A pricing parameter dynamic calculation method and system based on multi-source data

Abstract

The application provides a multi-source data-based pricing parameter dynamic calculation method and system. The method comprises the following steps: obtaining effective multi-source evaluation dimension data by obtaining multi-source evaluation dimension data of a target object and preprocessing the multi-source evaluation dimension data, combining current time matching effective strategy configuration, adopting multi-type adjustment functions to adapt to different scenes, calculating score deviation and dimension adjustment amount, obtaining final pricing data after optimization by constraint rules, and finally determining the pricing state through boundary check; thereby realizing the technology of calculating the score deviation value and the dimension adjustment amount data through the preprocessing of the multi-source evaluation dimension data, the effective strategy configuration, the optimization of the preset constraint rules to obtain the optimized dimension adjustment amount data, and the calculation of the final pricing data and the judgment of the pricing state.

Description

Technical Field

[0001] This application relates to the fields of computer data processing and financial technology applications, and more specifically, to a method and system for dynamically calculating pricing parameters based on multi-source data. Background Technology

[0002] In various business scenarios requiring precise pricing, such as finance, industrial services, and credit assessment, the rationality of pricing parameters directly impacts business revenue and risk control effectiveness. Traditional pricing methods generally suffer from numerous technical shortcomings: On the one hand, they often rely on single-dimensional evaluation data or static experience parameters, lacking effective integration and preprocessing of multi-source heterogeneous data such as basic security scores, industry strategic value scores, ESG scores, and credit scores. This makes it difficult to comprehensively characterize the risk level and value characteristics of the target object, and abnormal data is often handled with simple discarding or fixed replacement, without scientific correction based on historical data stability and industry averages, resulting in insufficient data validity. On the other hand, pricing strategies are rigidly configured, unable to dynamically match and adapt benchmark parameters and adjustment rules according to the current time and market environment. The adjustment function type is singular, making it difficult to meet the complex mapping relationship between different dimensions of performance and pricing adjustments. At the same time, there is a lack of a sound constraint rule system and a full-process risk control mechanism, insufficient risk verification of pricing adjustment amounts, and the final pricing result is not subject to strict boundary range verification, making it prone to pricing anomalies. These problems result in traditional pricing methods having low accuracy, weak risk control, and poor adaptability, failing to meet the core needs of modern businesses for dynamic, accurate, and compliant pricing.

[0003] Effective technical solutions are urgently needed to address the above problems. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for dynamic calculation of pricing parameters based on multi-source data. This method can calculate the scoring deviation value and dimension adjustment amount data through preprocessing of multi-source evaluation dimension data and configuration of effective strategies. After optimization by preset constraint rules, the optimized dimension adjustment amount data is obtained, thereby realizing the calculation of the final pricing data and the judgment of the pricing status.

[0005] This application also provides a method for dynamically calculating pricing parameters based on multi-source data, including the following steps:

[0006] The multi-source evaluation dimension data of the target object is obtained, and after preprocessing, the effective multi-source evaluation dimension data is obtained. The effective multi-source evaluation dimension data is then stored in the input cache.

[0007] Get the current time data, and query the preset strategy configuration table based on the current time to obtain the valid strategy configuration;

[0008] The scoring deviation value and dimension adjustment amount are calculated based on effective multi-source evaluation dimension data and effective strategy configuration.

[0009] By using preset constraint rules to make logical judgments on effective multi-source evaluation dimension data, and adjusting the dimension adjustment amount data according to the logical judgment results, the optimized dimension adjustment amount data is obtained.

[0010] The final pricing data is calculated based on the pricing benchmark parameters and the adjustment data of the optimization dimensions.

[0011] The final pricing data is compared with the preset boundary check interval to obtain the pricing status.

[0012] Optionally, in the dynamic calculation method for pricing parameters based on multi-source data described in this application, the step of obtaining multi-source evaluation dimension data of the target object, performing preprocessing to obtain effective multi-source evaluation dimension data, and storing the effective multi-source evaluation dimension data in the input cache specifically includes:

[0013] Obtain multi-source evaluation data, including one or more of the following: basic security score, industry strategic value score, ESG score, or credit score;

[0014] Compare the corresponding dimension data in the multi-source evaluation dimension data with the corresponding preset effective range threshold;

[0015] If the corresponding dimension data are all within the preset valid range threshold, they are marked as valid multi-source evaluation dimension data;

[0016] If the corresponding dimension data is not within the preset valid range threshold, it is marked as abnormal dimension data, and the preset abnormal data processing model is triggered to process it to obtain valid multi-source evaluation dimension data.

[0017] Optionally, in the dynamic calculation method for pricing parameters based on multi-source data described in this application, the step of obtaining current time data and querying a preset strategy configuration table based on the current time to obtain an effective strategy configuration specifically includes:

[0018] Effective strategy configuration includes effective pricing benchmark parameters and adjustment functions;

[0019] The effective pricing benchmark parameters include benchmark parameters, basic risk premium data, and benchmark values ​​corresponding to each dimension in each effective multi-source evaluation dimension data;

[0020] The adjustment function includes one or more of the following: linear mapping function, piecewise linear mapping function, and nonlinear mapping function.

[0021] Optionally, in the dynamic calculation method for pricing parameters based on multi-source data described in this application, the step of calculating the scoring deviation value and dimension adjustment amount data based on effective multi-source evaluation dimension data and effective strategy configuration specifically includes:

[0022] The score deviation value for that dimension is obtained by subtracting the effective multi-source evaluation dimension data from the corresponding benchmark value.

[0023] Input the scoring deviation value into the corresponding adjustment function to calculate the corresponding dimension adjustment data.

[0024] Optionally, in the dynamic calculation method for pricing parameters based on multi-source data described in this application, the step of performing logical judgment on effective multi-source evaluation dimension data through preset constraint rules, and adjusting the dimension adjustment amount data according to the logical judgment result to obtain optimized dimension adjustment amount data, specifically includes:

[0025] If the valid multi-source data does not meet the preset constraint rules, the dimension adjustment data will be directly marked as the optimized dimension adjustment data.

