A used car dynamic refitting decision method and system for ROI maximization

Abstract

This invention discloses a dynamic refurbishment decision-making method and system for maximizing ROI in used cars, belonging to the field of business data processing and decision support systems. It includes: acquiring multi-source vehicle condition data to construct a dynamic digital twin of the vehicle; generating a vehicle condition evolution script through time-series analysis and fault extraction; performing supply and demand matching analysis between the vehicle condition evolution script and market data to generate candidate refurbishment schemes; performing virtual refurbishment operations on the dynamic vehicle digital twin to generate a virtual refurbished vehicle model; using market transaction behavior simulation logic to perform value assessment and liquidity extrapolation to generate expected market reaction data; and constructing a multi-dimensional decision space and visually encapsulating it to generate a decision control panel. This invention employs dynamic vehicle digital twin modeling, vehicle condition market coupling analysis, and virtual simulation extrapolation techniques to quantitatively predict the economic benefits of different refurbishment strategies, thereby providing scientific and intuitive decision support for maximizing business returns.

Description

Technical Field

[0001] This invention relates to the field of business data processing and decision support systems, and in particular to a dynamic refurbishment decision-making method and system for maximizing ROI of used cars. Background Technology

[0002] The used car market is a crucial component of the automotive circulation sector, boasting a massive market size. In the commercial operation of used cars, proper refurbishment and maintenance of acquired vehicles is a key element in enhancing vehicle value, accelerating inventory turnover, and maximizing profits. Developing a refurbishment plan—determining which items need repair, replacement, or enhancement, and how much budget to allocate—is a core business decision for used car dealers, directly impacting the return on investment for each vehicle and even the overall profitability of the business.

[0003] In related technologies, Chinese invention patent application CN108564415A discloses a method for intelligently predicting used car prices. This method includes selecting attributes needed for used car price regression based on influencing factors, quantifying these attributes, cleaning the attribute feature vectors and past transaction data, and training a decision regression tree model, setting update intervals and new transaction data indicators, and periodically updating the model. The selected vehicle attributes include brand, model series, configuration, transmission type, color, vehicle type, engine displacement, purchase time, mileage, number of transactions, condition, whether it has been severely collided, whether it has been submerged in water for a long time, and whether it has been burned.

[0004] However, while the above-mentioned solutions can use decision regression tree models to predict prices based on static attributes such as brand, mileage, and newness rate determined by subjective human scoring, they are essentially only a static statistical valuation method with limitations: First, they lack a "value-added decision" perspective and cannot use virtual simulation to extrapolate the increase in vehicle value and cost coverage of refurbishment solutions; second, the feature dimensions are coarse and subjective, lacking the ability to diagnose microscopic damage based on physical sensor data, making it difficult to quantify the impact of hidden faults on value; third, they ignore market liquidity, only predicting transaction prices without considering the impact of supply and demand dynamics on the sales cycle, thus failing to provide car dealers with dynamic business decision support based on maximizing return on investment (ROI). Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a dynamic refurbishment decision-making method and system for maximizing ROI in used cars. By employing dynamic vehicle digital twin modeling, vehicle condition market coupling analysis, and virtual simulation, it can quantitatively predict the economic benefits of different refurbishment strategies, thereby providing scientific and intuitive decision support for maximizing business returns.

[0006] The above objectives can be achieved through the following approach: A dynamic refurbishment decision-making method for used cars aimed at maximizing ROI includes: acquiring multi-source vehicle condition data of the used car to be evaluated, and constructing a dynamic digital twin of the vehicle to be evaluated; performing time-series analysis and fault feature extraction on the dynamic digital twin to generate a vehicle condition evolution script; performing supply and demand matching analysis on the vehicle condition evolution script and market data extracted from external data sources to generate candidate refurbishment schemes; performing virtual refurbishment operations on the dynamic digital twin based on the candidate refurbishment schemes to generate a virtual refurbished vehicle model; using market transaction behavior simulation logic to perform value assessment and liquidity deduction on the virtual refurbished vehicle model to generate expected market reaction data containing an expected rate of return spectrum; and constructing a multi-dimensional decision space based on the expected market reaction data and visually encapsulating it to generate a user-oriented decision control panel.

[0007] Optionally, obtaining multi-source vehicle condition data of the used car to be evaluated includes: parsing the encryption protocol of the official record storage interface to read the maintenance and repair records and accident records of the vehicle to be evaluated to obtain historical lifecycle data; analyzing the physical signals fed back by the sensor array performing non-contact scanning of the vehicle to be evaluated to obtain current physical state data; and using a timestamp alignment algorithm and a confidence weighting algorithm to process the historical lifecycle data and the current physical state data to generate multi-source vehicle condition data.

[0008] Optionally, constructing a dynamic vehicle digital twin of the vehicle to be evaluated includes: deconstructing the vehicle's inherent attribute parameters and standard geometric configuration data from the multi-source vehicle condition data to generate a basic digital skeleton of the vehicle; extracting abnormal features and locating spatial coordinates from the physical sensor signals and appearance image data in the multi-source vehicle condition data to generate an entity state feature set containing damage distribution features and performance degradation gradients; and using a data fusion algorithm to map the entity state feature set to the spatial nodes of the basic digital skeleton of the vehicle to generate a dynamic vehicle digital twin of the vehicle to be evaluated.

[0009] Optionally, the generation of the vehicle condition evolution script includes: mapping the state feature vector of the dynamic vehicle digital twin to a pre-set vehicle fault knowledge graph in the feature space to obtain an existing fault list and a potential risk prediction list; and performing causal chain deduction on the existing fault list and the potential risk prediction list based on the temporal information in the dynamic vehicle digital twin to generate a vehicle condition evolution script describing the trajectory of vehicle state changes over time.

[0010] Optionally, generating candidate renovation schemes includes: extracting market preference feature vectors and historical price decay curves of the same model from market data under the current time window to obtain market benchmark reference data; calculating the feature distance between the vehicle condition evolution script and the market benchmark reference data to determine the target sales strategy; retrieving renovation project storage space and filtering renovation process items and budget parameters according to the target sales strategy to generate candidate renovation schemes.

[0011] Optionally, generating the virtual refurbished vehicle model includes: parsing the refurbishment process items and component replacement indicators in the candidate refurbishment scheme to generate virtual vehicle condition correction parameters; mapping the virtual vehicle condition correction parameters to the corresponding fault feature nodes in the dynamic vehicle digital twin to perform state feature value replacement and compensation calculations to generate refurbished local state features; and fusing the refurbished local state features with the original inherent attribute data in the dynamic vehicle digital twin that are not affected by the refurbishment to generate the virtual refurbished vehicle model.

[0012] Optionally, generating expected market reaction data containing the expected rate of return spectrum includes: accessing the real-time transaction data stream of the used car trading platform, extracting buyer browsing behavior characteristics and transaction price distribution characteristics to obtain dynamic market environment parameters; using the dynamic market environment parameters to correct the transaction probability and the corresponding price sensitivity coefficient to obtain an updated market simulation environment; mapping the virtual refurbished vehicle model and the corresponding refurbishment cost parameters to the updated market simulation environment for iterative calculation to generate expected market reaction data containing the expected selling price range, sales speed probability distribution, and rate of return range.

