Bidding method and device for financial asset distribution optimization, equipment and storage medium

Abstract

The invention discloses a bidding method and device for financial asset allocation optimization, equipment and a storage medium, and relates to the technical field of resource allocation. The method comprises the following steps: receiving a total asset amount, a bidding amount of each product, a risk control trial amount, priority information, a key product mark, a global priority proportion and a target bid winning amount sent by a data acquisition module; determining an effective amount of each product according to the total asset amount, the bidding amount, the risk control trial amount, the key product mark and the global priority proportion; performing hierarchical distribution on each product according to the effective amount, the total asset amount and the priority information to obtain an initial distribution result; and sending the initial allocation result to a stream label processing module, so that the stream label processing module performs allocation optimization on the initial allocation result according to the target bid winning amount.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of resource allocation, and particularly relates to a bidding method and device for financial asset allocation optimization, equipment and a storage medium. BACKGROUND

[0002] In the field of financial technology, asset allocation and bidding management are core links in the daily operation of banks, securities, insurance and other institutions. Traditional asset allocation systems rely on static rules and manual intervention. However, this technical solution has obvious defects. First, its multi-objective coordination ability is seriously insufficient, and static rules are difficult to dynamically balance multi-dimensional constraints such as risk control limits and different product guarantees, resulting in slow response in the allocation process and inability to adapt to rapidly changing market demand. Second, the flow bidding processing efficiency is low, and the time for traditional identification of flow bidding, recovery of amount and redistribution is long, which easily causes asset idling and decrease in full bidding rate. In addition, it lacks dynamic adaptability and is difficult to automatically adjust the priority guarantee strategy when the bidding amount fluctuates or the asset investment range changes.

[0003] Therefore, how to improve the efficiency of resource optimization allocation under multi-dimensional constraints is a problem to be solved at present. SUMMARY

[0004] The main purpose of the present application is to provide a bidding method, device, equipment and storage medium for financial asset allocation optimization, aiming to solve the technical problem of how to improve the efficiency of resource optimization allocation under multi-dimensional constraints.

[0005] To achieve the above-mentioned purpose, the present application provides a bidding method for financial asset allocation optimization, which is applied to a dynamic coupling module in a system, the system comprising a data acquisition module, a dynamic coupling module and a flow bidding processing module, and the method comprises the following steps: receiving the total asset amount, the bidding amount of each product, the risk control trial amount, the priority information, the key product mark, the global priority ratio and the target winning amount sent by the data acquisition module; determining the effective amount of each product according to the total asset amount, the bidding amount, the risk control trial amount, the key product mark and the global priority ratio; performing hierarchical allocation on each product according to the effective amount, the total asset amount and the priority information to obtain an initial allocation result; sending the initial allocation result to the flow bidding processing module to make the flow bidding processing module perform allocation optimization on the initial allocation result according to the target winning amount.

[0006] In an embodiment, the step of determining the effective amount of each product according to the total asset amount, the bid amount, the risk control trial amount, the key product mark, and the global priority ratio comprises: determining a product type of each product according to the key product mark, the product type comprising a key product and a non-key product; when the product type of a current product is the key product, determining the effective amount of the current product according to the bid amount, the risk control trial amount, the global priority ratio, and the total asset amount.

[0007] In an embodiment, after the step of determining a product type of each product according to the key product mark, the product type comprising a key product and a non-key product, the method further comprises: when the product type of a current product is the non-key product, determining the effective amount of the current product according to the bid amount and the risk control trial amount; or, when the product type of a current product is the non-key product, obtaining an importance proportion and a historical importance proportion of the current product; determining a weight proportion of the current product according to the importance proportion and the historical importance proportion; determining the effective amount of the current product according to the weight proportion, the bid amount, and the risk control trial amount.

[0008] In an embodiment, the step of performing hierarchical allocation on each product according to the effective amount, the total asset amount, and the priority information to obtain an initial allocation result comprises: allocating the effective amount of each product whose product type is a key product to obtain an allocated amount; determining a remaining asset amount according to the total asset amount and the allocated amount; performing hierarchical allocation on the effective amount of each product whose product type is a non-key product according to a priority order in the priority information and the remaining asset amount to obtain an initial allocation result.

[0009] In an embodiment, the priority information comprises a priority level, and the step of performing hierarchical allocation on the effective amount of each product whose product type is a non-key product according to a priority order in the priority information and the remaining asset amount to obtain an initial allocation result comprises: sequentially traversing each priority level according to a descending order of the priority level; For the priority level currently traversed, a sum of the effective amounts of all non-key products in the priority level is calculated to obtain an effective amount sum; When the effective amount sum is greater than the remaining asset quota, the remaining asset quota is allocated to each non-key product of the priority level according to the proportion of the effective amount of the product type being a non-key product in the effective amount sum, to obtain an initial allocation result; When the effective amount sum is less than or equal to the remaining asset quota, the effective amount of the product type being a non-key product is allocated to each non-key product of the priority level as an allocation quota to obtain an initial allocation result.

[0010] In an embodiment, the step of performing allocation optimization on the initial allocation result according to the target bid-winning amount comprises: Traversing the initial allocation result, products with an allocation quota lower than the target bid-winning amount in the initial allocation result are marked as underbid products; The allocation quota in the underbid products is recovered, and the underbid products are removed from the current allocation sequence to obtain an updated allocation sequence; Based on the recovered allocation quota and the updated allocation sequence, hierarchical allocation is re-executed until no underbid product is generated or the total asset quota is lower than a preset quota, to complete optimization of bid allocation.

