Service processing method and device, electronic equipment and computer program product

Abstract

The invention discloses a service processing method and device, electronic equipment and a computer program product. Relates to the field of financial science and technology, and the method comprises the steps: obtaining a financial service of a financial institution in a target region, obtaining an initial distribution strategy according to the service type of the financial service and the region number of the target region, and determining M job nodes according to the initial distribution strategy; if the operation nodes in the abnormal state exist in the M operation nodes, determining an initial distribution model according to an initial distribution strategy, and extracting an initial distribution proportion of the M operation nodes from the initial distribution model; and adjusting the M initial distribution proportions to obtain M target distribution proportions, segmenting the financial business according to the M target distribution proportions, and distributing the M segmented financial businesses to the M job nodes. Through the service processing method and device, the technical problem of low service processing efficiency caused by non-uniform service distribution during cross-job node service processing in the related technology is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of financial technology, and in particular, to a business processing method and device, electronic equipment and computer program product. BACKGROUND

[0002] In the modern financial system, financial institutions adopt the strategy of establishing a head office level operation center to improve business processing efficiency and reduce operating costs. This centralized processing method refers to the centralized processing of repetitive and standardized businesses in various places, improving efficiency through specialized division of labor and assembly line operation, and reducing risks through standardized operation processes, so that front-line businesses can focus more on customer service, and back-end businesses can achieve intensive operation.

[0003] However, in actual operation, the business volume and personnel configuration of each operation center are not balanced, which leads to a series of problems: first, for the operation center with busy business and relatively few personnel, due to task backlog, the business processing period is prolonged, which not only reduces customer experience, but also easily leads to the decline of business service quality. In addition, for the operation center with small business volume and more personnel, there is a situation that human resources are not fully utilized.

[0004] In order to solve the above problems, the related technology optimizes the task allocation mechanism in the task pool, dynamically adjusts the processing of tasks by tellers using the binary search-backtracking-pruning algorithm, and realizes the best allocation of internal resources. However, this method is only suitable for the mobilization of internal resources in the center, and cannot realize the sharing of human resources between different operation centers, and cannot effectively cope with the challenges brought by external environmental changes and internal resource imbalance.

[0005] In view of the technical problem that the related technology has uneven business allocation when performing business processing across operation nodes, and further leads to low business processing efficiency, so far no effective solution has been proposed. SUMMARY

[0006] The main purpose of the present application is to provide a business processing method and device, electronic equipment and computer program product, to solve the technical problem that the related technology has uneven business allocation when performing business processing across operation nodes, and further leads to low business processing efficiency.

[0007] To achieve the above object, according to one aspect of the present application, a processing method of a service is provided. The method comprises: obtaining a financial service of a financial institution in a target region, obtaining an initial distribution strategy according to a service type of the financial service and a region number of the target region, and determining M job nodes according to the initial distribution strategy, wherein the initial distribution strategy is used to indicate the M job nodes receiving the distributed financial service, and M is a positive integer; if there is an abnormal job node in the M job nodes, determining an initial distribution model according to the initial distribution strategy, and extracting initial distribution proportions of the M job nodes from the initial distribution model, wherein the initial distribution model is used to indicate initial distribution proportions of the financial service distributed to the M job nodes; adjusting the M initial distribution proportions to obtain M target distribution proportions, and segmenting the financial service according to the M target distribution proportions to obtain M segmented financial services, and distributing the M segmented financial services to the M job nodes, wherein if the M job nodes receive the M segmented financial services, the M segmented financial services are processed.

[0008] Further, after the M job nodes are determined according to the initial distribution strategy, the method further comprises: obtaining a node log file of the M job nodes, extracting a historical adjustment time from the node log file, and determining a service obtaining time when the financial service is obtained, wherein the historical adjustment time is a time when the initial distribution proportions are adjusted in a historical time period; calculating a difference between the service obtaining time and the historical adjustment time to obtain a time difference, stopping the step of adjusting the M initial distribution proportions if the time difference is less than a preset time interval; and executing the step of adjusting the M initial distribution proportions if the time difference is greater than or equal to the preset time interval.

[0009] Further, adjusting the M initial distribution ratios to obtain M target distribution ratios comprises: dividing the M job nodes into M1 job nodes and M2 job nodes according to node states of the M job nodes, and setting target distribution ratios of the M1 job nodes to preset ratios, wherein the node state of each job node comprises at least one of an abnormal state and a normal state, the node states of the M1 job nodes are the abnormal state, the node states of the M2 job nodes are the normal state, M1+M2=M, M1 and M2 are positive integers; for one of the M2 job nodes, obtaining efficiency deviation data of the job node, and obtaining a first efficiency deviation threshold of the job node, wherein the efficiency deviation data refers to a ratio of a business processing efficiency of the job node deviating from a total business processing efficiency associated with the financial institution; in a case where the efficiency deviation data is less than or equal to the first efficiency deviation threshold, stopping adjusting the initial distribution ratio associated with the job node; in a case where an absolute value of the efficiency deviation data is greater than the first efficiency deviation threshold, adjusting the initial distribution ratios of the M2 job nodes to obtain M2 candidate distribution ratios, and adjusting the M2 candidate distribution ratios to obtain M2 target distribution ratios; and the M target distribution ratios are composed of the M1 preset ratios and the M2 target distribution ratios.

[0010] Further, the efficiency deviation data of each job node is obtained by: obtaining a total amount of business of the M job nodes, and obtaining processing time data of the M job nodes processing K types of business to obtain total time consumption data, calculating a ratio of the total time consumption data to the total amount of business to obtain a total business processing efficiency, wherein K is a positive integer; for one of the job nodes, obtaining a business volume of the job node according to a business type of the financial business, and obtaining processing time data of the job node processing K types of business to calculate a ratio of a sum of the K processing time data to the business volume to obtain a business processing efficiency of the job node; calculating a difference between the business processing efficiency of the job node and the total business processing efficiency to obtain a processing efficiency difference, and calculating a ratio of the processing efficiency difference to the total business processing efficiency to obtain initial deviation data; obtaining a second efficiency deviation threshold of the job node, in a case where the initial deviation data is less than zero and an absolute value of the initial deviation data is greater than the second efficiency deviation threshold, determining a negative number of the second efficiency deviation threshold as the efficiency deviation data; in a case where the initial deviation data is greater than zero and the initial deviation data is greater than the second efficiency deviation threshold, determining the second efficiency deviation threshold as the efficiency deviation data.

[0011] Further, the adjusting of the initial distribution ratio of the M2 job nodes to obtain M2 candidate distribution ratios comprises: for a job node, if the job node has not adjusted the distribution ratio in the historical time period, calculating a parameter difference value according to the efficiency deviation data, and calculating a product of the parameter difference value and the initial distribution ratio to obtain a candidate distribution ratio of the job node; if the job node has adjusted the distribution ratio in the historical time period, obtaining a historical distribution ratio associated with a historical adjustment time, and calculating a product of the parameter difference value and the historical distribution ratio to obtain the candidate distribution ratio of the job node.

[0012] Further, the adjusting of the M2 candidate distribution ratios comprises: for a job node, obtaining a distribution ratio threshold of the job node, wherein the distribution ratio threshold comprises an upper limit value of the distribution ratio and a lower limit value of the distribution ratio; in a case where the candidate distribution ratio of the job node indicates zero, determining the initial distribution ratio as the candidate distribution ratio; in a case where the candidate distribution ratio is not zero and the candidate distribution ratio is greater than the upper limit value of the distribution ratio, determining a minimum value between the candidate distribution ratio and the upper limit value of the distribution ratio as the candidate distribution ratio; in a case where the candidate distribution ratio is not zero and the candidate distribution ratio is less than the lower limit value of the distribution ratio, determining a maximum value between the candidate distribution ratio and the lower limit value of the distribution ratio as the candidate distribution ratio.

[0013] Further, the adjusting of the M2 candidate distribution ratios to obtain M2 target distribution ratios comprises: in a case where a sum of the M2 candidate distribution ratios is equal to one, determining the M2 candidate distribution ratios as the M2 target distribution ratios; in a case where the sum of the M2 candidate distribution ratios is not equal to one, calculating the sum of the M2 candidate distribution ratios to obtain a ratio sum; calculating a ratio value of each candidate distribution ratio and the ratio sum to obtain M2 ratio values, and determining the M2 ratio values as the M2 target distribution ratios.

