CN107153914B - System and method for evaluating automobile operation risk - Google Patents
System and method for evaluating automobile operation risk Download PDFInfo
- Publication number
- CN107153914B CN107153914B CN201710253270.7A CN201710253270A CN107153914B CN 107153914 B CN107153914 B CN 107153914B CN 201710253270 A CN201710253270 A CN 201710253270A CN 107153914 B CN107153914 B CN 107153914B
- Authority
- CN
- China
- Prior art keywords
- automobile
- input
- module
- evaluation
- fault type
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
Abstract
The invention belongs to the technical field of automobile safety, and discloses an automobile operation risk evaluation system and method, which comprises the following steps: determining the type of the automobile to be evaluated, and acquiring statistical data of all automobiles with the same type as the type of the automobile to be evaluated; determining the risk priority number of the part corresponding to the fault type according to the occurrence probability of the fault type, the severity after the fault type occurs and the detection degree of the fault type; thus, three evaluation indexes of each part are obtained: risk priority, criticality, and maintenance costs; determining the entropy weight of three evaluation indexes of each part according to an entropy weight method; determining a final risk index of each part according to the three evaluation indexes of each part and the entropy weights of the three evaluation indexes; determining the priority of early warning on the part according to the final risk index of the part; the risk assessment method can be used for objectively and accurately assessing the risk of the automobile.
Description
Technical Field
The invention belongs to the technical field of automobile safety, and particularly relates to an automobile operation risk evaluation system and method.
Background
In recent years, the quantity of motor vehicles in China is rapidly increased, and meanwhile, road traffic accidents happen occasionally, so that the life and property of people are seriously damaged. With the development of economic society, the enhancement of safety consciousness of people puts forward higher requirements on the running safety of automobiles, and particularly the requirements on the cognition and early warning of the running risks of the automobiles are urgent.
At present, the automobile safety evaluation mainly focuses on subjective evaluation or detection evaluation of partial performances and indexes of an automobile, such as evaluation of a braking system, a steering system, safety auxiliary facilities and the like, and for example, a safety performance evaluation system model of an automobile product is established from a vehicle safety device, vehicle speed safety, vehicle type safety factor and the like in Huang Yong, Pan Jian and the like of the Chinese automobile technical research center to evaluate the automobile safety. Meanwhile, national standards such as 'motor vehicle operation safety conditions' and the like are established in China, and the safety technical requirements of motor vehicles are provided.
However, in view of the current industrial technical situation, the safety technical evaluation of the automobile is mainly realized by detecting the performance of each technical situation of the automobile and subjectively evaluating the performance, and dynamic cognition on the running situation of the automobile is lacked, that is, the safety performance of the automobile cannot be dynamically and real-timely evaluated according to the running situation of the automobile, such as the driving mileage, maintenance and other factors closely related to the running safety of the automobile.
Disclosure of Invention
In view of the above disadvantages of the prior art, an object of the present invention is to provide a system and a method for evaluating an operation risk of an automobile, which can acquire maintenance record data of the automobile, perform statistical analysis on big data of the maintenance record data of the automobile of a certain type, obtain a calculation weight of each evaluation index closely related to the operation safety of the automobile of the certain type, and dynamically determine the operation risk in real time by combining with the specific maintenance record data of the certain automobile.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme.
The first technical scheme is as follows:
an evaluation system for automobile operation risk, the evaluation system comprising an automobile archive service platform, the evaluation system further comprising: the system comprises an information input module, a control module, an information acquisition module, an information storage module, an automobile operation risk prediction and evaluation module and an automobile operation risk output module;
the control module comprises five input and output ends which are respectively marked as a first input and output end of the control module, a second input and output end of the control module, a third input and output end of the control module, a fourth input and output end of the control module and a fifth input and output end of the control module;
the information acquisition module comprises two input and output ends which are respectively marked as a first input and output end of the information acquisition module and a second input and output end of the information acquisition module;
the output end of the information input module is in one-way communication connection with the first input and output end of the control module;
the second input/output end of the control module is in bidirectional communication connection with the first input/output end of the information acquisition module;
the third input/output end of the control module is in bidirectional communication connection with the input/output end of the information storage module;
a fourth input/output end of the control module is in bidirectional communication connection with an input/output end of the automobile operation risk prediction and evaluation module;
a fifth input/output end of the control module is in one-way communication connection with an input end of the automobile operation risk output module;
and a second input/output end of the information acquisition module is in bidirectional communication connection with the input/output end of the automobile file service platform.
