Financial platform optimization method and system

Abstract

The invention discloses a financial platform optimization method and system, and relates to the technical field of financial platform optimizing.A financial platform is divided into a plurality of sub-platforms according to each plate, and the optimization system monitors operation data of each sub-platform in real time in the operation process of the financial platform; the method comprises the following steps: acquiring operation data of each sub-platform, predicting traffic consumption of each sub-platform based on the operation data, dividing the sub-platforms into a traffic increase set or a traffic decrease set based on a traffic consumption prediction result, performing traffic optimization on the sub-platforms in the traffic increase set and the traffic decrease set, and analyzing the traffic increase set and the traffic decrease set. And judging whether the financial platform needs to additionally buy the flow, and generating a corresponding management strategy based on a judgment result. The flow configuration is dynamically adjusted after the flow prediction, so that the platform can quickly respond when the flow fluctuation is relatively large, the resource utilization rate is improved, and the unnecessary resource waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of financial platform optimization, and in particular to a financial platform optimization method and system. Background Art

[0002] With the advancement of data technology, financial platforms have begun to widely use big data and artificial intelligence technologies to optimize business processes. Financial platforms are digital platforms that provide users and enterprises with a variety of financial services, covering payment, lending, investment, insurance, asset management and other fields.

[0003] The prior art has the following defects:

[0004] 1. Traditional traffic management technologies usually allocate traffic based on preset static rules or indicators. The resource configuration of each sub-platform is determined at the time of deployment and cannot be flexibly adjusted. This allocation method is difficult to effectively adapt to fluctuations in traffic demand, resulting in rigid resource allocation. For sub-platforms with increased traffic demand, resources may be insufficient, while for sub-platforms with reduced demand, resources may be excessive, thereby reducing resource utilization;

[0005] 2. The traffic management systems of many financial platforms lack forward-looking forecasting capabilities and usually rely on current traffic status or historical data for passive regulation. Such platforms only take measures when resources are tight or traffic changes abnormally, and are unable to perceive impending traffic change trends in advance. This lack of predictive management can easily lead to insufficient system preparedness.

[0006] Based on this, the present invention proposes a financial platform optimization method and system, which dynamically adjusts the traffic configuration after traffic prediction, which helps the platform respond quickly when the traffic fluctuates greatly, thereby improving resource utilization and reducing unnecessary resource waste. Summary of the invention

[0007] The purpose of the present invention is to provide a financial platform optimization method and system to solve the deficiencies in the background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a financial platform optimization method, the optimization method comprising the following steps:

[0009] After the optimization system obtains the information of the financial platform, it divides the financial platform into several sub-platforms according to various sectors. During the operation of the financial platform, the optimization system monitors the operation data of each sub-platform in real time, and predicts the traffic consumption of each sub-platform based on the operation data. Based on the traffic consumption prediction results, the sub-platform is classified into a traffic increase set or a traffic decrease set;

[0010] Optimize the traffic of sub-platforms in the traffic increase set and traffic decrease set, analyze the traffic increase set and traffic decrease set, determine whether the financial platform needs to buy additional traffic, and generate corresponding management strategies based on the judgment results;

[0011] After regularly obtaining the relative importance of all sub-platforms in the financial platform, the relative importance of the sub-platforms and the traffic consumption forecast results are comprehensively analyzed, and a ranking coefficient is output for each sub-platform. All sub-platforms are sorted according to the ranking coefficient to generate a platform list. The next day, the priority of traffic input for each sub-platform is generated based on the platform list.

[0012] In a preferred embodiment, during the operation of the financial platform, the optimization system monitors the operation data of each sub-platform in real time, and predicts the traffic consumption of each sub-platform based on the operation data, including the following steps:

[0013] The optimization system monitors the operation data of each sub-platform in real time, including the user growth index, data transmission index and cache hit index of the sub-platform;

[0014] The user growth index, data transmission index and cache hit index are calculated comprehensively to obtain the operation coefficient of the sub-platform;

[0015] The obtained operation coefficient is compared with a preset operation threshold, and the operation threshold is used to predict the future traffic consumption status of the sub-platform;

[0016] If the operation coefficient is less than or equal to the operation threshold, it is predicted that the future traffic consumption of the sub-platform will decrease. If the operation coefficient is greater than the operation threshold, it is predicted that the future traffic consumption of the sub-platform will increase.

