CN115293825A - Method and device for determining loss cost of second-hand vehicle - Google Patents
Method and device for determining loss cost of second-hand vehicle Download PDFInfo
- Publication number
- CN115293825A CN115293825A CN202211219408.9A CN202211219408A CN115293825A CN 115293825 A CN115293825 A CN 115293825A CN 202211219408 A CN202211219408 A CN 202211219408A CN 115293825 A CN115293825 A CN 115293825A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- cost
- loss
- component
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000003745 diagnosis Methods 0.000 claims abstract description 59
- 238000012423 maintenance Methods 0.000 claims abstract description 35
- 238000012545 processing Methods 0.000 claims abstract description 32
- 238000003860 storage Methods 0.000 claims description 20
- 238000004590 computer program Methods 0.000 claims description 8
- 238000013077 scoring method Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 238000010586 diagram Methods 0.000 description 18
- 230000008569 process Effects 0.000 description 12
- 238000004891 communication Methods 0.000 description 10
- 238000001514 detection method Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 8
- 238000011156 evaluation Methods 0.000 description 7
- 230000003287 optical effect Effects 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 230000003068 static effect Effects 0.000 description 4
- 238000005520 cutting process Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 230000002093 peripheral effect Effects 0.000 description 3
- 230000008439 repair process Effects 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 239000002131 composite material Substances 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001902 propagating effect Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000003973 paint Substances 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000002791 soaking Methods 0.000 description 1
- 238000003892 spreading Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000011179 visual inspection Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
Landscapes
- Business, Economics & Management (AREA)
- Development Economics (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The disclosure provides a used vehicle loss cost determination method and device. The method comprises the following steps: acquiring vehicle-mounted automatic diagnosis system data of a second-hand vehicle; according to preset weight, carrying out weighting processing on data of a target vehicle-mounted automatic diagnosis system to obtain loss indexes of a plurality of components; determining whether each component needs to be replaced; determining a replacement cost of a first component requiring replacement; determining a depreciation cost of the second component that does not require replacement; and determining the loss cost of the second-hand vehicle according to the loss cost and the replacement cost. According to the method and the device, various data of the loss cost can be easily understood by non-professional persons, the loss condition of the second-hand car can be reflected more objectively, a more powerful price certificate is provided for a purchaser, the opacity of information is reduced, the purchaser can know the maintenance condition of the car, and the dependence on the foreign existing model is reduced.
Description
Technical Field
The disclosure relates to the technical field of computers, in particular to a method and a device for determining loss cost of a second-hand vehicle.
Background
The new car market in China enters the saturated slow-moving stage from high-speed growth in 2015, and the composite growth rate is only 1.7% in seven years to 2021, which is far lower than the second-hand car market (9.3%). Referring to the development tracks of major automobile countries such as the United states and the like, and the strong boosting in the policy aspect, the second-hand car is becoming a new growth point of the domestic automobile circulation market. According to statistics, new cars can be replaced after the average 3-4 years by new car users, the trading volume of second-hand cars in China reaches 1758 ten thousands in 2021, the average price is calculated by 10 thousands, and a trillion-level market with huge development potential is formed.
In the past two decades, the trading volume of the second-hand vehicles in China is doubled from 37 ten thousand in 2001 to nearly 50 times, and reaches 1758 ten thousand in 2021. Compared with the new car market, the second-hand car market is more energetic and has greater potential. A large amount of capital is attracted to the market before and after 2015, an e-commerce trading platform develops force comprehensively, the popularity of second-hand vehicles and e-commerce is greatly improved, the second-hand vehicles in China enter a developing express way, and the composite growth rate reaches 9.3% in seven years to 2021.
However, the market is still in the adjustment period at present, the system is not complete enough, the information is not transparent, the specificity of the second-hand vehicle is one-vehicle-condition and one-price, and the industry evaluation pricing standard and system are not accurate enough. The basic pricing depends on the evaluation of fire-blister-major accident vehicle detection standards by referring to a foreign KBB (Kelley Blue Book) model and the national standard, however, the static data cannot truly reflect the actual value of the vehicle. In the used-vehicle assessment process, as market experience cannot be clearly quantified, moral risks are easily incurred, and particularly when a dealer is in an alliance with a vehicle detection and assessment institution, consumers are in a serious transaction opaque situation. Most enterprises choose to adopt a KBB assessment mode of the United states head second-hand car assessment enterprise, and the KBB emphasizes static information, so that the method has the advantages of providing basic reference information, but cannot reflect factors such as a natural environment and driving habits of automobile driving, and cannot completely reflect real vehicle conditions.
The speaking rights of the buyer and the seller are not equal, the buyer cannot easily understand the professional terms in negotiation, on one hand, the fair price cannot be obtained, on the other hand, the vehicle purchasing period is increased, and the buyer and the seller are not good. The used-car industry lacks a set of pricing evaluation standards and system for evaluating pricing agreed by both buyers and sellers.
The information disclosed in this background section is only for enhancement of understanding of the general background of the application and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The embodiment of the disclosure provides a used vehicle loss cost determination method and device, which can determine loss conditions of each component in multiple aspects by using OBD data, so that loss costs of each component can be obtained based on actual meanings reflected by the OBD data and loss conditions of the components reflected by the OBD data, and further loss costs of the used vehicle can be determined based on replacement costs and depreciation costs of the components, and each item of data of the loss costs is easy to understand by non-professional persons, and the loss conditions of the used vehicle can be reflected more objectively, so that a more powerful price certificate is provided for a purchaser, the information opacity is reduced, the purchaser can know the maintenance conditions of the vehicle, and the dependence on the existing foreign models is reduced.
In a first aspect of the embodiments of the present disclosure, a method for determining a used vehicle loss cost is provided, including: acquiring vehicle-mounted automatic diagnosis system data of a second-hand vehicle; according to preset weight, carrying out weighting processing on target vehicle-mounted automatic diagnosis system data corresponding to a plurality of components of the used vehicle to obtain loss indexes of the components; determining whether various components of the used vehicle need to be replaced; determining a replacement cost of a first component requiring replacement; determining a depreciation cost for a second component that does not require replacement based on the loss index or depreciation index for the second component; and determining the loss cost of the second-hand car according to the loss cost and the replacement cost.
