Second-hand vehicle residual value dynamic evaluation method and system and electronic equipment

Abstract

The invention provides a second-hand vehicle residual value dynamic evaluation method and system and electronic equipment, and relates to the technical field of vehicle evaluation and analysis. Comprising the following steps: respectively inputting features corresponding to a vehicle individual data chain, features corresponding to external environment data and adjusted weights into a random forest model, a gradient lifting decision model and a seasonal autoregression comprehensive moving average exogenous regression model in time sequence analysis; obtaining a first second-hand vehicle residual value evaluation result, a second second-hand vehicle residual value evaluation result and a third second-hand vehicle residual value evaluation result; performing weighted fusion on the first second-hand vehicle residual value evaluation result, the second second-hand vehicle residual value evaluation result and the third second-hand vehicle residual value evaluation result to obtain a second-hand vehicle comprehensive residual value dynamic evaluation result; and outputting a second-hand vehicle residual value dynamic evaluation report based on the second-hand vehicle comprehensive residual value dynamic evaluation result, and pushing the second-hand vehicle residual value dynamic evaluation report to the user side. According to the method, factors such as external environment fluctuation are updated in real time, so that the hysteresis of a traditional valuation system is avoided, and the timeliness of valuation is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle evaluation and analysis, and in particular to a method, system and electronic equipment for dynamically evaluating the residual value of a used car. Background Art

[0002] With the continuous expansion of the used car market, vehicle valuation has become particularly important in used car transactions, financial mortgages, insurance claims and other scenarios.

[0003] Traditional residual value assessment of used cars relies on limited static parameters, such as vehicle brand, model, age and mileage, etc. These assessment methods may provide certain reference value in a relatively stable market environment. However, in the face of the rapid changes and uncertainties in the current market environment, especially the sharp fluctuations in new car market prices, the ever-changing supply and demand relationship of used cars, and the intertwined influence of multiple external factors such as the macro-economy and consumer preferences, traditional methods seem to be unable to cope with the situation.

[0004] Therefore, a method, system and electronic equipment for dynamic evaluation of residual value of used cars are proposed. Summary of the invention

[0005] This manual provides a method, system and electronic equipment for dynamic evaluation of residual value of used cars, which avoids the lag of traditional valuation systems and ensures the timeliness of valuation by updating external environmental fluctuations and other factors in real time.

[0006] This specification provides a method for dynamic evaluation of residual value of used cars, including:

[0007] Obtain vehicle individual data link and external environment data;

[0008] Extracting features from the vehicle individual data chain and the external environment data respectively to obtain features corresponding to the vehicle individual data chain and features corresponding to the external environment data;

[0009] Determine a current environment fluctuation index based on the characteristics corresponding to the external environment data, and dynamically adjust the weight using the current environment fluctuation index;

[0010] Input the features corresponding to the vehicle individual data link, the features corresponding to the external environment data, and the adjusted weights into the random forest model, the gradient boosting decision model, and the seasonal autoregressive integrated moving average exogenous regression model in the time series analysis, respectively, to obtain the first second-hand car residual value assessment result, the second second-hand car residual value assessment result, and the third second-hand car residual value assessment result;

[0011] Performing weighted fusion on the first second-hand car residual value assessment result, the second second-hand car residual value assessment result, and the third second-hand car residual value assessment result to obtain a dynamic assessment result of a comprehensive residual value of a second-hand car;

[0012] Based on the dynamic evaluation result of the comprehensive residual value of the used car, a dynamic evaluation report of the residual value of the used car is output and pushed to the user end.

[0013] Optionally, determining the current environment fluctuation index based on the features corresponding to the external environment data includes:

[0014] For each variable, the moving window volatility is used to capture the short-term volatility characteristics of each variable;

[0015] The short-term fluctuation characteristics of each variable are integrated to obtain a vector of multiple standardized variables;

[0016] Based on the covariance matrix, a current environment fluctuation index is determined.

[0017] Optionally, for each variable, the short-term volatility characteristics of each variable are captured by using a moving window volatility, including:

[0018]

[0019] Among them, σ t represents the volatility at time t, X t-i is the change value at time ti, represents the mean value within the window period.

[0020] Optionally, the calculating of the vectors of multiple standardized variables based on the covariance matrix to obtain the current environmental fluctuation index includes:

[0021]

[0022] ∑ t = Cov(X t )

[0023] Among them, MVI t represents the current environmental volatility index, ω represents the weight vector of each variable, ω T Represents the weight vector matrix transposed value of each variable, ∑ t represents the covariance matrix, X t A vector representing multiple standardized variables.