[0017] In a preferred embodiment, the user growth index, the data transmission index and the cache hit index are comprehensively calculated to obtain the operation coefficient of the sub-platform, and the expression is: In the formula, yx z is the operating coefficient, UGI is the user growth index, DTI is the data transmission index, CHI is the cache hit index, α, β, γ are the proportional coefficients of the user growth index, data transmission index and cache hit index respectively, and α, β, γ are all greater than 0.

[0018] In a preferred embodiment, based on the traffic consumption prediction result, the sub-platforms are classified into a traffic increase set or a traffic decrease set, and traffic optimization is performed on the sub-platforms in the traffic increase set and the traffic decrease set, including the following steps:

[0019] After obtaining the traffic forecast results of all sub-platforms, if the traffic consumption of the sub-platform is predicted to decrease in the future, the sub-platform is classified into the traffic reduction set; if the traffic consumption of the sub-platform is predicted to increase in the future, the sub-platform is classified into the traffic increase set;

[0020] Traffic optimization is performed on the sub-platforms in the traffic increase set and the traffic decrease set. The optimization algorithm is: In the formula, lg new For the optimized flow, lg old is the traffic before optimization, yx z is the operating coefficient, YZ is the operating threshold, yx z >YZ indicates that the sub-platform is included in the traffic increase set, yx z ≤YZ means that the sub-platform is included in the flow reduction set.

[0021] In a preferred embodiment, analyzing the flow increase set and the flow decrease set to determine whether the financial platform needs to purchase additional flow includes the following steps:

[0022] Calculate the flow overflow of the flow reduction set, the expression is: lg c =lg old -lg new , where lg new For the optimized flow, lg old is the flow before optimization, lg c is the excess flow;

[0023] Calculate the flow demand of the flow increase set, the expression is: lg x =lg new -lg old , where lg new For the optimized flow, lg old is the flow before optimization, lg x is the flow demand;

[0024] If the traffic demand is greater than the surplus traffic, it is judged that the financial platform needs to purchase additional traffic. If the traffic demand is less than or equal to the surplus traffic, it is judged that the financial platform does not need to purchase additional traffic.

[0025] In a preferred embodiment, after periodically obtaining the relative importance of all sub-platforms in the financial platform, the relative importance of the sub-platforms and the traffic consumption prediction results are comprehensively analyzed, and a ranking coefficient is output for each sub-platform. After all sub-platforms are ranked according to the ranking coefficients, a platform list is generated, including the following steps:

[0026] Obtain the historical transaction volume and transaction amount of the sub-platform, normalize the transaction volume and transaction amount, map the value range of the transaction volume and transaction amount to [0,1], obtain the normalized transaction volume value and the normalized transaction amount value, sum the normalized transaction volume value and the normalized transaction amount value to obtain the influence value of the sub-platform, sum the influence values ​​of all sub-platforms to obtain the total influence value, and obtain the influence ratio of the sub-platform by dividing the influence value by the total influence value;

[0027] Multiply the impact ratio of the sub-platform by the running coefficient to output the sorting coefficient, sort all sub-platforms from large to small according to the sorting coefficient, generate a platform list, and allocate initial traffic to each sub-platform in the positive order of the platform list the next day.

[0028] In a preferred embodiment, the calculation expression of the user growth index is: Where UGI is the user growth index, N t 、N t-1 Respectively represent the number of active users at the current time t and the previous time t-1, R t Indicates the current number of new user registrations, C t represents the number of active behaviors of users on the sub-platform, R max , C max represents the benchmark maximum value, W1, W2, W3 are weight factors, and W1+W2+W3=1, ∈ is the correction factor;

[0029] The calculation expression of the data transmission index is: Where DTI is the data transmission index, S i is the packet size of the i-th type request, C i is the data compression ratio of the i-th type request, L i is the transmission link efficiency of the i-th type of request, T total is the total transmission time, F retrans 、F total Respectively represent the amount of retransmitted data and the total amount of data;

[0030] The calculation expression of the cache hit index is: Where CHI is the cache hit index, H direct is the number of requests that directly hit the cache, H lazy Indicates the number of requests hit by lazy loading, T requests Indicates the total number of requests, M evicted Indicates the amount of data cleared from the cache, M total Indicates the total cache capacity, D reuse Indicates the amount of data reused, D total Indicates the amount of data in all caches.