[0014] In order to achieve the above object, according to another aspect of the present application, a processing device of a service is provided. The device comprises: a first obtaining unit, configured to obtain a financial service of a financial institution in a target region, obtain an initial distribution strategy according to a service type of the financial service and a region number of the target region, and determine M job nodes according to the initial distribution strategy, wherein the initial distribution strategy is used to indicate the M job nodes receiving the distributed financial service, and M is a positive integer; a determining unit, configured to, if there is an abnormal job node in the M job nodes, determine an initial distribution model according to the initial distribution strategy, and extract an initial distribution proportion of the M job nodes from the initial distribution model, wherein the initial distribution model is used to indicate the initial distribution proportion of the service of the financial service distributed to the M job nodes; an adjusting unit, configured to adjust the M initial distribution proportions to obtain M target distribution proportions, split the financial service according to the M target distribution proportions to obtain M split financial services, and distribute the M split financial services to the M job nodes, wherein, if the M split financial services are received by the M job nodes, the M split financial services are processed.

[0015] According to another aspect of the embodiments of the present application, a computer readable storage medium is also provided, which comprises a stored executable program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute the processing method of any one of the services when the executable program is executed.

[0016] According to another aspect of the embodiments of the present application, an electronic device is also provided, which comprises one or more processors and a memory, the memory stores an executable program, and the processor is configured to execute the program, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the processing method of any one of the services.

[0017] According to another aspect of the embodiments of the present application, a computer program product is also provided, which comprises a computer program, wherein the computer program, when executed by a processor, implements the processing method of any one of the services.

[0018] In the embodiment of the present application, the processing of the service is adopted, the financial service of the financial institution in the target region is obtained, the initial distribution strategy is obtained according to the service type of the financial service and the region number of the target region, and the M job nodes are determined according to the initial distribution strategy, wherein the initial distribution strategy is used to indicate the M job nodes receiving the distributed financial service, and M is a positive integer; if there is an abnormal job node in the M job nodes, the initial distribution model is determined according to the initial distribution strategy, and the initial distribution proportion of the M job nodes is extracted from the initial distribution model, wherein the initial distribution model is used to indicate the initial distribution proportion of the financial service distributed to the M job nodes; the M initial distribution proportions are adjusted to obtain M target distribution proportions, and the financial service is segmented according to the M target distribution proportions to obtain M segmented financial services, and the M segmented financial services are distributed to the M job nodes, wherein the M segmented financial services are processed in the M job nodes receiving the M segmented financial services, which solves the technical problem of uneven service allocation and low service processing efficiency in the related art when the service is processed across the job nodes. Through the initial distribution model determined according to the initial distribution strategy and the initial distribution proportion of each job node extracted from the initial distribution model when there is an abnormal job node in the job nodes, the initial distribution proportion is adjusted to obtain the target distribution proportion, and the financial service is distributed to different job nodes according to the target distribution proportion, thereby achieving the technical effects of improving the processing efficiency when the service is processed across the job nodes and the utilization rate of the job nodes. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments of the present application and its description are used to explain the present application and are not intended to limit the present application. In the drawings:

[0020] Figure 1 FIG. 1 is a hardware structure block diagram of a computer terminal (or mobile device) for implementing the processing method of the service according to the embodiment of the present application;

[0021] Figure 2 FIG. 2 is a flowchart of the processing method of the service according to the embodiment of the present application;

[0022] Figure 3 FIG. 3 is a schematic diagram of the distribution model according to the embodiment of the present application;

[0023] Figure 4 FIG. 4 is an optional flowchart of the processing method of the service according to the embodiment of the present application;

[0024] Figure 5 FIG. 5 is a schematic diagram of the processing device of the service according to the embodiment of the present application;

[0025] Figure 6 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties. For example, this system has interfaces with relevant users or organizations to provide users with corresponding operation data for them to choose to agree to or refuse automated decision-making results. Before obtaining relevant information, a request for obtaining the information needs to be sent to the aforementioned user or organization through the interface, and the relevant information is obtained after receiving consent from the aforementioned user or organization; if the user chooses to refuse, the expert decision-making process is initiated. Users can view the purpose of data use in real time through authorization decoding and have the right to withdraw authorization or delete data at any time. After the authorization is withdrawn, the system will terminate the relevant data processing within 24 hours.

[0029] It should be noted that the information collected in this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize use or refuse use.

[0030] Example 1

[0031] According to an embodiment of this application, a method embodiment for processing business is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) used to implement a business processing method, such as... Figure 1 As shown, computer terminal 10 (or mobile device) may include one or more ( Figure 1 The processor 102 (which may include, but is not limited to, a microprocessor MCU (Microcontroller Unit) or a programmable gate array (FPGA)) is shown as 102a, 102b, ..., 102n. It also includes a memory 104 for storing data and a transmission device 106 for communication functions. In addition, it may include: a display, an input / output interface, a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a keyboard, a cursor control device, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0033] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0034] The memory 104 can be used to store software programs of application software and modules, such as program instructions / data storage devices corresponding to the processing method of the service in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the processing method of the service described above. The memory 104 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely arranged with respect to the processor 102, which can be connected to the computer terminal 10 through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0035] The transmission device 106 is used to receive or send data via a network. Specific examples of the above-mentioned network can include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) and a network interface, which can be connected to other network devices through a base station so as to be able to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet in a wireless manner.

[0036] The display can be, for example, a touch screen type liquid crystal display (Liquid Crystal Display, LCD), which can enable the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0037] Under the above-mentioned operating environment, the present application provides a processing method of a service as shown in Figure 2 Figure 2 is a flowchart of the processing method of the service provided by the embodiments of the present application, as shown in Figure 2 the method comprises the following steps:

[0038] Step S201, obtaining a financial service of a financial institution in a target region, obtaining an initial distribution strategy according to a service type of the financial service and a region number of the target region, and determining M job nodes according to the initial distribution strategy, wherein the initial distribution strategy is used to indicate M job nodes receiving distributed financial services, and M is a positive integer.

[0039] ​It should be noted that the target region refers to the geographical region where the financial business is initiated, and the financial business refers to various transactions or service requests generated by the financial institutions in the target region, which need to be processed through different operation nodes. The operation node represents the operation center of the financial institution in different regions; the business distribution refers to the distribution of businesses from different channels, different types, and different outlets to different centers for processing according to the configured distribution strategy; the distribution strategy defines which operation center in different regions the financial business is distributed to for processing, and the strategy can be divided into fixed distribution strategy and dynamic distribution strategy, wherein the fixed distribution strategy refers to that a certain type of financial business in a certain region is fixedly distributed to a center for processing, and the dynamic distribution strategy refers to that a certain type of financial business in a certain region is dynamically calculated according to a distribution model to obtain the proportion of business distribution in different centers, and the business is distributed to different operation nodes for processing according to the proportion.

[0040] In order to realize the dynamic balanced distribution of the business among the multiple operation nodes, first, after obtaining the financial business initiated in the target institution, the corresponding initial distribution strategy can be queried according to the business type (such as transfer, etc.) of the financial business and the region number of the target region, wherein the initial distribution strategy defines different operation nodes (i.e., C1, C2, C3, etc.) to which the business needs to be distributed.

[0041] Further, the multiple operation nodes corresponding to the financial business are determined according to the initial distribution strategy, for example, the initial distribution strategy indicates that if the business B1 is distributed to the operation nodes C1, C2 and C3 according to the proportions of 0.2, 0.3 and 0.5 respectively, the operation nodes are composed of the above three operation nodes at this time, which provides basic data for subsequent dynamic distribution proportion adjustment.

[0042] In step S202, if there is an operation node in an abnormal state in the M operation nodes, an initial distribution model is determined according to the initial distribution strategy, and an initial distribution proportion of the M operation nodes is extracted from the initial distribution model, wherein the initial distribution model is used to indicate the initial distribution proportion of the financial business to the M operation nodes.