[0013] Optionally, the step of generating a user-oriented decision control panel includes: aggregating the expected market response data corresponding to all the candidate renovation schemes, constructing a three-dimensional decision space with renovation cost, expected sales cycle, and expected revenue as coordinate axes; calculating the revenue gradient that satisfies preset conditions under each combination of coordinate points in the three-dimensional decision space, and drawing an expected return on investment curve; and overlaying the three-dimensional decision space and the expected return on investment curve with a UI layer and binding them with touch events to generate a decision control panel.

[0014] Optionally, the method further includes: in response to an instruction from a user selecting from the candidate renovation schemes based on the decision control panel, determining a target renovation scheme; collecting the final transaction price and transaction cycle of the executed target renovation scheme in a real transaction scenario to obtain real market feedback data; calculating the difference between the real market feedback data and the expected market reaction data to generate prediction deviation data; analyzing the source dimensions of the prediction deviation data, and using the real market feedback data to optimize and adjust the market transaction behavior simulation logic and the reasoning logic of the vehicle condition evolution script.

[0015] Based on the same inventive concept, this invention also provides a dynamic used car refurbishment decision-making system for maximizing ROI, comprising: a digital twin construction module for acquiring multi-source vehicle condition data of the used car to be evaluated and constructing a dynamic vehicle digital twin of the vehicle to be evaluated; a vehicle condition evolution analysis module for performing time-series analysis and fault feature extraction on the dynamic vehicle digital twin to generate a vehicle condition evolution script; a refurbishment scheme generation module for performing supply and demand matching analysis between the vehicle condition evolution script and market data extracted from external data sources to generate candidate refurbishment schemes; a virtual refurbishment simulation module for performing virtual refurbishment operations on the dynamic vehicle digital twin based on the candidate refurbishment schemes to generate a virtual refurbished vehicle model; a market return projection module for using market transaction behavior simulation logic to perform value assessment and liquidity projection on the virtual refurbished vehicle model to generate expected market reaction data containing an expected rate of return spectrum; and a decision interaction visualization module for constructing a multi-dimensional decision space based on the expected market reaction data and visually encapsulating it to generate a user-oriented decision control panel.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] This invention constructs a dynamic digital twin of a vehicle and performs in-depth time-series analysis, achieving a comprehensive and high-precision digital characterization of vehicle condition from static detection to dynamic evolution. Compared to traditional assessment methods that rely on human experience, this method can objectively and quantitatively reveal the vehicle's existing faults, potential risks, and their inherent causal relationships, providing a data foundation for subsequent decision-making and thus improving the scientific rigor and accuracy of renovation decisions.

[0018] This invention deeply couples and quantitatively matches the vehicle's internal condition with external market dynamics. By introducing market preference feature vectors and price decay curves, the system can calculate the specific impact of each vehicle defect on its market value and link the refurbishment plan with the target sales strategy. This transforms refurbishment investment from blind repair into a value investment driven by market demand, ensuring that every penny is used to enhance the vehicle's market competitiveness, thereby guaranteeing and maximizing the final return on investment.

[0019] This invention innovatively introduces a virtual renovation and market transaction simulation process. This mechanism allows decision-makers to conduct zero-risk, low-cost scenario simulations of the potential economic consequences of various candidate renovation schemes before incurring any actual costs. By generating expected market reaction data that includes a spectrum of anticipated selling prices, sales speed, and return on investment, decision-makers can evaluate and compare different strategies under multi-dimensional economic indicators, reducing business risks arising from information asymmetry and forecasting errors.

[0020] This invention, by constructing a closed-loop feedback optimization mechanism, endows the decision-making system with self-learning and adaptive capabilities. The system can compare actual transaction results with previous market forecasts, quantify prediction deviations, trace their sources, and then continuously optimize and adjust its own vehicle condition evolution reasoning logic and market transaction simulation logic using real data. This allows the accuracy of the entire decision-making system to continuously improve with increased application frequency, dynamically adapting to changes in the market environment and ensuring its long-term effectiveness.

[0021] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a dynamic refurbishment decision-making method for maximizing ROI in a used car, according to an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the confidence-weighted fusion logic of multi-source vehicle condition data according to an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the deformation of a digital twin node based on a spatial kernel function, according to an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram of the three-dimensional decision space and benefit gradient of the renovation scheme according to an embodiment of the present invention.

[0027] Figure 5 This is a schematic diagram of the structure of a dynamic used car refurbishment decision system aimed at maximizing ROI, according to an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Reference Figure 1 One embodiment of the present invention proposes a dynamic refurbishment decision-making method for maximizing ROI of used cars. It adopts dynamic vehicle digital twin modeling, vehicle condition market coupling analysis and virtual simulation deduction as technical means to quantitatively predict the economic benefits of different refurbishment strategies, thereby providing scientific and intuitive decision support for maximizing business returns.

[0030] The method described in this embodiment specifically includes: S1. Obtain multi-source vehicle condition data of the used car to be evaluated and construct a dynamic digital twin of the vehicle to be evaluated; Optionally, obtaining multi-source vehicle condition data of the used car to be evaluated includes: The official record storage interface's encryption protocol is parsed to read the vehicle's maintenance and accident records to obtain historical lifecycle data; The physical signals fed back by the sensor array during non-contact scanning of the vehicle to be evaluated are analyzed to obtain the current physical state data. The historical lifecycle data and the current physical state data are processed using a timestamp alignment algorithm and a confidence weighting algorithm to generate multi-source vehicle condition data.

[0031] Specifically, a security authentication request is initiated to the official record storage interface, such as the vehicle manufacturer's database or insurance information sharing platform, through a pre-defined application programming interface (API). Upon successful authentication, reverse engineering or an officially authorized software development kit (SDK) is used to parse its proprietary encryption protocol, such as an AES-256-based symmetric encryption algorithm, to read the vehicle's maintenance and accident records. These records are returned in structured data formats such as JSON or XML, containing fields such as repair work order number, replacement parts list, accident damage assessment amount, and mileage. These records are then parsed and formatted into historical lifecycle data.

[0032] The sensor array, deployed at a dedicated inspection station, is activated. It includes non-contact inspection equipment such as a laser profile scanner, an ultrasonic paint thickness gauge, and an infrared thermal imaging camera. When the vehicle to be evaluated enters the designated location, the sensor array is triggered to perform a synchronous scan. The laser scanner captures data on body panel gaps and deformation with an accuracy of 0.1 mm, the thickness gauge measures the paint thickness of each component with an accuracy of 1 micrometer, and the thermal imaging camera records the temperature distribution of the engine and braking system after idling or short-distance driving. The physical signals fed back from these sensors are analyzed in real time, key feature values ​​are extracted, and current physical state data is generated.

[0033] A timestamp alignment algorithm is applied to map event timestamps in historical lifecycle data to the current physical state data collection timestamps onto a common timeline. A confidence-weighted algorithm is used to assign differentiated weights to data from different sources and with varying timeliness. The core calculation logic of this algorithm can be represented by the following formula: , in, The final state value representing the specific vehicle condition characteristics after fusion; These are feature status values ​​extracted from historical lifecycle data, such as determining whether a component has been "replaced" based on maintenance records. The corresponding characteristic state value is measured from the current physical state data, such as the sensor detecting a "minor scratch" on the "replaced" part; It is the confidence weight of historical data. Its value is determined by the authority of the data source. For example, the weight of the manufacturer's official record is 0.9. It is also determined by the length of time since the record. The longer the time, the lower the weight. This represents the confidence weight of the current physical data. Its value depends on the sensor's accuracy, the measurement's signal-to-noise ratio, and the consistency between multi-sensor data. For example, the weight for stable readings that conform to physical laws can be set to 0.95. The final result is a high-fidelity, structured dataset with time-series attributes and confidence labels, i.e., multi-source vehicle condition data. Figure 2 As shown in the figure, this diagram visually illustrates how to process heterogeneous data from historical lifecycles and physical scans. By applying a confidence-weighted formula, weights are dynamically allocated based on the timeliness and authority of the data source, and feature values ​​from different dimensions are merged into a final high-fidelity vehicle condition value.