[0024] Optionally, dynamically adjusting the weight using the current environment fluctuation index includes:

[0025] ω′ internal =ω internal *[1-f(MVI)]

[0026] ω′ external =ω external *f(MVI)

[0027]

[0028] Among them, ω′ internal represents the feature weight corresponding to the adjusted vehicle individual data link, ω internal represents the feature weight corresponding to the vehicle individual data link, f(MVI) represents the linear adjustment function based on the current environmental fluctuation index, ω′ external represents the feature weight corresponding to the adjusted external environment data, ω external It represents the feature weight corresponding to the external environment data, MVI represents the environment volatility index, and MVI represents the maximum value of the environment volatility index.

[0029] Optionally, the characteristics corresponding to the vehicle individual data link, the characteristics corresponding to the external environment data and the adjusted weights are respectively input into the seasonal autoregressive integrated moving average exogenous regression model in the time series analysis to obtain the third used car residual value assessment result, including:

[0030]

[0031] Among them, Y t It indicates the residual value evaluation result of the third used car. All represent constant parameters, X t Represents a vector of multiple standardized variables, ε t represents the error term.

[0032] Optionally, the weighted fusion of the first second-hand car residual value assessment result, the second second-hand car residual value assessment result, and the third second-hand car residual value assessment result to obtain a dynamic assessment result of a comprehensive residual value of a second-hand car includes:

[0033] P ffnal =W RF *P RF +W GBDT *P GBDT +W SARIMAX *P SARIMAX

[0034] Among them, P final Represents the dynamic evaluation result of the comprehensive residual value of used cars, PR F represents the residual value evaluation result of the first used car, P GBDT represents the residual value evaluation result of the second used car, P SARIMAX represents the residual value evaluation result of the third used car, W RF , W GBDT , W SARIMAX are all weighting coefficients.

[0035] This specification provides a used car residual value dynamic evaluation system, including:

[0036] Acquisition module, used to acquire vehicle individual data link and external environment data;

[0037] An extraction module, used to extract features from the vehicle individual data chain and the external environment data, respectively, to obtain features corresponding to the vehicle individual data chain and features corresponding to the external environment data;

[0038] An adjustment module, configured to determine a current environment fluctuation index based on the features corresponding to the external environment data, and dynamically adjust the weight using the current environment fluctuation index;

[0039] An input module, used to input the features corresponding to the vehicle individual data link, the features corresponding to the external environment data and the adjusted weights into a random forest model, a gradient boosting decision model, and a seasonal autoregressive integrated moving average exogenous regression model in time series analysis, respectively, to obtain a first second-hand car residual value assessment result, a second second-hand car residual value assessment result, and a third second-hand car residual value assessment result;

[0040] A fusion module, used for weighted fusion of the first second-hand car residual value evaluation result, the second second-hand car residual value evaluation result, and the third second-hand car residual value evaluation result to obtain a dynamic evaluation result of a comprehensive residual value of a second-hand car;

[0041] The output module is used to output a dynamic evaluation report on the residual value of a used car based on the dynamic evaluation result of the comprehensive residual value of the used car, and push it to the user end.

[0042] Optionally, the adjustment module includes:

[0043] For each variable, the moving window volatility is used to capture the short-term volatility characteristics of each variable;

[0044] The short-term fluctuation characteristics of each variable are integrated to obtain a vector of multiple standardized variables;

[0045] Based on the covariance matrix, a current environment fluctuation index is determined.

[0046] Optionally, for each variable, the short-term volatility characteristics of each variable are captured by using a moving window volatility, including:

[0047]

[0048] Among them, σ t represents the volatility at time t, X t-i is the change value at time ti, represents the mean value within the window period.

[0049] Optionally, the calculating of the vectors of multiple standardized variables based on the covariance matrix to obtain the current environmental fluctuation index includes:

[0050]

[0051] ∑ t = Cov(X t )

[0052] Among them, MVI t represents the current environmental volatility index, ω represents the weight vector of each variable, ω T Represents the weight vector matrix transposed value of each variable, ∑ t represents the covariance matrix, X t A vector representing multiple standardized variables.

[0053] Optionally, the adjustment module includes:

[0054] ω′ internal =ω internal *[1-f(MVI)]

[0055] ω′ external =ω external *f(MVI)

[0056]

[0057] Among them, ω′ internal represents the feature weight corresponding to the adjusted vehicle individual data link, ω internal represents the feature weight corresponding to the vehicle individual data link, f(MVI) represents the linear adjustment function based on the current environmental fluctuation index, ω′ external represents the feature weight corresponding to the adjusted external environment data, ω external It represents the feature weight corresponding to the external environment data, MVI represents the environment volatility index, and MVI represents the maximum value of the environment volatility index.