[0043] It should be noted that the abnormal state refers to that the operation node is in a disaster mode, for example, technical failure, personnel shortage or external emergency event, etc. problem, which causes its business processing capacity to decrease significantly or temporarily unable to receive new business, at this time the operation node is in an abnormal state. The initial distribution model refers to a model that defines adjustment parameters based on the initial distribution strategy, which is used to quantify and guide how the financial business is initially distributed among multiple operation nodes. The model contains related parameters such as initial distribution proportion, adjustment frequency, deviation degree, refresh frequency, tolerance, distribution proportion range, etc. These parameters determine the initial allocation pattern of the business among different operation nodes.

[0044] When it is detected that there is an abnormal job node in the job node corresponding to the financial business, it means that at least one job node cannot receive and process the business according to the distribution proportion under normal circumstances. At this time, the initial distribution model corresponding to the initial distribution strategy matching the business type and the target region is first found. Then the initial distribution proportion of each job node is extracted, wherein the initial distribution proportion refers to the proportion of the business amount that each job node should receive when there is no abnormality, which can be determined according to business prediction, job node processing capacity and historical data and other factors.

[0045] Further, the dynamic distribution proportion is recalculated on the basis of the initial distribution proportion to ensure that the business can be smoothly and efficiently transferred to other job nodes that can normally operate, avoid business stagnation or delay, and at the same time, reduce the additional burden of normal job nodes and maintain the overall balance of business processing. It should be noted that, Figure 3 is a schematic diagram of a distribution model provided by an embodiment of the present application, as Figure 3 shown, each distribution strategy corresponds to a distribution model; multiple distribution strategies can use the same distribution model, for example, financial business B1 is connected to initial distribution model M1 through region number P, and the initial distribution model M1 sets the business distribution proportion of job node C1 to 0.2, the business distribution proportion of job node C2 to 0.3, and the business distribution proportion of job node C3 to 0.5; financial business B2 is connected to initial distribution model M2 through region number P, and the initial distribution model M2 sets the business distribution proportion of job node C1 to 0.2, the business distribution proportion of job node C2 to 0.5, and the business distribution proportion of job node C3 to 0.4.

[0046] Step S203, adjusting the M initial distribution proportions to obtain M target distribution proportions, and segmenting the financial business according to the M target distribution proportions to obtain M segmented financial businesses, and distributing the M segmented financial businesses to the M job nodes, wherein, in the case that the M job nodes receive the M segmented financial businesses, the M segmented financial businesses are processed.

[0047] Specifically, after obtaining the initial distribution proportion of each job node, in order to maintain the balance of business processing, so that the actual business amount of each job node matches its processing capacity and resource configuration, thereby achieving the state of maximum overall efficiency, the initial distribution proportions can be adjusted to obtain the target distribution proportion corresponding to each job node. During the adjustment process, factors such as whether the processing efficiency of a job node is lower or higher than the average level, and whether there is an abnormal job node, are considered, so as to calculate a new and more suitable business distribution proportion.

[0048] When the latest target distribution ratio is obtained, the received financial business can be divided according to the ratio, each part corresponding to the amount of business that each job node should process according to its target distribution ratio, thereby obtaining a plurality of divided financial businesses. By allocating financial businesses in proportion, each job node can receive an amount of business that is suitable for its processing capacity. Finally, the divided financial businesses are sent to the corresponding job nodes respectively. When the job nodes receive the divided financial businesses, they can process the businesses according to their respective task flow and procedure.

[0049] The processing method of the business provided by the embodiments of the present application, by obtaining the financial business of the financial institution in the target region, obtaining the initial distribution strategy according to the business type of the financial business and the region number of the target region, and determining M job nodes according to the initial distribution strategy, wherein the initial distribution strategy is used to indicate the M job nodes that receive the distributed financial business, and M is a positive integer; if there is an abnormal job node in the M job nodes, an initial distribution model is determined according to the initial distribution strategy, and the initial distribution ratio of the M job nodes is extracted from the initial distribution model, wherein the initial distribution model is used to indicate the initial distribution ratio of the financial business distributed to the M job nodes; the M initial distribution ratios are adjusted to obtain M target distribution ratios, and the financial business is divided according to the M target distribution ratios to obtain M divided financial businesses, and the M divided financial businesses are distributed to the M job nodes, wherein if the M job nodes receive the M divided financial businesses, the M divided financial businesses are processed, which solves the technical problem of uneven business distribution and low business processing efficiency when the business is processed across job nodes in the related art, and by determining the initial distribution model according to the initial distribution strategy and extracting the initial distribution ratio of each job node from the initial distribution model when there is an abnormal job node in the job nodes, the initial distribution ratio is adjusted to obtain the target distribution ratio, and the financial business is distributed to different job nodes according to the target distribution ratio, thereby achieving the technical effects of improving the processing efficiency when the business is processed across job nodes and the utilization rate of the job nodes.

[0050] Optionally, in the method for processing the service provided by the embodiment of the application, after the M job nodes are determined according to the initial distribution strategy, the method further comprises: obtaining node log files of the M job nodes, extracting a historical adjustment time from the node log files, and determining a service obtaining time when the financial service is obtained, wherein the historical adjustment time refers to a time when the initial distribution ratio is adjusted in a historical time period; calculating a difference between the service obtaining time and the historical adjustment time to obtain a time difference, and stopping the step of adjusting the M initial distribution ratios in a case where the time difference is less than a preset time interval; and performing the step of adjusting the M initial distribution ratios in a case where the time difference is greater than or equal to the preset time interval.

[0051] After the plurality of job nodes associated with the financial service are determined, the method of determining whether the initial distribution ratio needs to be adjusted can be determined not only by the node state of the job node, that is, when the node state of the job node is an abnormal state, the initial distribution ratio is directly triggered to be adjusted, but also by the adjustment frequency. Specifically, first, the node log files of the job nodes are obtained, wherein the node log file refers to a log document generated and recorded by each job node for service processing activities, and can include information such as a related time of processing the financial service and a time (that is, a historical adjustment time) when each job node adjusts the distribution ratio in a historical time period. At the same time, the service obtaining time when the financial service is received also needs to be obtained.

[0052] Further, the difference between the service obtaining time and the historical adjustment time is calculated, and if the obtained time difference is less than a preset time interval, the distribution ratio does not need to be adjusted, so as to prevent too frequent adjustment and cause instability of service processing and unnecessary resource consumption; otherwise, if the time difference is greater than or equal to the preset time interval, it indicates that there is enough time to evaluate the effect of the last adjustment, and the initial distribution ratio of the job node can be adjusted at this time to cope with the change of processing capacity of each job node or the fluctuation of service quantity, so as to ensure the fairness and efficiency of service processing.

[0053] The embodiment determines whether the initial distribution ratio needs to be adjusted by analyzing the node log file, avoids affecting the service processing efficiency due to the time consumption of model adjustment, reduces the burden and service processing uncertainty caused by the adjustment strategy, and at the same time ensures that the actual processing situation of each job node can be responded in time after a sufficient time interval, and the service distribution is adjusted, so as to improve the efficiency and resource utilization of the whole service processing.

[0054] Optionally, in the service processing method provided by the embodiments of the present application, adjusting the M initial distribution ratios to obtain M target distribution ratios comprises: dividing the M job nodes into M1 job nodes and M2 job nodes according to the node states of the M job nodes, and setting the target distribution ratio of the M1 job nodes to a preset ratio, wherein the node state of each job node comprises at least one of the following: an abnormal state and a normal state, the node state of the M1 job nodes is the abnormal state, the node state of the M2 job nodes is the normal state, M1+M2=M, and M1 and M2 are positive integers; for one of the M2 job nodes, obtaining efficiency deviation data of the job node, and obtaining a first efficiency deviation threshold of the job node, wherein the efficiency deviation data refers to the ratio of the service processing efficiency of the job node deviating from the total service processing efficiency associated with the financial institution; in the case that the efficiency deviation data is less than or equal to the first efficiency deviation threshold, stopping adjusting the initial distribution ratio associated with the job node; in the case that the absolute value of the efficiency deviation data is greater than the first efficiency deviation threshold, adjusting the initial distribution ratio of the M2 job nodes to obtain M2 candidate distribution ratios, and adjusting the M2 candidate distribution ratios to obtain M2 target distribution ratios; and the M target distribution ratios are composed of the M1 preset ratios and the M2 target distribution ratios.