[0026] If the valid multi-source data meets the preset constraint rules, constraint adjustment is triggered. After adjusting the dimension adjustment data according to the preset adjustment rules, the optimized dimension adjustment data is obtained and a constraint trigger mark is generated.

[0027] Optionally, in the dynamic calculation method for pricing parameters based on multi-source data described in this application, the step of calculating the final pricing data according to the pricing benchmark parameters and optimization dimension adjustment data specifically includes:

[0028] The final pricing data is obtained by combining the benchmark parameters, basic risk premium, and optimization dimension adjustment data with preset weighting coefficients and then calculating the weighted sum.

[0029] Optionally, in the dynamic calculation method for pricing parameters based on multi-source data described in this application, the step of comparing the final pricing data with a preset boundary check interval to obtain the pricing status specifically includes:

[0030] The preset boundary check interval includes a first boundary value and a second boundary value, and the first boundary value is less than the second boundary value;

[0031] The final pricing data is compared with the first and second boundary values ​​to obtain the pricing status, including normal or abnormal status.

[0032] If the final pricing data is less than the first boundary value or greater than the second boundary value, the pricing status is abnormal.

[0033] If the final pricing data is greater than or equal to the first boundary value and less than or equal to the second boundary value, then the pricing status is normal.

[0034] Secondly, this application provides a dynamic pricing parameter calculation system based on multi-source data. The system includes a memory and a processor. The memory includes a program for a dynamic pricing parameter calculation method based on multi-source data. When executed by the processor, the program for the dynamic pricing parameter calculation method based on multi-source data performs the following steps:

[0035] The multi-source evaluation dimension data of the target object is obtained, and after preprocessing, the effective multi-source evaluation dimension data is obtained. The effective multi-source evaluation dimension data is then stored in the input cache.

[0036] Get the current time data, and query the preset strategy configuration table based on the current time to obtain the valid strategy configuration;

[0037] The scoring deviation value and dimension adjustment amount are calculated based on effective multi-source evaluation dimension data and effective strategy configuration.

[0038] By using preset constraint rules to make logical judgments on effective multi-source evaluation dimension data, and adjusting the dimension adjustment amount data according to the logical judgment results, the optimized dimension adjustment amount data is obtained.

[0039] The final pricing data is calculated based on the pricing benchmark parameters and the adjustment data of the optimization dimensions.

[0040] The final pricing data is compared with the preset boundary check interval to obtain the pricing status.

[0041] Optionally, in the dynamic pricing parameter calculation system based on multi-source data described in this application, the step of obtaining multi-source evaluation dimension data of the target object, preprocessing it to obtain effective multi-source evaluation dimension data, and storing the effective multi-source evaluation dimension data in the input cache specifically includes:

[0042] Obtain multi-source evaluation data, including one or more of the following: basic security score, industry strategic value score, ESG score, or credit score;

[0043] Compare the corresponding dimension data in the multi-source evaluation dimension data with the corresponding preset effective range threshold;

[0044] If the corresponding dimension data are all within the preset valid range threshold, they are marked as valid multi-source evaluation dimension data;

[0045] If the corresponding dimension data is not within the preset valid range threshold, it is marked as abnormal dimension data, and the preset abnormal data processing model is triggered to process it to obtain valid multi-source evaluation dimension data.

[0046] Optionally, in the dynamic pricing parameter calculation system based on multi-source data described in this application, the step of obtaining current time data and querying a preset strategy configuration table based on the current time to obtain an effective strategy configuration specifically includes:

[0047] Effective strategy configuration includes effective pricing benchmark parameters and adjustment functions;

[0048] The effective pricing benchmark parameters include benchmark parameters, basic risk premium data, and benchmark values ​​corresponding to each dimension in each effective multi-source evaluation dimension data;

[0049] The adjustment function includes one or more of the following: linear mapping function, piecewise linear mapping function, and nonlinear mapping function.

[0050] As described above, this application provides a method and system for dynamically calculating pricing parameters based on multi-source data. This method obtains effective multi-source evaluation dimension data by acquiring and preprocessing multi-source evaluation dimension data of the target object. It then combines this with the current time to match effective strategy configurations, employs multiple adjustment functions to adapt to different scenarios, calculates the scoring deviation and dimension adjustment amount, optimizes it using constraint rules, and finally obtains the final pricing data. Boundary checks are then used to determine the pricing status. Thus, through the preprocessing of multi-source evaluation dimension data, the configuration of effective strategies, the calculation of scoring deviation values ​​and dimension adjustment amount data, and the optimization of optimized dimension adjustment amount data through preset constraint rules, the method achieves the calculation of final pricing data and the determination of pricing status.

[0051] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart illustrating a method for dynamically calculating pricing parameters based on multi-source data, provided in this application embodiment;

[0054] Figure 2 A flowchart illustrating the method for dynamically calculating pricing parameters based on multi-source data, as provided in this application embodiment, shows how to obtain effective multi-source evaluation dimension data.

[0055] Figure 3 This application provides a schematic diagram of the strategy configuration version switching logic for a dynamic calculation method for pricing parameters based on multi-source data, as illustrated in an embodiment of the present application.

[0056] Figure 4 A high-level flowchart of a dynamic calculation method for pricing parameters based on multi-source data provided in this application embodiment. Detailed Implementation

[0057] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0058] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0059] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for dynamically calculating pricing parameters based on multi-source data, as described in some embodiments of this application. This method is used in terminal devices, such as computers and mobile phones. The method includes the following steps:

[0060] S11. Obtain multi-source evaluation dimension data of the target object, perform preprocessing to obtain effective multi-source evaluation dimension data, and store the effective multi-source evaluation dimension data in the input cache.

[0061] S12. Obtain the current time data, and query the preset strategy configuration table based on the current time to obtain the valid strategy configuration;

[0062] S13. Calculate the scoring deviation value and dimension adjustment amount data based on the effective multi-source evaluation dimension data and effective strategy configuration;

[0063] S14. Logically judge the effective multi-source evaluation dimension data through preset constraint rules, and adjust the dimension adjustment amount data according to the logical judgment result to obtain the optimized dimension adjustment amount data.

