A banking business scheduling method and system for online payment front-end

Abstract

The present invention relates to the field of data processing technology, and in particular to a banking business scheduling method and system for an online payment front-end, which integrates the payment initiator and the payment receiving end of the payment operation to be scheduled to perform banking business evaluation and obtain a financial business map; integrates multiple business service scenarios of multiple business tracks in the financial business track format and performs business security prediction to obtain multiple business service trust scores; performs risk prediction based on dynamic payment data to obtain candidate risk weights; obtains W trust tracks from multiple business tracks; fits W trust tracks to the financial business map according to the financial business track format to obtain a trust business map; performs banking business evaluation to obtain candidate payment services, and performs payment scheduling management on candidate payment funds. The present invention can effectively judge key payment tasks, realize effective adjustment of related tasks of business entities, and save human resources and costs.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a banking business scheduling method and system for an online payment front-end. Background Art

[0002] Risk management is very important for financial institutions. For example, in consumer finance, in order to protect the asset security of financial institutions and reduce financial risks, it is necessary to monitor financial institutions and customers in the process of credit granting, credit use, loan issuance, and quota adjustment, so as to provide timely warnings when abnormalities occur.

[0003] However, in the process of executing business tasks, some tasks may take longer than the specified time to achieve the task goal due to improper execution or insufficient resources, which may affect the execution of subsequent business tasks and make it impossible to complete them within the specified time. When a financial payment business needs to be executed, the security of the payment business is generally evaluated and predicted based on historical transaction records, but it is impossible to accurately and effectively schedule the bank's diverse businesses, or the omission of some non-critical tasks will have a greater impact on the final business indicators. It is impossible to effectively judge key tasks, making it difficult to effectively adjust business tasks, resulting in a waste of human resources and costs. Summary of the invention

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] According to a first aspect of the present invention, the present invention claims a banking service scheduling method for an online payment front-end, the method comprising:

[0006] Integrate and obtain the payment initiator and payment receiver of the payment job to be scheduled;

[0007] Perform banking business evaluation with the payment initiator and the payment receiver as restrictions to obtain a financial business map;

[0008] Perform payment node detection on the financial business graph, and set track nodes on the financial business graph based on the detection records, and transform the financial business graph into a financial business track format;

[0009] Integrate and obtain multiple business service scenarios of multiple business tracks in the financial business track format, and perform business security prediction based on the multiple business service scenarios to obtain multiple business service trust scores;

[0010] Integrate the payment operation monitoring system of the candidate payment funds, obtain dynamic payment data of the candidate funds, and perform risk prediction based on the dynamic payment data to obtain candidate risk weights, wherein the candidate payment funds are allocated funds for executing the payment operation to be scheduled;

[0011] Filtering and obtaining W trust tracks from the multiple business tracks based on the candidate risk weights and the multiple business service trust scores;

[0012] Fitting the W trust trajectories to the financial business graph according to the financial business trajectory format to obtain a trust business graph;

[0013] A banking business evaluation is performed based on the trusted business graph to obtain candidate payment businesses, and the candidate payment businesses are used to perform payment scheduling management on the candidate payment funds.

[0014] Furthermore, a banking business evaluation is performed with the payment initiator and the payment receiver as restrictions to obtain a financial business graph, and the method further includes:

[0015] Based on the payment initiator and the payment receiver, data is called to obtain a candidate business graph;

[0016] Integrate and obtain the payment time limit and scheduling speed limit of the payment job to be scheduled;

[0017] Based on the payment initiator and the payment receiver executing banking business planning on the candidate business graph, a financial business set is obtained, wherein the financial business set includes a plurality of reference financial businesses and a plurality of business constraint features;

[0018] After the scheduling speed limit is aligned with the multiple business constraint features, payment time is calculated to obtain multiple reference financial time consumptions;

[0019] The payment time limit is used to traverse the multiple reference financial services, and W theoretical financial services are obtained from the multiple reference financial services, where W is a positive integer;

[0020] In the candidate business graph, the financial business graph is obtained based on the W theoretical financial business fittings.

[0021] Further, a plurality of business service scenarios of a plurality of business tracks in the financial business track format are integrated and obtained, and business security prediction is performed based on the plurality of business service scenarios to obtain a plurality of business service trust scores. The method further includes:

[0022] Pre-constructing a service security matching parameter set, wherein the service security matching parameter set includes an accident frequency parameter, a delay frequency parameter, a service cycle parameter, and a service subject confidence parameter;

[0023] The first business service scenario of the first business trajectory is obtained by calling the financial business graph with the business security matching parameter set as a restriction, wherein the first business trajectory is any one of the multiple business trajectories, and the first business service scenario includes a first business accident frequency, a first business extension frequency, a first business cycle feature, and a first business subject confidence feature;

[0024] Performing multi-level division on the first business trajectory based on the first business cycle feature and the first business subject confidence feature to obtain a first business trajectory feature;

[0025] A business security prediction model is pre-built, and the first business trajectory feature is synchronized to the business security prediction model to perform business security prediction, so as to obtain a first business service trust score.

