CN113920812A - New energy automobile fault diagnosis training examination and scheme push system - Google Patents
New energy automobile fault diagnosis training examination and scheme push system Download PDFInfo
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- 238000012549 training Methods 0.000 title claims abstract description 38
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- G09B9/00—Simulators for teaching or training purposes
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- G—PHYSICS
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- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
Abstract
The invention provides a new energy automobile fault diagnosis training examination and scheme pushing system, which comprises: the presentation layer is used for collecting user input information and providing feedback information for a user; the middle layer is used for acquiring user input information, starting a related program according to the user input information, and extracting data by the related program; the data access layer is used for storing data and feeding back data information according to instructions of related programs, the data access layer comprises a detection measure database, and the detection measure database comprises: a primary measure library, a secondary measure library and a tertiary measure library. The invention realizes classification of vehicle faults by adopting the presentation layer, the intermediate layer and the data access layer and utilizing the primary measure library, the secondary measure library and the tertiary measure library, and is convenient for users to use as required.
Description
Technical Field
The invention relates to an automobile fault diagnosis training examination and scheme pushing system, in particular to a new energy automobile fault diagnosis training examination and scheme pushing system.
Background
The power system power supply voltage adopted by the new energy automobile is mostly more than 300V, higher requirements are put on the safety of automobile use and maintenance, and a maintenance department is required to have stronger new energy automobile fault detection and diagnosis technology. The existing electric control system of the automobile is maintained by means of fault code guidance of a self-diagnosis system, and the fault code usually only prompts that a signal received or output by a certain control unit is abnormal and exceeds a threshold range, so that the fault code cannot be completely and accurately positioned. In particular, the fault of the integrated system of machine, electricity, liquid and the like, the specific fault position and reason need to be detected and data analyzed by maintenance personnel, and the requirement on the technical capability of the maintenance personnel is higher. Simultaneously new energy automobile power supply system and electric drive system are greatly different with traditional car, and structural arrangement is compacter, adopts trinity or four unification assemblies to encapsulate a plurality of high-voltage subsystem subassemblies in a seal box often, from the security with structural consideration all do not benefit to maintenance technical staff's study and cultivation. The lack of effective guidance for the technical personnel in the industry and the slow accumulation of theoretical knowledge and practical experience lead to the serious lack of diagnosis and maintenance technical personnel related to new energy automobiles in the market.
According to the talent training scheme of new energy automobile vocational education and the examination standards of the medium and high-grade professional skill level certificate, medium and high-grade new energy automobile maintenance technicians need to have comprehensive fault diagnosis capability, master the structures, functional principles, fault modes and symptoms of vehicle systems and parts, analyze fault reasons and draw up and optimize the capability of a fault diagnosis scheme according to a general diagnosis principle. The part of teaching training and examination is a difficult point in professional education and technical training, and is lack of sufficient teachers and materials and effective teaching support means and equipment.
Disclosure of Invention
The invention provides a new energy automobile fault diagnosis training, examination and scheme pushing system, which can remotely, randomly, massively and intelligently implement the training and the capability examination of a new energy automobile fault diagnosis technology, can remotely guide a user to overhaul the new energy automobile fault, and improves the efficiency of talent culture, capability evaluation and vehicle overhaul.
The invention provides a new energy automobile fault diagnosis training examination and scheme pushing system, which comprises:
the presentation layer is used for collecting user input information and providing feedback information for a user;
the middle layer is used for acquiring user input information, starting a related program according to the user input information, and extracting data by the related program;
the data access layer is used for storing data and feeding back data information according to instructions of related programs, the data access layer comprises a detection measure database, and the detection measure database comprises:
the system comprises a first-level measure library, a second-level measure library and a third-level measure library, wherein the first-level measure library is used for storing automobile fault phenomena and corresponding first-level diagnosis measures, and each first-level diagnosis measure is numbered in sequence according to a set priority, and the smaller the number is, the higher the priority is;
the system comprises a secondary measure library, a database and a database, wherein the secondary measure library is used for storing system fault symptoms and corresponding secondary diagnosis measures, and each secondary diagnosis measure is numbered in sequence according to a set priority, and the smaller the number is, the higher the priority is;
and the three-level measure library is used for storing the component-level fault symptoms and the corresponding three-level diagnosis measures, and each three-level diagnosis measure is numbered in sequence according to the set priority, and the smaller the number is, the higher the priority is.