[0064] S15. Calculate the final pricing data based on the pricing benchmark parameters and optimization dimension adjustment data;

[0065] S16. Compare the final pricing data with the preset boundary check interval to obtain the pricing status.

[0066] Understandably, the multi-source evaluation dimension data of the target object is obtained through a multi-dimensional scoring input interface.

[0067] First, preprocessing operations such as protocol parsing, format validation, and effective range threshold comparison are performed to filter out valid data and store it in the input cache. Abnormal data triggers a dedicated processing model for correction. Then, the current timestamp (accurate to milliseconds) is obtained, and the preset strategy configuration table is queried. The preset strategy configuration table is stored in a relational database or distributed configuration center. Through the filtering logic of "effective time matching + status validity", the effective strategy configuration adapted to the current scenario is extracted. The scoring deviation value is obtained by subtracting the effective data from the benchmark values ​​of each dimension in the strategy configuration. Then, the deviation value is substituted into the preset adjustment function to calculate and generate the dimension adjustment amount data for each dimension. The preset constraint rule engine is called to perform logical judgment on the effective data. If the risk constraint conditions are met, the adjustment is executed according to the rules and a structured constraint trigger mark is generated. If not, the original adjustment amount is directly used to obtain the optimized dimension adjustment amount data. The final pricing data is calculated by the weighted summation formula. Before the calculation, all parameter units are ensured to be consistent. Finally, the final pricing data is compared with the preset boundary check interval. If it is within the interval, it is determined to be in a normal state and directly output to the downstream. If it exceeds the interval, it is a pricing anomaly, triggering manual review or truncation.

[0068] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating how to obtain effective multi-source evaluation dimension data based on multi-source data, as provided in an embodiment of this application. According to an embodiment of the present invention, the steps of obtaining multi-source evaluation dimension data of a target object, performing preprocessing to obtain effective multi-source evaluation dimension data, and storing the effective multi-source evaluation dimension data in an input cache specifically include:

[0069] S21. Obtain multi-source evaluation dimension data, including one or more of the following: basic security score, industry strategic value score, ESG score, or credit score;

[0070] S22. Compare the corresponding dimension data in the multi-source evaluation dimension data with the corresponding preset effective range threshold;

[0071] S23. If the corresponding dimension data are all within the preset effective range threshold, they are marked as valid multi-source evaluation dimension data.

[0072] S24. If the corresponding dimension data is not within the preset effective range threshold, it is marked as abnormal dimension data, and the preset abnormal data processing model is triggered to process it to obtain effective multi-source evaluation dimension data.

[0073] Understandably, the multi-dimensional scoring input interface module automatically receives multi-source evaluation dimension data sent by the upstream evaluation system via internal API, message queue, or remote call interface. Among the multi-source evaluation dimension data, the basic safety score focuses on the risk bottom line, the industrial strategic value score focuses on long-term value, the ESG score focuses on sustainable development, and the credit score focuses on performance capabilities. Whether to use one or more can be set according to the needs of the target object. The ESG score is a comprehensive evaluation score of the three dimensions of Environmental, Social, and Governance, used to quantitatively assess the sustainable development and social responsibility of enterprises, projects, or organizations. Performance in fulfilling obligations and corporate governance are core indicators for measuring non-financial risks and long-term value. The preprocessing stage is the first line of defense for data validity. A specific valid range threshold is preset for each evaluation dimension. The processor compares the data with the threshold dimension by dimension. By verifying the values ​​against the preset valid range threshold, valid data and abnormal data are filtered out. Valid data is directly stored in the input cache, while abnormal data is corrected by a dedicated processing model before being stored. Valid multi-source evaluation dimension data corresponds to the multi-source evaluation dimension data and includes one or more of the following: valid basic security score, valid industry strategic value score, valid ESG score, or valid credit score. The preset abnormal data processing model is obtained by training with historical data.

[0074] Please refer to Figure 3 , Figure 3 This is a schematic diagram illustrating the strategy configuration version switching logic of a dynamic pricing parameter calculation method based on multi-source data provided in an embodiment of this application. According to an embodiment of the present invention, obtaining current time data and querying a preset strategy configuration table based on the current time to obtain a valid strategy configuration specifically includes:

[0075] Effective strategy configuration includes effective pricing benchmark parameters and adjustment functions;

[0076] The effective pricing benchmark parameters include benchmark parameters, basic risk premium data, and benchmark values ​​corresponding to each dimension in each effective multi-source evaluation dimension data;

[0077] The adjustment function includes one or more of the following: linear mapping function, piecewise linear mapping function, and nonlinear mapping function.

[0078] Understandably, an effective strategy configuration is a set of standardized pricing rules adapted to the current scenario. Its core comprises two types of technical elements: First, effective pricing benchmark parameters, encompassing benchmark parameters as the basis for pricing (e.g., a base interest rate of 3.45%), basic risk premium data covering common industry risks (e.g., 1.50%), and benchmark values ​​corresponding to each evaluation dimension. These are set by business experts in conjunction with industry data and updated with the strategy version. All three types of parameters are stored in the strategy configuration table and bound to adjustment functions and constraint rules to ensure logical consistency. Second, adjustment functions, providing three types adapted to different business scenarios, are preset in the effective strategy configuration: the linear mapping function is suitable for scenarios where dimensional performance and adjustment amount are directly / inversely proportional. The parameter is a linear coefficient k, and the calculation formula is "Dimensional adjustment amount data = Scoring deviation value × k" (a positive k indicates a higher score). (The larger the integer value, the better; a negative k indicates the opposite). Piecewise linear mapping functions are suitable for scenarios where different dimensional performance intervals correspond to different adjustment intensities. The parameters are a list of piecewise thresholds and a list of linear coefficients for the corresponding intervals. First, determine the interval to which the scoring deviation value belongs, and then substitute it into the corresponding coefficients for calculation. Nonlinear mapping functions are suitable for scenarios where the dimensional performance and adjustment amount have a complex relationship such as exponential or logarithmic. The parameters are function expressions such as exponential functions and correlation coefficients. The adjustment amount is obtained directly by calculating according to the expression. The strategy configuration table contains corresponding version information, including version number, version effective time, and version expiration time. By comparing the current time with the version effective time and version expiration time, the corresponding version number can be obtained, thereby determining the effective strategy configuration. In this embodiment, if the current time is between the version effective time and version expiration time, then the current version is the effective strategy configuration.

