CN117829907A - Method and device for determining consumption of spare parts of vehicle - Google Patents
Method and device for determining consumption of spare parts of vehicle Download PDFInfo
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Abstract
The invention discloses a method and a device for determining consumption of spare parts of a vehicle, and relates to the technical field of intelligent supply chains. The specific implementation mode of the method comprises the following steps: acquiring the transaction quantity of a target vehicle, and transaction time and spare part types of various vehicle spare parts applicable to the target vehicle; selecting a target vehicle spare part and target spare part data corresponding to the target vehicle spare part according to the transaction time, the spare part type and the transaction quantity; inputting the target spare part data into a preset target consumption model corresponding to the target vehicle spare part; and determining the consumption of the spare parts of the target vehicle according to the output of the target consumption model. According to the method and the device for predicting the spare part consumption, the new vehicle data can be fully utilized, the influence degree of various influence factors on the spare part consumption is determined, and a proper spare part consumption prediction model is selected, so that the consumption of the spare part of the vehicle is accurately determined, the waste of storage cost, the lack of spare parts and the like are prevented, the requirements of users cannot be met, and the user satisfaction degree and the new vehicle data utilization rate are improved.
Description
Technical Field
The invention relates to the technical field of intelligent supply chains, in particular to a method and a device for determining consumption of spare parts of a vehicle.
Background
The spare parts of the vehicle are parts which are frequently required to be replaced in the using process of the vehicle, and the consumption rule of the spare parts shows the trend of smaller marketing period, gradually increased sales growth period, smaller approximate average fluctuation of sales stationary period and gradually reduced market return period in different stages of the life cycle of the spare parts.
In the existing consumption prediction process of spare parts of a vehicle, the prediction is usually performed based on spare part data in a stationary phase, because the consumption condition, sales data and the like of the spare parts in the stationary phase are mature, the historical data volume is large, and more accurate spare part consumption can be obtained for storage.
However, unlike the spare part consumption prediction of the mature vehicle, various sales, replacement, loss and other data of the spare part of the new vehicle are small, when the spare part consumption of the new vehicle is predicted, enough spare part data cannot be obtained as a sample, the spare part consumption of the new vehicle needs to be predicted manually, the accuracy is low, the spare part storage error is large, and meanwhile, the spare part data of the new vehicle is not utilized effectively.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a method and an apparatus for determining consumption of spare parts of a vehicle, which can fully utilize new vehicle data, determine the degree of influence of various influencing factors such as spare part attributes, seasons, etc. on the consumption of spare parts, and select a suitable spare part consumption prediction model, so as to accurately determine the consumption of spare parts of the vehicle, prevent the spare parts from accumulating and wasting storage costs or the spare parts from being insufficient and being unable to supply to user demands, improve user satisfaction, and improve the utilization rate of the new vehicle data.
To achieve the above object, according to an aspect of the embodiments of the present invention, there is provided a method of determining consumption of a spare part of a vehicle, including:
acquiring the transaction quantity of a target vehicle, and transaction time and spare part types of various vehicle spare parts applicable to the target vehicle; wherein the transaction number corresponds to the transaction time;
selecting a target vehicle spare part and target spare part data corresponding to the target vehicle spare part according to the transaction time, the spare part type and the transaction quantity;
inputting the target spare part data into a preset target consumption model corresponding to the target vehicle spare part; the consumption model is obtained through training according to historical vehicle data and historical spare part data;
and determining the consumption of the target vehicle spare part according to the output of the target consumption model.
Optionally, the selecting a target vehicle spare part and target spare part data corresponding to the target vehicle spare part according to the transaction time, the spare part type and the transaction number includes:
determining intermediate spare part data greater than a preset relationship coefficient threshold according to relationship coefficients between the transaction time of the plurality of vehicle spare parts and the transaction quantity of the target vehicle;
Determining a target vehicle spare part according to the spare part type, selecting intermediate spare part data corresponding to the target vehicle spare part from the intermediate spare part data, and calibrating corresponding classification characteristics;
and clustering the intermediate spare part data carrying the classification characteristics to obtain the target spare part data.
Optionally, the determining the intermediate spare part data greater than the preset relation coefficient threshold according to the relation coefficient between the transaction time of the plurality of vehicle spare parts and the transaction quantity of the target vehicle includes:
determining the relationship coefficient between the vehicle spare part and the target vehicle according to the product of the transaction time and the transaction quantity;
and respectively comparing the relation coefficient with the preset relation coefficient threshold value, and selecting intermediate spare part data with the relation coefficient larger than or equal to the preset relation coefficient threshold value.
Optionally, the clustering processing is performed on the intermediate spare part data carrying the classification feature to obtain the target spare part data, including:
respectively determining feature values of intermediate spare part data of different classification features;
for each of the classification features, a plurality of feature thresholds are set:
Dividing the middleware data according to the relation between the characteristic value and the characteristic threshold value, and determining a plurality of mean square errors of the middleware data under different classification characteristics;
and determining the target spare part data according to the minimum value of the mean square error.