Further, the system operation process comprises:
s1 represents the layer to collect the function type and fault information;
s2, the middle layer starts relative program according to the function type selected by the user, the relative program extracts data from the data access layer according to the function type, and generates a reference diagnosis scheme;
s3 represents a layer output reference diagnostic protocol.
Further, the function types comprise a training function, an examination function and a fault diagnosis function, and the data access layer comprises a test question module which is used for generating test questions.
The step of starting a related program by the intermediate layer of S2 according to the function type selected by the user, extracting data from the data access layer by the related program according to the function type, and generating a reference diagnostic solution specifically includes:
the middle layer of S2 starts the related program according to the function type selected by the user;
if the function type is a training function or an examination function, the test question module generates a corresponding test question as a reference diagnosis scheme according to the input fault information;
if the function type is a fault diagnosis function, the data access layer analyzes input fault information, and respectively sends the fault information to a primary measure library, a secondary measure library and a tertiary measure library according to the type of the fault information, extracts corresponding diagnostic measures, respectively establishes a primary diagnostic measure set Ai ═ B1, B2, …, Bn }, a secondary diagnostic measure set Bj ═ C1, C2, … and Cm }, i is not less than 1 and not more than n, a tertiary diagnostic measure set Ck ═ C1, C2, … and ch }, and k is not less than 1 and not more than m, and establishes a reference diagnostic scheme.
Further, the S3 layer output reference diagnosis scheme specifically includes:
if the function type is a training function or an assessment function, the operation program is as follows:
s311 represents that the layer outputs the test questions;
s312, the presentation layer collects answer information input by a user, and the middle layer compares the input answer information with answers of the test questions;
s313, the middle layer carries out system scoring according to the comparison result, outputs the scoring result and outputs the answer of the test question;
if the function type is the fault diagnosis function, the operation program is as follows:
s321 determines an output pattern of the reference diagnostic protocol;
s322 performs output of the reference diagnostic protocol according to the output mode of the reference diagnostic protocol.
Still further, the output modes of the reference diagnostic scheme include a guided output mode, an integral output mode;
the guided output mode operation process comprises:
s323, according to the binary tree model, the step-by-step diagnosis is carried out on the input fault information, the diagnosis step is output, and a user is guided to confirm the information required by detection until a fault point is positioned;
the integral output mode includes:
s324, according to the serial numbers of the primary measure library, the secondary measure library and the tertiary measure library, the diagnosis measures are output and sequenced to form a diagnosis step.
Further, step-by-step diagnosis is performed on the input fault information in S323 according to the binary tree model, and the step of diagnosis is output to instruct the user to confirm the information required for detection, which specifically includes the following steps until the fault point is located:
s3231, according to a binary tree model, step-by-step diagnosis is carried out on input fault information, and a measure scheme set Ck of three-level fault symptoms is output firstly, wherein the measure scheme set Ck is { c1, c2, … and ch };
s3232 collecting user feedback information, and stopping if a fault is eliminated; if the fault is not eliminated, outputting a secondary diagnostic measure set Bj ═ { C1, C2, …, Cm };
s3233, collecting user feedback information, and stopping if a fault is eliminated; and if the fault is not eliminated, outputting a primary diagnostic measure set Ai ═ { B1, B2, …, Bn }.
Furthermore, the test question module comprises a case library, and the system operation process further comprises:
and S4, the layer acquires the result fed back by the user, packs the input fault information, the reference diagnosis scheme and the result fed back by the user and stores the packed result in the case library.
