteristic data A. a ;

[0099] It should be explained that the country risk level refers to the corresponding geographical risk score generated based on different recipient location data. For example, the score for "Country A" is 0.8, and the score for "Country B" is 0.1.

[0100] Q3: Based on currency preference data S b Calculate the historical volatility of the currency to obtain currency stability characteristic data A. b ;

[0101] Q4: Based on product category data S d And based on the product risk level, match them to obtain product risk characteristic data A. c ;

[0102] It should be explained that product risk level refers to, for example, the risk level of electronic products is 0.6, and the risk level of agricultural products is 0.2;

[0103] Q5: Calculate real-time exchange rate fluctuation data S e The absolute value of the fluctuation influence characteristic data A is obtained. d ;

[0104] Q6: Based on tariff threshold data S f And based on the tariff risk level, it is matched to obtain tariff risk characteristic data A. e ;

[0105] It should be explained that the tariff risk level refers to the fact that the larger the value of the tariff threshold data, the larger the corresponding value of the tariff risk characteristic data, and the tariff risk level data is normalized to the range of [0, 1].

[0106] Q7: Calculate the data S minus the reporting deadline. g Obtain the urgent characteristic data A of the application. f ;

[0107] Q8: Combine geographical risk characteristic data, currency stability characteristic data, product risk characteristic data, volatility impact characteristic data, tariff risk characteristic data, declaration urgency characteristic data, and historical success rate data to obtain a feature vector;

[0108] It should be explained that the sub-data items in the basic data involved in steps Q2 to Q8 and all subsequent modules are data values ​​that have been cleaned and normalized.

[0109] The compliance risk prediction module is used to analyze the feature vector and output a business risk prediction value.

[0110] Further steps in analyzing the eigenvectors include:

[0111] Step 1: Obtain a set of historical feature vectors stored in the database, and compare them with the current time based on the timestamp. Group the comparison results from smallest to largest and label them accordingly. The labeling results are L1, L2, L3, ..., Ln. Use the labeling results as the sample set.

[0112] Step 2: Divide the sample set into 70% training set, 15% test set and 15% validation set, and build a business risk prediction model based on the sample set.

[0113] Step 3: Based on the historical feature vectors in the training set, select the historical feature vectors of the past three months as the first reference dataset D1, select the historical feature vectors of the past twelve months as the second reference dataset D2, and select the historical feature vectors of similar products of the past three months as the third reference dataset D3.

[0114] Step 4: Based on the reference dataset and the base learner, calculate the first predicted value. The specific formula for the calculation is as follows:

[0115]

[0116] Obtain the first predicted value of the k-th reference dataset. Among them, T k η is the number of decision trees generated for the k-th reference dataset. t Let φ be the weight factor of the t-th tree. t (D p (referencing dataset D) p The output value of the t-th tree, where λ is the regularization coefficient and ||Ω|| is the complexity penalty term of the tree;

[0117] It should be explained that the reference dataset is the set of the first reference dataset, the second reference dataset, and the third reference dataset;

[0118] Step 5: Perform multiple evaluation and validation based on the first reference dataset, the second reference dataset, the third reference dataset, and the first predicted value. The specific formula set for multiple evaluation and validation is as follows:

[0119]

[0120] The feature-weighted verification value C was obtained respectively. a Time decay verification value of the k-th reference dataset The residual correction validation value of the k-th reference dataset Where, ω i Let i be the global weight factor for the i-th feature data. Let f be the partial derivative of the i-th feature data with respect to the first predicted value of the k-th reference dataset, max be the maximization function, ε be the minimum value, γ be the decay coefficient, and Δm be the partial derivative of the i-th feature data with respect to the first predicted value of the k-th reference dataset. k Let N be the latest data time difference of the k-th reference dataset, and let ROC-AUC be the area under the curve of the reference dataset on the test set. k The number of samples in the reference dataset is given, and tanh is the hyperbolic tangent function. Let σ be the prediction residual for the j-th sample. k Let k be the standard deviations of the reference datasets;

[0121] It needs to be explained that the maximization function is used to select the maximum value immediately within the parentheses as the output value; the minimization function is used to avoid division by zero errors, for example, taking the value 10 to the power of -6; the hyperbolic tangent function is used to constrain the output value immediately within the parentheses to the range [-1, 1].

