A method for analyzing a vehicle maintenance habit of a vehicle based on vehicle maintenance data

Abstract

The application provides a method for analyzing vehicle maintenance habits based on vehicle maintenance data, which comprises the following steps: standardizing vehicle maintenance record data; distinguishing vehicle CC maintenance records; calculating a vehicle maintenance average interval time score of the vehicle CC; calculating a vehicle maintenance average interval mileage score of the vehicle CC; calculating a time score from the last maintenance record of the vehicle CC; and calculating a vehicle maintenance habit score of the vehicle CC. The method for analyzing vehicle maintenance habits based on vehicle maintenance data provided by the application realizes standardized analysis, objective analysis, and comprehensive, dynamic, and quantitative evaluation of vehicle maintenance habits, and can improve the fairness and transparency of the second-hand vehicle transaction market.

Description

Technical Field

[0001] This invention belongs to the field of vehicle maintenance analysis technology, and specifically relates to a method for analyzing vehicle maintenance habits based on vehicle repair and maintenance data. Background Technology

[0002] In the used car market, accurate vehicle valuation is crucial for ensuring fair transactions. This valuation goes beyond simply providing a price tag; it requires a meticulous evaluation process where each vehicle is assessed individually. The key to this lies in a thorough analysis and assessment of the vehicle's condition. As an important part of this analysis, the owner's maintenance habits and related parameters provide potential buyers with a clear and detailed understanding. These parameters not only reflect the vehicle's usage but also reveal a significant portion of its overall condition.

[0003] It's particularly worth mentioning that there's a clear positive correlation between a car owner's maintenance habits and the vehicle's condition. Generally speaking, the more care a car owner takes of their vehicle, the better its maintenance will be, resulting in a better-condition vehicle. This positive correlation not only provides buyers with an important reference for assessing a vehicle's value but also offers sellers an effective way to showcase the vehicle's advantages.

[0004] By focusing on vehicle maintenance, we can gain a deeper understanding of the vehicle's condition. For example, maintenance records, the frequency of parts replacement, and the specific details of maintenance procedures are all important indicators reflecting the vehicle's condition. Through meticulous review and analysis of this information, we can make a clearer and more accurate assessment of the vehicle's overall condition, thus providing strong support for precise used car valuation.

[0005] In conclusion, accurate valuation of a used car requires comprehensive consideration of multiple factors, among which maintenance habits and relevant parameter information are crucial and cannot be ignored. By deeply exploring and analyzing this information, we can create a fairer and more transparent trading environment for the used car market.

[0006] Existing methods for analyzing used car maintenance habits have the following drawbacks:

[0007] First, there is a lack of standardization and objectivity. Currently, assessments of vehicle maintenance habits largely rely on subjective observation and experience, lacking unified and clear standards. This results in assessments that vary from person to person, lacking objectivity and accuracy. Furthermore, different assessors may arrive at drastically different judgments about the maintenance habits of the same vehicle, leading to inconsistent assessment results.

[0008] Secondly, it's difficult to quantify. Vehicle maintenance habits are a relatively abstract concept, difficult to quantify with specific values ​​or indicators. This often results in assessments that remain at a vague descriptive level, failing to provide precise data support. This lack of quantitative assessment makes it difficult for buyers and sellers to reach a clear consensus during the transaction process.

[0009] Furthermore, there is a lack of comprehensiveness and integration. Existing analytical methods often focus only on certain aspects of a vehicle, such as exterior maintenance and interior cleanliness, while neglecting other factors that may influence vehicle maintenance habits, such as repair records and usage patterns. This one-sided assessment approach may lead to misjudgments of the vehicle's true condition, thereby affecting the valuation and transaction of used cars.

[0010] In summary, existing methods for analyzing vehicle maintenance habits suffer from shortcomings such as insufficient standardization and objectivity, difficulty in quantification and evaluation, lack of comprehensiveness and integration, and lack of real-time and dynamic data. To address these deficiencies, a more scientific, objective, comprehensive, and dynamic evaluation system is needed to improve the fairness and transparency of the used car market. Summary of the Invention

[0011] To address the above problems, this invention provides a method for analyzing vehicle maintenance habits based on vehicle maintenance data.

[0012] The method for analyzing vehicle maintenance habits based on vehicle maintenance data provided by this invention includes the following steps:

[0013] 1. Standardization of vehicle maintenance and repair record data;

[0014] 2. Distinguish the maintenance records of the vehicle's CC;

[0015] 3. Calculate the vehicle maintenance average interval score for the vehicle CC.

[0016] maintenanceSpanTimeScore;

[0017] 4. Calculate the vehicle maintenance average interval mileage score for the vehicle CC.

[0018] maintenanceSpanMileScore;

[0019] 5. Calculate the time since the most recent maintenance record for the vehicle's CC.

[0020] lastMaintenanceScroe;

[0021] 6. Calculate the vehicle maintenance habit score (HabbritScore) for the vehicle CC.

[0022] further,

[0023] Step 1 includes converting the data format of any vehicle maintenance record A into a unified data structure.

[0024] The data structure includes six fields:

[0025] The first field is named "Repair Type" and its value is "repairType". It means the repair type in any vehicle repair and maintenance record and its type is String, i.e., text format.

[0026] The second field is named "Materials and Accessories" and its value is "material". It means all materials, including accessories, used in any vehicle maintenance record. The type is String and the materials are separated by semicolons.

[0027] The third field is named Repair Start Date, and its value is repairBeginDate, which means the start time of any vehicle maintenance record. Its type is DateTime, i.e., date type.

[0028] The fourth field is named "Mileage" and its value is "mileage". It means the mileage of the vehicle at the time of the formation of any vehicle maintenance record. Its type is Int, i.e., numeric.

[0029] The fifth field is named "Fault Reason" and its value is "troubleReason". It means the fault reason made by the maintenance personnel based on the vehicle condition in any vehicle maintenance record. Its type is String.

[0030] The sixth field is named "Repair Items" and its value is "recordItems". It represents all the repair and maintenance items in any given vehicle repair and maintenance record. The type of the field is String and the items are separated by semicolons.