The first technical scheme of the invention has the characteristics and further improvements that:
the automobile archive service platform at least records maintenance record statistical data of all models of automobiles, wherein the maintenance record statistical data of all models of automobiles at least comprises all parts of the models of automobiles, fault types corresponding to the parts after the parts break down, occurrence probability of the fault types, severity after the fault types occur, detection degree of the fault types, hazard degree corresponding to each part and maintenance cost required by each part;
the information input module is used for inputting the vehicle identification code of the automobile to be evaluated; the vehicle identification code to be evaluated is sent to the control module;
the control module is used for sending the vehicle identification code to be evaluated to an information acquisition module;
the information acquisition module is used for sending the vehicle identification code to be evaluated to an automobile file service platform;
the automobile file service platform is used for acquiring the model of the automobile to be evaluated and maintenance record statistical data of all automobiles with the same model as the automobile to be evaluated in a platform database according to the automobile identification code of the automobile to be evaluated, and sending the maintenance record statistical data of all automobiles with the same model as the automobile to be evaluated to the information acquisition module;
the information acquisition module is also used for forwarding maintenance record statistical data of all automobiles with the same model as the automobile to be evaluated to the information storage module under the control of the control module;
the information storage module is used for caching maintenance record statistical data of all automobiles with the same model as the automobile to be evaluated;
the control module is also used for taking out maintenance record statistical data required by the automobile operation risk prediction and evaluation module from the information storage module and sending the maintenance record statistical data to the automobile operation risk prediction and evaluation module when the automobile operation risk prediction and evaluation module predicts and evaluates the operation risk of the automobile of the model;
and the automobile operation risk output module is used for outputting the evaluation result obtained by the automobile operation risk prediction evaluation module.
The second technical scheme is as follows:
an evaluation method for automobile operation risk, which is applied to the evaluation system of any one of the technical solutions, the method comprising:
the method comprises the steps of obtaining a vehicle identification code of an automobile to be evaluated, determining the type of the automobile to be evaluated according to the vehicle identification code, and obtaining maintenance record statistical data of all automobiles with the same type as the automobile to be evaluated, wherein the maintenance record statistical data at least comprise all parts of the automobile with the type, fault types corresponding to the parts after faults occur, occurrence probability of the fault types, severity after the fault types occur, detection degree of the fault types, hazard degree corresponding to each part, and maintenance cost required by each part;
wherein, the severity after the fault type occurs refers to the damage degree of the personnel or the vehicle caused after the fault type occurs; the detection degree of the fault type refers to the possibility that the fault type is detected and repaired; the hazard degree corresponding to each part is the proportion of the hazard caused by the part after the part fails to account for the total hazard caused by the automobile system after the automobile system fails;
determining the risk priority number of the part corresponding to the fault type according to the occurrence probability of the fault type, the severity after the fault type occurs and the detection degree of the fault type; thus, three evaluation indexes of each part are obtained: risk priority, criticality, and maintenance costs;
determining the entropy weight of three evaluation indexes of each part according to an entropy weight method;
determining a final risk index of each part according to the three evaluation indexes of each part and the entropy weights of the three evaluation indexes;
and determining the priority for carrying out early warning on the part according to the final risk index of the part, wherein the higher the final risk index of the part is, the higher the priority for carrying out early warning on the part is.
The chair point of the second technical scheme of the invention is further improved as follows:
(1) according to the occurrence probability of the fault type, the severity after the fault type occurs and the detection degree of the fault type, determining the risk priority number of the part corresponding to the fault type, specifically:
the risk priority number of the part corresponding to the fault type is the product of the occurrence probability of the fault type, the severity after the fault type occurs and the detection degree of the fault type.
