CN110866770A - Prediction method and prediction system for vehicle maintenance scheme - Google Patents
Prediction method and prediction system for vehicle maintenance scheme Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a system for predicting a vehicle maintenance scheme, wherein the method for predicting the vehicle maintenance scheme comprises the following steps: collecting vehicle owner data, vehicle data and vehicle type maintenance indexes; according to the owner data, the vehicle data and the vehicle type maintenance index, performing preliminary maintenance prediction by combining a maintenance cycle; and carrying out accurate maintenance prediction according to the consumption weight value and the result of the preliminary maintenance prediction to obtain a maintenance scheme. The accurate vehicle maintenance scheme predicted by the method is more suitable for the consumption requirements of the user, and the user can independently participate in the formulation of the maintenance plan, so that the value chain accuracy and the customer satisfaction can be improved. And collect the vehicle data that on-vehicle sensor and user submitted, form the data collection closed loop, and generate vehicle maintenance scheme can enlarge the user scope that can carry out vehicle maintenance, promotes customer experience and company's profit.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a prediction method and a prediction system for a vehicle maintenance scheme.
Background
The intelligent vehicle networking system represented by the Anji star provides online service for a vehicle owner through a wireless technology and a global positioning system satellite, assists the vehicle owner to solve problems through voice help of a customer service center on one hand, and performs vehicle maintenance guidance on the vehicle owner through real-time monitoring of vehicle indexes on the other hand. Wherein the customer service voice collaboration comprises: collision automatic help seeking, emergency rescue assistance, full-voice control hands-free telephone and full-voice control navigation service; data guidance for network and vehicle sensors includes: vehicle condition detection report, stolen vehicle positioning, remote vehicle door emergency opening and remote vehicle searching.
The automobile networking system in the market is operated based on a brand, a main data source is main component information which is periodically transmitted to an automobile networking service end by a sensor preset by a vehicle, the current main component information prompts an automobile owner to maintain periodically by combining data transmitted back by the vehicle sensor with a preset value plan maintained by a component, the main component information generally comprises reports of an engine, a gearbox, an ABS (anti-lock brake system), an electronic stability control system, an air bag module, tires and the like, the automobile owner is helped to know the condition of the automobile, and the automobile owner is advised to complete maintenance in a 4S shop. However, such techniques have drawbacks: presetting data returned based on a sensor, wherein only higher configured vehicles have the function, and enjoying service vehicle type thresholds; the related data report is only opened by the 4S shop, the owner cannot participate in the maintenance decision and the maintenance plan making, cannot select other maintenance shops for maintenance selection, and loses the decision autonomy; and the maintenance suggestions cannot be combined with maintenance products, and the vehicle owner cannot select and judge the maintenance materials such as the applied engine oil, the engine filter and the like in advance.
Therefore, the inventor believes that the method for predicting and recommending the maintenance scheme has great limitations, only a few owners can execute several established maintenance plans, and the method has the problems that the requirements of users are difficult to meet and the application range is small.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and a system for predicting a vehicle maintenance scheme, which support data collection of a vehicle-mounted sensor and collection of data autonomously submitted by a user based on network connection, evaluate a maintenance state of a vehicle according to collected owner data and vehicle data, generate a preliminary judgment maintenance package by combining technologies such as cloud-based equipment management and big data analysis in combination with a vehicle model maintenance index, obtain a consumption weight value according to maintenance history data, and predict a secondary accurate vehicle maintenance scheme according to the consumption weight value, so that a user range capable of performing vehicle maintenance is expanded, and customer experience and company profits are improved.
According to a first aspect of the present invention, there is provided a vehicle maintenance protocol prediction method, comprising: collecting vehicle owner data, vehicle data and vehicle type maintenance indexes; according to the owner data, the vehicle data and the vehicle type maintenance index, performing preliminary maintenance prediction by combining a maintenance cycle; and carrying out accurate maintenance prediction according to the consumption weight value and the result of the preliminary maintenance prediction to obtain a maintenance scheme.
Preferably, the prediction method further comprises: the user submits owner data.
Preferably, the prediction method further comprises: vehicle sensors and users submit vehicle data.
Preferably, the prediction method further comprises: and obtaining the consumption weight value according to the vehicle owner data, the vehicle data and the maintenance history data.
Preferably, the maintenance scheme comprises a recommended maintenance package, and the maintenance product type and the price of the maintenance package are selectable.
Preferably, the vehicle data includes vehicle basic information data, maintenance data, vehicle condition data, and travel data; the vehicle type maintenance index comprises preset vehicle maintenance schemes of vehicles of various vehicle types.
Preferably, according to the owner data, the vehicle data and the vehicle type maintenance index, performing preliminary maintenance prediction by combining a maintenance cycle includes: traversing all vehicles, and establishing vehicle models of all vehicles according to the vehicle data and the maintenance historical data; establishing a first simulation condition, and removing scrapped vehicles of which the vehicle data do not meet the first simulation condition from all vehicles; establishing a second simulation condition, selecting the vehicles of which the vehicle data accord with the second simulation condition as the vehicles to be maintained, and taking the rest vehicles as the rest vehicles; and establishing a third simulation condition and a fourth simulation condition, and selecting the vehicles of which the vehicle data simultaneously accord with the third simulation condition and the fourth simulation condition from the rest vehicles as the vehicles to be maintained.
