CN113128409B - Multi-dimensional association rule model-based vehicle multi-source signal flexibility testing method - Google Patents
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Abstract
The invention discloses a finished automobile multi-source signal flexibility testing method based on a multi-dimensional association rule model, which is characterized in that under the environment of finished automobile cloud testing, testing states are divided on line at a cloud end, and a characteristic set of an actual testing state and a target testing state of a finished automobile is established; then intercepting a signal domain of the current time period in the multi-source signal flow, and calculating and updating a strong association rule set of the bus signal and the electrical property signal detection point by constructing a multi-dimensional association rule model; and finally, taking the bus signal as input, deciding an electrical performance signal detection point in the next time period by matching a strong association rule set, controlling the test terminal equipment to dynamically adjust the detection point according to the decision, and improving the flexible response capability of the test system. The invention can improve the capability of the test system for dealing with the change of the state and the test requirement of the whole vehicle, promote the cooperativity of the bus signal and the electrical property signal detection point, ensure that the test process is more accurate and automatic, and provide powerful support for cloud end development and intellectualization of the test and diagnosis of the whole vehicle.
Description
Technical Field
The invention belongs to the technical field of vehicle test diagnosis development, and particularly relates to a vehicle multi-source signal flexibility test method based on a multi-dimensional association rule model.
Background
In recent years, the intense competition of the automobile industry stimulates the urgent pursuit of the whole automobile factory on the development efficiency of new automobile models. In order to reduce the research and development cost of the whole vehicle, a whole vehicle manufacturer draws a flexible manufacturing idea for reference, introduces the 'flexibility' idea into the whole vehicle test diagnosis technology, and improves the capability of a test system for responding to the change of test requirements and the flexibility of a test process; in order to shorten the whole vehicle research and development period, the whole vehicle test diagnosis development technology develops towards networking, cloud and intellectualization, and each large and whole vehicle manufacturer provides a respective whole vehicle remote test development system to meet the multi-vehicle type and multi-system concurrent test diagnosis requirements.
However, the current test development system has the following problems in the test acquisition method: most types of finished automobile test systems need professional test diagnosis developers to give test schemes, so that the accuracy of the finished automobile test process, the multi-source signal cooperativity and the accuracy of test results seriously depend on professional qualities of the testers; and a part of types of test development systems are always in an intensive collection state during running, the starting and dormancy of each detection point of the test system cannot be intelligently adjusted according to the running state of the whole vehicle, and a large amount of invalid data is collected, so that the running cost of the server is increased, interference is brought to the results of later analysis and diagnosis, and the research and development period is prolonged. Therefore, how to automatically adjust the test correlation according to the change of the test requirement and accurately adjust the electrical performance signal detection point according to the real-time running state change of the whole vehicle is a problem which needs to be solved urgently to realize the test automation and the intellectualization.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the whole vehicle multi-source signal flexible testing method based on the multi-dimensional association rule model, so that the capability of a testing system for responding to the state of the whole vehicle and the change of testing requirements is improved, the cooperativity of bus signals and electrical property signal detection points is improved, the testing process is more accurate and automatic, and powerful support is provided for cloud end development and intellectualization of whole vehicle testing diagnosis.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention relates to a multi-source signal flexibility test method for a whole vehicle based on a multi-dimensional association rule model, which is characterized by comprising the following steps of:
step 1: establishing a characteristic set of an actual test state and a target test state of the whole vehicle in a T time period before the current moment:
step 1.1: dividing a T time period before the current time into N test time periods with the length of T, namely T is N multiplied by T, taking a set formed by bus signals and electrical performance signal detection points in the actual test state of the whole vehicle in each test time period as a feature set of the actual test state, thereby obtaining N feature sets of the actual test state, which are marked as A 1 ,A 2 ,...,A N (ii) a Wherein A is N A feature set representing an Nth actual test state;
step 1.2: respectively counting all bus signals and electrical performance signal detection points in the T time period to obtain a bus signal set S ═ S 1 ,S 2 ,...,S n P and set of electrical signal detection points P ═ P 1 ,P 2 ,...,P m In which S is n Indicating the nth bus signal, P, during the period T m The method comprises the steps of representing the mth electrical performance signal detection point in a T time period, wherein n represents the number of bus signals in the T time period, m represents the number of electrical performance signal detection points in the T time period, n is larger than or equal to 1, and m is larger than or equal to 0;