(2) Determining the entropy weight of three evaluation indexes of each part according to an entropy weight method, which specifically comprises the following steps:
entropy of i-th evaluation index
Where m is 3, three evaluation indexes of a component are represented, j represents the jth element determining the size of the ith evaluation index, n is the total number of elements determining the size of the ith evaluation index, and fijIndicates the importance of the jth element of the ith evaluation index, and
αijthe value of the j element of the ith evaluation index after normalization processing is represented; ln represents logarithm;
the elements for determining the risk priority index at least comprise three elements of the occurrence probability of the fault type corresponding to the part, the severity after the fault type of the part occurs and the detection degree of the fault type corresponding to the part; the elements for determining the harmfulness index at least comprise the service time of the parts, the installation positions of the parts and the models of the parts; the elements for determining the maintenance cost index at least comprise the cost of replacing parts and the cost of maintenance working hours;
thereby the entropy weight of the ith evaluation index
(3) Determining a final risk indicator RCP of each part according to the three evaluation indicators of each part and the entropy weights of the three evaluation indicators, specifically:
RCP＝w1R+w2C+w3P
wherein R is the risk priority number, C is the degree of harm, and P is the maintenance cost; w is a1Entropy weights corresponding to risk priorities, w2Entropy weights corresponding to criticality, w3Is vitamin A toAnd (4) repairing the corresponding entropy weight of the cost.
The technical scheme of the invention has the advantages that: the automobile maintenance record data are collected in real time by utilizing an automobile file service platform, and the automobile identification codes are used as automobile identification marks, so that the automobile maintenance record data of different automobile types are summarized, counted and analyzed, and the automobile operation risk early warning is completed. The automobile archive service platform can summarize maintenance record data of all automobiles in the country, so that the maintenance record data analysis of different automobile types is realized, and the data is real, reliable and comprehensive; the automobile operation risk is evaluated based on the automobile maintenance record data, the automobile real-time state relevance is wide in universality, convenient and high, objectivity and accuracy are achieved, and the automobile safety requirements of the majority of automobile owners can be met.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for evaluating an automobile operation risk according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an evaluation system for an automobile operation risk according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The loss of automobile parts is one of the important reasons for automobile safety accidents. The invention provides an automobile operation risk evaluation method based on summarizing the failure mode, the hazard and the maintenance economy of parts influencing the automobile operation safety from the importance and the economy of automobile part maintenance, the reliability of a system and the safety of a whole automobile, which comprises the following steps: the method comprises the steps of firstly analyzing Risk Priority Number (RPN) and hazard degree of automobile parts, establishing an automobile operation risk influence factor evaluation index system, then determining RPN value, hazard degree and maintenance cost index weight, and quantitatively grading the automobile parts through a comprehensive grading method to finally realize evaluation of automobile operation risk.
The embodiment of the invention provides an automobile operation risk evaluation method, as shown in fig. 1, the method comprises the following steps:
step 1, determining the type of an automobile to be evaluated according to an automobile identification code, and acquiring maintenance record statistical data of all automobiles with the same type as the automobile to be evaluated, wherein the maintenance record statistical data at least comprises all parts of the automobile with the type, fault type occurrence probability, severity after the fault type occurs, detection degree of the fault type, hazard degree corresponding to each part and maintenance cost required by each part, wherein the fault type corresponds to each part after the part fails;
wherein, the severity after the fault type occurs refers to the damage degree of personnel and vehicles caused after the fault type occurs; the detection degree of the fault type refers to the possibility that the fault type is detected and repaired; the hazard degree corresponding to each part is the proportion of the hazard caused by the part after the part fails to account for the total hazard caused by the automobile system after the automobile system fails;
step 2, determining the risk priority number of the part corresponding to the fault type according to the occurrence probability of the fault type, the severity after the fault type occurs and the detection degree of the fault type; thus, three evaluation indexes of each part are obtained: risk priority, criticality, and maintenance costs;
step 3, determining the entropy weight of three evaluation indexes of each part according to an entropy weight method;
step 4, determining the final risk index of each part according to the three evaluation indexes of each part and the entropy weights of the three evaluation indexes;
and 5, determining the priority for early warning the part according to the final risk index of the part, wherein the higher the final risk index of the part is, the higher the priority for early warning the part is.