[0026] Further, a business security prediction model is pre-built, and the first business trajectory feature is synchronized to the business security prediction model to perform business security prediction to obtain a first business service trust score. The method also includes:

[0027] The business security prediction model includes a local business security prediction layer and a global business security integration layer;

[0028] Pre-construct a local business security prediction function, and synchronize the local business security prediction function to the local business security prediction layer, wherein the local business security prediction function is as follows:

[0029] ;

[0030] in, Scoring business service trust, is the weight of the previous business, is the weight of the back-end business, The business cycle of the previous business. The business cycle of the back-end business. The confidence level of the business subject of the previous business. The confidence level of the business entities in the back-end business. is the business accident frequency, Frequency of business extensions;

[0031] Assigning a weight to the first service trajectory feature based on the service length to obtain a first service weight feature;

[0032] Synchronizing the first business trajectory feature to the local business security prediction layer of the business security prediction model to obtain a first business prediction feature;

[0033] The first business prediction feature and the first business weight feature are weightedly calculated at the global business security integration layer of the business security prediction model to obtain the first business service trust score.

[0034] Further, the payment operation monitoring system of the candidate payment funds is integrated to obtain dynamic payment data of the candidate funds, and risk prediction is performed based on the dynamic payment data to obtain candidate risk weights. The method also includes:

[0035] Integrate and obtain static payment data of candidate funds, wherein the static payment data includes payment term data, payment scope data, payment accident frequency data and payment delay frequency data;

[0036] The dynamic payment data includes cumulative payment time, service window interval characteristics and real-time request frequency;

[0037] A risk prediction function is pre-built, and the risk prediction function is as follows:

[0038] ;

[0039] in, is the risk weight, For the payment period, For payment range, To cover unexpected frequencies, To pay the deferral frequency, For the cumulative payment time, is the first The business window interval time, is the real-time request frequency, is the static weight, is the dynamic weight;

[0040] Substitute the static payment data and the dynamic payment data into the risk prediction function to obtain the candidate risk weights.

[0041] Further, based on the candidate risk weights and the multiple business service trust scores, W trust tracks are obtained by screening from the multiple business tracks, and the method further includes:

[0042] Pre-constructing a plurality of sample risk weight thresholds, wherein the plurality of sample risk weight thresholds have a plurality of sample business service trust score threshold identifiers;

[0043] Based on the candidate risk weight, the multiple sample risk weight thresholds are called to obtain a candidate business service trust score threshold;

[0044] The candidate business service trust score threshold is used to filter out the W trust tracks from the multiple business tracks.

[0045] Further, based on the trusted business graph, a banking business evaluation is performed to obtain a candidate payment business, and the candidate payment business is used to perform payment scheduling management on the candidate payment funds. The method further includes:

[0046] Using the W theoretical financial services to verify the trusted service graph, obtaining P feasible payment services, where P is a positive integer less than W;

[0047] Integrate the financial business graph to obtain P business overload data;

[0048] The P service overload data are characterized and candidate payment services are obtained from the P feasible payment service calls based on a minimum value.

[0049] According to a second aspect of the present invention, the present invention claims a banking service scheduling system for an online payment front-end, which is used to execute the banking service scheduling method for an online payment front-end, and the system comprises:

[0050] A data acquisition unit, used for integrating and acquiring the payment initiator and the payment receiver of the payment job to be scheduled;

[0051] A business evaluation unit, used to evaluate banking business with the payment initiating end and the payment receiving end as restrictions, and obtain a financial business map;

[0052] A track format conversion unit, used to perform payment node detection on the financial business graph, and set track nodes on the financial business graph based on the detection records, and convert the financial business graph into a financial business track format;

[0053] A trust score acquisition unit, used to integrate and obtain multiple business service scenarios of multiple business tracks in the financial business track format, and perform business security prediction based on the multiple business service scenarios to obtain multiple business service trust scores;

[0054] A risk weight acquisition unit, used for integrating a payment operation monitoring system of candidate payment funds, obtaining dynamic payment data of candidate funds, and performing risk prediction based on the dynamic payment data to obtain candidate risk weights, wherein the candidate payment funds are allocated funds for executing the payment operation to be scheduled;

[0055] A business screening unit, configured to screen and obtain W trust tracks from the multiple business tracks based on the candidate risk weights and the multiple business service trust scores;

[0056] A trust business acquisition unit, configured to fit the W trust trajectories to the financial business graph according to the financial business trajectory format to obtain a trust business graph;

[0057] A scheduling management unit is used to perform banking business evaluation based on the trusted business map, obtain candidate payment businesses, and use the candidate payment businesses to perform payment scheduling management on the candidate payment funds.

[0058] The present invention relates to the field of data processing technology, and in particular to a banking business scheduling method and system for an online payment front-end, which integrates the payment initiator and the payment receiving end of the payment operation to be scheduled to perform banking business evaluation and obtain a financial business map; integrates multiple business service scenarios of multiple business tracks in the financial business track format and performs business security prediction to obtain multiple business service trust scores; performs risk prediction based on dynamic payment data to obtain candidate risk weights; obtains W trust tracks from multiple business tracks; fits W trust tracks to the financial business map according to the financial business track format to obtain a trust business map; performs banking business evaluation to obtain candidate payment services, and performs payment scheduling management on candidate payment funds. The present invention can effectively judge key payment tasks, realize effective adjustment of related tasks of business entities, and save human resources and costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A work flow chart of a banking service scheduling method for an online payment front-end as claimed in an embodiment of the present invention;

[0060] Figure 2 A structural module diagram of a banking service scheduling system for an online payment front-end for which protection is requested in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] The terms "first", "second" and "third" in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0063] Reference to "embodiments" herein means that a particular feature, format, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or reference embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0064] The embodiment of the present invention provides a banking service scheduling method and system for an online payment front-end. Figure 1 As shown, the method includes:

[0065] Integrate and obtain the payment initiator and payment receiver of the payment job to be scheduled.