[0031] A financial platform optimization system, comprising a traffic consumption prediction module, an optimization module, and a priority generation module;

[0032] Traffic consumption prediction module: After obtaining the financial platform information, the financial platform is divided into several sub-platforms according to various sectors. During the operation of the financial platform, the optimization system monitors the operation data of each sub-platform in real time and predicts the traffic consumption of each sub-platform based on the operation data;

[0033] Optimization module: Based on the traffic consumption prediction results, the sub-platforms are classified into the traffic increase set or the traffic decrease set, and the traffic of the sub-platforms in the traffic increase set and the traffic decrease set is optimized. The traffic increase set and the traffic decrease set are analyzed to determine whether the financial platform needs to purchase additional traffic, and the corresponding management strategy is generated based on the judgment results;

[0034] Priority generation module: After regularly obtaining the relative importance of all sub-platforms in the financial platform, the relative importance of the sub-platforms and the traffic consumption forecast results are comprehensively analyzed, and a ranking coefficient is output for each sub-platform. All sub-platforms are sorted according to the ranking coefficient to generate a platform list. The next day, the priority of traffic input for each sub-platform is generated based on the platform list.

[0035] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0036] The present invention divides the financial platform into several sub-platforms according to each section. During the operation of the financial platform, the optimization system monitors the operation data of each sub-platform in real time, and predicts the flow consumption of each sub-platform based on the operation data. Based on the flow consumption prediction result, the sub-platform is classified into a flow increase set or a flow reduction set, and the flow of the sub-platforms in the flow increase set and the flow reduction set is optimized. The flow increase set and the flow reduction set are analyzed to determine whether the financial platform needs to buy additional flow, and a corresponding management strategy is generated based on the judgment result. By dynamically adjusting the flow configuration after flow prediction, it is helpful for the platform to respond quickly when the flow fluctuation is large, thereby improving resource utilization and reducing unnecessary resource waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0038] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Example 1: Please refer to Figure 1 As shown, the financial platform optimization method described in this embodiment includes the following steps:

[0041] After the optimization system obtains the financial platform information, it divides the financial platform into several sub-platforms according to various sectors. During the operation of the financial platform, the optimization system monitors the operation data of each sub-platform in real time, and predicts the traffic consumption of each sub-platform based on the operation data. Based on the traffic consumption prediction results, the sub-platform is classified into a traffic increase set or a traffic decrease set, and the traffic of the sub-platforms in the traffic increase set and the traffic decrease set is optimized. The traffic increase set and the traffic decrease set are analyzed to determine whether the financial platform needs to purchase additional traffic. Based on the judgment results, the corresponding management strategy is generated. After regularly obtaining the relative importance of all sub-platforms in the financial platform, the relative importance of the sub-platform and the traffic consumption prediction results are comprehensively analyzed, and a ranking coefficient is output for each sub-platform. All sub-platforms are sorted according to the ranking coefficient to generate a platform list, and the priority of traffic input for each sub-platform is generated according to the platform list the next day.

[0042] This application divides the financial platform into several sub-platforms according to various sectors. During the operation of the financial platform, the optimization system monitors the operation data of each sub-platform in real time, and predicts the traffic consumption of each sub-platform based on the operation data. Based on the traffic consumption prediction results, the sub-platforms are classified into a traffic increase set or a traffic reduction set, and the traffic of the sub-platforms in the traffic increase set and the traffic reduction set is optimized. The traffic increase set and the traffic reduction set are analyzed to determine whether the financial platform needs to buy additional traffic, and the corresponding management strategy is generated based on the judgment results. Dynamically adjusting the traffic configuration after traffic prediction helps the platform respond quickly when traffic fluctuations are large, thereby improving resource utilization and reducing unnecessary resource waste.

[0043] Embodiment 2: After the optimization system obtains the financial platform information, the financial platform is divided into several sub-platforms according to each section, including the following steps:

[0044] Obtain basic information of the financial platform through API, database, or other data interfaces, including data and operating status of each functional module.