According to an embodiment of the present disclosure, the method further comprises: screening the target vehicle-mounted automatic diagnosis system data from the plurality of vehicle-mounted automatic diagnosis system data according to the relationship between the vehicle-mounted automatic diagnosis system data and the driving habits, the maintenance conditions and the vehicle appearance of the used vehicle; and determining the weight of the target vehicle-mounted automatic diagnosis system data according to an expert scoring method.
According to the embodiment of the disclosure, weighting target vehicle-mounted automatic diagnosis system data corresponding to a plurality of components of the used vehicle according to preset weight to obtain loss indexes of the components comprises: according to the standard range of the target vehicle-mounted automatic diagnosis system data, carrying out normalization processing on the target vehicle-mounted automatic diagnosis system data to obtain a target vehicle-mounted automatic diagnosis system index; and according to the weight, weighting the target vehicle-mounted automatic diagnosis system index to obtain the loss indexes of the plurality of components.
According to an embodiment of the present disclosure, the method further comprises: determining the depreciation index d according to the formula d = part usage/part expected life, wherein the part usage is a used age or a used mileage of the part and the part expected life is an expected used age or an expected used mileage of the part.
According to an embodiment of the present disclosure, determining a depreciation cost of a second component that does not require replacement based on the loss or depreciation index of the second component includes: determining a depreciation cost of the second component based on a market price of the second component and a maximum of the loss index or the depreciation index.
According to the embodiment of the disclosure, determining the loss cost of the used vehicle according to the loss cost and the replacement cost comprises: according to the formula E =
Determining a loss cost E for the used car, wherein,
in the ith partThe cost of replacement in the case of a piece requiring replacement,
for depreciation costs in the case where the ith component does not need to be replaced,
=
x d, wherein d is the depreciation index,
for the market price of the ith part,
for the loss cost in the case where the ith component does not need to be replaced,
x l, where l is the loss exponent.
According to an embodiment of the present disclosure, the method further comprises: acquiring the estimated price of the second-hand vehicle; and obtaining the transaction price of the used vehicle according to the estimated price of the used vehicle and the loss cost of the used vehicle.
In a second aspect of the embodiments of the present disclosure, there is provided a used vehicle loss cost determination apparatus, including: the data acquisition module is used for acquiring vehicle-mounted automatic diagnosis system data of the second-hand vehicle; the loss index acquisition module is used for weighting target vehicle-mounted automatic diagnosis system data corresponding to a plurality of components of the used vehicle according to preset weight to obtain loss indexes of the components; a replacement determination module for determining whether components of the used vehicle need to be replaced; a replacement cost determination module for determining a replacement cost of a first component to be replaced; the depreciation cost determination module is used for determining depreciation cost of the second component according to the loss index or depreciation index of the second component which does not need to be replaced; and the loss cost determining module is used for determining the loss cost of the second-hand car according to the breaking cost and the replacement cost.
According to an embodiment of the present disclosure, the apparatus is further configured to: screening the target vehicle-mounted automatic diagnosis system data from the plurality of vehicle-mounted automatic diagnosis system data according to the relationship between the vehicle-mounted automatic diagnosis system data and the driving habits, the maintenance conditions and the vehicle appearance of the second-hand vehicle; and determining the weight of the target vehicle-mounted automatic diagnosis system data according to an expert scoring method.
According to an embodiment of the disclosure, the loss exponent acquisition module is further configured to: according to the standard range of the target vehicle-mounted automatic diagnosis system data, carrying out normalization processing on the target vehicle-mounted automatic diagnosis system data to obtain a target vehicle-mounted automatic diagnosis system index; and according to the weight, weighting the target vehicle-mounted automatic diagnosis system index to obtain the loss indexes of the plurality of components.
According to an embodiment of the present disclosure, the apparatus is further configured to: determining the depreciation index d according to the formula d = part usage/part expected life, wherein the part usage is a used age or a used mileage of the part and the part expected life is an expected used age or an expected used mileage of the part.
According to an embodiment of the disclosure, the depreciation cost determination module is further configured to: determining a depreciation cost of the second component based on a market price of the second component and a maximum of the loss index or the depreciation index.
According to an embodiment of the disclosure, the loss cost determination module is further configured to: according to the formula E =
Determining a cost of depletion, E, of the used vehicle, wherein,
for replacement costs in the case where the ith component needs to be replaced,
for depreciation costs in the case where the ith component does not need to be replaced,
=
x d, wherein d is the depreciation index,
for the market price of the ith part,
for the loss cost in the case where the ith component does not need to be replaced,
x l, where l is the loss exponent.
According to an embodiment of the present disclosure, the apparatus is further configured to: acquiring the estimated price of the second-hand vehicle; and obtaining the trading price of the used car according to the estimated price of the used car and the loss cost of the used car.
In a third aspect of the disclosed embodiments, there is provided an apparatus comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions, when executed by a processor, implement the above method.
Drawings
Fig. 1 schematically illustrates a flow chart of a second-hand vehicle loss cost determination method according to an embodiment of the present disclosure;
fig. 2 schematically illustrates a diagram of a second-hand vehicle loss exponent according to an embodiment of the disclosure;
FIG. 3 schematically illustrates a graph of target on-board automatic diagnostic system data according to an embodiment of the present disclosure;
fig. 4 schematically illustrates a flow chart for determining used vehicle loss cost according to an embodiment of the present disclosure;
fig. 5 is a graph schematically illustrating essential information of a used vehicle according to an embodiment of the present disclosure;
FIG. 6 is a chart that exemplarily illustrates replacement costs of a first component of an embodiment of the present disclosure;
FIG. 7 schematically illustrates a graph of the depreciation cost of a second component of an embodiment of the disclosure;
fig. 8 schematically illustrates a block diagram of a used vehicle loss cost determination apparatus according to an embodiment of the present disclosure;
FIG. 9 is a block diagram illustrating an electronic device in accordance with an exemplary embodiment;
fig. 10 is a block diagram illustrating a used vehicle loss cost determination device according to an exemplary embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The terms "first," "second," "third," "fourth," and the like in the description and claims of this disclosure and in the above-described drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in other sequences than those illustrated or described herein.
