CN109886439B - Remote diagnosis system and method for electric control vehicle - Google Patents
Remote diagnosis system and method for electric control vehicle Download PDFInfo
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Abstract
The invention discloses a remote diagnosis system for an electric control vehicle, which comprises: the system comprises an acquisition module, a monitoring module and a monitoring module, wherein the acquisition module is arranged on a vehicle to be monitored and is used for acquiring vehicle information; the remote diagnosis module is connected with the acquisition module through a CAN network and is used for diagnosing the information acquired by the acquisition module; and the server is used for issuing diagnosis information to the remote diagnosis module, receiving and analyzing a diagnosis result fed back by the remote diagnosis module, storing the diagnosis result and analyzing vehicle data according to the diagnosis result. The invention provides an electric control vehicle remote diagnosis system which can monitor and collect information of a vehicle to be monitored in real time and diagnose the running condition of the vehicle; the invention also provides a remote diagnosis method of the electric control vehicle, which can diagnose the vehicle to be monitored by calculating the preset risk assessment index and the risk assessment index.
Description
Technical Field
The invention relates to an operation monitoring technology of an electric control vehicle, in particular to a remote diagnosis system of the electric control vehicle and a diagnosis method thereof.
Background
With the continuous progress of remote communication, bus technology and the like, the fault diagnosis and monitoring technology of the vehicle is greatly developed, so that the vehicle can realize remote diagnosis and monitoring in the running process of the vehicle.
The development of the automobile electronic control technology improves the economy, dynamic property, safety, environmental protection, comfort and controllability of automobiles, and simultaneously, the structure of an automobile electronic control system is more and more complex, and the requirement on an automobile fault diagnosis system is more and more high. Meanwhile, maintenance technicians and experts capable of tracking and mastering the related technologies in the automobile field are increasingly deficient, and related personnel cannot be trained and supported by the technology in time and cannot meet professional requirements of maintenance technicians of various maintenance branches. At present, the proportion of electronic products in the cost of the whole automobile is generally 23% -30%, the proportion of electronic products in the cost of the whole automobile is 50% -60% in high-grade luxury saloon cars, 70% of innovative technologies of automobiles are concentrated on automobile electronics, and the development of the automobile industry must apply electronic control technologies more. Many conventional fault diagnosis methods and diagnostic apparatuses are difficult to adapt to the development of modern automotive technology, in terms of reliability of diagnosis, convenience of use, and data sharing. How to rapidly and accurately diagnose the fault of the automobile electronic control system is a big problem in the current automobile maintenance industry. Aiming at the situation, a corresponding fault self-diagnosis system is added in the development process of the electronic control unit, so that the running conditions of all components of the electronic control system can be continuously monitored in the running process of the vehicle, most faults in the electronic control system can be detected, and the faults are stored in a memory of the electronic control unit in the form of fault codes, so that the normal running of the vehicle can be ensured, and the maintenance of the vehicle and the electronic control system by maintenance personnel is facilitated, and the online or offline fault diagnosis of the vehicle is realized.
In the prior art, the remote diagnosis of the vehicle generally adopts a background or user-triggered fault diagnosis mode, and the time for initiating the fault diagnosis by adopting the background or the user has uncertainty, so that the remote fault diagnosis of the vehicle has certain randomness.
Disclosure of Invention
The invention provides an electric control vehicle remote diagnosis system for solving the technical defects at present, which can monitor and collect the information of the vehicle to be monitored in real time and diagnose the running condition of the vehicle;
the invention also provides a remote diagnosis method of the electric control vehicle, which can diagnose the vehicle to be monitored by calculating the preset risk assessment index and the risk assessment index.
The technical scheme provided by the invention is as follows: an electronically controlled vehicle remote diagnostic system comprising:
the system comprises an acquisition module, a monitoring module and a monitoring module, wherein the acquisition module is arranged on a vehicle to be monitored and is used for acquiring vehicle information;
the remote diagnosis module is connected with the acquisition module through a CAN network and is used for diagnosing the information acquired by the acquisition module;
and the server is used for issuing diagnosis information to the remote diagnosis module, receiving and analyzing a diagnosis result fed back by the remote diagnosis module, storing the diagnosis result and analyzing vehicle data according to the diagnosis result.