[0034] For example, using a mid-size sedan of a certain brand as the vehicle to be evaluated, the system first initiates a security authentication request to the vehicle manufacturer's official database through a preset API interface. After successful authentication, the system uses the officially authorized SDK to parse the proprietary encryption protocol based on the AES-256 algorithm and successfully reads the vehicle's maintenance and accident records. These records are returned in a JSON structured format, from which the system parses fields such as the repair order number "RX-2024-998", the list of replaced parts including "left front fender", the accident damage assessment amount, and mileage, and formats them into historical lifecycle data. Subsequently, when the vehicle enters the dedicated inspection station, the system automatically activates the sensor array for synchronous scanning. The laser contour scanner captures the sheet metal gaps and deformation data at the left front fender with an accuracy of 0.1 mm, the ultrasonic paint thickness gauge measures the average paint thickness at this location to be 180 micrometers with an accuracy of 1 micrometer, and the infrared thermal imaging camera records the temperature distribution of the engine after idling. The system extracts the key feature values ​​of these physical signals in real time to form the current physical state data. Next, a timestamp alignment algorithm was applied to map the repair record time point from two years ago to the current physical scan time point onto the same timeline, and a confidence-weighted algorithm was enabled. For the left front fender feature, the feature state value was determined based on the "bodywork and painting" repair record from two years ago. The initial weight of 0.9 for authoritative sources was reduced to 0.5 due to the passage of time; instead, it was reduced to the historical confidence weight. Based on the high micro-waviness and abnormal thickness of the paint surface measured by the sensor, the current characteristic state value is determined. The weight is set to 0.4, because the data from multiple sensors are consistent and highly accurate, thus setting the physical confidence level weight. Substitute into the formula to calculate: Ultimately, the system generates multi-source vehicle condition data with time-series attributes, incorporating the high-fidelity assessment score. This method effectively addresses the limitations of a single data source by integrating encrypted protocol parsing with high-precision multi-dimensional physical scanning and utilizing a confidence-weighted fusion algorithm. This ensures the dual accuracy of the assessment results in both the time and physical dimensions, thereby improving the fidelity of the vehicle condition data.

[0035] Optionally, constructing a dynamic vehicle digital twin of the vehicle to be evaluated includes: The inherent attribute parameters and standard geometric configuration data of the vehicle are deconstructed from the multi-source vehicle condition data to generate the basic digital skeleton of the vehicle. Anomaly features are extracted and spatial coordinates are located from the physical sensor signals and appearance image data in the multi-source vehicle condition data to generate an entity state feature set containing damage distribution features and performance degradation gradients. The entity state feature set is mapped to the spatial nodes of the vehicle's basic digital skeleton using a data fusion algorithm to generate a dynamic vehicle digital twin of the vehicle to be evaluated.

[0036] Specifically, static vehicle information is extracted from multi-source vehicle condition data. By parsing the Vehicle Identification Number (VIN), the standard 3D CAD model data and factory configuration table of the vehicle are retrieved from an internally or externally connected vehicle model database. This data defines the vehicle's standard geometric configuration and inherent attribute parameters, such as body dimensions, wheelbase, and engine model. These standard data collectively constitute the vehicle's basic digital skeleton, which is an ideal 3D mesh model composed of millions of spatial nodes carrying coordinates and connectivity relationships.

[0037] Focusing on dynamic changes in multi-source vehicle condition data, namely physical sensor signals and exterior image data, a set of entity state features is generated. Using deep learning-based image recognition algorithms, such as YOLOv7 or later models, pixel-level analysis is performed on the exterior image data to automatically detect and define damage such as scratches, dents, and rust, outputting the damage type, severity, and pixel coordinates in the 2D image. Simultaneously, for physical sensor signals such as laser scanning and ultrasonic thickness measurement, signal processing algorithms, such as Fourier transform and wavelet analysis, are employed to extract anomalous features deviating from the standard baseline, such as deformations exceeding 0.5 mm in sheet metal or local thickening of paint exceeding 200 micrometers. By fusing camera calibration parameters and sensor spatial layout information, all detected anomalous features are spatially located, mapping them from 2D pixel coordinates or 1D signal sequences to the 3D world coordinate system of the vehicle's basic digital skeleton, thus forming a set of entity state features containing damage distribution characteristics and performance degradation gradients. The performance degradation gradient refers to the component wear rate inferred from historical data, such as the percentage of remaining brake pad life calculated based on maintenance records.

[0038] The data fusion algorithm is executed to "attach" the entity state feature set to the vehicle's basic digital skeleton. The core is state updating: for each located anomaly, the state attributes of spatial nodes within its influence range are modified. This update process employs a weighted interpolation algorithm based on a spatial kernel function, which can be described by the following formula: , in, Represents spatial nodes The updated state vector contains the geometric positions of the nodes. Physical and appearance attributes; This is the original standard state vector of the node; It affects this node The total number of all abnormal features; The first one extracted from the entity state feature set An attribute vector of an anomalous feature, for example, for the feature "car door dent", Includes {maximum depth} Coordinates of the center of the depression Radius of influence Shape factor }; It is a feature mapping function used to convert the attribute vector of abnormal features into a change vector with the same dimension as the node state vector, such as mapping the indentation depth value to node coordinates. The negative displacement of the shaft; It is the center of abnormal features With spatial nodes The Euclidean distance between them; This is a spatial decay kernel function used to calculate the influence weights of anomalous features on nodes at different distances, employing a Gaussian decay model: ,when hour, This function ensures the physical realism of a smooth transition of damage features from the center to the edge. The above formula automatically performs nonlinear corrections: for example, for a depression feature with a depth of 5mm and a radius of 10cm, it calculates the distances between all mesh nodes within this range and the center of the depression. Using Gaussian kernel function Displacement weights are derived, and the geometric coordinates of each node are adjusted accordingly to realistically "press" out indentations on the digital twin. For scratch features, the appearance is reproduced by adjusting the node color and gloss attributes. Finally, a dynamic digital twin of the vehicle to be evaluated is generated. This twin is a high-fidelity, multi-dimensional digital asset that not only macroscopically reproduces the vehicle's three-dimensional appearance but also microscopically records the location, shape, and extent of each damage, as well as the performance degradation of key components. Figure 3 As shown in the figure, this illustrates the process of mapping physical damage features to the vehicle's underlying digital skeleton.