It should be understood that, in various embodiments of the present disclosure, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
It should be understood that in the present disclosure, "including" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present disclosure, "plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that all three of A, B, C comprise, "comprises A, B or C" means that one of three of A, B, C is comprised, "comprises A, B and/or C" means that any 1 or any 2 or 3 of the three of A, B, C is comprised.
It should be understood that in this disclosure, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, from which B can be determined. Determining B from a does not mean determining B from a alone, but may be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, "if" can be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection", depending on context.
The technical solution of the present disclosure is explained in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
The used vehicle market has not yet formed a systematic and complete used vehicle assessment pricing mechanism. Moreover, the OBD dynamic data mainly used for automobile maintenance can reflect more real automobile conditions, is not fully utilized, and is beneficial to providing more objective automobile condition information by utilizing the OBD data, so that buyers and sellers can win-win.
Aiming at the characteristic of one vehicle condition of a second-hand vehicle, the conventional detection mainly focuses on the vehicle with fire and water bubble accidents, and the vehicle condition is detected by adopting methods such as ruler measurement, visual inspection, a paint film instrument and the like. In fact, the On Board Diagnostics (OBD) data can more effectively detect the operation parameters of the systems of the automobile, such as the information of the centralized control lock. OBD data for about millions of vehicles per day are detected and recorded, but this data currently does not play a significant role in the estimated pricing of used vehicles.
The method can mine the deep value of the OBD data, endow the parameter with practical significance, and try to re-evaluate the used vehicle condition from constructing the used vehicle loss index. On the basis, theoretical loss and practical loss are fully considered, the second-hand car accessories are used as main objects, a second-hand car potential cost estimation model is built, and a larger bargaining space is strived for consumers.
Specifically, the method deeply excavates the value of the OBD data of the vehicle-mounted automatic diagnosis system, and quantifies the main concern in the used vehicle transaction process. And constructing a new vehicle condition evaluation system by taking the second-hand vehicle loss index as a main body. The basic logic is as follows: based on the vehicle valuation and price quotation generated by a Kelley Blue Book (hereinafter, abbreviated as 'KBB') model and three-item detection, the additional value generated by additional information is focused. For example, theoretical loss and actual loss are fully considered, the potential loss condition of the vehicle is hooked with the transaction price, a potential cost estimation model of the used vehicle is constructed, the loss cost of the used vehicle is calculated, meanwhile, data are converted, a set of vehicle condition description and value estimation system of the used vehicle, which is understood by consumers, is constructed, the driving and maintenance habits of the vehicle owners are reflected, a clearer bargaining space is provided for the consumers, doubts can be eliminated as soon as possible, and the successful transaction of buyers and sellers is facilitated.
Fig. 1 schematically illustrates a flowchart of a second-hand vehicle loss cost determination method according to an embodiment of the present disclosure, where as shown in fig. 1, the method includes:
s1, acquiring vehicle-mounted automatic diagnosis system data of a second-hand vehicle;
s2, according to a preset weight, carrying out weighting processing on target vehicle-mounted automatic diagnosis system data corresponding to a plurality of components of the second-hand vehicle to obtain loss indexes of the components;
s3, determining whether each component of the second-hand vehicle needs to be replaced;
s4, determining the replacement cost of the first component needing to be replaced;
s5, determining the depreciation cost of the second component according to the loss index or depreciation index of the second component which does not need to be replaced;
and S6, determining the loss cost of the second-hand vehicle according to the loss cost and the replacement cost.
According to the embodiment of the present disclosure, in step S1, raw data, that is, on-board diagnostics (OBD) data of the used vehicle, which is a great variety of OBD data, and not every OBD data is related to the evaluation of the value of the used vehicle, may be acquired through an OBD interface, so that the OBD data related to the evaluation of the value of the used vehicle may be determined from tens of thousands of OBD data, for example, the OBD data related to the loss of a plurality of components of the used vehicle may be determined from tens of thousands of OBD data. The loss of each component is generally related to the driving habits, maintenance and vehicle appearance of the used vehicle, so that the OBD data related to the value evaluation of the used vehicle can be screened out from tens of thousands of OBD data based on the correlation between the OBD data and the driving habits, maintenance and vehicle appearance of the used vehicle.
According to an embodiment of the present disclosure, the method further comprises: screening the target vehicle-mounted automatic diagnosis system data from the plurality of vehicle-mounted automatic diagnosis system data according to the relationship between the vehicle-mounted automatic diagnosis system data and the driving habits, the maintenance conditions and the vehicle appearance of the used vehicle; and determining the weight of the target vehicle-mounted automatic diagnosis system data according to an expert scoring method.
According to the embodiment of the disclosure, the OBD data can be selected as the target on-board automatic diagnosis system data, namely the target OBD data, by comprehensively considering the driving habits of historical owners of used cars, the historical maintenance condition of the vehicles and the appearance of the vehicles. And the weight of the target OBD data can be determined, so that the loss index is obtained by performing weighted summation processing based on the obtained target OBD data and the weight thereof. The loss index may be used as a basis for assessing used vehicle loss and possible replacement or maintenance costs.
According to the embodiment of the disclosure, the driving habits of historical owners of used cars are the least transparent information which is most easily concealed, and the bad driving habits and environments can increase the loss of the cars and have great influence on transaction prices. After the car is purchased, the buyer needs extra cost for maintenance and repair, which provides bargaining space.
According to the embodiment of the disclosure, although the maintenance condition of the vehicle can be found in some organizations, the maintenance condition is not complete enough in details such as actual maintenance effect, maintenance time limit, maintenance cost and the like, and the maintenance condition is directly reflected on the state of the accessories and is also an aspect which may be paid extra attention in the transaction process.
According to the embodiment of the disclosure, two major aspects of driving environment and driving skill are mainly considered from the driving habit of the historical owner. The driving environment refers to that a vehicle owner often drives on a slow urban road or a high speed, and often drives in a wet, salt-spreading snow or dry environment. The driving skills comprise braking habits, such as not stepping on pre-braking during braking, preferring emergency braking and the like. Poor handling can cause unrecoverable damage to components such as automobile engines, which can be inferred from OBD data. Similarly, the effect of the maintenance effect and the maintenance time period on the part also determine corresponding data. From the appearance of the vehicle, data from the instrument panel, wipers, rearview mirrors, washers, handles, etc. can be acquired.