[0058] Optionally, the input module includes:

[0059]

[0060] Among them, Y t It indicates the residual value evaluation result of the third used car. All represent constant parameters, X t Represents a vector of multiple standardized variables, ε t represents the error term.

[0061] Optionally, the fusion module includes:

[0062] P ffnal=W RF *P RF +W GBDT *P GBDT +W SARIMAX *P SARIMAX

[0063] Among them, P final represents the dynamic evaluation result of the comprehensive residual value of used cars, P RF represents the residual value evaluation result of the first used car, P GBDT represents the residual value evaluation result of the second used car, P SARIMAX represents the residual value evaluation result of the third used car, W RF , W GBDT , W SARIMAX are all weighting coefficients.

[0064] This specification also provides an electronic device, wherein the electronic device includes:

[0065] processor; and,

[0066] A memory storing computer executable instructions, which when executed cause the processor to perform any of the above methods.

[0067] The present specification also provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, any of the above methods is implemented.

[0068] The present invention avoids the lag of the traditional valuation system and ensures the timeliness of the valuation by updating factors such as external environmental fluctuations in real time. At the same time, it makes full use of the individual historical data of the vehicle, such as usage history, maintenance records and accident claims records, to achieve a more personalized and accurate valuation, and better reflect the actual status and usage of the vehicle. In addition, the method incorporates external environmental factors into the valuation system by constructing an environmental fluctuation index, thereby enhancing the environmental sensitivity of the valuation. Finally, it performs automated valuation updates based on real-time data streams, which not only improves the efficiency and accuracy of the valuation, but also avoids potential problems caused by manual intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0070] Figure 1 A schematic diagram of the principle of a method for dynamically evaluating the residual value of a used car provided in an embodiment of this specification;

[0071] Figure 2 A schematic diagram of the implementation process of the dynamic evaluation of the residual value of a used car provided in the embodiments of this specification;

[0072] Figure 3 A schematic diagram of the architecture of a used car residual value dynamic evaluation system provided in the embodiments of this specification;

[0073] Figure 4 A structural diagram of a used car residual value dynamic evaluation system provided in an embodiment of this specification;

[0074] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification;

[0075] Figure 6 A schematic diagram of a computer-readable medium provided for an embodiment of this specification. DETAILED DESCRIPTION

[0076] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0077] The following is combined with Figure 1-6 The exemplary embodiments of the present invention are described more fully. However, the exemplary embodiments can be implemented in various forms, and it should not be understood that the present invention is limited to the embodiments set forth herein. On the contrary, providing these exemplary embodiments can make the present invention more comprehensive and complete, and it is more convenient to fully convey the inventive concept to those skilled in the art. The same reference numerals in the figures represent the same or similar elements, components or parts, and thus their repeated description will be omitted.

[0078] Under the premise of being consistent with the technical concept of the present invention, the features, structures, characteristics or other details described in a specific embodiment do not exclude that they can be combined in one or more other embodiments in a suitable manner.

[0079] In the description of specific embodiments, the features, structures, characteristics or other details described in the present invention are intended to enable those skilled in the art to fully understand the embodiments. However, it does not exclude that those skilled in the art can practice the technical solutions of the present invention without one or more of the specific features, structures, characteristics or other details.

[0080] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0081] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0082] The term "and / or" or "and / or" includes all combinations of any one or more of the associated listed items.

[0083] Figure 1 A schematic diagram of the principle of a method for dynamically evaluating the residual value of a used car provided in an embodiment of this specification, the method may include:

[0084] S110: Acquire vehicle individual data link and external environment data;

[0085] In the specific implementation of this specification, the vehicle individual data link includes the following: 1) basic data such as factory information, original configuration, VIN code (vehicle identification number) provided by the vehicle manufacturer; 2) vehicle repair and maintenance history records provided by authorized 4S stores, independent repair shops, and maintenance service centers through API integration, which include detailed information such as repair time, maintenance items, accessories used, and repair costs; 3) vehicle insurance purchase information, claim records, accident reporting information, etc. provided by insurance companies through API integration; 4) registration information, license plate number changes, annual inspection records, etc. provided by vehicle management departments; 5) vehicle transaction records and assessed values ​​provided by used car trading platforms, auction platforms, and financial companies through API integration; 6) real-time driving data provided by on-board Internet of Things devices is obtained through the vehicle information system (Telematics) or OBD-Ⅱ interface, such as vehicle mileage, fuel consumption, engine status, etc.; 7) vehicle information actively uploaded by the owner is obtained through a mobile phone APP or Web page, such as: vehicle mileage, etc. It is worth noting that the authenticity of the vehicle information uploaded by the owner can be verified through the docked third-party system.

