CN117788038B - Intelligent monitoring, analyzing and processing method for platform data in automobile industry - Google Patents
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Abstract
The invention belongs to the technical field of data monitoring, analyzing and processing, and particularly discloses an intelligent monitoring, analyzing and processing method for platform data in the automobile industry, which comprises the following steps: importing search information; the method comprises the steps of calling relevant data of an automobile to be analyzed from an automobile information platform; analyzing the operation state information of the automobile to be analyzed; judging the operation state of the automobile to be analyzed, confirming the optimization scheme of the automobile to be analyzed when the operation state of the automobile to be analyzed is abnormal, feeding back, counting the operation state coincidence index of the automobile to be analyzed when the operation state of the automobile to be analyzed is normal, and feeding back; the method effectively solves the problem of poor guarantee of the current processing, avoids the unilateral performance and the limitation of the general processing on the current breadth, realizes the long-term tracking processing of the automobile sales data, ensures the referential and the effectiveness of the mining data, provides assistance and convenience for the development of the subsequent automobile sales operation work, and improves the control degree of the automobile sales operation work.
Description
Technical Field
The invention belongs to the technical field of data monitoring, analyzing and processing, and relates to an intelligent monitoring, analyzing and processing method for platform data in the automobile industry.
Background
With the rapid development and digital transformation of the automotive industry, a large amount of data is generated and accumulated, including vehicle information, user behavior, traffic conditions, and the like. The monitoring analysis and processing of the data have important significance in pushing the automobile industry to intelligent and digital transformation, optimizing the operation of the vehicle, improving the user experience, improving the traffic management and the like.
At present, the main focus of platform data monitoring in the automobile industry is concentrated on a sales level so as to analyze the sales condition of an automobile, but at present, the platform data monitoring analysis processing aspect in the automobile industry has some existing defects and shortages, and mainly comprises the following aspects: 1. the guarantee of the treatment is not strong: the current data mining depth is insufficient, belongs to general data processing on breadth, and is not convenient for the development of subsequent automobile operation work due to the fact that basic statistical analysis and report generation are concentrated, and tracking of long-term sales data of automobiles is lacking, so that the reference property and the effectiveness of mining data are not strong.
2. Insufficient assay availability: at present, the sales volume of an enterprise is only analyzed, and the sales regularity analysis is lacking, so that certain deviation exists in the accuracy and effectiveness of the judgment of the subsequent automobile operation state, and the reliability and rationality of the subsequent automobile sales decision cannot be ensured.
3. The treatment pertinence is not strong: at present, only a single sales volume is analyzed, corresponding sales processing is further performed, and fusion analysis is not performed on sales data and maintenance data of an automobile, so that the directionality of optimization of subsequent vehicle operation is not clear enough, and further the subsequent optimization effect is difficult to expect due to high probability.
Disclosure of Invention
In view of this, in order to solve the problems set forth in the background art, an intelligent monitoring, analyzing and processing method for platform data in the automotive industry is now provided.
The aim of the invention can be achieved by the following technical scheme: the invention provides an intelligent monitoring, analyzing and processing method for platform data in the automobile industry, which comprises the following steps: s1, importing search information: and extracting the search car corresponding to the input of the target car production enterprise as the car to be analyzed.
S2, calling automobile platform data: and the relevant data of the automobile to be analyzed is called from the automobile information platform, wherein the relevant data comprise the announced input ratio, after-sales service data, time of sale, sales volume in each sale month and planned sales volume.
S3, analyzing the operation state of the automobile: and analyzing the operation state information of the automobile to be analyzed based on the sales volume and the planned sales volume of each sales month.
S4, evaluating the operation state of the vehicle: and judging the operation state of the automobile to be analyzed, and starting the step S5 when the operation state of the automobile to be analyzed is normal, otherwise starting the step S6.
S5, vehicle operation data statistics: and counting the operation state coincidence index of the automobile to be analyzed.
S6, vehicle operation optimization decision: and confirming an optimization scheme of the automobile to be analyzed according to the announced input ratio and the after-sales service data.
S7, vehicle operation analysis feedback: and (3) feeding back the analysis result in the step S5 or the step S6 to a corresponding retrieval interface of the target automobile production enterprise.
Preferably, the analyzing the operation state information of the automobile to be analyzed includes: and constructing a sales change curve of the automobile to be analyzed by taking the sales month as an abscissa and the sales quantity as an ordinate, and extracting the sales month and the sales quantity of each valley point.
And calculating the difference between the sales volume of each valley point and the planned sales volume of the sales month of each valley point, and recording the difference as the sales plan difference.
If the sales plan difference of a certain valley point is negative and smaller than the set reference sales difference, the valley point is marked as a concerned point, and each concerned point is screened out.
And sequencing all the attention points according to the sales time of the attention points, marking the attention point in the first sequence as a selected point, marking the sales month of the selected point as the starting low Gu Yue, and taking the sales change curve and the starting low valley month as the operation state information of the automobile to be analyzed.