[0079] According to an embodiment of the present invention, the step of calculating the scoring deviation value and dimension adjustment amount data based on effective multi-source evaluation dimension data and effective strategy configuration specifically includes:

[0080] The score deviation value for that dimension is obtained by subtracting the effective multi-source evaluation dimension data from the corresponding benchmark value.

[0081] Input the scoring deviation value into the corresponding adjustment function to calculate the corresponding dimension adjustment data.

[0082] Understandably, the first step involves calculating the rating deviation value. The processor reads valid multi-source evaluation dimension data from the input cache, extracts the benchmark value for the corresponding dimension from the valid strategy configuration, and quantifies the difference between the target object and the benchmark level in that dimension through the difference operation of "rating deviation value = valid dimension data - corresponding benchmark value"—a positive deviation indicates performance better than the benchmark, which may correspond to a pricing discount, while a negative deviation indicates performance worse than the benchmark, which may correspond to a pricing increase. The second step involves calculating the dimension adjustment amount data. Based on the adjustment function type specified for that dimension in the valid strategy configuration, the rating deviation value is substituted into the function as an input parameter: if it is a linear mapping function... The system executes the calculation "Adjustment Amount = Deviation Value × Linear Coefficient k" (if k is positive, the larger the deviation, the larger the adjustment amount; if k is negative, the opposite is true). If it is a piecewise linear mapping function, it first determines the threshold interval to which the deviation value belongs, and then calls the corresponding linear coefficient to calculate the adjustment amount. If it is a non-linear mapping function, it executes the calculation according to the preset expression (such as ΔP=a×e^(b×ΔS)+c, where ΔP is the dimension adjustment amount data, ΔS is the scoring deviation value, and a, b, and c are preset non-linear mapping coefficients). Finally, it outputs the dimension-specific dimension adjustment amount data (in percentage points or base points), realizing the transformation of dimension performance differences into pricing adjustment range.

[0083] According to an embodiment of the present invention, the step of performing logical judgment on effective multi-source evaluation dimension data through preset constraint rules, and adjusting the dimension adjustment amount data according to the logical judgment result to obtain optimized dimension adjustment amount data, specifically includes:

[0084] If the valid multi-source data does not meet the preset constraint rules, the dimension adjustment data will be directly marked as the optimized dimension adjustment data.

[0085] If the valid multi-source data meets the preset constraint rules, constraint adjustment is triggered. After adjusting the dimension adjustment data according to the preset adjustment rules, the optimized dimension adjustment data is obtained and a constraint trigger mark is generated.

[0086] Understandably, firstly, the preset constraint rules are stored in the effective policy configuration in JSON format. Each rule contains four elements: "trigger condition - adjustment strategy - constraint flag - priority". For example, "trigger condition: basic security score < 60; adjustment strategy: reset ESG dimension discount adjustment amount (ΔP < 0) to 0; constraint flag: low_BSS_blocked_discount (i.e., discounts are blocked due to low basic security score); priority: 1". The processor calls the constraint rule engine, traverses the rules from high to low according to the preset priority, extracts effective multi-source evaluation dimension data and substitutes it into the trigger condition (supporting single or compound conditions such as "basic security score < 60 and credit score < 55"), and executes it through expressions. The system determines whether the conditions are met. If the valid multi-source data does not meet any constraint rules, it means that the triggering condition cannot be met, and the adjustment amount has no risk. The original dimension adjustment amount data is directly marked as the optimized dimension adjustment amount data. If a constraint rule is met, it means that the triggering condition has been met. The constraint adjustment process is immediately triggered, and atomic adjustment is performed on the dimension adjustment amount data according to the preset adjustment strategy of the rule (if the adjustment fails, it is rolled back to the original value). Only the adjustment amount of the target dimension is modified, without affecting other data. At the same time, a structured constraint trigger mark is generated (recording the rule ID, triggering condition, and adjustment dimension). The mark is bound to the optimized adjustment amount data for storage to ensure that the risk adjustment is traceable. Finally, the optimized dimension adjustment amount data that takes into account both personalization and compliance is obtained.

[0087] According to an embodiment of the present invention, the step of calculating the final pricing data based on pricing benchmark parameters and optimization dimension adjustment data specifically includes:

[0088] The final pricing data is obtained by combining the benchmark parameters, basic risk premium, and optimization dimension adjustment data with preset weighting coefficients and then calculating the weighted sum.

[0089] Understandably, the benchmark parameters and basic risk premium are fixed values ​​in the effective strategy configuration, reflecting the basic business pricing and general risk costs; the optimization dimension adjustment data are the dynamically corrected values ​​of each dimension after the constraint rules are adjusted, reflecting the personalized characteristics of the target object; the preset weight coefficients are the proportion of each dimension's influence on pricing, which can be obtained by normalizing the data of each dimension and analyzing historical data, such as giving the highest weight to the basic safety score to ensure the risk bottom line is prioritized; before calculation, ensure that all parameters are in the same unit (all percentages or basis points), and the final pricing data calculation formula is:

[0090] ;

[0091] in, For the final pricing data, As the baseline parameter, Based on the risk premium, These are the weight coefficients for each dimension (preset in the strategy configuration, and all of them sum to 1). Adjust the volume data to optimize dimensions.

[0092] According to an embodiment of the present invention, the step of comparing the final pricing data with a preset boundary check interval to obtain the pricing status specifically includes:

[0093] The preset boundary check interval includes a first boundary value and a second boundary value, and the first boundary value is less than the second boundary value;

[0094] The final pricing data is compared with the first and second boundary values ​​to obtain the pricing status, including normal or abnormal status.

[0095] If the final pricing data is less than the first boundary value or greater than the second boundary value, the pricing status is abnormal.

[0096] If the final pricing data is greater than or equal to the first boundary value and less than or equal to the second boundary value, then the pricing status is normal.