Fig. 2 is a schematic diagram illustrating an example of a second-hand vehicle loss index according to an embodiment of the present disclosure, and as shown in fig. 2, target OBD data may be selected based on aspects capable of reflecting historical driving habits (driving environment, driving skill), maintenance conditions (maintenance effect, maintenance time limit), vehicle appearance (vehicle body shell, vehicle body accessories), and the like, and the loss index may be obtained based on weighted summation of preset weights. The loss index is then entered into the used vehicle cost estimation system to determine the cost of the used vehicle loss, which may reflect the cost of the used vehicle loss for some of its components, in other words, the possible cost of maintenance (depreciation of the parts to be replaced, repaired) or replacement (market price of the parts to be replaced, repaired immediately) for these components.
According to the embodiment of the present disclosure, as described above, when the OBD data is selected, the OBD data related to the wear of the plurality of components may be selected, and the wear of the plurality of components is associated with the three aspects of the driving habit, the maintenance condition, and the vehicle appearance.
FIG. 3 schematically illustrates a graph of target on-board automatic diagnostic system data according to an embodiment of the present disclosure. As shown in fig. 3, OBD data related to the wear of a plurality of components may be determined from tens of thousands of OBD data as target OBD data, and these target OBD data are respectively related to corresponding components, for example, "desired duty ratio of clutch motor" is OBD data of clutch, obviously OBD data related to clutch, and various target OBD data related to clutch may reflect wear condition of clutch and maintenance and repair cost of clutch, and various target OBD data of clutch may reflect influence on clutch from driving habits, maintenance condition and the like of the historical owner of the used vehicle. The target OBD data in fig. 3 is only an example, and the present disclosure does not limit the specific type of the target OBD data as long as the wear of components and the driving habits, maintenance conditions, and vehicle appearance can be reflected. Moreover, the OBD data can be associated with realistic meanings, so that purchasers can easily understand the meanings indicated by the data and can easily and intuitively understand the wear conditions of the components.
According to the embodiment of the present disclosure, the weight of the target OBD data may be determined, in an example, the weight of the target OBD data may be determined using an expert scoring method, for example, in OBD data related to the storage battery in a historical driving habit-driving environment of the vehicle owner, three data of "temperature of the storage battery", "accuracy of a state of charge value of the storage battery", and "built-in battery sensor" may be included, and the weights of the three data may be respectively determined as 0.4, 0.3, and 0.3, so that after the three data are weighted and summed, the loss index of the storage battery may be determined.
According to the embodiment of the present disclosure, in step S2, after the target OBD data of the plurality of components and the weight of each target OBD data are determined, the loss index of each component may be determined.
According to embodiments of the present disclosure, the aperture of the various target OBD data is non-uniform, e.g., non-uniform in units and/or non-uniform in numerical ranges. For example, the temperature of the storage battery is in centigrade, and the accuracy of the state-of-charge value of the storage battery is in percentage, so if weighted summation of data with inconsistent data apertures is required, the data apertures can be unified first. Step S2 may include: according to the standard range of the target vehicle-mounted automatic diagnosis system data, carrying out normalization processing on the target vehicle-mounted automatic diagnosis system data to obtain a target vehicle-mounted automatic diagnosis system index; and according to the weight, weighting the target vehicle-mounted automatic diagnosis system index to obtain the loss indexes of the plurality of components.
According to embodiments of the present disclosure, each target on-board automatic diagnostic system data may be normalized, for example, a ratio of the target OBD data to a standard range of OBD data of that type may be determined as a target on-board automatic diagnostic system index. For example, the target on-board automatic diagnostic system index may be determined using the following equation (1):
target vehicle-mounted automatic diagnostic system index =
（1）
And the upper limit of the target OBD data is the upper limit of the standard range of the OBD data, and the lower limit of the target OBD data is the lower limit of the standard range of the OBD data. The target on-board automatic diagnostic system index may be determined based on equation (1).
According to the embodiment of the disclosure, the target on-board automatic diagnosis system indexes can be subjected to weighted summation, for example, a plurality of target on-board automatic diagnosis system indexes of the ith component can be subjected to weighted summation to obtain the loss index of the ith component. The above process may be iterated multiple times until the loss index of each component is calculated.
Fig. 4 schematically illustrates a flow chart for determining a used vehicle loss cost according to an embodiment of the present disclosure.
According to the embodiment of the disclosure, a detailed detection report of the used vehicle can be firstly obtained to determine whether the used vehicle is subjected to an event which seriously affects the price of the used vehicle, such as an overweight accident, water soaking, burning and the like. If these events can be eliminated, the cost of the used vehicle loss can be calculated.
According to an embodiment of the present disclosure, in step S3, it may be determined whether various components of the used vehicle need to be replaced. For example, it may be determined whether the various components have been damaged or have reached a useful life. If a component has been damaged or has reached its useful life, it is determined that the component needs to be replaced, otherwise, the component does not need to be replaced. In an example, the component that needs to be replaced can be determined to be a first component and the component that does not need to be replaced can be determined to be a second component.
According to an embodiment of the present disclosure, in step S4, a replacement cost of the first component that needs to be replaced is determined. In an example, a replacement cost for a first component may be determined based on a sum of a market price and a labor cost for the first component
(replacement cost in case the ith component needs to be replaced).
According to an embodiment of the present disclosure, in step S5, a cost of breakage of the second component that does not need replacement may be determined. The depreciation cost of the second component may include a depreciation cost or a loss cost. If the second component is in normal operation, but there is a normal degree of wear, the depreciation cost may be determined based on the depreciation cost. If the second component is artificially depleted due to driving habits, maintenance conditions and other factors of a former owner, than the normal consumption, the depreciation cost can be determined based on the depletion cost.
According to an embodiment of the present disclosure, the method further comprises: determining the depreciation index d according to equation (2):
d = degree of use of part/expected life of part (2)
Wherein the component usage level is an expected age or an expected mileage of the component and the component expected life is an expected age or an expected mileage of the component.
According to an embodiment of the present disclosure, in the case where the component is normally used, the depreciation index d may be determined using a ratio of the degree of use of the component to an expected life of the component (a total life of the component). If the second component is in a normal operating condition, but there is only a normal degree of wear, the cost of depreciation of the second component may be determined based on the depreciation index d
And then determining the depreciation cost.