[0086] External environment data include the following: 1) New car market data disclosed by automobile manufacturers or sales platforms, such as new car prices, sales, inventory, etc.; 2) Used car market data provided by used car trading platforms, dealers or auction websites, such as used car listing prices, transaction prices, supply-demand ratios, transaction volumes, etc.; 3) Through sentiment analysis of text data such as social media, news, and market comments, to extract market sentiment fluctuation data on specific brands or models, for example, when a certain model receives widespread attention due to an event, the price of the model may rise due to market heat; 4) Obtain government regulatory policies, tax policies, environmental policies, etc. on used car transactions through government websites. For example, policies restricting used car transactions in other places may reduce used car valuations; while policies encouraging used car transactions may increase valuations; 5) Obtain new car sales policies. If new car sales policies encourage consumers to buy new cars, it may reduce the demand and valuation of used cars; conversely, if new car sales policies restrict or increase the cost of purchasing new cars, it may stimulate the used car market and increase valuations.

[0087] All collected data is symmetrically encrypted (such as AES-256) and stored in a database or blockchain to ensure that even if the data is obtained by a third party, it cannot be directly read.

[0088] When data is transmitted between nodes, SSL / TLS encryption protocol is used to prevent data from being eavesdropped or tampered with during transmission. Different nodes (such as repair shops, insurance companies, vehicle management departments, etc.) have different access rights. Through the identity authentication mechanism, each node can only access or update specific data to ensure secure access to data.

[0089] S120: extracting features from the vehicle individual data chain and the external environment data respectively, to obtain features corresponding to the vehicle individual data chain and features corresponding to the external environment data;

[0090] In the specific implementation of this specification, Figure 2 As shown, by extracting the features of the above-mentioned vehicle individual data chain, a series of features that can comprehensively reflect the vehicle condition, history, usage and market value can be obtained.

[0091] The characteristics corresponding to the external environment data specifically include: 1) Characteristics of used car transaction policies: the degree of policy restrictions, which measures the degree of government restrictions on used car transactions, such as restrictions on cross-regional transactions, tax policies, etc.; the degree of policy encouragement, which measures the degree of government encouragement for used car transactions, such as subsidy policies, tax incentives, etc.; 2) Characteristics of new car sales policies: the cost of purchasing a new car, which reflects the purchase cost of the new car market, including price, taxes, etc.; new car sales incentives, which measure government incentives for new car sales, such as car purchase subsidies, loan discounts, etc. 3) Characteristics of used car supply: market supply, reflecting the overall supply situation in the used car market; supply change trend, measuring the changing trend of used car supply, such as increase, decrease, etc.; 4) Characteristics of consumer demand: consumer demand intensity, reflecting the degree of consumer demand for used cars; demand change trend, measuring the changing trend of consumer demand for used cars, such as increase, decrease, etc.; brand / model / configuration demand, reflecting consumer demand for specific brands, models or configurations; 5) Geographical location characteristics: regional economic development level, reflecting the economic development level of different regions, such as first-tier cities, second-tier cities, etc.; consumer purchasing power, measuring the purchasing power level of consumers in different regions; 6) Climate conditions characteristics: annual precipitation, reflecting the precipitation situation in the region, which has an impact on the corrosion and rust of the vehicle; annual average temperature, reflecting the temperature situation in the region, which has an impact on the use and maintenance of the vehicle; humidity level, reflecting the humidity situation in the region, which also has a certain impact on the use and maintenance of the vehicle.

[0092] By extracting features from the vehicle's individual data chain and external environment data, we can obtain a series of features that can reflect the factors affecting used car valuation. These features can be used to build a used car valuation prediction model.

[0093] S130: determining a current environment fluctuation index based on the features corresponding to the external environment data, and dynamically adjusting a weight using the current environment fluctuation index;

[0094] Optionally, determining the current environment fluctuation index based on the features corresponding to the external environment data includes:

[0095] For each variable, the moving window volatility is used to capture the short-term volatility characteristics of each variable;

[0096] The short-term fluctuation characteristics of each variable are integrated to obtain a vector of multiple standardized variables;

[0097] Based on the covariance matrix, a current environment fluctuation index is determined.