[0097] Understandably, a boundary check interval is first preset, including a first boundary value (minimum) and a second boundary value (maximum). This interval is set based on the business cost floor, risk tolerance, and industry competition level, with the first boundary value being less than the second boundary value. Then, the calculated final pricing data is compared with these two boundary values ​​to determine the pricing status: if the final pricing data is less than the first boundary value or greater than the second boundary value, it indicates that the pricing exceeds the reasonable range and is judged as an abnormal pricing, requiring manual review or correction. If the final pricing data is greater than or equal to the first boundary value and less than or equal to the second boundary value, it indicates that the pricing meets business requirements and is judged as a normal state, which can be directly output to the downstream business system.

[0098] It is worth mentioning that if the corresponding dimension data is abnormal, it also includes:

[0099] Obtain historical rating data for the corresponding dimensions of the object, as well as concurrent rating data for objects of the same size and industry scale.

[0100] Historical moving average and historical variance are calculated based on historical scoring data, and industry average is calculated based on scoring data from the same period.

[0101] The effective weights are obtained by querying the preset corrected weight table based on the historical variance.

[0102] The correction value for abnormal dimension data is obtained by weighting and summing the historical moving average, industry average, and effective weights.

[0103] Understandably, the process involves first acquiring historical rating data for the target object in that dimension and concurrent rating data for similar-sized objects in the same industry. In this embodiment, the historical rating data is rolling data from the past 6 months, and the concurrent rating data includes at least 30 sets to ensure sample representativeness. Then, the historical moving average and historical variance are calculated, along with the industry average for concurrent rating data in the same industry. Subsequently, a preset correction weight table is consulted based on the historical variance (the smaller the variance, the higher the reliability of the historical data, and the greater the weight; for example, when the variance is ≤5, the historical weight is 0.6 and the industry weight is 0.4; when the variance is >5, the historical weight is 0.3 and the industry weight is 0.7). Finally, the corrected effective data is obtained through a weighted summation operation of "Abnormal dimension data correction value = historical moving average × historical weight + industry average × industry weight," ensuring that abnormal data does not affect the overall pricing logic.

[0104] Please refer to Figure 4 , Figure 4 A high-level flowchart of a dynamic calculation method for pricing parameters based on multi-source data provided in this application embodiment.

[0105] This invention also discloses a dynamic pricing parameter calculation system based on multi-source data, including a memory and a processor. The memory stores a program for a dynamic pricing parameter calculation method based on multi-source data. When the processor executes the program, the dynamic pricing parameter calculation method based on multi-source data performs the following steps:

[0106] The multi-source evaluation dimension data of the target object is obtained, and after preprocessing, the effective multi-source evaluation dimension data is obtained. The effective multi-source evaluation dimension data is then stored in the input cache.

[0107] Get the current time data, and query the preset strategy configuration table based on the current time to obtain the valid strategy configuration;

[0108] The scoring deviation value and dimension adjustment amount are calculated based on effective multi-source evaluation dimension data and effective strategy configuration.

[0109] By using preset constraint rules to make logical judgments on effective multi-source evaluation dimension data, and adjusting the dimension adjustment amount data according to the logical judgment results, the optimized dimension adjustment amount data is obtained.

[0110] The final pricing data is calculated based on the pricing benchmark parameters and the adjustment data of the optimization dimensions.

[0111] The final pricing data is compared with the preset boundary check interval to obtain the pricing status.

[0112] Understandably, the multi-source evaluation dimension data of the target object is obtained through a multi-dimensional scoring input interface.

[0113] First, preprocessing operations such as protocol parsing, format validation, and effective range threshold comparison are performed to filter out valid data and store it in the input cache. Abnormal data triggers a dedicated processing model for correction. Then, the current timestamp (accurate to milliseconds) is obtained, and the preset strategy configuration table is queried. The preset strategy configuration table is stored in a relational database or distributed configuration center. Through the filtering logic of "effective time matching + status validity", the effective strategy configuration adapted to the current scenario is extracted. The scoring deviation value is obtained by subtracting the effective data from the benchmark values ​​of each dimension in the strategy configuration. Then, the deviation value is substituted into the preset adjustment function to calculate and generate the dimension adjustment amount data for each dimension. The preset constraint rule engine is called to perform logical judgment on the effective data. If the risk constraint conditions are met, the adjustment is executed according to the rules and a structured constraint trigger mark is generated. If not, the original adjustment amount is directly used to obtain the optimized dimension adjustment amount data. The final pricing data is calculated by the weighted summation formula. Before the calculation, all parameter units are ensured to be consistent. Finally, the final pricing data is compared with the preset boundary check interval. If it is within the interval, it is determined to be in a normal state and directly output to the downstream. If it exceeds the interval, it is a pricing anomaly, triggering manual review or truncation.

[0114] According to an embodiment of the present invention, the step of obtaining multi-source evaluation dimension data of the target object, performing preprocessing to obtain effective multi-source evaluation dimension data, and storing the effective multi-source evaluation dimension data in the input cache specifically includes:

[0115] Obtain multi-source evaluation data, including one or more of the following: basic security score, industry strategic value score, ESG score, or credit score;

[0116] Compare the corresponding dimension data in the multi-source evaluation dimension data with the corresponding preset effective range threshold;

[0117] If the corresponding dimension data are all within the preset valid range threshold, they are marked as valid multi-source evaluation dimension data;

[0118] If the corresponding dimension data is not within the preset valid range threshold, it is marked as abnormal dimension data, and the preset abnormal data processing model is triggered to process it to obtain valid multi-source evaluation dimension data.

[0119] Understandably, the multi-dimensional scoring input interface module automatically receives multi-source evaluation dimension data sent by the upstream evaluation system via internal API, message queue, or remote call interface. Among the multi-source evaluation dimension data, the basic safety score focuses on the risk bottom line, the industrial strategic value score focuses on long-term value, the ESG score focuses on sustainable development, and the credit score focuses on performance capabilities. Whether to use one or more can be set according to the needs of the target object. The ESG score is a comprehensive evaluation score of the three dimensions of Environmental, Social, and Governance, used to quantitatively assess the sustainable development and social responsibility of enterprises, projects, or organizations. Performance in fulfilling obligations and corporate governance are core indicators for measuring non-financial risks and long-term value. The preprocessing stage is the first line of defense for data validity. A specific valid range threshold is preset for each evaluation dimension. The processor compares the data with the threshold dimension by dimension. By verifying the values ​​against the preset valid range threshold, valid data and abnormal data are filtered out. Valid data is directly stored in the input cache, while abnormal data is corrected by a dedicated processing model before being stored. Valid multi-source evaluation dimension data corresponds to the multi-source evaluation dimension data and includes one or more of the following: valid basic security score, valid industry strategic value score, valid ESG score, or valid credit score. The preset abnormal data processing model is obtained by training with historical data.