Preferably, the judging the operation state of the automobile to be analyzed specifically includes: and extracting the selling month from the selling time, if the selling month is the same as the initial valley month, cutting out a selling change curve segment positioned after the initial valley month from the selling change curve, and judging the operation state of the automobile to be analyzed according to an operation state judging rule, wherein the operation state is one of normal and abnormal.
If the initial low valley month is positioned after the selling month and the number of the interval months between the initial low Gu Yue and the selling month is larger than or equal to the set reference interval month, the operation state of the automobile to be analyzed is normally taken as the operation state of the automobile to be analyzed, and if the number of the interval months between the initial low Gu Yue and the selling month is smaller than the set reference interval month, the abnormal operation state is taken as the operation state of the automobile to be analyzed.
Preferably, the specific decision process of the operation state decision rule is as follows: marking the sales change curve segment after the initial valley month as an analysis curve segment, extracting the slope of the analysis curve segmentAnd will/>And a determination condition 1 as an operation state determination rule.
Cutting out the total length of the curve segment above the sales corresponding to the initial low Gu Yue from the analysis curve segment, and marking asAt the same time, the length of the analysis curve segment is recorded as/>Will/>And a determination condition 2 as an operation state determination rule.
And positioning the position of the fluctuation point from the analysis curve segment, dividing the analysis curve segment according to the fluctuation point to obtain each sub analysis curve segment after division, and extracting the slope of each sub analysis curve segment.
And taking the sub-analysis curve segment with the slope larger than or equal to 0 as a standard curve segment, and taking the analysis curve segment with the slope smaller than 0 as a non-standard curve segment.
Extracting the lengths of all standard curve segments and all substandard curve segments, and summing to obtain the total length of standard curve segmentsSum total length of substandard curve segment/>Will/>And a determination condition 3 as an operation state determination rule.
If the analysis curve segment meets any one of the judgment condition 1, the judgment condition 2 or the judgment condition 3, the normal operation state of the automobile to be analyzed is taken as the normal operation state.
And if the analysis curve segments do not meet the judgment condition 1, the judgment condition 2 and the judgment condition 3, taking the abnormality as the operation state of the automobile to be analyzed.
Preferably, the statistics of the running state matching index of the automobile to be analyzed includes: the sales vehicles with the same sales month as the corresponding sales month of the vehicle to be analyzed are recorded as the synchronous sales vehicles, and the number of the synchronous sales vehicles is counted。
The sales of the vehicles sold in the same period in each sales month is extracted from the vehicle information platform and recorded as，/>Representing the number of cars sold contemporaneously,/>，/>Representing sales month number,/>And the sales of the automobile to be analyzed in each sales month is recorded as/>。
The number of months between the start of low Gu Yue and the month of sale was taken as the number of months of sale maintenanceAccording to/>The confirmation mode of (a) confirms and obtains the sales maintenance month number/>, of each contemporaneous sales automobile。
Extracting average market ratio of corresponding sales vehicles of target automobile manufacturers from an automobile information platform, and marking the average market ratio as。
Counting the operation state coincidence index of the automobile to be analyzed，，/>For setting reference maintenance month difference,/>And evaluating the compensation factors for the set operation state anastomosis.
Preferably, the specific setting process of the operation state anastomosis evaluation compensation factor is as follows: extracting the number of fluctuation points from the sales change curve of the automobile to be analyzedAmplitude/>And slope/>。
If it is0 Is taken as an operation state anastomosis evaluation compensation factor/>，/>Sales change rate for the set reference.
If it isWill/>As an operational status anastomosis evaluation compensation factor/>，The first fluctuation point number and the first change amplitude of the set reference are respectively set.
If it isWill/>As an operational status anastomosis evaluation compensation factor/>，The number of the second fluctuation points and the second change amplitude value are respectively set as references.
Preferably, the identifying the optimization scheme of the automobile to be analyzed includes: the number of after-sales services, the license plate number of each after-sales service, the time, the after-sales lead and the after-sales log are extracted from the after-sales service data.
After-sales service of after-sales guide due to part fault and accident maintenance is respectively marked as class I service and class II service, and the number of class I service times is countedAnd class II service times/>。
Screening the class I service and the class II service, respectively marking the screened class I service and class II service as the number of times of paying attention to the class I service and the number of times of paying attention to the class II service, and counting the number of times of paying attention to the class I serviceAnd pay attention to class II service times/>。
Will beAnd/>And (3) importing the optimization category identification model, and outputting the optimization category of the automobile to be analyzed, wherein the optimization category is one or more of declaration improvement, part improvement and structural improvement.
When the optimization category is the announced improvement, extracting the announced input ratio of each contemporaneous sales automobile from the automobile information platform, evaluating the improved announced input ratio through an announced evaluation model, and taking the improved announced input ratio as optimization information.
When the optimization category is part improvement, the improvement part is confirmed and used as optimization information.
When the optimization category is structural improvement, the improvement structure is confirmed and used as optimization information.