[0055] Specifically, in order to maintain the overall service processing efficiency and customer service quality at a high level even when some job nodes encounter operational obstacles, when adjusting the initial distribution ratio, it is necessary to first monitor the current node state of all job nodes in real time, wherein the node state covers the operational health of the job node, including an abnormal state or a normal state. Then, based on the different node states, two subsets are divided: M1 job nodes and M2 job nodes, wherein the node state of the M1 job nodes is the abnormal state, and the node state of the M2 job nodes is the normal state, so that the financial service can be directed to those job nodes that can operate normally and have stable processing capacity, while avoiding job nodes that face technical or human resource bottlenecks and other problems.

[0056] For job nodes marked as an abnormal state, the target distribution ratio thereof can be set to 0 (i.e., the preset ratio), that is, the distribution of new services to these job nodes is suspended until their state returns to normal, preventing the backlog of services at job nodes with limited processing capacity, avoiding further delays and inefficiencies, which can be represented as: =0. Then, for all distribution strategies involving the job node, the distribution ratio of the financial service to other job nodes is adjusted.

[0057] Further, for job nodes in a normal state ~ , first, the efficiency deviation data (i.e. deviation degree) of each job node needs to be obtained, wherein the efficiency deviation data refers to the deviation ratio between the business processing efficiency of the job node and the overall business processing efficiency of the financial institution. Then, the efficiency deviation data of each job node is compared with the first efficiency deviation threshold (i.e. tolerable deviation degree DT) of the job node, wherein the tolerable deviation degree refers to the deviation amount of the business processing efficiency of the job node from the platform business processing efficiency without the need for distribution ratio adjustment.

[0058] If the efficiency deviation data is less than or equal to the first efficiency deviation threshold, it indicates that the efficiency performance of the job node is still within the acceptable range without significant deviation, so there is no need to adjust the distribution ratio of these nodes, which can be represented by the following formula: if <= , no adjustment is needed. On the contrary, if the absolute value of the efficiency deviation data exceeds the first efficiency deviation threshold, it indicates that the business processing efficiency of the job node has significant fluctuations, which needs to be balanced by adjusting the distribution ratio, and the initial distribution ratio of other job nodes involved in the model also needs to be adjusted, i.e. it can be represented by the following formula: if |> , the initial distribution ratio of the job node corresponding to the business needs to be adjusted. After adjusting the initial distribution ratio of these job nodes, the corresponding candidate distribution ratio can be obtained, and then the candidate distribution ratio is updated to obtain the target distribution ratio. Finally, the preset ratio of M1 job nodes is combined with the target distribution ratio of M2 job nodes to form a complete target distribution ratio.

[0059] By adjusting the initial distribution ratio of the job node, this embodiment not only realizes the effective isolation of the abnormal state job node and avoids the interruption of business processing, but also achieves the optimal allocation of resources by dynamically adjusting the distribution ratio of the normal state job node, promotes the smoothness and efficiency of the overall business processing flow, and greatly improves the balancing and optimization ability of the internal job efficiency of the financial institution.

[0060] Optionally, in the service processing method provided by the embodiments of the present application, the efficiency deviation data of each job node is calculated by the following method: obtaining the total amount of services of the M job nodes, obtaining the processing time data of the M job nodes for processing K types of services, obtaining the total processing time data, calculating the ratio of the total processing time data to the total amount of services, and obtaining the total service processing efficiency, wherein K is a positive integer; for a job node, obtaining the amount of services of the job node according to the service type of the financial service, obtaining the processing time data of the job node for processing K types of services, calculating the ratio of the sum of the K processing time data to the amount of services, and obtaining the service processing efficiency of the job node; calculating the difference between the service processing efficiency of the job node and the total service processing efficiency, obtaining the processing efficiency difference, and calculating the ratio of the processing efficiency difference to the total service processing efficiency, obtaining the initial deviation data; obtaining the second efficiency deviation threshold of the job node, in the case that the initial deviation data is less than zero and the absolute value of the initial deviation data is greater than the second efficiency deviation threshold, determining the negative number of the second efficiency deviation threshold as the efficiency deviation data; in the case that the initial deviation data is greater than zero and the initial deviation data is greater than the second efficiency deviation threshold, determining the second efficiency deviation threshold as the efficiency deviation data.

[0061] Before adjusting the service distribution ratio of the job nodes to ensure efficient and balanced service processing, the efficiency deviation data of each job node needs to be calculated. When the job nodes involved are , the amount of services of each job node and the total amount of services of all job nodes are first obtained, wherein the total amount of services refers to the total number of service requests received and to be processed by all job nodes within a certain period of time, that is, different types of services are classified according to different service lines and different functions, and the amount of services of each job is different. At the same time, the processing time data of each job node for each type of service is also obtained. Then the total service processing efficiency of all job nodes is calculated by the following formula: .

[0062] For any job node, the ratio of the sum of the processing time data to the amount of services can be calculated according to the above data to obtain the service processing efficiency of each job node: . Then the initial deviation data is obtained by the difference between the service processing efficiency of the job node and the total service processing efficiency, and the ratio of the difference to the total service processing efficiency, that is, the initial deviation data is calculated by the following formula: =( - ) / . ​​​

[0063] In order to ensure the rationality of the deviation data, the deviation data of each job node needs to be corrected, and the second efficiency deviation threshold of each job node is obtained, that is, the maximum deviation of the business processing efficiency of the job node from the total business processing efficiency is obtained DM When the initial deviation data is less than zero, and the absolute value of the initial deviation data is greater than the second efficiency deviation threshold, the absolute value of the initial deviation data is limited within the second efficiency deviation threshold, that is, the efficiency deviation data is set to the negative of the second efficiency deviation threshold, and at this time, it can be expressed by the following formula: if <0 and > , .

[0064] On the contrary, when the initial deviation data is greater than zero and greater than the second efficiency deviation threshold, the efficiency deviation data is set to the second efficiency deviation threshold, and at this time, it can be expressed by the following formula: if >0 and > , .

[0065] The embodiment corrects the deviation degree through the job node business processing efficiency analysis and the deviation degree correction mechanism of actual data, adjusts the deviation degree reasonably, ensures the dynamic optimization of the business distribution ratio, avoids the excessive concentration of resources, prevents the uneven business processing caused by efficiency difference, avoids the frequent ratio adjustment caused by small amplitude efficiency fluctuation, and further improves the business processing capacity and customer satisfaction of the entire financial institution, guarantees the stability and continuity of business processing.

[0066] Optionally, in the business processing method provided in the embodiment of the application, the initial distribution ratio of the M2 job nodes is adjusted to obtain M2 candidate distribution ratios, including: for a job node, if the job node has not adjusted the distribution ratio in the historical time period, a parameter difference value is calculated according to the efficiency deviation data, and a product of the parameter difference value and the initial distribution ratio is calculated to obtain the candidate distribution ratio of the job node; if the job node has adjusted the distribution ratio in the historical time period, a historical distribution ratio associated with the historical adjustment time is obtained, and a product of the parameter difference value and the historical distribution ratio is calculated to obtain the candidate distribution ratio of the job node.

[0067] Specifically, if >0, it indicates that the current job node processing is slow, and the distribution ratio needs to be lowered; if ​​<0, it indicates that the current job node processing is fast, and the distribution ratio needs to be increased. When adjusting the initial distribution ratio of the job node, it is first necessary to determine whether the job node is adjusted for the first time. That is, check if there is a record of adjusting the business distribution ratio in the historical time period. If the job node has not adjusted the distribution ratio in this historical time period, the parameter difference (1- ) is calculated according to the current efficiency deviation data, wherein the efficiency deviation data reflects the deviation between the business processing efficiency of the job node and the total business processing efficiency of the financial institution as a whole, and the parameter difference is a further quantification of the deviation. Then, the parameter difference is multiplied by the initial distribution ratio of the node to generate a candidate distribution ratio, which can be represented as = (1- ).