Optionally, the dividing the middleware data according to the relation between the feature value and the feature threshold value, and determining a plurality of mean square errors of the middleware data under different classification features includes:
dividing the intermediate spare part data corresponding to the classification features into first sample data and second sample data according to each feature threshold value respectively; wherein the characteristic value of the first sample data is smaller than or equal to the characteristic threshold value, and the characteristic value of the second sample data is larger than the characteristic threshold value;
determining a first average value of all the characteristic values included in the first sample data and a second average value of all the characteristic values included in the second sample data;
and calculating the mean square error of the first sample data and the second sample data.
Optionally, the determining the target spare part data according to the minimum value of the mean square error includes:
And taking the corresponding first sample data and second sample data as target spare part data of the intermediate spare part data according to the minimum value of the mean square error corresponding to each characteristic threshold.
Optionally, the classification features include a repair class and a maintenance class.
Optionally, the method further comprises:
preprocessing the historical vehicle data and the historical spare part data;
training a plurality of consumption models respectively by taking the preprocessed historical vehicle data and the preprocessed historical spare part data as input and a plurality of historical spare part consumption as output;
comparing training results of a plurality of consumption models, and respectively determining the target consumption models applicable to the target vehicle spare parts.
Optionally, before the clustering processing is performed on the middleware data carrying the classification feature, the method further includes:
and calibrating the seasonal features of the middleware data carrying the classification features.
According to still another aspect of the embodiment of the present invention, there is provided a device for determining the consumption of a spare part of a vehicle, including:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the transaction quantity of a target vehicle, and the transaction time and spare part types of various vehicle spare parts applicable to the target vehicle; wherein the transaction number corresponds to the transaction time;
The selecting module is used for selecting a target vehicle spare part and target spare part data corresponding to the target vehicle spare part according to the transaction time, the spare part type and the transaction quantity;
the input module is used for inputting the target spare part data into a preset target consumption model corresponding to the target vehicle spare part; the consumption model is obtained through training according to historical vehicle data and historical spare part data;
and the output module is used for determining the consumption of the spare parts of the target vehicle according to the output of the target consumption model.
According to another aspect of an embodiment of the present invention, there is provided an electronic apparatus for determining consumption of a vehicle spare part, including:
one or more processors;
storage means for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method for determining the consumption of the spare parts of the vehicle provided by the invention.
According to still another aspect of the embodiments of the present invention, there is provided a computer-readable medium having stored thereon a computer program which, when executed by a processor, implements the method of determining the consumption of a spare part of a vehicle provided by the present invention.
One embodiment of the above invention has the following advantages or benefits: because the intermediate spare part data with the relation coefficient larger than the preset relation coefficient threshold value is screened out from the initial spare part data by adopting the relation coefficient between the transaction time of various vehicle spare parts and the transaction quantity of the target vehicle; selecting intermediate spare part data of a target vehicle (namely a new vehicle type) according to the spare part type, and calibrating classification characteristics, price characteristics and seasonal characteristics of the intermediate spare part data; dividing the calibrated intermediate spare part data according to the characteristic threshold value of each characteristic, calculating the mean square error of the divided sample data, thus aggregating the calibrated intermediate spare part data, determining target spare part data, inputting a pre-screening target consumption model to determine/predict the future spare part consumption of the target vehicle, overcoming the technical means that the existing consumption prediction of the vehicle spare part cannot acquire enough spare part data as a sample, ensuring that the manual prediction accuracy is too low, ensuring that the spare part reserve error is larger, simultaneously ensuring that the spare part data of a new vehicle is not effectively utilized, further achieving the technical effects of fully utilizing the new vehicle data, determining the influence degree of various influencing factors such as spare part attribute, season and the like on the spare part consumption, selecting a proper spare part consumption prediction model, accurately determining the consumption of the vehicle spare part, preventing the spare part from accumulating to waste storage cost or the spare part shortage to supply the user demand, improving the user satisfaction and improving the utilization rate of the new vehicle data.
Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic diagram of a main flow of a method of determining the consumption amount of a vehicle spare part according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the main flow of a method of determining target spare part data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of sample partitioning according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the main flow of a method of determining a consumption model according to an embodiment of the present invention;
fig. 5 is a schematic view of main modules of a determination device of the consumption amount of a spare part of a vehicle according to an embodiment of the present invention;
fig. 6 shows an exemplary system architecture diagram of a determination method of the consumption amount of a vehicle spare part or a determination device of the consumption amount of a vehicle spare part, which is suitable for being applied to an embodiment of the present invention;
fig. 7 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of a method for determining consumption of a vehicle spare part according to an embodiment of the present invention, as shown in fig. 1, the method for determining consumption of a vehicle spare part of the present invention includes the steps of:
in the life cycle of each vehicle model, the sales volume of the vehicle usually shows the change trend of gradual increase in the marketing period, rapid increase in the growth period, stable fluctuation in the maturation period and rapid decrease in the market return period. The existing spare part prediction (i.e. predicting the future consumption of each part of the vehicle) is generally to determine the spare part consumption of the vehicle in the sales maturity stage based on the vehicle data in the maturity stage, for the newly marketed vehicle model, the vehicle data is very little, and further the spare part consumption of the new vehicle determined according to the vehicle data is very inaccurate, further, the vehicle data includes a plurality of influencing factors (such as sales volume, season, price, spare part type, spare part source, spare part quality, etc.) on the spare part consumption, and under the condition that the mastered data volume is very little, the existing prediction method directly mixes the new vehicle data with the historical data, and does not individually identify the new vehicle, so the new vehicle data is not effectively utilized, and the mixed use cannot clearly determine the influencing factors having great influence on the spare part consumption of the new vehicle.