Further, according to the occurrence probability of the fault type, the severity after the fault type occurs, and the detection degree of the fault type, determining a risk priority number RPN of the component corresponding to the fault type, specifically:
the risk priority number RPN of the component corresponding to the fault type is the product of the occurrence probability of the fault type, the severity after the fault type occurs, and the detection degree of the fault type.
Further, determining the entropy weights of the three evaluation indexes of each part according to an entropy weight method specifically includes:
entropy of i-th evaluation index
Where m is 3, three evaluation indexes of a component are represented, j represents the jth element determining the size of the ith evaluation index, n is the total number of elements determining the size of the ith evaluation index, and fijIndicates the importance of the jth element of the ith evaluation index, and
αijthe value of the j element of the ith evaluation index after normalization processing is represented; ln represents logarithm;
the elements for determining the risk priority index at least comprise three elements of the occurrence probability of the fault type corresponding to the part, the severity after the fault type of the part occurs and the detection degree of the fault type corresponding to the part; the elements for determining the harmfulness index at least comprise the service time of the parts, the installation positions of the parts and the models of the parts; the elements for determining the maintenance cost index at least comprise the cost of replacing parts, the cost of maintenance working hours and the compensation cost;
thereby the entropy weight of the ith evaluation index
Further, determining a final risk indicator RCP of each component according to the three evaluation indicators of each component and the entropy weights of the three evaluation indicators, specifically:
RCP＝w1R+w2C+w3P
wherein R is the risk priority number, C is the degree of harm, and P is the maintenance cost; w is a1Entropy weights corresponding to risk priorities, w2Entropy weights corresponding to criticality, w3The corresponding entropy weight of the maintenance cost.
In an exemplary embodiment of the present invention, the occurrence probability of the fault type, the severity after the fault type occurs, and the detection degree of the fault type are classified by expert, the score range is 1-10, 10 represents the most severe product with the highest occurrence frequency or the least easy product to detect, and the specific scoring criteria are shown in table 1.
TABLE 1 Fault probability, severity and detection difficulty grade table
Note: alpha is alphaj-frequency of failure/(%)
For example, the embodiment of the invention establishes the grading standard of the component hazard degree for the automobile braking system as shown in the table 2. For convenience of analysis, the proportion of the hazard caused by the fault of the part to the total hazard caused by the fault of the automobile system is used as the hazard degree of the part, and C is usedjIs calculated as follows:
in the formula: cj-is as followsThe harmfulness of the j parts accounts for the proportion of the total harmfulness of the braking system, and is dimensionless; crjThe harmfulness of the jth part is dimensionless; n-is the total number of types of the brake system components;
Crj＝λDj·αj·βj·t
in the formula: alpha is alphajFailure frequency (j percentage of component failures to total system failures);
βjprobability of fault effect (conditional probability of system failure due to j component failure, generally determined empirically by an analyst, usually as β in the standard GJB/Z1391-2006jIs quantitatively estimated);
t-working time or kilometer of operation;
λDj-equivalent failure rate, times/1000 km;
rDj-j component equivalent failure number;
εian i-th failure coefficient (each having a value of ε)1＝100，ε2＝10，ε3＝1，ε4＝0.2)；
ri-number of failures of class i.
The economy is a factor which must be considered in the maintenance work process, and the spare parts of expensive parts are high in cost, the number of spare parts is small, and the direct economic loss caused by faults is large. The maintenance cost is an economic indicator for evaluating the maintainability, and therefore, the maintenance cost can be used as an important indicator for evaluating maintenance important and key components of the brake system.
In summary, the comprehensive scoring criteria for the automobile parts are shown in table 2.
TABLE 2 comprehensive scoring criteria for automobile parts
And determining the priority of early warning on the part according to the final risk index of the part, wherein the higher the final risk index of the part is, the higher the priority of early warning on the part is.