According to an embodiment of the present disclosure, if the loss of the second component artificially caused by driving habits of former owners, maintenance conditions and other factors is larger than normal loss, the loss cost of the second component can be determined using the loss index l determined based on the target OBD data
And then determine the depreciation cost.
According to an embodiment of the present disclosure, step S5 includes: determining a depreciation cost of the second component based on a market price of the second component and a maximum of the loss index or the depreciation index. Thus, the maximum value of the loss index l and the depreciation index d of the second component can be determined, and if d > l, the second component is in normal operation, except for the presence of normal levels of loss. If d < l, the second part is more lossy than normal due to artifacts.
In accordance with the present disclosureThus, if d > l, the market price of the second part can be passed
Determining the depreciation cost by multiplying the depreciation index d
And will be
The cost of breakage of the second component is determined. If d < l, the market price of the second part can be passed
The product of the loss exponent l determines a loss cost, and the loss cost of the second component is determined as the break cost of the second component. That is, the second member has a breakage cost of
)。
According to an embodiment of the present disclosure, in step S6, the sum of the replacement cost of each component and the breakage cost of each component may be determined as the loss cost of the used vehicle. That is, the purchase of this used car is expected to incur the cost of a maintenance car that is still spent.
According to an embodiment of the present disclosure, step S6 may include: determining a loss cost E of the used vehicle according to a formula (3):
E=
（3）
wherein,
for replacement costs in the case where the ith component needs to be replaced,
for depreciation costs in the case where the ith component does not need to be replaced,
=
x d, wherein d is the depreciation index,
for the market price of the ith part,
for the loss cost in the case where the ith component does not need to be replaced,
x l, wherein l is the loss exponent.
According to an embodiment of the present disclosure, if the ith component needs to be replaced, its replacement cost is
. When the ith component needs to be replaced, the breaking cost of the ith component does not need to be calculated
) That is, if the ith component needs to be replaced, only the replacement cost of the ith component needs to be determined
The breakage cost of the ith part may be set to 0.
According to embodiments of the present disclosure, if the ith component does not need to be replaced, its depreciation cost may be determined
) Without determining the replacement cost thereof
. I.e., if the ith component does not need to be replaced,replacement cost of ith component
May be set to 0.
According to an embodiment of the present disclosure, the replacement cost or the depreciation cost of each component may be determined based on equation (3), thereby determining the total cost of all components, i.e., the depreciation cost E of the used vehicle.
The following is an application example of the second-hand vehicle loss cost determination method.
Fig. 5 exemplarily shows a graph of essential information of a used vehicle according to an embodiment of the present disclosure.
According to an embodiment of the present disclosure, basic information of a vehicle, which is a normal non-commercial private car, may be determined through a vehicle detection report, as shown in fig. 5. And the condition that the price of the second-hand vehicle is seriously influenced by fire, water flooding, serious accidents and the like of the vehicle can be eliminated.
According to the embodiment of the present disclosure, through the above detection, it can be determined that the first component such as the valve chamber cover pad, the engine peripheral belt, or the like needs to be replaced.
Fig. 6 exemplarily illustrates a graph of replacement costs of a first component of an embodiment of the present disclosure.
As shown in fig. 6, the first component, such as the valve cover gasket, the peripheral belt of the engine, etc., needs to be replaced, either in line or, if not damaged to a significant extent, repaired. The cost of repair or replacement is shown in fig. 6. The total replacement cost of each first component is 1250 dollars.
According to the embodiment of the disclosure, through the detection, the second components such as the radiator, the water pump and the fan can be determined not to be replaced, and the breaking cost of the second components can be determined.
Fig. 7 schematically illustrates a graph of the depreciation cost of the second component of an embodiment of the disclosure.
As shown in fig. 7, the sum of the market price (i.e., replacement cost) and labor cost of each second part, and the depreciation index d of each second part are given, and the loss index I of each second part calculated based on the OBD data is also given. The depreciation cost of each second component can thus be determined based on the sum of the replacement part cost and the labor cost multiplied by the maximum of the depreciation index d and the loss index I, e.g. the depreciation cost of the radiator is such that the sum 375 of the replacement part cost and the labor cost is multiplied by the loss index 0.11, resulting in a depreciation cost of 41 yuan, the depreciation cost of the water pump is such that the sum 380 of the replacement part cost and the labor cost is multiplied by the depreciation index d, resulting in a depreciation cost of 38 yuan … …, the depreciation cost of each second component can be calculated in this way, e.g. 6853 yuan.
In summary, the loss cost of the used vehicle is 1250+6853=7391 yuan according to the embodiment of the present disclosure. The graph can visually reflect the loss condition and the depreciation cost of each part, is easy to understand by purchasers, and is favorable for the purchasers to know the vehicle condition and powerful evidence of cutting price.
According to the embodiment of the disclosure, the transaction price of the used vehicle can be calculated based on the loss cost of the used vehicle. For example, the cost of the used vehicle is the expected maintenance cost after the used vehicle is purchased. Can be used as the basis for cutting the price of the second-hand vehicle. The method further comprises the following steps: acquiring the estimated price of the second-hand vehicle; and obtaining the trading price of the used car according to the estimated price of the used car and the loss cost of the used car.
In an example, the estimated price is an offer of the used-hand car detected based on the above-mentioned KBB model, or an offer of a former owner, or an offer of a dealer of the used-hand car. The loss cost of the used car is the cost which needs to be spent after the used car is purchased, so the loss cost can be used as the basis for cutting the price of the used car. For example, chopping can be performed within the scope of cost of attrition. And finally, subtracting the cut price on the basis of the estimated price to obtain the trading price of the second-hand car.
According to the used vehicle loss cost determination method, the loss conditions of all parts in multiple aspects can be determined by using the OBD data, so that the loss cost of each part can be obtained based on the actual meaning reflected by the OBD data and the loss conditions of the parts reflected by the OBD data, the loss cost of the used vehicle can be determined based on the replacement cost and depreciation cost of the parts, all data of the loss cost can be easily understood by non-professional persons, the loss conditions of the used vehicle can be reflected more objectively, a more powerful price certificate is provided for a purchaser, the information opacity is reduced, the purchaser can know the maintenance conditions of the vehicle, and the dependence on the existing foreign models is reduced.