Furthermore, if the function type selected by the user is a training function or a fault diagnosis function, the system opens the query authority of the case base and the detection measure database to the user, and the user can freely query the information in the data access layer.
Furthermore, the test question module comprises an automatic examination question generation process and a case library inquiry test question generation process.
Furthermore, if the function type is an assessment function, the S3 presentation layer output reference diagnosis scheme further includes an assessment process, in the assessment process, the system acquires answers of the user to the test questions, matches the answers with the diagnosis measures in the detection measure database, sets a primary measure weight of 0.2, a secondary measure weight of 0.3, and a tertiary measure weight of 0.5, scores, and feeds back by the presentation layer.
And (4) scoring the answers of the users according to the correct number of the keywords of the diagnosis measures in each level in the diagnosis measure and system reference diagnosis scheme. Wherein the weight of the first-level measure is 0.2, the weight of the second-level measure is 0.3, and the weight of the third-level measure is 0.5.
Compared with the prior art, the vehicle fault classification method has the advantages that the vehicle fault classification method adopts the presentation layer, the middle layer and the data access layer, and utilizes the primary measure library, the secondary measure library and the tertiary measure library to realize the classification of the vehicle faults, so that a user can use the vehicle fault classification method conveniently according to the needs.
Drawings
FIG. 1 is a schematic diagram of a system operation process according to an embodiment of the present invention;
FIG. 2 is a diagram of an integrated output mode operation process according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a guided output mode operation process according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments.
The invention provides a new energy automobile fault diagnosis training examination and scheme pushing system, which comprises:
the presentation layer is used for collecting user input information and providing feedback information for a user;
the middle layer is used for acquiring user input information, starting a related program according to the user input information, and extracting data by the related program;
the data access layer is used for storing data and feeding back data information according to instructions of related programs, the data access layer comprises a detection measure database, and the detection measure database comprises:
the system comprises a first-level measure library, a second-level measure library and a third-level measure library, wherein the first-level measure library is used for storing automobile fault phenomena and corresponding first-level diagnosis measures, and each first-level diagnosis measure is numbered in sequence according to a set priority, and the smaller the number is, the higher the priority is;
the system comprises a secondary measure library, a database and a database, wherein the secondary measure library is used for storing system fault symptoms and corresponding secondary diagnosis measures, and each secondary diagnosis measure is numbered in sequence according to a set priority, and the smaller the number is, the higher the priority is;
and the three-level measure library is used for storing the component-level fault symptoms and the corresponding three-level diagnosis measures, and each three-level diagnosis measure is numbered in sequence according to the set priority, and the smaller the number is, the higher the priority is.
The system adopts a browser server (B/S) mode, and an application server is separated from a database server, so that a user can conveniently access, maintain and upgrade the system through Internet connection. The system adopts a 3-layer system architecture, namely a presentation layer, a middle layer and a data access layer. The data access process is the bottom layer, and mainly applies the relational database process in the background. The middle layer mainly realizes business logic through software programs to complete related functions. The presentation layer is provided for the user to view and input, and is mainly realized by JSP, Ext and the like. As the system is used in an open network environment, technical processing in terms of system concurrency, availability, security, and the like is emphasized.
The system provided by the embodiment of the invention can be applied to human-computer interaction interfaces and internet systems of various terminal devices, and can realize multiple functions of remote training, learning, assessment, guidance and application and the like of new energy automobile fault diagnosis. Meanwhile, the system provided by the embodiment of the invention can fully utilize expert experience to establish a model and a database, collect fault information of a relevant case aiming at the characteristics of a certain fault of the new energy automobile proposed by a user, provide description of a simulated learning situation, and facilitate the user to analyze the fault by combining the learning situation information and formulate and optimize a maintenance scheme. Meanwhile, various learning cases are provided, students are trained to quickly master the vehicle system composition and the structure and function of components, the fault mode of parts can be quickly related to the characteristics of automobile faults, and reasonable diagnosis processes and steps can be formulated. The system can establish a referential 'standard' diagnosis scheme according to the established model, the knowledge base and the database so as to be used for students to learn in a contrast way.