[0039] For example, taking a mid-size sedan as an example, the system first parses its Vehicle Identification Number (VIN), retrieves the corresponding standard 3D CAD model data and factory configuration table from the built-in vehicle database, and constructs a basic digital skeleton of the vehicle consisting of millions of spatial nodes carrying coordinates and connections. Next, the system focuses on dynamic information in multi-source vehicle condition data, using the YOLOv7 deep learning model to perform pixel-level analysis on the exterior images, detecting a "left front door dent" and outputting its pixel coordinates and type in the image. Simultaneously, wavelet analysis is used to process the laser scanning signal, confirming that the sheet metal deformation exceeds the baseline standard of 0.5mm, and the remaining lifespan percentage of the brake pads is calculated based on maintenance records. Camera calibration parameters map this information to a 3D world coordinate system, forming a set of entity state features. Subsequently, the system executes a data fusion algorithm to extract the attribute vector of the left front door dent feature. , including maximum depth The world coordinates of the concave center Millimeters, radius of influence The system selects areas within the influence zone and at a distance from the center of the depression. A spatial grid node at the location As the state update object, the node's original Z-axis coordinate The length is 300mm. The system uses a spatial kernel function based on the Gaussian decay model: Then the feature mapping function is applied. The 5mm indentation depth is converted into a negative Z-axis displacement. The displacement of the node is calculated using weights, and the updated Z-axis coordinate of the node is finally obtained. The system performs this nonlinear correction operation on thousands of nodes within its range and records performance degradation at the microscopic level, ultimately generating a high-fidelity dynamic vehicle digital twin. This method achieves a precise mapping from two-dimensional images and one-dimensional sensor signals to a three-dimensional spatial entity, making the digital twin not merely an appearance model, but an intelligent entity with real-time feedback capabilities for physical properties. The application of a Gaussian kernel function ensures the smoothness and physical realism of the damage features transitioning from the center to the edge, providing a geometric and physical foundation for subsequent maintenance simulation and value assessment.

[0040] S2. Perform time-series analysis and fault feature extraction on the dynamic vehicle digital twin to generate a vehicle condition evolution script; Optionally, the generated vehicle condition evolution script includes: The state feature vector of the dynamic vehicle digital twin is mapped to the feature space of the pre-set vehicle fault knowledge graph to obtain the existing fault list and the potential risk prediction list. Based on the temporal information in the dynamic vehicle digital twin, causal chain deduction is performed on the existing fault list and potential risk prediction list to generate a vehicle condition evolution script describing the trajectory of vehicle status changes over time.

[0041] Specifically, the quantified state information in the dynamic vehicle digital twin is vectorized to form a high-dimensional state feature vector. This vector contains hundreds of dimensions, such as the vibration frequency of a specific engine cylinder, the viscosity attenuation coefficient of the transmission fluid, and anomalies in the paint film thickness of specific areas of the vehicle body. Subsequently, this state feature vector is mapped to a pre-built automotive fault knowledge graph in its feature space. This knowledge graph is a vast graph database where nodes represent entities such as automotive parts, fault phenomena, and fault codes, and edges represent causal, correlation, or subordinate relationships between them, containing over a million pieces of expert knowledge and repair case data. The mapping process is achieved by calculating the cosine similarity or Euclidean distance between the state feature vector and various fault mode feature templates defined in the knowledge graph. The matching degree can be expressed as: , in, It is the first in the current vehicle status and knowledge graph The matching confidence score of each fault mode, with a value range of [0,1]; It is the total number of feature dimensions involved in the calculation; It is the first in the state feature vector One measured feature value; It is the first The fault mode corresponding to the first For each feature, a benchmark template is provided. For continuous variables, Includes expected mean and allowable variance For discrete variables, For the target set; It is the first The diagnostic weight coefficients for each feature are extracted from historical maintenance data based on the information gain algorithm. These coefficients characterize the distinguishability of the feature for diagnosed faults. For example, the weight of the core OBD fault code is set to 0.8, while the weight of minor scratches on the exterior is set to 0.1. It is a normalized feature similarity function, defined piecewise for different data types: for continuous physical parameters, the membership degree is calculated using the Gaussian radial basis function. This ensures that the greater the deviation, the lower the score; for discrete state parameters, the Kronecker algorithm is used. Function: If Belongs to set ,but ,otherwise Through the above calculations, the matching results of each micro-feature are aggregated into a macro-level fault confidence score. When When the confidence level exceeds a preset high-confidence threshold, such as 0.9, the corresponding fault is recorded in the existing fault list; when If the confidence level is in the medium range, such as 0.6-0.9, it is considered a potential risk and is recorded in the potential risk prediction list.

[0042] After mapping is complete, causal chain deduction is initiated. This deduction utilizes the time-series information embedded in the dynamic vehicle digital twin, such as timestamps of maintenance records and historical trends of sensor data, to logically connect the items in the list. A graph traversal algorithm is executed on the knowledge graph, starting from the diagnosed faults in the existing fault list, such as "low efficiency of the three-way catalytic converter," and tracing back along the "is_caused_by" relationship edges to find its upstream causes, such as "long-term use of inferior fuel" or "ignition system misfire." Then, evidence supporting these upstream causes is searched in the vehicle's historical lifecycle data. Conversely, starting from the items in the potential risk prediction list, forward deduction is performed along the "can_cause" relationship edges to predict the possible downstream cascading faults and assess their probability of occurrence. Finally, a structured vehicle condition evolution script is generated. This script is not a plain text description but a data object, typically stored in JSON or XML format. Its core content includes a verified and attributed list of existing failures, with each failure associated with its most likely upstream cause; a list of potential risks ranked by probability and urgency, with each risk accompanied by a potential chain of downstream failures; and a series of causal path diagrams depicting the causal path of failures from their inception to their development and manifestation.

[0043] For example, the multidimensional quantitative information in the dynamic vehicle digital twin is vectorized and recombined to construct a high-dimensional state feature vector containing hundreds of dimensions, such as engine block vibration frequency, transmission fluid viscosity attenuation coefficient, and abnormal values ​​of vehicle body paint film. Subsequently, the system performs feature space mapping, comparing this vector with an automotive fault knowledge graph containing over one million expert cases and causal relationships between components. In the analysis of the vehicle's engine operating data, the system extracts the measured feature value of the "ignition delay angle of cylinder 2 under idling conditions." The value is 4.5 degrees, which is consistent with the standard reference template for the "spark plug electrode aging" failure mode in the knowledge graph, and the expected mean is... Degree, allowable variance The system maps features based on their degree of similarity. The Gaussian radial basis function formula is used to derive the feature similarity results: Combined with preset weights Other characteristics, and the overall fault confidence level calculated by the system. The score was 0.85. Since this score falls within the medium confidence threshold range of 0.6 to 0.9, the system identifies it as a potential risk rather than a confirmed fault. Using a time-series data initiation graph traversal algorithm within the twin, causal chain deduction is performed. Finally, the system outputs a structured data object encapsulated in JSON or XML format, forming a vehicle condition evolution script through logical connections. This script explicitly records the predicted conclusion: "There is a risk of spark plug aging, with a confidence level of 85%, and a misfire fault is expected after 5000 kilometers," along with a list of existing faults and risk classifications containing upstream and downstream causal paths. This method transforms discrete physical sensor data into fault diagnosis conclusions with deep logical connections, achieving an intelligent leap from simply "reporting fault codes" to possessing causal reasoning capabilities. In particular, the quantitative calculation combined with Gaussian and weighted algorithms not only locates existing problems but also predicts potential risks in probabilistic form, providing users with a vehicle health management solution.

[0044] S3. Perform supply and demand matching analysis on the vehicle condition evolution script and market data extracted from external data sources to generate candidate renovation plans; Optionally, the generation of candidate renovation schemes includes: Market preference feature vectors and historical price decay curves of the same model are extracted from market data for the current time window to obtain market benchmark reference data. Calculate the feature distance between the vehicle condition evolution script and the market benchmark reference data to determine the target sales strategy; The system retrieves the renovation project storage space and filters renovation process items and budget parameters according to the target sales strategy to generate candidate renovation plans.