[0066] A banking business assessment is performed with the payment initiating end and the payment receiving end as restrictions to obtain a financial business map.

[0067] Payment node detection is performed on the financial business graph, and track nodes are set on the financial business graph based on the detection records, so as to transform the financial business graph into a financial business track format.

[0068] A plurality of business service scenarios of a plurality of business tracks in the financial business track format are obtained by integration, and business security prediction is performed based on the plurality of business service scenarios to obtain a plurality of business service trust scores.

[0069] The payment initiator and the payment receiver of the payment job to be scheduled are integrated, wherein the payment initiator is the starting position of the payment job to be scheduled, the payment receiver is the final arrival position of the payment job to be scheduled, and the payment job to be scheduled is a pre-configured payment job. The banking business is evaluated with the payment initiator and the payment receiver as restrictions, and the business including the payment initiator and the payment receiver is obtained for evaluation to obtain a financial business graph. The payment node is detected in the financial business graph, and the payment node is the starting node and the ending node of the business that passes through the multiple financial business graphs. And the track node is set in the financial business graph based on the detection record, that is, the detection record is marked in the financial business graph to complete the setting of the track node, and the financial business graph is transformed into a financial business track format based on the track node in the financial business graph, and the financial business track format only contains the connection business services and track node data of each line. Integrate the business security record database to obtain multiple business service scenarios of multiple business tracks in the financial business track format, and record multiple parameter data in the business security record database, such as accident frequency parameter data, extension frequency parameter data, business cycle parameter data, business subject confidence parameter data, etc. The above parameter data is obtained after data integration based on various big data platforms, and the parameter data is updated according to a preset time period. And perform business security prediction based on the multiple business service scenarios to obtain multiple business service trust scores.

[0070] In this embodiment, the transformation of the financial business graph into the financial business trajectory format means that the graph in the form of nodes and connections between nodes is converted into a trajectory in the data stream format to represent the financial business;

[0071] Each data stream in the financial business track represents a key processing step of the corresponding financial business, including the interaction subjects involved in each step, interaction process details, etc.

[0072] The method provided in the embodiment of the present invention also includes:

[0073] Based on the payment initiator and the payment receiver, data is called to obtain a candidate business graph.

[0074] The payment time limit and scheduling speed limit of the payment job to be scheduled are obtained by integration.

[0075] Based on the payment initiator and the payment receiver executing banking business planning on the candidate business graph, a financial business set is obtained, wherein the financial business set includes a plurality of reference financial businesses and a plurality of business constraint features.

[0076] The scheduling speed limit is used to align the multiple business constraint features to perform payment time calculation to obtain multiple reference financial time consumptions.

[0077] The payment time limit is used to traverse the multiple reference financial services, and W theoretical financial services are obtained from the multiple reference financial services, where W is a positive integer.

[0078] In the candidate business graph, the financial business graph is obtained based on the W theoretical financial business fittings.

[0079] The banking business is evaluated with the payment initiator and the payment receiver as restrictions to obtain a financial business graph. The method also includes: performing data calls based on the payment initiator and the payment receiver to obtain the business between the payment initiator and the payment receiver to obtain a candidate business graph. The payment time limit and the scheduling speed limit of the payment job to be scheduled are integrated to obtain the payment time limit, the time limit is the completion time limit of the payment job, and the scheduling speed limit is the maximum speed limit during the completion of the payment job. Bank business planning is executed based on the payment initiator and the payment receiver in the candidate business graph, that is, all connected businesses of the payment initiator and the payment receiver in the candidate business graph are obtained to obtain a financial business set, wherein the financial business set includes multiple reference financial businesses and multiple business constraint features. The business constraint feature is all constraint parameters of the reference business, including constraint intervals and constraint values. The payment time is calculated after the dispatch speed limit is used to align the multiple business constraint features, that is, the constraints higher than the dispatch speed limit in the multiple business constraint features are aligned, and the constraints higher than the dispatch speed limit are replaced with the dispatch speed limit. Based on the business constraint features and the distance, multiple reference financial times of multiple reference businesses are obtained. Further, the payment time limit is used to traverse the multiple reference financial times, and the reference businesses whose financial time is greater than the payment time limit are removed from the multiple reference financial businesses, and W theoretical financial businesses are screened to obtain, where W is a positive integer. The financial business map is obtained by fitting the W theoretical financial businesses.

[0080] The method provided in the embodiment of the present invention also includes:

[0081] A business security matching parameter set is pre-built, wherein the business security matching parameter set includes an accident frequency parameter, a delay frequency parameter, a business cycle parameter, and a business subject confidence parameter.

[0082] The first business service scenario of the first business trajectory is obtained by calling the financial business graph with the business security matching parameter set as the restriction, wherein the first business trajectory is any one of the multiple business trajectories, and the first business service scenario includes a first business accident frequency, a first business extension frequency, a first business cycle characteristic and a first business subject confidence characteristic.