Optionally, as shown in fig. 1, the system operation process includes:
s1 represents the layer to collect the function type and fault information;
s2, the middle layer starts relative program according to the function type selected by the user, the relative program extracts data from the data access layer according to the function type, and generates a reference diagnosis scheme;
s3 represents a layer output reference diagnostic protocol.
Wherein the content of the first and second substances,
examples of the invention
Optionally, the function types include a training function, an examination function, and a fault diagnosis function, and the data access layer includes a test question module, and the test question module is configured to generate test questions.
The step of starting a related program by the intermediate layer of S2 according to the function type selected by the user, extracting data from the data access layer by the related program according to the function type, and generating a reference diagnostic solution specifically includes:
the middle layer of S2 starts the related program according to the function type selected by the user;
if the function type is a training function or an examination function, the test question module generates a corresponding test question as a reference diagnosis scheme according to the input fault information;
if the function type is a fault diagnosis function, as shown in fig. 2, the data access layer analyzes input fault information, and sends the fault information to the primary measure library, the secondary measure library and the tertiary measure library respectively according to the type of the fault information, extracts corresponding diagnostic measures, establishes a primary diagnostic measure set Ai ═ { B1, B2, …, Bn }, a secondary diagnostic measure set Bj { (C1, C2, …, Cm }, i ≦ 1 ≦ n, a tertiary diagnostic measure set Ck ═ C1, C2, …, ch }, and k ≦ 1 ≦ m, and establishes a reference diagnostic scheme.
Wherein, according to the first-level fault symptom (vehicle fault phenomenon), the database can be searched to obtain the relevant diagnosis measures and the coding conditions thereof as shown in table 1.
TABLE 1 first-class Fault symptom diagnostic measures Table of acceleration deterioration
Based on the secondary symptoms of the fault, the database is searched to obtain a table of relevant diagnostic measures, as shown in table 2.
TABLE 2 Secondary diagnostic measures table for "Motor System Fault
If no three-level fault symptom exists, the system searches three-level diagnostic measures corresponding to the numbers of the two-level diagnostic measures in the three-level diagnostic measure library one by one according to the two-level diagnostic measures, and all the three-level diagnostic measures are listed according to the priority order (the priority with small numerical value) of the numbers.
If the secondary measures read the fault code in the application process, the motor coolant temperature (sensor signal voltage) is found to be too high, the three-level fault sign of the motor heat dissipation cooling system abnormity is input, and the measures shown in the table 3 can be searched in the three-level diagnosis measures.
TABLE 3 "abnormal motor cooling system" three-stage diagnosis measure
The sequencing of the output measures of the same-level diagnosis scheme is arranged from small to large according to the number. The sequencing of the three-level diagnostic measures can also be performed according to the fact that the fault occurrence probability of the reasons corresponding to the three-level measures in case statistics is sequenced from large to small.
The sequencing of the output measures of the same-level diagnosis scheme is arranged from small to large according to the number. The sequencing of the three-level diagnostic measures can also be performed according to the fact that the fault occurrence probability of the reasons corresponding to the three-level measures in case statistics is sequenced from large to small.
Examples of the invention
Specifically, the S3 layer output reference diagnosis protocol specifically includes:
if the function type is a training function or an assessment function, the operation program is as follows:
s311 represents that the layer outputs the test questions;
s312, the presentation layer collects answer information input by a user, and the middle layer compares the input answer information with answers of the test questions;
s313, the middle layer carries out system scoring according to the comparison result, outputs the scoring result and outputs the answer of the test question;
if the function type is the fault diagnosis function, the operation program is as follows:
s321 determines an output pattern of the reference diagnostic protocol;
s322 performs output of the reference diagnostic protocol according to the output mode of the reference diagnostic protocol.
In the embodiment of the present invention, the training function is set to function 1, the examination function is set to function 2, and the failure diagnosis function is set to function 3.