Illustratively, the embodiment of the present invention provides a three-level hierarchical prediction method:
a part of the type: the RPN value is large for maintaining key parts, the probability of fault occurrence is high, and the harmfulness of the parts is also large. If the fault has serious influence on the brake system and the whole vehicle safety, high priority, emphasis and strict control should be given to the brake system and the whole vehicle safety in the maintenance work.
A second type of parts: in order to maintain the parts in a key mode, the RPN value is large, the probability of fault occurrence is high, and the damage degree of the parts is also large. If faults occur, the brake system and the whole vehicle safety are affected to a certain degree, so normal management is usually carried out in maintenance work, and high priority and conventional control are given in special cases.
Three types of parts: the RPN value is not large, the probability of fault occurrence is not high, and the harmfulness of the parts is not large. If the fault has certain influence on the brake system, the lowest priority is given to the brake system in the maintenance work, and the brake system is generally treated and simply controlled.
TABLE 3 three-class hierarchical prediction table
An embodiment of the present invention further provides an evaluation system for an automobile operation risk, as shown in fig. 2, where the evaluation system includes an automobile archive service platform, and the evaluation system further includes: the system comprises an information input module, a control module, an information acquisition module, an information storage module, an automobile operation risk prediction and evaluation module and an automobile operation risk output module;
the control module comprises five input and output ends which are respectively marked as a first input and output end of the control module, a second input and output end of the control module, a third input and output end of the control module, a fourth input and output end of the control module and a fifth input and output end of the control module;
the information acquisition module comprises two input and output ends which are respectively marked as a first input and output end of the information acquisition module and a second input and output end of the information acquisition module;
the output end of the information input module is in one-way communication connection with the first input and output end of the control module;
the second input/output end of the control module is in bidirectional communication connection with the first input/output end of the information acquisition module;
the third input/output end of the control module is in bidirectional communication connection with the input/output end of the information storage module;
a fourth input/output end of the control module is in bidirectional communication connection with an input/output end of the automobile operation risk prediction and evaluation module;
a fifth input/output end of the control module is in one-way communication connection with an input end of the automobile operation risk output module;
and a second input/output end of the information acquisition module is in bidirectional communication connection with the input/output end of the automobile file service platform.
Specifically, the information input module is used for inputting a vehicle identification code of an automobile to be evaluated; the vehicle identification code to be evaluated is sent to the control module;
the control module is used for sending the vehicle identification code to be evaluated to an information acquisition module;
the information acquisition module is used for sending the vehicle identification code to be evaluated to an automobile file service platform;
the automobile file service platform is used for acquiring the model of the automobile and maintenance record statistical data of all automobiles with the same model as the automobile to be evaluated in a platform database according to the automobile identification code of the automobile to be evaluated, and sending the maintenance record statistical data of all automobiles with the same model as the automobile to be evaluated to the information acquisition module; the maintenance record statistical data of all automobiles with the same type as the automobile to be evaluated at least comprises all parts of the automobile with the type, fault types corresponding to the parts after the parts have faults, the occurrence probability of the fault types, the severity after the fault types occur, the detection degree of the fault types, the corresponding hazard degree of each part and the maintenance cost required by each part;
the information acquisition module is also used for forwarding maintenance record statistical data of all automobiles with the same model as the automobile to be evaluated to the information storage module under the control of the control module;
the information storage module is used for caching maintenance record statistical data of all automobiles with the same model as the automobile to be evaluated;
the control module is also used for taking out maintenance record statistical data required by the automobile operation risk prediction and evaluation module from the information storage module and sending the maintenance record statistical data to the automobile operation risk prediction and evaluation module when the automobile operation risk prediction and evaluation module predicts and evaluates the operation risk of the automobile of the model;
and the automobile operation risk output module is used for outputting the evaluation result obtained by the automobile operation risk prediction evaluation module.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (2)
1. The method for evaluating the automobile running risk is characterized in that an evaluation system based on the automobile running risk comprises an automobile archive service platform, and the evaluation system further comprises: the system comprises an information input module, a control module, an information acquisition module, an information storage module, an automobile operation risk prediction and evaluation module and an automobile operation risk output module;
the control module comprises five input and output ends which are respectively marked as a first input and output end of the control module, a second input and output end of the control module, a third input and output end of the control module, a fourth input and output end of the control module and a fifth input and output end of the control module;
the information acquisition module comprises two input and output ends which are respectively marked as a first input and output end of the information acquisition module and a second input and output end of the information acquisition module;
the output end of the information input module is in one-way communication connection with the first input and output end of the control module;