[0011] In an embodiment, before the step of traversing the initial allocation result, products with an allocation quota lower than the target bid-winning amount in the initial allocation result are marked as underbid products, the method further comprises: Obtaining a historical underbid frequency of each product and a bid saturation of a current market; Adjusting the target bid-winning amount according to the historical underbid frequency and the bid saturation to obtain an underbid judgment amount; Replacing the target bid-winning amount with the underbid judgment amount.

[0012] In addition, to achieve the above object, the application further provides a bid device for financial asset allocation optimization, which comprises: A data acquisition module is configured to acquire a total asset quota, a bid amount of each product, a risk control trial amount, priority information, a key product mark, a global priority ratio, and a target bid-winning amount; A dynamic coupling module is configured to receive the total asset quota, the bid amount of each product, the risk control trial amount, the priority information, the key product mark, the global priority ratio, and the target bid-winning amount sent by the data acquisition module; The dynamic coupling module is also used to determine the effective amount of each product based on the total asset amount, the bid amount, the risk control trial amount, the key product mark and the global priority ratio; The dynamic coupling module is also used to allocate each product in a hierarchical manner according to the effective amount, the total asset amount and the priority information to obtain the initial allocation result; The failed bid processing module is used to optimize the allocation of the initial allocation results based on the target winning bid amount.

[0013] Furthermore, to achieve the above objectives, this application also proposes a bidding device for optimizing the allocation of financial assets, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the bidding method for optimizing the allocation of financial assets as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the bidding method for optimizing financial asset allocation as described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the bidding method for optimizing financial asset allocation as described above.

[0016] This application provides a bidding method for optimizing financial asset allocation. The method includes: receiving total asset amount, bid amount for each product, risk control calculation amount, priority information, key product markers, global priority ratio, and target winning bid amount from a data acquisition module; determining the effective amount for each product based on the total asset amount, bid amount, risk control calculation amount, key product markers, and global priority ratio; performing tiered allocation on each product based on the effective amount, total asset amount, and priority information to obtain an initial allocation result; and sending the initial allocation result to a failed bid processing module, so that the failed bid processing module optimizes the allocation based on the target winning bid amount. In summary, this application, by constructing a three-layer dynamically coupled engine including priority tiered allocation, key product priority, and risk control adaptive constraints, achieves automatic coordination and dynamic balance of asset allocation under multi-dimensional constraints; and combines a failed bid intelligent recovery and redistribution mechanism to form a closed-loop processing, thereby solving the technical difficulties of traditional methods in multi-objective coordination, failed bid processing efficiency, key product protection, and risk control, and improving the efficiency and adaptability of asset allocation. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating the first embodiment of the bidding method for optimizing financial asset allocation in this application; Figure 2 A flowchart illustrating the second embodiment of the bidding method for optimizing financial asset allocation in this application; Figure 3 This is a schematic diagram of the overall system flow in one embodiment of the bidding method for optimizing financial asset allocation in this application; Figure 4 A flowchart illustrating the third embodiment of the bidding method for optimizing financial asset allocation in this application; Figure 5 This is a schematic diagram of the module structure of a bidding device for optimizing financial asset allocation according to an embodiment of this application; Figure 6 This is a schematic diagram of the hardware operating environment involved in the bidding method for optimizing financial asset allocation in the embodiments of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is as follows: receiving the total asset amount, bid amount of each product, risk control trial amount, priority information, key product mark, global priority ratio, and target winning bid amount sent by the data acquisition module; determining the effective amount of each product based on the total asset amount, bid amount, risk control trial amount, key product mark, and global priority ratio; performing hierarchical allocation of each product based on the effective amount, total asset amount, and priority information to obtain an initial allocation result; and sending the initial allocation result to the bid failure processing module so that the bid failure processing module can optimize the allocation of the initial allocation result based on the target winning bid amount.

[0024] In the fintech field, asset allocation and bidding management are core aspects of the daily operations of institutions such as banks, securities firms, and insurance companies. Traditional asset allocation systems largely rely on static rules and manual intervention. However, this technological solution has significant drawbacks. First, its multi-objective coordination capabilities are severely insufficient. Static rules struggle to dynamically balance multi-dimensional constraints such as risk control limits and the protection of different products, resulting in a slow response time in the allocation process and an inability to adapt to rapidly changing market demands. Second, the efficiency of handling failed bids is low. Traditional methods of identifying failed bids, recovering quotas, and redistributing them are time-consuming, easily leading to idle assets and a decline in the bid completion rate. Furthermore, it lacks dynamic adaptability, making it difficult to automatically adjust priority protection strategies when bid amounts fluctuate or the range of assets available for investment changes. Therefore, how to improve the efficiency of resource optimization and allocation under multi-dimensional constraints is a pressing issue that needs to be addressed.

[0025] This application constructs a three-layer dynamic coupling engine that includes priority-based allocation, priority for key products, and adaptive risk control constraints. This engine enables automatic coordination and dynamic balancing of asset allocation under multi-dimensional constraints. Combined with a mechanism for intelligent recovery and redistribution of lost bids, it forms a closed-loop process. This solves the technical challenges of traditional methods in multi-objective coordination, efficiency of lost bid processing, protection of key products, and risk control, thereby improving the efficiency and adaptability of asset allocation.

[0026] It should be noted that the executing entity in this embodiment can be a bidding system for optimizing financial asset allocation, a computing service device with data processing, network communication, and program execution functions, or an electronic device capable of implementing the aforementioned bidding function for optimizing financial asset allocation. This embodiment does not specifically limit it in this way. The following uses a bidding system for optimizing financial asset allocation as an example to describe this embodiment and the following embodiments.