Fig. 8 is a block diagram schematically illustrating a used vehicle loss cost determination apparatus according to an embodiment of the present disclosure, and as shown in fig. 8, the apparatus includes:
the data acquisition module 11 is used for acquiring vehicle-mounted automatic diagnosis system data of the second-hand vehicle;
the loss index acquisition module 12 is configured to perform weighting processing on target vehicle-mounted automatic diagnosis system data corresponding to multiple components of the second-hand vehicle according to a preset weight, so as to obtain loss indexes of the multiple components;
a replacement determining module 13 for determining whether each component of the used vehicle needs to be replaced;
a replacement cost determination module 14 for determining a replacement cost of the first component requiring replacement;
a depreciation cost determination module 15, configured to determine a depreciation cost of a second component that does not need to be replaced, according to the loss index or depreciation index of the second component;
and a loss cost determination module 16, configured to determine a loss cost of the used vehicle according to the loss cost and the replacement cost.
According to an embodiment of the present disclosure, the apparatus is further configured to: screening the target vehicle-mounted automatic diagnosis system data from the plurality of vehicle-mounted automatic diagnosis system data according to the relationship between the vehicle-mounted automatic diagnosis system data and the driving habits, the maintenance conditions and the vehicle appearance of the used vehicle; and determining the weight of the target vehicle-mounted automatic diagnosis system data according to an expert scoring method.
According to an embodiment of the disclosure, the loss exponent acquisition module is further configured to: according to the standard range of the target vehicle-mounted automatic diagnosis system data, carrying out normalization processing on the target vehicle-mounted automatic diagnosis system data to obtain a target vehicle-mounted automatic diagnosis system index; and according to the weight, weighting the target vehicle-mounted automatic diagnosis system index to obtain the loss indexes of the plurality of components.
According to an embodiment of the present disclosure, the apparatus is further configured to: determining the depreciation index d according to the formula d = part usage/part expected life, wherein the part usage is a used age or a used mileage of the part and the part expected life is an expected used age or an expected used mileage of the part.
According to an embodiment of the disclosure, the depreciation cost determination module is further configured to: determining a depreciation cost of the second component based on a market price of the second component and a maximum of the loss index or the depreciation index.
According to an embodiment of the disclosure, the loss cost determination module is further configured to: according to the formula E =
Determining a cost of depletion, E, of the used vehicle, wherein,
for replacement costs in the case where the ith component needs to be replaced,
for depreciation costs in the case where the ith component does not need to be replaced,
=
x d, wherein d is the depreciation index,
for the market price of the ith part,
for the loss cost in the case where the ith component does not need to be replaced,
x l, wherein l is the loss exponent.
According to an embodiment of the present disclosure, the apparatus is further configured to: acquiring the estimated price of the second-hand vehicle; and obtaining the transaction price of the used vehicle according to the estimated price of the used vehicle and the loss cost of the used vehicle.
FIG. 9 is a block diagram illustrating an electronic device in accordance with an exemplary embodiment; as shown, the apparatus includes one or more of the following components: processing components 1502, memory 1504, power components 1506, multimedia components 1508, audio components 1510, input/output (I/O) interfaces 1512, sensor components 1514, and communication components 1516.
The processing component 1502 generally controls overall operation of the device 1500, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 1502 may include one or more processors 1520 executing instructions to perform all or a portion of the steps of the methods described above. Further, processing component 1502 may include one or more modules that facilitate interaction between processing component 1502 and other components. For example, processing component 1502 may include a multimedia module to facilitate interaction between multimedia component 1508 and processing component 1502.
The memory 1504 is configured to store various types of data to support operation at the device 1500. Examples of such data include instructions for any application or method operating on device 1500, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 1504 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 1506 provides power to the various components of the device 1500. The power components 1506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 1500.
The audio component 1510 is configured to output and/or input audio signals. For example, the audio component 1510 includes a Microphone (MIC) configured to receive external audio signals when the device 1500 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 1504 or transmitted via the communication component 1516. In some embodiments, audio component 1510 also includes a speaker for outputting audio signals.
The I/O interface 1512 provides an interface between the processing component 1502 and peripheral interface modules, which can be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 1514 includes one or more sensors for providing status assessment of various aspects of the device 1500. For example, the sensor component 1514 can detect an open/closed state of the device 1500, the relative positioning of components, such as a display and keypad of the device 1500, the sensor component 1514 can also detect a change in position of the device 1500 or a component of the device 1500, the presence or absence of user contact with the device 1500, orientation or acceleration/deceleration of the device 1500, and a change in temperature of the device 1500. The sensor assembly 1514 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 1514 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1514 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 1516 is configured to facilitate wired or wireless communication between the device 1500 and other devices. The device 1500 can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G, 5G, or a combination thereof, or an intercom network. In an exemplary embodiment, the communication component 1516 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1516 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the device 1500 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer readable storage medium comprising instructions, such as the memory 1504 comprising instructions, executable by the processor 1520 of the device 1500 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Fig. 10 is a block diagram illustrating a used vehicle loss cost determination device according to an exemplary embodiment. For example, the apparatus 1600 may be provided as a server. Device 1600 includes a processing component 1602 that further includes one or more processors, and memory resources, represented by memory 1603, for storing instructions, e.g., applications, executable by processing component 1602. The application programs stored in memory 1603 may include one or more modules each corresponding to a set of instructions. Further, the processing component 1602 is configured to execute instructions to perform the above-described methods.
The device 1600 may also include a power component 1606 configured to perform power management for the device 1600, a wired or wireless network interface 1605 configured to connect the device 1600 to a network, and an input/output (I/O) interface 1608. The device 1600 may operate based on an operating system stored in memory 1603, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, or the like.
The present invention may be methods, apparatus, systems and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therein for carrying out aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as a punch card or an in-groove protruding structure with instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It is noted that, unless expressly stated otherwise, all features disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features. Where used, further, preferably, still further and more preferably is a brief introduction to the description of the other embodiment based on the foregoing embodiment, the combination of the contents of the further, preferably, still further or more preferably back strap with the foregoing embodiment being a complete construction of the other embodiment. Several further, preferred, still further or more preferred arrangements of the belt after the same embodiment may be combined in any combination to form a further embodiment.