[0120] According to an embodiment of the present invention, the step of obtaining current time data and querying a preset strategy configuration table based on the current time to obtain a valid strategy configuration specifically includes:

[0121] Effective strategy configuration includes effective pricing benchmark parameters and adjustment functions;

[0122] The effective pricing benchmark parameters include benchmark parameters, basic risk premium data, and benchmark values ​​corresponding to each dimension in each effective multi-source evaluation dimension data;

[0123] The adjustment function includes one or more of the following: linear mapping function, piecewise linear mapping function, and nonlinear mapping function.

[0124] Understandably, an effective strategy configuration is a set of standardized pricing rules adapted to the current scenario. Its core comprises two types of technical elements: First, effective pricing benchmark parameters, encompassing benchmark parameters as the basis for pricing (e.g., a base interest rate of 3.45%), basic risk premium data covering common industry risks (e.g., 1.50%), and benchmark values ​​corresponding to each evaluation dimension. These are set by business experts in conjunction with industry data and updated with the strategy version. All three types of parameters are stored in the strategy configuration table and bound to adjustment functions and constraint rules to ensure logical consistency. Second, adjustment functions, providing three types adapted to different business scenarios, are preset in the effective strategy configuration: the linear mapping function is suitable for scenarios where dimensional performance and adjustment amount are directly / inversely proportional. The parameter is a linear coefficient k, and the calculation formula is "Dimensional adjustment amount data = Scoring deviation value × k" (a positive k indicates a higher score). (The larger the integer value, the better; a negative k indicates the opposite). Piecewise linear mapping functions are suitable for scenarios where different dimensional performance intervals correspond to different adjustment intensities. The parameters are a list of piecewise thresholds and a list of linear coefficients for the corresponding intervals. First, determine the interval to which the scoring deviation value belongs, and then substitute it into the corresponding coefficients for calculation. Nonlinear mapping functions are suitable for scenarios where the dimensional performance and adjustment amount have a complex relationship such as exponential or logarithmic. The parameters are function expressions such as exponential functions and correlation coefficients. The adjustment amount is obtained directly by calculating according to the expression. The strategy configuration table contains corresponding version information, including version number, version effective time, and version expiration time. By comparing the current time with the version effective time and version expiration time, the corresponding version number can be obtained, thereby determining the effective strategy configuration. In this embodiment, if the current time is between the version effective time and version expiration time, then the current version is the effective strategy configuration.

[0125] According to an embodiment of the present invention, the step of calculating the scoring deviation value and dimension adjustment amount data based on effective multi-source evaluation dimension data and effective strategy configuration specifically includes:

[0126] The score deviation value for that dimension is obtained by subtracting the effective multi-source evaluation dimension data from the corresponding benchmark value.

[0127] Input the scoring deviation value into the corresponding adjustment function to calculate the corresponding dimension adjustment data.

[0128] Understandably, the first step involves calculating the rating deviation value. The processor reads valid multi-source evaluation dimension data from the input cache, extracts the benchmark value for the corresponding dimension from the valid strategy configuration, and quantifies the difference between the target object and the benchmark level in that dimension through the difference operation of "rating deviation value = valid dimension data - corresponding benchmark value"—a positive deviation indicates performance better than the benchmark, which may correspond to a pricing discount, while a negative deviation indicates performance worse than the benchmark, which may correspond to a pricing increase. The second step involves calculating the dimension adjustment amount data. Based on the adjustment function type specified for that dimension in the valid strategy configuration, the rating deviation value is substituted into the function as an input parameter: if it is a linear mapping function... The system executes the calculation "Adjustment Amount = Deviation Value × Linear Coefficient k" (if k is positive, the larger the deviation, the larger the adjustment amount; if k is negative, the opposite is true). If it is a piecewise linear mapping function, it first determines the threshold interval to which the deviation value belongs, and then calls the corresponding linear coefficient to calculate the adjustment amount. If it is a non-linear mapping function, it executes the calculation according to the preset expression (such as ΔP=a×e^(b×ΔS)+c, where ΔP is the dimension adjustment amount data, ΔS is the scoring deviation value, and a, b, and c are preset non-linear mapping coefficients). Finally, it outputs the dimension-specific dimension adjustment amount data (in percentage points or base points), realizing the transformation of dimension performance differences into pricing adjustment range.

[0129] According to an embodiment of the present invention, the step of performing logical judgment on effective multi-source evaluation dimension data through preset constraint rules, and adjusting the dimension adjustment amount data according to the logical judgment result to obtain optimized dimension adjustment amount data, specifically includes:

[0130] If the valid multi-source data does not meet the preset constraint rules, the dimension adjustment data will be directly marked as the optimized dimension adjustment data.

[0131] If the valid multi-source data meets the preset constraint rules, constraint adjustment is triggered. After adjusting the dimension adjustment data according to the preset adjustment rules, the optimized dimension adjustment data is obtained and a constraint trigger mark is generated.