[0045] Information Type:

[0046] The overall business classification of the platform (such as payment, lending, investment, insurance, etc.).

[0047] Historical traffic data and real-time monitoring data of each functional module.

[0048] Hardware and software resource consumption of each functional module.

[0049] Dependency and collaboration model between functional modules.

[0050] Classify platform sections by functional type (such as trading services, user management, risk control services, etc.).

[0051] Determine the interdependencies and resource sharing patterns among sectors.

[0052] Key indicators extraction:

[0053] Extract the core indicators of each functional module, such as traffic consumption, processing delay, error rate, etc.

[0054] Identify the importance of each module to the overall operation of the platform.

[0055] Division by function: Divide the financial platform into multiple independent sub-platforms according to different business functions (such as payment, investment, risk control, etc.).

[0056] Classification by resource consumption: Analyze the resource consumption of each module (CPU, memory, bandwidth) and classify the modules with high resource consumption separately.

[0057] Division by user traffic: High-frequency access modules and low-frequency access modules are managed separately based on the user access frequency or traffic scale of the section.

[0058] Divide by importance: Identify critical business modules (such as core payment systems) and non-critical modules (such as user recommendation systems).

[0059] Sub-platform definition:

[0060] Based on the division rules, the boundaries and responsibilities of each sub-platform are defined.

[0061] Determine the functional scope, resource allocation, and corresponding dependencies of the sub-platform.

[0062] Data distribution:

[0063] Map the operation data and status data of each functional module to the corresponding sub-platform.

[0064] Generate independent data models for each sub-platform to ensure data accuracy and completeness.

[0065] Platform mapping: Build a logical mapping relationship between the sub-platform and the overall financial platform to facilitate the unified management of the subsequent optimization system.

[0066] During the operation of the financial platform, the optimization system monitors the operation data of each sub-platform in real time and predicts the traffic consumption of each sub-platform based on the operation data, including the following steps:

[0067] The optimization system monitors the operation data of each sub-platform in real time, including the user growth index, data transmission index and cache hit index of the sub-platform;

[0068] The user growth index, data transmission index, and cache hit index are calculated comprehensively to obtain the sub-platform operation coefficient, which is expressed as follows: In the formula, yx z is the operation coefficient, UGI is the user growth index, DTI is the data transmission index, CHI is the cache hit index, α, β, γ are the proportional coefficients of the user growth index, data transmission index and cache hit index respectively, and α, β, γ are all greater than 0;

[0069] The larger the operating coefficient of a sub-platform, the greater the future traffic consumption of the sub-platform. The obtained operating coefficient is compared with the preset operating threshold. The operating threshold is used to predict the future traffic consumption of the sub-platform. If the operating coefficient is less than or equal to the operating threshold, the future traffic consumption of the sub-platform is predicted to decrease. If the operating coefficient is greater than the operating threshold, the future traffic consumption of the sub-platform is predicted to increase.

[0070] The calculation expression of user growth index is:

[0071] Where UGI is the user growth index, N t 、N t-1 Respectively represent the number of active users at the current time t and the previous time t-1, R t Indicates the current number of new user registrations, C t Indicates the number of active behaviors of users on the sub-platform (such as login, operation request, etc.), R max , C max represents the benchmark maximum value, W1, W2, W3 are weight factors, and W1+W2+W3=1, ∈ is the correction factor to avoid the denominator being 0.

[0072] The higher the user growth index (UGI), the more users and activity of the sub-platform will increase, which will lead to an increase in traffic consumption. The introduction of new users (such as registration, first login) will increase the amount of data requests and content transmission. The increase in the frequency of user behavior (such as browsing, downloading, etc.) will directly lead to more traffic consumption.

[0073] The calculation expression of data transmission index is: Where DTI is the data transmission index, S i is the packet size of the i-th type request, C i is the data compression ratio of the i-th type request, L i is the transmission link efficiency of the i-th request, T total is the total transmission time, F retrans 、F total Represent the amount of retransmitted data and the total amount of data respectively.

[0074] The higher the data transmission index (DTI), the higher the data transmission efficiency of the sub-platform, thereby reducing the traffic consumption per unit request. High data compression rate (such as small data volume transmitted) directly reduces traffic. High transmission link efficiency (such as low latency and low packet loss rate) reduces the additional traffic caused by retransmission or redundant operations. Priority optimization optimizes high-traffic requests and avoids inefficient data transmission.