[0027] Based on this, embodiments of this application provide a bidding method for optimizing financial asset allocation, referring to... Figure 1 , Figure 1This is a flowchart illustrating the first embodiment of the bidding method for optimizing financial asset allocation in this application.

[0028] In this embodiment, the bidding method for optimizing financial asset allocation is applied to the dynamic coupling module in the system, which includes a data acquisition module, a dynamic coupling module, and a failed bid processing module. The bidding method for optimizing financial asset allocation includes steps S10 to S40: Step S10: Receive the total asset amount, bid amount for each product, risk control calculation amount, priority information, key product marking, global priority ratio, and target winning bid amount sent by the data acquisition module.

[0029] It should be noted that in this step, the dynamic coupling module obtains all the input parameters required for the allocation process from the data acquisition module. These parameters include: total asset amount T (i.e., total allocable funds), bid amount B_i for each product i (the amount requested by the product), risk control calculation amount R_i (the maximum allocation amount allowed by the risk control system), priority information P_i (the priority level of the product, e.g., levels 1-5, with level 1 being the highest priority), key product marker F_i (a Boolean value used to identify whether a product is a key product), and global priority ratio. (used to calculate the priority allocation ratio for key products, such as 5%), and the target winning bid amount M (the minimum effective allocation amount set by the system, such as 10 million yuan).

[0030] Additionally, it should be noted that the risk control trial amount R_i is the maximum amount that can be allocated to each product, calculated in real time by the risk control system based on compliance rules (such as quota limits, risk exposure, etc.). Its purpose is to prevent over-investment or illegal allocation. Priority information P_i can include fixed priority (pre-set by the business department), dynamic priority (calculated based on the product's real-time performance), or special priority (temporarily set for specific project assets), thereby achieving precise control over the allocation order. The target winning bid amount M is a configurable threshold used to determine whether the allocation is valid and to avoid small, invalid allocations.

[0031] Step S20: Determine the effective amount for each product based on the total asset amount, the bid amount, the risk control trial amount, the key product marker, and the global priority ratio.

[0032] It should be noted that in this step, the system's dynamic coupling module calculates the effective amount E_i for each product based on the input parameters. The effective amount is the upper limit of the quota available for a product in actual allocation, and its calculation depends on the product type (key product or non-key product). For key products, the effective amount is simultaneously affected by the bid amount, the risk control trial amount, and the global priority amount (…). The system employs a triple constraint (× T); for non-priority products, the effective amount is limited only by the bid amount and the risk control trial amount. Understandably, this step is one of the core components of the three-layer dynamic coupling engine, ensuring both compliance and efficiency in the allocation process through built-in risk control constraints and priority protection mechanisms.

[0033] Additionally, it should be noted that the effective amount E_i is a dynamically calculated value, reflecting the actual available credit for the product during the allocation process. By using the minimum value operation (min function), the system rigidly limits the allocation ceiling, preventing risk control violations and excessive resource concentration. Global priority ratio It is an adjustable parameter that allows businesses to dynamically adjust the level of protection for key products based on their needs.

[0034] In one feasible implementation, step S20 specifically includes: Step S201: Determine the product type of each product based on the key product marking, wherein the product type includes key products and non-key products.

[0035] It should be noted that in this step, the system will iterate through all products and classify them according to the priority product label F_i. If F_i is True, the product is marked as a priority product; if F_i is False, the product is marked as a non-priority product. Furthermore, it should be noted that priority products are typically core or strategic products that require priority in business operations and receive special treatment in their allocation; non-priority products are ordinary products and have a relatively lower allocation priority.

[0036] Step S202: When the product type of the current product is the key product, determine the effective amount of the current product based on the bid amount, the risk control trial amount, the global priority ratio, and the total asset amount.

[0037] It should be noted that in this step, for key products, the system uses the formula E_i = min(B_i, R_i, The effective amount is calculated using the formula (× T). Where B_i is the bid amount, and R_i is the risk control trial amount. × T represents the global priority amount. This step ensures that the allocation of funds to key products simultaneously meets their own needs, risk control limits, and priority protection limits, thus prioritizing core business operations with limited resources.

[0038] Additionally, it should be noted that the `min` function is used to find the minimum value among the three parameters, thus strictly limiting the allocation ceiling. Global priority amount. × T is dynamically calculated, and its value changes with the total asset amount T, ensuring that key products can obtain a certain proportion of protection under any circumstances.

[0039] In one feasible implementation, after step S20, the method further includes: Step A10: When the product type of the current product is the non-key product, determine the effective amount of the current product based on the bid amount and the risk control trial amount.

[0040] It should be noted that in this step, for non-priority products, the system directly calculates the effective amount using the formula E_i = min(B_i, R_i). This means that the effective amount for non-priority products is only subject to their own bidding needs and risk control restrictions, and does not participate in the priority guarantee mechanism. Understandably, this step ensures that the allocation of non-priority products is within the risk control range, while avoiding the performance overhead caused by complex calculations.

[0041] In one feasible implementation, after step S20, the method further includes: Step A10': When the product type of the current product is the non-key product, obtain the importance percentage and historical importance percentage of the current product.

[0042] It should be noted that in this step, the system retrieves the product's importance percentage W_current (based on the product's current business importance) and historical importance percentage W_historical (based on the product's historical allocation performance or performance indicators) from the business configuration or historical database. These percentages are used in subsequent weight calculations to more finely adjust the effective amount. Furthermore, it should be noted that the importance percentage and historical importance percentage are dynamic parameters that can be adjusted according to business strategies, such as based on product contribution, market priority, or risk rating.