By the method for determining the consumption of the spare parts of the vehicle, the new vehicle data can be fully utilized, all influence factors of the consumption of the spare parts are analyzed, influence factors with larger influence on the consumption of the spare parts are determined, the characteristics of the new vehicle are calibrated, a proper prediction model is selected, and the consumption of the spare parts of the new vehicle is accurately determined.
Step S101, acquiring the transaction quantity of a target vehicle, and transaction time and spare part types of various vehicle spare parts applicable to the target vehicle; wherein the transaction number corresponds to the transaction time.
In the embodiment of the invention, any newly marketed vehicle type is selected as a target vehicle, vehicle data of the target vehicle is obtained, and the vehicle data can comprise the transaction quantity of the target vehicle corresponding to the transaction time of various vehicle spare parts of the target vehicle; wherein the transaction time includes sales time since the new vehicle was marketed for various vehicle spare parts. For example, a SY model on the market in 5 months 2019 is selected as a target vehicle, and vehicle data of the SY model is obtained, including sales (or holding amounts) since the SY vehicle brake pad transaction of the SY model, as shown in table 1 below:
TABLE 1
Wherein, SY vehicle brake pad of SY motorcycle type is 5 months in 2019 on the market time equally.
In the embodiment of the invention, the determination of the target vehicle can be to calibrate the model of the target vehicle for which the vehicle spare part is applicable, so as to distinguish the needed model data.
In the embodiment of the invention, initial spare part data of various vehicle spare parts for a target vehicle is acquired, wherein the initial spare part data can comprise transaction time and spare part types of various vehicle spare parts for the target vehicle; the spare part types are classified according to product classification and workshop classification, the product classification comprises an Accessory (Access), an electronic (DG), a conveyor belt (exchange), a manual (manual), a fine (Merchantize), a conventional (regular), a Tool (Tool), a tire (tyre) and the like, and the workshop classification comprises a sheet metal (B$P), a fine (buy$sel), an electronic device (equivalence), a maintenance (repair), a service (service), a Tool (Tool), a wearing part (W$T) and the like.
Further, the initial spare part data may further include transaction prices of various vehicle spare parts for the target vehicle, according to the transaction prices of the vehicle spare parts, a price zone to which the vehicle spare parts belong during transaction may be determined, the price zone corresponds to a plurality of price intervals, including a first type price zone, a second type price zone, three types price zones, … …, and the prices rise in sequence; one type of price band includes a value of 0, which corresponds to a complimentary vehicle spare part (e.g., a tag).
In the embodiment of the invention, the transaction time of the vehicle spare part can be determined according to the SOL label of the vehicle.
It should be noted that the method for determining the consumption of spare parts of a vehicle of the present invention is applicable to all new vehicle models, and the front-rear replacement vehicle models are not in this consideration.
Step S102, selecting a target vehicle spare part and target spare part data corresponding to the target vehicle spare part according to the transaction time, the spare part type and the transaction number.
According to the method, intermediate spare part data larger than a preset coefficient threshold value are screened from initial spare part data according to a plurality of relation coefficients of the transaction quantity of a plurality of vehicle spare parts and a target vehicle under different transaction times, then the target vehicle spare parts are selected according to the spare part types of the vehicle spare parts on the basis of the intermediate spare part data, classification characteristics of the target vehicle spare parts are marked, and marking is carried out on the intermediate spare part data of the target vehicle spare parts according to transaction prices and seasonal attributes of the vehicle spare parts, so that the target spare part data are obtained.
In the embodiment of the present invention, as shown in fig. 2, the method for determining target spare part data according to the present invention includes the following steps:
step S201, determining intermediate spare part data greater than a preset relationship coefficient threshold according to relationship coefficients between the transaction time of the plurality of vehicle spare parts and the transaction number of the target vehicle.
According to the method and the device for determining the relation coefficient of the multiple vehicle spare parts and the target vehicle, according to the transaction time of the multiple vehicle spare parts for the target vehicle and the transaction quantity of the target vehicle, initial spare part data of the vehicle spare parts with the relation coefficient larger than a preset relation coefficient threshold value are selected, and intermediate spare part data are obtained.
Step S2011, determining the relationship coefficient between the vehicle spare part and the target vehicle according to the product of the transaction time and the transaction number.