When the optimization category is part improvement and structure improvement, the improved part and the improved structure are used as optimization information, and the optimization category and the optimization information under the optimization category are combined to generate an optimization scheme of the automobile to be analyzed.
Preferably, the screening of each class i service and each class ii service includes: and setting the reference maintenance interval duration of each class I service and each class II service based on the after-sales log.
And taking the interval duration between the time and the selling time of each class I service and each class II service as the selling interval duration of each class I service and each class II service.
And marking the class I service and the class II service with the sale interval time smaller than the set reference maintenance interval time as the class I service and the class II service, and screening each class I service and each class II service.
Preferably, the specific expression formula of the optimization category identification model is:，/> The assessment conditions are identified for each optimization category, Representation/>，/>Representation/>And/>，/>Representation/>And/>，/>Representation of，/>The after-market service ratio is advanced for setting the reference license.
Preferably, the specific evaluation process of the announced evaluation model is as follows: and marking the sales change curve of the automobile to be analyzed as a comparison curve, constructing the sales change curve of each contemporaneous sales automobile, and performing superposition comparison with the comparison curve to obtain the total length of the non-superposition curve segment and the total length of the curve segment above the comparison curve.
Will beThe contemporaneous sales car of (1) is recorded as a reference sales car, and the announced input ratio of the car to be analyzed is recorded as/>Further, the announce input ratio and/>, of each reference sales vehicleAnd performing poor making to obtain a poor announced input ratio.
The reference sales vehicle having the announced input ratio difference within the set reference announced input ratio difference interval is referred to as a b-class vehicle.
The reference sales vehicle with the announced input ratio difference larger than the upper limit value of the set reference announced input ratio difference interval is recorded as a class a vehicle, the announced input ratio difference of the class a vehicle is subjected to mean value calculation, and the calculation result is recorded as。
The reference sales vehicle with the announced input ratio difference smaller than the lower limit value of the set reference announced input ratio difference interval is referred to as a c-class vehicle.
Counting the numbers of the class a automobiles, the class b automobiles and the class c automobiles, and respectively marking as、/>And/>。
Will be、/>And/>As an input of the announcement evaluation model, an improved announcement input ratio is used as an output of the announcement evaluation model, and the announcement evaluation model specifically shows the following formula: /(I)，/>To improve the announce input ratio,/>To set the compensation announce input ratio.
Compared with the prior art, the invention has the following beneficial effects: (1) According to the invention, after-sales service data, the time for selling and the sales volume and the planned sales volume of the automobile to be analyzed are obtained from the automobile information platform, so that the analysis of the automobile operation state and the judgment of the operation state are carried out, and corresponding processing is carried out according to the judgment result of the operation state, thereby effectively solving the problem of weak guarantee of the current processing, avoiding the one-sided and limited aspects of the general processing on the current breadth, realizing the long-term tracking processing of the automobile sales data, ensuring the referential and effectiveness of the mining data, providing assistance and convenience for the development of the subsequent automobile sales operation work, and further improving the control degree of the automobile sales operation work.
(2) According to the invention, the starting low Gu Yue is determined, the judging conditions are set from three aspects of the change trend, the overall growth aspect ratio and the fluctuation rule, and then the operation state judging rule is set, so that the operation state judgment is carried out, the problem of insufficient analysis effectiveness of the current operation state is solved, the defect that the analysis is carried out only from the sales volume is avoided, the accuracy and the effectiveness of the judgment of the automobile operation state are ensured, and the reliability and the rationality of the subsequent automobile sales decision are further promoted.
(3) When the operation state is normal, the invention carries out comparison analysis by introducing the synchronous sales vehicle to carry out sales volume level and sales time maintenance level, thereby counting the operation state coincidence index of the vehicle to be analyzed, ensuring the authenticity and rationality of the operation state analysis result of the vehicle to be analyzed, intuitively displaying the market condition and sales condition of the vehicle to be analyzed, and simultaneously being beneficial to more accurately identifying the change of sales trend and better understanding the sales strategy effect.
(4) According to the invention, the operational state anastomosis evaluation compensation factor is set from three layers of fluctuation conditions, increasing trend and changing amplitude of the sales change curve, so that the reliability and the adhesiveness of the operational state anastomosis index of the automobile to be analyzed are improved, and the error generated when the change is large is eliminated as much as possible.
(5) When the operation state is abnormal, the optimization category and the optimization information are confirmed according to the information such as the after-sale cause and the after-sale log, so that the defect of weak pertinence of the current processing is overcome, the fusion analysis of the sales data and the maintenance data is realized, the directivity of the sales operation of the subsequent vehicles is clarified, and the optimization effect of the sales operation of the subsequent vehicles is further ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a method for intelligently monitoring, analyzing and processing platform data in the automobile industry, which comprises the following steps: s1, importing search information: and extracting the search car corresponding to the input of the target car production enterprise as the car to be analyzed.