[0122] Step Six: Calculate the business risk prediction value A based on the first predicted value, feature-weighted verification value, time-decay verification value, and residual-corrected verification value of the k-th reference dataset. h The specific formula for calculation is:

[0123]

[0124] Where Sigmoid is the Sigmoid activation function;

[0125] It should be explained that the Sigmoid activation function is used to constrain the output value within the immediately adjacent brackets to the range (0,1);

[0126] Step 7: Output the business risk forecast values ​​to the risk decision-making and response module and the resilient supply chain control module;

[0127] The risk decision-making and response module is used to process the predicted business risk values ​​and output a risk decision report based on the processing results.

[0128] Furthermore, methods for processing business risk forecasts and outputting risk decision reports based on the processing results include:

[0129] A low-risk report is generated when the predicted business risk value is less than 0.3; a medium-risk report is generated when the predicted business risk value is greater than or equal to 0.3 or less than 0.6; and a high-risk report is generated when the predicted business risk value is greater than or equal to 0.6.

[0130] It should be explained that the threshold range consisting of 0.3 and 0.6 was determined manually and then input into the system;

[0131] The low-risk report indicates that the predicted compliance risk for commodity exports is low, and staff should proceed with their work according to the pre-arranged schedule.

[0132] The medium-risk report includes an explanation of the predicted compliance risks for commodity exports, requesting staff to strengthen monitoring of the commodity process and to manually intervene in the commodity review process;

[0133] The high-risk report includes a description of the high predicted compliance risk for the export of the goods, requesting staff to immediately suspend the goods transactions and activate the emergency mechanism;

[0134] When a medium-risk or high-risk report is received, the global weight factor ω of the i-th feature data with the largest contribution value is detected. i And generate a feature data contribution report;

[0135] The feature data contribution report includes an explanation of the most problematic underlying data represented by the i-th feature data item;

[0136] It needs to be explained that the i-th feature data represents the seven feature data in the feature vector. For example, when the weight factor ω7 is the largest, the feature data contribution report is expressed as: the 7th feature data represents the urgent time limit for the application.

[0137] Package low-risk reports, medium-risk reports, high-risk reports, and feature data contribution reports to obtain a risk decision report;

[0138] The resilient supply chain control module is used to simulate extreme environments based on business risk forecasts and influencing factors, and output a supply chain restructuring report based on the simulation results.

[0139] Furthermore, methods for simulating extreme environments based on business risk forecasts and influencing factors, and then outputting a supply chain restructuring report based on the simulation results, include:

[0140] The comprehensive score is obtained by weighted summation of the predicted business risk values ​​and the various sub-data points of the impact dataset.

[0141] It should be explained that the impact dataset consists of various related impact data derived from influencing factors, such as the severity of weather and the degree of port congestion, and all data in the impact dataset are normalized and dimensionless values.

[0142] When the comprehensive evaluation value is greater than R1 or less than R2, a partial reconstruction report is generated; when the comprehensive evaluation value is greater than or equal to R2, a full reconstruction report is generated.

[0143] It should be explained that the threshold range consisting of R1 and R2 is determined manually and then input into the system.

[0144] The partial restructuring report includes an explanation that the supply chain risk assessment level is medium, and the system recommends selecting other ports as backup ports for docking;

[0145] The complete restructuring report includes an explanation that the supply chain risk assessment level is high, and the system recommends abandoning the existing routes and selecting alternative routes.

[0146] By packaging the partial and full restructuring reports, a supply chain restructuring report is obtained.

[0147] The autonomous decision-making agent module is used to analyze the supply chain restructuring report and generate an autonomous decision-making agent report based on the analysis results.

[0148] Furthermore, methods for analyzing supply chain restructuring reports include:

[0149] Identify the content of the supply chain restructuring report;

[0150] When the supply chain restructuring report is a partial restructuring report, partial operation instructions are generated; when the supply chain restructuring report is a full restructuring report, all operation instructions are generated.