In an embodiment of the present invention, the relationship coefficient may be a pearson correlation coefficient (i.e., pearson correlation coefficient), where the pearson correlation coefficient r may be determined according to the following equation:
wherein:
x represents different transaction times for the vehicle spare parts of the target vehicle, e.g., x is the respective sales times for the SY vehicle brake pads shown in Table 1;
y represents the transaction number of the target vehicle at different transaction times, for example, y is the accumulated sales of SY vehicle types shown in Table 1;
n represents the number of x, y data pairs.
Step S2012, comparing the relationship coefficient with the preset relationship coefficient threshold, and selecting intermediate spare part data with the relationship coefficient greater than or equal to the preset relationship coefficient threshold.
In the embodiment of the invention, the relation coefficient of each vehicle spare part and the target vehicle is respectively compared with a preset relation coefficient threshold value, and initial spare part data of the vehicle spare part, of which the relation coefficient is greater than or equal to the preset relation coefficient threshold value, is selected from the initial spare part data to serve as intermediate spare part data.
In the embodiment of the invention, the preset relation coefficient threshold value can be 0.3%, and when the relation coefficient r of the spare parts of the vehicle and the target vehicle is more than or equal to 0.3%, the consumption of the spare parts of the vehicle is considered to have larger correlation with the sales of the target vehicle, and the data of the spare parts of the vehicle are selected for subsequent marking processing to obtain the data of the target spare parts.
In the embodiment of the invention, the relation coefficient between the transaction time of the spare parts of the vehicle and the transaction quantity of the target vehicle is analyzed, so that whether the consumption of the spare parts of the vehicle is related to the sales of the target vehicle and the degree of mutual association can be determined.
Step S202, determining a target vehicle spare part according to the spare part type, selecting intermediate spare part data corresponding to the target vehicle spare part from the intermediate spare part data, and calibrating corresponding classification characteristics.
In the embodiment of the invention, the intermediate spare part data is screened according to the spare part type, the data which is not suitable for model tuning is removed, and the intermediate spare part data of the attached target vehicle (namely the selected new vehicle type) is selected.
In the embodiment of the invention, according to research and discovery of spare part data, in the vehicle spare parts of product classification:
tools and tires belong to general spare parts and are not specific to any vehicle type, so that the tools and tires can be processed by using the existing method for consumption of spare parts in the mature period; the internal pickup phenomenon of the precision products (such as clothes, periphery and the like) needs to distinguish the consumption data of the user from the pickup data of the internal staff;
vehicle spare parts categorized in workshops:
similar to product classification, tools belong to general spare parts, and the top-quality class has internal pickup phenomenon;
when the intermediate spare part data is selected, tools, tires, precision products in product classification and tools and precision products in workshop classification are removed, and the remaining spare part types are summarized together to serve as target vehicle spare parts.
In the embodiment of the invention, intermediate spare part data of the target vehicle spare part is selected, and classification characteristics of the target vehicle spare part are calibrated, wherein the classification characteristics comprise maintenance class (also mainly called maintenance), maintenance class (mainly called maintenance) and other classes (also called indistinguishable).
In the embodiment of the invention, the maintenance period of various vehicle spare parts can be determined according to the maintenance manual description of the target vehicle spare parts, the maintenance class is subdivided, and the maintenance period characteristics of the target vehicle spare parts are calibrated.
In the embodiment of the invention, the seasonal characteristics of the spare parts of the target vehicle can be calibrated according to the transaction time of the spare parts of the target vehicle, for example, as shown in the following table 2:
TABLE 2
The season characteristic of the calibration brake pad is spring and autumn; calibrating season features of the wiper blade, the silencer, the air conditioner part and the radiator pipe to be summer; the season characteristic of the calibration temperature regulator is autumn; the season features of the calibrated engine oil pressure switch, engine oil and the easy-to-crash automobile body parts are winter. For other target vehicle spare parts than table 2, its seasonal characteristics are calibrated to be empty. The seasonal features may be calibrated by Q1, Q2, Q3, and Q4, for example, Q1 in spring, Q2 in summer, Q3 in autumn, and Q4 in winter.
In the table, the spring (3-5 months) is a good season for opening the car window, and the car window is windy and dry, so that dust easily enters a brake system of the car, and abnormal noise or loss is generated, so that the loss of a brake pad and the like is larger; in summer (6-8 months), the rain on the road surface is easy to rust, and the paint passing through the high Wen Rizhao and the vehicle body and some resin and plastic parts are easy to age and damage, so that the loss of the wiper blade, the silencer, the air-conditioning part, the radiator pipe and the like is larger; the temperature is gradually cool in autumn (9-11 months), so that the best time for excessively using the engine, the air conditioner, the brake and the electrical system in summer is checked, and the consumption of the temperature regulator, the brake pad and the like is larger; winter (12-2 months) is a serious test period for automobiles, and unexpected traffic accidents caused by a frozen engine, an air conditioning system for continuous heating and a snow or frozen road at night lead to larger consumption of engine oil pressure switches, engine oil, easy-to-crash automobile body parts and the like.