S2, calling automobile platform data: and the relevant data of the automobile to be analyzed is called from the automobile information platform, wherein the relevant data comprise the announced input ratio, after-sales service data, time of sale, sales volume in each sale month and planned sales volume.
Specifically, the after-sales service data includes the number of times of after-sales service, the license plate number of each after-sales service, time, after-sales lead, and after-sales log.
In one particular embodiment, after-market causes include, but are not limited to, maintenance, part failure, vehicle refitting, and accident repair.
S3, analyzing the operation state of the automobile: and analyzing the operation state information of the automobile to be analyzed based on the sales volume and the planned sales volume of each sales month.
Illustratively, analyzing operational status information of an automobile to be analyzed includes: s31, constructing a sales change curve of the automobile to be analyzed by taking the sales month as an abscissa and the sales quantity as an ordinate, and extracting the sales month and the sales quantity of each valley point.
S32, calculating the difference between the sales volume of each valley point and the planned sales volume of the sales month of each valley point, and marking the difference as the sales plan difference.
And S33, if the sales plan difference of a certain valley point is a negative value and smaller than the set reference sales difference, marking the valley point as a concerned point, and screening out each concerned point.
S34, sorting all the attention points according to the sales time sequence, marking the attention point in the first sorting position as a selected point, marking the sales month in which the selected point is positioned as a starting low Gu Yue, and taking the sales change curve and the starting low valley month as the operation state information of the automobile to be analyzed.
S4, evaluating the operation state of the vehicle: and judging the operation state of the automobile to be analyzed, and starting the step S5 when the operation state of the automobile to be analyzed is normal, otherwise starting the step S6.
Illustratively, determining the operating state of the vehicle to be analyzed, the specific determination includes: s41, extracting a selling month from the selling time, if the selling month is the same as the initial low valley month, cutting out a selling change curve segment positioned after the initial low valley month from the selling change curve, and judging the operation state of the automobile to be analyzed according to an operation state judging rule, wherein the operation state is one of normal and abnormal.
Further, the specific decision process of the operation state decision rule is as follows: u1, marking a sales change curve segment positioned after the initial valley month as an analysis curve segment, and extracting the slope of the analysis curve segmentAnd will/>And a determination condition 1 as an operation state determination rule.
It should be added that the slopes of the curve and the curve segment refer to the slopes of the corresponding regression lines of the curve and the curve segment.
U2, cutting out the total length of the curve segment above the sales corresponding to the initial low Gu Yue from the analysis curve segment, and marking asAt the same time, the length of the analysis curve segment is recorded as/>Will/>And a determination condition 2 as an operation state determination rule.
And U3, positioning the position of a fluctuation point from the analysis curve segment, dividing the analysis curve segment according to each fluctuation point to obtain each sub analysis curve segment after division, and extracting the slope of each sub analysis curve segment.
It should be noted that the fluctuation point refers to a point with opposite left-right variation trend in the curve, such as a point with left-side rising and right-side falling or a point with left-side falling and right-side rising.
And U4, taking the sub-analysis curve segment with the slope larger than or equal to 0 as a standard curve segment, and taking the analysis curve segment with the slope smaller than 0 as a non-standard curve segment.
U5, extracting the length of each standard curve segment and the length of each substandard curve segment, and respectively summing to obtain the total length of the standard curve segmentsSum total length of substandard curve segment/>Will/>And a determination condition 3 as an operation state determination rule.
And U6, if the analysis curve segment meets any one of the judgment condition 1, the judgment condition 2 or the judgment condition 3, taking the normal operation state as the operation state of the automobile to be analyzed.
And U7, if the analysis curve segments do not meet the judgment condition 1, the judgment condition 2 and the judgment condition 3, taking the abnormality as the operation state of the automobile to be analyzed.
S42, if the initial low valley month is positioned after the selling month and the number of the interval months between the initial low Gu Yue and the selling month is larger than or equal to the set reference interval month, the operation state of the automobile to be analyzed is normally taken as the operation state of the automobile to be analyzed, and if the number of the interval months between the initial low Gu Yue and the selling month is smaller than the set reference interval month, the abnormal operation state is taken as the operation state of the automobile to be analyzed.
In one embodiment, the reference number of months may specifically be 2.
According to the embodiment of the invention, the starting low Gu Yue is determined, the judging conditions are set from three aspects of the change trend, the overall growth aspect ratio and the fluctuation rule, and then the operation state judging rule is set, so that the operation state is judged according to the judging conditions, the problem of insufficient analysis effectiveness of the current operation state is solved, the defect that the analysis is only carried out from the sales volume is avoided, the accuracy and the effectiveness of the judgment of the automobile operation state are ensured, and the reliability and the rationality of the subsequent automobile sales decision are further promoted.
S5, vehicle operation data statistics: and counting the operation state coincidence index of the automobile to be analyzed.