[0043] Step A20': Determine the weight percentage of the current product based on the importance percentage and the historical importance percentage.

[0044] It should be noted that in this step, the system calculates the weighted proportion W_i by combining the overall importance proportion and the historical importance proportion. For example, a weighted average formula can be used: W_i = α × W_current + β × W_historical, where α and β are configurable weighting coefficients (e.g., α=0.6, β=0.4). The weighted proportion W_i reflects the overall importance of the product and is used to flexibly adjust the effective amount.

[0045] Step A30': Determine the effective amount of the current product based on the weighting percentage, the bid amount, and the risk control trial amount.

[0046] It should be noted that the weight ratio can adjust the effective amount, but the risk control trial calculation amount R_i remains a hard constraint, ensuring the compliance of the allocation. In this step, the system will calculate the effective amount of non-key products using the formula E_i = min(B_i, R_i) × W_i. By introducing the weight ratio, products can obtain an adjusted quota based on risk control and demand, and important non-key products may have a higher allocation chance. It can be understood that this method enhances the flexibility and adaptability of the allocation.

[0047] Step S30: Perform hierarchical allocation on each product according to the effective amount, the total asset quota, and the priority information to obtain an initial allocation result.

[0048] It should be noted that as Figure 2 shown, in this step, the dynamic coupling module will perform priority hierarchical allocation. Specifically, the system will first allocate all key products and update the remaining total asset quota (sum of the allocated key products). Then, non-key products will be processed in descending order of priority (e.g., P = 1 to P = 5) until all products are processed or the total asset quota is exhausted. It can be understood that this step realizes the optimized allocation of multi-dimensional resources, ensures the priority allocation of high-priority products, and avoids quota fragmentation through proportional allocation.

[0049] In addition, it should be noted that the risk control adaptive constraint has been built in through the calculation of the effective amount, so no additional risk control verification is required during the allocation process, improving the efficiency. The priority hierarchical allocation mechanism allows the system to dynamically adapt to business changes. For example, when the demand for high-priority products fluctuates, low-priority products can automatically fill the quota.

[0050] Step S40: Send the initial allocation result to the bid failure processing module so that the bid failure processing module can optimize the initial allocation result according to the target winning bid amount.

[0051] It should be noted that in this step, the dynamic coupling module of the system sends the initial allocation result (including the winning bid amount A_i of each product) to the bid failure processing module. The bid failure processing module will automatically check whether the A_i of each product is lower than the target winning bid amount M. If A_i < M, the product will be marked as a bid failure, its winning bid amount will be recovered, and the product will be removed from the allocation sequence; at the same time, a new product will be substituted from the candidate product pool. Subsequently, the bid failure processing module triggers the reallocation process and re-executes the allocation logic starting from step S20 until no bid failure occurs or further optimization is impossible. This step forms an intelligent closed-loop processing, significantly improving the asset full bid rate and allocation efficiency.

[0052] Understandably, the failed bid handling module addresses the issues of manual processing delays and quota fragmentation through real-time recycling and redistribution. The redistribution process can iterate multiple times to ensure maximum utilization of the total asset quota.

[0053] This embodiment provides a bidding method for optimizing financial asset allocation. The method includes: receiving total asset amount, bid amount for each product, risk control calculation amount, priority information, key product markers, global priority ratio, and target winning bid amount from the data acquisition module; determining the effective amount for each product based on the total asset amount, bid amount, risk control calculation amount, key product markers, and global priority ratio; performing tiered allocation on each product based on the effective amount, total asset amount, and priority information to obtain an initial allocation result; and sending the initial allocation result to a failed bid processing module, so that the failed bid processing module optimizes the allocation based on the target winning bid amount. In summary, this embodiment, by constructing a three-layer dynamically coupled engine including priority tiered allocation, key product priority, and risk control adaptive constraints, achieves automatic coordination and dynamic balance of asset allocation under multi-dimensional constraints; and combines a failed bid intelligent recovery and redistribution mechanism to form a closed-loop processing, thereby solving the technical difficulties of traditional methods in multi-objective coordination, failed bid processing efficiency, key product protection, and risk control, and improving the efficiency and adaptability of asset allocation.

[0054] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the bidding method for optimizing financial asset allocation in this application. Step S30 specifically includes: Step S301: Allocate the effective amount for the key products among the products to obtain the allocated amount.

[0055] It should be noted that in this step, the system first processes all products marked as key products (F_i = True). For each key product, the system directly allocates its effective amount E_i as its bid amount A_i, i.e., A_i = E_i. This process ensures that key products receive their calculated effective amount first and in full, thereby guaranteeing resources for core business operations. After all key products have been allocated, the system summarizes the total allocation amount, denoted as ΣA_i.

[0056] Additionally, it should be noted that the allocation here refers to formally determining the calculated effective amount E_i as the pre-allocated winning bid amount A_i for the product. The effective amount E_i for key products has already been determined in step S20 using the formula E_i = min(B_i, R_i, The value (×T) has been determined to meet the product's own needs, risk control limits, and overall priority protection limits, so there is no need to compete for limits or reduce the proportion.

[0057] Step S302: Determine the remaining asset amount based on the total asset amount and the allocated amount.