Further, in the actual prediction process of the spare part consumption, the influence of the seasonal features on the accuracy of the consumption prediction is not obvious, but the seasonal features have better feedback on the interpretability of the spare part consumption prediction model, are friendly to output, and can intuitively see the influence of seasons on the sales of the spare part.
In the embodiment of the invention, the price characteristics of the target vehicle can be calibrated according to the transaction prices of various vehicle spare parts, for example, the price characteristics of the target vehicle spare parts of a type of price band (comprising a value with a price of 0) are calibrated to be P1, and the price characteristics of the target vehicle spare parts are sequentially calibrated to be P2, P3 and … … along with the increase of the price interval corresponding to the price band.
And step S203, clustering the intermediate spare part data carrying the classification features to obtain the target spare part data.
Step S2031, determining feature values of the middleware data of different classification features respectively.
In the embodiment of the invention, the intermediate spare part data for calibrating various features is traversed, the feature value of each feature is determined, and the i is used for representing the data features (including classification features and subdivision thereof, seasonal features and price features) to determine the feature value of the intermediate spare part data for calibrating various features. For example, as shown in fig. 3, i represents the damage rate of the maintenance class.
Step S2032 sets a plurality of feature thresholds for each of the classification features.
In the embodiment of the present invention, s is used to represent the feature threshold under different i features, for example, s is 5% as shown in fig. 3.
Step S2033, dividing the middleware data according to the relation between the feature value and the feature threshold, and determining a plurality of mean square errors of the middleware data under different classification features.
Step S20331, dividing the middleware data corresponding to the classification feature into first sample data and second sample data according to each feature threshold; wherein the characteristic value of the first sample data is smaller than or equal to the characteristic threshold value, and the characteristic value of the second sample data is larger than the characteristic threshold value.
In the embodiment of the invention, the characteristic value x of each i characteristic is calculated i Comparing with the feature threshold s of the corresponding i feature, dividing the intermediate spare part data corresponding to the i feature into first sample data R for each i feature 1 And second sample data R 2 . Wherein the first sample data R 1 Refers to the feature value x of the i feature i Middleware data of the feature threshold s of the i feature or less; second sample data R 2 Refers to the feature value x of the i feature i Middleware data greater than a feature threshold s for the i feature.
For example, as shown in fig. 3, if the feature threshold s of the damage rate feature is 5%, the intermediate spare part data with the damage rate less than or equal to 5% is divided into a first damage rate sample, and the intermediate spare part data with the damage rate greater than 5% is divided into a second damage rate sample.
Step S20332, determining a first average value of all the feature values included in the first sample data and a second average value of all the feature values included in the second sample data.
In an embodiment of the present invention, the first average value of all the feature values included in the first sample data is c 1 Representing a second average of all feature values included in the second sample data with c 2 A representation; the average value c is the ratio of the sum of all the characteristic values included in the sample data to the number of the characteristic values.
Step S20333, calculating a mean square error of the first sample data and the second sample data.
In the embodiment of the invention, for each i feature, a first mean square error between each feature value of the first sample data and a first mean value and a second mean square error between each feature value of the second sample data and a second mean value are calculated; the mean square error is the square of the difference between the characteristic value and the mean value of the sample data, the first mean square error is the sum of the mean square errors of the characteristic values included in the first sample data, and the second mean square error is the sum of the mean square errors of the characteristic values included in the second sample data.
Step S2034, determining the target spare part data according to the minimum value of the mean square error. In an embodiment of the invention, a minimum value of the sum of the first mean square error and the second mean square error is determined,
the smaller the sum of the first mean square error and the second mean square error is, the smaller the sample difference between the first sample data and the second sample data is, that is, the more accurately the first sample data and the second sample data are divided, so that the sample data with mutual commonality are found.
Further, the divided first sample data and second sample data are used as target spare part data according to the minimum value of the sum of the first mean square error and the second mean square error corresponding to each characteristic threshold value s.
According to the method for determining the target spare part data, provided by the embodiment of the invention, the intermediate spare part data with the relation coefficient larger than the preset relation coefficient threshold value can be screened from the initial spare part data according to the relation coefficient between the transaction time of various vehicle spare parts and the transaction quantity of the target vehicle; selecting intermediate spare part data of a target vehicle (namely a new vehicle type) according to the spare part type, and calibrating classification characteristics, price characteristics and seasonal characteristics of the intermediate spare part data; dividing the calibrated intermediate spare part data according to the characteristic threshold value of each characteristic, calculating the mean square error of the divided sample data, and therefore aggregating the calibrated intermediate spare part data, determining target spare part data, and realizing characteristic screening of the spare part data, so that spare part consumption can be accurately predicted on the basis of the target spare part data, and the utilization rate of new vehicle data is improved.
Step S103, inputting the target spare part data into a preset target consumption model corresponding to the target vehicle spare part; the consumption model is trained according to historical vehicle data and historical spare part data.