[0083] The first business trajectory is divided into multiple levels based on the first business cycle feature and the first business subject confidence feature to obtain a first business trajectory feature.

[0084] A business security prediction model is pre-built, and the first business trajectory feature is synchronized to the business security prediction model to perform business security prediction, so as to obtain a first business service trust score.

[0085] Integrate and obtain multiple business service scenarios of multiple business trajectories in the financial business trajectory format, and perform business security prediction based on the multiple business service scenarios to obtain multiple business service trust scores. The method also includes: pre-building a business security matching parameter set, wherein the business security matching parameter set includes an accident frequency parameter, an extension frequency parameter, a business cycle parameter, and a business subject confidence parameter.

[0086] The first business service scenario of the first business trajectory is obtained by calling the financial business graph with the business security matching parameter set as the restriction, wherein the first business trajectory is any one of the multiple business trajectories from the payment initiator to the payment receiver, and the first business service scenario is obtained, including the first business accident frequency, the first business extension frequency, the first business cycle characteristics and the first business subject confidence characteristics. Based on the first business cycle characteristics and the first business subject confidence characteristics, the first business trajectory is divided into multiple levels, that is, the businesses with the same business cycle and the same business subject confidence are divided into one segment of business, so that the business cycle and business subject confidence of each segment of the business are consistent, and the first business trajectory characteristics are obtained. Further, a business security prediction model is pre-built, and the first business trajectory characteristics are synchronized to the business security prediction model for business security prediction to obtain the first business service trust score.

[0087] The method provided in the embodiment of the present invention also includes:

[0088] The business security prediction model includes a local business security prediction layer and a global business security integration layer.

[0089] Pre-construct a local business security prediction function, and synchronize the local business security prediction function to the local business security prediction layer, wherein the local business security prediction function is as follows:

[0090] ;

[0091] in, Scoring business service trust, is the weight of the previous business, is the weight of the back-end business, The business cycle of the previous business. The business cycle of the back-end business. The confidence level of the business subject of the previous business. The confidence level of the business entities in the back-end business. is the business accident frequency, The frequency of business extension.

[0092] A weight is assigned to the first service trajectory feature based on the service length to obtain a first service weight feature.

[0093] The first business trajectory feature is synchronized to the local business security prediction layer of the business security prediction model to obtain a first business prediction feature.

[0094] The first business prediction feature and the first business weight feature are weightedly calculated at the global business security integration layer of the business security prediction model to obtain the first business service trust score.

[0095] Pre-building a business security prediction model, and synchronizing the first business trajectory feature to the business security prediction model to perform business security prediction, and obtaining a first business service trust score, the method further includes:

[0096] The business security prediction model includes a local business security prediction layer and a global business security integration layer. When constructing the local business security prediction layer, a local business security prediction function is pre-constructed. The local business security prediction function is synchronized to the local business security prediction layer. The local business security prediction function is as follows:

[0097] ;

[0098] in, Scoring business service trust, is the weight of the previous business, is the weight of the back-end business, The business cycle of the previous business. The business cycle of the back-end business. The confidence level of the business subject of the previous business. The confidence level of the business entities in the back-end business. is the business accident frequency, The business service divided by the first business is input into the local business security prediction function, and the business service trust score obtained when calculating The larger the value, the better the payment characteristics, the smoother the business, the slower the confidence of the business subject, the weaker the business changes, and the more trusted the business service. The first business trajectory feature is weighted based on the business length, that is, the weight of the divided business is obtained based on the ratio of the length of each segment of the business divided in the first business trajectory feature to the total length, and the first business weight feature is obtained. The first business trajectory feature is synchronized to the local business security prediction layer of the business security prediction model, that is, the business service divided by the first business is input into the local business security prediction function, and the prediction characteristics of each divided business service in the first business are obtained to obtain the first business prediction feature. Finally, the first business prediction feature and the first business weight feature are weightedly calculated in the global business security integration layer of the business security prediction model, that is, the obtained first business prediction feature and the first business weight feature are input into the global business security integration layer of the business security prediction model for weighted summation, and the sum record is obtained to obtain the first business service trust score.

[0099] A payment operation monitoring system is integrated with candidate payment funds to obtain dynamic payment data of the candidate funds, and risk prediction is performed based on the dynamic payment data to obtain candidate risk weights, wherein the candidate payment funds are allocated funds for executing the payment operation to be scheduled.

[0100] W trust tracks are obtained by screening the multiple business tracks based on the candidate risk weights and the multiple business service trust scores.

[0101] The W trust trajectories are fitted to the financial business graph according to the financial business trajectory format to obtain a trust business graph.

[0102] A banking business evaluation is performed based on the trusted business graph to obtain candidate payment businesses, and the candidate payment businesses are used to perform payment scheduling management on the candidate payment funds.