[0031] further,

[0032] Step 2 includes the following steps:

[0033] 2a. Organize the key maintenance items of the vehicle and enter them into the database as a corpus of key vehicle maintenance. The data structure S1 of the key maintenance items is a two-dimensional structure. One dimension is the item content element, and each item content element stores one item content. The other dimension is the content element sub-element. Each item content element contains two content element sub-elements: verb sub-element termV and noun sub-element termN. Each of them stores, i.e., contains a verb VV and a noun NN. The combination of verb VV and noun VV yields the corresponding item content.

[0034] 2b. Determine from the materials and parts of any vehicle maintenance record A whether the key maintenance item is included in any vehicle maintenance record A, including:

[0035] 2b1. Standardize the second field of the vehicle maintenance record A, namely the material and parts text B: remove spaces from the text B and convert the Chinese punctuation marks in the text B to English punctuation marks;

[0036] 2b2. Using semicolons as delimiters, divide the text B into a first text array. The first text array includes at least one first text element. If the text B does not contain semicolons, store the entire content of the text B into one of the first text elements of the first text array. If the material and accessories text B contains N1 semicolons, where N1 is an integer greater than 0, when the text B does not end with a semicolon, the N1 semicolons sequentially divide the text B into N1+1 words. When the text B ends with a semicolon, the N1 semicolons sequentially divide the text B into N1 words. Store the N1+1 words or N1 words sequentially and one-to-one into the N1+1 or N1 first text elements of the first text array.

[0037] 2b3. From the first text array, search for a text element that contains the noun stored in the noun sub-element termN of any item content element AA in the corpus. If found, and the verb stored in the verb sub-element termV of the item content element AA is "replace", then the vehicle maintenance record A contains a key vehicle maintenance item. Mark the category of the vehicle maintenance record A, i.e., the first field, as maintenance. If not found, proceed to step 2c below.

[0038] 2c. Determine whether the vehicle maintenance record A contains the critical maintenance item based on the maintenance items in the vehicle maintenance record A, including:

[0039] 2c1. Standardize the sixth field, namely the repair item text C, in the vehicle maintenance record A: remove spaces from the text C and convert the Chinese punctuation marks in the text C to English punctuation marks;

[0040] 2c2. Using semicolons as delimiters, divide the text C into a second text array. The second text array includes at least one second text element. If the text C does not contain any semicolons, store the entire content of the text C into one of the second text elements of the second text array. If the text C contains N2 semicolons, where N2 is an integer greater than 0, then when the text C does not end with a semicolon, the N2 semicolons sequentially divide the text C into N2+1 words. When the text C ends with a semicolon, the N2 semicolons sequentially divide the text C into N2 words. Store the N2+1 words or N2 words sequentially and one-to-one into the N2+1 or N2 second text elements of the second text array.

[0041] 2c3. Using all nouns NN and verbs VV stored in the corpus, the words stored in each of the second text elements D are segmented. That is, all nouns NN and verbs VV are read and matched with the words stored in the second text element D according to the principle of prioritizing the nouns NN or verbs VV with more symbols from front to back, so as to segment the words stored in the second text element D into nouns NN or verbs VV in sequence. The data structure S2 of the segmentation result L is a two-dimensional structure. One dimension is the segmentation element E, and each segmentation element E stores a segmentation result of the second text element D in a one-to-one correspondence. The other dimension is the segmentation sub-element, and each segmentation element E contains at least one segmentation sub-element F. Each word obtained after segmenting the words stored in the second text element D is stored in each segmentation sub-element F in a one-to-one correspondence in sequence.

[0042] 2c4. Match the word segmentation result L. If there is a word segmentation element E1 in the word segmentation result L, and the words stored in all the word segmentation sub-elements of the word segmentation element E1 simultaneously contain the verb VV1 and noun NN1 stored in the verb sub-element termV1 and noun sub-element termN1 of any vehicle key maintenance item in the data structure S1 of the corpus, then there is a vehicle key maintenance item in the vehicle maintenance record A. Mark the category of the vehicle maintenance record A, i.e., the first field, as maintenance.

[0043] Repeat steps 2b and 2c to determine whether all vehicle maintenance records A of vehicle CC contain critical maintenance items. Then, combine all vehicle maintenance records A containing critical maintenance items into a record array materialRecordList. Each vehicle maintenance record A containing critical maintenance items is stored as an element in the record array materialRecordList, i.e., a record array element.

[0044] further,

[0045] Step 3 includes the following steps:

[0046] 3a Calculates the vehicle maintenance interval for the vehicle CC based on the record array materialRecordList, including the following steps:

[0047] 3a1. Remove the first and last vehicle repair records from the record array materialRecordList in chronological order;

[0048] 3a2. Take the earliest valid repair start date firstDate and the latest valid repair start date lastDate from the vehicle repair record array repairRecordList1, which is composed of all vehicle repair and maintenance records of the vehicle CC, and count the number of record array elements nTimes in the record array materialRecordList. Here, "valid" means that any vehicle repair and maintenance record in the vehicle repair record array repairRecordList1 has a correct repair start date and a mileage greater than 0.

[0049] 3a3. Calculate the time interval spanYear between the latest maintenance start date lastDate and the earliest maintenance start date firstDate, and convert the unit of the time interval spanYear to years;

[0050] 3a4. Calculate the average maintenance interval (avgMaintenceSpanYear), in years per maintenance interval:

[0051] avgMaintenceSpanYear=spanYear÷nTimes,

[0052] 3b. Calculate the vehicle maintenance average interval score (maintenanceSpanTimeScore) based on the average maintenance interval (avgMaintenceSpanYear). The maximum score for the maintenance SpanTimeScore is 5, and the minimum score is 1. The calculation includes the following steps:

[0053] 3b1. Round the average maintenance interval time avgMaintenceSpanYear to the nearest integer to obtain the average maintenance interval time avgMaintenceSpanYear1.

[0054] 3b2. If the average maintenance interval avgMaintenceSpanYear1 is greater than or equal to 0 and less than or equal to 10,

[0055] maintenanceSpanTimeScore=2.5×cos(0.31×avgMaintenceSpanYear-0.0505)+2.5,

[0056] Otherwise, if avgMaintenceSpanYear1 is less than 0 or greater than or equal to 10, then

[0057] maintenanceSpanTimeScore=1;

[0058] 3b3. If the calculated score maintenanceSpanTimeScore is less than 1, then take...

[0059] maintenanceSpanTimeScore=1.