Preferably, the method further comprises the following steps:
the wireless communication module is used for communicating with the server through a wireless network and receiving the diagnosis information sent by the server;
the control processing module generates a CAN diagnosis message according to the diagnosis information, sends the CAN diagnosis message to the acquisition module in the vehicle, receives a corresponding CAN message sent by the acquisition module, generates a diagnosis result according to the corresponding CAN message, and transmits the diagnosis result to the wireless communication module;
the storage module is used for storing the data processed by the control processing module;
a wireless communication module for sending the diagnosis result to the server.
It is preferable that the first and second liquid crystal layers are formed of,
the acquisition module comprises:
a vehicle speed sensor provided at a hub of a vehicle for detecting a vehicle speed;
a tire pressure detector provided at a wheel of a vehicle for detecting a tire pressure of the vehicle;
a temperature sensor provided in a water tank of a vehicle, for detecting a temperature of water in the water tank;
and a rotation speed sensor provided at a radiator fan of the vehicle for detecting a rotation speed of the radiator fan.
The remote diagnosis method for the electric control vehicle comprises the following steps:
the method comprises the steps of firstly, collecting data of a vehicle to be monitored, obtaining a corresponding preset risk assessment index tau according to the data of the vehicle to be monitored, and when tau is larger than or equal to tauSThen, carrying out risk assessment on the vehicle to be monitored; wherein, tauSComparing the risk assessment indicators;
step two, collecting the speed, the tire pressure, the water temperature in the water tank and the like of the vehicle to be monitored,The rotating speed of the cooling fan processes the preset risk evaluation index to obtain a risk evaluation index xi, and when xi is more than or equal to xiSThen, judging the risk state of the vehicle to be monitored; wherein ξSComparing the risk assessment indices;
and thirdly, judging the risk state of the vehicle to be monitored according to the speed of the vehicle to be monitored, the tire pressure of the wheels, the water temperature in the water tank, the rotating speed of the cooling fan and the risk evaluation index, so as to monitor the vehicle to be monitored.
It is preferable that the first and second liquid crystal layers are formed of,
in the first step, the calculation process of the preset risk assessment index τ is as follows:
wherein, kappa is a correction coefficient, S is the number of years the vehicle to be monitored has been used, S is the total kilometer number of the vehicle to be monitored, S' is the number of kilometers of the vehicle after the last maintenance, f is the number of times of vehicle maintenance, and t is the time from the last maintenance of the vehicle to be monitored to the present.
It is preferable that the first and second liquid crystal layers are formed of,
τSthe value was 1.08.
It is preferable that the first and second liquid crystal layers are formed of,
the risk assessment index xi calculation process is as follows:
wherein V is the speed of the vehicle to be monitored, P is the tire pressure of the vehicle to be monitored, T is the water temperature in the water tank of the vehicle to be monitored, N is the rotating speed of the cooling fan, and V is0For comparison of vehicle speeds, P0For comparison of tire pressures of the wheels, N0For comparing the rotational speeds, T, of the cooling fans0And e is the base number of the natural logarithm when comparing the water temperature in the water tank.