[0103] A payment operation monitoring system integrating candidate payment funds, the payment operation monitoring system is used to extract payment data of payment personnel, including: dynamic payment data and static payment data, obtain dynamic payment data of candidate funds, and input risk prediction function based on the dynamic payment data and static payment data to perform risk prediction, and obtain candidate risk weights, wherein the candidate payment funds are allocated funds for executing the payment operation to be scheduled. W trust tracks are screened from the multiple business tracks based on the candidate risk weights and the multiple business service trust scores. According to the correspondence between the financial business track format and the financial business graph, the W trust tracks are fitted to the financial business graph to obtain a trust business graph. Bank business evaluation is performed based on the trust business graph to obtain candidate payment business with the least overload parameters, and the candidate payment business is used to perform payment scheduling management on the candidate payment funds. The technical problem that the payment scheduling management in the prior art focuses on improving payment efficiency and reducing payment costs, and ignores the security of payment funds and payment scheduling business is solved. By comprehensively considering business security, fund risk status and scheduling efficiency, an intelligent payment scheduling management method is provided to improve the service quality and scheduling security of the payment industry. The technical effect of executing payment scheduling management on payment funds based on the obtained candidate payment businesses, optimizing the payment scheduling process, and improving payment efficiency and security has been achieved.

[0104] The method provided in the embodiment of the present invention also includes:

[0105] The static payment data of the candidate funds is obtained through integration, wherein the static payment data includes payment term data, payment scope data, payment accident frequency data and payment extension frequency data.

[0106] The dynamic payment data includes accumulated payment time, business window interval characteristics and real-time request frequency.

[0107] A risk prediction function is pre-built, and the risk prediction function is as follows:

[0108] ;

[0109] in, is the risk weight, For the payment period, For payment range, To cover unexpected frequencies, To pay the deferral frequency, For the cumulative payment time, is the first The business window interval time, is the real-time request frequency, is the static weight, is a dynamic weight.

[0110] Substitute the static payment data and the dynamic payment data into the risk prediction function to obtain the candidate risk weights.

[0111] Integrate the payment operation monitoring system of the candidate payment funds, obtain the dynamic payment data of the candidate funds, and perform risk prediction based on the dynamic payment data to obtain the candidate risk weights. The method also includes: integrating and obtaining the static payment data of the candidate funds, wherein the static payment data includes payment years data, payment scope data, payment accident frequency data and payment extension frequency data. The dynamic payment data includes the accumulated payment time, business window interval characteristics and real-time request frequency, wherein the business window interval characteristics are the intervals recorded when the business window is performed during the payment process, and the real-time request frequency is the request initiation frequency data obtained within a preset time interval.

[0112] A risk prediction function is pre-built, and the risk prediction function is pre-built. The risk prediction function is as follows:

[0113] ;

[0114] in, is the risk weight, For the payment period, For payment range, To cover unexpected frequencies, To pay the deferral frequency, For the cumulative payment time, is the first The business window interval time, is the real-time request frequency, is the static weight, is a dynamic weight, wherein the static weight and the dynamic weight are weight parameters preset based on preference requirements, and the sum of the two is 1 and the dynamic weight is greater than the static weight. Substitute the acquired static payment data and dynamic payment data into the risk prediction function to obtain the candidate risk weight.

[0115] The method provided in the embodiment of the present invention also includes:

[0116] A plurality of sample risk weight thresholds are pre-constructed, wherein the plurality of sample risk weight thresholds have a plurality of sample business service trust score threshold identifiers.

[0117] A candidate business service trust score threshold is obtained by calling the multiple sample risk weight thresholds based on the candidate risk weight.

[0118] The candidate business service trust score threshold is used to filter out the W trust tracks from the multiple business tracks.

[0119] Based on the candidate risk weight and the multiple business service trust scores, W trust tracks are obtained from the multiple business tracks. The method also includes: pre-building multiple sample risk weight thresholds, wherein the multiple sample risk weight thresholds have multiple sample business service trust score threshold identifiers, that is, different sample risk weight thresholds have corresponding sample business service trust score thresholds. When the risk weight of the payee is within the sample risk weight threshold, the trust score of the corresponding matching business service can be obtained. Based on the candidate risk weight corresponding to the payment job to be scheduled, the candidate business service trust score threshold is called at the multiple sample risk weight thresholds to obtain the candidate business service trust score threshold, and the sample business service trust score threshold and the sample risk weight threshold are both interval parameters. The candidate business service trust score threshold is used to screen the business service trust scores corresponding to the multiple business tracks to obtain the W trust tracks that meet the candidate business service trust score threshold.

[0120] The method provided in the embodiment of the present invention also includes:

[0121] The W theoretical financial services are used to verify the trust business graph to obtain P feasible payment services, where P is a positive integer less than W.

[0122] The financial service graph is integrated to obtain P service overload data.

[0123] The P service overload data are characterized and candidate payment services are obtained from the P feasible payment service calls based on a minimum value.

[0124] Based on the trust business map, a banking business evaluation is performed to obtain candidate payment businesses, and the candidate payment businesses are used to perform payment scheduling management on the candidate payment funds. The method also includes: using the W theoretical financial businesses to verify the trust business map, that is, searching the trust business map in the W theoretical financial businesses, judging whether the trust business map exists in the W theoretical financial businesses, obtaining the existing trust business map, avoiding errors in the process of obtaining the trust business map that lead to the non-existence of the actual route, and obtaining P feasible payment businesses, where P is a positive integer less than W. Integrate the financial business map to obtain P business overload data, and the business overload data includes the total overload duration. Characterize the P business overload data, extract the minimum value in the P business overload data, call the corresponding payment business from the P feasible payment businesses, and obtain the candidate payment business.