Preferably, according to the owner data, the vehicle data and the vehicle type maintenance index, performing preliminary maintenance prediction by combining a maintenance cycle further includes: calling the accessory standard index value data of all vehicles meeting the first simulation condition; and generating a preliminary judgment maintenance package according to the vehicle type maintenance indexes aiming at all the vehicles to be maintained, wherein the preliminary judgment maintenance package comprises a maintenance product set for vehicle maintenance.
Preferably, the vehicle model is: the maintenance data includes Car, type, factor, maintain, status, and trade, where Car is a certain Car registered by the Car owner t, type is Car model information, factor is a Car leaving situation, maintain is maintenance data, status is Car condition data, and trade is maintenance history data.
Preferably, the first simulation condition is C1 ═ CarAvailαβ(Car) wherein (α ═ 20, β ═ 600000) denotes a vehicle with an age of 20 years or less and a kilometer count of 60 kilometers, C2 ═ C1. status. erro, wherein status. erro denotes a vehicle condition failure and an alarm is to be issued, and C3 ═ camap ═ third simulation conditionkey(C1.staus，Tσh) Where C1. status is the latest vehicle condition data, TσhThe function CarMap represents the standard index value of the accessory corresponding to the vehicle C1keyRepresenting some of the accessory keys in the accessory index value data in the vehicle condition data corresponding to the vehicle C1The current index number is not in the accessory standard index value TσhWithin the range; the fourth simulation condition is
Date represents a maintenance date set,
for the most recent successive date in the maintenance date, now is the current date, (T)σhPeriod) is the recommended maintenance period for the vehicle, λ is the number of floating days;
the fitting standard index value TσhIs denoted by Tσh＝valYear(CarTypeσh) Wherein σ is the current age of the C1 vehicle, h is the current kilometer number of the C1 vehicle, and CarTypeσh＝[key,minValue,maxValue]Wherein key is the accessory identification, minValue is the minimum health value of the accessory within h kilometers of the sigma year, and maxValue is the maximum health value of the accessory within h kilometers of the sigma year.
Preferably, obtaining the consumption weight value according to the owner data, the vehicle data and the maintenance history data comprises: traversing the owner data, the vehicle data and the maintenance history data of the vehicle to be maintained; respectively summing all historical expense amounts and all historical maintenance times of each vehicle to be maintained to be used as a first model parameter and a second model parameter; taking the difference value between the non-maintenance time and the maintenance period of each vehicle to be maintained as a third model parameter, and generating a fourth model parameter according to the third model parameter; and generating a consumption weight value according to the first parameter model, the second parameter model and the fourth parameter model.
Preferably, the accurate maintenance prediction is performed according to the consumption weight value and the result of the preliminary maintenance prediction, and obtaining the maintenance scheme includes: generating a maintenance product sum closed interval according to the consumption weight value; selecting the maintenance product set of which the sum of money falls within the sum closed interval from all the maintenance product sets in the initial judgment maintenance package; and using the selected maintenance product set as the recommended maintenance package.
Preferably, the first model parameter is
The second model parameter is
The third model parameter is d3＝(now-maxC.maintain.date)-TσhPeriod, where maintainCDate is the latest date in the maintenance date, now is the current date, TσhPeriod is the recommended maintenance period (days); the fourth model parameter is determined by the third model parameter when d3D is less than or equal to 04When d is not more than 03Then, then
d4 represents the vehicle risk rate, the longer the last service is, the greater the vehicle risk rate.
Preferably, the consumption weight value is
Wherein the consumption mean value
The higher, and the vehicle risk ratio d4The higher the consumption weight value is; the sum of money is within a closed interval of
According to a second aspect of the present invention, there is provided a vehicle maintenance protocol prediction system comprising: the data acquisition unit is used for collecting vehicle owner data, vehicle data and vehicle type maintenance indexes; the preliminary maintenance prediction unit is used for performing preliminary maintenance prediction by combining a maintenance cycle according to the vehicle owner data, the vehicle data and the vehicle type maintenance index; and the maintenance scheme generation unit is used for carrying out accurate maintenance prediction according to the consumption weight value and the result of the preliminary maintenance prediction to obtain a maintenance scheme.
Preferably, the prediction system further comprises: and the consumption weight value generating unit is used for obtaining the consumption weight value according to the owner data, the vehicle data and the maintenance history data.
Preferably, the preliminary maintenance prediction unit includes: a vehicle model establishing unit for establishing vehicle models of all vehicles according to the vehicle data and the maintenance history data; the simulation condition establishing unit is used for establishing first to fourth simulation conditions and selecting a vehicle to be maintained according to the coincidence degree of the vehicle data and the first to fourth simulation conditions; and a preliminary judgment package generation unit, configured to generate a preliminary judgment maintenance package according to the vehicle type maintenance index for all the vehicles to be maintained, where the preliminary judgment maintenance package includes a maintenance product set for vehicle maintenance.
Preferably, the maintenance plan generating unit includes: the sum closed interval generating unit is used for generating a maintenance product sum closed interval according to the consumption weight value; a selection unit, configured to select the maintenance product set with an amount of money falling within the money closed interval from all the maintenance product sets in the preliminary judgment maintenance package; and a recommended package generation unit which takes the selected maintenance product set as the recommended maintenance package.
According to a third aspect of the present invention, there is provided a computer readable storage medium having stored thereon computer instructions which, when executed, implement the prediction method as described above.