Specifically, the statistics of the operational state fitness index of the automobile to be analyzed includes: s51, marking the sales vehicles which are the same as the corresponding sales month of the vehicle to be analyzed as synchronous sales vehicles, and counting the number of the synchronous sales vehicles。
S52, extracting sales of each synchronous sales vehicle in each sales month from the vehicle information platform, and marking the sales as，/>Representing the number of cars sold contemporaneously,/>，/>Representing sales month number,/>And the sales of the automobile to be analyzed in each sales month is recorded as/>。
In one embodiment of the present invention, in one embodiment,、In order to take the value of any positive integer greater than 1, wherein,。
S53, taking the interval month number of the initial low Gu Yue and the selling month as the selling maintenance month numberAccording to/>The confirmation mode of (a) confirms and obtains the sales maintenance month number/>, of each contemporaneous sales automobile。
S54, extracting average market ratio of the corresponding sales vehicles of the target automobile production enterprises from the automobile information platform, and marking the average market ratio as。
S55, counting the operation state anastomosis index of the automobile to be analyzed，，/>For setting reference maintenance month difference,/>And evaluating the compensation factors for the set operation state anastomosis.
According to the embodiment of the invention, when the operation state is normal, the comparison analysis is performed by introducing the synchronous sales vehicle to perform the sales volume level and the sales time maintenance level, so that the operation state coincidence index of the vehicle to be analyzed is counted, the authenticity and rationality of the operation state analysis result of the vehicle to be analyzed are ensured, the market condition and the sales condition of the vehicle to be analyzed are intuitively displayed, and meanwhile, the change of the sales trend is more accurately identified and the sales strategy effect is better understood.
The specific setting process of the operation state anastomosis evaluation compensation factor is as follows: g1, extracting the number of fluctuation points from a sales change curve of an automobile to be analyzedAmplitude/>And slope/>。
G2, if0 Is taken as an operation state anastomosis evaluation compensation factor/>，/>Sales change rate for the set reference.
G3, ifWill/>As an operational status anastomosis evaluation compensation factor/>，The first fluctuation point number and the first change amplitude of the set reference are respectively set.
G4, ifWill/>As an operational status anastomosis evaluation compensation factor/>，/>The number of the second fluctuation points and the second change amplitude value are respectively set as references.
In one embodiment, the first number of the fluctuation points may be 2, and the second number of the fluctuation points may be 2，/>Representing an upward rounding symbol, wherein the values of the first and second magnitudes of variation are set with specific reference to sales, i.e./>And/>Positive integers with values greater than 0.
According to the embodiment of the invention, the operational state anastomosis evaluation compensation factor is set from three layers of fluctuation conditions, increasing trend and changing amplitude of the sales change curve, so that the reliability and the adhesiveness of the operational state anastomosis index of the automobile to be analyzed are improved, and errors generated when the change is large are eliminated as much as possible.
S6, vehicle operation optimization decision: and confirming an optimization scheme of the automobile to be analyzed according to the announced input ratio and the after-sales service data.
Illustratively, confirming an optimization scheme of the automobile to be analyzed comprises: and X1, extracting the number of after-sale services, the license plate number of each after-sale service, the time, the after-sale cause and the after-sale log from the after-sale service data.
X2, marking after-sales service of after-sales guide due to part fault and accident maintenance as I-type service and II-type service respectively, and counting the times of the I-type serviceAnd class II service times/>。
X3, screening each class I service and each class II service, respectively marking the screened class I service and class II service as the number of times of paying attention to the class I service and the number of times of paying attention to the class II service, and counting the number of times of paying attention to the class I serviceAnd pay attention to the number of class II services。
Further, screening the class I services and the class II services comprises: and J1, setting and setting the reference maintenance interval duration of each class I service and each class II service based on the after-sales log.
The specific setting mode of the reference maintenance interval duration of each class I service is as follows: and J11, locating the names of the parts after sale from the post-sale logs of the class I services.
And J12, positioning the reference stable use time length of the after-sales parts corresponding to each class I service from an automobile information base according to the after-sales part names of each class I service, and further using the reference stable use time length as the reference maintenance interval time length of each class I service.
And J13, positioning the after-sale structure from the after-sale log of each class II service, positioning the reference stable use time length of the after-sale structure corresponding to each class I service from the automobile information base, and taking the reference stable use time length as the reference maintenance interval time length of each class II service.
And J2, taking the interval duration between the time and the selling time of each class I service and each class II service as the selling interval duration of each class I service and each class II service.
And J3, marking the class I service and the class II service with the selling interval time smaller than the set reference maintenance interval time as the class I service and the class II service, and screening each class I service and each class II service.
X4, willAnd/>And (3) importing the optimization category identification model, and outputting the optimization category of the automobile to be analyzed, wherein the optimization category is one or more of declaration improvement, part improvement and structural improvement.
Further, the concrete expression formula of the optimization category identification model is as follows:，/> identifying assessment conditions for various optimization categories,/> Representation/>，/>Representation/>And/>，/>Representation/>And/>，/>Representation of，/>The after-market service ratio is advanced for setting the reference license.