[0058] It should be noted that in this step, the system calculates the remaining asset amount T_remaining that can be used for subsequent allocation of non-priority products. The calculation formula is: T_remaining = T - ΣA_i, where T is the initial total asset amount, and ΣA_i is the sum of the amounts already allocated to all priority products in step S301. It can be understood that this step dynamically reflects the resource consumption during the allocation process, providing an accurate basis for the available amount in subsequent priority-based allocation.

[0059] Additionally, it should be noted that the remaining asset amount T_remaining is a dynamic variable whose value is continuously updated as the allocation process progresses. In this step, it specifically refers to the total amount of assets remaining for competitive allocation to non-key products after the allocation of key products has been completed.

[0060] Step S303: Based on the priority order in the priority information and the remaining asset amount, the effective amount of the product type of non-key products in each product is allocated in a hierarchical manner to obtain the initial allocation result.

[0061] It should be noted that in this step, the system begins processing non-priority products in priority order (F_i = False). The allocation process starts with the highest priority (e.g., P=1) and proceeds sequentially to lower priorities (e.g., P=2, P=3, ...). For each priority level, the system treats all non-priority products within that level as an allocation group and decides whether to use full allocation or proportional allocation based on the relationship between the total effective amount of the group and the current remaining asset amount T_remaining. This step ensures that high-priority non-priority products receive resources first, while proportional allocation within each level guarantees fairness within the same priority level and effectively utilizes all available asset amounts.

[0062] Additionally, it should be noted that the priority order is usually pre-defined by the business, for example, level 1 is the highest priority and level 5 is the lowest priority. The system strictly follows the order from high to low, and only after products of higher priority levels have been allocated will the remaining quota be allocated to the next priority level.

[0063] In one feasible implementation, step S303 specifically includes: Step C10: According to the priority level from high to low, traverse each priority level in turn.

[0064] It's important to note that in this step, the system initializes a loop, selecting the highest priority level (e.g., P=1) from the priority list as the starting point. Then, it iterates through each priority level containing non-critical products, following the order of increasing priority values ​​(i.e., decreasing priority). This step forms the outer loop of the hierarchical allocation, ensuring the strategic nature of the allocation order—prioritizing the needs of high-priority products.

[0065] Additionally, it should be noted that traversal refers to the system automatically and sequentially accessing each priority level. This process is systematic and requires no manual intervention until all priority levels have been processed or the remaining asset amount T_remaining has decreased to zero.

[0066] Step C20: For the current priority level, calculate the sum of the effective amounts of all non-key products under that priority level to obtain the total effective amount.

[0067] It should be noted that, for the current priority level being processed (e.g., P=k), the system filters out all products belonging to that level and whose product type is non-priority products, and calculates the sum of the effective amounts E_i for these products, denoted as ΣE_j (P=k). This step aims to assess the total demand for remaining asset quotas at that priority level, providing a quantitative basis for determining the allocation strategy in the next step.

[0068] Additionally, it should be noted that the total effective amount ΣE_j (P=k) reflects the maximum amount of assets that this priority level can consume under ideal conditions (i.e., without limit).

[0069] Step C30: When the total effective amount is greater than the remaining asset limit, the remaining asset limit is allocated to each non-key product of that priority level according to the proportion of the effective amount of the non-key product in the total effective amount, to obtain the initial allocation result.

[0070] It should be noted that if the total effective amount ΣE_j (P=k) of the current tier is greater than the current remaining asset amount T_remaining, it indicates that the products in this tier cannot all receive their full effective amount. The system then uses a proportional allocation algorithm: for each non-priority product i within this tier, the winning bid amount A_i is calculated as: A_i = (E_i / ΣE_j (P=k)) × T_remaining. After allocation, the allocation for this tier ends, the remaining asset amount T_remaining is set to zero, and subsequent lower priority tiers will not receive any allocation.

[0071] Additionally, it should be noted that proportional allocation ensures that within the same priority level, each product can fairly share the limited remaining quota on demand (represented by its effective amount). This mechanism avoids arbitrary quota allocation and ensures that the quota is completely exhausted at the current highest allocable level.

[0072] Step C40: When the total effective amount is less than or equal to the remaining asset limit, the effective amount of the product type being non-key product is used as the allocation limit and allocated to each non-key product of that priority level to obtain the initial allocation result.

[0073] It should be noted that if the total effective amount ΣE_j (P=k) of the current tier is less than or equal to the current remaining asset amount T_remaining, it indicates that the remaining amount is sufficient to meet the full allocation requirements of all products in that tier. The system then directly allocates the effective amount E_i of each non-priority product within that tier as its winning bid amount A_i, i.e., A_i = E_i. Subsequently, the system updates the remaining asset amount: T_remaining = T_remaining - ΣE_j (P=k). The updated T_remaining will be used for the next lower priority allocation stream.

[0074] Additionally, it should be noted that full allocation means that the product tier has received its maximum expected available credit (within risk control and bidding amount limits). The credit carryover mechanism (allocating remaining credit to the next priority level) ensures that the total asset quota is utilized to the maximum extent and reduces idle credit.

[0075] In this embodiment, by constructing a priority-based hierarchical allocation mechanism, the system first ensures the priority and full allocation of key products, and then performs sequential traversal and hierarchical proportional allocation of non-key products according to priority level. This realizes the automated and orderly allocation of asset quotas under multi-dimensional constraints, effectively solves the technical problems of multi-objective coordination difficulties and resource fragmentation in traditional allocation methods, and significantly improves the efficiency and full allocation rate of asset allocation.