According to a fourth aspect of the present invention, there is provided a vehicle maintenance protocol selection device, comprising: a memory for storing computer instructions; a processor coupled to the memory, the processor configured to perform a prediction method implemented as described above based on computer instructions stored by the memory.
The embodiment of the invention has the following advantages or beneficial effects: through collecting car owner data, vehicle data and motorcycle type maintenance index, according to the maintenance state of these data evaluation vehicles and carry out preliminary maintenance prediction according to the maintenance cycle, carry out accurate maintenance prediction according to the result of consumption weight and preliminary maintenance prediction again, obtain the maintenance scheme. According to the method, the consumption weight value is added into the selected vehicle maintenance scheme, so that the method is more suitable for the consumption requirements of users, secondary prediction is carried out, package recommendation is more accurate, and the users can independently participate in the formulation of maintenance plans, so that the value chain accuracy and the customer satisfaction can be improved.
Another preferred embodiment of the present invention has the following advantages or benefits: the vehicle-mounted sensor and the user submit vehicle data, a data collection closed loop is realized, the vehicle maintenance scheme selected by the method can be suitable for most users, is not limited by vehicle types, and the service range is expanded.
Another preferred embodiment of the present invention has the following advantages or benefits: and obtaining a consumption weight value according to the vehicle data and the maintenance historical data, so that the recommendation of the maintenance scheme is more consistent with the consumption level of the customer.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing embodiments of the present invention with reference to the following drawings, in which:
FIG. 1 illustrates a flow chart of a method of predicting a vehicle maintenance protocol in an embodiment of the present invention;
FIG. 2 illustrates a flow chart of a method for predicting an aggregated vehicle maintenance schedule in an embodiment of the present disclosure;
fig. 3 shows a detailed flowchart of step S102 shown in fig. 1;
fig. 4 shows a detailed flowchart of step S205 and step S206 shown in fig. 2;
FIG. 5 is a block diagram illustrating a predictive system for a consolidated vehicle maintenance schedule in an embodiment of the present invention;
FIGS. 6a and 6b are block diagrams illustrating the preliminary service prediction unit and the service plan generation unit of FIG. 5, respectively;
fig. 7 is a block diagram illustrating a vehicle maintenance protocol selection apparatus according to an embodiment of the present invention.
Detailed Description
The present invention will be described below based on examples, but the present invention is not limited to only these examples. In the following detailed description of the present invention, certain specific details are set forth. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details. Well-known methods, procedures, and procedures have not been described in detail so as not to obscure the present invention. The figures are not necessarily drawn to scale.
Fig. 1 shows a flowchart of a method for predicting a vehicle maintenance schedule according to an embodiment of the present invention, and the specific steps include S101 to S103.
In step S101, vehicle owner data, vehicle data, and vehicle type maintenance index are collected.
In step S102, a preliminary maintenance prediction is performed according to the owner data, the vehicle data, and the vehicle type maintenance index, in combination with the maintenance cycle.
In step S103, an accurate maintenance prediction is performed according to the consumption weight value and the result of the preliminary maintenance prediction, so as to obtain a maintenance scheme.
As described in the background section, the prediction and recommendation of the existing vehicle maintenance schemes are based on the periodic return data of the on-board sensors, and the maintenance is performed periodically according to a preset maintenance schedule, which is not selected by the user, and thus, the user is in short of selectivity.
Therefore, in the embodiment, vehicle owner data, vehicle data and vehicle type maintenance indexes are collected firstly, and the data are correspondingly sorted according to the vehicle; then, evaluating the maintenance state of the vehicle according to the collected data, analyzing the collected vehicle data of different vehicles respectively by combining the maintenance period, determining whether the vehicle needs to be maintained, and carrying out initial judgment on whether the vehicle needs to be maintained; generating a rough initial judgment maintenance package for the vehicle (to-be-maintained vehicle) needing to be maintained according to the vehicle type maintenance data; and finally, screening from the primary judgment maintenance packages according to the consumption weight values, selecting the recommended maintenance packages meeting the consumption weight values, and generating a maintenance scheme, wherein the consumption weight values are added to the pre-judgment results, secondary prediction is carried out, and the consumption weight values represent the consumption demands of the user on the maintenance scheme and can influence the recommended maintenance time and the price of the maintenance scheme.
The vehicle maintenance scheme selected by the method is suitable for most users, secondary prediction is carried out, package recommendation is more accurate, the vehicle maintenance scheme is predicted by adding the consumption weight value, the method is more suitable for the consumption requirements of the users, the users can independently participate in the formulation of the maintenance plan, and therefore the value chain accuracy and the customer satisfaction can be improved.
Fig. 2 is a flowchart illustrating a method for predicting a vehicle maintenance schedule according to an embodiment of the present invention, which includes the following steps.
In step S201, the user submits owner data.
In step S202, the in-vehicle sensors and the user submit vehicle data.
In step S203, vehicle owner data, vehicle data, and vehicle type maintenance index are collected.
In step S204, a preliminary maintenance prediction is performed according to the owner data, the vehicle data, and the vehicle type maintenance index, in combination with the maintenance cycle.
In step S205, a consumption weight value is obtained from the owner data, the vehicle data, and the maintenance history data.
In step S206, an accurate maintenance prediction is performed according to the consumption weight value and the result of the preliminary maintenance prediction, so as to obtain a maintenance plan.