[0060] further,

[0061] Step 3 also includes:

[0062] If the record array materialRecordList is empty, that is, there are no maintenance records, then

[0063] maintenanceSpanTimeScore=1.

[0064] further,

[0065] Step 4 includes the following steps:

[0066] 4a. Calculate the vehicle maintenance interval mileage based on the record array materialRecordList, including the following steps:

[0067] 4a1. Take the earliest recorded mileage firstMile and the latest recorded mileage lastMile from the record array materialRecordList, and then calculate the difference between the latest recorded mileage lastMile and the earliest recorded mileage firstMile as spanMile, in kilometers.

[0068] 4a2. Calculate the average maintenance interval mileage (avgMaintenceSpanMileage):

[0069] When the number of elements in the record array, nTimes, is greater than 1,

[0070] avgMaintenceSpanMileage=spanMile÷(nTimes-1),

[0071] otherwise,

[0072] avgMaintenceSpanMileage=-1;

[0073] 4b. Calculate the vehicle maintenance average interval mileage score (maintenanceSpanMileScore), where the maintenanceSpanMileScore has a maximum score of 5 and a minimum score of 1. The calculation includes the following steps:

[0074] 4b1. When the average maintenance interval mileage (avgMaintenceSpanMileage) is greater than 1 and less than or equal to 25000,

[0075] maintenanceSpanMileScore=2.5×

[0076] cos(0.000124×avgMaintenceSpanMileage-0.0505)+2.5,

[0077] otherwise,

[0078] maintenanceSpanMileScore=1;

[0079] 4b2. If the calculated score maintenanceSpanMileScore is greater than 5, then take maintenanceSpanMileScore = 5.

[0080] further,

[0081] Step 4 also includes:

[0082] If the record array materialRecordList is empty, i.e. there are no maintenance records, then maintenanceSpanMileScore = 1.

[0083] further,

[0084] Step 5 includes the following steps:

[0085] 5a. Find the most recent maintenance record time (i.e., the maintenance start date) from the record array materialRecordList, and denot it as lastMaintenanceTime. Subtract lastMaintenanceTime from the current time curTime to obtain the time interval spanYear, in years.

[0086] 5b. Calculate the time to present score (lastMaintenanceScroe) for the most recent maintenance record. The lastMaintenanceScroe score has a maximum of 5 and a minimum score of 1. The calculation includes the following steps:

[0087] 5b1. When the time interval spanYear is greater than or equal to 0 and less than 2.5,

[0088] lastMaintenanceScroe=2.5×cos(1.238×spanYear-0.0505)+2.5,

[0089] otherwise,

[0090] lastMaintenanceScroe = 1;

[0091] 5b2. If the calculated score lastMaintenanceScroe is less than 1, then take...

[0092] lastMaintenanceScroe = 1.

[0093] further,

[0094] Step 5 further includes:

[0095] If the record array materialRecordList is empty, i.e. there are no maintenance records, then lastMaintenanceScroe = 1.

[0096] further,

[0097] Step 6 includes:

[0098] When any one of the scores maintenanceSpanTimeScore, lastMaintenanceScore, and maintenanceSpanTimeScore is 1, the vehicle maintenance habit score maintenanceHabbritScore is 1.

[0099] otherwise,

[0100] maintenanceHabbritScore=(maintenanceSpanTimeScore+lastMaintenanceScroe

[0101] +maintenanceSpanTimeScor)÷3.

[0102] The method for analyzing vehicle maintenance habits based on vehicle maintenance data provided by this invention achieves standardized, objective analysis and comprehensive, dynamic and quantitative evaluation of vehicle maintenance habits, which can improve the fairness and transparency of the used car trading market.

[0103] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0104] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0105] Figure 1 A flowchart of a method for analyzing vehicle maintenance habits based on vehicle maintenance data according to an embodiment of the present invention is shown. Detailed Implementation

[0106] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0107] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. The terms "first," "second," "third," etc., in the specification, claims, or accompanying drawings of this application are used to distinguish different objects, not to describe a specific order or hierarchy. The term "multiple" in this application refers to two or more (including two).

[0108] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0109] Figure 1 A flowchart illustrating the method for analyzing vehicle maintenance habits based on vehicle maintenance data, provided by this invention, is shown. See also... Figure 1 The method of analyzing vehicle maintenance habits based on vehicle maintenance data mainly includes the following steps.

[0110] 1. Standardization of vehicle maintenance data.

[0111] Because vehicle maintenance records from different sources have varying data formats, it is necessary to convert these data formats into a unified data structure to facilitate data analysis. The dataset composed of vehicle maintenance records used in this invention is denoted as the vehicle maintenance record array `repairRecordList`. `repairRecordList` contains multiple (more than one) vehicle maintenance records (referred to as records) A with the same data structure. Table 1 shows the data structure of one vehicle maintenance record A.

[0112] Table 1. Data structure of a vehicle maintenance record.

[0113]

[0114]

[0115] As shown in Table 1, the data structure of vehicle maintenance record A includes six fields:

[0116] The first field is named "Repair Type" and its value is "repairType". It means the type of maintenance and repair in this record and its type is String (i.e., text format).

[0117] The second field is named "Materials and Accessories," with the value "material," which represents all materials (including accessories) used in this record. The field is of type String, and the materials are separated by semicolons.

[0118] The third field is named "Repair Start Date" and its value is "repairBeginDate". It means the start time of this record and its type is DateTime (i.e., date type).

[0119] The fourth field is named "Mileage" and its value is "mileage". It represents the vehicle's mileage at the time this record was created, and its type is Int (i.e., numeric type).

[0120] The fifth field is named "Fault Reason" and its value is "troubleReason". It means the fault reason made by the repair personnel based on the vehicle condition in this record, and its type is String.

[0121] The sixth field is named "Maintenance Items" and its value is "recordItems". It represents all maintenance and repair items in this record. The field type is String and the items are separated by semicolons.

[0122] 2. Distinguish the maintenance records of the vehicle's CC.

[0123] 2a. Vehicle maintenance technicians compile key vehicle maintenance items, such as oil changes and four-wheel alignments, and input them into a database as a corpus of key vehicle maintenance data. The data structure S1 for key vehicle maintenance items is a two-dimensional structure. One dimension consists of item content elements, each storing one item content. The other dimension consists of content element sub-elements, each containing two content element sub-elements: a verb sub-element termV and a noun sub-element termN. Each sub-element stores (i.e., contains) a verb VV and a noun NN. The combination of verb VV and noun VV yields the corresponding item content. Table 1 shows an example of the data structure for key vehicle maintenance items obtained from the previously listed key maintenance items.