[0045] Specifically, this process begins with extracting market data in batches from multiple external data sources, such as public data interfaces of mainstream used car trading platforms and automotive vertical portals. Applying natural language processing and data mining algorithms, the process analyzes the descriptive text, user reviews, and transaction records of tens of thousands of vehicles of the same model for sale over the past 90 days, extracting keyword frequency and sentiment to construct a market preference feature vector. This vector quantitatively expresses the vehicle attributes that consumers currently value most; for example, "no abnormal engine noise" has a weight of 0.9, and "interior cleanliness" has a weight of 0.75. Simultaneously, regression analysis is performed on historical transaction price data for the same model over two years to generate a historical price decay curve for the same model. This curve describes the benchmark market value of the model at different ages and mileages. These two sets of data together constitute the market benchmark reference data.

[0046] The core step in performing supply and demand matching analysis is calculating the negative impact of various defects in the vehicle condition evolution scenario on market value. The calculation logic for this impact value can be expressed as follows: , in, The first scene in the script representing the evolution of the vehicle's condition The expected market value loss caused by a fault or defect; This is a parameter representing the severity of the defect, quantified by the dynamic vehicle digital twin, with a value ranging from 0 to 1, where 1 represents complete damage; This is the weight of the market preference corresponding to the defect, which is obtained directly from the market preference feature vector; This refers to the average discount relative to the market benchmark price when the defect exists, obtained by analyzing the deviation of historical price decay curves and comparing the transaction price difference of similar vehicles with and without this defect. All defects... The values ​​are accumulated to obtain the total value reduction of the vehicle. Based on the ratio of the total reduction value to the vehicle's base value, a target sales strategy is automatically determined. For example, if the total reduction value is less than 5% of the base value, the "refinement" strategy is adopted; if it is between 5% and 20%, the "core function repair" strategy is adopted.

[0047] Based on the established target sales strategy, a search is performed within the built-in refurbishment project storage space. This storage space is a structured database containing thousands of refurbishment process entries, each associated with a corresponding fault type, repair cost range, required man-hours, and applicable sales strategy tags. Filtering is performed using strategy tags, combined with a defect list, to generate one or more candidate refurbishment solutions. Each solution is a data package detailing the recommended refurbishment process entries, the budget parameters for each entry, and the overall expected cost of the solution.

[0048] For example, the system first applies natural language processing and data mining techniques to extract data from various trading platforms over the past 90 days, constructing a market preference feature vector including dimensions such as "no abnormal engine noise" (weight 0.9) and "interior cleanliness" (weight 0.75), and then regression fitting to obtain the historical price decay curve for the same model. Regarding the "dent on the left front door" defect, based on a market benchmark price of 150,000 yuan and a weight for exterior integrity... Average discount for similar defects Meta and severity parameters The system calculates that: The system determined that the loss was significantly higher than the corresponding PDR (Preservation and Repair) cost of 300 yuan, and therefore adopted the "Premium Preparation" strategy, outputting candidate refurbishment plans that included this repair process. This method transforms vehicle condition defects from a technical perspective into value loss indicators from an economic perspective, achieving precise quantitative evaluation of refurbishment decisions. This mechanism avoids ineffective investment caused by blind repairs, ensuring that every selected refurbishment project is economically reasonable, and helping car dealers identify key preparation points that can increase vehicle premiums.

[0049] S4. Perform a virtual renovation operation on the dynamic vehicle digital twin based on the candidate renovation scheme to generate a virtual renovated vehicle model; Optionally, generating the virtual refurbished vehicle model includes: Analyze the refurbishment process items and component replacement indicators in the candidate refurbishment schemes to generate virtual vehicle condition correction parameters; The virtual vehicle condition correction parameters are mapped to the corresponding fault feature nodes in the dynamic vehicle digital twin to perform state feature value replacement and compensation calculations, generating local state features after refurbishment. By integrating the local state features after the renovation with the original inherent attribute data of the dynamic vehicle digital twin that are not affected by the renovation, a virtual renovated vehicle model is generated.

[0050] Specifically, the selected candidate renovation schemes are analyzed. These schemes are structured data objects from which renovation process items and component replacement indicators are extracted. For example, a process item "replace the left front fender" is analyzed as {Part ID: FL-Fender, Operation: Replace, Target Status: Brand New Original Part}; a process item "full vehicle paint polishing" is analyzed as {Part Group ID: Body_Panels, Operation: Repair, Target Attribute: Paint Gloss, Target Value: >95GU}. All these extracted instruction sets are then uniformly encapsulated into virtual vehicle condition correction parameters.

[0051] Based on the component ID or component group ID in the virtual vehicle condition correction parameters, the corresponding fault feature nodes are located in the dynamic vehicle digital twin. Replacement and compensation operations are performed on the state feature values ​​of these nodes. The replacement operation is used for component replacement, directly replacing the entire state vector of the target node with the standard state vector of the new component. The compensation operation is used for repair operations; its update logic uses a linear interpolation algorithm based on target approximation, which can be expressed by the following formula: , in, It is a fault characteristic node The new state vector after virtual renovation; It is the original state vector before renovation, which contains measured values ​​of multiple dimensions such as damage level and performance parameters; It is a target state vector set according to the refurbishment standard, representing the ideal result expected by the process, such as the "zero surface defects" state or "100% gloss" state of the new car standard; This is the repair efficiency coefficient of the refurbishment process, with a value ranging from [0, 1], used to quantify the physical limitations of the process. For example, the repair efficiency of ordinary polishing processes for deep scratches... Setting it to 0.9 means it can eliminate 90% of the depth, but still leaves 10% of the traces; and for "sheet metal reshaping"... A value of 0.8 indicates that the curvature cannot be fully restored to its original factory value. It is a feature dimension selection matrix, used to filter out specific state dimensions affected by the process through matrix multiplication. For example, when performing a "spray painting" operation, Set the corresponding positions for the "Color" and "Gloss" dimensions to 1, and the positions for the "Material Hardness" or "Geometric Coordinates" dimensions to 0. This ensures that the repair operation does not incorrectly modify the vehicle's physical structural parameters. For example, for paint polishing, [the following is an example of setting the position of ... The "scratch depth" component value is reduced by 90%, and the "gloss" component value is increased to the target value. By performing this operation on all nodes to be refurbished, a series of local state features after refurbishment are generated.

[0052] Using the original dynamic vehicle digital twin as a blueprint, a copy is created. All newly generated refurbished local state features are then "welded" back to their corresponding positions in the copy, replacing the original fault states. Components not touched by candidate refurbishment schemes, such as the unrepaired engine interior or the intact right side of the vehicle body, retain their original inherent attribute data. This results in a completely new data object representing the ideal state of the refurbished vehicle—a virtual refurbished vehicle model. This model maintains consistency with the original dynamic vehicle digital twin in terms of geometry and the state of the unrefurbished parts, but in the refurbished areas, the state feature values ​​have been updated to reflect the repaired ideal state.