[0151] Some operation instructions contain a set of characters representing the execution of a partial adjustment strategy;

[0152] It should be explained that some of the adjustment strategies include: dynamic inventory transfer: 30% of the original port inventory will be transferred to backup port warehouses, with a transfer cycle of no more than 48 hours; pre-sale ratio adjustment: the pre-sale quota in the original port coverage area will be reduced to 60%, while that in the backup port area will be increased to 150%; logistics timeliness update: the delivery time of goods on the original port route will be extended by 3 days, and the delivery time of goods on the backup port route will be extended by 1 day; priority reallocation: high-value goods will be prioritized for allocation to backup port routes.

[0153] All operation instructions contain a set of characters representing the execution of all adjustment strategies;

[0154] It should be explained that the entire adjustment strategy includes: emergency inventory migration: completing the transfer of 100% of the original port's inventory to the backup port cluster within 72 hours; pre-sale strategy switch: immediately stopping pre-sales in the original port area and switching 100% to the backup port pre-sale channel; emergency timeliness declaration: extending the delivery time benchmark for all goods by 7 days, and activating air freight agreements for highly sensitive goods (including but not limited to fresh produce and pharmaceuticals); customer compensation plan: automatically issuing 15% coupons to affected orders and prioritizing VIP customer orders; supplier collaboration: activating the secondary supplier stocking agreement, reducing the minimum order quantity to 40% of the normal value;

[0155] Package some operation instructions and all operation instructions to obtain an autonomous decision-making agent report;

[0156] The immersive decision support module is used to support manual information retrieval and, in conjunction with real-time logistics trajectory heatmaps, to obtain a comprehensive information reference report;

[0157] Furthermore, methods for supporting manual information retrieval and combining real-time logistics trajectory heatmaps to obtain comprehensive information reference reports include:

[0158] Generate a query report based on manually queried information.

[0159] Based on real-time logistics trajectories and rendered using WebGL, a heatmap of the logistics trajectory is obtained. When the customs clearance status is delayed, the real-time logistics trajectory is red; when the quality inspection status is in progress, the real-time material trajectory is yellow; otherwise, the real-time logistics trajectory is green.

[0160] By combining query information reports and logistics trajectory heatmaps, a comprehensive information reference report is obtained;

[0161] The system data management module is used to store system datasets, display risk decision reports, supply chain restructuring reports, and comprehensive information reference reports through a visualization panel, and process autonomous decision-making agency reports.

[0162] Furthermore, methods for processing reports on autonomous decision-making delegation include:

[0163] Obtain autonomous decision-making agency reports;

[0164] For some or all operation instructions, use a template engine to parse the instruction text, split the content of the instruction text, and send the split content to the corresponding operation department's email address.

[0165] It needs to be explained that, for example, when the autonomous decision-making agency report is a partial operation instruction, based on the content contained in the partial adjustment strategy, it is broken down into dynamic inventory transfer, pre-sale ratio adjustment, logistics timeliness update and priority reallocation. Based on the breakdown content, the content of dynamic inventory transfer, logistics timeliness update and priority reallocation is sent to the warehouse department's email address, and the content of pre-sale ratio adjustment is sent to the sales department's email address.

[0166] The system dataset includes business datasets, basic datasets, business risk forecasts, risk decision reports, supply chain restructuring reports, autonomous decision-making agency reports, and comprehensive information reference reports;

[0167] In this embodiment, the beneficial effects are achieved by collecting a business dataset, including payee location data, currency preference data, historical success rate data, and product category data, and a risk dataset, including real-time exchange rate fluctuation data, tariff threshold data, and declaration deadline data. The business dataset and the basic dataset are preprocessed, and data interaction feature extraction is performed to obtain feature vectors. These feature vectors are analyzed to output business risk prediction values. The business risk prediction values ​​are then processed, and a risk decision report is output based on the processing results. Based on the business risk prediction values ​​and influencing factors, extreme environment simulations are conducted, and a supply chain restructuring report is output based on the simulation results. The supply chain restructuring report is analyzed, and based on the analysis results, an autonomous decision-making agency report is generated, supporting manual information retrieval. Combined with a real-time logistics trajectory heatmap, a comprehensive information reference report is obtained. The system dataset is stored, and the risk decision report, supply chain restructuring report, and comprehensive information reference report are integrated. The report is displayed through a visualization panel, and the system processes the autonomous decision-making agency report, enabling it to accurately quantify and predict the compliance risks of goods under the influence of multiple dynamic data. This gives enterprises the opportunity to make proactive adjustments, greatly reducing the risk of losses caused by the passive response of traditional business and finance integrated platforms. In addition, through in-depth analysis of business risk prediction values, the system can transform data into intuitive and actionable response decisions, greatly reducing the time cost of manual decision-making while ensuring the reliability of decisions. At the same time, by considering extreme factors, the system can effectively cope with and handle the impact of extreme scenarios on enterprises, greatly improving the applicability of the system. Finally, by combining manual query information with real-time logistics trajectory heatmaps, the system can further improve operational efficiency. Overall, the present invention has significant advantages such as strong compliance risk prediction capabilities, good decision-making assistance effects, and great effectiveness in responding to extreme scenarios.