[0132] Understandably, firstly, the preset constraint rules are stored in the effective policy configuration in JSON format. Each rule contains four elements: "trigger condition - adjustment strategy - constraint flag - priority". For example, "trigger condition: basic security score < 60; adjustment strategy: reset ESG dimension discount adjustment amount (ΔP < 0) to 0; constraint flag: low_BSS_blocked_discount (i.e., discounts are blocked due to low basic security score); priority: 1". The processor calls the constraint rule engine, traverses the rules from high to low according to the preset priority, extracts effective multi-source evaluation dimension data and substitutes it into the trigger condition (supporting single or compound conditions such as "basic security score < 60 and credit score < 55"), and executes it through expressions. The system determines whether the conditions are met. If the valid multi-source data does not meet any constraint rules, it means that the triggering condition cannot be met, and the adjustment amount has no risk. The original dimension adjustment amount data is directly marked as the optimized dimension adjustment amount data. If a constraint rule is met, it means that the triggering condition has been met. The constraint adjustment process is immediately triggered, and atomic adjustment is performed on the dimension adjustment amount data according to the preset adjustment strategy of the rule (if the adjustment fails, it is rolled back to the original value). Only the adjustment amount of the target dimension is modified, without affecting other data. At the same time, a structured constraint trigger mark is generated (recording the rule ID, triggering condition, and adjustment dimension). The mark is bound to the optimized adjustment amount data for storage to ensure that the risk adjustment is traceable. Finally, the optimized dimension adjustment amount data that takes into account both personalization and compliance is obtained.

[0133] According to an embodiment of the present invention, the step of calculating the final pricing data based on pricing benchmark parameters and optimization dimension adjustment data specifically includes:

[0134] The final pricing data is obtained by combining the benchmark parameters, basic risk premium, and optimization dimension adjustment data with preset weighting coefficients and then calculating the weighted sum.

[0135] Understandably, the benchmark parameters and basic risk premium are fixed values ​​in the effective strategy configuration, reflecting the basic business pricing and general risk costs; the optimization dimension adjustment data are the dynamically corrected values ​​of each dimension after the constraint rules are adjusted, reflecting the personalized characteristics of the target object; the preset weight coefficients are the proportion of each dimension's influence on pricing, which can be obtained by normalizing the data of each dimension and analyzing historical data, such as giving the highest weight to the basic safety score to ensure the risk bottom line is prioritized; before calculation, ensure that all parameters are in the same unit (all percentages or basis points), and the final pricing data calculation formula is:

[0136] ;

[0137] in, For the final pricing data, As the baseline parameter, Based on the risk premium, These are the weight coefficients for each dimension (preset in the strategy configuration, and all of them sum to 1). Adjust the volume data to optimize dimensions.

[0138] According to an embodiment of the present invention, the step of comparing the final pricing data with a preset boundary check interval to obtain the pricing status specifically includes:

[0139] The preset boundary check interval includes a first boundary value and a second boundary value, and the first boundary value is less than the second boundary value;

[0140] The final pricing data is compared with the first and second boundary values ​​to obtain the pricing status, including normal or abnormal status.

[0141] If the final pricing data is less than the first boundary value or greater than the second boundary value, the pricing status is abnormal.

[0142] If the final pricing data is greater than or equal to the first boundary value and less than or equal to the second boundary value, then the pricing status is normal.

[0143] Understandably, a boundary check interval is first preset, including a first boundary value (minimum) and a second boundary value (maximum). This interval is set based on the business cost floor, risk tolerance, and industry competition level, with the first boundary value being less than the second boundary value. Then, the calculated final pricing data is compared with these two boundary values ​​to determine the pricing status: if the final pricing data is less than the first boundary value or greater than the second boundary value, it indicates that the pricing exceeds the reasonable range and is judged as an abnormal pricing, requiring manual review or correction. If the final pricing data is greater than or equal to the first boundary value and less than or equal to the second boundary value, it indicates that the pricing meets business requirements and is judged as a normal state, which can be directly output to the downstream business system.

[0144] It is worth mentioning that if the corresponding dimension data is abnormal, it also includes:

[0145] Obtain historical rating data for the corresponding dimensions of the object, as well as concurrent rating data for objects of the same size and industry scale.

[0146] Historical moving average and historical variance are calculated based on historical scoring data, and industry average is calculated based on scoring data from the same period.

[0147] The effective weights are obtained by querying the preset corrected weight table based on the historical variance.

[0148] The correction value for abnormal dimension data is obtained by weighting and summing the historical moving average, industry average, and effective weights.

[0149] Understandably, the process involves first acquiring historical rating data for the target object in that dimension and concurrent rating data for similar-sized objects in the same industry. In this embodiment, the historical rating data is rolling data from the past 6 months, and the concurrent rating data includes at least 30 sets to ensure sample representativeness. Then, the historical moving average and historical variance are calculated, along with the industry average for concurrent rating data in the same industry. Subsequently, a preset correction weight table is consulted based on the historical variance (the smaller the variance, the higher the reliability of the historical data, and the greater the weight; for example, when the variance is ≤5, the historical weight is 0.6 and the industry weight is 0.4; when the variance is >5, the historical weight is 0.3 and the industry weight is 0.7). Finally, the corrected effective data is obtained through a weighted summation operation of "Abnormal dimension data correction value = historical moving average × historical weight + industry average × industry weight," ensuring that abnormal data does not affect the overall pricing logic.

[0150] This invention discloses a method and system for dynamically calculating pricing parameters based on multi-source data. It obtains effective multi-source evaluation dimension data by acquiring and preprocessing multi-source evaluation dimension data of the target object. Combined with the current time and effective strategy configuration, it employs multiple types of adjustment functions to adapt to different scenarios, calculates the scoring deviation and dimension adjustment amount, optimizes the data using constraint rules, and finally determines the pricing status through boundary checks. Thus, through the preprocessing of multi-source evaluation dimension data, effective strategy configuration, calculation of scoring deviation and dimension adjustment amount data, and optimization of the dimension adjustment amount data using preset constraint rules, the technology achieves the calculation of final pricing data and the determination of pricing status.

[0151] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0152] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0153] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0154] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0155] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for dynamically calculating pricing parameters based on multi-source data, characterized in that, include: The multi-source evaluation dimension data of the target object is obtained, and after preprocessing, the effective multi-source evaluation dimension data is obtained. The effective multi-source evaluation dimension data is then stored in the input cache. Get the current time data, and query the preset strategy configuration table based on the current time to obtain the valid strategy configuration; The scoring deviation value and dimension adjustment amount are calculated based on effective multi-source evaluation dimension data and effective strategy configuration. By using preset constraint rules to make logical judgments on effective multi-source evaluation dimension data, and adjusting the dimension adjustment amount data according to the logical judgment results, the optimized dimension adjustment amount data is obtained. The final pricing data is calculated based on the pricing benchmark parameters and the adjustment data of the optimization dimensions. The final pricing data is compared with the preset boundary check interval to obtain the pricing status.