According to the embodiment of the invention, different consumption models are used for determining the target spare part data with different classification characteristics according to the training results of the historical data, and the consumption of the spare part of the vehicle can be predicted more accurately by inputting the target spare part data into the target consumption model corresponding to the classification characteristics of the spare part of the target vehicle. From the training results, the maintenance class adopts a machine learning model (such as an XGB algorithm), and the maintenance class adopts a time sequence class model (such as an algorithm arima, prophet and the like).
In the embodiment of the present invention, as shown in fig. 4, the method for determining the consumption model of the present invention includes the following steps:
step S401, preprocessing the historical vehicle data and the historical spare part data.
According to the method for determining the target spare part data, historical vehicle data and the historical spare part data are preprocessed, and historical target spare part data are selected.
And step S402, training a plurality of consumption models respectively by taking the preprocessed historical vehicle data and the preprocessed historical spare part data as input and a plurality of historical spare part consumption as output.
In the embodiment of the invention, the historical target spare part data is used as the input of a plurality of consumption models, the historical spare part consumption is used as the output of the plurality of consumption models, and the plurality of consumption models are respectively trained.
In the embodiment of the invention, the training stage can respectively input spare part data for calibrating various characteristics into each consumption model for training so as to select the optimal spare part data, for example, the training stage can respectively train XGBoost, lightBM, BHT-ARIMA, change-Point and other algorithm models, and select a target consumption model suitable for various calibration characteristics.
Step S403, comparing training results of the plurality of consumption models, and determining the target consumption models applicable to the target vehicle spare parts respectively.
In the embodiment of the invention, a plurality of training results are subjected to cross verification, so that the consumption model suitable for various features, particularly the consumption model suitable for classified features, can be determined, and a corresponding relation table of various features and models can be constructed, thereby facilitating the subsequent selection of a target consumption model to predict the consumption of the spare parts of the vehicle.
According to the method for determining the consumption model, the historical target spare part data can be input into the multiple models for training, so that the consumption model adapting to various characteristics is selected from the multiple models, and the consumption of the spare part can be predicted conveniently.
Step S104, determining the consumption of the spare parts of the target vehicle according to the output of the target consumption model.
According to the embodiment of the invention, the consumption of various target vehicle spare parts can be accurately determined according to the output of the target consumption model suitable for various data characteristics, so that a user can reserve various new vehicle spare parts suitable for various new vehicle types according to the accurately predicted consumption of the spare parts, waste of storage cost, production cost, labor cost and the like caused by stocking the spare parts is prevented, or the user complaints, lost users and the like caused by incapability of timely supplying the user due to insufficient reserve of the spare parts are greatly improved, and the data utilization rate of the new vehicle is improved.
In the embodiment of the invention, the transaction quantity of the target vehicle, the transaction time and the spare part types of various vehicle spare parts applicable to the target vehicle are acquired; wherein the transaction number corresponds to the transaction time; selecting a target vehicle spare part and target spare part data corresponding to the target vehicle spare part according to the transaction time, the spare part type and the transaction quantity; inputting the target spare part data into a preset target consumption model corresponding to the target vehicle spare part; the consumption model is obtained through training according to historical vehicle data and historical spare part data; determining the consumption of the spare parts of the target vehicle according to the output of the target consumption model, and the like, so that new vehicle data can be fully utilized, the influence degree of various influence factors such as spare part attributes, seasons and the like on the consumption of the spare parts can be determined, and a proper spare part consumption prediction model is selected, so that the accurately determined consumption of the spare parts of the vehicle is prevented, the storage cost of the spare parts is prevented from being accumulated, or the spare parts cannot be supplied to user demands, the user satisfaction is improved, and the new vehicle data utilization rate is improved.
Fig. 5 is a schematic diagram of main modules of a device for determining consumption of a vehicle spare part according to an embodiment of the present invention, and as shown in fig. 5, a device 500 for determining consumption of a vehicle spare part of the present invention includes:
an obtaining module 501, configured to obtain a transaction number of a target vehicle, and transaction time and spare part types of multiple vehicle spare parts applicable to the target vehicle; wherein the transaction number corresponds to the transaction time.
In the embodiment of the invention, any newly marketed vehicle type is selected as the target vehicle, the vehicle data of the target vehicle is acquired, and the vehicle data can comprise the transaction quantity of the target vehicle corresponding to the transaction time of various vehicle spare parts of the target vehicle.
In an embodiment of the present invention, the obtaining module 501 obtains initial spare part data of multiple vehicle spare parts for the target vehicle, where the initial spare part data may include transaction time and spare part types of multiple vehicle spare parts for the target vehicle.
And a selecting module 502, configured to select a target vehicle spare part and target spare part data corresponding to the target vehicle spare part according to the transaction time, the spare part type and the transaction number.
In the embodiment of the present invention, according to a plurality of relation coefficients of the transaction numbers of the plurality of vehicle spare parts and the target vehicle under different transaction times, the selection module 502 screens intermediate spare part data greater than a preset coefficient threshold from the initial spare part data, further selects the target vehicle spare part according to the spare part type of the vehicle spare part on the basis of the intermediate spare part data, marks the classification feature of the target vehicle spare part, and marks the intermediate spare part data of the target vehicle spare part according to the transaction price and the seasonal attribute of the vehicle spare part to obtain the target spare part data.