[0053] For example, the system receives structured candidate refurbishment scheme data and dynamically analyzes it through a virtual vehicle condition correction module. The scheme data stream is first deconstructed into specific execution instruction sets. For instance, "replace the left front fender" is converted into a parameter group of {operation: replacement, target: original part}, while "full vehicle paint polishing" is extracted into control parameters of {ID: body panel, target: >95GU}. Subsequently, the system locates key fault nodes in the dynamic vehicle digital twin based on the component ID. For repair instructions that need to be calculated, the system reads the original state data and sets the original gloss level of the current node. The ideal target value set by the renovation standard The system simultaneously calls upon the physical characteristic parameters of the process to determine the repair efficiency coefficient. and feature dimension selection matrix The system uses a linear interpolation formula based on target approximation to calculate the renovated state value in real time. The value 94.4 GU was then backfilled into the corresponding node of the digital twin copy, completing the local feature reconstruction. Simultaneously, the system integrated the updated local features with the original inherent attribute data of the engine, chassis, etc., which were unaffected by the refurbishment, ultimately generating a high-fidelity virtual refurbished vehicle model. By converting the refurbishment scheme into mathematical operation instructions and introducing efficiency coefficient constraints, the system can simulate the non-ideal effects of physical repair, preserving the vehicle's objective physical attributes while quantifying the state improvement brought about by the refurbishment. This provides a high-confidence model foundation that conforms to physical laws for subsequent ROI prediction.

[0054] S5. Use market transaction behavior simulation logic to perform value assessment and liquidity deduction on the virtual refurbished vehicle model, and generate expected market reaction data containing the expected investment return rate spectrum. Optionally, generating the expected market reaction data, which includes a spectrum of expected investment returns, includes: By accessing the real-time transaction data stream of the used car trading platform, buyer browsing behavior characteristics and transaction price distribution characteristics are extracted to obtain dynamic market environment parameters; The trading probability and the corresponding price sensitivity coefficient are corrected using the dynamic market environment parameters to obtain the updated market simulation environment; The virtual refurbished vehicle model and corresponding refurbishment cost parameters are mapped to the updated market simulation environment for iterative calculation to generate expected market reaction data, including expected price range, sales speed probability distribution, and return on investment range.

[0055] Specifically, real-time transaction data streams from one or more large used car trading platforms are accessed via real-time APIs. By continuously monitoring these data streams and utilizing stream processing frameworks such as Apache Flink, buyer browsing behavior characteristics are extracted in real time. These characteristics include average page dwell time for specific car models, the ratio of inquiries to favorites, and the price distribution of similar vehicles over the past 24 hours. This highly timely data is aggregated into dynamic market environment parameters to reflect current market activity and consumer sentiment.

[0056] These parameters are used to dynamically correct the pre-set market transaction behavior simulation logic, generating an updated market simulation environment. The core of this correction process is adjusting the transaction probability and the corresponding price sensitivity coefficient. For example, if the number of views for a certain car model increases by 20% within 72 hours, the base transaction probability for that model is increased by a certain percentage. The adjustment of the price sensitivity coefficient is driven by the following model: , in, It is the updated price sensitivity coefficient, which directly affects buyers' reactions to prices in the simulation; It is the benchmark price sensitivity coefficient; The rate of change in the popularity of buyer browsing behavior; This represents the rate of change in the supply of similar vehicles in the market. and This is an adjustment parameter, the value of which is determined through regression analysis of historical trading data, and is typically between 0.1 and 0.5. This formula implies that when market demand is strong... Or supply reduction When prices are high, price sensitivity decreases, and vice versa.

[0057] The virtual refurbished vehicle model and its corresponding refurbishment cost parameters are treated as new virtual commodities and subjected to Monte Carlo iterative calculations in an updated market simulation environment. In each iteration, the behavior of a large number of potential buyers is simulated, with each buyer's preferences, budget, and current price sensitivity coefficient all considered. The system then bids on the virtual refurbished vehicle model. Based on supply and demand and a price game model, after tens of thousands of simulated transactions, the distribution of transaction prices for this vehicle model is statistically analyzed. By analyzing this distribution, the expected selling price range is derived, for example, the price range within a 95% confidence interval, and the probability distribution of sales speed is determined, for example, the probability of selling within 7 days, 30 days, and 90 days. Finally, the range of return on investment (ROI) is calculated.

[0058] The final result is a comprehensive report on anticipated market reactions. This report, presented in a structured format, centers on a detailed analysis of the expected return on investment (ROI) spectrum. It not only provides a single ROI forecast but also offers probability distributions, demonstrating the various ROI outcomes achievable under different sales cycles and prices. For example, the report might explicitly state: "This plan has a 70% probability of selling within 30 days, with an expected selling price range of 105,000 to 112,000 yuan, corresponding to an ROI range of 15% to 22%."

[0059] For example, when generating expected market reaction data containing a spectrum of expected return on investment, the system first accesses the used car transaction data stream processed by Apache Flink via a real-time API. It then extracts buyer browsing behavior characteristics such as the average page dwell time for specific car models, the ratio of inquiries to favorites, and the price distribution of similar vehicles over the past 24 hours. This high-time data is aggregated into dynamic market environment parameters. Next, the system reads the monitored rate of change in browsing activity. Let's set it to +0.2, which is the ratio of a 20% increase to the rate of change in supply. Set it to -0.1, which means a 10% reduction, combined with the baseline coefficient. and adjustment parameters , Call the model to perform correction calculations: After obtaining a correction coefficient of 1.62, the system determined that the market was in a highly active state. It then input the virtual refurbished vehicle model and refurbishment cost parameters into the updated environment to perform tens of thousands of Monte Carlo iterations. Based on the simulated buyer bidding game, the system statistically determined the selling price range within a 95% confidence interval and the probability of sales speed over 7 days, 30 days, and 90 days. Finally, the system outputs a structured report indicating: "In the current highly sensitive market, the recommended pricing range is 152,000-155,000, with an expected probability of selling within 7 days increasing to 85%, corresponding to a specific ROI range." This method, by integrating real-time stream processing and dynamic correction algorithms, transforms abstract market sentiment into concrete mathematical parameters, achieving millisecond-level response of the simulated environment to real market fluctuations. The system abandons static experience-based pricing; through probability distribution data generated by extensive iterative calculations, it can quantify the risk of unsold inventory and profit margins under different pricing strategies, providing decision-makers with a scientific basis for balancing sales speed and return on investment.

[0060] S6. Based on the expected market response data, construct a multi-dimensional decision space and visualize it to generate a user-oriented decision control panel.

[0061] Optionally, the generation of the user-oriented decision control panel includes: Aggregate the expected market response data corresponding to all the candidate renovation schemes to construct a three-dimensional decision space with renovation cost, expected sales cycle, and expected revenue as coordinate axes; Calculate the return gradient that satisfies the preset conditions under each combination of coordinate points in the three-dimensional decision space, and draw the expected rate of return curve. The three-dimensional decision space and the expected rate of return curve are overlaid with a UI layer and bound to touch events to generate a decision control panel.

[0062] Specifically, the expected market response data for all candidate renovation plans is aggregated. The key decision variables for each plan—renovation cost, expected sales cycle, and expected revenue—are extracted. These three variables are defined as three mutually orthogonal coordinate axes, collectively constructing a three-dimensional decision space. Each candidate renovation plan, due to its unique combination of (cost, cycle, revenue), becomes a discrete coordinate point in this three-dimensional space. Expected sales cycle and expected revenue are usually not single values, but rather intervals or probability distributions. The expected value or median is taken as the position of the coordinate point, while the range information of the interval is stored as an additional attribute of that point.