the second input/output end of the control module is in bidirectional communication connection with the first input/output end of the information acquisition module;
the third input/output end of the control module is in bidirectional communication connection with the input/output end of the information storage module;
a fourth input/output end of the control module is in bidirectional communication connection with an input/output end of the automobile operation risk prediction and evaluation module;
a fifth input/output end of the control module is in one-way communication connection with an input end of the automobile operation risk output module;
a second input/output end of the information acquisition module is in bidirectional communication connection with an input/output end of the automobile file service platform;
the automobile archive service platform at least records maintenance record statistical data of all models of automobiles, wherein the maintenance record statistical data of any model of automobile at least comprises all parts of the model of automobile, fault types corresponding to the parts after the parts break down, occurrence probability of the fault types, severity after the fault types occur, detection degree of the fault types, hazard degree corresponding to each part and maintenance cost required by each part;
the information input module is used for inputting the vehicle identification code of the automobile to be evaluated; the vehicle identification code to be evaluated is sent to the control module;
the control module is used for sending the vehicle identification code to be evaluated to an information acquisition module;
the information acquisition module is used for sending the vehicle identification code to be evaluated to an automobile file service platform;
the automobile file service platform is used for acquiring the model of the automobile to be evaluated and maintenance record statistical data of all automobiles with the same model as the automobile to be evaluated in a platform database according to the automobile identification code of the automobile to be evaluated, and sending the maintenance record statistical data of all automobiles with the same model as the automobile to be evaluated to the information acquisition module;
the information acquisition module is also used for forwarding maintenance record statistical data of all automobiles with the same model as the automobile to be evaluated to the information storage module under the control of the control module;
the information storage module is used for caching maintenance record statistical data of all automobiles with the same model as the automobile to be evaluated;
the control module is also used for taking out maintenance record statistical data required by the automobile operation risk prediction and evaluation module from the information storage module and sending the maintenance record statistical data to the automobile operation risk prediction and evaluation module when the automobile operation risk prediction and evaluation module predicts and evaluates the operation risk of the automobile of the model;
the automobile operation risk output module is used for outputting the evaluation result obtained by the automobile operation risk prediction evaluation module;
the method comprises the following steps:
the method comprises the steps of obtaining a vehicle identification code of an automobile to be evaluated, determining the type of the automobile to be evaluated according to the vehicle identification code, and obtaining maintenance record statistical data of all automobiles with the same type as the automobile to be evaluated, wherein the maintenance record statistical data at least comprise all parts of the automobile with the type, fault types corresponding to the parts after faults occur, occurrence probability of the fault types, severity after the fault types occur, detection degree of the fault types, hazard degree corresponding to each part, and maintenance cost required by each part;
wherein, the severity after the fault type occurs refers to the damage degree of the personnel or the vehicle caused after the fault type occurs; the detection degree of the fault type refers to the possibility that the fault type is detected and repaired; the hazard degree corresponding to each part is the proportion of the hazard caused by the part after the part fails to account for the total hazard caused by the automobile system after the automobile system fails;
determining the risk priority number of the part corresponding to the fault type according to the occurrence probability of the fault type, the severity after the fault type occurs and the detection degree of the fault type; thus, three evaluation indexes of each part are obtained: risk priority, criticality, and maintenance costs;
determining the entropy weight of three evaluation indexes of each part according to an entropy weight method;
determining a final risk index of each part according to the three evaluation indexes of each part and the entropy weights of the three evaluation indexes;
the method specifically comprises the following steps:
entropy of i-th evaluation index
Where m is 3, three evaluation indexes of a component are represented, j represents the jth element determining the size of the ith evaluation index, n is the total number of elements determining the size of the ith evaluation index, and fijIndicates the importance of the jth element of the ith evaluation index, and
αijthe value of the j element of the ith evaluation index after normalization processing is represented; ln represents logarithm;
the elements for determining the risk priority index at least comprise three elements of the occurrence probability of the fault type corresponding to the part, the severity after the fault type of the part occurs and the detection degree of the fault type corresponding to the part; the elements for determining the harmfulness index at least comprise the service time of the parts, the installation positions of the parts and the models of the parts; the elements for determining the maintenance cost index at least comprise the cost of replacing parts and the cost of maintenance working hours;
thereby the entropy weight of the ith evaluation index
Determining the priority of early warning on the part according to the final risk index of the part, wherein the higher the final risk index of the part is, the higher the priority of early warning on the part is;
according to the occurrence probability of the fault type, the severity after the fault type occurs and the detection degree of the fault type, determining the risk priority number of the part corresponding to the fault type, specifically:
the risk priority number of the part corresponding to the fault type is the product of the occurrence probability of the fault type, the severity after the fault type occurs and the detection degree of the fault type.
