Intelligent Price Comparison Method and System for Commodity Procurement Based on Big Data

Abstract

This invention discloses a smart price comparison method and system for commodity procurement based on big data, belonging to the field of commodity price comparison technology. The method specifically includes: real-time collection of commodity information from multi-source heterogeneous platforms; parsing the commodity information; establishing an implicit difference index system; constructing a virtual procurement scenario based on the implicit difference index system; performing simulated transaction calculations on candidate commodities under different budget, timeliness, and risk parameters; generating corresponding virtual total cost curves; extracting the optimal solution set under different constraints based on the virtual total cost curves; and generating a smart price comparison result with a nonlinear decision path through cross-scenario synthetic comparison. This application achieves a comprehensive consideration of explicit and implicit costs, making the price comparison process closer to the real transaction environment and obtaining commodities with higher cost-effectiveness.

Description

Technical Field

[0001] This invention belongs to the field of commodity price comparison technology, specifically a smart price comparison method and system for commodity procurement based on big data. Background Technology

[0002] With the development of e-commerce and supply chain digitalization, commodity procurement has gradually shifted from the traditional offline negotiation model to a model that relies on online platforms and data analysis. Existing price comparison methods mainly focus on price comparison on a single platform, cross-platform plug-in tools based on web crawlers, and statistical analysis based on historical data. These methods have improved procurement efficiency to some extent, but most of them still rely on the logic of judging the lowest price as the best.

[0003] In real-world large-scale procurement scenarios, price-centric comparison models are clearly insufficient. Buyers need to consider not only price but also implicit differences such as inventory fluctuations, logistics timeliness, supplier credit, after-sales risks, and exchange rates and tariffs in cross-border transactions. However, existing price comparison methods generally lack effective modeling of these implicit differences, leading to discrepancies between comparison results and actual procurement needs.

[0004] Furthermore, traditional price comparison processes are mostly based on static and linear decision-making methods, which cannot be flexibly adjusted according to the different objectives of the purchaser, and also lack the ability to adaptively correct and optimize based on the procurement results. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a smart price comparison method and system for commodity procurement based on big data. It constructs a smart price comparison method that can take into account both explicit and implicit factors and support dynamic simulation and multi-path decision output.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] Intelligent price comparison methods for commodity procurement based on big data include:

[0008] Real-time collection of product information from multiple heterogeneous platforms, including price, inventory, logistics timeliness, historical price fluctuation trends, supplier credit rating, and user review text;

[0009] The product information is analyzed to establish a latent difference indicator system, which includes: logistics penalty factor, after-sales risk factor and exchange rate fluctuation factor.

[0010] Based on the aforementioned implicit difference indicator system, a virtual procurement scenario is constructed, and simulated transaction calculations are performed on candidate products under different budget, timeliness, and risk parameters to generate corresponding virtual total cost curves.

[0011] Based on the virtual total cost curve, the optimal solution set under different constraints is extracted, and through cross-scenario synthesis and comparison, intelligent price comparison results of nonlinear decision paths are generated.

[0012] Specifically, the product information is analyzed to establish a hidden difference indicator system, including:

[0013] The product information is segmented according to the transaction process dimension to obtain hierarchical data fragments for representing price, inventory, logistics, after-sales service, and cross-border transactions;

[0014] Semantic mapping is performed on the unstructured text in the hierarchical data fragments to extract keyword information related to delays, returns, credit, and transaction anomalies, and a set of semantic factors is generated.

[0015] The semantic factor set is coupled with corresponding numerical data such as price, inventory, and logistics across dimensions to generate differential candidate indicators containing potential risk signals.

[0016] The candidate indicators for differences are archived according to the transaction stage, and a mapping index is established between different stages to form an implicit difference indicator system for price comparison.

[0017] Specifically, the candidate difference indicators are archived according to the transaction stage, and a mapping index is established between different stages to form an implicit difference indicator system for price comparison calls, including:

[0018] The transaction process is divided into the inquiry stage, the order placement stage, the fulfillment stage, and the after-sales stage, and the aforementioned candidate indicators of difference are assigned to the corresponding stages respectively;

[0019] In each stage, identify candidate indicators of differences that have a causal or dependent relationship with the previous stage, and establish initial link links between stages;

[0020] Based on the initial link, multi-dimensional cross-mapping is performed on the candidate indicators with cross-stage impact, so that the same indicator can form parallel nodes in multiple stages.

[0021] The phased sets and their cross-mapping results are integrated into a unified index table to form an implicit difference indicator system for price comparison.

[0022] Specifically, based on the aforementioned implicit difference indicator system, a virtual procurement scenario is constructed. Simulated transactions are performed on candidate goods under different budget, timeframe, and risk parameters, and corresponding virtual total cost curves are generated, including:

[0023] Based on the purchaser's preset conditions, a set of scenario parameters is generated, including budget constraints, delivery time constraints, and risk tolerance constraints.

[0024] The set of scenario parameters is decomposed according to the constraint type to form budget-driven scenarios, time-driven scenarios, and risk-driven scenarios;

[0025] Within each scenario, the implicit difference indicators of candidate products are mapped to the corresponding scenario parameters to generate a combination of constraints for simulating transactions.

[0026] Based on the aforementioned combination of constraints, simulated transaction processes are executed respectively, and transaction consumption results under different scenarios are output. A comparison matrix is ​​then established between different scenarios.

[0027] The transaction consumption results are integrated with the comparison matrix to generate a virtual total cost curve, which is then indexed and associated with each scenario.

[0028] Specifically, the transaction consumption results are integrated with the comparison matrix to generate a virtual total cost curve, and an indexed association is maintained with each scenario, including:

[0029] The transaction consumption results obtained from various scenarios are aggregated according to budget-driven, time-driven, and risk-driven scenarios to form grouped consumption sets;

[0030] The grouped consumption set is mapped to the corresponding comparison matrix to generate the difference comparison relationship between scenarios, and a unique identifier is assigned to each difference comparison relationship between scenarios;

[0031] Based on the grouped consumption set and the comparison relationship between scenarios, a continuous virtual total cost curve is constructed, and the node index corresponding to different scenario groups is marked on the curve.