[0075] The calculation expression of cache hit index is:

[0076] Where CHI is the cache hit index, H direct is the number of requests that directly hit the cache, H lazy Indicates the number of requests hit by lazy loading (such as caching after lazy loading), T requests Indicates the total number of requests, M evicted Indicates the amount of data cleared from the cache, M total Indicates the total cache capacity, D reuse Indicates the amount of data reused, D total Indicates the amount of data in all caches.

[0077] The higher the cache hit index (CHI), the more requests are usually hit by the cache, thereby reducing the need for backend data transmission and reducing overall traffic consumption. Direct cache hits reduce the traffic of requesting backend databases or services. When the data reuse rate is high, repeated requests do not need to obtain data from the backend again, further reducing traffic. Improving cache management efficiency (such as reducing cache clearing) can maintain the validity of cached data and reduce repeated transmissions.

[0078] Based on the traffic consumption prediction result, the sub-platforms are classified into a traffic increase set or a traffic decrease set, and traffic optimization is performed on the sub-platforms in the traffic increase set and the traffic decrease set, including the following steps:

[0079] The larger the operating coefficient of a sub-platform, the greater the future traffic consumption of the sub-platform. The obtained operating coefficient is compared with the preset operating threshold. The operating threshold is used to predict the future traffic consumption of the sub-platform. If the operating coefficient is less than or equal to the operating threshold, the future traffic consumption of the sub-platform is predicted to decrease. If the operating coefficient is greater than the operating threshold, the future traffic consumption of the sub-platform is predicted to increase.

[0080] After obtaining the traffic forecast results of all sub-platforms, if the traffic consumption of the sub-platform is predicted to decrease in the future, the sub-platform is classified into the traffic reduction set; if the traffic consumption of the sub-platform is predicted to increase in the future, the sub-platform is classified into the traffic increase set;

[0081] Traffic optimization is performed on the sub-platforms in the traffic increase set and the traffic decrease set. The optimization algorithm is: In the formula, lg new For the optimized flow, lg old is the traffic before optimization, yx z is the operating coefficient, YZ is the operating threshold, yx z >YZ represents the sub-platform into the traffic increase set, yx z ≤YZ means that the sub-platform is included in the flow reduction set.

[0082] Analyze the traffic increase set and traffic decrease set to determine whether the financial platform needs to purchase additional traffic, and generate corresponding management strategies based on the judgment results, including the following steps:

[0083] Calculate the flow overflow of the flow reduction set, the expression is: lg c =lg old -lg new , where lg new For the optimized flow, lg old is the flow before optimization, lg c is the excess flow;

[0084] Calculate the flow demand of the flow increase set, the expression is: lg x =lg new -lg old , where lg new For the optimized flow, lg old is the flow before optimization, lg x is the flow demand;

[0085] If the traffic demand is greater than the surplus traffic, it is determined that the financial platform needs to purchase additional traffic; if the traffic demand is less than or equal to the surplus traffic, it is determined that the financial platform does not need to purchase additional traffic;

[0086] 1) If the traffic demand is greater than the traffic overflow, it means that the actual traffic demand of the sub-platform exceeds the existing available traffic. At this time, it is necessary to purchase additional traffic to meet the platform's demand.

[0087] Strategy 1: Buy traffic immediately

[0088] Description: When the platform's traffic demand exceeds the overflow, immediately purchase the required traffic based on the current traffic gap.

[0089] Execution: Calculate the traffic required based on real-time data and purchase it from the traffic provider.

[0090] Conditions: This policy is preferred during peak traffic demand periods or when an increase in load is expected.

[0091] Considerations:

[0092] Real-time traffic monitoring: Ensure that the platform monitors traffic consumption in real time to avoid traffic interruptions due to budget exceeding or supplier restrictions.

[0093] Traffic purchase price prediction: Predict traffic purchase costs based on historical data and supplier quotations to avoid ineffective purchases.

[0094] Strategy 2: Traffic purchasing plan and budget adjustment

[0095] Description: Purchase a certain amount of traffic in advance to optimize traffic procurement and budget in view of the peak traffic demand in the future.