It will be appreciated by persons skilled in the art that the embodiments of the invention described above and shown in the drawings are given by way of example only and are not limiting of the invention. The objects of the invention have been fully and effectively accomplished. The functional and structural principles of the present invention have been shown and described in the examples, and any variations or modifications of the embodiments of the present invention may be made without departing from the principles.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (10)
1. A second-hand vehicle loss cost determination method is characterized by comprising the following steps:
acquiring vehicle-mounted automatic diagnosis system data of a second-hand vehicle;
according to preset weight, carrying out weighting processing on target vehicle-mounted automatic diagnosis system data corresponding to a plurality of components of the used vehicle to obtain loss indexes of the components;
determining whether various components of the used vehicle need to be replaced;
determining a replacement cost of a first component requiring replacement;
determining a depreciation cost of a second component that does not require replacement based on the loss index or depreciation index of the second component;
and determining the loss cost of the second-hand vehicle according to the loss cost and the replacement cost.
2. The method of claim 1, further comprising:
screening the target vehicle-mounted automatic diagnosis system data from the plurality of vehicle-mounted automatic diagnosis system data according to the relationship between the vehicle-mounted automatic diagnosis system data and the driving habits, the maintenance conditions and the vehicle appearance of the used vehicle;
and determining the weight of the target vehicle-mounted automatic diagnosis system data according to an expert scoring method.
3. The method according to claim 1, wherein weighting target vehicle-mounted automatic diagnostic system data corresponding to a plurality of components of the used vehicle according to a preset weight to obtain loss indexes of the plurality of components comprises:
according to the standard range of the target vehicle-mounted automatic diagnosis system data, carrying out normalization processing on the target vehicle-mounted automatic diagnosis system data to obtain a target vehicle-mounted automatic diagnosis system index;
and weighting the target vehicle-mounted automatic diagnosis system index according to the weight to obtain the loss indexes of the plurality of components.
4. The method of claim 1, further comprising:
determining the depreciation index d according to the formula d = part usage/part expected life, wherein the part usage is a used age or a used mileage of the part and the part expected life is an expected used age or an expected used mileage of the part.
5. The method of claim 1, wherein determining a depreciation cost for a second component that does not require replacement based on the loss or depreciation index for the second component comprises:
determining a depreciation cost of the second component based on a market price of the second component and a maximum of the loss index or the depreciation index.
6. The method of claim 1, wherein determining a cost of depletion of the used vehicle from the cost of depletion and the cost of replacement comprises:
according to the formula E =
Determining a cost of depletion, E, of the used vehicle, wherein,
for replacement costs in the case where the ith component needs to be replaced,
for depreciation costs in the case where the ith component does not need to be replaced,
=
x d, wherein d is the depreciation index,
for the market price of the ith part,
for the loss cost in the case where the ith component does not need to be replaced,
x l, wherein l is the loss exponent.
7. The method of claim 1, further comprising:
acquiring an estimated price of the second-hand car;
and obtaining the transaction price of the used vehicle according to the estimated price of the used vehicle and the loss cost of the used vehicle.
8. A used vehicle loss cost determination apparatus, comprising:
the data acquisition module is used for acquiring vehicle-mounted automatic diagnosis system data of the second-hand vehicle;
the loss index acquisition module is used for weighting target vehicle-mounted automatic diagnosis system data corresponding to a plurality of components of the used vehicle according to preset weight to obtain loss indexes of the components;
a replacement determination module for determining whether components of the used vehicle need to be replaced;
a replacement cost determination module for determining a replacement cost of a first component to be replaced;
a depreciation cost determination module for determining a depreciation cost of a second component that does not require replacement based on the loss index or depreciation index of the second component;
and the loss cost determining module is used for determining the loss cost of the second-hand car according to the loss cost and the replacement cost.
9. An apparatus, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the memory-stored instructions to perform the method of any one of claims 1 to 7.
10. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211219408.9A CN115293825A (en) | 2022-10-08 | 2022-10-08 | Method and device for determining loss cost of second-hand vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211219408.9A CN115293825A (en) | 2022-10-08 | 2022-10-08 | Method and device for determining loss cost of second-hand vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115293825A true CN115293825A (en) | 2022-11-04 |
Family
ID=83834593
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211219408.9A Pending CN115293825A (en) | 2022-10-08 | 2022-10-08 | Method and device for determining loss cost of second-hand vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115293825A (en) |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005050022A (en) * | 2003-07-31 | 2005-02-24 | Toyota Motor Corp | Part use history collection system |
| US20070156311A1 (en) * | 2005-12-29 | 2007-07-05 | Elcock Albert F | Communication of automotive diagnostic data |
| US20080027882A1 (en) * | 2006-07-31 | 2008-01-31 | Robert Allen | Price assessment method for used equipment |
| CN101241573A (en) * | 2007-02-09 | 2008-08-13 | 沈阳 | Method for providing credit data for used car transaction based on OBD technology |
| US20100005010A1 (en) * | 2005-06-24 | 2010-01-07 | Chenn Ieon C | Method of Providing Diagnostic Information in Connection with the Sale of Pre-Owned Vehicles |