[0124] Table 1 shows an example of the data structure for key vehicle maintenance items.

[0125] Change the engine oil replace engine oil Four-wheel alignment position Four wheels

[0126] Table 1 shows two item content elements derived from the key maintenance items listed above. The first item content element stores the item content "change engine oil". The verb sub-element termV and the noun sub-element termN of the first item content element contain the verb "make it smoother" and the noun "engine oil", respectively. Similarly, the second item content element stores the item content "four-wheel alignment". The verb sub-element termV and the noun sub-element termN of the second item content element contain the verb "alignment" and the noun "four wheels", respectively.

[0127] 2b. Determine from the materials and parts data of any vehicle maintenance record A whether the critical maintenance item is included in that vehicle maintenance record A. This includes the following steps:

[0128] 2b1. Standardize the second field of the vehicle maintenance record A, namely the material and parts text B: remove spaces from the material and parts text B, and convert the Chinese punctuation marks in the material and parts text B to English punctuation marks.

[0129] 2b2. Using semicolons as delimiters, divide text B into a first text array, which includes at least one first text element. If text B contains no semicolons, the entire content of text B is stored in a first text element of the first text array. If text B contains N1 semicolons, where N1 is a positive integer, these N1 semicolons sequentially divide text B into either N1+1 words (text B does not end with a semicolon) or N1 words (text B ends with a semicolon). These N1+1 words or N1 words are then sequentially stored one-to-one in the N1+1 or N1 first text elements of the first text array. For example, if the material text after step 2b1 is "engine oil; oil filter element; spark plug kit;", the first text array shown in Table 2 is obtained after this step.

[0130] Table 2. Example of the first text array.

[0131]

[0132]

[0133] 2b3. From the first text array obtained after step 2b2, search for a text element containing a noun stored in the noun sub-element termN of any item content element AA in the corpus. If found, and the verb stored in the verb sub-element termV of the item content element AA is "replace", then the vehicle maintenance record A contains a key vehicle maintenance item. Mark the category, i.e., the first field, of the vehicle maintenance record A as maintenance. If not found, proceed to step 2c below. For example, if the first element in Table 2 contains the word "engine oil", which is a noun stored in the noun sub-element termN of the first item content element in Table 1, and the verb stored in the verb sub-element termV of the first item content element is "replace", then mark the category, i.e., the first field, of the vehicle maintenance record containing the material text "engine oil; oil filter element; spark plug kit;" as maintenance.

[0134] 2c. Determine whether the current maintenance record contains critical maintenance items based on the maintenance items in vehicle maintenance record A.

[0135] 2c1. Standardize the sixth field of the vehicle maintenance record A, namely the maintenance item text C: remove spaces in the maintenance item text C, and convert the Chinese punctuation marks in the maintenance item text C to English punctuation marks.

[0136] 2c2. Using semicolons as delimiters, divide the repair project text C into a second text array, which includes at least one second text element. If the repair project text C does not contain semicolons, the entire content of the repair project text C is stored in one of the second text elements of the second text array. If the repair project text C contains N2 semicolons, where N2 is a positive integer, these N2 semicolons sequentially divide the repair project text C into either N2+1 words (the repair project text C does not end with a semicolon) or N2 words (the repair project text C ends with a semicolon). These N2+1 words or N2 words are then sequentially stored one-to-one in the N2+1 or N2 second text elements of the second text array. For example, if the material text processed in step 2c1 is "PDI inspection; four-wheel alignment; front bumper left side removal and installation; replacement of left side control arm guide arm;", the second text array shown in Table 3 is obtained after this step.

[0137] Table 3. Example of the second text array.

[0138] 1 PDI check 2 Four-wheel alignment 3 Front bumper left side removal and installation 4 Replace the left swing arm guide arm

[0139] 2c3. Using all nouns (NN) and verbs (VV) stored in the corpus, segment the words stored in each of the second text elements (D). Specifically, read all nouns (NN) and verbs (VV) and match them against the words stored in the second text element (D) according to the principle of prioritizing nouns (NN) or verbs (VV) containing more symbols (the more symbols a noun (NN) or verb (VV) contains, the higher its priority). This process segments the words stored in the second text element (D) into nouns (NN) or verbs (VV) sequentially. The data structure S2 of the segmentation result L is a two-dimensional structure. One dimension is the segmentation element E, where each segmentation element E stores a segmentation result corresponding to one of the second text element (D). The other dimension is the segmentation sub-element F, where each segmentation element E contains at least one segmentation sub-element F. Each word obtained after segmenting the words stored in the second text element (D) is sequentially stored in each segmentation sub-element F. For example, Table 4 shows the segmentation results obtained from the second text element shown in Table 3. For example, the word segmentation results “PDI” and “Check” stored in the second text element 1 in Table 3 are stored one-to-one in the segmentation sub-element 1 and segmentation sub-element 2 of segmentation element 1 in Table 4.

[0140] The word segmentation results of the second text element are shown in Tables 4 and 3.

[0141]

[0142] 2c4. Match the segmentation result L. If any segmentation element E1 in the segmentation result L contains words stored in all its sub-elements that simultaneously include the verb VV1 and noun NN1 stored in the verb sub-element termV1 and noun sub-element termN1 of the data structure S1 for a certain vehicle key maintenance item in the corpus, then the vehicle maintenance record A contains a vehicle key maintenance item, and the category of the vehicle maintenance record A, i.e., the first field, is marked as maintenance. For example, segmentation element 2 in Table 4 matches the key maintenance item listed above in Table 1.

[0143] Repeat steps 2b and 2c to determine whether all vehicle maintenance records A of vehicle CC contain critical maintenance items. Then, combine all vehicle maintenance records A containing critical maintenance items into a record array materialRecordList. Each vehicle maintenance record A containing critical maintenance items is correspondingly used as or stored as an element in the record array materialRecordList, i.e., a record array element.

[0144] 3. Calculate the vehicle CC's average maintenance interval score.

[0145] 3a. Calculate the vehicle maintenance interval for vehicle CC based on the record array materialRecordList, including the following steps.