The present embodiment is a more sophisticated method of predicting a vehicle maintenance schedule than the previous embodiments. Steps S203 to 204 and S206 are the same as steps S101 to S103 in fig. 1, and are not described again here.
Step S201-step S202 are preparation steps for collecting data for anticipation, and relate to the source of the data. In step S201, the user submits owner data, centralizes all owner data in the owner data center, and waits for further processing. In step S02, the vehicle sensors and the user submit vehicle data, the vehicle data is automatically uploaded to the internet of vehicles in the vehicle with the vehicle sensors, the user can autonomously submit the vehicle data in the vehicle without the vehicle sensors, so as to realize the versatility of data collection, and the vehicle owner data and the vehicle data are matched with each other.
Step S206 involves the acquisition of a consumption weight value, the calculation of the consumption weight value is carried out according to vehicle data and maintenance history data provided by a user, secondary prediction is carried out according to the consumption level and selection preference of a client, and the recommended maintenance package is more in line with the preference and demand of the client.
According to the embodiment of the invention, a closed loop of data collection is realized, and the consumption weight value is calculated according to the vehicle data and the maintenance history data, so that the service range is expanded, more customers can enjoy the recommendation of the maintenance package, the recommendation of the maintenance scheme is more consistent with the consumption level of the customers, and the satisfaction degree of the customers is improved.
In one embodiment, the maintenance scheme comprises a recommended maintenance package, and the maintenance product type and the price of the maintenance package are selectable, so that a customer can perform maintenance selection according to the condition of the customer and participate in a maintenance plan, different requirements of different customers are met, and the service quality is improved.
In one embodiment, the vehicle data includes vehicle telematics data, maintenance data, vehicle condition data, and travel data. With reference to fig. 1, the health status of the vehicle can be quickly determined according to the vehicle condition data and the driving data of the vehicle, and whether maintenance is needed in the near future can be quickly predicted with reference to the maintenance cycle of the vehicle; according to the basic information data and the vehicle condition data of the vehicle, some maintenance schemes which are possibly suitable for the vehicle user can be roughly predicted; the requirements of the owner of the vehicle on the grade and the price of the maintenance scheme can be known by combining the maintenance data and the maintenance historical data, the consumption weight value is set accordingly, and the final recommended package is selected according to the consumption weight value, so that the recommended accurate vehicle maintenance scheme is more in line with the actual consumption condition of the client.
Fig. 3 shows a flowchart of step S102 shown in fig. 1, which is a specific process of preliminary maintenance prediction, and specifically includes the following steps.
In step S1021, all vehicles are traversed, and vehicle models of all vehicles are built based on the vehicle data and the maintenance history data. And sorting all the collected vehicle data from the vehicle-mounted sensor and the vehicle data submitted by the users independently, wherein all the vehicles corresponding to each user and the vehicle data of each vehicle are sorted in the same data table. The vehicle data includes, for example, basic vehicle information data, maintenance data, vehicle condition data, and travel data.
For example, the vehicle model of a certain vehicle registered by the owner t is [ type, factor, maintain, status, track ], wherein, Car is a certain vehicle q registered by the owner t, type is vehicle type information, and factor is vehicle leaving condition.
The following describes how to traverse the vehicle to form the prognosis of the vehicle to be serviced according to the defined vehicle data and rules in detail with reference to steps S1022 to S1025. Firstly, judging a vehicle Car of a vehicle owner, confirming whether the vehicle enters a to-be-maintained stage or not to obtain a pre-judgment result, and then carrying out preliminary prediction on a vehicle maintenance scheme according to the pre-judgment result.
In step S1022, a first simulation condition is established, and all scrapped vehicles whose vehicle data do not meet the first simulation condition are excluded. Set the first simulation condition to C1 ═ CarAvailαβ(Car) wherein (α -20, β -600000) and C1 denote vehicles having an age of 20 years or less and a travel mileage of 60 kilometers or less, travel data such as the travel mileage and the travel period from the purchase of the vehicle is extracted from vehicle data corresponding to the vehicle q, and the travel data is transmittedAnd substituting the data into the first simulation condition, wherein the data meets the condition C1, the vehicle is considered to be normal, and if the vehicle age is more than 20 years or the kilometers of the vehicle are more than 60 kilometers, the vehicle is considered to be invalid data or a scrapped vehicle, and the data is excluded. And unnecessary package recommendation can be omitted by excluding invalid data, so that the time is saved, and the recommendation is more accurate.
In step S1023, a second simulation condition is established, and vehicles whose vehicle data meet the first simulation condition and the second simulation condition are selected as vehicles to be maintained, and the remaining vehicles are used as remaining vehicles. The second simulated condition is C2 ═ C1. status. erro, where status. erro indicates a vehicle condition fault and an alarm will be issued.
In the last step, the driving condition of the vehicle is detected, the scrapped vehicle is removed, the rest vehicles are traversed, the vehicle condition data staus is detected, whether the vehicle condition is good or not is judged, the vehicle condition data staus meets the second simulation condition, namely, the vehicle condition is failed, an alarm is given out, the alarm data is returned, and the vehicle needs to be maintained.
In step S1024, accessory standard index value data of all vehicles that meet the first simulation condition is retrieved. In one embodiment, the vehicles screened under the condition C1 are counted, and the accessory standard index value data of each vehicle is retrieved.
Setting data of standard index values of accessories in h kilometers in the sigma year of a certain vehicle type as follows: cartypeσh＝ [key,minValue,maxValue]Wherein, key is the part identifier, minValue is the minimum health value of the part (may be null air) within h kilometers of the third year, maxValue is the maximum health value of the part (may not be null air) within h kilometers of the third year, σ is within 20 years, and h is within 60 kilometres. If the index value of some accessories of a certain vehicle q exceeds the maximum health value, the health state of the vehicle is considered to need attention, and maintenance is recommended.
Then obtaining a function T of the accessory standard index value based on the age sigma and the kilometer number h of any vehicle q under the condition of C1σh，Tσh＝valYear(CarTypeσh) Wherein, σ is the current age of the C1 vehicle, h is the current kilometer number of the C1 vehicle, if the CarTypeσhIn the function, the age of the car is matched with the kilometer numberAnd selecting one group of indexes with the closest kilometer number.
In step S1025, a third simulation condition and a fourth simulation condition are established, and a vehicle of which the vehicle data simultaneously satisfies the third simulation condition and the fourth simulation condition among the remaining vehicles is selected as a vehicle to be maintained. In step S1023, the vehicle with good vehicle conditions is determined as a remaining vehicle, and other detection is performed to determine whether maintenance is necessary.
Setting the third simulation condition to C3 ═ CarMapkey(C1.staus，Tσh) Where C1. status is the latest vehicle condition data, TσhFunction CarMap representing standard index value of fitting part corresponding to vehicle C1keyThe current index number indicating that some of the accessory keys in the accessory index value data in the vehicle condition data corresponding to the vehicle C1 are not at the accessory standard index value TσhWithin the range; the third simulation condition C3 therefore represents a pass through the traversal function CarMapkeyThe accessory standard index value T corresponding to the vehicle C1σhIf the number of the current accessories is greater than the health value maxValue, the vehicle is used as a candidate vehicle to be maintained, and the fourth simulation condition is detected.
Setting the fourth simulation condition as
Date represents a maintenance date set,
for the most recent successive date in the maintenance date, now is the current date, (T)σhPeriod) is the recommended maintenance period for the vehicle, λ is the number of floating days; condition C4 represents a vehicle whose current date is closer to the nearest maintenance date than the recommended maintenance period plus the number of floating days, and which has a long non-maintenance time and may have a potential risk of recommending maintenance.
If the vehicle q has an accessory key whose accessory index value is greater than the maximum health value and the non-maintenance time of the vehicle exceeds the maximum allowable time, the vehicle that meets the condition C3 is detected under the condition C4, and the vehicle must be maintained.
In the above steps, the vehicle is traversed through steps S1022 to S1025 according to the set simulation conditions C1 to C4, and the preliminary maintenance prediction is performed according to the owner data, the vehicle data and the vehicle type maintenance index, in combination with the maintenance cycle, so as to form the prediction result of the vehicle to be maintained, and the vehicle to be maintained is suitable to be selected as the vehicle to be maintained if the vehicle condition fault and the accessory index exceed the maximum health value and are not maintained for a long time.
In step S1026, for all vehicles to be serviced, a preliminary care package including a set of care items for vehicle maintenance is generated according to the vehicle type maintenance index.
And integrating a set of new primary maintenance schemes according to the vehicle type maintenance data stored in advance and the maintenance data of other similar vehicle types to the vehicle type to be maintained, wherein the maintenance schemes comprise various maintenance product types, and the maintenance product set of the corresponding maintenance scheme is sku [ C ], so that a primary judgment maintenance package is generated.
The above process is a selection flow of an initial maintenance period, and a matching process of the maintenance scheme is further described below based on fig. 4.
Fig. 4 shows a detailed flowchart of step S205 and step S206 shown in fig. 2. The method specifically comprises the following steps: step S401 to step S404 are calculation procedures of the consumption weight value, and step S405 to step S407 are procedures of matching the precise maintenance plan according to the consumption weight value.
In step S401, the owner data, the vehicle data, and the maintenance history data of the vehicle to be maintained are traversed. And traversing the maintenance historical data and the maintenance consumption record of the vehicle to be maintained, so that the maintenance consumption weight value D of the vehicle can be conveniently calculated subsequently.
In step S402, all the historical maintenance amounts and all the historical maintenance times of each vehicle to be maintained are respectively summed as a first model parameter and a second model parameter. Wherein the first model parameter is
The second model parameter is
In step S403, a difference between the maintenance period and the non-maintenance time of each vehicle to be maintained is used as a third model parameter, and a fourth model parameter is generated according to the third model parameter.
The third model parameter is d3＝(now-maxC.maintain.date)-TσhPeriod, where maintainCDate is the latest date in the maintenance date, now is the current date, TσhPeriod is the recommended maintenance period (days); the third model parameter d3 represents the time difference between the current unworked time and the recommended maintenance cycle.
The fourth model parameter is determined by the third model parameter when d3Less than or equal to 0, i.e. the non-maintenance time does not exceed the recommended maintenance cycle time period, d40, d4 represents the vehicle risk rate; when d is more than or equal to 03I.e. not maintained for a long time, then
d4 represents the vehicle risk rate, the longer the time since the last maintenance, the greater the vehicle risk rate (the number of days of delay in maintenance, the proportion of the number of days of the recommended maintenance cycle).
In step S404, a consumption weight value is generated according to the first parametric model, the second parametric model, and the fourth parametric model.