[0032] Specifically, based on the virtual total cost curve, the optimal solution set under different constraints is extracted, and through cross-scenario synthetic comparison, an intelligent price comparison result for nonlinear decision paths is generated, including:

[0033] On the virtual total cost curve, a set of corresponding candidate solutions is selected according to different combinations of budget constraints, time constraints, and risk constraints;

[0034] In each set of candidate solutions, the optimal solution for a stage is obtained by prioritizing the scenario parameters, forming a set of grouped optimal solutions.

[0035] The optimal solution set of the group is cross-synthesized among budget-driven, time-driven, and risk-driven approaches to generate a composite solution set containing multi-dimensional solution chains;

[0036] In the composite solution set, a nonlinear decision path is constructed based on the constraint continuity between different solution points;

[0037] The nonlinear decision path and the corresponding set of scenario parameters are merged to output the intelligent price comparison result.

[0038] Specifically, in the composite solution set, a nonlinear decision path is constructed based on the constraint continuity between different solution points, including:

[0039] The solution points in the composite solution set are sorted according to the priority order of budget constraints, time constraints, and risk constraints to obtain an ordered sequence of solution points;

[0040] In the ordered solution sequence, the continuity relationship between adjacent solution points in terms of budget, timeliness, or risk parameters is detected, and solution point pairs that meet preset transition conditions are marked;

[0041] The solution point pairs that satisfy the preset transition conditions are connected sequentially to generate a link set across parameter dimensions;

[0042] The aforementioned link set is combined and assembled to form a nonlinear decision path that includes cross-scenario jump nodes.

[0043] Specifically, the nonlinear decision path is merged with the corresponding set of scenario parameters to output an intelligent price comparison result, including:

[0044] Each jump node in the nonlinear decision path is indexed, and the corresponding set of indexes for budget parameters, timeliness parameters, and risk parameters is determined.

[0045] The index set is aggregated according to the jump order to form a sequence of scene parameters that corresponds one-to-one with the decision path;

[0046] The scenario parameter sequence and the nonlinear decision path are merged and modeled to generate a unified decision structure that describes the mapping relationship of path warning parameters;

[0047] Based on the unified decision-making structure, the merging results of different paths are arranged in parallel to output intelligent price comparison results.

[0048] The big data-based intelligent price comparison system for commodity procurement is used to implement the big data-based intelligent price comparison method for commodity procurement, including: a commodity information collection module, an indicator system establishment module, a cost curve generation module, and a price comparison generation module.

[0049] The product information collection module is used to collect product information from multiple heterogeneous platforms in real time. The product information includes price, inventory, logistics timeliness, historical price fluctuation trends, supplier credit rating, and user review text.

[0050] The indicator system establishment module is used to analyze the product information and establish a latent difference indicator system, which includes: logistics penalty factor, after-sales risk factor and exchange rate fluctuation factor.

[0051] The cost curve generation module is used to construct a virtual procurement scenario based on the implicit difference indicator system, perform simulated transaction calculations on candidate products under different budget, timeliness and risk parameters, and generate the corresponding virtual total cost curve.

[0052] The price comparison generation module is used to extract the optimal solution set under different constraints based on the virtual total cost curve, and generate intelligent price comparison results for nonlinear decision paths through cross-scenario synthetic comparison.

[0053] Specifically, the price comparison generation module includes: a solution set generation unit, a decision path construction unit, and a price comparison result output unit;

[0054] The solution set generation unit is used to generate grouped optimal solution sets and cross-synthesize the grouped optimal solution sets among budget-driven, time-driven, and risk-driven approaches to generate composite solution sets containing multi-dimensional solution chains.

[0055] The decision path construction unit is used to construct a nonlinear decision path in the composite solution set based on the constraint continuity between different solution points.

[0056] The price comparison result output unit is used to merge the nonlinear decision path with the corresponding scenario parameter set and output intelligent price comparison results.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] This invention proposes a big data-based intelligent price comparison method and system for commodity procurement. It establishes an implicit difference indicator system encompassing price, inventory, logistics, credit, after-sales service, and cross-border transactions. Based on this system, a virtual procurement scenario is constructed to simulate candidate commodities under different budget, time-sensitive, and risk conditions, generating a virtual total cost curve. This is then combined with multi-dimensional constraint screening and cross-scenario comparison to form a non-linear decision path and output intelligent price comparison results. This method overcomes the limitations of traditional price comparisons that rely solely on price or a single parameter, achieving a comprehensive consideration of both explicit and implicit costs. This makes the price comparison process closer to the real transaction environment, thus providing procurement parties with more scientific, flexible, and adaptive optimization capabilities in complex and ever-changing supply chain conditions. Attached Figure Description

[0059] Figure 1 A flowchart of the intelligent price comparison method for commodity procurement based on big data provided by this invention;

[0060] Figure 2 A flowchart for generating price comparison results provided by this invention;

[0061] Figure 3 This invention provides an architecture diagram of a big data-based intelligent price comparison system for commodity procurement. Detailed Implementation

[0062] To facilitate understanding of the technical means, creative features, and achieved objectives and effects of this invention, it should be noted in the description of this invention that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "number one," "number two," and "number three" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The invention will be further described below in conjunction with specific embodiments.

[0063] Example 1

[0064] Please see Figures 1-2 The present invention provides an embodiment of a method comprising the following specific steps:

[0065] Step S1: Collect product information from multiple heterogeneous platforms in real time. The product information includes price, inventory, logistics timeliness, historical price fluctuation trends, supplier credit rating, and user review text.

[0066] In this embodiment, the product field structure of different platforms is defined in a unified manner, and elements such as price, inventory, logistics timeliness, historical price fluctuation curve, supplier credit score, and user review text are mapped to a standardized data template to ensure consistency in subsequent processing. It should be noted that during the collection process, price and inventory information are mainly obtained through the platform's structured interface data, while logistics timeliness and historical price fluctuation trends are derived through time series sampling and incremental crawling, and supplier credit score and user review text are extracted by parsing transaction records and natural language processing technology.

[0067] Step S2: Analyze the product information and establish a latent difference indicator system, which includes: logistics penalty factor, after-sales risk factor and exchange rate fluctuation factor.

[0068] The specific steps of step S2 are as follows:

[0069] Step S201: The product information is segmented according to the transaction process dimension to obtain hierarchical data segments for representing price, inventory, logistics, after-sales service and cross-border transactions.