[0146] 3a1. Remove the first and last vehicle repair records from the materialRecordList array in chronological order.

[0147] 3a2. Retrieve the earliest valid maintenance start date (firstDate) and latest valid maintenance start date (lastDate) from the vehicle maintenance record array (repairRecordList1) of vehicle CC. Also, count the number of elements in the record array (nTimes) in materialRecordList (representing the total number of vehicle maintenance records). Validity means that every vehicle maintenance record in repairRecordList1 has a correct maintenance start date and a mileage greater than 0.

[0148] 3a3. Calculate the time interval spanYear between lastDate and firstDate, and convert the unit of spanYear to years.

[0149] 3a4. Calculate the average maintenance interval (avgMaintenceSpanYear) (unit: year / time):

[0150] avgMaintenceSpanYear=spanYear÷nTimes.

[0151] 3b. Calculate the vehicle maintenance average interval time score (maintenanceSpanTimeScore) based on the average maintenance interval time (avgMaintenceSpanYear). The maximum score for the maintenance SpanTimeScore is 5, and the minimum score is 1. The calculation includes the following steps.

[0152] 3b1. Round the average maintenance interval avgMaintenceSpanYear to the nearest integer to obtain the average maintenance interval avgMaintenceSpanYear1.

[0153] 3b2. If the average maintenance interval avgMaintenceSpanYear1 is greater than or equal to 0 and less than or equal to 10,

[0154] maintenanceSpanTimeScore=2.5×cos(0.31×avgMaintenceSpanYear-0.0505)+2.5.

[0155] Otherwise, if avgMaintenceSpanYear1 is less than 0 or greater than or equal to 10, then maintenanceSpanTimeScore = 1.

[0156] 3b3. If the calculated maintenanceSpanTimeScore is less than 1, then take...

[0157] maintenanceSpanTimeScore=1.

[0158] 3c. If the materialRecordList is empty, meaning there are no maintenance records, then

[0159] maintenanceSpanTimeScore=1.

[0160] The vehicle maintenance average interval score (maintenanceSpanTimeScore) falls within different score ranges, representing different levels of vehicle maintenance intervals:

[0161] When the maintenanceSpanTimeScore is 4.5-5, the vehicle CC exhibits excellent maintenance practices: the average maintenance interval is very short, far below the manufacturer's recommended schedule. This indicates that the owner places great importance on vehicle maintenance, ensuring timely servicing to guarantee performance and reliability. This maintenance habit extends the vehicle's lifespan and reduces the failure rate.

[0162] When the maintenanceSpanTimeScore is 3.5-4.5, the vehicle CC has a good maintenance schedule: the average maintenance interval is close to or slightly longer than the manufacturer's recommended maintenance interval. The owner is able to perform maintenance as recommended by the manufacturer, maintaining the vehicle's normal operation and stable performance.

[0163] When the maintenanceSpanTimeScore is 3-3.5, the vehicle CC's maintenance cycle is generally: the average maintenance interval slightly exceeds the manufacturer's recommended interval, but is still within an acceptable range. The owner may have neglected vehicle maintenance, but it hasn't reached the point of seriously affecting vehicle performance. It is recommended that the owner strengthen their maintenance awareness and follow the manufacturer's recommendations.

[0164] When the maintenanceSpanTimeScore is 2-3, the vehicle CC has a poor maintenance schedule: the average maintenance interval significantly exceeds the manufacturer's recommended maintenance schedule. This indicates that the owner does not pay enough attention to vehicle maintenance, which may lead to decreased vehicle performance and increased risk of malfunctions. It is recommended that the owner have the vehicle serviced as soon as possible and strengthen their maintenance awareness.

[0165] When the maintenanceSpanTimeScore is 1-2, the vehicle CC has a poor maintenance cycle: the average interval between maintenance is very long, far exceeding the manufacturer's recommended maintenance schedule. This indicates that the owner has seriously neglected vehicle maintenance, and the vehicle may have developed serious performance problems or safety hazards. It is strongly recommended that the owner have the vehicle serviced immediately and strengthen their maintenance awareness to ensure the vehicle's safety and reliability.

[0166] 4. Calculate the vehicle maintenance average interval mileage score for vehicle CC.

[0167] 4a. Calculating the vehicle maintenance interval mileage based on the record array materialRecordList includes the following steps:

[0168] 4a1. Take the earliest recorded mileage (firstMile) and the latest recorded mileage (lastMile) from the record array materialRecordList, and then calculate the difference between lastMile and firstMile as spanMile (unit: kilometers).

[0169] 4a2. Calculate the average maintenance interval mileage (avgMaintenceSpanMileage):

[0170] When nTimes is greater than 1

[0171] avgMaintenceSpanMileage=spanMile÷(nTimes-1),

[0172] otherwise,

[0173] avgMaintenceSpanMileage=-1.

[0174] 4b. Calculate the vehicle maintenance average interval mileage score (maintenanceSpanMileScore). The maintenanceSpanMileScore has a maximum score of 5 and a minimum score of 1. The calculation includes the following steps:

[0175] 4b1. When avgMaintenceSpanMileage is greater than 1 and less than or equal to 25000, maintenanceSpanMileScore = 2.5 ×

[0176] cos(0.000124×avgMaintenceSpanMileage-0.0505)+2.5,

[0177] otherwise,

[0178] maintenanceSpanMileScore=1.

[0179] 4b2. If the calculated maintenanceSpanMileScore is greater than 5, then take...

[0180] maintenanceSpanMileScore=5.

[0181] 4c. If the materialRecordList is empty, meaning there are no maintenance records, then

[0182] maintenanceSpanMileScore=1.

[0183] The average maintenance interval mileage (avgMaintenceSpanMileage) falls within different score ranges, representing different levels of vehicle maintenance mileage (CC).

[0184] When the average Maintenance Span Mileage (avgMaintenceSpanMileage) is 4.5-5, the vehicle CC exhibits excellent maintenance mileage: the average maintenance interval is significantly lower than the manufacturer's recommended mileage, indicating that the owner places great emphasis on vehicle maintenance. This maintenance habit ensures the vehicle is always kept in optimal condition, reducing malfunctions and wear, and extending the vehicle's lifespan.