[0076] Based on the first and second embodiments of the present application, in the third embodiment of the present application, the same or similar content as in the above-mentioned first and second embodiments can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 4 , Figure 4 which is a schematic flowchart of the third embodiment of the bidding method for optimizing the allocation of financial assets in the present application. The specific steps of step S40 include: Step S401: Traverse the initial allocation result, and mark the products with the allocated amount lower than the target winning bid amount in the initial allocation result as the products that have failed the bid.

[0077] It should be noted that in this step, the bid failure processing module will check the winning bid amount A_i of each product in the initial allocation result one by one. The system compares A_i with the preset target winning bid amount M (for example, 10 million yuan). If A_i < M, then this product is marked as a product that has failed the bid. Additionally, it should be noted that a product that has failed the bid refers to a product that has participated in the allocation process but has been determined by the system to be invalid for this allocation due to the too low winning bid amount. Its allocated amount will be recovered for reuse.

[0078] Step S402: Recover the allocated amount in the products that have failed the bid, and remove the products that have failed the bid from the current allocation sequence to obtain an updated allocation sequence.

[0079] It should be noted that in this step, the system will sum up the winning bid amounts A_i of all the products marked as having failed the bid, and recover this total amount into the allocable asset pool, that is, update the remaining asset amount T_remaining = T_remaining + ΣA_i (failed bid). At the same time, these products that have failed the bid are removed from the subsequent allocation candidate list (i.e., the allocation sequence). This step realizes the instant recovery of quota resources and the purification of the allocation sequence, clears the obstacles for efficient reallocation, and effectively solves the problem of quota fragmentation.

[0080] Additionally, it should be noted that the allocation sequence refers to an ordered set of products participating in hierarchical allocation. The updated allocation sequence no longer includes the products that have failed the bid identified this time, but new candidate products can be supplemented from the standby product pool to increase the success rate of reallocation.

[0081] Step S403: Based on the recovered allocated amount and the updated allocation sequence, re - execute hierarchical allocation until no product fails the bid or the total asset amount is lower than the preset amount to complete the optimization of the bidding allocation.

[0082] It should be noted that in this step, the system uses the recovered quota as the new available asset and the updated product sequence as the new allocation target, re-triggering and executing the complete allocation logic that started from step S20 (calculating the effective amount) and step S30 (tiered allocation). This redistribution process will be repeated cyclically, forming an intelligent closed loop of allocation-checking-recovery-redistribution, until the loop termination condition is met: either there are no more failed products in the newly generated allocation results, or the remaining total distributable quota after multiple rounds of recovery has fallen below a small preset threshold (indicating that effective allocation is no longer possible). Understandably, this step, through iterative optimization, ensures that the asset quota is allocated to the maximum extent and effectively, significantly improving the full-stake rate.

[0083] In one feasible implementation, prior to step S401, the method further includes: Step B10: Obtain the historical frequency of failed bids and the current market saturation for each product.

[0084] It should be noted that in this step, the system will obtain auxiliary decision-making information from the historical database and the real-time market data interface. The historical failure frequency refers to the proportion of times a specific product has been marked as a failed bid in the past period out of the total number of allocations; the bid saturation refers to the ratio of the total bid amount of all products in the current period to the total asset amount, reflecting the strength of market demand.

[0085] Step B20: Adjust the target winning bid amount based on the historical failure frequency and the bid saturation to obtain the failure judgment amount.

[0086] It should be noted that in this step, the system uses a predefined algorithm model to dynamically adjust the fixed target winning bid amount M based on the acquired historical bid failure frequency and bid saturation, outputting a temporary, more intelligent bid failure judgment amount M'. For example, for products with a high historical bid failure frequency, the bid failure judgment threshold can be appropriately lowered; when the market bid saturation is high, the bid failure judgment amount can be generally increased to filter out higher-quality products. The calculation formula can be M' = M * f(historical bid failure frequency, bid saturation), where f is the adjustment function.

[0087] Understandably, this step changes the criteria for determining failure status from static to dynamic, enabling it to adapt to the business environment and product characteristics, and achieve more refined management.

[0088] Step B30: Replace the target winning bid amount with the failed bid determination amount.

[0089] It should be noted that in this failed bid check, the system will use a dynamically calculated failed bid judgment amount M' instead of the original fixed target winning bid amount M as the new standard for determining whether a product is a failed bid in step S401. This means that this step dynamically switches the judgment threshold, making the failed bid identification mechanism more flexible and intelligent. Furthermore, it should be noted that this replacement can be global (using the same M' for all products) or targeted (using different M'_i for different products), depending on the configuration of the business rules.

[0090] In this embodiment, by constructing a closed loop of dynamic bid failure determination and intelligent redistribution, the system first dynamically adjusts the bid failure determination criteria based on historical data and market conditions, and then automatically identifies, reclaims invalid allocations and triggers iterative optimization, realizing intelligent and adaptive bid failure processing. This effectively solves the technical problems of rigid determination and low recovery efficiency in traditional solutions, and significantly improves the asset full-bid rate and allocation efficiency.

[0091] This application also provides a bidding device for optimizing financial asset allocation; please refer to [reference needed]. Figure 5 The bidding device for optimizing financial asset allocation includes: Data acquisition module 10 is used to obtain total asset amount, bid amount of each product, risk control trial amount, priority information, key product marking, global priority ratio and target winning bid amount; The dynamic coupling module 20 is used to receive the total asset amount, bid amount of each product, risk control trial amount, priority information, key product mark, global priority ratio and target winning bid amount sent by the data acquisition module; The dynamic coupling module 20 is also used to determine the effective amount of each product based on the total asset amount, the bid amount, the risk control trial amount, the key product mark and the global priority ratio; The dynamic coupling module 20 is also used to perform hierarchical allocation of each product according to the effective amount, the total asset amount and the priority information to obtain the initial allocation result; The failed bid processing module 30 is used to optimize the allocation of the initial allocation result based on the target winning bid amount.