First, the ratio of the first model parameter d1 and the second model parameter d2 obtained in step S402 is used to obtain the consumption average value of each maintenance
This is only one implementation and other ways, such as weighted averaging to obtain the maintenance consumption average, may be used first. Then, in connection with step S403, in connection with the risk rate d4, the consumption weight values obtained are functions related to d1, d2 and d 4:
wherein the consumption mean value
The higher and the higher the vehicle risk rate d4, the higher the consumption weight value.
In step S405, a maintenance product amount closed section is generated based on the consumption weight value.
A consumption weight value of
Wherein the consumption mean value
The higher, and the vehicle risk ratio d4The higher the consumption weight index value is; recommending a maintenance set sku [ C ] based on the vehicle maintenance consumption weight D]The consumption amount of (1) is in a closed interval, e.g. the amount is in a closed interval
Then:
in step S406, a maintenance product set in which the amount of money falls within the money closed interval is selected from all the maintenance product sets in the preliminary judgment maintenance package.
In step S407, the selected maintenance product set is set as a recommended maintenance package.
And selecting the maintenance product set sku [ C ] meeting the conditions as a recommended maintenance package, recommending the final accurate vehicle maintenance scheme to the user by combining the types and the prices of the maintenance products, and enabling the user to select the maintenance product set sku [ C ].
In one embodiment, more directional weight values are implanted as constraints for selecting an accurate vehicle maintenance scheme, and a maintenance scheme more meeting the requirements of a customer is found.
In another embodiment, a maintenance product suggestion or a weight suggestion of an expert technician may be added, and a user may confirm whether the maintenance prediction considers the participation of the expert suggestion through an autonomous judgment, so as to improve the participation degree of more vehicle owner decisions.
It should be noted that the present invention is not limited to the simulation conditions and the parameter model rules provided in the above embodiments, and other simulation conditions and parameter model rules may be used to practice the vehicle maintenance schedule prediction method provided in the embodiments of the present invention.
FIG. 5 is a block diagram illustrating a predictive system for a consolidated vehicle maintenance schedule in an embodiment of the present invention; the vehicle maintenance plan prediction system 500 includes a data acquisition unit 501, a preliminary maintenance prediction unit 502, a consumption weight value generation unit 503, and a maintenance plan generation unit 504.
The data acquisition unit 501 is used for collecting vehicle owner data, vehicle data, and vehicle type maintenance indexes.
The preliminary maintenance prediction unit 502 is used for performing preliminary maintenance prediction according to vehicle owner data, vehicle data and vehicle type maintenance indexes by combining a maintenance cycle.
The consumption weight value generation unit 503 is configured to obtain a consumption weight value according to the owner data, the vehicle data, and the maintenance history data.
The maintenance plan generating unit 504 is configured to perform accurate maintenance prediction according to the consumption weight value and the result of the preliminary maintenance prediction, and obtain a maintenance plan.
In this embodiment, according to the car owner data, the vehicle data and the motorcycle type maintenance index of collecting, combine the maintenance cycle to carry out preliminary maintenance prediction, predict the maintenance scheme twice according to the weighted value that the vehicle data and the consumption data of collecting formed again, can promote the degree of accuracy of prediction, the accurate vehicle maintenance scheme of predicting through this method is applicable to user's consumption demand more, and the user can independently participate in the formulation of maintenance plan to can promote value chain precision and customer satisfaction.
Meanwhile, the consumption weight value is calculated according to the vehicle data and the maintenance historical data, so that the recommendation of the maintenance scheme is more consistent with the consumption level of the customer.
In one embodiment, the consumption weight value generation unit 503 includes: a data screening unit (not shown in the figure) model parameter establishing unit (not shown in the figure) and a calculating unit (not shown in the figure), wherein the screening unit is used for traversing vehicle data and maintenance history data of the vehicle to be maintained; the model parameter establishing unit is used for establishing first to fourth model parameters according to the vehicle data and the maintenance historical data; the calculating unit is used for generating a consumption weight value according to the first parameter model, the second parameter model and the fourth parameter model.
Fig. 6a and 6b show block diagrams of the preliminary maintenance prediction unit 502 and the maintenance plan generation unit 504 in fig. 5, respectively. As shown in fig. 6a and 6b, the preliminary maintenance prediction unit 502 includes: a vehicle model establishing unit 5021, a simulation condition establishing unit 5022 and a preliminary judgment package generating unit 5023. The maintenance plan generating unit 504 includes: a money closed interval generation unit 5041, a selection unit 5042, and a precision package generation unit 5043.
The vehicle model building unit 5021 is used for building vehicle models of all vehicles according to vehicle data and maintenance historical data; the simulation condition establishing unit 5022 is used for establishing first to fourth simulation conditions and selecting the vehicle to be maintained according to the coincidence degree of the vehicle data and the first to fourth simulation conditions; the preliminary judgment package generation unit 5023 is used for generating a preliminary judgment maintenance package according to the vehicle model maintenance index for all the vehicles to be maintained, wherein the preliminary judgment maintenance package comprises a maintenance product set for vehicle maintenance.
The sum closed interval generating unit 5041 is configured to generate a maintenance sum closed interval according to the consumption weight value; the selection unit 5042 is used for selecting the maintenance product set of which the gold amount falls within the money closed interval in all the maintenance product sets in the preliminary judgment maintenance package; the precise package generation unit 5043 is configured to set the selected maintenance product set as a recommended maintenance package.