[0070] In this embodiment, the collected product information is semantically labeled and decomposed into a process, and the raw data is segmented according to the typical stages of the transaction to form a multi-level segmented result. Specifically, the transaction process is first modeled and divided into the pre-pricing stage, the intermediate fulfillment stage, and the subsequent after-sales stage, with customs clearance and settlement stages added in the cross-border scenario. Subsequently, the corresponding structured data such as price, inventory, and logistics timeliness are mapped to the pricing and fulfillment stages, after-sales records and user evaluation information are included in the after-sales stage, and data related to exchange rates, tariffs, and cross-border compliance are included in the cross-border transaction stage. It should be noted that the segmentation process not only relies on the field attributes of the data, but also combines transaction timestamps and event trigger points for sequential division to ensure that the data fragments can accurately reflect the business logic of different stages. Through the above processing, a set of hierarchical data fragments is finally obtained.

[0071] Step S202: Perform semantic mapping on the unstructured text in the layered data fragments, extract keyword information related to delays, returns, credit and transaction anomalies, and generate a set of semantic factors.

[0072] In this embodiment, unstructured text in hierarchical data fragments is processed using natural language parsing and semantic feature mapping to identify and extract key information related to transaction risks. Specifically, firstly, word segmentation and part-of-speech tagging techniques are used to convert evaluation texts, after-sales descriptions, and credit records into word sequences. Then, through a constructed transaction semantic dictionary, words and phrases involving delays, returns, credit, and abnormal transactions are matched. During the matching process, a context window is used to determine semantic tendencies. For example, delay-related words combined with time expressions are marked as performance risk signals. It should be noted that the extracted keywords are not stored in isolation but are converted into several semantic factor units through semantic tagging and merged according to semantic categories to form a semantic factor set covering dimensions such as delays, returns, credit, and abnormal transactions.

[0073] Step S203: Perform cross-dimensional coupling operation on the set of semantic factors and the corresponding numerical data such as price, inventory, and logistics to generate differential candidate indicators containing potential risk signals.

[0074] In this embodiment, a cross-dimensional data fusion method is used to match and jointly calculate the aforementioned set of semantic factors with structured numerical data to generate candidate indicators of difference that can reflect potential risks. Specifically, firstly, based on the timestamp of the transaction process and the hierarchical identifier of the data fragment, a mapping relationship is established between the semantic factors and the corresponding numerical attributes such as price, inventory, and logistics timeliness. For example, delayed semantic factors are bound to the logistics timeliness field, and returned semantic factors are bound to the inventory turnover rate field. Subsequently, a difference measurement model is used to jointly calculate the bound semantic factors and numerical data to identify potential abnormal signals. For example, when the logistics timeliness data is normal but the semantic factors contain a large number of delayed words, a new candidate indicator of difference is generated to characterize the deviation between explicit data and semantic perception. It should be noted that the generated candidate indicator of difference not only retains the original numerical and semantic factor attributes, but also includes cross-dimensional mapping labels for subsequent grouping and indexing in the indicator archiving stage. Through the above processing, the originally separate semantic information and numerical information are transformed into a coupled set of indicators.

[0075] Step S204: Archive the candidate difference indicators according to the transaction stage, and establish a mapping index between different stages to form an implicit difference indicator system for price comparison.

[0076] The specific steps of step S204 are as follows:

[0077] Step S2041: Divide the transaction process into the inquiry stage, order placement stage, fulfillment stage and after-sales stage, and assign the candidate difference indicators to the corresponding stages.

[0078] In this embodiment, by performing phased modeling of the entire transaction process, differential candidate indicators are assigned according to business context to form a structured set of phased indicators. Specifically, based on the transaction time sequence and event triggering logic, the entire process is divided into four main stages: inquiry stage, order placement stage, fulfillment stage, and after-sales stage. The inquiry stage covers indicators related to price fluctuations and inventory availability; the order placement stage covers indicators related to credit scores and order success rates; the fulfillment stage covers indicators related to logistics timeliness and delivery stability; and the after-sales stage covers indicators related to returns, complaints, and abnormal transaction records. It should be noted that the allocation process does not rely solely on the surface attributes of the indicators, but combines the aforementioned cross-dimensional coupling results to assign differential candidate indicators containing potential risk signals to the most relevant transaction stage according to their impact points. For example, if an indicator involves both semantic factors of logistics delays and inventory fluctuation data, it is preferentially assigned to the fulfillment stage to ensure that the indicator assignment has interpretability and business coherence. Through the above division, a set of phased indicators corresponding to the transaction process is finally formed.

[0079] Step S2042: In each stage, identify candidate indicators of differences that have a causal or dependent relationship with the previous stage, and establish initial link links between stages.

[0080] In this embodiment, causal chain reasoning and dependency detection methods are used to compare candidate indicators with differences in each stage layer by layer, thereby constructing an initial link across stages. Specifically, firstly, under the same transaction process, the indicator set of each stage is correlated with the indicator set of the previous stage. Potential causal relationships are identified by comparing the triggering conditions and results of the indicators. For example, inventory fluctuations in the inquiry stage may directly affect the order success rate in the order placement stage. Subsequently, a dependency determination mechanism is used to screen the coupling strength between indicators. If the change pattern of a certain indicator is highly correlated with the indicator in the previous stage, a dependency relationship is determined to exist between the two and recorded in the form of a link. It should be noted that this link is not a simple linear mapping, but allows one-to-many or many-to-one correspondences to ensure that cross-dependencies in complex scenarios can be expressed. Through the above processing, an initial link network covering the inquiry, order placement, fulfillment, and after-sales stages is finally formed.

[0081] Step S2043: Based on the initial link, perform multi-dimensional cross-mapping on the candidate indicators with cross-stage impact, so that the same indicator can form parallel nodes in multiple stages.