[0185] When the average MaintenanceSpanMileage (avgMaintenceSpanMileage) is 3.5-4.5, the vehicle CC has good maintenance mileage: the average maintenance interval is close to or slightly longer than the manufacturer's recommended maintenance schedule. This means that the owner is able to maintain the vehicle according to the recommended maintenance intervals, which helps ensure the vehicle's normal operation and stable performance.

[0186] When the average MaintenanceSpanMileage (avgMaintenceSpanMileage) is 3-3.5, the vehicle CC's maintenance mileage is generally average: the average maintenance interval slightly exceeds the manufacturer's recommended maintenance cycle. This indicates that the owner may have been somewhat negligent in vehicle maintenance, but it hasn't reached the point of seriously affecting vehicle performance. It is recommended that the owner pay closer attention to the vehicle's maintenance needs and follow the manufacturer's recommendations.

[0187] When the average MaintenanceSpanMileage (avgMaintenceSpanMileage) is 2-3, the vehicle CC's maintenance mileage is poor: the average maintenance interval significantly exceeds the manufacturer's recommended maintenance cycle. This indicates that the owner does not pay enough attention to vehicle maintenance, which may lead to decreased vehicle performance, increased risk of malfunctions, and potentially adversely affect the vehicle's lifespan. It is recommended that the owner have the vehicle serviced as soon as possible and strengthen their maintenance awareness.

[0188] When avgMaintenceSpanMileage is 1-2, the vehicle CC's maintenance mileage difference indicates an extremely long average maintenance interval, far exceeding the manufacturer's recommended maintenance schedule. This suggests the owner has seriously neglected vehicle maintenance, and the vehicle may have developed serious performance problems or safety hazards. It is strongly recommended that the owner immediately have the vehicle serviced and strengthen their maintenance awareness to ensure the vehicle's safety and reliability.

[0189] 5. Calculate the time since the vehicle's most recent maintenance record.

[0190] 5a. Find the most recent maintenance record time (i.e., the maintenance start date, denoted as lastMaintenanceTime) from the record array materialRecordList, and subtract lastMaintenanceTime from the current time curTime to obtain the time interval spanYear (unit: years).

[0191] 5b. Calculate the LastMaintenanceScroe score, which has a maximum score of 5 and a minimum score of 1. The calculation includes the following steps:

[0192] 5b1. When the time interval spanYear is greater than or equal to 0 and less than 2.5,

[0193] lastMaintenanceScroe=2.5×cos(1.238×spanYear-0.0505)+2.5,

[0194] otherwise,

[0195] lastMaintenanceScroe = 1.

[0196] 5b2. If the calculated lastMaintenanceScroe is less than 1, then take...

[0197] lastMaintenanceScroe = 1.

[0198] 5c. If the materialRecordList is empty, meaning there are no maintenance records, then

[0199] lastMaintenanceScroe = 1.

[0200] The "Last Maintenance Record (Date to Present)" score ranges from which the vehicle's maintenance record (CC) scores differ, representing varying levels of vehicle maintenance.

[0201] When the lastMaintenanceScale (SCR) is 4.5-5, the vehicle CC is in excellent condition: the last maintenance record is very recent, almost within the most recent recommended maintenance cycle. This indicates that the owner maintains the vehicle very promptly, effectively ensuring its performance and reliability.

[0202] When the lastMaintenanceScale (SCR) is between 3.5 and 4.5, the vehicle CC is in good condition: although the last maintenance record is some time ago, it is still within a reasonable maintenance cycle. This indicates that the owner pays attention to vehicle maintenance and is able to perform maintenance according to the manufacturer's recommendations or a certain cycle.

[0203] When the lastMaintenanceScale (SCR) is 3-3.5, the vehicle's maintenance level is generally average: the vehicle's most recent maintenance record is close to or slightly past the recommended maintenance interval. This may mean that the owner has neglected vehicle maintenance to some extent, but it has not yet reached the point of seriously affecting vehicle performance.

[0204] When lastMaintenanceScale (cc) is 2-3, the vehicle's maintenance level is poor: the last maintenance record is significantly older than the recommended maintenance interval. This may lead to decreased vehicle performance, increased risk of malfunction, and potentially adversely affect the vehicle's lifespan.

[0205] When lastMaintenanceScale is 1-2, the vehicle's maintenance level is poor: the last maintenance record is very old, far exceeding the recommended maintenance cycle. This indicates that the owner has seriously neglected vehicle maintenance, and the vehicle may have serious performance problems or safety hazards, urgently requiring maintenance and repair.

[0206] 6. Calculate the vehicle maintenance habit score for vehicle CC.

[0207] When any one of maintenanceSpanTimeScore, lastMaintenanceScore, or maintenanceSpanTimeScore is 1, the vehicle maintenance habit score maintenanceHabbritScore is 1.

[0208] otherwise,

[0209] maintenanceHabbritScore=(maintenanceSpanTimeScore+lastMaintenanceScroe

[0210] +maintenanceSpanTimeScor)÷3.

[0211] The vehicle maintenance habit rating (HabbritScore) falls within different score ranges, representing varying degrees of vehicle maintenance habits (CC).

[0212] A maintenance Habbrit Score of 4.5-5 indicates that owners of the CC vehicle have excellent vehicle maintenance habits: they place great importance on vehicle maintenance and strictly follow the manufacturer's recommendations for regular servicing, including changing key components such as engine oil, air filter, and brake pads. In addition, they perform routine checks on the vehicle, such as checking tire pressure, braking system, and lights, to ensure the vehicle is always in optimal condition. Vehicles owned by these owners tend to have a longer lifespan and more stable performance.

[0213] A maintenance Habbrit Score of 3.5-4.5 indicates that owners of vehicles in the CC category have good vehicle maintenance habits: they perform regular maintenance, but may not strictly follow the manufacturer's recommendations. They may determine the frequency and items of maintenance based on the actual usage of the vehicle. Nevertheless, these owners still perform necessary inspections and maintenance to ensure the vehicle functions properly. Their vehicles typically maintain good performance and a long lifespan.

[0214] A maintenance Habbrit Score of 3-3.5 indicates that owners of vehicles in the CC category generally have average vehicle maintenance habits: they don't prioritize vehicle maintenance and may overlook some subtle problems. They might only perform maintenance when the vehicle malfunctions or requires repair. This maintenance habit can lead to decreased vehicle performance and a shortened lifespan. However, as long as owners address vehicle issues promptly and prevent them from worsening, their vehicles can still maintain basic operational status.