In one embodiment, the proportion of vehicle licensed after-market services may vary from one automotive manufacturer to another and market situation, and typically is between 5% and 15%. That is, about 5% to 15% of the vehicles will require after-market service, including warranty, maintenance, replacement of parts, etc., during the sales process of the vehicle, in order to facilitate the specific analysis of the present invention, that isThe specific value can be 0.15.
And X5, when the optimization category is the release improvement, extracting the release input ratio of each contemporaneously sold automobile from the automobile information platform, and evaluating the improved release input ratio through a release evaluation model to serve as optimization information.
Further, the specific evaluation process of the announced evaluation model is as follows: and X51, marking the sales change curve of the automobile to be analyzed as a comparison curve, constructing the sales change curve of each synchronous sales automobile in the same way according to the construction mode of the sales change curve of the automobile to be analyzed, and performing superposition comparison with the comparison curve to obtain the total length of the curve sections which are not superposed and the total length of the curve sections which are positioned above the comparison curve.
X52, willThe contemporaneous sales car of (1) is recorded as a reference sales car, and the announced input ratio of the car to be analyzed is recorded as/>Further, the announce input ratio and/>, of each reference sales vehicleAnd performing poor making to obtain a poor announced input ratio.
And X53, marking the reference sales vehicles with the announced input ratio difference within the set reference announced input ratio difference interval as b-class vehicles.
X54, marking the reference sales vehicle with the announced input ratio difference larger than the upper limit value of the set reference announced input ratio difference interval as a class a vehicle, and simultaneously, carrying out average value calculation on the announced input ratio difference of the class a vehicle, and marking the calculation result as。
And X55, marking the reference sales vehicle with the announced input ratio difference smaller than the lower limit value of the set reference announced input ratio difference interval as a class-c vehicle.
X56, counting the number of a-class automobiles, b-class automobiles and c-class automobiles, and respectively recording as、/>And/>。
X57, will、/>And/>As an input of the announcement evaluation model, an improved announcement input ratio is used as an output of the announcement evaluation model, and the announcement evaluation model specifically shows the following formula: /(I)，To improve the announce input ratio,/>To set the compensation announce input ratio.
And X6, when the optimization category is part improvement, confirming the improved part and taking the improved part as optimization information.
It should be added that the verification principle of the improved part is the same as that of the subsequent improved structure, wherein the specific verification process of the improved part is as follows: and V1, extracting license plate numbers of all I-class services, and carrying out duplication removal processing on the I-class services of the same license plate number to obtain all I-class services subjected to duplication removal processing, wherein all I-class services are used as all I-class services of a bicycle.
And V2, comparing names of after-sale parts of the after-sale parts with the same names, and summing up times of the after-sale parts with the same names to obtain times of the after-sale parts.
V3, the service times of the bicycle I type of each after-sales part are countedThe ratio was made as the after-market service ratio for each after-market part.
V4 compares the after-service ratio of each after-service part with the set reference after-service ratio of each after-service part, and takes the after-service part with the after-service ratio larger than the reference after-service ratio as the improved part.
In one embodiment, the automotive parts include, but are not limited to, lamps, tires, battery and electronics systems, brake systems and engines, and in practical application scenarios, the maintenance requirement ratio of the engine is about 20%, the maintenance requirement ratio of the brake system is about 15%, the maintenance requirement ratio of the battery and electronics system battery and charging system is about 10%, the maintenance requirement ratio of the tires is about 12%, and the maintenance requirement ratio of the lamps is about 5%, i.e., the reference after-market service ratios of lamps, tires, battery and electronics systems, brake systems and such after-market parts of the engine can be sequentially 0.05, 0.12, 0.1, 0.15 and 0.2.
And X7, when the optimization category is structural improvement, confirming the improved structure and taking the improved structure as optimization information.
And X8, when the optimization category is part improvement and structure improvement, taking the improved part and improved structure as optimization information, and combining the optimization category and the optimization information under the optimization category to generate an optimization scheme of the automobile to be analyzed.
According to the embodiment of the invention, when the operation state is abnormal, the optimization category and the optimization information are confirmed according to the information such as the after-sale cause and the after-sale log, so that the defect of weak pertinence of the current processing is overcome, the fusion analysis of the sales data and the maintenance data is realized, the sales operation direction of the subsequent vehicles is clarified, and the optimization effect of the sales operation of the subsequent vehicles is further ensured.
S7, vehicle operation analysis feedback: and (3) feeding back the analysis result in the step S5 or the step S6 to a corresponding retrieval interface of the target automobile production enterprise.