[0098] Optionally, for each variable, the short-term volatility characteristics of each variable are captured by using a moving window volatility, including:

[0099]

[0100] Among them, σ t represents the volatility at time t, X t-i is the change value at time ti, represents the mean value within the window period.

[0101] Optionally, the calculating of the vectors of multiple standardized variables based on the covariance matrix to obtain the current environmental fluctuation index includes:

[0102]

[0103] ∑ t = Cov(X t )

[0104] Among them, MVI t represents the current environmental volatility index, ω represents the weight vector of each variable, ω T Represents the weight vector matrix transposed value of each variable, ∑ t represents the covariance matrix, X t A vector representing multiple standardized variables.

[0105] In a specific embodiment of the present specification, the environmental volatility index is an indicator used to measure the degree of change in a specific environment or market conditions. It can set different update frequencies according to different data types and importance to ensure the accuracy and timeliness of the data. For key data, such as trading volume and other important indicators that directly affect market or environmental fluctuations, a higher update frequency (such as daily) is set. The real-time nature of these data is essential for capturing market dynamics and formulating timely strategies. For non-critical but influential indicators such as sentiment data, it is more appropriate to set a lower update frequency (such as monthly). Sentiment data generally involves the public's views and attitudes towards the market, policies or specific events. Although these data are important, they change relatively slowly, and frequent updates may bring unnecessary noise. Therefore, updating sentiment data through monthly surveys or analyses can more accurately capture trends and changes in public sentiment.

[0106] Optionally, dynamically adjusting the weight using the current environment fluctuation index includes:

[0107] ω′ internal =ω internal *[1-f(MVI)]

[0108] ω′ external =ω external *f(MVI)

[0109]

[0110] Among them, ω′internal represents the feature weight corresponding to the adjusted vehicle individual data link, ω internal represents the feature weight corresponding to the vehicle individual data link, f(MVI) represents the linear adjustment function based on the current environmental fluctuation index, ω′ external represents the feature weight corresponding to the adjusted external environment data, ω external It represents the feature weight corresponding to the external environment data, MVI represents the environment volatility index, and MVI represents the maximum value of the environment volatility index.

[0111] S140: Input the features corresponding to the vehicle individual data link, the features corresponding to the external environment data, and the adjusted weights into the random forest model, the gradient boosting decision model, and the seasonal autoregressive integrated moving average exogenous regression model in the time series analysis, respectively, to obtain the first second-hand car residual value assessment result, the second second-hand car residual value assessment result, and the third second-hand car residual value assessment result;

[0112] Optionally, the characteristics corresponding to the vehicle individual data link, the characteristics corresponding to the external environment data and the adjusted weights are respectively input into the seasonal autoregressive integrated moving average exogenous regression model in the time series analysis to obtain the third used car residual value assessment result, including:

[0113]

[0114] Among them, Y t It indicates the residual value evaluation result of the third used car. All represent constant parameters, X t Represents a vector of multiple standardized variables, ε t represents the error term.

[0115] In a specific implementation of the present specification, the gradient boosting decision model includes a GBDT model, and the seasonal autoregressive integrated moving average exogenous regression model in time series analysis includes a SARIMAX model.

[0116] Random Forest Model: Generates vehicle valuation through nonlinear regression based on historical vehicle data and environmental fluctuation data through the integration method of multiple decision trees.

[0117] Gradient Boosted Decision Tree (GBDT): Through multiple rounds of iterations, it captures the complex impact of environmental fluctuations and vehicle individual data on valuation.

[0118] Time series model: Forecast time series data and short-term environmental fluctuations, and generate valuations based on the historical prices of vehicles and the characteristics of exogenous environmental variables.

[0119] S150: performing weighted fusion on the first second-hand car residual value evaluation result, the second second-hand car residual value evaluation result, and the third second-hand car residual value evaluation result to obtain a dynamic evaluation result of a comprehensive residual value of a second-hand car;

[0120] Optionally, the weighted fusion of the first second-hand car residual value assessment result, the second second-hand car residual value assessment result, and the third second-hand car residual value assessment result to obtain a dynamic assessment result of a comprehensive residual value of a second-hand car includes:

[0121] P final =W RF *P RF +W GBDT *P GBDT +W SARIMAX *P SARIMAX

[0122] Among them, P final represents the dynamic evaluation result of the comprehensive residual value of used cars, P RF represents the residual value evaluation result of the first used car, P GBDT represents the residual value evaluation result of the second used car, P SARIMAX represents the residual value evaluation result of the third used car, W RF , W GBDT , W SARIMAX are all weighting coefficients.

[0123] S160: Outputting a used car residual value dynamic evaluation report based on the used car comprehensive residual value dynamic evaluation result, and pushing it to the user end.