[0063] The revenue gradient is calculated within this three-dimensional decision space. Here, the revenue gradient is defined as the net return obtained per unit time, a key indicator for measuring capital efficiency. For any coordinate point in the space, representing a renovation plan, the revenue gradient is calculated as follows: , in, This represents the profit gradient of the scheme; This refers to the expected revenue of the plan, i.e., the expected selling price; This refers to the renovation cost of the plan; This represents the expected sales cycle of the proposed renovation plan. The coordinates of all candidate renovation plans are iterated through, and the value of each point is calculated. The discrete points are then connected using an interpolation algorithm, such as cubic spline interpolation, to create one or more smooth curves. These curves represent the trend of expected return on investment under different input and time strategies; this is known as the expected return on investment curve.

[0064] Using front-end visualization libraries such as D3.js or Three.js, the constructed 3D decision space and the plotted expected return on investment (ROI) curve are rendered to generate an interactive 3D graphical interface. Each candidate renovation plan is represented by a clickable geometry in the space, whose size or color can be used to encode fourth-dimensional information, such as the specific numerical value of the ROI. UI layers are overlaid on these geometries, and touch or mouse hover events are bound to each geometry. When a user interacts with a point, an information window pops up, displaying a detailed list of renovation projects, cost details, expected selling price range, sales speed probability distribution, and a complete ROI spectrum for that plan. The final result is a fully functional and information-rich decision control panel. This panel simplifies complex decision-making problems into selecting different points in the space through 3D visualization. Users can intuitively see the trade-offs between different renovation strategies in terms of cost, time, and revenue, and gain a deeper understanding of each plan's details through interactive operations. For example, users can easily find the renovation plan that will bring the highest expected return within a limited 30-day sales period, or find the minimum renovation cost required to achieve a 15% ROI. like Figure 4 As shown in the figure, the diagram constructs a three-dimensional decision space consisting of renovation costs, expected sales cycle, and expected revenue.

[0065] For example, the system first aggregates the expected market response data of all candidate renovation schemes, extracts key decision variables—renovation cost, expected sales cycle, and expected revenue—and constructs a three-dimensional orthogonal decision space. The system maps each scheme to discrete coordinate points in the space and then applies this mapping to specific schemes. Data retrieval: Refurbishment costs The expected selling price is 2000 yuan. The expected sales period is 155,000 yuan. The period is 15 days. The system executes the revenue gradient calculation program, using the formula... Quantifying the efficiency of funds per unit of time: Yuan / day. After calculating a result of 10200 yuan / day, the system traverses all coordinate points within the space, uses cubic spline interpolation to connect discrete points, and draws a smooth expected return on investment curve. Subsequently, the system calls visualization libraries such as Three.js to render the 3D space, instantiating the solution into a clickable geometry, and overlaying UI layers and binding touch events. When the user triggers the interaction event of Solution A, the system immediately pops up a details window, displaying a list of renovation projects, cost details, expected selling price range, and sales speed probability distribution, intuitively presenting the advantage of Solution A over Solution B in terms of capital turnover efficiency. This method reduces the dimensionality of complex multidimensional data, intuitively demonstrating the balance between cost, revenue, and time through the composite indicator of "return gradient." It helps users find the optimal balance between pursuing high profits and rapid turnover, improving the efficiency and scientific nature of business decisions.

[0066] Optionally, the method further includes: In response to the user's instruction to select from the candidate renovation plans based on the decision control panel, a target renovation plan is determined; Collect the final transaction price and transaction cycle of the executed target renovation plan in real transaction scenarios to obtain real market feedback data; Calculate the difference between the actual market feedback data and the expected market reaction data to generate prediction deviation data; The source dimensions of the prediction deviation data are analyzed, and the reasoning logic of the market transaction behavior simulation logic and the vehicle condition evolution script is optimized and adjusted using the real market feedback data.

[0067] Specifically, in response to a user-selected instruction, which specifies the target renovation plan to be adopted and implemented from multiple candidate renovation plans, this target renovation plan and its associated complete expected market response data are archived and marked as "pending verification".

[0068] Entering asynchronous monitoring mode, it connects with the enterprise's internal sales management system or vehicle inventory system via a data interface to continuously monitor the sales status of the specific vehicle. Once the transaction of the vehicle is completed, it automatically collects the final transaction price and transaction cycle of the executed target refurbishment plan in a real transaction scenario, forming real market feedback data.

[0069] The actual market feedback data is quantitatively compared with the previously archived expected market reaction data for the plan to generate prediction deviation data. This deviation is quantified using a normalized relative squared error model, as shown in the following formula: , in, This represents the total prediction bias loss value, used to guide the optimization direction of the model; It is the actual value of the final transaction price of the vehicle in a real transaction; It is the median expected selling price predicted by the model; It is the actual transaction period from when a vehicle is listed to when it is sold; It is the expected sales cycle predicted by the model; and These are weighting coefficients used to balance the importance of price accuracy and turnover efficiency in the evaluation system, and they satisfy... For example, in a market environment where there is significant pressure to recover funds, price weights can be set. Time weight .

[0070] Using real market feedback data with clearly labeled sources of bias, the corresponding predictive models are optimized and adjusted. Machine learning algorithms such as gradient descent are employed to minimize the loss function. To achieve this, the model parameters are fine-tuned. For example, for the market transaction behavior simulation logic, new transaction data points are used to update the price sensitivity coefficient or correct the transaction probability model. For the reasoning logic of the vehicle condition evolution scenario, if a risk point rated as "minor" in the scenario is found to actually lead to a significant extension of the vehicle sales cycle, the negative impact weight of this risk on liquidity in the knowledge graph is increased. Each real transaction becomes a valuable learning opportunity, enabling the reasoning logic of the vehicle condition evolution scenario to map the correlation between physical vehicle condition and market value, while the market transaction behavior simulation logic can also capture and adapt to the ever-changing market environment. Under long-term operation, prediction bias... The value will converge to a lower, stable level, thus providing users with increasingly accurate support for ROI-maximizing renovation decisions.

[0071] For example, in response to a user's selection of Option A on the decision control panel, the system marks the option and its associated expected market response data as "pending verification" and archives it. Subsequently, the system switches to asynchronous monitoring mode, monitoring vehicle status through integration with the sales management system. Once the vehicle transaction is completed, the system automatically collects the final transaction price based on the actual transaction scenario. With transaction cycle The system detected that vehicles corresponding to Plan A were sold and collected data. Yuan, Wow. Compared to the median selling price predicted by the previous model. Yuan and expected sales cycle The system operates based on the set weights. , The deviation is calculated using the normalized relative square error model: System analysis revealed that the deviation primarily stemmed from the time dimension. Therefore, an optimization and adjustment process was initiated using this data set: a gradient descent algorithm was employed to minimize... To achieve this, the system fine-tunes the time decay parameter in the market trading behavior simulation logic, correcting optimistic estimates of the sales cycle. By converting each real transaction into a model training sample, the system can quantify the prediction deviations in price and cycle dimensions and automatically calibrate the underlying inference logic and simulation parameters. This allows the system to dynamically adapt to market changes during long-term operation, thus correcting prediction deviations. The value converges to a low level, thereby providing users with increasingly accurate and battle-tested ROI decision support.