[0082] In this embodiment, by extending the initial link, multi-dimensional cross-mapping is performed on the differential candidate indicators that play a role across stages, enabling the parallel expression of indicators in different transaction stages. Specifically, firstly, differential candidate indicators that have established causal or dependency relationships in the previous stage are identified, and it is detected whether they have a continuous impact on the business logic of the subsequent two or more stages. For example, logistics delays in the fulfillment stage may simultaneously affect the return rate in both the fulfillment stage itself and the after-sales stage. Subsequently, these cross-stage indicators are replicated into multiple nodes, and location markers are established in different stage sets. At the same time, cross-mapping labels are retained between nodes to characterize their source consistency. It should be noted that the cross-mapping process not only ensures that the indicator content remains consistent across different stages, but also establishes a synchronization relationship between indicators through an index structure, so that the same indicator can form parallel nodes in multiple stages. Through the above processing, a cross-mapping network covering multiple stages is finally obtained.

[0083] Step S2044: Integrate the phased sets and their cross-mapping results into a unified index table to form an implicit difference index system for price comparison.

[0084] In this embodiment, a unified indexing mechanism is used to integrate the divided set of phased indicators and their cross-phase cross-mapping results to generate a readily accessible implicit difference indicator system. Specifically, each phase's indicator set is first assigned a phase identifier, and a source label is attached to the cross-mapping nodes to ensure that the association of the same indicator in different phases can be uniquely identified. Subsequently, all phase sets and cross-mapping nodes are integrated according to preset index fields to establish a unified index table containing phase numbers, indicator identifiers, and cross-reference relationships. It should be noted that this index table is not simply a list storage, but is bidirectionally arranged according to the transaction process sequence and cross-phase associations, so that price comparison calls can be performed by phase and extended along cross links. Through the above steps, a structured and traceable implicit difference indicator system is finally formed.

[0085] Step S3: Based on the implicit difference index system, construct a virtual procurement scenario, perform simulated transaction calculations on candidate products under different budget, timeliness and risk parameters, and generate corresponding virtual total cost curves.

[0086] The specific steps of step S3 are as follows:

[0087] Step S301: Generate a set of scenario parameters including budget constraints, delivery time constraints, and risk tolerance constraints based on the purchaser's preset conditions.

[0088] In this embodiment, by parsing and classifying the condition parameters input by the purchaser, a set of scenario parameters describing different constraints is generated. Specifically, firstly, the budget range set by the purchaser is numerically expressed, and the upper and lower limits of the budget are determined by combining historical price fluctuation information. Secondly, the delivery time limit requirement is quantified in the time dimension, combining the latest delivery date with the reachability time of the logistics link to form a timeliness constraint parameter. Thirdly, the risk tolerance setting is graded, mapping factors such as supplier credit, return frequency, and transaction abnormality probability to a unified risk parameter space, and classifying them into high, medium, and low levels according to the tolerance threshold. It should be noted that the three types of constraints—budget, timeliness, and risk—are each marked with a unique index during generation and are combined and stored as a set of scenario parameters. This set can be directly called as input conditions in the subsequent construction of virtual procurement scenarios, thereby ensuring that the simulation results are consistent with the purchaser's decision preferences.

[0089] Step S302: Decompose the set of scenario parameters according to the constraint type to form budget-driven scenarios, time-driven scenarios, and risk-driven scenarios.

[0090] In this embodiment, by splitting and classifying the attribute types of the aforementioned set of scenario parameters, independent scenario groups describing different decision-making orientations are generated. Specifically, firstly, budget constraint parameters belonging to the cost control dimension are extracted from the set and associated with price fluctuation range and inventory cost to construct budget-driven scenarios. Secondly, delivery time limit parameters belonging to the time dimension are identified from the set and correlated with logistics timeliness and supply chain fulfillment cycle to form timeliness-driven scenarios. Thirdly, parameters related to the risk dimension are extracted from the set, including supplier credit rating, return rate threshold, and abnormal transaction probability, and integrated into risk-driven scenarios. It should be noted that in the decomposition process, not only is the parameter type mechanically divided, but the boundary is also corrected through the mutual constraint relationship between parameters to ensure that the same parameter is not incorrectly classified into multiple scenarios. Through this processing method, three independent scenarios—budget-driven, timeliness-driven, and risk-driven—are finally obtained.

[0091] Step S303: In each scenario, the implicit difference indicators of candidate products are mapped to the corresponding scenario parameters to generate a combination of constraints for simulating transactions.

[0092] In this embodiment, implicit difference indicators of candidate products are mapped one-to-one with scenario parameters through parameter binding and indicator matching, thereby generating constraint combinations that can be used for simulated transactions. Specifically, in the budget-driven scenario, price fluctuation indicators are compared with the upper and lower limits of the budget, and inventory turnover-related indicators are introduced to correct the purchase scale. In the timeliness-driven scenario, logistics delay indicators are mapped with delivery time limit parameters, and combined with supply chain fulfillment cycle indicators to form a complete timeliness constraint chain. In the risk-driven scenario, indicators such as supplier credit, return rate, and transaction anomaly rate are matched with risk tolerance parameters to generate a risk-sensitive constraint set. It should be noted that this mapping process is not only a correspondence of a single field, but also includes joint constraint relationships between multiple indicators. For example, in the budget-driven scenario, a valid combination is formed only when price and inventory indicators simultaneously meet the conditions. Through the above processing, each scenario obtains a difference indicator combination that matches its constraint conditions.

[0093] Step S304: Based on the constraint combination, execute the simulated transaction process respectively, output the transaction consumption results under different scenarios, and establish a comparison matrix between cross scenarios.

[0094] In this embodiment, quantifiable transaction consumption results are generated by performing scenario-by-scenario calculations and comparisons on the aforementioned constraint combinations, and a cross-scenario comparison matrix is ​​established. Specifically, in the budget-driven scenario, transaction costs are calculated with price and inventory matching as the core, and consumption curves under different budget constraints are recorded. In the time-driven scenario, indicators such as logistics timeliness, delivery delay, and fulfillment volatility are incorporated into the calculation to generate a consumption distribution over time. In the risk-driven scenario, potential loss values ​​are derived based on the constraint combination of supplier credit rating, return rate, and anomaly probability. It should be noted that the transaction consumption results of each scenario are not stored independently, but are horizontally correlated by constructing a comparison matrix. The rows of the comparison matrix represent different scenario types, and the columns represent the consumption results of the same candidate product, thereby enabling the comparison of results for the same product under different constraint conditions. In this way, the final comparison matrix can be used as input for the subsequent generation of the virtual total cost curve.

[0095] Step S305: Integrate the transaction consumption results with the comparison matrix to generate a virtual total cost curve, and maintain an index association with each scenario.