[0168] Example 2

[0169] Please see Figure 2As shown, the parts not described in detail in this embodiment are described in Embodiment 1. A business and finance integration method for cross-border end-to-end collaborative operations is provided. The method includes: S1: collecting business datasets, which include payee location data, currency preference data, historical success rate data, and product category data;

[0170] S2: Collect risk datasets, which include real-time exchange rate fluctuation data, tariff threshold data, and declaration deadline data;

[0171] S3: Preprocess the business dataset and the basic dataset, and extract data interaction features to obtain feature vectors;

[0172] S4: Analyze the feature vectors and output the predicted business risk values;

[0173] S5: Process the predicted business risks and output a risk decision report based on the processing results;

[0174] S6: Simulate extreme environments based on business risk forecasts and influencing factors, and output a supply chain restructuring report based on the simulation results;

[0175] S7: Analyze the supply chain restructuring report and generate an autonomous decision-making agency report based on the analysis results;

[0176] S8: Supports manual information query and combines it with real-time logistics trajectory heat map to obtain comprehensive information reference report;

[0177] S9: Stores system datasets, displays risk decision reports, supply chain restructuring reports, and comprehensive information reference reports through a visualization panel, and processes autonomous decision-making agency reports.

[0178] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the present invention.

Claims

1. A cross-border, end-to-end collaborative operation platform integrating business and finance, characterized in that: The platform includes: a compliance risk prediction module, a risk decision-making and response module, a resilient supply chain control module, an autonomous decision-making agent module, and an immersive decision-making assistance module, wherein: The compliance risk prediction module is used to analyze the feature vector and output a business risk prediction value. The risk decision-making and response module is used to process the predicted business risk values ​​and output a risk decision report based on the processing results. The resilient supply chain control module is used to simulate extreme environments based on business risk forecasts and influencing factors, and output a supply chain restructuring report based on the simulation results. The autonomous decision-making agent module is used to analyze the supply chain restructuring report and generate an autonomous decision-making agent report based on the analysis results. The immersive decision support module is used to support manual information retrieval and, in conjunction with real-time logistics trajectory heatmaps, to obtain a comprehensive information reference report.

2. The cross-border end-to-end collaborative operation integrated business and finance platform according to claim 1, characterized in that, The platform also includes: a business data acquisition module, a risk data acquisition module, a basic data interaction module, and a system data management module, wherein: The business data acquisition module is used to collect business datasets, which include payee location data, currency preference data, historical success rate data, and product category data. The risk data acquisition module is used to collect risk datasets, which include real-time exchange rate fluctuation data, tariff threshold data, and declaration deadline data. The basic data interaction module is used to preprocess the business dataset and the basic dataset, and to extract data interaction features to obtain feature vectors. The system data management module is used to store system datasets, display risk decision reports, supply chain restructuring reports, and comprehensive information reference reports through a visualization panel, and process autonomous decision-making agency reports.