[0092] The bidding device for optimizing financial asset allocation provided in this application, employing the bidding method for optimizing financial asset allocation in the above embodiments, can solve the technical problem of how to improve the efficiency of resource allocation optimization under multidimensional constraints. Compared with the prior art, the beneficial effects of the bidding device for optimizing financial asset allocation provided in this application are the same as those of the bidding method for optimizing financial asset allocation provided in the above embodiments, and other technical features in the bidding device for optimizing financial asset allocation are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0093] In one embodiment, the dynamic coupling module 20 is further configured to determine the product type of each product based on the key product marker, wherein the product type includes key products and non-key products; when the product type of the current product is the key product, the effective amount of the current product is determined based on the bid amount, the risk control trial amount, the global priority ratio, and the total asset amount.

[0094] In one embodiment, the dynamic coupling module 20 is further configured to: determine the effective amount of the current product based on the bid amount and the risk control trial amount when the product type of the current product is the non-key product; or, when the product type of the current product is the non-key product, obtain the importance ratio and historical importance ratio of the current product; determine the weight ratio of the current product based on the importance ratio and the historical importance ratio; and determine the effective amount of the current product based on the weight ratio, the bid amount, and the risk control trial amount.

[0095] In one embodiment, the dynamic coupling module 30 is further configured to allocate the effective amount of the product type being a key product among the products to obtain the allocated amount; determine the remaining asset amount based on the total asset amount and the allocated amount; and perform hierarchical allocation of the effective amount of the product type being a non-key product among the products according to the priority order in the priority information and the remaining asset amount to obtain an initial allocation result.

[0096] In one embodiment, the dynamic coupling module 20 is further configured to traverse each priority level sequentially according to the priority level from high to low; for the currently traversed priority level, calculate the sum of the effective amounts of all non-key products under that priority level to obtain the total effective amount; when the total effective amount is greater than the remaining asset limit, allocate the remaining asset limit to each non-key product of that priority level according to the proportion of the effective amount of the non-key product type in the total effective amount, to obtain an initial allocation result; when the total effective amount is less than or equal to the remaining asset limit, allocate the effective amount of the non-key product type as the allocation limit to each non-key product of that priority level to obtain an initial allocation result.

[0097] In one embodiment, the failed bid processing module 30 is further configured to traverse the initial allocation results, mark products in the initial allocation results whose allocation amount is lower than the target winning bid amount as failed bid products; recover the allocation amount in the failed bid products, and remove the failed bid products from the current allocation sequence to obtain an updated allocation sequence; based on the recovered allocation amount and the updated allocation sequence, re-execute the hierarchical allocation until no failed bid products are generated or the total asset amount is lower than the preset amount, so as to complete the optimization of the bid allocation.

[0098] In one embodiment, the failed bid processing module 30 is further configured to obtain the historical failed bid frequency and the current market bid saturation for each product; adjust the target winning bid amount according to the historical failed bid frequency and the bid saturation to obtain the failed bid determination amount; and replace the target winning bid amount with the failed bid determination amount.

[0099] This application provides a bidding device for optimizing the allocation of financial assets. The bidding device for optimizing the allocation of financial assets includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the bidding method for optimizing the allocation of financial assets in the first embodiment described above.

[0100] The following is for reference. Figure 6This document illustrates a structural schematic diagram of a bidding device suitable for implementing embodiments of this application for optimizing financial asset allocation. The bidding device for optimizing financial asset allocation in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The bidding device shown for optimizing financial asset allocation is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0101] like Figure 6 As shown, the bidding device for optimizing financial asset allocation may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the bidding device for optimizing financial asset allocation. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the bidding equipment for financial asset allocation optimization to exchange data wirelessly or via wired communication with other devices. Although the figure shows bidding equipment for financial asset allocation optimization with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0102] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0103] The bidding device for optimizing financial asset allocation provided in this application, employing the bidding method for optimizing financial asset allocation in the above embodiments, can solve the technical problem of how to improve the efficiency of resource allocation optimization under multidimensional constraints. Compared with the prior art, the beneficial effects of the bidding device for optimizing financial asset allocation provided in this application are the same as those of the bidding method for optimizing financial asset allocation provided in the above embodiments, and other technical features of the bidding device for optimizing financial asset allocation are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0104] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0105] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0106] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the bidding method for optimizing financial asset allocation in the above embodiments.

[0107] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0108] The aforementioned computer-readable storage medium may be included in the bidding equipment for optimizing the allocation of financial assets; or it may exist independently and not be assembled into the bidding equipment for optimizing the allocation of financial assets.

[0109] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a bidding device for optimizing financial asset allocation, the bidding device for optimizing financial asset allocation causes the following to be received from the data acquisition module: total asset amount, bid amount for each product, risk control trial amount, priority information, key product marker, global priority ratio, and target winning bid amount; determine the effective amount for each product based on the total asset amount, bid amount, risk control trial amount, key product marker, and global priority ratio; perform tiered allocation of each product based on the effective amount, total asset amount, and priority information to obtain an initial allocation result; and send the initial allocation result to a bid failure processing module so that the bid failure processing module optimizes the allocation based on the target winning bid amount.