[0096] The specific steps of step S305 are as follows:

[0097] Step S3051: Aggregate the transaction consumption results obtained in each scenario according to the scenarios driven by budget, timeliness, and risk to form a grouped consumption set.

[0098] In this embodiment, transaction consumption results under different scenarios are categorized and aggregated to form a grouped consumption set with scenario characteristics. Specifically, firstly, the consumption results in budget-driven scenarios are extracted and summarized hierarchically according to budget constraint intervals to reflect cost changes under different budget conditions. Secondly, the consumption results in timeliness-driven scenarios are collected and aggregated according to the matching of delivery time nodes and logistics timeliness to obtain a consumption curve in the timeliness dimension. Thirdly, the consumption results in risk-driven scenarios are gathered together and grouped and integrated according to risk tolerance levels to form a potential loss set under different risk preferences. It should be noted that the aggregation process is not a simple addition or splicing, but rather the consumption results under the same driving scenario are archived through a unified index, and the mapping relationship with candidate products is maintained within each group. In this way, the grouped consumption sets of three dimensions—budget, timeliness, and risk—are finally obtained.

[0099] Step S3052: Map the grouped consumption set to the corresponding comparison matrix to generate the difference comparison relationship between scenarios, and assign a unique identifier to each difference comparison relationship between scenarios.

[0100] In this embodiment, by mapping the grouped consumption sets to the aforementioned comparison matrix item by item, a difference comparison relationship between scenarios is established, and a unique identifier is generated for each difference relationship. Specifically, firstly, consumption data of corresponding candidate products are extracted from the three types of grouped consumption sets: budget-driven, time-driven, and risk-driven, and the result values ​​of the same product under different scenarios are retrieved in the comparison matrix. Subsequently, the retrieved results are paired up to determine the difference magnitude and corresponding parameter dimensions between different scenarios, thereby constructing a difference comparison relationship between scenarios. It should be noted that while generating the difference comparison relationship, a unique identifier code is assigned to each pair of relationships. This identifier code contains three types of information: product number, scenario type, and difference sequence number, ensuring that unique retrieval can be achieved in subsequent calls. In this way, a structured difference comparison set is finally formed.

[0101] Step S3053: Based on the group consumption set and the comparison relationship between scene differences, construct a continuous virtual total cost curve and mark the node index corresponding to different scene groups in the curve.

[0102] In this embodiment, a virtual total cost curve reflecting continuous changes across multiple scenarios is generated by comprehensively modeling the grouped consumption sets and the differences between scenarios. Specifically, the consumption results of budget-driven, time-driven, and risk-driven groups are first sorted according to scenario priority, and the connection intervals between adjacent scenarios are determined using the differences, thus forming a continuous consumption trajectory. Subsequently, corresponding consumption value points are embedded on each trajectory, and interpolation and smoothing are used to maintain the continuity of the curve across scenarios. Next, node indexes are established on the curve for key positions corresponding to different scenario groups. Each index contains scenario category, constraint parameters, and candidate product identifiers to ensure that the curve can both display global trends and support scenario-based calls. It should be noted that this virtual total cost curve not only integrates grouped results under different constraints but also achieves cross-scenario traceability under the marking of node indexes, providing an input basis for the subsequent construction of nonlinear decision paths.

[0103] Step S4: Based on the virtual total cost curve, extract the optimal solution set under different constraints, and generate intelligent price comparison results for nonlinear decision paths through cross-scenario synthesis and comparison.

[0104] like Figure 2 As shown, the specific steps of step S4 are as follows:

[0105] Step S401: On the virtual total cost curve, according to different combinations of budget constraints, time constraints and risk constraints, select the corresponding set of candidate solutions.

[0106] In this embodiment, step S401 introduces a multi-dimensional constraint screening mechanism on the virtual total cost curve to extract a set of candidate solutions that meet the requirements of different parameter combinations. Specifically, firstly, the parameter range related to the budget constraint is retrieved in the curve node index, and candidate points located within the upper and lower limits of the budget are screened out. Then, the screened candidate points are cross-compared with the timeliness constraint conditions, and nodes that do not meet the delivery time limit requirements are eliminated. Next, the remaining nodes are filtered hierarchically according to the risk constraint parameters to distinguish effective solutions under high, medium, and low risk tolerance. It should be noted that during the screening process, the combination of constraints is not a simple superposition, but a set of candidate solutions under different combination scenarios is formed through layer-by-layer interactive screening, thereby ensuring that each set can correspond to a specific constraint combination. Through this processing method, a set of structured candidate solutions is finally obtained.

[0107] Step S402: In each set of candidate solution points, the optimal solution for each stage is obtained by screening based on the priority of the scenario parameters, forming a set of optimal solutions for each group.

[0108] In this embodiment, by hierarchically screening the candidate solution set, the optimal solution for each stage is determined layer by layer according to the priority of scenario parameters, thereby forming a grouped optimal solution set. Specifically, firstly, in each candidate solution set, nodes are initially sorted according to the importance of budget constraints, and the solution that meets the budget limit and has the lowest cost is selected as the first-level candidate. Then, this candidate set is compared with the timeliness constraint parameters, and nodes that meet the delivery time limit requirements are retained first, and logistics-related indicators are further compared to determine nodes that meet the timeliness priority. Next, the remaining nodes are screened according to risk tolerance, and risk indicators such as supplier credit, return probability, and abnormal transaction rate are compared with the tolerance threshold one by one, and nodes that do not meet the conditions are eliminated. It should be noted that each screening stage is based on parameter priority to ensure that the finally selected solution has the characteristics of a stage under the constraints. In this way, grouped optimal solution sets are finally formed in the three dimensions of budget-driven, timeliness-driven, and risk-driven.

[0109] Step S403: Cross-synthesize the grouped optimal solution set among budget-driven, time-driven, and risk-driven approaches to generate a composite solution set containing multi-dimensional solution chains.