In addition, the system scores the assessment user diagnosis scheme according to the reference scheme, and the justice, reasonableness and efficiency of the assessment are guaranteed. Through the diagnosis reference scheme of the system, the user with the function 3 can obtain the guidance of new energy automobile fault diagnosis, the maintenance efficiency and accuracy are improved, and the risks of misjudgment and incorrect maintenance are reduced. By applying case information and relevant statistical analysis data, valuable data for subsequent learning can be provided, and the accumulation of practical experience is assisted.
In particular, the output modes of the reference diagnostic scheme include a guided output mode, an integral output mode;
the guided output mode operation process comprises:
s323, according to the binary tree model, the step-by-step diagnosis is carried out on the input fault information, the diagnosis step is output, and a user is guided to confirm the information required by detection until a fault point is positioned;
s324, according to the serial numbers of the primary measure library, the secondary measure library and the tertiary measure library, the diagnosis measures are output and sequenced to form a diagnosis step.
Specifically, step-by-step diagnosis is performed on the input fault information in S323 according to the binary tree model, and the step of diagnosis is output to instruct the user to confirm the information required for detection until the fault point is located:
s3231, according to a binary tree model, step-by-step diagnosis is carried out on input fault information, and a measure scheme set Ck of three-level fault symptoms is output firstly, wherein the measure scheme set Ck is { c1, c2, … and ch };
s3232 collecting user feedback information, and stopping if a fault is eliminated; if the fault is not eliminated, outputting a secondary diagnostic measure set Bj ═ { C1, C2, …, Cm };
s3233, collecting user feedback information, and stopping if a fault is eliminated; and if the fault is not eliminated, outputting a primary diagnostic measure set Ai ═ { B1, B2, …, Bn }.
Wherein, the abnormal heat dissipation of the motor is used as a search term.
(1) The overall reference scheme output mode is as follows:
as shown in fig. 2, the first level measures: checking the appearance condition of the vehicle; the vehicle brake system has no drag; vehicle EPB system (normal) with disarm; the drive axle has no oil leakage and abnormal sound; the SOC of the instrument shows that the electric quantity is sufficient; reading a vehicle fault code; entering a diagnosis of an electric drive system anomaly ("enter" means entering the next stage of diagnostic measures). If the fault is not eliminated by the last diagnosis measure, the fault diagnosis of the motor inverter is carried out; and if the fault is not eliminated by the last diagnosis measure, entering the fault diagnosis of the motor controller.
Secondary measures are as follows: checking the appearance condition of the electric drive system; reading a fault code and a data stream of a motor system; detecting the input current and voltage of the motor; entering fault diagnosis of a driving motor assembly; entering a motor cooling system for fault diagnosis; entering motor inverter fault diagnosis; and entering motor controller fault diagnosis.
And (3) three-level measures: checking the quality and quantity condition of the cooling liquid; checking a cooling liquid circulating pipeline; detecting by a temperature sensor; detecting a temperature sensor circuit; detecting a motor of the cooling liquid pump; detecting a motor circuit of the cooling liquid pump; detecting a cooling fan; detecting a cooling fan controller and a circuit; and (6) checking the performance of the radiator.
(2) The guided diagnostic scheme output modes are:
as shown in FIG. 3, the guidance adopts reverse reasoning, and the corresponding measure set C of three-level fault symptom is adoptediAnd starting to push the measures one by one until the faults are eliminated or the measures are pushed. If the fault can not be eliminated, other items of secondary measures are carried out; after traversing the secondary measures, pushing the primary measures one by one, and acquiring related information in a man-machine interaction mode of a binary tree until a fault point is determined.
In the embodiment of the invention, under the training function and the fault diagnosis function, a user can select an integral or guided diagnosis scheme output mode. Under the assessment function, the user can only use an integral diagnosis scheme to output a mode so as to facilitate system scoring assessment. The users under the training function and the fault diagnosis function can check the case base statistical data and output the occurrence rate of each three-level measure as the reference of the priority of the diagnosis step.