[0215] A maintenance Habbrit Score of 2-3 indicates that owners of vehicles with a CC rating have poor vehicle maintenance habits: they place little importance on vehicle maintenance and often neglect its needs. They may not change critical components such as engine oil and air filters for extended periods, and they may not perform routine checks. Such maintenance habits can seriously affect the vehicle's performance and lifespan, and may even lead to serious safety hazards.

[0216] A maintenance Habbrit Score of 1-2 indicates that owners of vehicles with a CC rating have poor vehicle maintenance habits: they almost never perform any maintenance and ignore the vehicle's maintenance needs. They may leave their vehicles in a broken-down state for extended periods without repair, which not only seriously affects the vehicle's performance and lifespan but also poses a serious safety hazard to other road users. Vehicles owned by these owners often require frequent repairs and parts replacements, significantly increasing both economic and time costs.

[0217] The method for analyzing vehicle maintenance habits based on vehicle maintenance data provided by this invention achieves standardized, objective analysis and comprehensive, dynamic and quantitative evaluation of vehicle maintenance habits, which can improve the fairness and transparency of the used car trading market.

[0218] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing vehicle maintenance habits based on vehicle maintenance data, characterized in that, Including the following steps: 1) Standardization of vehicle maintenance and repair record data; 2) Distinguish the maintenance records of the vehicle's CC; 3) Calculate the vehicle CC's average maintenance interval time score, maintenanceSpanTimeScore. 4) Calculate the vehicle maintenance average interval mileage score for the vehicle CC. 5) Calculate the lastMaintenanceScroe score for the most recent maintenance record of the vehicle CC; 6) Calculate the vehicle maintenance habit score (HabbritScore) for the vehicle CC. Step 2 includes the following steps: 2a. Organize the key maintenance items of the vehicle and enter them into the database as a corpus of key vehicle maintenance. The data structure S1 of the key maintenance items is a two-dimensional structure. One dimension is the item content element, and each item content element stores one item content. The other dimension is the content element sub-element. Each item content element contains two content element sub-elements: verb sub-element termV and noun sub-element termN. Each of them stores, i.e., contains a verb VV and a noun NN. The combination of verb VV and noun VV yields the corresponding item content. 2b. Determine from the materials and parts of any vehicle maintenance record A whether the key maintenance item is included in any vehicle maintenance record A, including: 2b1. Standardize the second field (materials and parts text B) in the vehicle maintenance record A: remove spaces from text B and convert Chinese punctuation marks in text B to English punctuation marks. The second field is named "Materials and Accessories," and its value is "material." It means all materials, including accessories, used in any vehicle maintenance record. The type is String, and the materials are separated by semicolons. 2b2. Using semicolons as delimiters, divide the text B into a first text array. The first text array includes at least one first text element. If the text B does not contain any semicolons, store the entire content of the text B into one of the first text elements of the first text array. If the material / parts text B contains N1 semicolons, where N1 is a positive integer, when the text B does not end with a semicolon, the N1 semicolons sequentially divide the text B. The text is divided into N1+1 words. When the text B ends with an English semicolon, the N1 English semicolons divide the text B into N1 words in sequence. The N1+1 words or N1 words are stored in the N1+1 or N1 first text elements of the first text array in sequence. 2b3. From the first text array, search for a text element that contains the noun stored in the noun sub-element termN of any item content element AA in the corpus. If found, and the verb stored in the verb sub-element termV of the item content element AA is "replace", then the vehicle maintenance record A contains a key vehicle maintenance item. Mark the category of the vehicle maintenance record A, i.e., the first field, as maintenance. If not found, proceed to step 2c below. 2c. Determine whether the vehicle maintenance record A contains the critical maintenance item based on the maintenance items in the vehicle maintenance record A, including: 2c1. Standardize the sixth field (repair item text C) in the vehicle maintenance record A: remove spaces from text C and convert Chinese punctuation marks in text C to English punctuation marks. The sixth field is named "Repair Items" and its value is "recordItems". It represents all the repair and maintenance items in any given vehicle repair and maintenance record. The type of the field is String and the items are separated by semicolons. 2c2. Using semicolons as delimiters, divide the text C into a second text array. The second text array includes at least one second text element. If the text C does not contain any semicolons, store the entire content of the text C into one of the second text elements of the second text array. If the text C contains N2 semicolons, where N2 is an integer greater than 0, then when the text C does not end with a semicolon, the N2 semicolons sequentially divide the text C into N2+1 words. When the text C ends with a semicolon, the N2 semicolons sequentially divide the text C into N2 words. Store the N2+1 words or N2 words sequentially and one-to-one into the N2+1 or N2 second text elements of the second text array. 2c3. Using all nouns NN and verbs VV stored in the corpus, the words stored in each of the second text elements D are segmented. That is, all nouns NN and verbs VV are read and matched with the words stored in the second text element D according to the principle of prioritizing the nouns NN or verbs VV with more symbols from front to back, so as to segment the words stored in the second text element D into nouns NN or verbs VV in sequence. The data structure S2 of the segmentation result L is a two-dimensional structure. One dimension is the segmentation element E, and each segmentation element E stores a segmentation result of the second text element D in a one-to-one correspondence. The other dimension is the segmentation sub-element, and each segmentation element E contains at least one segmentation sub-element F. Each word obtained after segmenting the words stored in the second text element D is stored in each segmentation sub-element F in a one-to-one correspondence in sequence. 2c4. Match the word segmentation result L. If there is a word segmentation element E1 in the word segmentation result L, and the words stored in all the word segmentation sub-elements of the word segmentation element E1 simultaneously contain the verb VV1 and noun NN1 stored in the verb sub-element termV1 and noun sub-element termN1 of any vehicle key maintenance item in the data structure S1 of the corpus, then there is a vehicle key maintenance item in the vehicle maintenance record A. Mark the category of the vehicle maintenance record A, i.e., the first field, as maintenance. Repeat steps 2b and 2c to determine whether all vehicle maintenance records A of vehicle CC contain critical maintenance items. Then, combine all vehicle maintenance records A containing critical maintenance items into a record array materialRecordList. Each vehicle maintenance record A containing critical maintenance items is stored as an element in the record array materialRecordList, i.e., a record array element.