[0110] In this embodiment, a composite solution set that simultaneously reflects multidimensional constraints is constructed by cross-synthesizing the optimal solution sets grouped into three categories: budget-driven, time-driven, and risk-driven. Specifically, firstly, based on the budget-driven optimal solution set, nodes are matched one by one with those in the time-driven optimal solution set to select solution chains that satisfy the constraints on both cost and delivery time. Subsequently, these solution chains are matched a second time with the risk-driven optimal solution set to jointly verify the compatibility of risk parameters with budget and time-driven parameters, generating composite solution points that cross three-dimensional constraints. Next, these composite solution points are connected according to their source paths to form a composite solution set containing multiple solution chains. It should be noted that the synthesis process not only considers whether the parameters satisfy various constraints but also ensures that the composite solution chains can cover the continuity of multiple constraint combinations by comparing the boundary conditions between different drivers. In this way, the final composite solution set can comprehensively reflect the equilibrium solutions of candidate products under multidimensional constraints.

[0111] Step S404: In the composite solution set, construct a nonlinear decision path based on the constraint continuity between different solution points.

[0112] The specific steps of step S404 are as follows:

[0113] Step S4041: Sort the solution points in the composite solution set according to the priority order of budget constraints, time constraints and risk constraints to obtain an ordered sequence of solution points.

[0114] In this embodiment, an ordered sequence of solutions that reflects the constraint order is generated by comparing the priorities of each solution point in the composite solution set across multiple dimensions. Specifically, firstly, based on the pre-set priority of scenario parameters, budget constraints are used as the first sorting dimension to initially rank all solutions points in the composite solution set according to cost satisfaction. Subsequently, based on the budget ranking, time constraints are introduced to rank solutions points with the same budget level according to the urgency of delivery deadlines. Next, risk constraint parameters are introduced to the ranked solutions points, using supplier credit, return rate, and transaction anomaly rate as references, to determine the final priority of solutions points under the same budget and timeliness level. It should be noted that the ranking process adopts a hierarchical approach, with each dimension's constraints being refined based on the results of the previous dimension, thereby ensuring that the ordered sequence of solutions points not only conforms to the overall priority setting but also reflects the weight relationship between different constraint dimensions. Through this method, the final ordered sequence of solutions points is obtained.

[0115] Step S4042: In the ordered solution sequence, detect the continuity relationship between adjacent solution points in terms of budget, timeliness or risk parameters, and mark the solution point pairs that meet the preset transition conditions.

[0116] In this embodiment, by comparing the parameter values ​​of adjacent nodes one by one in the ordered solution point sequence, the continuity relationship of the nodes in terms of budget, timeliness, or risk is identified, and solution point pairs that meet the preset transition conditions are marked. Specifically, firstly, the budget parameters of adjacent solution points are checked for differences. If the difference is within the allowable budget fluctuation range, it is determined that they are continuous in the budget dimension. Subsequently, the timeliness parameters of the solution point pairs that meet the budget conditions are further checked. If the difference in delivery time limits between the two solution points is within a tolerable time interval, the solution point pair is marked as continuous in the timeliness dimension. Next, the risk parameters of the solution point pairs that have passed the budget and timeliness checks are checked. If the difference in risk tolerance levels between the two solution points does not exceed a preset threshold, it is determined that they also maintain continuity in the risk dimension. It should be noted that the continuity determination does not require all three dimensions to be met simultaneously, but rather determines single or multi-dimensional continuous solution point pairs according to the preset transition conditions. In this way, solution point pairs that serve as path connection points are finally marked in the ordered solution point sequence.

[0117] Step S4043: Connect the solution point pairs that meet the preset transition conditions in sequence to generate a cross-parameter dimension link set.

[0118] In this embodiment, a set of links capable of spanning different dimensions such as budget, timeliness, and risk is formed by sequentially connecting solution point pairs marked as meeting preset transition conditions. Specifically, firstly, adjacent and marked solution point pairs are connected step-by-step in the ordered solution point sequence according to time sequence and priority, and the constraint dimension label to which they belong is recorded during connection. Subsequently, when a solution point belongs to multiple transition relationships, it is allowed to be extended to multiple link branches in parallel to ensure coverage of potential multipaths. Next, the generated links are grouped and classified, separating single-dimensional continuous links from multi-dimensional intersecting continuous links, and indexes are retained between link nodes to track source solutions and transition dimensions. It should be noted that this set of links is not a fixed structure, but is allowed to contain tree or mesh topologies to reflect dynamic connection relationships under different constraint conditions. Through the above processing, a set of links is finally formed.

[0119] Step S4044: Combine and assemble the link set to form a nonlinear decision path containing cross-scene jump nodes.

[0120] In this embodiment, a nonlinear decision path that covers multidimensional constraints and includes cross-scenario jump nodes is generated by combining and reconstructing the link set. Specifically, firstly, single-dimensional links and multi-dimensional cross links in the link set are filtered and spliced, and the interconnected link segments are assembled into continuous paths according to priority. Subsequently, during the assembly process, shared solution points belonging to two or more links are identified, and these solution points are set as cross-scenario jump nodes to enable switching between different constraint dimensions. Next, each path is traversed to ensure that each path can fully cover at least one dominant condition in the budget, timeliness, or risk dimensions, and a traceable index chain is formed within the path. It should be noted that this assembly process allows the path to present a branching structure or a circular connection to ensure feasibility under complex constraint scenarios. Through the above method, the final nonlinear decision path not only retains the continuity between solution points, but also realizes flexible jumps across scenarios, providing diversified decision trajectories for the generation of intelligent price comparison results.

[0121] Step S405: Merge the nonlinear decision path with the corresponding set of scene parameters and output the intelligent price comparison result.

[0122] The specific steps of step S405 are as follows:

[0123] Step S4051: Index each jump node in the nonlinear decision path and determine the index set of its corresponding budget parameters, timeliness parameters and risk parameters.

[0124] In this embodiment, the indexing of nodes and the corresponding constraint parameter index set are determined by parsing and binding parameters to each jump node in the nonlinear decision path. Specifically, firstly, all jump nodes in the path are uniquely identified, and the path sequence number is embedded in the identifier to ensure node traceability. Then, the constraint sources associated with each jump node during its generation process are retrieved, and the cost range in the budget dimension, the delivery time requirement in the timeliness dimension, and the tolerance level in the risk dimension are extracted respectively. Next, the above parameters are structured and stored according to the dimension type, and a corresponding parameter index set is generated under the node number to achieve a one-to-one correspondence between nodes and multidimensional constraints. It should be noted that the index set is not only used to mark parameter values, but also includes parameter category and threshold boundary information to ensure the integrity during subsequent merging and invocation. In this way, an index set for the budget, timeliness, and risk dimensions is finally established on each jump node in the nonlinear decision path.