[0096] Execution: Purchases are made based on the platform’s predicted traffic trends (e.g. based on seasonality, promotions, market fluctuations, etc.).

[0097] Condition: When the platform has a clear expectation of fluctuations in traffic demand, purchasing traffic in advance can reduce the urgency of immediate purchases.

[0098] Considerations:

[0099] Long-term forecasting: Predict future changes in platform traffic demand based on historical data or machine learning to avoid over-purchasing or traffic waste.

[0100] Negotiate with suppliers: Negotiate long-term agreements with traffic suppliers to obtain lower unit prices and better purchasing conditions.

[0101] Strategy 3: Optimize the use of existing resources

[0102] Description: In addition to purchasing additional traffic, traffic pressure can be alleviated by optimizing the configuration and use of existing resources, reducing the need to purchase additional traffic.

[0103] Execution: Optimize platform architecture, reasonably distribute traffic load, and use CDN and other technologies to improve traffic processing efficiency.

[0104] Conditions: When the budget is limited or traffic purchase cannot be completed immediately, try to use existing resources to relieve pressure.

[0105] 2) The traffic demand is less than or equal to the traffic overflow (no need to buy additional traffic)

[0106] If the traffic demand is less than or equal to the traffic overflow, it means that the platform's existing traffic resources can meet current demand and there is no need to purchase additional traffic.

[0107] Strategy 1: Optimize the allocation of existing traffic resources

[0108] Description: When the platform traffic demand is low, the resource utilization efficiency is further improved by optimizing the allocation of existing traffic resources.

[0109] Execution: Maximize the utilization of existing traffic through load balancing, traffic scheduling, cache optimization, etc.

[0110] Condition: When the platform has uneven traffic demand between different regions or sub-platforms, adjust the traffic allocation strategy to improve the efficiency of traffic resource utilization in each region.

[0111] Strategy 2: Improve the flexibility of platform traffic resources

[0112] Description: Improve the platform's traffic resource scheduling capabilities so that it can respond flexibly to traffic fluctuations and avoid over-purchasing of traffic.

[0113] Execution: Adjust traffic quota management policies to allocate more bandwidth to sub-platforms with less traffic demand and make rapid adjustments during high demand periods.

[0114] Conditions: Applicable to situations where traffic demand fluctuates periodically, and resource waste can be reduced through dynamic traffic adjustment.

[0115] Strategy 3: Save costs and optimize traffic configuration

[0116] Description: Since you don't need to purchase additional traffic, you can save costs by optimizing existing configurations, compressing data, and reducing invalid traffic consumption.

[0117] Execution: Implement data compression, reduce unnecessary data transfers or requests, and reduce the frequency of low-value operations.

[0118] Conditions: Cost savings are particularly important when the platform is facing budget pressures or needs to improve long-term profitability.

[0119] After regularly obtaining the relative importance of all sub-platforms in the financial platform, the relative importance of the sub-platforms and the traffic consumption forecast results are comprehensively analyzed, and a ranking coefficient is output for each sub-platform. All sub-platforms are sorted according to the ranking coefficient to generate a platform list. The next day, the priority of traffic input for each sub-platform is generated based on the platform list, including the following steps:

[0120] Obtain the historical transaction volume and transaction amount of the sub-platform, normalize the transaction volume and transaction amount, map the value range of the transaction volume and transaction amount to [0,1], obtain the normalized transaction volume value and the normalized transaction amount value, sum the normalized transaction volume value and the normalized transaction amount value to obtain the influence value of the sub-platform, sum the influence values ​​of all sub-platforms to obtain the total influence value, and obtain the influence ratio of the sub-platform by dividing the influence value by the total influence value;

[0121] Multiply the influence ratio of the sub-platform by the running coefficient to output the sorting coefficient, sort all sub-platforms from large to small according to the sorting coefficient, generate a platform list, and allocate initial traffic to each sub-platform in the positive order of the platform list the next day. The higher the sub-platform is ranked in the platform list, the more initial traffic is allocated to the sub-platform.