Specifically, as shown in fig. 1, the test question module includes a case library, and the system operation process further includes:
and S4, the layer acquires the result fed back by the user, packs the input fault information, the reference diagnosis scheme and the result fed back by the user and stores the packed result in the case library.
Particularly, if the function type selected by the user is a training function or a fault diagnosis function, the system opens the inquiry authority of the case base and the detection measure database to the user, and the user can freely inquire the information in the data access layer.
Particularly, the test question module comprises an automatic examination question generation process and a case library inquiry test question generation process.
Particularly, if the function type is the assessment function, the S3 presentation layer output reference diagnosis scheme further includes an assessment process, in the assessment process, the system acquires answers of the user to the test questions, matches the answers with the diagnosis measures in the detection measure database, sets a primary measure weight of 0.2, a secondary measure weight of 0.3, and a tertiary measure weight of 0.5, scores, and feeds back by the presentation layer.
And scoring according to the correct number of the keywords of each level of diagnosis measures in the diagnosis measures and the system reference diagnosis scheme. Wherein the weight of the first-level measure is 0.2, the weight of the second-level measure is 0.3, and the weight of the third-level measure is 0.5. The system can set the grade of the achievement and store the relevant information.
Finally, it should be noted that the above-mentioned embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the modifications and equivalents of the specific embodiments of the present invention can be made by those skilled in the art after reading the present specification, but these modifications and variations do not depart from the scope of the claims of the present application.
Claims (10)
1. The new energy automobile fault diagnosis training examination and scheme pushing system is characterized by comprising:
the presentation layer is used for collecting user input information and providing feedback information for a user;
the middle layer is used for acquiring user input information, starting a related program according to the user input information, and extracting data by the related program;
the data access layer is used for storing data and feeding back data information according to instructions of related programs, the data access layer comprises a detection measure database, and the detection measure database comprises:
the system comprises a first-level measure library, a second-level measure library and a third-level measure library, wherein the first-level measure library is used for storing automobile fault phenomena and corresponding first-level diagnosis measures, and each first-level diagnosis measure is numbered in sequence according to a set priority, and the smaller the number is, the higher the priority is;
the system comprises a secondary measure library, a database and a database, wherein the secondary measure library is used for storing system fault symptoms and corresponding secondary diagnosis measures, and each secondary diagnosis measure is numbered in sequence according to a set priority, and the smaller the number is, the higher the priority is;
and the three-level measure library is used for storing the component-level fault symptoms and the corresponding three-level diagnosis measures, and each three-level diagnosis measure is numbered in sequence according to the set priority, and the smaller the number is, the higher the priority is.
2. The new energy vehicle fault diagnosis training assessment and scheme pushing system as claimed in claim 1, wherein the system operation process comprises:
s1 represents the layer to collect the function type and fault information;
s2, the middle layer starts relative program according to the function type selected by the user, the relative program extracts data from the data access layer according to the function type, and generates a reference diagnosis scheme;
s3 represents a layer output reference diagnostic protocol.
3. The new energy vehicle fault diagnosis training assessment and scheme pushing system as claimed in claim 1, wherein the function types comprise a training function, an assessment function and a fault diagnosis function, the data access layer comprises a test question module, and the test question module is used for generating test questions.
The step of starting a related program by the intermediate layer of S2 according to the function type selected by the user, extracting data from the data access layer by the related program according to the function type, and generating a reference diagnostic solution specifically includes:
the middle layer of S2 starts the related program according to the function type selected by the user;
if the function type is a training function or an examination function, the test question module generates a corresponding test question as a reference diagnosis scheme according to the input fault information;
if the function type is a fault diagnosis function, the data access layer analyzes input fault information, and respectively sends the fault information to a primary measure library, a secondary measure library and a tertiary measure library according to the type of the fault information, extracts corresponding diagnostic measures, respectively establishes a primary diagnostic measure set Ai ═ B1, B2, …, Bn }, a secondary diagnostic measure set Bj ═ C1, C2, … and Cm }, i is not less than 1 and not more than n, a tertiary diagnostic measure set Ck ═ C1, C2, … and ch }, and k is not less than 1 and not more than m, and establishes a reference diagnostic scheme.