[0125] Step S4052: Aggregate the index set according to the jump order to form a sequence of scene parameters that corresponds one-to-one with the decision path.

[0126] In this embodiment, a sequence of scenario parameters corresponding one-to-one with the nonlinear decision path is generated by sequentially aggregating the aforementioned node index set. Specifically, firstly, the index set of each jump node is read sequentially according to the jump order of the decision path to ensure that the parameter aggregation process is consistent with the path's direction of travel. Subsequently, parameters belonging to different dimensions such as budget, timeliness, and risk are retained with their dimension labels during aggregation and arranged sequentially according to the order of node appearance to form a multidimensional parameter chain. Next, parameters with duplicates or conflicts between adjacent nodes are merged during the aggregation process, prioritizing the retention of parameter values ​​with higher constraint levels or higher path priorities to ensure the consistency and executability of the sequence. It should be noted that the aggregated scenario parameter sequence not only reflects the temporal relationship of the path nodes but also retains the hierarchical constraint logic of the multidimensional parameters, thus enabling the sequence to serve as a direct input for subsequent merge modeling. Through the above methods, the final scenario parameter sequence achieves a one-to-one correspondence with the nonlinear decision path.

[0127] Step S4053: Merge the scene parameter sequence with the nonlinear decision path to generate a unified decision structure that describes the mapping relationship of path warning parameters.

[0128] In this embodiment, a unified modeling framework is established by merging the generated scene parameter sequence with the nonlinear decision path to describe the mapping relationship between path nodes and multidimensional constraint parameters. Specifically, firstly, the corresponding scene parameter sequence is embedded node by node, with index references for parameters such as budget, timeliness, and risk added at the node positions, based on the node sequence of the nonlinear decision path. Subsequently, the parameter chains between nodes are integrated through merging operations to establish a continuous parameter mapping structure from the path entry point to the path end point. Next, warning parameter identifiers are set for key nodes in this structure to record boundary conditions that may trigger budget overruns, timeliness breaches, or risk exceeding thresholds, thereby providing immediate risk warning capabilities when the path is invoked. It should be noted that merging modeling not only preserves the nonlinear characteristics of the path but also solidifies the multidimensional parameter relationships in the form of a unified decision structure. Through the above methods, the final unified decision structure can intuitively present the mapping relationship between path nodes and parameter constraints.

[0129] Step S4054: Based on the unified decision structure, the merging results of different paths are arranged in parallel to output intelligent price comparison results.

[0130] In this embodiment, the structured output of intelligent price comparison results is achieved by parallelizing the merging results of multiple nonlinear decision paths. Specifically, firstly, within the framework of a unified decision structure, the merging parameter sets of each path are extracted, while maintaining the node order and parameter mapping relationship. Subsequently, the parameter sets of different paths are arranged in parallel according to the dimensions of budget, timeliness, and risk, and the differences and critical conditions of each path are compared under the same dimension. Next, during the arrangement process, duplicate nodes or parameter items are deduplicated and categorized, while retaining path identifiers to maintain independence, thereby forming a set of price comparison results with multiple parallel paths. It should be noted that this arrangement process logically demonstrates the combination of advantages and disadvantages of each path, making the output results both comparable and traceable. Through the above methods, the final intelligent price comparison results not only cover the optimal solution set under different paths but also provide intuitive cross-path comparisons, providing a clear reference for the purchasing party to make decisions under complex constraints.

[0131] Example 2

[0132] Please see Figure 3 Another embodiment of the present invention provides: a big data-based intelligent price comparison system for commodity procurement, comprising: a commodity information collection module, an indicator system establishment module, a cost curve generation module, and a price comparison generation module;

[0133] The product information collection module is used to collect product information from multiple heterogeneous platforms in real time. The product information includes price, inventory, logistics timeliness, historical price fluctuation trends, supplier credit rating, and user review text.

[0134] The indicator system establishment module is used to analyze the product information and establish a latent difference indicator system, which includes: logistics penalty factor, after-sales risk factor and exchange rate fluctuation factor.

[0135] The cost curve generation module is used to construct a virtual procurement scenario based on the implicit difference indicator system, perform simulated transaction calculations on candidate products under different budget, timeliness and risk parameters, and generate the corresponding virtual total cost curve.

[0136] The price comparison generation module is used to extract the optimal solution set under different constraints based on the virtual total cost curve, and generate intelligent price comparison results for nonlinear decision paths through cross-scenario synthetic comparison.

[0137] The price comparison generation module includes: a solution set generation unit, a decision path construction unit, and a price comparison result output unit;

[0138] The solution set generation unit is used to generate grouped optimal solution sets and cross-synthesize the grouped optimal solution sets among budget-driven, time-driven, and risk-driven approaches to generate composite solution sets containing multi-dimensional solution chains.

[0139] The decision path construction unit is used to construct a nonlinear decision path in the composite solution set based on the constraint continuity between different solution points.

[0140] The price comparison result output unit is used to merge the nonlinear decision path with the corresponding scenario parameter set and output intelligent price comparison results.

[0141] In addition, the parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0142] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A big data based intelligent price comparison method for commodity procurement, characterized in that, The method comprises the following steps: Real-time collection of commodity information from multiple heterogeneous platforms, including price, inventory, logistics timeliness, historical price fluctuation trend, supplier credit score, and user evaluation text; Analyzing the commodity information and establishing an implicit difference index system, including logistics penalty factor, after-sales risk factor, and exchange rate fluctuation factor; Based on the implicit difference index system, a virtual purchasing scenario is constructed, and candidate commodities are simulated and operated under different budget, timeliness and risk parameters to generate corresponding virtual total cost curves; According to the virtual total cost curve, the optimal solution set under different constraint conditions is extracted, and the intelligent price comparison result of the nonlinear decision path is generated through cross-scene comparison; Analyzing the commodity information and establishing an implicit difference index system, including: Divide the commodity information according to the transaction process dimension to obtain hierarchical data segments representing price, inventory, logistics, after-sales and cross-border transactions; Perform semantic mapping on the unstructured text in the hierarchical data segments, extract keyword information related to delay, return, credit and transaction anomalies, and generate a set of semantic factors; Cross-dimension coupling operation is performed on the set of semantic factors and the corresponding price, inventory, logistics numerical data to generate difference candidate indicators containing potential risk signals; Archive the difference candidate indicators according to the transaction stage, and establish a mapping index between different stages to form an implicit difference index system for price comparison; Archive the difference candidate indicators according to the transaction stage, and establish a mapping index between different stages to form an implicit difference index system for price comparison, including: Divide the transaction process into the inquiry stage, order placement stage, performance stage and after-sales stage, and distribute the difference candidate indicators to the corresponding stages respectively; Identify the difference candidate indicators that have causal or dependent relationship with the previous stage in each stage, and establish the initial linking link between stages; Based on the initial linking link, the difference candidate indicators that have cross-stage influence are cross-mapped in multiple dimensions, so that the same indicator can form parallel nodes in multiple stages; Integrate the stage-by-stage set and its cross-mapping results in the form of a unified index table to form an implicit difference index system for price comparison.