[0068] If the job node has experienced adjustment of the distribution ratio in the historical time period, the historical adjustment time needs to be traced back and the corresponding historical distribution ratio is extracted, wherein the historical distribution ratio is the adjusted distribution ratio corresponding to the historical adjustment time. On the basis of obtaining the historical distribution ratio, the parameter difference is calculated based on the efficiency deviation data, and then the parameter difference is multiplied by the historical distribution ratio to generate a candidate distribution ratio, which can be represented as: = (1- ).

[0069] The embodiment can calculate the candidate distribution ratio of the job node, which not only quickly responds to the instantaneous changes of the job node efficiency, but also adjusts the distribution ratio in time through the calculation of the parameter difference, avoids the situation that the job node is overloaded or underloaded, and also considers the recent work dynamics of the job node to prevent the distribution ratio from changing dramatically due to the efficiency fluctuation in the short term, ensuring the rationality and stability of business distribution, so that the adjustment is more close to the actual demand.

[0070] Optionally, in the method for processing a service provided by the embodiments of the present application, adjusting the M candidate distribution ratios comprises: for a job node, obtaining a distribution ratio threshold of the job node, wherein the distribution ratio threshold comprises an upper limit value of the distribution ratio and a lower limit value of the distribution ratio; in a case where the candidate distribution ratio of the job node is indicated as zero, determining the initial distribution ratio as the candidate distribution ratio; in a case where the candidate distribution ratio is not zero and the candidate distribution ratio is greater than the upper limit value of the distribution ratio, determining the minimum value between the candidate distribution ratio and the upper limit value of the distribution ratio as the candidate distribution ratio; in a case where the candidate distribution ratio is not zero and the candidate distribution ratio is less than the lower limit value of the distribution ratio, determining the maximum value between the candidate distribution ratio and the lower limit value of the distribution ratio as the candidate distribution ratio.

[0071] Specifically, after obtaining the candidate distribution ratios, each candidate distribution ratio needs to be fine-tuned to achieve service distribution that meets the demand of maximum efficiency and does not violate the preset upper and lower limit rules, thereby constructing a more robust and controllable dynamic service distribution. First, the distribution ratio threshold corresponding to each job node needs to be obtained, which contains the upper limit value of the distribution ratio and the lower limit value of the distribution ratio . These two values respectively define the highest and lowest limits of the proportion of services that can be distributed to the job nodes. For example, if the upper limit value of the distribution ratio is set to 0.3, it means that the proportion of services received by a job node cannot exceed 30% in any case, and the lower limit value of the distribution ratio is set to 0.05 to ensure that all job nodes should at least bear a certain amount of services, avoiding extreme concentration or idling of resources.

[0072] Further, it is judged whether the candidate distribution ratio is indicated as zero. If the candidate distribution ratio is 0, it represents that, according to the efficiency deviation data and the analysis of the historical adjustment time, a certain job node should not receive new services in theory, at this time, the initial distribution ratio of the job node can be directly used as the candidate distribution ratio, that is, when =0, no adjustment is needed. If the candidate distribution ratio is not zero but its value exceeds the preset upper limit value of the distribution ratio, measures can be taken to limit its growth to comply with the upper limit rule of service allocation, that is, by calculating the minimum value between the candidate distribution ratio and the upper limit value of the distribution ratio, which can be represented as: when ≠0, if > , =min( , ). For example, if the candidate distribution ratio is calculated as 0.4 and the upper limit value is 0.3, the adjusted candidate distribution ratio will be set to 0.3, i.e., the upper limit value.

[0073] If the candidate distribution ratio is lower than the lower limit value of the distribution ratio, it indicates that the amount of traffic allocated to the job node is too small to fully utilize its processing capacity or maintain a certain stability of traffic. At this time, the maximum value between the candidate distribution ratio and the lower limit value can be selected as the adjusted candidate distribution ratio, which can be expressed as: = max( , ). For example, the candidate distribution ratio is 0.03, and the lower limit value is 0.05, so the adjusted candidate distribution ratio is 0.05, i.e., the lower limit value.

[0074] In the process of dynamically adjusting the distribution ratio, the candidate distribution ratio is compared with the preset upper and lower limits and adjusted, so that even in the face of complex and variable business processing environment, the business allocation of the job node can be adjusted in a smart and stable manner, avoiding processing delay caused by excessive concentration of business, and preventing resource idling caused by insufficient allocation.

[0075] Optionally, in the business processing method provided in the embodiments of the present application, adjusting the M2 candidate distribution ratios to obtain M2 target distribution ratios includes: when the sum of the M2 candidate distribution ratios is equal to one, determining the M2 candidate distribution ratios as the M2 target distribution ratios; when the sum of the M2 candidate distribution ratios is not equal to one, calculating the sum of the M2 candidate distribution ratios to obtain a ratio sum, calculating the ratio of each candidate distribution ratio to the ratio sum to obtain M2 ratio values, and determining the M2 ratio values as the M2 target distribution ratios.

[0076] The adjustment of the candidate distribution ratio can also be judged according to the sum of the candidate distribution ratios. If the sum of the candidate distribution ratios of the job nodes (i.e., the job nodes in normal state) is exactly equal to one, the target distribution ratio can be set to the calculated and fine-tuned candidate distribution ratio , which can be expressed as: = 1; the target distribution ratio = .

[0077] However, due to calculation errors or the characteristics of the adjustment algorithm, the sum of the candidate distribution ratios may not be equal to one, at which time the adjustment stage needs to be entered to ensure that the total sum of the distribution ratios meets the requirements. That is, in order to make the sum of the distribution ratios of the M2 job nodes become one again, each candidate distribution ratio is adjusted according to its proportion of the ratio sum, i.e., a scaling operation is performed on the original ratio to ensure that the total sum of the adjusted distribution ratios of each job node meets the requirement of 100%, and thus the integrity and balance of business allocation are maintained, which can be expressed as: if​​ >1 or <1; = / .

[0078] The embodiment verifies the candidate distribution ratio, avoids the service distribution imbalance phenomenon caused by calculation errors or algorithm limitations in the dynamic adjustment process, improves the accuracy of service distribution, and guarantees the optimization of resource utilization.

[0079] The application embodiment further provides a service processing method, Figure 4 is a flowchart of an optional service processing method provided by the application embodiment, as shown in Figure 4 To achieve dynamic balanced distribution of financial services, first, after obtaining a financial service initiated by a target institution, the initial distribution strategy is obtained according to the service type of the financial service and the region number of the target region, then the initial distribution model is determined according to the initial distribution strategy, and the initial distribution ratio of the work node is extracted from the initial distribution model. Before the service runs, the initial distribution ratio of the financial service in each work node is adjusted according to the service running situation to obtain the corresponding target distribution ratio, and finally the financial service is segmented according to the target distribution ratio to obtain a plurality of segmented financial services, and the segmented financial services are distributed to the corresponding work nodes, and the services are distributed between the centers according to the ratio.

[0080] In the case where there is an abnormal work node in the work node, the initial distribution model is determined according to the initial distribution strategy, and the initial distribution ratio of each work node is extracted from the initial distribution model, the initial distribution ratio is adjusted to obtain the target distribution ratio, and the financial service is distributed to different work nodes according to the target distribution ratio, thereby achieving the technical effect of improving the processing efficiency and utilization rate of the work node when processing the service across the work nodes.

[0081] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0082] Embodiment 2

[0083] The application embodiment further provides a service processing device, and it should be noted that the service processing device of the application embodiment can be used to execute the service processing method provided by the application embodiment. The service processing device provided by the application embodiment is introduced as follows.

[0084] According to the embodiments of the present application, a device for implementing the processing method of the service is also provided, Figure 5 is a schematic diagram of a processing device of a service according to the embodiments of the present application, as shown in the figure, the device comprises a first acquisition unit 50, a determination unit 51, an adjustment unit 52. Figure 5

[0085] The first acquisition unit 50 is configured to acquire the financial services of the financial institutions in the target region, acquire an initial distribution strategy according to the service type of the financial services and the region number of the target region, and determine M job nodes according to the initial distribution strategy, wherein the initial distribution strategy is used to indicate the M job nodes receiving the distributed financial services, and M is a positive integer;

[0086] The determination unit 51 is configured to, if there is a job node in an abnormal state in the M job nodes, determine an initial distribution model according to the initial distribution strategy, and extract an initial distribution proportion of the M job nodes from the initial distribution model, wherein the initial distribution model is used to indicate the initial distribution proportion of the financial services distributed to the M job nodes;

[0087] The adjustment unit 52 is configured to adjust the M initial distribution proportions to obtain M target distribution proportions, segment the financial services according to the M target distribution proportions to obtain M segmented financial services, distribute the M segmented financial services to the M job nodes, and perform service processing on the M segmented financial services if the M segmented financial services are received by the M job nodes.