An input module 503, configured to input the target spare part data into a preset target consumption model corresponding to the target vehicle spare part; the consumption model is trained according to historical vehicle data and historical spare part data.
In the embodiment of the invention, according to the training result of the historical data, different consumption models are used for determining the target spare part data with different classification characteristics, and the consumption of the spare part of the vehicle can be predicted more accurately by inputting the target spare part data into the input module 503 of the target consumption model corresponding to the classification characteristics of the spare part of the target vehicle. According to training results, the maintenance class adopts a machine learning model, and the maintenance class uses a time sequence class model.
An output module 504 for determining the consumption of the target vehicle spare part according to the output of the target consumption model.
In the embodiment of the invention, according to the output of the output module 504 of the target consumption model applicable to various data features, the consumption of various target vehicle spare parts can be accurately determined, so that a user can reserve various new vehicle spare parts applicable to various new vehicle types according to the accurately predicted spare part consumption, the waste of storage cost, production cost, labor cost and the like caused by the stock of the spare parts is prevented, or the user complaints, lost users and the like caused by the fact that the user cannot be timely supplied due to the insufficient reserve of the spare parts are prevented, the user satisfaction is greatly improved, and the new vehicle data utilization rate is improved.
In the embodiment of the invention, the modules such as the acquisition module, the selection module, the input module and the output module can fully utilize new vehicle data, determine the influence degree of various influence factors such as spare part attributes, seasons and the like on spare part consumption, and select a proper spare part consumption prediction model, so that the consumption of the spare parts of the vehicle is accurately determined, the storage cost is prevented from being accumulated and wasted or the spare parts are not enough to supply to user demands, the user satisfaction is improved, and the new vehicle data utilization rate is improved.
Fig. 6 shows an exemplary system architecture diagram of a determination method of consumption of a vehicle spare part or a determination device of consumption of a vehicle spare part that is suitable for application to an embodiment of the present invention, and as shown in fig. 6, an exemplary system architecture of a determination method of consumption of a vehicle spare part or a determination device of consumption of a vehicle spare part of an embodiment of the present invention includes:
as shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 is used as a medium to provide communication links between the terminal devices 601, 602, 603 and the server 105. The network 604 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 605 via the network 604 using the terminal devices 601, 602, 603 to receive or send messages, etc. Various communication client applications, such as spare part class applications, shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc., may be installed on the terminal devices 601, 602, 603.
The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 605 may be a server providing various services, such as a background management server providing support for spare part class websites browsed by the user using the terminal devices 601, 602, 603. The background management server may perform processing such as analysis on the received data such as the spare part consumption determination request, and feed back the processing result (e.g., target spare part consumption) to the terminal devices 601, 602, 603.
It should be noted that, the method for determining the consumption of the spare parts of the vehicle provided by the embodiment of the present invention is generally executed by the server 605, and accordingly, the device for determining the consumption of the spare parts of the vehicle is generally disposed in the server 605.
It should be understood that the number of terminal devices, networks and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 7 is a schematic structural diagram of a computer system suitable for use in implementing a terminal device or a server according to an embodiment of the present invention, and as shown in fig. 7, a computer system 700 of a terminal device or a server according to an embodiment of the present invention includes:
a Central Processing Unit (CPU) 701, which can execute various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data required for the operation of the system 700 are also stored. The CPU701, ROM702, and RAM703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output portion 707 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is mounted into the storage section 708 as necessary.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 701.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts 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 code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, 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 or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor includes an acquisition module, a selection module, an input module, and an output module. The names of these modules do not constitute a limitation on the module itself in some cases, and for example, the selection module may also be described as "a module that selects a target vehicle spare part, and target spare part data corresponding to the target vehicle spare part, according to the transaction time, the spare part type, and the transaction number".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: acquiring the transaction quantity of a target vehicle, and transaction time and spare part types of various vehicle spare parts applicable to the target vehicle; wherein the transaction number corresponds to the transaction time; selecting a target vehicle spare part and target spare part data corresponding to the target vehicle spare part according to the transaction time, the spare part type and the transaction quantity; inputting the target spare part data into a preset target consumption model corresponding to the target vehicle spare part; the consumption model is obtained through training according to historical vehicle data and historical spare part data; and determining the consumption of the target vehicle spare part according to the output of the target consumption model.
According to the technical scheme of the embodiment of the invention, the correct identification of the vehicle spare parts with relevant influences brought by a new vehicle is ensured through the relation coefficient between the spare part transaction time and the vehicle holding quantity and the new vehicle type marking of the target vehicle; the method for determining the consumption of the spare parts of the vehicle is more suitable for spare part prediction of a new vehicle scene, and can provide category commonalities and interpretability for the existing spare parts of the vehicle which cannot be predicted and are not suitable for model prediction so as to ensure that barriers similar to other methods under applicable scenes can be constructed when the method is used for subsequent projects.