| CN101661532A (en) * | 2008-08-26 | 2010-03-03 | 通用汽车环球科技运作公司 | System and method to compute vehicle health index from aggregate data |
| WO2013141796A1 (en) * | 2012-03-20 | 2013-09-26 | Scania Cv Ab | Scheduling of maintenance |
| CN104535930A (en) * | 2014-12-15 | 2015-04-22 | 北京九五智驾信息技术股份有限公司 | Vehicle battery diagnosis system based on vehicle networking |
| CN105139475A (en) * | 2014-06-03 | 2015-12-09 | 福特全球技术公司 | Apparatus and System for Generating Vehicle Usage Model |
| CN105469273A (en) * | 2014-09-29 | 2016-04-06 | 福特全球技术公司 | Determining second hand car price |
| CN107909416A (en) * | 2017-12-29 | 2018-04-13 | 深圳市轱辘车联数据技术有限公司 | The method and vehicle price evaluation device of vehicle price evaluation |
| CN110232452A (en) * | 2019-06-12 | 2019-09-13 | 中国神华能源股份有限公司 | Repair method and system based on track car team state of the art |
| US20200167811A1 (en) * | 2016-01-12 | 2020-05-28 | Sandeep Aggarwal | Used-vehicle algorithmic pricing engine method and system |
| CN111667320A (en) * | 2020-06-23 | 2020-09-15 | 安徽新桥融富科技服务有限公司 | Second-hand vehicle detection and value estimation system |
| WO2021004205A1 (en) * | 2019-07-11 | 2021-01-14 | 深圳壹账通智能科技有限公司 | Loss determination method and apparatus for vehicle components, and computer device and storage medium |
| US20220051304A1 (en) * | 2020-08-12 | 2022-02-17 | Backlotcars, Inc. | System and method of matching a seller of a vehicle to a buyer of a vehicle |
| CN114742633A (en) * | 2021-01-08 | 2022-07-12 | 上海擎感智能科技有限公司 | Vehicle management method and device, electronic equipment and computer storage medium |
| CN115115209A (en) * | 2022-06-24 | 2022-09-27 | 一汽解放汽车有限公司 | Method and device for evaluating vehicle condition of second-hand vehicle, computer equipment and storage medium |
-
2022
- 2022-10-08 CN CN202211219408.9A patent/CN115293825A/en active Pending
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005050022A (en) * | 2003-07-31 | 2005-02-24 | Toyota Motor Corp | Part use history collection system |
| US20100005010A1 (en) * | 2005-06-24 | 2010-01-07 | Chenn Ieon C | Method of Providing Diagnostic Information in Connection with the Sale of Pre-Owned Vehicles |
| US20070156311A1 (en) * | 2005-12-29 | 2007-07-05 | Elcock Albert F | Communication of automotive diagnostic data |
| US20080027882A1 (en) * | 2006-07-31 | 2008-01-31 | Robert Allen | Price assessment method for used equipment |
| CN101241573A (en) * | 2007-02-09 | 2008-08-13 | 沈阳 | Method for providing credit data for used car transaction based on OBD technology |
| CN101661532A (en) * | 2008-08-26 | 2010-03-03 | 通用汽车环球科技运作公司 | System and method to compute vehicle health index from aggregate data |
| WO2013141796A1 (en) * | 2012-03-20 | 2013-09-26 | Scania Cv Ab | Scheduling of maintenance |
| CN105139475A (en) * | 2014-06-03 | 2015-12-09 | 福特全球技术公司 | Apparatus and System for Generating Vehicle Usage Model |
| CN105469273A (en) * | 2014-09-29 | 2016-04-06 | 福特全球技术公司 | Determining second hand car price |
| CN104535930A (en) * | 2014-12-15 | 2015-04-22 | 北京九五智驾信息技术股份有限公司 | Vehicle battery diagnosis system based on vehicle networking |
| US20200167811A1 (en) * | 2016-01-12 | 2020-05-28 | Sandeep Aggarwal | Used-vehicle algorithmic pricing engine method and system |
| CN107909416A (en) * | 2017-12-29 | 2018-04-13 | 深圳市轱辘车联数据技术有限公司 | The method and vehicle price evaluation device of vehicle price evaluation |
| CN110232452A (en) * | 2019-06-12 | 2019-09-13 | 中国神华能源股份有限公司 | Repair method and system based on track car team state of the art |
| WO2021004205A1 (en) * | 2019-07-11 | 2021-01-14 | 深圳壹账通智能科技有限公司 | Loss determination method and apparatus for vehicle components, and computer device and storage medium |
| CN111667320A (en) * | 2020-06-23 | 2020-09-15 | 安徽新桥融富科技服务有限公司 | Second-hand vehicle detection and value estimation system |
| US20220051304A1 (en) * | 2020-08-12 | 2022-02-17 | Backlotcars, Inc. | System and method of matching a seller of a vehicle to a buyer of a vehicle |
| CN114742633A (en) * | 2021-01-08 | 2022-07-12 | 上海擎感智能科技有限公司 | Vehicle management method and device, electronic equipment and computer storage medium |
| CN115115209A (en) * | 2022-06-24 | 2022-09-27 | 一汽解放汽车有限公司 | Method and device for evaluating vehicle condition of second-hand vehicle, computer equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11354899B2 (en) | Visual inspection support using extended reality | |
| CA2937152C (en) | System and method for reclaiming residual value of personal electronic devices | |
| US10135777B2 (en) | Leveraging an internet of things to initiate a physical object to perform a specific act that enhances an interaction of a user with the physical object | |
| US11074766B2 (en) | Managing a vehicle incident | |
| US20160180428A1 (en) | Methods and sytem for simultaneously generating vehicle history reports and preapproved financing options | |
| WO2017201522A1 (en) | Vehicle management services | |
| CA3018681A1 (en) | Electronic platform for selling and buying used vehicles | |
| US20160042423A1 (en) | Vehicle information delivery and management system and method | |
| US20170061459A1 (en) | Augmented reality solution for price evaluation | |
| CN109163914A (en) | A kind of vehicle remote detection method and device | |
| US20150348142A1 (en) | System and Method of Vehicle Value Assessment | |
| US11756037B2 (en) | Product analysis platform to perform a facial recognition analysis to provide information associated with a product to a user | |
| JP6883494B2 (en) | Used car assessment system | |
| KR102234821B1 (en) | Electronic device for performing a predection for a price of a product using big data and machine learning model and method for operating thereof | |
| CN113792943B (en) | Group decision method and device | |
| CN115293825A (en) | Method and device for determining loss cost of second-hand vehicle | |
| JP7314442B2 (en) | point signage business system | |
| KR102275036B1 (en) | Method and system for providing recommendation information for trading of used cars | |
| US10755215B2 (en) | Generating wastage estimation using multiple orientation views of a selected product | |
| CN116091249A (en) | Transaction risk assessment method, device, electronic equipment and medium | |
| US10552000B2 (en) | Network interaction optimization | |
| CN115661619A (en) | Network model training method, ultrasonic image quality evaluation method, device and electronic equipment | |
| CN115311062A (en) | Method, device, equipment and storage medium for determining public subsidy amount of private car | |
| KR102126451B1 (en) | Method for vehicle painting service | |
| US10319253B2 (en) | Cognitive workload awareness |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20221104 |