According to the embodiment of the invention, the after-sales service data, the time of sale, the sales volume in each sale month and the planned sales volume of the automobile to be analyzed are obtained from the automobile information platform, the automobile operation state analysis and the operation state judgment are carried out according to the after-sales service data, the sales volume and the planned sales volume, and the corresponding processing is carried out according to the operation state judgment result, so that the problem of weak guarantee of the current processing is effectively solved, the one-sided performance and the limitation of the general processing on the current breadth are avoided, the long-term tracking processing of the automobile sales data is realized, the referential property and the effectiveness of the mining data are ensured, the assistance and the convenience are provided for the development of the subsequent automobile sales operation work, and the control degree of the automobile sales operation work is further improved.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (4)
1. An intelligent monitoring, analyzing and processing method for platform data in the automobile industry is characterized in that: the method comprises the following steps:
S1, importing search information: extracting a search car corresponding to the input of a target car production enterprise as a car to be analyzed;
S2, calling automobile platform data: the method comprises the steps of calling relevant data of an automobile to be analyzed from an automobile information platform, wherein the relevant data comprise announced input ratio, after-sales service data, time of sale, sales volume in each sale month and planned sales volume;
s3, analyzing the operation state of the automobile: analyzing operation state information of the automobile to be analyzed based on sales volume and planned sales volume of each sales month;
S4, evaluating the operation state of the vehicle: judging the operation state of the automobile to be analyzed, and starting the step S5 when the operation state of the automobile to be analyzed is normal, otherwise starting the step S6;
s5, vehicle operation data statistics: counting the operation state coincidence index of the automobile to be analyzed;
S6, vehicle operation optimization decision: according to the announced input ratio and the after-sales service data, confirming an optimization scheme of the automobile to be analyzed;
S7, vehicle operation analysis feedback: feeding back the analysis result of the step S5 or the step S6 to a corresponding retrieval interface of a target automobile production enterprise;
The analyzing the operation state information of the automobile to be analyzed comprises the following steps:
Taking a sales month as an abscissa and a sales amount as an ordinate, constructing a sales change curve of the automobile to be analyzed, and extracting the sales month and the sales amount of each valley point;
Calculating the difference between the sales volume of each valley point and the planned sales volume of the sales month of each valley point, and recording the difference as sales plan difference;
If the sales plan difference of a certain valley point is a negative value and smaller than the set reference sales difference, marking the valley point as a concerned point, and screening out each concerned point;
Sequencing all the attention points according to the sales time of the attention points, marking the attention point in the first sequence as a selected point, marking the sales month of the selected point as the starting low Gu Yue, and taking the sales change curve and the starting low valley month as the operation state information of the automobile to be analyzed;
The judging of the operation state of the automobile to be analyzed specifically comprises the following steps:
Extracting a selling month from the selling time, if the selling month is the same as the initial valley month, cutting out a selling change curve segment positioned after the initial valley month from the selling change curve, and judging the operation state of the automobile to be analyzed according to an operation state judging rule, wherein the operation state is one of normal and abnormal;
If the initial low valley month is positioned after the selling month and the number of the interval months between the initial low Gu Yue and the selling month is larger than or equal to the set reference interval month number, normally taking the operation state of the automobile to be analyzed, and if the number of the interval months between the initial low Gu Yue and the selling month is smaller than the set reference interval month number, taking the abnormality as the operation state of the automobile to be analyzed;
the specific judging process of the operation state judging rule is as follows:
marking the sales change curve segment after the initial valley month as an analysis curve segment, extracting the slope of the analysis curve segment And will/>A determination condition 1 as an operation state determination rule;
Cutting out the total length of the curve segment above the sales corresponding to the initial low Gu Yue from the analysis curve segment, and marking as At the same time, the length of the analysis curve segment is recorded as/>Will/>A determination condition 2 as an operation state determination rule;
Positioning the position of a fluctuation point from the analysis curve segment, dividing the analysis curve segment according to each fluctuation point to obtain each sub analysis curve segment after division, and extracting the slope of each sub analysis curve segment;
taking a sub analysis curve segment with the slope larger than or equal to 0 as a standard curve segment, and taking an analysis curve segment with the slope smaller than 0 as a non-standard curve segment;
Extracting the lengths of all standard curve segments and all substandard curve segments, and summing to obtain the total length of standard curve segments Sum total length of substandard curve segment/>Will/>A determination condition 3 as an operation state determination rule;
If the analysis curve segment meets any one of the judgment condition 1, the judgment condition 2 or the judgment condition 3, taking the normal operation state as the operation state of the automobile to be analyzed;
if the analysis curve segments do not meet the judgment conditions 1, 2 and 3, taking the abnormality as the operation state of the automobile to be analyzed;
The statistics of the operational state anastomosis index of the automobile to be analyzed comprises the following steps:
The sales vehicles with the same sales month as the corresponding sales month of the vehicle to be analyzed are recorded as the synchronous sales vehicles, and the number of the synchronous sales vehicles is counted ；
The sales of the vehicles sold in the same period in each sales month is extracted from the vehicle information platform and recorded as，/>Representing the number of cars sold contemporaneously,/>，/>Representing sales month number,/>And the sales of the automobile to be analyzed in each sales month is recorded as/>；