2. The method for analyzing vehicle maintenance habits based on vehicle maintenance data according to claim 1, characterized in that, Step 1 includes converting the data format of any vehicle maintenance record A into a unified data structure. The data structure includes six fields: The first field is named "Repair Type" and its value is "repairType". It means the repair type in any vehicle repair and maintenance record and its type is String, i.e., text format. The second field is named "Materials and Accessories" and its value is "material". It means all materials, including accessories, used in any vehicle maintenance record. The type is String and the materials are separated by semicolons. The third field is named Repair Start Date, and its value is repairBeginDate, which means the start time of any vehicle maintenance record. Its type is DateTime, i.e., date type. The fourth field is named "Mileage" and its value is "mileage". It means the mileage of the vehicle at the time of the formation of any vehicle maintenance record. Its type is Int, i.e., numeric. The fifth field is named "Fault Reason" and its value is "troubleReason". It means the fault reason made by the maintenance personnel based on the vehicle condition in any vehicle maintenance record. Its type is String. The sixth field is named "Repair Items" and its value is "recordItems". It represents all the repair and maintenance items in any given vehicle repair and maintenance record. The type of the field is String and the items are separated by semicolons.

3. The method for analyzing vehicle maintenance habits based on vehicle maintenance data according to claim 1, characterized in that, Step 3 includes the following steps: 3a. Calculate the vehicle maintenance interval for the vehicle CC based on the record array materialRecordList, including the following steps: 3a1. Remove the first and last vehicle repair records from the record array materialRecordList in chronological order; 3a2. Take the earliest valid repair start date firstDate and the latest valid repair start date lastDate from the vehicle repair record array repairRecordList1, which is composed of all vehicle repair and maintenance records of the vehicle CC, and count the number of record array elements nTimes in the record array materialRecordList. Here, "valid" means that any vehicle repair and maintenance record in the vehicle repair record array repairRecordList1 has a correct repair start date and a mileage greater than 0. 3a3. Calculate the time interval spanYear between the latest maintenance start date lastDate and the earliest maintenance start date firstDate, and convert the unit of the time interval spanYear to years; 3a4. Calculate the average maintenance interval (avgMaintenceSpanYear), in years per maintenance interval: avgMaintenceSpanYear=spanYear÷nTimes, 3b. Calculate the vehicle maintenance average interval score (maintenanceSpanTimeScore) based on the average maintenance interval (avgMaintenceSpanYear). The maximum score for the maintenance SpanTimeScore is 5, and the minimum score is 1. The calculation includes the following steps: 3b1. Round the average maintenance interval time avgMaintenceSpanYear to the nearest integer to obtain the average maintenance interval time avgMaintenceSpanYear1. 3b2. If the average maintenance interval avgMaintenceSpanYear1 is greater than or equal to 0 and less than or equal to 10, maintenanceSpanTimeScore=2.5×cos(0.31×avgMaintenceSpanYear-0.0505)+2.5, Otherwise, if avgMaintenceSpanYear1 is less than 0 or greater than or equal to 10, then maintenanceSpanTimeScore=1; 3b3. If the calculated score maintenanceSpanTimeScore is less than 1, then take... maintenanceSpanTimeScore=1.

4. The method for analyzing vehicle maintenance habits based on vehicle maintenance data according to claim 3, characterized in that, Step 3 also includes: If the contents of the record array materialRecordList are empty, that is, there are no maintenance records, then maintenanceSpanTimeScore = 1.

5. The method for analyzing vehicle maintenance habits based on vehicle maintenance data according to claim 3 or 4, characterized in that, Step 4 includes the following steps: 4a. Calculate the vehicle maintenance interval mileage based on the record array materialRecordList, including the following steps: 4a1. Take the earliest recorded mileage firstMile and the latest recorded mileage lastMile from the record array materialRecordList, and then calculate the difference between the latest recorded mileage lastMile and the earliest recorded mileage firstMile as spanMile, in kilometers. 4a2. Calculate the average maintenance interval mileage (avgMaintenceSpanMileage): When the number of elements in the record array, nTimes, is greater than 1, avgMaintenceSpanMileage=spanMile÷(nTimes-1), otherwise, avgMaintenceSpanMileage=-1; 4b. Calculate the vehicle maintenance average interval mileage score (maintenanceSpanMileScore), where the maintenanceSpanMileScore has a maximum score of 5 and a minimum score of 1. The calculation includes the following steps: 4b1. When the average maintenance interval mileage (avgMaintenceSpanMileage) is greater than 1 and less than or equal to 25000, maintenanceSpanMileScore = 2.5 × cos(0.000124 × avgMaintenceSpanMileage - 0.0505) + 2.

5. otherwise, maintenanceSpanMileScore=1; 4b2. If the calculated score maintenanceSpanMileScore is greater than 5, then take maintenanceSpanMileScore = 5.

6. The method for analyzing vehicle maintenance habits based on vehicle maintenance data according to claim 5, characterized in that, Step 4 also includes: If the record array materialRecordList is empty, i.e. there are no maintenance records, then maintenanceSpanMileScore = 1.

7. The method for analyzing vehicle maintenance habits based on vehicle maintenance data according to claim 6, characterized in that, Step 5 includes the following steps: 5a. Find the most recent maintenance record time (i.e., the maintenance start date) from the record array materialRecordList, and denot it as lastMaintenanceTime. Subtract lastMaintenanceTime from the current time curTime to obtain the time interval spanYear, in years. 5b. Calculate the time to present score (lastMaintenanceScroe) for the most recent maintenance record. The lastMaintenanceScroe score has a maximum of 5 and a minimum score of 1. The calculation includes the following steps: 5b1. When the time interval spanYear is greater than or equal to 0 and less than 2.5, lastMaintenanceScroe=2.5×cos(1.238×spanYear-0.0505)+2.5, otherwise, lastMaintenanceScroe = 1; 5b2. If the calculated score lastMaintenanceScroe is less than 1, then take lastMaintenanceScroe = 1.

8. The method for analyzing vehicle maintenance habits based on vehicle maintenance data according to claim 7, characterized in that, Step 5 further includes: If the record array materialRecordList is empty, i.e. there are no maintenance records, then lastMaintenanceScroe = 1.

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