[0125] In the above, refer to Figure 1A banking service scheduling method for an online payment front-end based on an embodiment of the present invention is described in detail. Figure 2 A banking service scheduling system for an online payment front-end based on an embodiment of the present invention is described, which is used to execute the banking service scheduling method for an online payment front-end.

[0126] A banking business scheduling system for an online payment front-end based on an embodiment of the present invention solves the technical problem that the payment scheduling management in the prior art focuses on improving payment efficiency and reducing payment costs, but ignores the security of payment funds and payment scheduling services. By comprehensively considering business security, fund risk status and scheduling efficiency, an intelligent payment scheduling management method is provided to improve the service quality and scheduling security of the payment industry. The technical effect of executing payment scheduling management on payment funds based on the obtained candidate payment services, optimizing the payment scheduling process, and improving payment efficiency and security is achieved. A banking business scheduling system for an online payment front-end includes: a data acquisition unit 11, a business evaluation unit 12, a track format conversion unit 13, a trust score acquisition unit 14, a risk weight acquisition unit 15, a business screening unit 16, a trust business acquisition unit 17, and a scheduling management unit 18.

[0127] The data acquisition unit 11 is used to integrate and obtain the payment initiator and the payment receiver of the payment job to be scheduled.

[0128] The business evaluation unit 12 is used to perform banking business evaluation with the payment initiating end and the payment receiving end as restrictions to obtain a financial business map.

[0129] The track format conversion unit 13 is used to perform payment node detection on the financial business graph, and set track nodes on the financial business graph based on the detection records, so as to convert the financial business graph into a financial business track format.

[0130] The trust score acquisition unit 14 is used to integrate and obtain multiple business service scenarios of multiple business traces in the financial business trace format, and perform business security prediction based on the multiple business service scenarios to obtain multiple business service trust scores.

[0131] The risk weight acquisition unit 15 is used to integrate the payment operation monitoring system of the candidate payment funds, obtain the dynamic payment data of the candidate funds, and perform risk prediction based on the dynamic payment data to obtain the candidate risk weights, wherein the candidate payment funds are the allocated funds for executing the payment operation to be scheduled.

[0132] The business screening unit 16 is configured to screen the multiple business tracks to obtain W trust tracks based on the candidate risk weights and the multiple business service trust scores.

[0133] The trusted business acquisition unit 17 is used to fit the W trusted trajectories to the financial business graph according to the financial business trajectory format to obtain a trusted business graph.

[0134] The scheduling management unit 18 is used to perform banking business evaluation based on the trusted business map, obtain candidate payment services, and use the candidate payment services to perform payment scheduling management on the candidate payment funds.

[0135] The specific configuration of the business evaluation unit 12 will be described in detail below. The business evaluation unit 12 may further include: performing banking business evaluation with the payment initiator and the payment receiving end as restrictions to obtain a financial business graph, and the method may further include: performing data calls based on the payment initiator and the payment receiving end to obtain a candidate business graph. Integrate and obtain the payment time limit and the scheduling speed limit of the payment job to be scheduled. Based on the payment initiator and the payment receiving end, execute banking business planning in the candidate business graph to obtain a financial business set, wherein the financial business set includes multiple reference financial businesses and multiple business constraint features. After using the scheduling speed limit to align the multiple business constraint features, calculate the payment time to obtain multiple reference financial time consumptions. Using the payment time limit to traverse the multiple reference financial time consumptions, screen and obtain W theoretical financial businesses from the multiple reference financial businesses, wherein W is a positive integer. In the candidate business graph, the financial business graph is obtained based on the W theoretical financial businesses.

[0136] The specific configuration of the trust score acquisition unit 14 will be described in detail below. The trust score acquisition unit 14 may further include: integrating and obtaining multiple business service scenarios of multiple business trajectories in the financial business trajectory format, and performing business security prediction based on the multiple business service scenarios to obtain multiple business service trust scores. The method also includes: pre-building a business security matching parameter set, wherein the business security matching parameter set includes an accident frequency parameter, a delay frequency parameter, a business cycle parameter, and a business subject confidence parameter. The business security matching parameter set is used as a restriction to call the first business service scenario of the first business trajectory in the financial business graph, wherein the first business trajectory is any one of the multiple business trajectories, and the first business service scenario includes a first business accident frequency, a first business delay frequency, a first business cycle feature, and a first business subject confidence feature. Based on the first business cycle feature and the first business subject confidence feature, the first business trajectory is divided into multiple levels to obtain a first business trajectory feature. A business security prediction model is pre-built, and the first business trajectory feature is synchronized to the business security prediction model to perform business security prediction to obtain a first business service trust score.

[0137] The specific configuration of the trust score acquisition unit 14 will be described in detail below. The trust score acquisition unit 14 further includes: pre-building a business security prediction model, and synchronizing the first business trajectory feature to the business security prediction model to perform business security prediction, and obtaining a first business service trust score. The method also includes: the business security prediction model includes a local business security prediction layer and a global business security integration layer. Pre-building a local business security prediction function, and synchronizing the local business security prediction function to the local business security prediction layer, wherein the local business security prediction function is as follows:

[0138] ;

[0139] in, Scoring business service trust, is the weight of the previous business, is the weight of the back-end business, The business cycle of the previous business. The business cycle of the back-end business. The confidence level of the business subject of the previous business. The confidence level of the business entities in the back-end business. is the business accident frequency, is the frequency of business extension. Weighting the first business trajectory feature based on the business length to obtain a first business weight feature. Synchronizing the first business trajectory feature to the local business security prediction layer of the business security prediction model to obtain a first business prediction feature. Weighting the first business prediction feature and the first business weight feature at the global business security integration layer of the business security prediction model to obtain the first business service trust score.