3. The cross-border end-to-end collaborative operation integrated business and finance platform according to claim 2, characterized in that, The steps for preprocessing the business dataset and the basic dataset, and extracting data interaction features include: Q1: Clean the data by removing outliers and normalize all sub-data items in the basic dataset to the range of [0, 1] according to the normalization formula; Q2: Based on the recipient's location data S a And based on the geographical risk level, it is matched to obtain geographical risk characteristic data A. a ; Q3: Based on currency preference data S b Calculate the historical volatility of the currency to obtain currency stability characteristic data A. b ; Q4: Based on product category data S d And based on the product risk level, match them to obtain product risk characteristic data A. c ; Q5: Calculate real-time exchange rate fluctuation data S e The absolute value of the fluctuation influence characteristic data A is obtained. d ; Q6: Based on tariff threshold data S f And based on the tariff risk level, it is matched to obtain tariff risk characteristic data A. e ; Q7: Calculate the data S minus the reporting deadline. g Obtain the urgent characteristic data A of the application. f ; Q8: Combine geographical risk characteristic data, currency stability characteristic data, product risk characteristic data, volatility impact characteristic data, tariff risk characteristic data, declaration urgency characteristic data, and historical success rate data to obtain a feature vector.

4. The cross-border end-to-end collaborative operation integrated business and finance platform according to claim 1, characterized in that, The steps for analyzing eigenvectors include: Step 1: Obtain a set of historical feature vectors stored in the database, and compare them with the current time based on the timestamp. Group the comparison results from smallest to largest and label them accordingly. The labeling results are L1, L2, L3, ..., Ln. Use the labeling results as the sample set. Step 2: Divide the sample set into 70% training set, 15% test set and 15% validation set, and build a business risk prediction model based on the sample set. Step 3: Based on the historical feature vectors in the training set, select the historical feature vectors of the past three months as the first reference dataset D1, select the historical feature vectors of the past twelve months as the second reference dataset D2, and select the historical feature vectors of similar products of the past three months as the third reference dataset D3. Step 4: Based on the reference dataset and the base learner, calculate the first predicted value. The specific formula for the calculation is as follows: Obtain the first predicted value of the k-th reference dataset. Among them, T k η is the number of decision trees generated for the k-th reference dataset. t Let φ be the weight factor of the t-th tree. t (D p (referencing dataset D) p The output value of the t-th tree, where λ is the regularization coefficient and ||Ω|| is the complexity penalty term of the tree; Step 5: Perform multiple evaluation and validation based on the first reference dataset, the second reference dataset, the third reference dataset, and the first predicted value. The specific formula set for multiple evaluation and validation is as follows: The feature-weighted verification value C was obtained respectively. a Time decay verification value of the k-th reference dataset The residual correction validation value of the k-th reference dataset Where, ω i Let i be the global weight factor for the i-th feature data. Let f be the partial derivative of the i-th feature data with respect to the first predicted value of the k-th reference dataset, max be the maximization function, ε be the minimum value, γ be the decay coefficient, and Δm be the partial derivative of the i-th feature data with respect to the first predicted value of the k-th reference dataset. k Let N be the latest data time difference of the k-th reference dataset, and let ROC-AUC be the area under the curve of the reference dataset on the test set. k The number of samples in the reference dataset is given, and tanh is the hyperbolic tangent function. Let σ be the prediction residual for the j-th sample. k Let k be the standard deviations of the reference datasets; Step Six: Calculate the business risk prediction value A based on the first predicted value, feature-weighted verification value, time-decay verification value, and residual-corrected verification value of the k-th reference dataset. h The specific formula for calculation is: Where Sigmoid is the Sigmoid activation function; Step 7: Output the business risk forecast values ​​to the risk decision-making and response module and the resilient supply chain control module.

5. The cross-border end-to-end collaborative operation integrated business and finance platform according to claim 1, characterized in that, Methods for processing business risk forecasts and generating risk decision reports based on the processing results include: A low-risk report is generated when the predicted business risk value is less than 0.3; a medium-risk report is generated when the predicted business risk value is greater than or equal to 0.3 or less than 0.6; and a high-risk report is generated when the predicted business risk value is greater than or equal to 0.