using the u-th non-empty subset Q of the set S of bus signals u And the v-th subset O of the electrical property signal detection points P v Constructing a feature set of the ith target test state
1≤u≤2 n-1 ，1≤v≤2 m ，1≤i≤2 n-1 ×2 m Thereby obtaining 2 n-1 ×2 m The characteristic set of each target test state corresponds to one target test state;
step 1.3: according to the feature set V of the ith target test state i The number of elements k being card (V) i ) The target test state is classified as a k-source test state, wherein k is more than or equal to 1 and less than or equal to n + m; card (-) indicates the number of collection elementsA function;
step 2: and traversing and counting the occurrence frequency of each target test state in the actual test state within the T time period, calculating the support degree of each frequent test state, and acquiring the feature set of the most frequent test states:
step 2.1: defining k as iteration number, and initializing k to 1;
taking all union sets of the minimum subset of the bus signal set S and the minimum subset of the electrical performance signal detection point set P as a candidate 1-source test state set W 1 I.e. by
Wherein,
a feature set for any 1-source test state;
step 2.2: traversing characteristic set A of actual test state in T time period 1 ,A 2 ,...,A N The psi th candidate k-source test state is counted
Number of occurrences in actual test conditions
1≤ψ≤card(W k )；
Step 2.3: calculating the psi th candidate k-source test state by equation (1)
Degree of support of alpha k,ψ ；
In the formula (1), the reaction mixture is,
a feature set representing a ψ th candidate k-source test state; c. CThe count (·) represents the number of times the statistical test state or association rule occurs in the actual test state.
Step 2.4: according to a preset support minimum threshold value alpha min Truncation of alpha k,ψ ＜α min To obtain a k-th iteration solved frequent k-source test state set M k I.e. by
Wherein,
for any frequent k-source test state feature set,
for arbitrary feature sets
The degree of support of (c);
step 2.5: assigning k +1 to k;
step 2.6: frequent (k-1) -source test state set M obtained by k-1 iterations k-1 The feature sets corresponding to the target test state in (1) are parallel-connected, so that a candidate k-source test state set W is generated k I.e. by
Feature sets of the g-th and h-th test states in the frequent (k-1) -source test state set, respectively, i.e.
Step 2.7: repeating the process from the step 2.2 to the step 2.6 until the support degree alpha of any candidate k-source test state k,ψ Are all less than alpha min I.e. no frequent test states of higher source are generated, the statistical calculation is ended, thus obtaining the frequent k max -source test state set
Namely, it is
Wherein,
at an arbitrary frequency k max -a feature set of source test states,
for arbitrary feature sets
The degree of support of (a) is,
is candidate k max -a set of source test states;
and step 3: using said frequency k max -source test state set
Each frequency k of max -the feature set of source test state gets and updates the strongly associated rule set:
step 3.1: will frequently k max -source test state set
The δ -th frequent k max -feature set of source test states
Separating the bus signal from the electrical performance signal detection point to obtain a frequent bus signal set
And frequent electrical performance signal detection point set
Step 3.2: initializing δ to 1;
step 3.3: separately evaluating a set of frequent bus signals
Of (d) a non-empty subset
And frequent electrical performance signal detection point set
Of (a) a non-empty subset
Thereby establishing an association rule U of bus signal and electrical property signal detection points τ ∪H λ Is marked as U τ →H λ Thereby calculating the association rule U using equation (2) τ →H λ Degree of support of
And abandon
The association rule of (1);
step 3.4: calculating the association rule U by using the formula (3) τ →H λ Reliability of (2)
And according to a preset minimum threshold value beta of the reliability min Abandon
Is associated with the rule, therebyObtaining a strongly associated rule set
Step 3.5: after delta +1 is assigned to delta, the sequence is returned to the step 3.3 to be executed until the sequence is finished
So as to obtain a strong association rule set of all bus signal and electrical performance signal detection points in the T time period
And 4, step 4: according to the bus signal and electrical property signal detection point strong association rule set F, setting the bus signal set R as { R } at the next moment of the current moment 1 ,R 2 ...R z Set of bus signals as inputs, where R z Is the z-th signal of the input bus signal set, z is less than or equal to n;
acquiring an electrical performance signal detection point set G (G) by matching a strong association rule set F 1 ,G 2 ...G q In which G q And the q detection point of the detection point set is issued to the test terminal to adjust the electrical performance signal detection point to complete the flexible test, and q is less than or equal to m.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention can lead the whole vehicle test development system to judge the state of the whole vehicle according to the bus signal of the next time period by learning the association rule set of the bus signal and the electrical property signal detection point of the previous time period and finally actively adjust the electrical property signal detection point of the whole vehicle, thereby realizing the multi-source signal cooperative test and meeting the cloud-end, automatic and intelligent requirements of the whole vehicle test diagnosis development.