2. The method for dynamically calculating pricing parameters based on multi-source data according to claim 1, characterized in that, The process of acquiring multi-source evaluation dimension data of the target object, preprocessing it to obtain valid multi-source evaluation dimension data, and storing the valid multi-source evaluation dimension data in the input cache specifically includes: Obtain multi-source evaluation data, including one or more of the following: basic security score, industry strategic value score, ESG score, or credit score; Compare the corresponding dimension data in the multi-source evaluation dimension data with the corresponding preset effective range threshold; If the corresponding dimension data are all within the preset valid range threshold, they are marked as valid multi-source evaluation dimension data; If the corresponding dimension data is not within the preset valid range threshold, it is marked as abnormal dimension data, and the preset abnormal data processing model is triggered to process it to obtain valid multi-source evaluation dimension data.

3. The method for dynamically calculating pricing parameters based on multi-source data according to claim 2, characterized in that, The step of obtaining the current time data and querying the preset strategy configuration table based on the current time to obtain the effective strategy configuration specifically includes: Effective strategy configuration includes effective pricing benchmark parameters and adjustment functions; The effective pricing benchmark parameters include benchmark parameters, basic risk premium data, and benchmark values ​​corresponding to each dimension in each effective multi-source evaluation dimension data; The adjustment function includes one or more of the following: linear mapping function, piecewise linear mapping function, and nonlinear mapping function.

4. The method for dynamically calculating pricing parameters based on multi-source data according to claim 3, characterized in that, The calculation of scoring deviation values ​​and dimension adjustment amounts based on effective multi-source evaluation dimension data and effective strategy configuration specifically includes: The score deviation value for that dimension is obtained by subtracting the effective multi-source evaluation dimension data from the corresponding benchmark value. Input the scoring deviation value into the corresponding adjustment function to calculate the corresponding dimension adjustment data.

5. The method for dynamically calculating pricing parameters based on multi-source data according to claim 4, characterized in that, The step of logically judging the effective multi-source evaluation dimension data through preset constraint rules, and adjusting the dimension adjustment amount data according to the logical judgment results to obtain optimized dimension adjustment amount data, specifically includes: If the valid multi-source data does not meet the preset constraint rules, the dimension adjustment data will be directly marked as the optimized dimension adjustment data. If the valid multi-source data meets the preset constraint rules, constraint adjustment is triggered. After adjusting the dimension adjustment data according to the preset adjustment rules, the optimized dimension adjustment data is obtained and a constraint trigger mark is generated.

6. The method for dynamically calculating pricing parameters based on multi-source data according to claim 5, characterized in that, The calculation of the final pricing data based on the pricing benchmark parameters and optimization dimension adjustment data specifically includes: The final pricing data is obtained by combining the benchmark parameters, basic risk premium, and optimization dimension adjustment data with preset weighting coefficients and then calculating the weighted sum.

7. The method for dynamically calculating pricing parameters based on multi-source data according to claim 6, characterized in that, The step of comparing the final pricing data with a preset boundary check interval to obtain the pricing status specifically includes: The preset boundary check interval includes a first boundary value and a second boundary value, and the first boundary value is less than the second boundary value; The final pricing data is compared with the first and second boundary values ​​to obtain the pricing status, including normal or abnormal status. If the final pricing data is less than the first boundary value or greater than the second boundary value, the pricing status is abnormal. If the final pricing data is greater than or equal to the first boundary value and less than or equal to the second boundary value, then the pricing status is normal.

8. A dynamic pricing parameter calculation system based on multi-source data, characterized in that, The system includes a memory and a processor. The memory contains a program for dynamically calculating pricing parameters based on multi-source data. When executed by the processor, the program for dynamically calculating pricing parameters based on multi-source data performs the following steps: The multi-source evaluation dimension data of the target object is obtained, and after preprocessing, the effective multi-source evaluation dimension data is obtained. The effective multi-source evaluation dimension data is then stored in the input cache. Get the current time data, and query the preset strategy configuration table based on the current time to obtain the valid strategy configuration; The scoring deviation value and dimension adjustment amount are calculated based on effective multi-source evaluation dimension data and effective strategy configuration. By using preset constraint rules to make logical judgments on effective multi-source evaluation dimension data, and adjusting the dimension adjustment amount data according to the logical judgment results, the optimized dimension adjustment amount data is obtained. The final pricing data is calculated based on the pricing benchmark parameters and the adjustment data of the optimization dimensions. The final pricing data is compared with the preset boundary check interval to obtain the pricing status.

9. The dynamic calculation system for pricing parameters based on multi-source data according to claim 8, characterized in that, The process of acquiring multi-source evaluation dimension data of the target object, preprocessing it to obtain valid multi-source evaluation dimension data, and storing the valid multi-source evaluation dimension data in the input cache specifically includes: Obtain multi-source evaluation data, including one or more of the following: basic security score, industry strategic value score, ESG score, or credit score; Compare the corresponding dimension data in the multi-source evaluation dimension data with the corresponding preset effective range threshold; If the corresponding dimension data are all within the preset valid range threshold, they are marked as valid multi-source evaluation dimension data; If the corresponding dimension data is not within the preset valid range threshold, it is marked as abnormal dimension data, and the preset abnormal data processing model is triggered to process it to obtain valid multi-source evaluation dimension data.

10. The dynamic calculation system for pricing parameters based on multi-source data according to claim 9, characterized in that, The step of obtaining the current time data and querying the preset strategy configuration table based on the current time to obtain the effective strategy configuration specifically includes: Effective strategy configuration includes effective pricing benchmark parameters and adjustment functions; The effective pricing benchmark parameters include benchmark parameters, basic risk premium data, and benchmark values ​​corresponding to each dimension in each effective multi-source evaluation dimension data; The adjustment function includes one or more of the following: linear mapping function, piecewise linear mapping function, and nonlinear mapping function.

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