[0124] In the specific implementation of this specification, Figure 3 As shown, based on the market analysis results, provide suggestions such as the best time to sell and recommended sales channels. According to the current status of the vehicle, provide maintenance and maintenance suggestions to extend the service life and increase the value of the vehicle. Use professional report generation tools to integrate all information into easy-to-understand, visually friendly PDF or HTML format reports to ensure that the report content is accurate, the logic is clear, and the charts are intuitive. Use the API interface to achieve real-time data synchronization with the intelligent valuation engine to ensure that the valuation information in the report is always up to date. After the user logs in to the system, the latest valuation report is automatically displayed, and the option of downloading or viewing online is provided. Support third-party applications to obtain the latest valuation report through the API interface, which is convenient for integration into other management systems or service platforms of users. Notify users that the valuation report has been updated through email, SMS or APP push notifications, and provide quick access links. Set up user feedback channels to collect users' opinions and suggestions on report quality, update frequency, and push methods. Provide online customer service or telephone support to answer questions encountered by users in the process of reading reports and enhance user satisfaction.

[0125] The present invention avoids the lag of the traditional valuation system and ensures the timeliness of the valuation by updating factors such as external environmental fluctuations in real time. At the same time, it makes full use of the individual historical data of the vehicle, such as usage history, maintenance records and accident claims records, to achieve a more personalized and accurate valuation, and better reflect the actual status and usage of the vehicle. In addition, the method incorporates external environmental factors into the valuation system by constructing an environmental fluctuation index, thereby enhancing the environmental sensitivity of the valuation. Finally, it performs automated valuation updates based on real-time data streams, which not only improves the efficiency and accuracy of the valuation, but also avoids potential problems caused by manual intervention.

[0126] Figure 4 A schematic diagram of a system for dynamically evaluating residual value of a used car provided in an embodiment of this specification, the system may include:

[0127] The acquisition module 10 is used to acquire the vehicle individual data link and external environment data;

[0128] An extraction module 20, used to extract features from the vehicle individual data chain and the external environment data, respectively, to obtain features corresponding to the vehicle individual data chain and features corresponding to the external environment data;

[0129] An adjustment module 30, configured to determine a current environment fluctuation index based on the features corresponding to the external environment data, and dynamically adjust the weight using the current environment fluctuation index;

[0130] An input module 40 is used to input the features corresponding to the vehicle individual data link, the features corresponding to the external environment data, and the adjusted weights into a random forest model, a gradient boosting decision model, and a seasonal autoregressive integrated moving average exogenous regression model in time series analysis, respectively, to obtain a first second-hand car residual value assessment result, a second second-hand car residual value assessment result, and a third second-hand car residual value assessment result;

[0131] A fusion module 50 is used to perform weighted fusion on the first second-hand car residual value evaluation result, the second second-hand car residual value evaluation result, and the third second-hand car residual value evaluation result to obtain a dynamic evaluation result of a comprehensive residual value of a second-hand car;

[0132] The output module 60 is used to output a dynamic evaluation report of the residual value of a used car based on the dynamic evaluation result of the comprehensive residual value of the used car, and push it to the user end.

[0133] Optionally, the adjustment module 30 includes:

[0134] For each variable, the moving window volatility is used to capture the short-term volatility characteristics of each variable;

[0135] The short-term fluctuation characteristics of each variable are integrated to obtain a vector of multiple standardized variables;

[0136] Based on the covariance matrix, a current environment fluctuation index is determined.

[0137] Optionally, for each variable, the short-term volatility characteristics of each variable are captured by using a moving window volatility, including:

[0138]

[0139] Among them, σ t represents the volatility at time t, X t-i is the change value at time ti, represents the mean value within the window period.

[0140] Optionally, the calculating of the vectors of multiple standardized variables based on the covariance matrix to obtain the current environmental fluctuation index includes:

[0141]

[0142] ∑ t = Cov(X t )

[0143] Among them, MVI t represents the current environmental volatility index, ω represents the weight vector of each variable, ω T Represents the weight vector matrix transposed value of each variable, ∑ t represents the covariance matrix, X t A vector representing multiple standardized variables.

[0144] Optionally, the adjustment module 30 includes:

[0145] ω′ internal =ω internal *[1-f(MVI)]

[0146] ω′ external =ω external *f(MVI)

[0147]

[0148] Among them, ω′ internal represents the feature weight corresponding to the adjusted vehicle individual data link, ω internal represents the feature weight corresponding to the vehicle individual data link, f(MVI) represents the linear adjustment function based on the current environmental fluctuation index, ω′ external represents the feature weight corresponding to the adjusted external environment data, ω external It represents the feature weight corresponding to the external environment data, MVI represents the environment volatility index, and MVI represents the maximum value of the environment volatility index.