4. The new energy vehicle fault diagnosis training assessment and scheme pushing system as claimed in claim 3, wherein the S3 representation layer output reference diagnosis scheme specifically comprises:
if the function type is a training function or an assessment function, the operation program is as follows:
s311 represents that the layer outputs the test questions;
s312, the presentation layer collects answer information input by a user, and the middle layer compares the input answer information with answers of the test questions;
s313, the middle layer carries out system scoring according to the comparison result, outputs the scoring result and outputs the answer of the test question;
if the function type is the fault diagnosis function, the operation program is as follows:
s321 determines an output pattern of the reference diagnostic protocol;
s322 performs output of the reference diagnostic protocol according to the output mode of the reference diagnostic protocol.
5. The new energy vehicle fault diagnosis training assessment and scheme pushing system as claimed in claim 1, wherein the output modes of the reference diagnosis scheme comprise a guided output mode and an integrated output mode;
the guided output mode operation process comprises:
s323, according to the binary tree model, the step-by-step diagnosis is carried out on the input fault information, the diagnosis step is output, and a user is guided to confirm the information required by detection until a fault point is positioned;
the integral output mode includes:
s324, according to the serial numbers of the primary measure library, the secondary measure library and the tertiary measure library, the diagnosis measures are output and sequenced to form a diagnosis step.
6. The new energy vehicle fault diagnosis training assessment and scheme pushing system as claimed in claim 4, wherein the step S323 is used for step-by-step diagnosis of input fault information according to a binary tree model, outputting a diagnosis step, and guiding a user to confirm information required for detection until a fault point is located, and specifically comprises:
s3231, according to a binary tree model, step-by-step diagnosis is carried out on input fault information, and a measure scheme set Ck of three-level fault symptoms is output firstly, wherein the measure scheme set Ck is { c1, c2, … and ch };
s3232 collecting user feedback information, and stopping if a fault is eliminated; if the fault is not eliminated, outputting a secondary diagnostic measure set Bj ═ { C1, C2, …, Cm };
s3233, collecting user feedback information, and stopping if a fault is eliminated; and if the fault is not eliminated, outputting a primary diagnostic measure set Ai ═ { B1, B2, …, Bn }.
7. The new energy vehicle fault diagnosis training assessment and scheme pushing system as claimed in claim 1, wherein the test question module comprises a case library, and the system operation process further comprises:
and S4, the layer acquires the result fed back by the user, packs the input fault information, the reference diagnosis scheme and the result fed back by the user and stores the packed result in the case library.
8. The new energy vehicle fault diagnosis training assessment and scheme pushing system as claimed in claim 7, wherein if the function type selected by the user is a training function or a fault diagnosis function, the system opens the query authority of a case base and a detection measure database to the user, and the user can freely query information in a data access layer.
9. The new energy vehicle fault diagnosis training examination and scheme pushing system as claimed in claim 7, wherein the test question module comprises an automatic examination question generation process and a case library query examination question generation process.
10. The new energy vehicle fault diagnosis training assessment and scheme pushing system as claimed in claim 3, wherein if the function type is assessment function, the S3 presentation layer output reference diagnosis scheme further comprises an assessment process, in the assessment process, the system obtains answers of users to test questions, matches the answers with diagnosis measures in the detection measure database, sets a primary measure weight of 0.2, a secondary measure weight of 0.3 and a tertiary measure weight of 0.5, scores, and feeds back by the presentation layer.
And (4) scoring the answers of the users according to the correct number of the keywords of the diagnosis measures in each level in the diagnosis measure and system reference diagnosis scheme. Wherein the weight of the first-level measure is 0.2, the weight of the second-level measure is 0.3, and the weight of the third-level measure is 0.5.
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