It should be understood that the system and method of embodiments of the present invention are corresponding and, thus, are performed in a relatively brief manner in the description of the system.
Fig. 7 is a block diagram illustrating a vehicle maintenance protocol selection apparatus according to an embodiment of the present invention. The apparatus shown in fig. 7 is only an example and should not limit the functionality and scope of use of embodiments of the present invention in any way.
Referring to fig. 7, the vehicle maintenance protocol selection apparatus includes a processor 701, a memory 702, and an input-output device 703 connected by a bus. The memory 702 includes a Read Only Memory (ROM) and a Random Access Memory (RAM), and various computer instructions and data required to perform system functions are stored in the memory 702, and the processor 701 reads the various computer instructions from the memory 702 to perform various appropriate actions and processes. An input/output device including an input portion of a keyboard, a mouse, and the like; an output section including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section including a hard disk and the like; and a communication section including a network interface card such as a LAN card, a modem, or the like. The memory 702 also stores the following computer instructions to perform the operations specified in the method for predicting a vehicle maintenance schedule of an embodiment of the present invention: collecting vehicle owner data, vehicle data and vehicle type maintenance indexes; performing preliminary maintenance prediction by combining a maintenance cycle according to vehicle owner data, vehicle data and vehicle type maintenance indexes; and carrying out accurate maintenance prediction according to the consumption weighted value and the result of the preliminary maintenance prediction to obtain a maintenance scheme.
Accordingly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that, when executed, implement the operations specified by the vehicle maintenance schedule prediction method described above.
The flowcharts and block diagrams in the figures and block diagrams illustrate the possible architectures, functions, and operations of the systems, methods, and apparatuses according to the embodiments of the present invention, and may represent a module, a program segment, or merely a code segment, which is an executable instruction for implementing a specified logical function. It should also be noted that the executable instructions that implement the specified logical functions may be recombined to create new modules and program segments. The blocks of the drawings, and the order of the blocks, are thus provided to better illustrate the processes and steps of the embodiments and should not be taken as limiting the invention itself.
The various modules or units of the system may be implemented in hardware, firmware or software. The software includes, for example, a code program formed using various programming languages such as JAVA, C/C + +/C #, SQL, and the like. Although the steps and sequence of steps of the embodiments of the present invention are presented in method and method diagrams, the executable instructions of the steps implementing the specified logical functions may be re-combined to create new steps. The sequence of the steps should not be limited to the sequence of the steps in the method and the method illustrations, and can be modified at any time according to the functional requirements. Such as performing some of the steps in parallel or in reverse order.
Systems and methods according to the present invention may be deployed on a single server or on multiple servers. For example, different modules may be deployed on different servers, respectively, to form a dedicated server. Alternatively, the same functional unit, module or system may be deployed in a distributed fashion across multiple servers to relieve load stress. The server includes but is not limited to a plurality of PCs, PC servers, blades, supercomputers, etc. on the same local area network and connected via the Internet.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (20)
1. A method of predicting a vehicle maintenance schedule, comprising:
collecting vehicle owner data, vehicle data and vehicle type maintenance indexes;
according to the owner data, the vehicle data and the vehicle type maintenance index, performing preliminary maintenance prediction by combining a maintenance cycle; and
and carrying out accurate maintenance prediction according to the consumption weight value and the result of the preliminary maintenance prediction to obtain a maintenance scheme.
2. The prediction method according to claim 1, further comprising: and the user submits the vehicle owner data.
3. The prediction method according to claim 1, further comprising: the vehicle-mounted sensors and the user submit the vehicle data.
4. The prediction method according to claim 1, further comprising: and obtaining the consumption weight value according to the vehicle owner data, the vehicle data and the maintenance history data.
5. The prediction method of claim 1, wherein the maintenance plan comprises a recommended maintenance package, and the maintenance item class and price of the maintenance package are selectable.
6. The prediction method according to claim 1,
the vehicle data comprises basic information data of the vehicle, maintenance data, vehicle condition data and driving data;
the vehicle type maintenance index comprises preset vehicle maintenance schemes of vehicles of various vehicle types.
7. The prediction method of claim 4, wherein performing the preliminary maintenance prediction in combination with a maintenance cycle based on the owner data, the vehicle data, and the vehicle type maintenance indicator comprises:
traversing all vehicles, and establishing vehicle models of all vehicles according to the vehicle data and the maintenance historical data;
establishing a first simulation condition, and removing scrapped vehicles of which the vehicle data do not meet the first simulation condition from all vehicles;
establishing a second simulation condition, selecting the vehicles of which the vehicle data accord with the second simulation condition as the vehicles to be maintained, and taking the rest vehicles as the rest vehicles; and
and establishing a third simulation condition and a fourth simulation condition, and selecting the vehicles of which the vehicle data simultaneously accord with the third simulation condition and the fourth simulation condition from the rest vehicles as the vehicles to be maintained.
8. The prediction method according to claim 7, further comprising:
calling the accessory standard index value data of all vehicles meeting the first simulation condition; and
and generating a preliminary judgment maintenance package for all the vehicles to be maintained according to the vehicle type maintenance indexes, wherein the preliminary judgment maintenance package comprises a maintenance product set for vehicle maintenance.
9. The prediction method according to claim 8, characterized in that the vehicle model is: the maintenance data includes Car, type, factor, maintain, status, and trade, where Car is a certain Car registered by the Car owner t, type is Car model information, factor is a Car leaving situation, maintain is maintenance data, status is Car condition data, and trade is maintenance history data.
10. The prediction method according to claim 9,
the first simulation condition is C1 ═ CarAvailαβ(Car), wherein (α ═ 20, β ═ 600000), denotes a vehicle whose age is 20 years or less and whose number of kilometers traveled is 60 kilometers or less;
the second simulated condition is C2 ═ C1. status. erro, where status. erro indicates a vehicle condition fault, an alarm will be raised;
the third simulation condition is C3 ═ CarMapkey(C1.staus，Tσh) Where C1. status is the latest vehicle condition data, TσhThe function CarMap represents the standard index value of the accessory corresponding to the vehicle C1keyThe current index number of some parts key in the parts index value data in the vehicle condition data corresponding to the vehicle C1 is not in the parts standard index value TσhWithin the range;
the fourth simulation condition is
Date represents a maintenance date set,
for the most recent successive date in the maintenance date, now is the current date, (T)σhPeriod) is the recommended maintenance period for the vehicle, λ is the number of floating days;
the fitting standard index value TσhIs denoted by Tσh＝valYear(CarTypeσh) Wherein σ is the current age of the C1 vehicle, h is the current kilometer number of the C1 vehicle, and CarTypeσh＝[key,minValue,maxValue]Wherein key is the accessory identification, minValue is the minimum health value of the accessory within h kilometers of the sigma year, and maxValue is the maximum health value of the accessory within h kilometers of the sigma year.
11. The prediction method of claim 8, wherein obtaining the consumption weight value based on the owner data, the vehicle data, and the maintenance history data comprises:
traversing the owner data, the vehicle data and the maintenance history data of the vehicle to be maintained;
respectively summing all historical expense amounts and all historical maintenance times of each vehicle to be maintained to be used as a first model parameter and a second model parameter;
taking the difference value between the non-maintenance time and the maintenance period of each vehicle to be maintained as a third model parameter, and generating a fourth model parameter according to the third model parameter; and
generating a consumption weight value according to the first parameter model, the second parameter model and the fourth parameter model.
12. The forecasting method according to claims 5 and 11, wherein the accurate maintenance forecast is performed according to the consumption weight value and the result of the preliminary maintenance forecast, and obtaining a maintenance plan comprises:
generating a maintenance product sum closed interval according to the consumption weight value;
selecting the maintenance product set of which the sum of money falls within the sum closed interval from all the maintenance product sets in the initial judgment maintenance package; and
and taking the selected maintenance product set as the recommended maintenance package.
13. The prediction method according to claim 12,
the first model parameter is
The second model parameter is
The third model parameter is d3＝(now-maxC.maintain.date)-TσhPeriod, where maintainCDate is the latest date in the maintenance date, now is the current date, TσhPeriod is the recommended maintenance period (days);
the fourth model parameter is determined by the third model parameter when d3D is less than or equal to 04When d is not more than 03Then, then
d4 represents the vehicle risk rate, the longer the last service is, the greater the vehicle risk rate.
14. The prediction method according to claim 13,
said consumption ofWeighted value of
Wherein the consumption mean value
The higher, and the vehicle risk ratio d4The higher the consumption weight value is;
the sum of money is within a closed interval of
15. A system for predicting a vehicle maintenance protocol, comprising:
the data acquisition unit is used for collecting vehicle owner data, vehicle data and vehicle type maintenance indexes;
the preliminary maintenance prediction unit is used for performing preliminary maintenance prediction by combining a maintenance cycle according to the vehicle owner data, the vehicle data and the vehicle type maintenance index; and
and the maintenance scheme generating unit is used for carrying out accurate maintenance prediction according to the consumption weight value and the preliminary maintenance prediction result to obtain a maintenance scheme.
16. The prediction system of claim 15, further comprising:
and the consumption weight value generating unit is used for obtaining the consumption weight value according to the owner data, the vehicle data and the maintenance history data.
17. The prediction system of claim 16, wherein the preliminary service prediction unit comprises:
a vehicle model establishing unit for establishing vehicle models of all vehicles according to the vehicle data and the maintenance history data;
the simulation condition establishing unit is used for establishing first to fourth simulation conditions and selecting a vehicle to be maintained according to the coincidence degree of the vehicle data and the first to fourth simulation conditions; and
and the preliminary judgment package generation unit is used for generating a preliminary judgment maintenance package according to the vehicle type maintenance indexes for all the vehicles to be maintained, and the preliminary judgment maintenance package comprises a maintenance product set for vehicle maintenance.
18. The prediction system of claim 17, wherein the maintenance protocol generation unit comprises:
the sum closed interval generating unit is used for generating a maintenance product sum closed interval according to the consumption weight value;
a selection unit, configured to select the maintenance product set with an amount of money falling within the money closed interval from all the maintenance product sets in the preliminary judgment maintenance package; and
and the recommended package generating unit takes the selected maintenance product set as the recommended maintenance package.
19. A computer-readable storage medium storing computer instructions which, when executed, implement a prediction method as claimed in any one of claims 1 to 14.
20. A vehicle maintenance plan prediction device, comprising:
a memory for storing computer instructions;
a processor coupled to the memory, the processor configured to perform implementing the prediction method of any of claims 1-14 based on computer instructions stored by the memory.
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