[0088] ​The processing device for a service provided by the embodiment of the present application obtains the financial service of the financial institution in the target region through the first obtaining unit 50, obtains the initial distribution strategy according to the service type of the financial service and the region number of the target region, and determines the M job nodes according to the initial distribution strategy, wherein the initial distribution strategy is used to indicate the M job nodes receiving the distributed financial service, and M is a positive integer; the determining unit 51 determines the initial distribution model according to the initial distribution strategy if there is an abnormal job node in the M job nodes, and extracts the initial distribution proportion of the M job nodes from the initial distribution model, wherein the initial distribution model is used to indicate the initial distribution proportion of the service of the financial service distributed to the M job nodes; the adjusting unit 52 adjusts the M initial distribution proportions to obtain the M target distribution proportions, and segments the financial service according to the M target distribution proportions to obtain the M segmented financial services, and distributes the M segmented financial services to the M job nodes, wherein the service processing is performed on the M segmented financial services if the M segmented financial services are received by the M job nodes, thereby solving the technical problem of uneven service distribution and low service processing efficiency when the service processing is performed across the job nodes. According to the initial distribution strategy, the initial distribution model is determined and the initial distribution proportion of each job node is extracted from the initial distribution model if there is an abnormal job node in the job nodes, the initial distribution proportion is adjusted to obtain the target distribution proportion, and the financial service is distributed to different job nodes according to the target distribution proportion, thereby achieving the technical effects of improving the processing efficiency when the service processing is performed across the job nodes and the utilization rate of the job nodes.

[0089] Optionally, in the processing device for a service provided by the embodiment of the present application, the device further comprises: a second obtaining unit, configured to obtain the node log file of the M job nodes after the M job nodes are determined according to the initial distribution strategy, extract the historical adjustment time from the node log file, and determine the service obtaining time when the financial service is obtained, wherein the historical adjustment time refers to the time when the initial distribution proportion is adjusted in the historical time period; a calculating unit, configured to calculate the difference between the service obtaining time and the historical adjustment time to obtain the time difference, and stop the step of adjusting the M initial distribution proportions if the time difference is less than the preset time interval; and an executing unit, configured to execute the step of adjusting the M initial distribution proportions if the time difference is greater than or equal to the preset time interval.

[0090] Optionally, in the service processing apparatus provided by the embodiment of the present application, the adjusting unit 52 comprises: a division module, configured to divide the M job nodes into M1 job nodes and M2 job nodes according to the node states of the M job nodes, and set the target distribution ratio of the M1 job nodes as a preset ratio, wherein the node state of each job node comprises at least one of the following: an abnormal state and a normal state, the node state of the M1 job nodes is the abnormal state, the node state of the M2 job nodes is the normal state, M1+M2=M, M1 and M2 are positive integers; a first acquisition module, configured to acquire efficiency deviation data of a job node in the M2 job nodes and a first efficiency deviation threshold of the job node, wherein the efficiency deviation data refers to a ratio of the service processing efficiency of the job node deviating from the total service processing efficiency associated with the financial institution; a stopping module, configured to stop adjusting the initial distribution ratio associated with the job node in the case that the efficiency deviation data is less than or equal to the first efficiency deviation threshold; an adjusting module, configured to adjust the initial distribution ratio of the M2 job nodes in the case that the absolute value of the efficiency deviation data is greater than the first efficiency deviation threshold, to obtain M2 candidate distribution ratios, and adjust the M2 candidate distribution ratios to obtain M2 target distribution ratios; and a forming module, configured to form the M target distribution ratios from the M1 preset ratios and the M2 target distribution ratios.

[0091] Optionally, in the service processing apparatus provided by the embodiment of the present application, the adjusting unit 52 comprises: a second acquisition module, configured to acquire the total amount of services of the M job nodes and processing time consumption data of the M job nodes processing K types of services to obtain total time consumption data, calculate a ratio of the total time consumption data to the total amount of services to obtain the total service processing efficiency, wherein K is a positive integer; a third acquisition module, configured to acquire the amount of services of a job node according to the service type of the financial service and acquire processing time consumption data of the job node processing K types of services to calculate a ratio of the sum of the K processing time consumption data to the amount of services to obtain the service processing efficiency of the job node; a first calculation module, configured to calculate a difference between the service processing efficiency of the job node and the total service processing efficiency to obtain a processing efficiency difference, and calculate a ratio of the processing efficiency difference to the total service processing efficiency to obtain initial deviation data; a fourth acquisition module, configured to acquire a second efficiency deviation threshold of the job node, and determine the negative number of the second efficiency deviation threshold as the efficiency deviation data in the case that the initial deviation data is less than zero and the absolute value of the initial deviation data is greater than the second efficiency deviation threshold; and a first determination module, configured to determine the second efficiency deviation threshold as the efficiency deviation data in the case that the initial deviation data is greater than zero and the initial deviation data is greater than the second efficiency deviation threshold.

[0092] Optionally, in the service processing apparatus provided by the embodiment of the present application, the adjusting unit 52 comprises: a second calculation module, configured to, for a job node, if the job node has not adjusted the distribution ratio in the historical time period, calculate a parameter difference value according to the efficiency deviation data, and calculate a product of the parameter difference value and the initial distribution ratio to obtain a candidate distribution ratio of the job node; and a fifth acquisition module, configured to, if the job node has adjusted the distribution ratio in the historical time period, acquire a historical distribution ratio associated with a historical adjustment time, and calculate a product of the parameter difference value and the historical distribution ratio to obtain the candidate distribution ratio of the job node.

[0093] Optionally, in the service processing apparatus provided by the embodiment of the present application, the adjusting unit 52 comprises: a sixth acquisition module, configured to, for a job node, acquire a distribution ratio threshold of the job node, wherein the distribution ratio threshold comprises an upper limit value of the distribution ratio and a lower limit value of the distribution ratio; a second determination module, configured to, if the candidate distribution ratio of the job node indicates zero, determine the initial distribution ratio as the candidate distribution ratio; a third determination module, configured to, if the candidate distribution ratio is not zero and the candidate distribution ratio is greater than the upper limit value of the distribution ratio, determine a minimum value between the candidate distribution ratio and the upper limit value of the distribution ratio as the candidate distribution ratio; and a fourth determination module, configured to, if the candidate distribution ratio is not zero and the candidate distribution ratio is less than the lower limit value of the distribution ratio, determine a maximum value between the candidate distribution ratio and the lower limit value of the distribution ratio as the candidate distribution ratio.

[0094] Optionally, in the service processing apparatus provided by the embodiment of the present application, the adjusting unit 52 comprises: a fifth determination module, configured to, if the sum of the M2 candidate distribution ratios is equal to one, determine the M2 candidate distribution ratios as the M2 target distribution ratios; a third calculation module, configured to, if the sum of the M2 candidate distribution ratios is not equal to one, calculate the sum of the M2 candidate distribution ratios to obtain a ratio sum; and a fourth calculation module, configured to calculate a ratio value of each candidate distribution ratio and the ratio sum to obtain M2 ratio values, and determine the M2 ratio values as the M2 target distribution ratios.

[0095] It should be noted that the first acquisition unit 50, the determination unit 51 and the adjusting unit 52 correspond to the steps S201 to S203 in the embodiment 1, and have the same instances and application scenarios as the corresponding steps, but are not limited to the content disclosed in the above embodiment one. It should be noted that the above modules or units can be hardware components or software components stored in the memory (for example, the memory 104) and processed by one or more processors (for example, the processors 102a, 102b, …, 102n), and the above units can also be a part of the apparatus and can run in the computer terminal 10 provided in the embodiment one.