[0110] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0111] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0112] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0113] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the bidding method for optimizing financial asset allocation described above. This addresses the technical problem of improving resource allocation efficiency under multi-dimensional constraints. Compared to existing technologies, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the bidding method for optimizing financial asset allocation provided in the above embodiments, and will not be elaborated upon here.

[0114] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the bidding method for optimizing financial asset allocation as described above.

[0115] The computer program product provided in this application can solve the technical problem of how to improve the efficiency of resource optimization allocation under multidimensional constraints. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the bidding method for financial asset allocation optimization provided in the above embodiments, and will not be repeated here.

[0116] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A bidding method for optimizing the allocation of financial assets, characterized in that, The bidding method for optimizing financial asset allocation is applied to the dynamic coupling module of the system, which includes a data acquisition module, a dynamic coupling module, and a failed bid processing module. The method includes: Receive the total asset amount, bid amount for each product, risk control calculation amount, priority information, key product marking, global priority ratio, and target winning bid amount sent by the data acquisition module; The effective amount for each product is determined based on the total asset amount, the bid amount, the risk control trial amount, the key product marking, and the global priority ratio. Based on the effective amount, the total asset amount, and the priority information, the products are allocated in a tiered manner to obtain the initial allocation result; The initial allocation result is sent to the failed bid processing module so that the failed bid processing module can optimize the allocation of the initial allocation result according to the target winning bid amount.

2. The method as described in claim 1, characterized in that, The step of determining the effective amount for each product based on the total asset amount, the bid amount, the risk control calculation amount, the key product marker, and the global priority ratio includes: The product type of each product is determined based on the key product markings, and the product type includes key products and non-key products. When the product type of the current product is the key product, the effective amount of the current product is determined based on the bid amount, the risk control trial amount, the global priority ratio, and the total asset amount.

3. The method as described in claim 2, characterized in that, After the step of determining the product type of each product based on the key product marker, wherein the product type includes key products and non-key products, the method further includes: When the product type of the current product is the non-key product, the effective amount of the current product is determined based on the bid amount and the risk control trial amount. or, When the current product type is the non-key product, obtain the importance percentage and historical importance percentage of the current product; The weight percentage of the current product is determined based on the importance percentage and the historical importance percentage; The effective amount of the current product is determined based on the weighting percentage, the bid amount, and the risk control trial amount.

4. The method as described in claim 2, characterized in that, The step of allocating products in a tiered manner according to the effective amount, the total asset amount, and the priority information to obtain the initial allocation result includes: The effective amount for the key products among the products is allocated to obtain the allocated amount; The remaining asset amount is determined based on the total asset amount and the allocated amount; Based on the priority order in the priority information and the remaining asset amount, the effective amount of the non-key product type in each product is allocated in a tiered manner to obtain the initial allocation result.

5. The method as described in claim 4, characterized in that, The priority information includes priority levels. The step of allocating the effective amount of non-priority products among the products in each product category according to the priority order in the priority information and the remaining asset amount to obtain the initial allocation result includes: According to the priority level in descending order, traverse each priority level in turn; For the current priority level, calculate the sum of the effective amounts of all non-key products under that priority level to obtain the total effective amount; When the total effective amount is greater than the remaining asset limit, the remaining asset limit is allocated to each non-key product of that priority level according to the proportion of the effective amount of the non-key product in the total effective amount, to obtain the initial allocation result; If the total effective amount is less than or equal to the remaining asset limit, the effective amount of the product type (non-key product) is used as the allocation limit and allocated to each non-key product of that priority level to obtain the initial allocation result.

6. The method as described in claim 1, characterized in that, The step of optimizing the initial allocation result based on the target winning bid amount includes: Traverse the initial allocation results and mark products in the initial allocation results whose allocated amount is lower than the target winning bid amount as failed bid products; The allocated quota in the failed bid product is recovered, and the failed bid product is removed from the current allocation sequence to obtain an updated allocation sequence; Based on the recovered allocation amount and the updated allocation sequence, the tiered allocation is re-executed until no unsuccessful bids are generated or the total asset amount is lower than the preset amount, in order to complete the optimization of the bidding allocation.

7. The method as described in claim 6, characterized in that, Before the step of traversing the initial allocation results and marking products with allocation amounts lower than the target winning bid amount as failed bid products, the method further includes: Obtain the historical frequency of failed bids and the current market saturation of bids for each product; The target winning bid amount is adjusted based on the historical frequency of failed bids and the bid saturation level to obtain the bid failure determination amount; Replace the target winning bid amount with the bid failure determination amount.

8. A bidding device for optimizing the allocation of financial assets, characterized in that, The device includes: The data acquisition module is used to obtain the total asset amount, the bid amount for each product, the risk control trial amount, priority information, key product marking, global priority ratio, and target winning bid amount; The dynamic coupling module is used to receive the total asset amount, bid amount of each product, risk control trial amount, priority information, key product marking, global priority ratio and target winning bid amount sent by the data acquisition module; The dynamic coupling module is also used to determine the effective amount of each product based on the total asset amount, the bid amount, the risk control trial amount, the key product mark and the global priority ratio; The dynamic coupling module is also used to allocate each product in a hierarchical manner according to the effective amount, the total asset amount and the priority information to obtain the initial allocation result; The failed bid processing module is used to optimize the allocation of the initial allocation results based on the target winning bid amount.

9. A bidding device for optimizing the allocation of financial assets, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the bidding method for optimizing financial asset allocation as claimed in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the bidding method for optimizing financial asset allocation as described in any one of claims 1 to 7.

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