[0140] Below, the specific configuration of the risk weight acquisition unit 15 will be described in detail. The risk weight acquisition unit 15 further includes: integrating the payment operation monitoring system of the candidate payment funds, obtaining the dynamic payment data of the candidate funds, and performing risk prediction based on the dynamic payment data to obtain the candidate risk weights. The method also includes: integrating and obtaining the static payment data of the candidate funds, wherein the static payment data includes payment term data, payment range data, payment accident frequency data, and payment extension frequency data. The dynamic payment data includes cumulative payment time, business window interval characteristics, and real-time request frequency. Pre-construct a risk prediction function, and the risk prediction function is as follows:

[0141] ;

[0142] in, is the risk weight, For the payment period, For payment range, To cover unexpected frequencies, To pay the deferral frequency, For the cumulative payment time, is the first The business window interval time, is the real-time request frequency, is the static weight, is a dynamic weight.

[0143] Substitute the static payment data and the dynamic payment data into the risk prediction function to obtain the candidate risk weights.

[0144] The specific configuration of the business screening unit 16 will be described in detail below. The business screening unit 16 further includes: based on the candidate risk weight and the multiple business service trust scores, W trust tracks are obtained from the multiple business tracks, and the method also includes: pre-constructing multiple sample risk weight thresholds, wherein the multiple sample risk weight thresholds have multiple sample business service trust score threshold identifiers. Based on the candidate risk weight, the candidate business service trust score threshold is called at the multiple sample risk weight thresholds. The W trust tracks are obtained from the multiple business tracks using the candidate business service trust score threshold.

[0145] The specific configuration of the business screening unit 16 will be described in detail below. The business screening unit 16 further includes: performing a banking business evaluation based on the trust business graph to obtain a candidate payment business, and using the candidate payment business to perform payment scheduling management on the candidate payment funds. The method also includes: using the W theoretical financial businesses to verify the trust business graph to obtain P feasible payment businesses, where P is a positive integer less than W. Integrating the financial business graph to obtain P business overload data. Characterizing the P business overload data and obtaining a candidate payment business from the P feasible payment business calls based on the minimum value.

[0146] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0147] In addition, each functional unit in each embodiment of the present invention may be integrated into a scheduling unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit. The above is only an implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent format or equivalent process transformation made using the contents of the specification and drawings of the present invention, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present invention.

[0148] The specific implementation methods of the invention are described in detail above, but they are only examples, and the invention is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the invention, therefore, the equalization, modification, improvement, etc. made without departing from the spirit and principle of the invention should be included in the scope of the invention.

Claims

1. A banking service scheduling method for an online payment front-end, characterized in that: The method comprises: Integrate and obtain the payment initiator and payment receiver of the payment job to be scheduled; Perform banking business evaluation with the payment initiator and the payment receiver as restrictions to obtain a financial business map; Perform payment node detection on the financial business graph, and set track nodes on the financial business graph based on the detection records, and transform the financial business graph into a financial business track format; Integrate and obtain multiple business service scenarios of multiple business tracks in the financial business track format, and perform business security prediction based on the multiple business service scenarios to obtain multiple business service trust scores; Integrate the payment operation monitoring system of the candidate payment funds, obtain dynamic payment data of the candidate funds, and perform risk prediction based on the dynamic payment data to obtain candidate risk weights, wherein the candidate payment funds are allocated funds for executing the payment operation to be scheduled; Filtering and obtaining W trust tracks from the multiple business tracks based on the candidate risk weights and the multiple business service trust scores; Fitting the W trust trajectories to the financial business graph according to the financial business trajectory format to obtain a trust business graph; A banking business evaluation is performed based on the trusted business graph to obtain candidate payment businesses, and the candidate payment businesses are used to perform payment scheduling management on the candidate payment funds.

2. The method according to claim 1, characterized in that Performing banking business evaluation with the payment initiator and the payment receiver as restrictions to obtain a financial business map, the method further includes: Based on the payment initiator and the payment receiver, data is called to obtain a candidate business graph; Integrate and obtain the payment time limit and scheduling speed limit of the payment job to be scheduled; Based on the payment initiator and the payment receiver executing banking business planning on the candidate business graph, a financial business set is obtained, wherein the financial business set includes a plurality of reference financial businesses and a plurality of business constraint features; After the scheduling speed limit is aligned with the multiple business constraint features, payment time is calculated to obtain multiple reference financial time consumptions; The payment time limit is used to traverse the multiple reference financial services, and W theoretical financial services are obtained from the multiple reference financial services, where W is a positive integer; In the candidate business graph, the financial business graph is obtained based on the W theoretical financial business fittings.