[0122] Embodiment 3: A financial platform optimization system described in this embodiment includes a traffic consumption prediction module, an optimization module, and a priority generation module;

[0123] Traffic consumption prediction module: After obtaining the financial platform information, the financial platform is divided into several sub-platforms according to various sectors. During the operation of the financial platform, the optimization system monitors the operation data of each sub-platform in real time, and predicts the traffic consumption of each sub-platform based on the operation data. The prediction results are sent to the optimization module and the priority generation module;

[0124] Optimization module: Based on the traffic consumption prediction results, the sub-platforms are classified into the traffic increase set or the traffic decrease set, and the traffic of the sub-platforms in the traffic increase set and the traffic decrease set is optimized. The traffic increase set and the traffic decrease set are analyzed to determine whether the financial platform needs to purchase additional traffic, and the corresponding management strategy is generated based on the judgment results;

[0125] Priority generation module: After regularly obtaining the relative importance of all sub-platforms in the financial platform, the relative importance of the sub-platforms and the traffic consumption forecast results are comprehensively analyzed, and a ranking coefficient is output for each sub-platform. All sub-platforms are sorted according to the ranking coefficient to generate a platform list. The next day, the priority of traffic input for each sub-platform is generated based on the platform list.

[0126] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0127] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0128] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A financial platform optimization method, characterized in that: The optimization method comprises the following steps: After the optimization system obtains the information of the financial platform, it divides the financial platform into several sub-platforms according to various sectors. During the operation of the financial platform, the optimization system monitors the operation data of each sub-platform in real time, and predicts the traffic consumption of each sub-platform based on the operation data. Based on the traffic consumption prediction results, the sub-platform is classified into a traffic increase set or a traffic decrease set; Optimize the traffic of sub-platforms in the traffic increase set and traffic decrease set, analyze the traffic increase set and traffic decrease set, determine whether the financial platform needs to buy additional traffic, and generate corresponding management strategies based on the judgment results; After regularly obtaining the relative importance of all sub-platforms in the financial platform, the relative importance of the sub-platforms and the traffic consumption forecast results are comprehensively analyzed, and a ranking coefficient is output for each sub-platform. All sub-platforms are sorted according to the ranking coefficient to generate a platform list. The next day, the priority of traffic input for each sub-platform is generated based on the platform list.

2. A financial platform optimization method according to claim 1, characterized in that: During the operation of the financial platform, the optimization system monitors the operation data of each sub-platform in real time and predicts the traffic consumption of each sub-platform based on the operation data, including the following steps: The optimization system monitors the operation data of each sub-platform in real time, including the user growth index, data transmission index and cache hit index of the sub-platform; The user growth index, data transmission index and cache hit index are calculated comprehensively to obtain the operation coefficient of the sub-platform; The obtained operation coefficient is compared with a preset operation threshold, and the operation threshold is used to predict the future traffic consumption status of the sub-platform; If the operation coefficient is less than or equal to the operation threshold, it is predicted that the future traffic consumption of the sub-platform will decrease. If the operation coefficient is greater than the operation threshold, it is predicted that the future traffic consumption of the sub-platform will increase.

3. A financial platform optimization method according to claim 2, characterized in that: The user growth index, data transmission index, and cache hit index are calculated comprehensively to obtain the sub-platform operation coefficient, which is expressed as follows: In the formula, yx z is the operating coefficient, UGI is the user growth index, DTI is the data transmission index, CHI is the cache hit index, α, β, γ are the proportional coefficients of the user growth index, data transmission index and cache hit index respectively, and α, β, γ are all greater than 0.

4. A financial platform optimization method according to claim 3, characterized in that: Based on the traffic consumption prediction result, the sub-platforms are classified into a traffic increase set or a traffic decrease set, and traffic optimization is performed on the sub-platforms in the traffic increase set and the traffic decrease set, including the following steps: After obtaining the traffic forecast results of all sub-platforms, if the traffic consumption of the sub-platform is predicted to decrease in the future, the sub-platform is classified into the traffic reduction set; if the traffic consumption of the sub-platform is predicted to increase in the future, the sub-platform is classified into the traffic increase set; Traffic optimization is performed on the sub-platforms in the traffic increase set and the traffic decrease set. The optimization algorithm is: In the formula, lg new For the optimized flow, lg old is the traffic before optimization, yx z is the operating coefficient, YZ is the operating threshold, yx z >YZ represents the sub-platform into the traffic increase set, yx z ≤YZ means that the sub-platform is included in the flow reduction set.