2. The invention can enable the electrical performance signal test system to dynamically adjust the starting and sleeping states of the electrical performance signal test detection unit in the development system according to the bus signal (vehicle body state) in the next time period, thereby increasing the accuracy of the acquisition process, reducing the acquisition and storage of invalid data, and being beneficial to the large-scale and long-time test diagnosis development requirements of the whole vehicle.
3. The invention provides a method for acquiring a strong association rule between a bus signal and an electrical performance detection point of a whole vehicle, namely acquiring the strong association rule between the bus signal and the electrical performance detection point on line based on a multidimensional association rule model; the method considers the change of the state of the whole vehicle and the change of the test requirement of the tester in the test process, and updates the strong association rule of the bus signal and the electrical property detection point according to the changes, thereby improving the flexibility of the test system and the test process.
Drawings
FIG. 1 is a flow chart of a method for obtaining a strong association rule set of a bus signal and an electrical property signal detection point according to the present invention;
FIG. 2 is a basic flow chart of the multi-source signal flexibility test method for the whole vehicle.
Detailed Description
In the embodiment, the multi-source signal flexibility testing method for the whole vehicle based on the multi-dimensional association rule model is characterized in that a testing system intelligently and flexibly adjusts the electric performance signal detection points of the whole vehicle by monitoring the running state and the testing requirement change of the whole vehicle in real time in the cloud testing environment of the whole vehicle. The method comprises the following steps: firstly, online dividing test states at a cloud end, and establishing a characteristic set of an actual test state and a target test state of the whole vehicle; then intercepting a signal domain of the current time period in the multi-source signal flow, and calculating and updating a strong association rule set of the bus signal and the electrical property signal detection point by constructing a multi-dimensional association rule model; and finally, taking the bus signal as input, deciding the electrical performance signal detection point in the next time period by matching a strong association rule set, controlling the test terminal equipment to dynamically adjust the detection point according to the decision, and improving the flexible response capability of the test system. As shown in fig. 2, specifically, the following steps are performed:
step 1: establishing a characteristic set of an actual test state and a target test state of the whole vehicle in a T time period before the current moment:
step 1.1: dividing the equal length of the T time period before the current moment into N test time periods with the length of T, namely T is N multiplied by T, taking a bus signal set and an electrical performance signal detection point set in the actual test state of the whole vehicle in each test time period as a feature set of the actual test state, thereby obtaining N feature sets of the actual test state, which are marked as A 1 ,A 2 ,...,A N (ii) a Wherein A is N A feature set representing an Nth actual test state;
in this example, the previous T is 60s of test time, the previous T is divided into N60 equal-length time segments, that is, each time segment T is 1s, the set of bus signal and electrical performance signal detection points occurring in each time segment T is used as an actual test state, and the actual test state a is obtained 1 ,A 2 ,...,A 59 ,A 60 。
Step 1.2: respectively counting all bus signals and electrical performance signal detection points in the T time period to obtain a bus signal set S ═ S 1 ,S 2 ,...,S n P and set of electrical signal detection points P ═ P 1 ,P 2 ,...,P m In which S is n Indicating the nth bus signal, P, during the period T m The method comprises the steps of representing the mth electrical performance signal detection point in a T time period, representing the number of bus signals in the T time period by n, representing the number of electrical performance signal detection points in the T time period by m, wherein n is more than or equal to 1, and m is more than or equal to 0;
using the u-th non-empty subset Q of the set S of bus signals u And the v-th subset O of the electrical property signal detection points P v Constructing a feature set of the ith target test state
1≤u≤2 n-1 ，1≤v≤2 m ，1≤i≤2 n-1 ×2 m Thereby obtaining 2 n-1 ×2 m Feature sets of each target test state, and each feature set of each target test state corresponds to one target testA state;
step 1.3: according to the feature set V of the ith target test state i The number of elements k being card (V) i ) Classifying the target test state as a k-source test state, wherein k is more than or equal to 1 and less than or equal to n + m; card (-) denotes the function of finding the number of collection elements;
step 2 and step 3 are to obtain a rule set strongly associating the bus signal and the electrical performance signal detection point, as shown in fig. 1.
Step 2: and traversing and counting the occurrence frequency of each target test state in the actual test state within the T time period, calculating the support degree of each frequent test state, and acquiring the feature set of the most frequent test states:
step 2.1: defining k as iteration number, and initializing k to be 1;
taking all union sets of the minimum subset of the bus signal set S and the minimum subset of the electrical performance signal detection point set P as a candidate 1-source test state set W 1 I.e. by
Step 2.2: traversing characteristic set A of actual test state in T time period 1 ,A 2 ,...,A N The psi th candidate k-source test state is counted
Number of occurrences in actual test state
0＜ψ＜card(W k )；
Step 2.3: calculating the psi th candidate k-source test state by equation (1)
Degree of support of alpha k,ψ ；
In the formula (1), the acid-base catalyst,
a feature set representing a ψ th candidate k-source test state; count (·) represents the number of times a statistical test state or association rule occurs in an actual test state.
Step 2.4: according to a preset support degree minimum threshold value alpha min Rounding off alpha k,ψ ＜α min Obtaining a frequent k-source test state set M obtained by k times of iteration k I.e. by
In this example, take a min ＝0.6，α min Too large a value may lose a frequent test state, and too small a value results in M 1 And the number of elements in the set is too many, so that the iteration times are increased.
Step 2.5: assigning k +1 to k;
step 2.6: frequent (k-1) -source test state set M obtained by k-1 iterations k-1 Is performed on the feature set corresponding to each target test state in the target test state to generate a candidate k-source test state set W k I.e. by
Feature sets of the g-th and h-th test states in the frequent (k-1) -source test state set, respectively, i.e.
Step 2.7: repeating the process from step 2.2 to step 2.6 until alpha k,ψ Are all less than alpha min I.e. no frequent test states of higher source are generated, the statistical calculation is ended, thus obtaining the frequent k max -source test state set
Namely, it is
Wherein,
is candidate k max -a set of source test states;
and step 3: using said frequency k max -source test state set
Each frequency k of max -the feature set of source test state gets and updates the strongly associated rule set:
step 3.1: will frequently k max -source test state set
Middle delta frequency k max -feature set of source test states
Separating the bus signal from the electrical performance signal detection point to obtain a frequent bus signal set
And frequent electrical performance signal detection point set
Step 3.2: initializing delta to be 1;
step 3.3: separately determining a set of frequent bus signals
Of (d) a non-empty subset
And frequent electrical performance signal detection point set
λ non-empty subset of
Thereby establishing an association rule U of bus signal and electrical property signal detection points τ ∪H λ Is marked as U τ →H λ Thereby calculating the association rule U using equation (2) τ →H λ Degree of support of
And abandon
Step 3.4: calculating the association rule U by using the formula (3) τ →H λ Reliability of (2)
And according to a preset confidence minimum threshold value beta min Abandon
Thereby obtaining a set of strongly associated rules
In this example taken from min ＝0.6，β min If the value is too large, strong association rules of some bus signals and electrical performance signal detection points can be lost, and if the value is too small, the obtained strong association rules lose association significance.
Step 3.5: after the delta +1 is assigned to the delta, the sequence is returned to the step 3.3 until the sequence is executed until the
So as to obtain a strong association rule set of all bus signal and electrical performance signal detection points in the T time period
And 4, step 4: according to the bus signal and electrical performance signal detection point strong association rule set F, the bus signal set R of the next moment of the current moment is equal to { R ═ R 1 ,R 2 ...R z As the set of bus signals at the inputs, where R z Is the z-th signal of the input bus signal set, z is less than or equal to n;
acquiring an electrical performance signal detection point set G (G) by matching a strong association rule set F 1 ,G 2 ...G q In which G q And the q detection point of the detection point set is issued to the test terminal to adjust the electrical performance signal detection point to complete the flexible test, and q is less than or equal to m.
In conclusion, the invention can provide accurate electrical performance signal detection points according to the running state of the whole vehicle, reduce the interference of invalid test data on test diagnosis results while improving the test cooperativity of bus signals and electrical performance signals of a test development system of the whole vehicle, improve the flexible reaction capability of the test system and provide favorable conditions for large-batch and long-time test diagnosis development of the whole vehicle in a complex environment.
Claims (1)
1. A vehicle multisource signal flexibility testing method based on a multidimensional association rule model is characterized by comprising the following steps:
step 1: establishing a characteristic set of an actual test state and a target test state of the whole vehicle in a T time period before the current moment:
step 1.1: dividing T time period before the current time into N test times with the length of TAnd taking a set formed by bus signals and electrical performance signal detection points in the actual test state of the whole vehicle in each test time period as a feature set of the actual test state, thereby obtaining feature sets of N actual test states, which are marked as A 1 ,A 2 ,...,A N (ii) a Wherein A is N A feature set representing an Nth actual test state;
step 1.2: respectively counting all bus signals and electrical performance signal detection points in the T time period to obtain a bus signal set S ═ S 1 ,S 2 ,...,S n P and set of electrical signal detection points P ═ P 1 ,P 2 ,...,P m In which S n Indicating the nth bus signal, P, during the period T m The method comprises the steps of representing the mth electrical performance signal detection point in a T time period, representing the number of bus signals in the T time period by n, representing the number of electrical performance signal detection points in the T time period by m, wherein n is more than or equal to 1, and m is more than or equal to 0;
using the u-th non-empty subset Q of the set S of bus signals u And the v-th subset O of the electrical property signal detection points P v Constructing a feature set of the ith target test state
1≤u≤2 n-1 ，1≤v≤2 m ，1≤i≤2 n-1 ×2 m Thereby obtaining 2 n-1 ×2 m The characteristic set of each target test state corresponds to one target test state;
step 1.3: according to the feature set V of the ith target test state i The number of elements K ═ card (V) i ) The target test state is classified as a K-source test state, wherein K is more than or equal to 1 and less than or equal to n + m; card (-) denotes the function of finding the number of collection elements;
step 2: and traversing and counting the occurrence frequency of each target test state in the actual test state in the T time period, calculating the support degree of each frequent test state, and acquiring a feature set of the most frequent test states:
step 2.1: defining k as iteration number, and initializing k to be 1;
taking all union sets of the minimum subset of the bus signal set S and the minimum subset of the electrical performance signal detection point set P as a candidate 1-source test state set W 1 I.e. by
Wherein,
a feature set for any 1-source test state;
step 2.2: traversing the feature set A of the actual test state in the T time period 1 ,A 2 ,...,A N The psi th candidate k-source test state is counted
Number of occurrences C in actual test conditions k,ψ ＝count(W k ψ )，
1≤ψ≤card(W k )；
Step 2.3: calculating the psi th candidate k-source test state by equation (1)
Degree of support of alpha k,ψ ；
In the formula (1), the reaction mixture is,
a feature set representing a ψ th candidate k-source test state; count (·) represents the number of times that the statistical test state or the association rule appears in the actual test state;
step 2.4: according to a predetermined threshold for minimum supportValue alpha min Truncation of alpha k,ψ ＜α min To obtain a k-th iteration solved frequent k-source test state set M k I.e. by
Wherein,
for any frequent k-source test state feature set,
for arbitrary feature sets
The degree of support of (c);
step 2.5: assigning k +1 to k;
step 2.6: frequent (k-1) -source test state set M obtained by k-1 iterations k-1 The feature sets corresponding to the target test state in (1) are parallel-connected, so that a candidate k-source test state set W is generated k I.e. by
Feature sets of the g-th and h-th test states in the frequent (k-1) -source test state set, respectively, i.e.
Step 2.7: repeating the process from the step 2.2 to the step 2.6 until the support degree alpha of any candidate k-source test state k,ψ Are all less than alpha min I.e. no frequent test states of higher source are generated, the statistical calculation is ended, thus obtaining the frequent k max -source test state set
Namely, it is
Wherein,
at an arbitrary frequency k max -a feature set of source test states,
for arbitrary feature sets
The degree of support of (a) is,
is candidate k max -a set of source test states;
and step 3: using said frequency k max -source test state set
Each frequency k of max -the feature set of source test state gets and updates the strongly associated rule set:
step 3.1: will frequently k max -source test state set
The δ -th frequent k max -feature set of source test states
Separating the bus signal from the electrical performance signal detection point to obtain a frequent bus signal set
And frequent electrical performance signal detection pointsCollection
Step 3.2: initializing delta to be 1;
step 3.3: separately determining a set of frequent bus signals
Of (d) a non-empty subset
And frequent electrical performance signal detection point set
λ non-empty subset of
Thereby establishing an association rule U of bus signals and electrical property signal detection points τ ∪H λ Is marked as U τ →H λ Thereby calculating the association rule U using equation (2) τ →H λ Degree of support of
And abandon
The association rule of (1);
step 3.4: calculating association rule U by using formula (3) τ →H λ Reliability of (2)
And according to a preset confidence minimum threshold value beta min To leave away
Thereby obtaining a set of strongly associated rules
Step 3.5: after delta +1 is assigned to delta, the sequence is returned to the step 3.3 to be executed until the sequence is finished
So as to obtain a strong association rule set of all bus signal and electrical performance signal detection points in the T time period
And 4, step 4: according to the bus signal and electrical property signal detection point strong association rule set F, setting the bus signal set R as { R } at the next moment of the current moment 1 ,R 2 ...R z As the set of bus signals at the inputs, where R z Is the z-th signal of the input bus signal set, z is less than or equal to n;
acquiring an electrical performance signal detection point set G (G) by matching a strong association rule set F 1 ,G 2 ...G q In which G q And the q detection point of the detection point set is issued to the test terminal to adjust the electrical performance signal detection point to complete the flexible test, and q is less than or equal to m.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086804A (en) * | 2018-07-12 | 2018-12-25 | 中石化石油机械股份有限公司 | A kind of hydraulic device fault forecast method merged based on multi source status monitoring information and reliability characteristic |
| CN110991668A (en) * | 2019-11-29 | 2020-04-10 | 合肥国轩高科动力能源有限公司 | Electric vehicle power battery monitoring data analysis method based on association rule |
| CN111337034A (en) * | 2020-03-27 | 2020-06-26 | 南京普斯迪尔电子科技有限公司 | Shared electric vehicle positioning system based on big data technology |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086804A (en) * | 2018-07-12 | 2018-12-25 | 中石化石油机械股份有限公司 | A kind of hydraulic device fault forecast method merged based on multi source status monitoring information and reliability characteristic |
| CN110991668A (en) * | 2019-11-29 | 2020-04-10 | 合肥国轩高科动力能源有限公司 | Electric vehicle power battery monitoring data analysis method based on association rule |
| CN111337034A (en) * | 2020-03-27 | 2020-06-26 | 南京普斯迪尔电子科技有限公司 | Shared electric vehicle positioning system based on big data technology |
Non-Patent Citations (2)
| Title |
|---|
| "Optimal Control of Multi-Source Electric Vehicles in Real Time Using Advisory Dynamic Programming";A. M. Ali等;《IEEE Transactions on Vehicular Technology》;20191130;第68卷(第11期);全文 * |
| "基于多源数据挖掘及关联规则分析的事故事件等级判定";林裕新等;《电力设备管理》;20201130;第2020年卷(第11期);全文 * |
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