3. The method according to claim 1, characterized in that Integrate and obtain multiple business service scenarios of multiple business tracks in the financial business track format, and perform business security prediction based on the multiple business service scenarios to obtain multiple business service trust scores. The method also includes: Pre-constructing a service security matching parameter set, wherein the service security matching parameter set includes an accident frequency parameter, a delay frequency parameter, a service cycle parameter, and a service subject confidence parameter; The first business service scenario of the first business trajectory is obtained by calling the financial business graph with the business security matching parameter set as a restriction, wherein the first business trajectory is any one of the multiple business trajectories, and the first business service scenario includes a first business accident frequency, a first business extension frequency, a first business cycle feature, and a first business subject confidence feature; Performing multi-level division on the first business trajectory based on the first business cycle feature and the first business subject confidence feature to obtain a first business trajectory feature; A business security prediction model is pre-built, and the first business trajectory feature is synchronized to the business security prediction model to perform business security prediction, so as to obtain a first business service trust score.

4. The method according to claim 3, characterized in that Pre-building a business security prediction model, and synchronizing the first business trajectory feature to the business security prediction model to perform business security prediction, and obtaining a first business service trust score, the method further includes: The business security prediction model includes a local business security prediction layer and a global business security integration layer; Pre-construct a local business security prediction function, and synchronize the local business security prediction function to the local business security prediction layer, wherein the local business security prediction function is as follows: ； in, Scoring business service trust, is the weight of the previous business, is the weight of the back-end business, The business cycle of the previous business. The business cycle of the back-end business. The confidence level of the business subject of the previous business. The confidence level of the business entities in the back-end business. is the business accident frequency, Frequency of business extensions; Assigning a weight to the first service trajectory feature based on the service length to obtain a first service weight feature; Synchronizing the first business trajectory feature to the local business security prediction layer of the business security prediction model to obtain a first business prediction feature; The first business prediction feature and the first business weight feature are weightedly calculated at the global business security integration layer of the business security prediction model to obtain the first business service trust score.

5. The method according to claim 1, characterized in that Integrate the payment operation monitoring system of the candidate payment funds, obtain dynamic payment data of the candidate funds, and perform risk prediction based on the dynamic payment data to obtain candidate risk weights. The method also includes: Integrate and obtain static payment data of candidate funds, wherein the static payment data includes payment term data, payment scope data, payment accident frequency data and payment delay frequency data; The dynamic payment data includes cumulative payment time, service window interval characteristics and real-time request frequency; A risk prediction function is pre-built, and the risk prediction function is as follows: ； in, is the risk weight, For the payment period, For payment range, To cover unexpected frequencies, To pay the deferral frequency, For the cumulative payment time, is the first The business window interval time, is the real-time request frequency, is the static weight, is the dynamic weight; Substitute the static payment data and the dynamic payment data into the risk prediction function to obtain the candidate risk weights.

6. The method according to claim 1, characterized in that Filtering and obtaining W trust tracks from the multiple business tracks based on the candidate risk weights and the multiple business service trust scores, the method further includes: Pre-constructing a plurality of sample risk weight thresholds, wherein the plurality of sample risk weight thresholds have a plurality of sample business service trust score threshold identifiers; Based on the candidate risk weight, the multiple sample risk weight thresholds are called to obtain a candidate business service trust score threshold; The candidate business service trust score threshold is used to filter out the W trust tracks from the multiple business tracks.

7. The method according to claim 2, characterized in that Performing a banking business evaluation based on the trusted business graph to obtain a candidate payment business, and using the candidate payment business to perform payment scheduling management on the candidate payment funds, the method further includes: Using the W theoretical financial services to verify the trusted service graph, obtaining P feasible payment services, where P is a positive integer less than W; Integrate the financial business graph to obtain P business overload data; The P service overload data are characterized and candidate payment services are obtained from the P feasible payment service calls based on a minimum value.

8. A banking service scheduling system for an online payment front-end, used to execute a banking service scheduling method for an online payment front-end as claimed in any one of claims 1 to 7, characterized in that: The system comprises: A data acquisition unit, used for integrating and acquiring the payment initiator and the payment receiver of the payment job to be scheduled; A business evaluation unit, used to evaluate banking business with the payment initiating end and the payment receiving end as restrictions, and obtain a financial business map; A track format conversion unit, used to perform payment node detection on the financial business graph, and set track nodes on the financial business graph based on the detection records, and convert the financial business graph into a financial business track format; A trust score acquisition unit, used to integrate and obtain multiple business service scenarios of multiple business tracks in the financial business track format, and perform business security prediction based on the multiple business service scenarios to obtain multiple business service trust scores; A risk weight acquisition unit, used for integrating a payment operation monitoring system of candidate payment funds, obtaining dynamic payment data of candidate funds, and performing risk prediction based on the dynamic payment data to obtain candidate risk weights, wherein the candidate payment funds are allocated funds for executing the payment operation to be scheduled; A business screening unit, configured to screen and obtain W trust tracks from the multiple business tracks based on the candidate risk weights and the multiple business service trust scores; A trust business acquisition unit, configured to fit the W trust trajectories to the financial business graph according to the financial business trajectory format to obtain a trust business graph; A scheduling management unit is used to perform banking business evaluation based on the trusted business map, obtain candidate payment businesses, and use the candidate payment businesses to perform payment scheduling management on the candidate payment funds.

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