[0072] Based on the same inventive concept, such as Figure 5 As shown, the present invention also provides a dynamic used car refurbishment decision-making system for maximizing ROI, comprising: The digital twin construction module is used to acquire multi-source vehicle condition data of the used car to be evaluated and construct a dynamic digital twin of the vehicle to be evaluated. The vehicle condition evolution analysis module is used to perform time-series analysis and fault feature extraction on the dynamic vehicle digital twin, and generate a vehicle condition evolution script. The renovation plan generation module is used to perform supply and demand matching analysis between the vehicle condition evolution script and market data extracted from external data sources to generate candidate renovation plans. The virtual renovation simulation module is used to perform virtual renovation operations on the dynamic vehicle digital twin based on the candidate renovation scheme, and generate a virtual renovated vehicle model. The market return projection module is used to perform value assessment and liquidity projection on the virtual refurbished vehicle model using market transaction behavior simulation logic, and generate expected market reaction data containing the expected investment return rate spectrum. The decision-making interaction visualization module is used to construct a multi-dimensional decision space based on the expected market response data and to visualize and encapsulate it, generating a user-oriented decision control panel.

[0073] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0074] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A dynamic refurbishment decision-making method for used cars aimed at maximizing ROI, characterized in that, The method includes: Acquire multi-source vehicle condition data of the used car to be evaluated, and construct a dynamic digital twin of the vehicle to be evaluated; Perform time-series analysis and fault feature extraction on the dynamic vehicle digital twin to generate a vehicle condition evolution script; The vehicle condition evolution script is matched with market data extracted from external data sources to generate candidate renovation plans by supply and demand analysis. Based on the candidate renovation scheme, a virtual renovation operation is performed on the dynamic vehicle digital twin to generate a virtual renovated vehicle model; The value assessment and liquidity simulation of the virtual refurbished vehicle model are performed using market transaction behavior simulation logic, generating expected market reaction data that includes the expected rate of return spectrum; Based on the expected market response data, a multi-dimensional decision space is constructed and visualized to generate a user-oriented decision control panel.

2. The dynamic refurbishment decision-making method for used cars aimed at maximizing ROI as described in claim 1, characterized in that, The acquisition of multi-source vehicle condition data for the used car to be evaluated includes: The official record storage interface's encryption protocol is parsed to read the vehicle's maintenance and accident records to obtain historical lifecycle data; The physical signals fed back by the sensor array during non-contact scanning of the vehicle to be evaluated are analyzed to obtain the current physical state data. The historical lifecycle data and the current physical state data are processed using a timestamp alignment algorithm and a confidence weighting algorithm to generate multi-source vehicle condition data.

3. The dynamic refurbishment decision-making method for used cars aimed at maximizing ROI as described in claim 1, characterized in that, The construction of the dynamic vehicle digital twin of the vehicle to be evaluated includes: The inherent attribute parameters and standard geometric configuration data of the vehicle are deconstructed from the multi-source vehicle condition data to generate the basic digital skeleton of the vehicle. Anomaly features are extracted and spatial coordinates are located from the physical sensor signals and appearance image data in the multi-source vehicle condition data to generate an entity state feature set containing damage distribution features and performance degradation gradients. The entity state feature set is mapped to the spatial nodes of the vehicle's basic digital skeleton using a data fusion algorithm to generate a dynamic vehicle digital twin of the vehicle to be evaluated.

4. The dynamic refurbishment decision-making method for used cars aimed at maximizing ROI as described in claim 1, characterized in that, The generated vehicle condition evolution script includes: The state feature vector of the dynamic vehicle digital twin is mapped to the feature space of the pre-set vehicle fault knowledge graph to obtain the existing fault list and the potential risk prediction list. Based on the temporal information in the dynamic vehicle digital twin, causal chain deduction is performed on the existing fault list and potential risk prediction list to generate a vehicle condition evolution script describing the trajectory of vehicle status changes over time.

5. The dynamic refurbishment decision-making method for used cars aimed at maximizing ROI as described in claim 1, characterized in that, The generated candidate renovation schemes include: Market preference feature vectors and historical price decay curves of the same model are extracted from market data for the current time window to obtain market benchmark reference data. Calculate the feature distance between the vehicle condition evolution script and the market benchmark reference data to determine the target sales strategy; The system retrieves the renovation project storage space and filters renovation process items and budget parameters according to the target sales strategy to generate candidate renovation plans.

6. The dynamic refurbishment decision-making method for used cars aimed at maximizing ROI as described in claim 1, characterized in that, The generation of the virtual refurbished vehicle model includes: Analyze the refurbishment process items and component replacement indicators in the candidate refurbishment schemes to generate virtual vehicle condition correction parameters; The virtual vehicle condition correction parameters are mapped to the corresponding fault feature nodes in the dynamic vehicle digital twin to perform state feature value replacement and compensation calculations, generating local state features after refurbishment. By integrating the local state features after the renovation with the original inherent attribute data of the dynamic vehicle digital twin that are not affected by the renovation, a virtual renovated vehicle model is generated.

7. The dynamic refurbishment decision-making method for used cars aimed at maximizing ROI as described in claim 1, characterized in that, The generation of expected market reaction data, which includes a spectrum of expected investment returns, includes: By accessing the real-time transaction data stream of the used car trading platform, buyer browsing behavior characteristics and transaction price distribution characteristics are extracted to obtain dynamic market environment parameters; The trading probability and the corresponding price sensitivity coefficient are corrected using the dynamic market environment parameters to obtain the updated market simulation environment; The virtual refurbished vehicle model and corresponding refurbishment cost parameters are mapped to the updated market simulation environment for iterative calculation to generate expected market reaction data, including expected price range, sales speed probability distribution, and return on investment range.

8. The dynamic refurbishment decision-making method for used cars aimed at maximizing ROI as described in claim 1, characterized in that, The generated user-oriented decision control panel includes: Aggregate the expected market response data corresponding to all the candidate renovation schemes to construct a three-dimensional decision space with renovation cost, expected sales cycle, and expected revenue as coordinate axes; Calculate the return gradient that satisfies the preset conditions under each combination of coordinate points in the three-dimensional decision space, and draw the expected rate of return curve. The three-dimensional decision space and the expected rate of return curve are overlaid with a UI layer and bound to touch events to generate a decision control panel.

9. A dynamic ROI-maximizing decision-making method for used car refurbishment according to claim 1, characterized in that, The method further includes: In response to the user's instruction to select from the candidate renovation options based on the decision control panel, a target renovation option is determined; Collect the final transaction price and transaction cycle of the executed target renovation plan in real transaction scenarios to obtain real market feedback data; Calculate the difference between the actual market feedback data and the expected market reaction data to generate prediction deviation data; The source dimensions of the prediction deviation data are analyzed, and the reasoning logic of the market transaction behavior simulation logic and the vehicle condition evolution script is optimized and adjusted using the real market feedback data.

10. A dynamic refurbishment decision-making system for used cars aimed at maximizing ROI, characterized in that, The system includes: The digital twin construction module is used to acquire multi-source vehicle condition data of the used car to be evaluated and construct a dynamic digital twin of the vehicle to be evaluated. The vehicle condition evolution analysis module is used to perform time-series analysis and fault feature extraction on the dynamic vehicle digital twin, and generate a vehicle condition evolution script. The renovation plan generation module is used to perform supply and demand matching analysis between the vehicle condition evolution script and market data extracted from external data sources to generate candidate renovation plans. The virtual renovation simulation module is used to perform virtual renovation operations on the dynamic vehicle digital twin based on the candidate renovation scheme, and generate a virtual renovated vehicle model. The market return projection module is used to perform value assessment and liquidity projection on the virtual refurbished vehicle model using market transaction behavior simulation logic, and generate expected market reaction data containing the expected investment return rate spectrum. The decision-making interaction visualization module is used to construct a multi-dimensional decision space based on the expected market response data and to visualize and encapsulate it, generating a user-oriented decision control panel.

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