[0096] Embodiment 3

[0097] The embodiment of the present application can provide a computer terminal, which can be any one of computer terminal devices in a computer terminal group. Alternatively, in the embodiment, the computer terminal can be replaced by a mobile terminal or an electronic device or other terminal device.

[0098] Alternatively, in the embodiment, the computer terminal can be located in at least one of a plurality of network devices in a computer network.

[0099] In the embodiment, the computer terminal can execute program codes for the following steps in the processing method of the business: obtaining a financial business of a financial institution in a target region, obtaining an initial distribution strategy according to a business type of the financial business and a region number of the target region, and determining M job nodes according to the initial distribution strategy, wherein the initial distribution strategy is used to indicate M job nodes receiving distributed financial business, and M is a positive integer; if there is an abnormal job node in the M job nodes, determining an initial distribution model according to the initial distribution strategy, and extracting initial distribution proportions of the M job nodes from the initial distribution model, wherein the initial distribution model is used to indicate initial distribution proportions of the financial business distributed to the M job nodes; adjusting the M initial distribution proportions to obtain M target distribution proportions, and segmenting the financial business according to the M target distribution proportions to obtain M segmented financial businesses, and distributing the M segmented financial businesses to the M job nodes, wherein if the M job nodes receive the M segmented financial businesses, the M segmented financial businesses are processed.

[0100] Alternatively, the computer terminal can execute program codes for the following steps in the processing method of the business: obtaining a node log file of the M job nodes, extracting a historical adjustment time from the node log file, and determining a business obtaining time when the financial business is obtained, wherein the historical adjustment time is a time when the initial distribution proportion is adjusted in a historical time period; calculating a difference between the business obtaining time and the historical adjustment time to obtain a time difference, and stopping the step of adjusting the M initial distribution proportions if the time difference is less than a preset time interval; and executing the step of adjusting the M initial distribution proportions if the time difference is greater than or equal to the preset time interval.

[0101] Optionally, the computer terminal can execute program codes of the following steps in the business processing method: dividing the M job nodes into M1 job nodes and M2 job nodes according to the node states of the M job nodes, and setting the target distribution ratio of the M1 job nodes as a preset ratio, wherein the node state of each job node at least includes one of the following: an abnormal state and a normal state, the node state of the M1 job nodes is the abnormal state, the node state of the M2 job nodes is the normal state, M1+M2=M, M1 and M2 are positive integers; for one of the M2 job nodes, obtaining efficiency deviation data of the job node, and obtaining a first efficiency deviation threshold of the job node, wherein the efficiency deviation data refers to the ratio of the business processing efficiency of the job node deviating from the total business processing efficiency associated with the financial institution; in the case that the efficiency deviation data is less than or equal to the first efficiency deviation threshold, stopping adjusting the initial distribution ratio associated with the job node; in the case that the absolute value of the efficiency deviation data is greater than the first efficiency deviation threshold, adjusting the initial distribution ratio of the M2 job nodes to obtain M2 candidate distribution ratios, and adjusting the M2 candidate distribution ratios to obtain M2 target distribution ratios; the M target distribution ratios are composed of the M1 preset ratios and the M2 target distribution ratios.

[0102] Optionally, the computer terminal can execute program codes of the following steps in the business processing method: obtaining the total amount of business of the M job nodes, and obtaining processing time data of the M job nodes processing K types of business to obtain total time consumption data, calculating the ratio of the total time consumption data to the total amount of business to obtain the total business processing efficiency, wherein K is a positive integer; for one job node, obtaining the amount of business of the job node according to the business type of the financial business, and obtaining processing time data of the job node processing K types of business, calculating the ratio of the sum of the K processing time data to the amount of business to obtain the business processing efficiency of the job node; calculating the difference between the business processing efficiency of the job node and the total business processing efficiency to obtain a processing efficiency difference, and calculating the ratio of the processing efficiency difference to the total business processing efficiency to obtain an initial deviation data; obtaining a second efficiency deviation threshold of the job node, in the case that the initial deviation data is less than zero and the absolute value of the initial deviation data is greater than the second efficiency deviation threshold, determining the negative number of the second efficiency deviation threshold as the efficiency deviation data; in the case that the initial deviation data is greater than zero and the initial deviation data is greater than the second efficiency deviation threshold, determining the second efficiency deviation threshold as the efficiency deviation data.

[0103] Optionally, the computer terminal described above can execute the following steps in the business processing method: For a job node, if the job node has not adjusted the distribution ratio within the historical time period, calculate the parameter difference based on the efficiency deviation data, and calculate the product of the parameter difference and the initial distribution ratio to obtain the candidate distribution ratio of the job node; if the job node has adjusted the distribution ratio within the historical time period, obtain the historical distribution ratio associated with the historical adjustment time, and calculate the product of the parameter difference and the historical distribution ratio to obtain the candidate distribution ratio of the job node.

[0104] Optionally, the computer terminal described above can execute the following steps in the business processing method: For a job node, obtain the distribution ratio threshold of the job node, wherein the distribution ratio threshold includes an upper limit value and a lower limit value of the distribution ratio; when the candidate distribution ratio indication of the job node is zero, determine the initial distribution ratio as the candidate distribution ratio; when the candidate distribution ratio is not zero and the candidate distribution ratio is greater than the upper limit value of the distribution ratio, determine the minimum value between the candidate distribution ratio and the upper limit value of the distribution ratio as the candidate distribution ratio; when the candidate distribution ratio is not zero and the candidate distribution ratio is less than the lower limit value of the distribution ratio, determine the maximum value between the candidate distribution ratio and the lower limit value of the distribution ratio as the candidate distribution ratio.

[0105] Optionally, the computer terminal described above can execute the following steps in the business processing method: when the sum of the M2 candidate distribution ratios is equal to one, determine the M2 candidate distribution ratios as the M2 target distribution ratios; when the sum of the M2 candidate distribution ratios is not equal to one, calculate the sum of the M2 candidate distribution ratios to obtain a ratio sum; calculate the ratio of each candidate distribution ratio to the ratio sum to obtain M2 ratio values, and determine the M2 ratio values ​​as the M2 target distribution ratios.

[0106] Optionally, Figure 6 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 6 As shown, the electronic device may include: one or more ( Figure 6 (Only one is shown) Processor 602, memory 604, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0107] The memory can be configured to store software programs and modules, such as program instructions / modules corresponding to the service processing method and device in the embodiments of the present application. The processor executes various functions and data processing by running the software programs and modules stored in the memory, that is, implements the service processing method described above. The memory can include a high-speed random access memory, and can further include a nonvolatile memory, such as one or more magnetic storage devices, flash memories, or other nonvolatile solid-state memories. In some examples, the memory can further include a memory remotely arranged with respect to the processor, and the remote memory can be connected to the terminal through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0108] The processor can call information and application programs stored in the memory through the transmission device to execute the steps in the service processing method described above.

[0109] Those skilled in the art can understand that Figure 6 The structure shown is only schematic, and the electronic device can also be a terminal device such as a smart phone, a tablet computer, a palm computer, a mobile Internet device (MID), a PAD, and the like. Figure 6 It does not limit the structure of the electronic device described above. For example, the electronic device can further include more or less components (such as a network interface, a display device, and the like) than those shown in the figure, or have a different configuration from that shown in the figure. Figure 6 Figure 6 The structure shown is only schematic, and the electronic device can also be a terminal device such as a smart phone, a tablet computer, a palm computer, a mobile Internet device (MID), a PAD, and the like.

[0110] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by programs instructing the hardware of the terminal device, and the programs can be stored in a computer readable storage medium, which can include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and the like.

[0111] Embodiment 4

[0112] The embodiments of the present application also provide a storage medium. Optionally, in the present embodiment, the storage medium can be used to save the program code executed by the service processing method provided in Embodiment 1.