[0149] Optionally, the input module 40 includes:

[0150]

[0151] Among them, Y t It indicates the residual value evaluation result of the third used car. All represent constant parameters, X t Represents a vector of multiple standardized variables, ε t represents the error term.

[0152] Optionally, the fusion module 50 includes:

[0153] P final =W RF *P RF +W GBDT *P GBDT +W SARIMAX *P SARIMAX

[0154] Among them, P final represents the dynamic evaluation result of the comprehensive residual value of used cars, P RF represents the residual value evaluation result of the first used car, P GBDT represents the residual value evaluation result of the second used car, P SARIMAX represents the residual value evaluation result of the third used car, W RF , W GBDT , W SARIMAX are all weighting coefficients.

[0155] The functions of the system of the embodiment of the present invention have been described in the above method embodiment, so for details not fully described in this embodiment, please refer to the relevant description in the above embodiment, and no further description will be given here.

[0156] Based on the same inventive concept, an embodiment of this specification also provides an electronic device.

[0157] The following describes an electronic device embodiment of the present invention, which can be regarded as a specific physical implementation of the method and system embodiments of the present invention. The details described in the electronic device embodiment of the present invention should be regarded as a supplement to the above method or device embodiments; details not disclosed in the electronic device embodiment of the present invention can be implemented with reference to the above method or device embodiments.

[0158] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. Figure 5 The electronic device 300 according to the embodiment of the present invention is described. Figure 5The electronic device 300 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0159] like Figure 5 As shown, the electronic device 300 is in the form of a general computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, a bus 330 connecting different system components (including the storage unit 320 and the processing unit 310), a display unit 340, etc.

[0160] The storage unit stores program codes, which can be executed by the processing unit 310, so that the processing unit 310 performs the steps according to various exemplary embodiments of the present invention described in the above processing method section of this specification. For example, the processing unit 310 can perform the following steps: Figure 1 Steps shown.

[0161] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 3201 and / or a cache memory unit 3202 , and may further include a read-only memory unit (ROM) 3203 .

[0162] The storage unit 320 may also include a program / utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0163] Bus 330 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0164] The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable viewers to interact with the electronic device 300, and / or any device that enables the electronic device 300 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 350. Furthermore, the electronic device 300 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 360. The network adapter 360 may communicate with other modules of the electronic device 300 through the bus 330. It should be understood that although Figure 5 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0165] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present invention can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation method of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.) or on a network, including a number of instructions to enable a computing device (which can be a personal computer, server, or network device, etc.) to execute the above method according to the present invention. When the computer program is executed by a data processing device, the computer-readable medium can implement the above method of the present invention, that is: Figure 1 The method shown.

[0166] Figure 6 A schematic diagram of a computer-readable medium provided for an embodiment of this specification.

[0167] accomplish Figure 1 The computer program of the method shown can be stored on one or more computer readable media. The computer readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0168] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0169] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the viewer computing device, partially on the viewer device, as a stand-alone software package, partially on the viewer computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the viewer computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0170] In summary, the present invention can be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that general data processing devices such as microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0171] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the present invention is not inherently related to any specific computer, virtual device or electronic device, and various general devices can also implement the present invention. The above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0172] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0173] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A dynamic evaluation method for residual value of a used car, characterized in that: include: Obtain vehicle individual data link and external environment data; Extracting features from the vehicle individual data chain and the external environment data respectively to obtain features corresponding to the vehicle individual data chain and features corresponding to the external environment data; Determine a current environment fluctuation index based on the characteristics corresponding to the external environment data, and dynamically adjust the weight using the current environment fluctuation index; Input the features corresponding to the vehicle individual data link, the features corresponding to the external environment data, and the adjusted weights into the random forest model, the gradient boosting decision model, and the seasonal autoregressive integrated moving average exogenous regression model in the time series analysis, respectively, to obtain the first second-hand car residual value assessment result, the second second-hand car residual value assessment result, and the third second-hand car residual value assessment result; Performing weighted fusion on the first second-hand car residual value assessment result, the second second-hand car residual value assessment result, and the third second-hand car residual value assessment result to obtain a dynamic assessment result of a comprehensive residual value of a second-hand car; Based on the dynamic evaluation result of the comprehensive residual value of the used car, a dynamic evaluation report of the residual value of the used car is output and pushed to the user end.