According to the technical scheme provided by the embodiment of the invention, the new vehicle data can be fully utilized, the influence degree of various influence factors such as spare part attributes, seasons and the like on spare part consumption can be determined, and a proper spare part consumption prediction model is selected, so that the consumption of the spare parts of the vehicle can be accurately determined, the storage cost of the spare parts can be prevented from being accumulated and wasted or the spare parts can not be supplied to the user, the user satisfaction can be improved, and the new vehicle data utilization rate can be improved.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (12)
1. A method of determining consumption of a spare part of a vehicle, comprising:
acquiring the transaction quantity of a target vehicle, and transaction time and spare part types of various vehicle spare parts applicable to the target vehicle; wherein the transaction number corresponds to the transaction time;
selecting a target vehicle spare part and target spare part data corresponding to the target vehicle spare part according to the transaction time, the spare part type and the transaction quantity;
Inputting the target spare part data into a preset target consumption model corresponding to the target vehicle spare part; the consumption model is obtained through training according to historical vehicle data and historical spare part data;
and determining the consumption of the target vehicle spare part according to the output of the target consumption model.
2. The method of claim 1, wherein selecting a target vehicle spare part, target spare part data corresponding to the target vehicle spare part, based on the transaction time, the spare part type, and the transaction quantity, comprises:
determining intermediate spare part data greater than a preset relationship coefficient threshold according to relationship coefficients between the transaction time of the plurality of vehicle spare parts and the transaction quantity of the target vehicle;
determining a target vehicle spare part according to the spare part type, selecting intermediate spare part data corresponding to the target vehicle spare part from the intermediate spare part data, and calibrating corresponding classification characteristics;
and clustering the intermediate spare part data carrying the classification characteristics to obtain the target spare part data.
3. The method of claim 2, wherein the determining intermediate spare part data greater than a preset relationship coefficient threshold based on a relationship coefficient between transaction times of a plurality of the vehicle spare parts and a transaction number of the target vehicle comprises:
Determining the relationship coefficient between the vehicle spare part and the target vehicle according to the product of the transaction time and the transaction quantity;
and respectively comparing the relation coefficient with the preset relation coefficient threshold value, and selecting intermediate spare part data with the relation coefficient larger than or equal to the preset relation coefficient threshold value.
4. The method according to claim 2, wherein the clustering the intermediate spare part data carrying classification features to obtain the target spare part data includes:
respectively determining feature values of intermediate spare part data of different classification features;
for each of the classification features, a plurality of feature thresholds are set:
dividing the middleware data according to the relation between the characteristic value and the characteristic threshold value, and determining a plurality of mean square errors of the middleware data under different classification characteristics;
and determining the target spare part data according to the minimum value of the mean square error.
5. The method of claim 4, wherein the dividing the middleware data according to the relationship between the feature value and the feature threshold value, determining a plurality of mean square errors of the middleware data under different classification features, comprises:
Dividing the intermediate spare part data corresponding to the classification features into first sample data and second sample data according to each feature threshold value respectively; wherein the characteristic value of the first sample data is smaller than or equal to the characteristic threshold value, and the characteristic value of the second sample data is larger than the characteristic threshold value;
determining a first average value of all the characteristic values included in the first sample data and a second average value of all the characteristic values included in the second sample data;
and calculating the mean square error of the first sample data and the second sample data.
6. The method of claim 5, wherein said determining the target spare part data based on the minimum value of the mean square error comprises:
and taking the corresponding first sample data and second sample data as target spare part data of the intermediate spare part data according to the minimum value of the mean square error corresponding to each characteristic threshold.
7. The method of any one of claims 2 to 6, wherein the classification features include a repair class and a maintenance class.
8. The method as recited in claim 1, further comprising:
Preprocessing the historical vehicle data and the historical spare part data;
training a plurality of consumption models respectively by taking the preprocessed historical vehicle data and the preprocessed historical spare part data as input and a plurality of historical spare part consumption as output;
comparing training results of a plurality of consumption models, and respectively determining the target consumption models applicable to the target vehicle spare parts.
9. The method of claim 1, further comprising, prior to the clustering the middleware data carrying the classification feature:
and calibrating the seasonal features of the middleware data carrying the classification features.
10. A device for determining consumption of a spare part of a vehicle, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the transaction quantity of a target vehicle, and the transaction time and spare part types of various vehicle spare parts applicable to the target vehicle; wherein the transaction number corresponds to the transaction time;
the selecting module is used for selecting a target vehicle spare part and target spare part data corresponding to the target vehicle spare part according to the transaction time, the spare part type and the transaction quantity;
The input module is used for inputting the target spare part data into a preset target consumption model corresponding to the target vehicle spare part; the consumption model is obtained through training according to historical vehicle data and historical spare part data;
and the output module is used for determining the consumption of the spare parts of the target vehicle according to the output of the target consumption model.
11. An electronic device for determining consumption of a vehicle spare part, comprising:
one or more processors;
storage means for storing one or more programs,
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-9.
12. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-9.
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