[0113] Optionally, in the present embodiment, the storage medium can be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0114] ​Optionally, in the embodiment, the storage medium is configured to store program code for performing the following steps: obtaining the financial services of the financial institution in the target region, obtaining an initial distribution strategy according to the service type of the financial service and the region number of the target region, and determining M job nodes according to the initial distribution strategy, wherein the initial distribution strategy is used to indicate the M job nodes receiving the distributed financial service, and M is a positive integer; if there is an abnormal job node in the M job nodes, an initial distribution model is determined according to the initial distribution strategy, and an initial distribution ratio of the M job nodes is extracted from the initial distribution model, wherein the initial distribution model is used to indicate the initial distribution ratio of the financial service distributed to the M job nodes; the M initial distribution ratios are adjusted to obtain M target distribution ratios, and the financial service is segmented according to the M target distribution ratios to obtain M segmented financial services, and the M segmented financial services are distributed to the M job nodes, wherein in the case that the M job nodes receive the M segmented financial services, the M segmented financial services are processed.

[0115] The application also provides a computer program product, which is adapted to execute the steps of the processing method of the business when executed on a data processing device.

[0116] The above-mentioned sequence numbers of the embodiments of the application are only for description, and do not represent the advantages or disadvantages of the embodiments.

[0117] In the above-mentioned embodiments of the application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0118] In the several embodiments provided by the present application, it should be understood that the disclosed technology can be implemented in other ways. Of course, the unit embodiment described above is only illustrative, and for example, the division of units is only a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, unit or module, and can be electrical or other forms.

[0119] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment.

[0120] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0121] If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in the form of a contribution to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0122] The above is only the preferred embodiment of the present application, and it should be pointed out that, for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A method for processing business transactions, characterized in that, include: The system acquires financial transactions of financial institutions in a target region, obtains an initial distribution strategy based on the business type of the financial transactions and the region code of the target region, and determines M job nodes based on the initial distribution strategy. The initial distribution strategy is used to indicate the M job nodes that receive the distributed financial transactions, and M is a positive integer. If any of the M job nodes is in an abnormal state, an initial distribution model is determined according to the initial distribution strategy, and the initial distribution ratio of the M job nodes is extracted from the initial distribution model. The initial distribution model is used to indicate the initial distribution ratio of the financial business to be distributed to the M job nodes. The M initial distribution ratios are adjusted to obtain M target distribution ratios. The financial business is then divided according to the M target distribution ratios to obtain M segmented financial businesses. The M segmented financial businesses are then distributed to the M work nodes. When the M work nodes receive the M segmented financial businesses, they perform business processing on the M segmented financial businesses.

2. The method according to claim 1, characterized in that, After determining the M job nodes according to the initial distribution strategy, the method further includes: Obtain the node log files of the M job nodes, extract the historical adjustment time from the node log files, and determine the business acquisition time when the financial business is acquired. The historical adjustment time refers to the time when the initial distribution ratio is adjusted within a historical time period. Calculate the difference between the service acquisition time and the historical adjustment time to obtain the time difference. If the time difference is less than a preset time interval, stop executing the step of adjusting the M initial distribution ratios. If the time difference is greater than or equal to the preset time interval, the step of adjusting the initial distribution ratio of M units is performed.

3. The method according to claim 1, characterized in that, Adjusting the M initial distribution ratios yields M target distribution ratios, including: The M job nodes are divided into M1 job nodes and M2 job nodes according to their node states, and the target distribution ratio of the M1 job nodes is set to a preset ratio. The node state of each job node includes at least one of the following: abnormal state and normal state. The node state of the M1 job nodes is the abnormal state, and the node state of the M2 job nodes is the normal state. M1 + M2 = M, and M1 and M2 are positive integers. For one of the M2 job nodes, obtain the efficiency deviation data of the job node and obtain the first efficiency deviation threshold of the job node, wherein the efficiency deviation data refers to the ratio of the business processing efficiency of the job node to the total business processing efficiency associated with the financial institution; If the efficiency deviation data is less than or equal to the first efficiency deviation threshold, stop adjusting the initial distribution ratio associated with the job node; If the absolute value of the efficiency deviation data is greater than the first efficiency deviation threshold, the initial distribution ratio of the M2 job nodes is adjusted to obtain M2 candidate distribution ratios, and the M2 candidate distribution ratios are further adjusted to obtain M2 target distribution ratios. The M target distribution ratios are composed of M1 preset ratios and M2 target distribution ratios.

4. The method according to claim 3, characterized in that, The efficiency deviation data for each job node is calculated in the following way: Obtain the total workload of the M job nodes, and obtain the processing time data of the M job nodes for processing K types of business to obtain the total processing time data. Calculate the ratio of the total processing time data to the total workload to obtain the total business processing efficiency, where K is a positive integer. For a job node, the business volume of the job node is obtained according to the business type of the financial business, and the processing time data of the job node for processing the K types of business is obtained. The ratio of the sum of the K processing time data to the business volume is calculated to obtain the business processing efficiency of the job node. Calculate the difference between the business processing efficiency of the job node and the total business processing efficiency to obtain the processing efficiency difference, and calculate the ratio of the processing efficiency difference to the total business processing efficiency to obtain the initial deviation data; Obtain the second efficiency deviation threshold of the job node. If the initial deviation data is less than zero and the absolute value of the initial deviation data is greater than the second efficiency deviation threshold, determine the negative number of the second efficiency deviation threshold as the efficiency deviation data. If the initial deviation data is greater than zero and the initial deviation data is greater than the second efficiency deviation threshold, the second efficiency deviation threshold is determined as the efficiency deviation data.

5. The method according to claim 3, characterized in that, The initial distribution ratios of the M2 job nodes are adjusted to obtain M2 candidate distribution ratios, including: For a job node, if the job node has not adjusted the distribution ratio within the historical time period, the parameter difference is calculated based on the efficiency deviation data, and the product of the parameter difference and the initial distribution ratio is calculated to obtain the candidate distribution ratio of the job node. If the job node has adjusted the distribution ratio within a historical time period, the historical distribution ratio associated with the historical adjustment time is obtained, and the product of the parameter difference and the historical distribution ratio is calculated to obtain the candidate distribution ratio of the job node.

6. The method according to claim 3, characterized in that, Adjusting the distribution ratio of the M2 candidates includes: For a job node, obtain the distribution ratio threshold of the job node, wherein the distribution ratio threshold includes an upper limit value and a lower limit value of the distribution ratio; When the candidate distribution ratio indicator of the job node is zero, the initial distribution ratio is determined as the candidate distribution ratio; If the candidate distribution ratio is not zero and the candidate distribution ratio is greater than the upper limit of the distribution ratio, the minimum value between the candidate distribution ratio and the upper limit of the distribution ratio shall be determined as the candidate distribution ratio. If the candidate distribution ratio is not zero and the candidate distribution ratio is less than the lower limit of the distribution ratio, the maximum value between the candidate distribution ratio and the lower limit of the distribution ratio shall be determined as the candidate distribution ratio.

7. The method according to claim 3, characterized in that, The M2 candidate distribution ratios are adjusted to obtain M2 target distribution ratios, including: If the sum of the M2 candidate distribution ratios is equal to one, the M2 candidate distribution ratios are determined as the M2 target distribution ratios; If the sum of the distribution ratios of the M2 candidates is not equal to one, calculate the sum of the distribution ratios of the M2 candidates to obtain the ratio sum; Calculate the ratio of each candidate distribution ratio to the sum of the ratios to obtain M2 ratio values, and determine the M2 ratio values ​​as the M2 target distribution ratios.

8. A business processing apparatus, characterized in that, include: The first acquisition unit is used to acquire financial business of financial institutions in a target area, acquire an initial distribution strategy according to the business type of the financial business and the area code of the target area, and determine M job nodes according to the initial distribution strategy, wherein the initial distribution strategy is used to indicate the M job nodes that receive the distributed financial business, and M is a positive integer; The determining unit is configured to determine an initial distribution model based on the initial distribution strategy if there is an abnormal state among the M job nodes, and extract the initial distribution ratio of the M job nodes from the initial distribution model, wherein the initial distribution model is used to indicate the initial distribution ratio of the financial business to be distributed to the M job nodes. An adjustment unit is used to adjust M initial distribution ratios to obtain M target distribution ratios, and to divide the financial business according to the M target distribution ratios to obtain M divided financial businesses. The M divided financial businesses are then distributed to the M work nodes. When the M work nodes receive the M divided financial businesses, they perform business processing on the M divided financial businesses.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the processing method of the service according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the processing method of the service described in any one of claims 1 to 7.

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