2. The method for dynamic evaluation of residual value of a used car according to claim 1, characterized in that: The determining of the current environment fluctuation index based on the features corresponding to the external environment data includes: For each variable, the moving window volatility is used to capture the short-term volatility characteristics of each variable; The short-term fluctuation characteristics of each variable are integrated to obtain a vector of multiple standardized variables; Based on the covariance matrix, a current environment fluctuation index is determined.

3. The method for dynamic evaluation of residual value of a used car according to claim 2, characterized in that: For each variable, the moving window volatility is used to capture the short-term volatility characteristics of each variable, including: Among them, σ t represents the volatility at time t, X t-i is the change value at time ti, represents the mean value within the window period.

4. The method for dynamic evaluation of residual value of a used car according to claim 3, characterized in that: The calculation of the vectors of multiple standardized variables based on the covariance matrix to obtain the current environmental fluctuation index includes: Among them, MVI t represents the current environmental volatility index, ω represents the weight vector of each variable, ω T Represents the weight vector matrix transposed value of each variable, ∑ t represents the covariance matrix, X r A vector representing multiple standardized variables.

5. The method for dynamic evaluation of residual value of a used car according to claim 4, characterized in that: The dynamically adjusting the weight using the current environmental fluctuation index includes: ω′ internal =ω internal *[1-f(MVI)] ω′ external =ω external *f(MVI) Among them, ω′ internal represents the feature weight corresponding to the adjusted vehicle individual data link, ω internal represents the feature weight corresponding to the vehicle individual data link, f(MVI) represents the linear adjustment function based on the current environmental fluctuation index, ω′ external represents the feature weight corresponding to the adjusted external environment data, ω external It represents the feature weight corresponding to the external environment data, MVI represents the environment fluctuation index, and MVI represents the maximum value of the environment fluctuation index.

6. The method for dynamic evaluation of residual value of a used car according to claim 5, characterized in that: The characteristics corresponding to the vehicle individual data link, the characteristics corresponding to the external environment data and the adjusted weights are respectively input into the seasonal autoregressive integrated moving average exogenous regression model in the time series analysis to obtain the third second-hand car residual value assessment result, including: Among them, Y t represents the residual value evaluation result of the third second-hand car, c, ...、 θ1, ..., θ q All represent constant parameters, X t Represents a vector of multiple standardized variables, ε t represents the error term.

7. The method for dynamic evaluation of residual value of a used car according to claim 6, characterized in that: The weighted fusion of the first second-hand car residual value assessment result, the second second-hand car residual value assessment result, and the third second-hand car residual value assessment result to obtain a dynamic assessment result of a comprehensive residual value of a second-hand car includes: P final =W RF *P RF +W GBDT *P GBDT +W sARIMAX *P SARIMAX Among them, P final represents the dynamic evaluation result of the comprehensive residual value of used cars, P RF represents the residual value evaluation result of the first used car, P GBDT represents the residual value evaluation result of the second used car, P SARIMAX represents the residual value evaluation result of the third used car, W RF , W GBDT , W SARIMAX are all weighting coefficients.

8. A dynamic evaluation system for residual value of used cars, characterized in that: include: Acquisition module, used to acquire vehicle individual data link and external environment data; An extraction module, used to extract features from the vehicle individual data chain and the external environment data, respectively, to obtain features corresponding to the vehicle individual data chain and features corresponding to the external environment data; An adjustment module, configured to determine a current environment fluctuation index based on the features corresponding to the external environment data, and dynamically adjust the weight using the current environment fluctuation index; An input module, used to input the features corresponding to the vehicle individual data link, the features corresponding to the external environment data and the adjusted weights into a random forest model, a gradient boosting decision model, and a seasonal autoregressive integrated moving average exogenous regression model in time series analysis, respectively, to obtain a first second-hand car residual value assessment result, a second second-hand car residual value assessment result, and a third second-hand car residual value assessment result; A fusion module, used for weighted fusion of the first second-hand car residual value evaluation result, the second second-hand car residual value evaluation result, and the third second-hand car residual value evaluation result to obtain a dynamic evaluation result of a comprehensive residual value of a second-hand car; The output module is used to output a dynamic evaluation report on the residual value of a used car based on the dynamic evaluation result of the comprehensive residual value of the used car, and push it to the user end.

9. An electronic device, wherein: The electronic device includes: processor; and, A memory storing computer executable instructions which, when executed, cause the processor to perform a method according to any one of claims 1-7.

10. A computer-readable storage medium, wherein: The computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method of any one of claims 1 to 7 is implemented.

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