2. The big data based commodity procurement intelligent price comparison method according to claim 1, wherein, Based on the implicit difference index system, a virtual purchasing scenario is constructed, and candidate commodities are simulated and operated under different budget, timeliness and risk parameters to generate corresponding virtual total cost curves, including: According to the pre-set conditions of the purchaser, generate a set of scene parameters including budget constraints, delivery time constraints and risk tolerance constraints; Decompose the set of scene parameters according to the constraint type to form budget-driven scenes, timeliness-driven scenes and risk-driven scenes; In each scene, map the implicit difference indicators of the candidate commodities to the corresponding scene parameters to generate constraint combinations for simulated transactions; Based on the constraint combinations, respectively execute the simulated transaction process, output the transaction consumption results under different scenes, and establish a comparison matrix between cross-scenes; Integrate the transaction consumption result with the control matrix to generate a virtual total cost curve and keep an index association with each scenario.

3. The big data based commodity procurement intelligent price comparison method according to claim 2, wherein, Integrate the transaction consumption result with the control matrix to generate a virtual total cost curve and keep an index association with each scenario, including: Aggregate the transaction consumption results obtained under each scenario according to the budget-driven, time-driven and risk-driven scenarios to form a grouped consumption set; Map the grouped consumption set with the corresponding control matrix to generate an inter-scenario difference control relationship and assign a unique identifier to each inter-scenario difference control relationship; Based on the grouped consumption set and the inter-scenario difference control relationship, construct a continuous virtual total cost curve and mark the node index corresponding to different scenario groups in the curve.

4. The big data based commodity purchase intelligent price comparison method according to claim 3, wherein, According to the virtual total cost curve, extract the optimal solution set under different constraint conditions, and through cross-scene comparison, generate an intelligent comparison result of the non-linear decision path, including: On the virtual total cost curve, filter out the corresponding candidate solution point set according to different combinations of budget constraints, time constraints and risk constraints; In each candidate solution point set, based on the scenario parameter priority, filter out the stage optimal solution to form a grouped optimal solution set; Cross-synthesize the grouped optimal solution set between budget-driven, time-driven and risk-driven to generate a composite solution set containing multi-dimensional solution chains; In the composite solution set, construct a non-linear decision path according to the constraint continuity between different solution points; Merge the non-linear decision path with the corresponding scenario parameter set to output the intelligent comparison result.

5. The big data based commodity procurement intelligent price comparison method according to claim 4, wherein, In the composite solution set, construct a non-linear decision path according to the constraint continuity between different solution points, including: Sort each solution point in the composite solution set according to the priority order of budget constraints, time constraints and risk constraints to obtain an ordered solution point sequence; In the ordered solution point sequence, detect the continuity relationship between adjacent solution points in budget, time or risk parameters, and mark the solution point pairs that meet the preset transition conditions; Connect the solution point pairs that meet the preset transition conditions in turn to generate a link set across parameter dimensions; Combine and assemble the link set to form a non-linear decision path containing cross-scene jump nodes.

6. The big data based commodity procurement intelligent price comparison method according to claim 5, wherein, Merge the non-linear decision path with the corresponding scenario parameter set to output the intelligent comparison result, including: Index each jump node in the non-linear decision path and determine its index set of budget parameters, time parameters and risk parameters; Aggregate the index set according to the jump order to form a scenario parameter sequence corresponding to the decision path one by one; Merge the scenario parameter sequence with the non-linear decision path to model a unified decision structure describing the path warning parameter mapping relationship; Based on the unified decision structure, juxtapose the merging results of different paths to output the intelligent comparison result.

7. The big data-based commodity procurement intelligent price comparison system for implementing the big data-based commodity procurement intelligent price comparison method according to any one of claims 1-6, characterized in that, Including: a commodity information collection module, an index system establishment module, a cost curve generation module and a comparison generation module; The commodity information collection module is configured to collect commodity information from multiple heterogeneous platforms in real time, the commodity information including price, inventory, logistics timeliness, historical price fluctuation trend, supplier credit score, and user evaluation text; The index system establishment module is configured to analyze the commodity information and establish an implicit difference index system, the implicit difference index system including a logistics penalty factor, an after-sales risk factor, and an exchange rate fluctuation factor; The cost curve generation module is configured to construct a virtual purchasing scenario based on the implicit difference index system, perform simulation transaction operation on candidate commodities under different budget, timeliness, and risk parameters, and generate a corresponding virtual total cost curve; The price comparison generation module is configured to extract an optimal solution set under different constraint conditions according to the virtual total cost curve, and generate an intelligent price comparison result of a nonlinear decision path through cross-scene comparison and synthesis.

8. The big data based commodity procurement intelligent price comparison system as claimed in claim 7, wherein, The price comparison generation module includes a solution set generation unit, a decision path construction unit, and a price comparison result output unit. The solution set generation unit is configured to generate a grouped optimal solution set, and cross-synthesize the grouped optimal solution set between budget driving, timeliness driving, and risk driving to generate a composite solution set containing a multi-dimensional solution chain; The decision path construction unit is configured to construct a nonlinear decision path in the composite solution set according to constraint continuity between different solution points; The price comparison result output unit is configured to merge the nonlinear decision path with a corresponding scene parameter set and output an intelligent price comparison result.

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