6. The low-risk report indicates that the predicted compliance risk for commodity exports is low, and staff should proceed with their work according to the pre-arranged schedule. The medium-risk report includes an explanation of the predicted compliance risks for commodity exports, requesting staff to strengthen monitoring of the commodity process and to manually intervene in the commodity review process; The high-risk report includes a description of the high predicted compliance risk for the export of the goods, requesting staff to immediately suspend the goods transactions and activate the emergency mechanism; When a medium-risk or high-risk report is received, the global weight factor ω of the i-th feature data with the largest contribution value is detected. i And generate a feature data contribution report; The feature data contribution report includes an explanation of the most problematic underlying data represented by the i-th feature data item; The risk decision report is obtained by packaging low-risk reports, medium-risk reports, high-risk reports, and feature data contribution reports.

6. The integrated business and finance platform for cross-border end-to-end collaborative operations according to claim 1, characterized in that, The methods for simulating extreme environments based on business risk forecasts and influencing factors, and then outputting a supply chain restructuring report based on the simulation results, include: The comprehensive score is obtained by weighted summation of the predicted business risk values ​​and the various sub-data points of the impact dataset. When the comprehensive evaluation value is greater than R1 or less than R2, a partial reconstruction report is generated; when the comprehensive evaluation value is greater than or equal to R2, a full reconstruction report is generated. The partial restructuring report includes an explanation that the supply chain risk assessment level is medium, and the system recommends selecting other ports as backup ports for docking; The complete restructuring report includes an explanation that the supply chain risk assessment level is high, and the system recommends abandoning the existing routes and selecting alternative routes. By packaging the partial restructuring report and the full restructuring report, we obtain the supply chain restructuring report.

7. The cross-border end-to-end collaborative operation integrated business and finance platform according to claim 1, characterized in that, Methods for analyzing supply chain restructuring reports include: Identify the content of the supply chain restructuring report; When the supply chain restructuring report is a partial restructuring report, partial operation instructions are generated; when the supply chain restructuring report is a full restructuring report, all operation instructions are generated. Some operation instructions contain a set of characters representing the execution of a partial adjustment strategy; All operation instructions contain a set of characters representing the execution of all adjustment strategies; Package some operation instructions and all operation instructions to obtain an autonomous decision-making agent report.

8. The integrated business and finance platform for cross-border end-to-end collaborative operations according to claim 1, characterized in that, The methods for supporting manual information retrieval and combining real-time logistics trajectory heatmaps to obtain comprehensive information reference reports include: Generate a query report based on manually queried information. Based on real-time logistics trajectories and rendered using WebGL, a heatmap of the logistics trajectory is obtained. When the customs clearance status is delayed, the real-time logistics trajectory is red; when the quality inspection status is in progress, the real-time material trajectory is yellow; otherwise, the real-time logistics trajectory is green. By combining query information reports and logistics trajectory heatmaps, a comprehensive information reference report is obtained.

9. The cross-border end-to-end collaborative operation integrated business and finance platform according to claim 2, characterized in that, Methods for processing reports on autonomous decision-making delegation include: Obtain autonomous decision-making agency reports; For some or all operation instructions, use a template engine to parse the instruction text, split the content of the instruction text, and send the split content to the corresponding operation department's email address. The system dataset includes business datasets, basic datasets, business risk forecasts, risk decision reports, supply chain restructuring reports, autonomous decision-making agency reports, and comprehensive information reference reports.

10. A business-finance integration method for cross-border end-to-end collaborative operations, implemented using the business-finance integration platform for cross-border end-to-end collaborative operations according to any one of claims 1-9, characterized in that, The work includes the following steps: S1: Collect business datasets, which include payee location data, currency preference data, historical success rate data, and product category data; S2: Collect risk datasets, which include real-time exchange rate fluctuation data, tariff threshold data, and declaration deadline data; S3: Preprocess the business dataset and the basic dataset, and extract data interaction features to obtain feature vectors; S4: Analyze the feature vectors and output the predicted business risk values; S5: Process the predicted business risks and output a risk decision report based on the processing results; S6: Simulate extreme environments based on business risk forecasts and influencing factors, and output a supply chain restructuring report based on the simulation results; S7: Analyze the supply chain restructuring report and generate an autonomous decision-making agency report based on the analysis results; S8: Supports manual information query and combines it with real-time logistics trajectory heat map to obtain comprehensive information reference report; S9: Stores system datasets, displays risk decision reports, supply chain restructuring reports, and comprehensive information reference reports through a visualization panel, and processes autonomous decision-making agency reports.

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