The number of months between the start of low Gu Yue and the month of sale was taken as the number of months of sale maintenanceAccording to/>The confirmation mode of (a) confirms and obtains the sales maintenance month number/>, of each contemporaneous sales automobile；
Extracting average market ratio of corresponding sales vehicles of target automobile manufacturers from an automobile information platform, and marking the average market ratio as；
Counting the operation state coincidence index of the automobile to be analyzed，，/>For setting reference maintenance month difference,/>Evaluating a compensation factor for the set operation state anastomosis;
The specific setting process of the operation state anastomosis evaluation compensation factor is as follows:
Extracting the number of fluctuation points from the sales change curve of the automobile to be analyzed Amplitude/>And slope/>；
If it is0 Is taken as an operation state anastomosis evaluation compensation factor/>，/>Sales change rate for a set reference;
If it is Will/>As an operational status anastomosis evaluation compensation factor/>，The first fluctuation point number and the first change amplitude value of the set reference are respectively;
If it is Will/>As an operational status anastomosis assessment compensation factor，/>The number of the second fluctuation points is set as a reference, and the second change amplitude value is set as a reference;
the method for confirming the optimization scheme of the automobile to be analyzed comprises the following steps:
Extracting the number of after-sales service times, the license plate number of each after-sales service, the time, the after-sales lead and the after-sales log from the after-sales service data;
after-sales service of after-sales guide due to part fault and accident maintenance is respectively marked as class I service and class II service, and the number of class I service times is counted And class II service times/>；
Screening the class I service and the class II service, respectively marking the screened class I service and class II service as the number of times of paying attention to the class I service and the number of times of paying attention to the class II service, and counting the number of times of paying attention to the class I serviceAnd pay attention to class II service times/>；
Will beAnd/>Importing the optimization category identification model, and outputting the optimization category of the automobile to be analyzed, wherein the optimization category is one or more of declaration improvement, part improvement and structure improvement;
When the optimization category is announced improvement, extracting the announced input ratio of each contemporaneous sales automobile from an automobile information platform, evaluating the improved announced input ratio through an announced evaluation model, and taking the improved announced input ratio as optimization information;
When the optimization category is part improvement, confirming the improved part and taking the improved part as optimization information;
When the optimization category is structural improvement, confirming an improved structure and taking the improved structure as optimization information;
When the optimization category is part improvement and structure improvement, the improved part and the improved structure are used as optimization information, and the optimization category and the optimization information under the optimization category are combined to generate an optimization scheme of the automobile to be analyzed.
2. The intelligent monitoring, analyzing and processing method for the platform data of the automobile industry as claimed in claim 1, which is characterized in that: the screening of each class I service and each class II service comprises the following steps:
setting and setting reference maintenance interval duration of each class I service and each class II service based on the after-sales log;
Taking the interval duration between the time and the selling time of each class I service and each class II service as the selling interval duration of each class I service and each class II service;
And marking the class I service and the class II service with the sale interval time smaller than the set reference maintenance interval time as the class I service and the class II service, and screening each class I service and each class II service.
3. The intelligent monitoring, analyzing and processing method for the platform data of the automobile industry as claimed in claim 1, which is characterized in that: the concrete expression formula of the optimization category identification model is as follows:， identifying assessment conditions for various optimization categories,/> Representation/>，/>Representation ofAnd/>，/>Representation/>And/>，/>Representation/>，The after-market service ratio is advanced for setting the reference license.
4. The intelligent monitoring, analyzing and processing method for the platform data of the automobile industry as claimed in claim 1, which is characterized in that: the specific evaluation process of the announced evaluation model is as follows:
marking the sales change curve of the automobile to be analyzed as a comparison curve, constructing sales change curves of the automobiles sold in the same period, and performing superposition comparison with the comparison curve to obtain the total length of the curve sections which are not superposed and the total length of the curve sections which are positioned above the comparison curve;
Will be The contemporaneous sales car of (1) is recorded as a reference sales car, and the announced input ratio of the car to be analyzed is recorded as/>Further, the announce input ratio and/>, of each reference sales vehiclePerforming deviation making to obtain a dispersion input ratio deviation;
The method comprises the steps that a reference sales vehicle with a announced input ratio difference within a set reference announced input ratio difference interval is recorded as a b-type vehicle;
The reference sales vehicle with the announced input ratio difference larger than the upper limit value of the set reference announced input ratio difference interval is recorded as a class a vehicle, the announced input ratio difference of the class a vehicle is subjected to mean value calculation, and the calculation result is recorded as ；
The method comprises the steps that a reference sales vehicle with a announced input ratio difference smaller than a lower limit value of a set reference announced input ratio difference interval is marked as a class c vehicle;
Counting the numbers of the class a automobiles, the class b automobiles and the class c automobiles, and respectively marking as 、/>And/>；
Will be、/>And/>As an input of the announcement evaluation model, an improved announcement input ratio is used as an output of the announcement evaluation model, and the announcement evaluation model specifically shows the following formula:
，/> In order to improve the announced input ratio, To set the compensation announce input ratio.
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