2. The method for evaluating the running risk of the automobile according to claim 1, wherein the final risk indicator RCP of each component is determined according to the three evaluation indicators of each component and the entropy weights of the three evaluation indicators, and specifically comprises:
RCP＝w1R+w2C+w3P
wherein R is the risk priority number, C is the degree of harm, and P is the maintenance cost; w is a1Entropy weights corresponding to risk priorities, w2Entropy weights corresponding to criticality, w3The corresponding entropy weight of the maintenance cost.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710253270.7A CN107153914B (en) | 2017-04-18 | 2017-04-18 | System and method for evaluating automobile operation risk |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710253270.7A CN107153914B (en) | 2017-04-18 | 2017-04-18 | System and method for evaluating automobile operation risk |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107153914A CN107153914A (en) | 2017-09-12 |
| CN107153914B true CN107153914B (en) | 2021-01-26 |
Family
ID=59792716
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710253270.7A Expired - Fee Related CN107153914B (en) | 2017-04-18 | 2017-04-18 | System and method for evaluating automobile operation risk |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107153914B (en) |
Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108304943B (en) * | 2018-02-01 | 2022-03-04 | 湖南省烟草公司永州市公司 | Method for judging maintenance and updating of tobacco logistics vehicle parts |
| CN108647791B (en) * | 2018-03-30 | 2020-12-29 | 中国标准化研究院 | Multi-source automobile safety information processing method, device and system |
| CN108876137B (en) * | 2018-06-11 | 2021-05-28 | 中国标准化研究院 | Automobile safety risk early warning method and system based on multi-source information |
| CN108985642B (en) * | 2018-07-27 | 2021-10-01 | 浙江师范大学 | Subway vehicle fault mode risk degree identification method |
| CN109067893A (en) * | 2018-08-22 | 2018-12-21 | 北京小米移动软件有限公司 | Smart machine fault handling method, device, terminal and server |
| CN109443789B (en) * | 2018-09-30 | 2021-02-19 | 北京新联铁集团股份有限公司 | Method and system for processing locomotive vehicle health |
| CN109359703A (en) * | 2018-12-19 | 2019-02-19 | 佛山科学技术学院 | A kind of ceramic tile grade separation method based on decision tree |
| CN110287703B (en) * | 2019-06-10 | 2021-10-12 | 百度在线网络技术（北京）有限公司 | Method and device for detecting vehicle safety risk |
| CN110377793B (en) * | 2019-06-21 | 2022-09-06 | 深圳市轱辘车联数据技术有限公司 | Method, device and equipment for recommending video production information |
| CN110837948B (en) * | 2019-10-16 | 2022-04-01 | 北京理工大学 | New energy automobile safety index evaluation method |
| CN111223208B (en) * | 2019-12-30 | 2022-04-26 | 潍柴动力股份有限公司 | Maintenance reminding method and device for engine parts, storage medium and electronic equipment |
| CN111401690B (en) * | 2020-02-22 | 2023-06-23 | 中国平安财产保险股份有限公司 | Fleet risk identification method, electronic device and readable storage medium |
| CN112070419B (en) * | 2020-09-23 | 2023-11-07 | 交通运输部公路科学研究所 | Method for quantifying risk of potential dangerous situation of automobile driving |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150149240A1 (en) * | 2013-11-26 | 2015-05-28 | Bank Of America Corporation | Identifying control improvement opportunities for key processes |
| CN105046593A (en) * | 2015-09-16 | 2015-11-11 | 国网天津市电力公司 | Intelligent power distribution and utilization evaluation method conforming to low-carbon energy policy |
| CN205563566U (en) * | 2016-04-08 | 2016-09-07 | 交通运输部公路科学研究所 | Management platform of healthy archives of automotive electronics |