It is preferable that the first and second liquid crystal layers are formed of,
in the third step, the risk state is judged by establishing a BP neural network model, and the method comprises the following steps:
step 1, acquiring the speed V of a vehicle to be monitored, the tire pressure P of the vehicle, the water temperature T in a water tank and the rotating speed N of a cooling fan according to a sampling period, and determining a risk evaluation index xi;
step 2, normalizing the parameters in sequence, and determining an input layer neuron vector x ═ x of the three-layer BP neural network1,x2,x3,x4,x5In which x1For the speed coefficient, x, of the vehicle to be monitored2Coefficient of wheel pressure, x, for a vehicle to be monitored3Water temperature coefficient, x, in the water tank of a vehicle to be monitored4For the coefficient of rotation, x, of the radiator fan of the vehicle to be monitored5Evaluating an index coefficient for risk;
and 3, mapping the input layer vector to a hidden layer, wherein the hidden layer vector y is { y ═ y1,y2,…,ymM is the number of hidden nodes;
and 4, obtaining an output layer neuron vector o ═ o1,o2,o3,o4}; wherein o is1To a set 1 st risk level, o2To a set 2 nd risk level, o3To a set risk level of 3, o4For a set 4 th risk level, the output layer neuron value is
k is the output layer neuron sequence number, k is {1,2,3,4}, i is the set ith risk level, i is {1,2,3,4}, and when o iskIs 1, at this time, the vehicle to be monitored is at okA corresponding risk level;
step 5, the service module judges according to the output security level; the 1 st risk level is a safety state, and is right the vehicle to be monitored does not need to take measures, the 2 nd risk level is a warning state, and is right the vehicle to be monitored needs to take monitoring and early warning, the 3 rd risk level is a dangerous state, and is right the vehicle to be monitored needs to prompt early warning measures, the 4 th risk level is a high-risk level, and is right the vehicle to be monitored needs to take emergency warning measures.
It is preferable that the first and second liquid crystal layers are formed of,
the number m of the hidden layer nodes meets the following conditions:
wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
It is preferable that the first and second liquid crystal layers are formed of,
the tire pressure P of the vehicle to be monitored is the average tire pressure of four wheels.
The invention has the following beneficial effects: the invention provides an electric control vehicle remote diagnosis system which can monitor and collect information of a vehicle to be monitored in real time and diagnose the running condition of the vehicle;
the invention also provides a remote diagnosis method of the electric control vehicle, which judges the risk state by establishing the BP neural network model and diagnoses the vehicle to be monitored by calculating the preset risk evaluation index and the risk evaluation index.
Detailed Description
The present invention is described in further detail below to enable those skilled in the art to practice the invention with reference to the description.
The invention provides an electric control vehicle remote diagnosis system, comprising: the acquisition module is arranged on a vehicle to be monitored and used for acquiring vehicle information; the remote diagnosis module is connected with the acquisition module through a CAN network and is used for diagnosing the information acquired by the acquisition module; the server is used for issuing diagnosis information to the remote diagnosis module, receiving and analyzing a diagnosis result fed back by the remote diagnosis module, storing the diagnosis result and analyzing vehicle data according to the diagnosis result.
The wireless communication module is used for communicating with the server through a wireless network and receiving the diagnosis information sent by the server; the control processing module generates a CAN diagnosis message according to the diagnosis information, sends the CAN diagnosis message to the acquisition module in the vehicle, receives a corresponding CAN message sent by the acquisition module, generates a diagnosis result according to the corresponding CAN message, and transmits the diagnosis result to the wireless communication module; the storage module is used for storing the data processed by the control processing module; the wireless communication module is used for sending the diagnosis result to the server.
The acquisition module comprises: the vehicle speed sensor is arranged at a hub of the vehicle and used for detecting the vehicle speed of the vehicle; the tire pressure detector is arranged at a wheel of the vehicle and used for detecting the tire pressure of the vehicle; the temperature sensor is arranged in a water tank of the vehicle and used for detecting the water temperature in the water tank; the rotational speed sensor is provided at a radiator fan of a vehicle for detecting a rotational speed of the radiator fan.
The invention also provides a remote diagnosis method of the electric control vehicle, which specifically comprises the following steps:
the method comprises the steps of firstly, collecting data of a vehicle to be monitored, obtaining a corresponding preset risk assessment index tau according to the data of the vehicle to be monitored, and when tau is larger than or equal to tauSThen, carrying out risk assessment on the vehicle to be monitored; wherein, tauSComparing the risk assessment indicators;
step two, collecting the speed of the vehicle to be monitored, the pressure of the wheel tire, the water temperature in the water tank and the rotating speed of a cooling fan, processing the preset risk evaluation index to obtain a risk evaluation index xi, and when xi is more than or equal to xiSThen, judging the risk state of the vehicle to be monitored; wherein ξSComparing the risk assessment indices;
and thirdly, judging the risk state of the vehicle to be monitored according to the speed of the vehicle to be monitored, the tire pressure of the wheels, the water temperature in the water tank, the rotating speed of the cooling fan and the risk evaluation index, so as to monitor the vehicle to be monitored.
In another embodiment, the preset risk assessment indicator τ is calculated by:
wherein, kappa is a correction coefficient, and s is the number of used years of the vehicle to be monitored in unit year; s is the total kilometer of the vehicle to be monitored, S' is the kilometer of the vehicle after the last maintenance, f is the number of vehicle maintenance times, and t is the latest maintenance time of the vehicle to be monitored, and the unit year.
In another embodiment, k is 1.02 and τSThe value was 1.08.
In another embodiment, the risk assessment index ξ is calculated as:
wherein V is the speed of the vehicle to be monitored, P is the tire pressure of the vehicle to be monitored, T is the water temperature in the water tank of the vehicle to be monitored, N is the rotating speed of the cooling fan, and V is0For comparison of vehicle speeds, P0For comparison of tire pressures of the wheels, N0For comparing the rotational speeds, T, of the cooling fans0And e is the base number of the natural logarithm when comparing the water temperature in the water tank.
In another embodiment, in the third step, the determining the risk state by building a BP neural network model includes the following steps:
step 1, establishing a BP neural network model.
Fully interconnected connections are formed among neurons of each layer on the BP model, the neurons in each layer are not connected, and the output and the input of neurons in an input layer are the same, namely oi＝xi. The operating characteristics of the neurons of the intermediate hidden and output layers are
opj＝fj(netpj)
Where p represents the current input sample, ωjiIs the connection weight from neuron i to neuron j, opiIs the current input of neuron j, opjIs the output thereof; f. ofjIs a non-linear, slightly non-decreasing function, generally taken as a sigmoid function, i.e. fj(x)＝1/(1+e-x)。
The BP network system structure adopted by the invention consists of three layers, wherein the first layer is an input layer, n nodes are provided in total, n detection signals representing the working state of the equipment are correspondingly provided, and the signal parameters are given by a data preprocessing module; the second layer is a hidden layer, and has m nodes which are determined by the training process of the network in a self-adaptive mode; the third layer is an output layer, p nodes are provided in total, and the output is determined by the response actually needed by the system.
The mathematical model of the network is:
inputting a vector: x ═ x1,x2,...,xn)T
Intermediate layer vector: y ═ y1,y2,...,ym)T
Outputting a vector: o ═ o (o)1,o2,...,op)T
In the invention, the number of nodes of the input layer is n equals to 5, the number of nodes of the output layer is p equals to 4, and the number of nodes of the hidden layer m is estimated by the following formula:
the input layer 5 parameters are respectively expressed as: x is the number of1For the speed coefficient, x, of the vehicle to be monitored2Coefficient of wheel pressure, x, for a vehicle to be monitored3Water temperature coefficient, x, in the water tank of a vehicle to be monitored4For the coefficient of rotation, x, of the radiator fan of the vehicle to be monitored5Evaluating an index coefficient for risk;
the data acquired by the sensors belong to different physical quantities, and the dimensions of the data are different. Therefore, the data needs to be normalized to a number between 0-1 before it is input into the artificial neural network.
The normalized formula is
Wherein x isjFor parameters in the input layer vector, XjMeasurement parameters V, P, T, N, ξ, j ═ 1,2,3,4, 5; xjmaxAnd XjminAnd respectively adopting S-shaped functions for the maximum value and the minimum value in the corresponding measurement parameters.
Specifically, the vehicle speed V of the vehicle to be monitored measured by using a vehicle speed sensor is normalized to obtain a vehicle speed coefficient x of the vehicle to be monitored1：
Wherein, VmaxAnd VminThe maximum vehicle speed and the minimum vehicle speed of the vehicle to be monitored are measured by the vehicle speed sensor respectively.
Similarly, the tire pressure P of the vehicle to be monitored, measured by the tire pressure monitor, is normalized to obtain the tire pressure coefficient x of the vehicle to be monitored2：
Wherein, PmaxAnd PminThe maximum tire pressure and the minimum tire pressure of the wheels of the vehicle to be monitored are respectively measured by the tire pressure monitor.
Normalizing the water temperature T in the water tank of the vehicle to be monitored measured by using the temperature sensor to obtain the water temperature coefficient x in the water tank of the vehicle to be monitored3：
Wherein, TmaxAnd TminThe highest water temperature and the lowest water temperature in a water tank of the vehicle to be monitored are respectively measured by the tire pressure monitor.
The rotating speed N of the cooling fan of the vehicle to be monitored, which is measured by using the rotating speed sensor, is normalized to obtain the rotating speed coefficient x of the cooling fan of the vehicle to be monitored4：
Wherein N ismaxAnd NminThe maximum rotating speed and the minimum rotating speed of a cooling fan of the vehicle to be monitored are respectively measured by the tire pressure monitor.
Normalizing according to the calculated risk evaluation index xi to obtain a risk evaluation index coefficient x5：
Wherein ξminAnd ximaxThe minimum risk assessment index and the maximum risk assessment index which can be obtained by calculation are respectively.
In another embodiment, the tire pressure P of the vehicle to be monitored is the average tire pressure of four wheels.
The output layer 4 parameters are respectively expressed as: o1To a set 1 st risk level, o2To a set 2 nd risk level, o3To a set risk level of 3, o4For a set 4 th risk level, the output layer neuron value is
k is the output layer neuron sequence number, k is {1,2,3,4}, i is the set ith risk level, i is {1,2,3,4}, and when o iskIs 1, at this time, the vehicle to be monitored is at okThe corresponding risk level.
And 2, training the BP neural network.
After the BP neural network node model is established, the training of the BP neural network can be carried out. And obtaining a training sample according to historical experience data of the product, and giving a connection weight between the input node i and the hidden layer node j and a connection weight between the hidden layer node j and the output layer node k.
(1) Training method
Each subnet adopts a separate training method; when training, firstly providing a group of training samples, wherein each sample consists of an input sample and an ideal output pair, and when all actual outputs of the network are consistent with the ideal outputs of the network, the training is finished; otherwise, the ideal output of the network is consistent with the actual output by correcting the weight; the output samples for each subnet training are shown in table 1.
TABLE 1 output samples for network training
(2) Training algorithm
The BP network is trained by using a back Propagation (Backward Propagation) algorithm, and the steps can be summarized as follows:
the first step is as follows: and selecting a network with a reasonable structure, and setting initial values of all node thresholds and connection weights.
The second step is that: for each input sample, the following calculations are made:
(a) forward calculation: for j unit of l layer
In the formula (I), the compound is shown in the specification,
for the weighted sum of the j unit information of the l layer at the nth calculation,
is the connection weight between the j cell of the l layer and the cell i of the previous layer (i.e. the l-1 layer),
is the previous layer (i.e. l-1 layer, node number n)l-1) The operating signal sent by the unit i; when i is 0, order
Is the threshold of the j cell of the l layer.
If the activation function of the unit j is a sigmoid function, then
And is
If neuron j belongs to the first hidden layer (l ═ 1), then there are
If neuron j belongs to the output layer (L ═ L), then there are
(b) And (3) calculating the error reversely:
for output unit
Pair hidden unit
(c) Correcting the weight value:
The third step: inputting a new sample or a new period sample until the network converges, and randomly re-ordering the input sequence of the samples in each period during training.
The BP algorithm adopts a gradient descent method to solve the extreme value of a nonlinear function, and has the problems of local minimum, low convergence speed and the like. A more effective algorithm is a Levenberg-Marquardt optimization algorithm, which enables the network learning time to be shorter and can effectively inhibit the network from being locally minimum. The weight adjustment rate is selected as
Δω＝(JTJ+μI)-1JTe
Wherein J is a Jacobian (Jacobian) matrix of error to weight differentiation, I is an input vector, e is an error vector, and the variable mu is a scalar quantity which is self-adaptive and adjusted and is used for determining whether the learning is finished according to a Newton method or a gradient method.
When the system is designed, the system model is a network which is only initialized, the weight needs to be learned and adjusted according to data samples obtained in the using process, and therefore the self-learning function of the system is designed. Under the condition of appointing learning samples and quantity, the system can carry out self-learning so as to continuously improve the network performance.
Step 3, the service module judges according to the output security level; the 1 st risk level is a safety state, and is right the vehicle to be monitored does not need to take measures, the 2 nd risk level is a warning state, and is right the vehicle to be monitored needs to take monitoring and early warning, the 3 rd risk level is a dangerous state, and is right the vehicle to be monitored needs to prompt early warning measures, the 4 th risk level is a high-risk level, and is right the vehicle to be monitored needs to take emergency warning measures.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable to various fields of endeavor for which the invention may be embodied with additional modifications as would be readily apparent to those skilled in the art, and the invention is therefore not limited to the details given herein and to the embodiments shown and described without departing from the generic concept as defined by the claims and their equivalents.
Claims (4)
1. The remote diagnosis method for the electric control vehicle is characterized by comprising the following steps:
the method comprises the steps of firstly, collecting data of a vehicle to be monitored, obtaining a corresponding preset risk assessment index tau according to the data of the vehicle to be monitored, and when tau is larger than or equal to tauSThen, carrying out risk assessment on the vehicle to be monitored; wherein, tauSComparing the risk assessment indicators;
step two, collecting the speed of the vehicle to be monitored, the pressure of the wheel tire, the water temperature in the water tank and the rotating speed of a cooling fan, processing the preset risk evaluation index to obtain a risk evaluation index xi, and when xi is more than or equal to xiSThen, judging the risk state of the vehicle to be monitored; wherein ξSComparing the risk assessment indices;
thirdly, judging the risk state of the vehicle to be monitored according to the speed of the vehicle to be monitored, the tire pressure of the wheels, the water temperature in the water tank, the rotating speed of the cooling fan and the risk evaluation index, so as to monitor the vehicle to be monitored;
in the first step, the calculation process of the preset risk assessment index τ is as follows:
wherein, kappa is a correction coefficient, S is the number of years of use of the vehicle to be monitored, S is the total kilometer number of running of the vehicle to be monitored, S' is the number of kilometers of running of the vehicle after the last maintenance, f is the number of times of vehicle maintenance, and t is the time from the last maintenance of the vehicle to be monitored to the present; tau isSThe value is 1.08;
the risk assessment index xi calculation process is as follows:
wherein V is the speed of the vehicle to be monitored, P is the tire pressure of the vehicle to be monitored, T is the water temperature in the water tank of the vehicle to be monitored, N is the rotating speed of the cooling fan, and V is0For comparison of vehicle speeds, P0For comparison of tire pressures of the wheels, N0For comparing the rotational speeds, T, of the cooling fans0And e is the base number of the natural logarithm when comparing the water temperature in the water tank.
2. The method for remotely diagnosing the electrically controlled vehicle according to claim 1, wherein the step three for determining the risk status by building a BP neural network model comprises the steps of:
step 1, acquiring the speed V of a vehicle to be monitored, the tire pressure P of the vehicle, the water temperature T in a water tank and the rotating speed N of a cooling fan according to a sampling period, and determining a risk evaluation index xi;
step 2, normalizing the parameters in sequence, and determining an input layer neuron vector x ═ x of the three-layer BP neural network1,x2,x3,x4,x5In which x1For the speed coefficient, x, of the vehicle to be monitored2Coefficient of wheel pressure, x, for a vehicle to be monitored3Water temperature coefficient, x, in the water tank of a vehicle to be monitored4For the coefficient of rotation, x, of the radiator fan of the vehicle to be monitored5Evaluating an index coefficient for risk;
and 3, mapping the input layer vector to a hidden layer, wherein the hidden layer vector y is { y ═ y1,y2,…,ymM is the number of hidden nodes;
and 4, obtaining an output layer neuron vector o ═ o1,o2,o3,o4}; wherein o is1To a set 1 st risk level, o2To a set 2 nd risk level, o3To a set risk level of 3, o4For a set 4 th risk level, the output layer neuron value is
k is the output layer neuron sequence number, k is {1,2,3,4}, i is the set ith risk level, i is {1,2,3,4}, and when o iskIs 1, at this time, the vehicle to be monitored is at okA corresponding risk level;
step 5, the service module judges according to the output security level; the 1 st risk level is a safety state, and is right the vehicle to be monitored does not need to take measures, the 2 nd risk level is a warning state, and is right the vehicle to be monitored needs to take monitoring and early warning, the 3 rd risk level is a dangerous state, and is right the vehicle to be monitored needs to prompt early warning measures, the 4 th risk level is a high-risk level, and is right the vehicle to be monitored needs to take emergency warning measures.
3. The electronically controlled vehicle remote diagnosis method according to claim 2,
the number m of the hidden layer nodes meets the following conditions:
wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
4. The electronically controlled vehicle remote diagnosis method according to claim 3,
the tire pressure P of the vehicle to be monitored is the average tire pressure of four wheels.
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| CN111006692A (en) * | 2019-11-29 | 2020-04-14 | 东风商用车有限公司 | Method for rapidly correcting vehicle speed and mileage of commercial vehicle instrument based on development of diagnostic instrument |
| CN114740913B (en) * | 2021-01-07 | 2023-11-17 | 广州汽车集团股份有限公司 | Control method and device for vehicle temperature control module |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102074956A (en) * | 2011-02-11 | 2011-05-25 | 华北电网有限公司 | Power grid risk management method and system |
| CN107272649A (en) * | 2017-07-05 | 2017-10-20 | 东南（福建）汽车工业有限公司 | Malfunction remote diagnosis system and method |
| CN107784407A (en) * | 2016-08-25 | 2018-03-09 | 大连楼兰科技股份有限公司 | Vehicle risk monitoring system, platform and method based on cloud platform |
| CN107845039A (en) * | 2016-09-20 | 2018-03-27 | 得道车联网络科技（上海）有限公司 | A kind of adaptive car networking vehicle insurance Rating Model of scale free |
| CN109109787A (en) * | 2018-07-24 | 2019-01-01 | 辽宁工业大学 | A kind of vehicle running fault monitoring method |
| CN109583728A (en) * | 2018-11-19 | 2019-04-05 | 广州供电局有限公司 | Appraisal procedure, device, computer equipment and the storage medium of power supply car state |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10748451B2 (en) * | 2016-12-15 | 2020-08-18 | Duke University | Methods and systems for generating fluid simulation models |
| CN107341865A (en) * | 2017-07-04 | 2017-11-10 | 北京工业大学 | Vehicle condition remote diagnosis method based on smart mobile phone in shared automobile management platform |
-
2019
- 2019-04-24 CN CN201910332114.9A patent/CN109886439B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102074956A (en) * | 2011-02-11 | 2011-05-25 | 华北电网有限公司 | Power grid risk management method and system |
| CN107784407A (en) * | 2016-08-25 | 2018-03-09 | 大连楼兰科技股份有限公司 | Vehicle risk monitoring system, platform and method based on cloud platform |
| CN107845039A (en) * | 2016-09-20 | 2018-03-27 | 得道车联网络科技（上海）有限公司 | A kind of adaptive car networking vehicle insurance Rating Model of scale free |
| CN107272649A (en) * | 2017-07-05 | 2017-10-20 | 东南（福建）汽车工业有限公司 | Malfunction remote diagnosis system and method |
| CN109109787A (en) * | 2018-07-24 | 2019-01-01 | 辽宁工业大学 | A kind of vehicle running fault monitoring method |
| CN109583728A (en) * | 2018-11-19 | 2019-04-05 | 广州供电局有限公司 | Appraisal procedure, device, computer equipment and the storage medium of power supply car state |
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