5. A financial platform optimization method according to claim 4, characterized in that: Analyze the traffic increase set and traffic decrease set to determine whether the financial platform needs to purchase additional traffic, including the following steps: Calculate the flow overflow of the flow reduction set, the expression is: lg c =lg old -lg new , where lg new For the optimized flow, lg old is the flow before optimization, lg c is the excess flow; Calculate the flow demand of the flow increase set, the expression is: lg x =lg new -lg old , where lg new For the optimized flow, lg old is the flow before optimization, lg x is the flow demand; If the traffic demand is greater than the surplus traffic, it is judged that the financial platform needs to purchase additional traffic. If the traffic demand is less than or equal to the surplus traffic, it is judged that the financial platform does not need to purchase additional traffic.

6. A financial platform optimization method according to claim 5, characterized in that: After regularly obtaining the relative importance of all sub-platforms in the financial platform, the relative importance of the sub-platforms and the traffic consumption prediction results are comprehensively analyzed, and a ranking coefficient is output for each sub-platform. All sub-platforms are sorted according to the ranking coefficient to generate a platform list, including the following steps: Obtain the historical transaction volume and transaction amount of the sub-platform, normalize the transaction volume and transaction amount, map the value range of the transaction volume and transaction amount to [0,1], obtain the normalized transaction volume value and the normalized transaction amount value, sum the normalized transaction volume value and the normalized transaction amount value to obtain the influence value of the sub-platform, sum the influence values ​​of all sub-platforms to obtain the total influence value, and obtain the influence ratio of the sub-platform by dividing the influence value by the total influence value; Multiply the impact ratio of the sub-platform by the running coefficient to output the sorting coefficient, sort all sub-platforms from large to small according to the sorting coefficient, generate a platform list, and allocate initial traffic to each sub-platform in the positive order of the platform list the next day.

7. A financial platform optimization method according to claim 6, characterized in that: The calculation expression of the user growth index is: Where UGI is the user growth index, N t 、N t-1 Respectively represent the number of active users at the current time t and the previous time t-1, R t Indicates the current number of new user registrations, C t represents the number of active behaviors of users on the sub-platform, R max , C max represents the benchmark maximum value, W1, W2, W3 are weight factors, and W1+W2+W3=1, ∈ is the correction factor; The calculation expression of the data transmission index is: Where DTI is the data transmission index, S i is the packet size of the i-th type request, C i is the data compression ratio of the i-th type request, L i is the transmission link efficiency of the i-th request, T total is the total transmission time, F retrans 、F total Respectively represent the amount of retransmitted data and the total amount of data; The calculation expression of the cache hit index is: Where CHI is the cache hit index, H direct is the number of requests that directly hit the cache, H lazy Indicates the number of requests hit by lazy loading, T requests Indicates the total number of requests, M evicted Indicates the amount of data cleared from the cache, M total Indicates the total cache capacity, D reuse Indicates the amount of data reused, D total Indicates the amount of data in all caches.

8. A financial platform optimization system, used to implement the optimization method according to any one of claims 1 to 7, characterized in that: It includes traffic consumption prediction module, optimization module and priority generation module; Traffic consumption prediction module: After obtaining the financial platform information, the financial platform is divided into several sub-platforms according to various sectors. During the operation of the financial platform, the optimization system monitors the operation data of each sub-platform in real time and predicts the traffic consumption of each sub-platform based on the operation data; Optimization module: Based on the traffic consumption prediction results, the sub-platforms are classified into the traffic increase set or the traffic decrease set, and the traffic of the sub-platforms in the traffic increase set and the traffic decrease set is optimized. The traffic increase set and the traffic decrease set are analyzed to determine whether the financial platform needs to purchase additional traffic, and the corresponding management strategy is generated based on the judgment results; Priority generation module: After regularly obtaining the relative importance of all sub-platforms in the financial platform, the relative importance of the sub-platforms and the traffic consumption forecast results are comprehensively analyzed, and a ranking coefficient is output for each sub-platform. All sub-platforms are sorted according to the ranking coefficient to generate a platform list. The next day, the priority of traffic input for each sub-platform is generated based on the platform list.

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