-
2017
- 2017-04-18 CN CN201710253270.7A patent/CN107153914B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150149240A1 (en) * | 2013-11-26 | 2015-05-28 | Bank Of America Corporation | Identifying control improvement opportunities for key processes |
| CN105046593A (en) * | 2015-09-16 | 2015-11-11 | 国网天津市电力公司 | Intelligent power distribution and utilization evaluation method conforming to low-carbon energy policy |
| CN205563566U (en) * | 2016-04-08 | 2016-09-07 | 交通运输部公路科学研究所 | Management platform of healthy archives of automotive electronics |
Non-Patent Citations (3)
| Title |
|---|
| "以可靠性为中心的高速列车设备维修决策支持系统研究";王灵芝;《中国博士学位论文全文数据库工程科技Ⅱ辑》;20110915(第9期);正文第15-25页、第89-95页 * |
| "基于可靠性和维修成本分析的城轨车辆车门维修策略优化";张少东;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20160115(第1期);正文第20-43页 * |
| 张少东."基于可靠性和维修成本分析的城轨车辆车门维修策略优化".《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》.2016,(第1期), * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107153914A (en) | 2017-09-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107153914B (en) | System and method for evaluating automobile operation risk | |
| WO2020244288A1 (en) | Method and apparatus for evaluating truck driving behaviour based on gps trajectory data | |
| Shinar et al. | The validity of police reported accident data | |
| CN110222436B (en) | Method and device for evaluating health state of train parts and storage medium | |
| CN110222437A (en) | Appraisal procedure, device and the storage medium of train car team health status | |
| CN103617110A (en) | Server device condition maintenance system | |
| CN107085768A (en) | A kind of system and method for being used to evaluate vehicle operational reliability | |
| CN110210161B (en) | Method and device for evaluating health state of train and storage medium | |
| CN111506048B (en) | Vehicle fault early warning method and related equipment | |
| CN109242227A (en) | The driving risk and assessment models of car steering behavior | |
| CN110203249A (en) | Train repairs processing method, device and the storage medium of journey | |
| CN108763966B (en) | Tail gas detection cheating supervision system and method | |
| CN112131282A (en) | Vehicle life cycle data management method, electronic device, system and storage medium | |
| CN116245367A (en) | Dangerous truck transportation risk assessment method and system based on hierarchical fuzzy neural network | |
| CN113516798B (en) | Meter adjustment vehicle judging method and device | |
| Joni et al. | Analysis of traffic accident severity in Baghdad city using Binary Logistic Regression Model | |
| CN112685466A (en) | Vehicle overload prediction model training method, overload prediction method, system and medium | |
| CN115114124A (en) | Host risk assessment method and device | |
| CN112232553A (en) | Bayesian network-based high-speed rail train late influence factor diagnosis method | |
| CN115479949B (en) | Bridge safety monitoring and early warning method and system based on big data | |
| CN116579125A (en) | Bearing remaining life prediction method, device, equipment and readable storage medium | |
| Alsahli et al. | Investigation of the correlation between track geometry defect occurrence and wood tie condition | |
| CN111984676B (en) | Method and device for determining damage of internal parts of vehicle | |
| CN111275847B (en) | Method and device for detecting shunting vehicle | |
| Asplund et al. | Assessment of the data quality of wayside wheel profile measurements. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210126 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |