CN113465935A - Vehicle cooling circuit detection method and device, computer equipment and storage medium - Google Patents
Vehicle cooling circuit detection method and device, computer equipment and storage medium Download PDFInfo
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- CN113465935A CN113465935A CN202010246116.9A CN202010246116A CN113465935A CN 113465935 A CN113465935 A CN 113465935A CN 202010246116 A CN202010246116 A CN 202010246116A CN 113465935 A CN113465935 A CN 113465935A
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M17/00—Testing of vehicles
- G01M17/007—Wheeled or endless-tracked vehicles
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- G—PHYSICS
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- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
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- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P21/00—Testing or calibrating of apparatus or devices covered by the preceding groups
- G01P21/02—Testing or calibrating of apparatus or devices covered by the preceding groups of speedometers
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Abstract
The application provides a method, a device, a computer device and a storage medium for detecting a cooling loop of a vehicle, which are characterized in that a target operation parameter and related operation parameters in the cooling loop are obtained, the target operation parameter and the related operation parameters are input into a preset regression model to obtain a target operation parameter predicted value, the difference value between the target operation parameter predicted value and the target operation parameter is compared with a preset range, whether the detection is normal or not is judged according to the comparison result, an abnormity judgment mechanism is established according to the regression model, when the deviation between the predicted value and the actual value of the target operation parameter in the cooling loop is detected to be overlarge, an abnormity prompt is sent out, the change condition of the target operation parameter in the cooling loop is considered, the related operation parameters which are related to the target operation parameter are also considered, and the abnormity judgment is carried out through a single operation parameter in the prior art, the abnormal situation can be more accurately judged.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to a method and a device for detecting a vehicle cooling circuit, computer equipment and a storage medium.
Background
In the prior art, vehicle cooling system early warning systems are arranged in vehicles, each vehicle cooling system early warning system comprises a sensor arranged on a cooling system, a vehicle-mounted controller connected with an output end of the sensor, and a prompting device connected with an output end of the vehicle-mounted controller, the sensors are used for monitoring operating parameters of the cooling system in real time and sending the operating parameters to the vehicle-mounted controller, the vehicle-mounted controller judges through comparison with threshold values of various parameters based on the operating parameters, detects abnormal types of the cooling system, and prompts through the prompting devices.
In the above technology, a threshold is set for a specific parameter, the real-time operation parameter of each parameter is compared with the threshold, and if the real-time operation parameter is lower than or exceeds the set threshold, an abnormal prompt is given, for example: and judging that the temperature of the anti-freezing cooling liquid is too hot by the vehicle control unit if the temperature of the anti-freezing cooling liquid detected by the temperature sensor is higher than a first temperature preset value. In the prior art, only one parameter is subjected to abnormity judgment when a cooling system is detected, the interaction and influence among the parameters cannot be comprehensively considered, abnormal parameters which do not reach a threshold value cannot be effectively detected, and a certain error exists in abnormity reminding.
Disclosure of Invention
The embodiment of the invention provides a method and a device for detecting a cooling circuit of a vehicle, computer equipment and a storage medium, which aim to solve the problem that in the prior art, when abnormality detection is carried out on the cooling circuit, only a single parameter is detected, so that the detection result has errors.
The present application provides in a first aspect a vehicle cooling circuit detection method, comprising:
acquiring target operating parameters and related operating parameters thereof in the cooling circuit;
inputting the target operation parameters and relevant operation parameters thereof into a preset regression model to obtain target operation parameter predicted values;
and judging whether the difference value between the target operation parameter and the target operation parameter predicted value is within a preset range, if so, judging that the detection result is normal, otherwise, judging that the detection result is abnormal and sending a prompt.
This application second aspect provides a train speed measuring equipment detection device, includes:
the data acquisition module is used for acquiring target operation parameters and relevant operation parameters in the cooling circuit;
the data prediction module is used for inputting the target operation parameters and the related operation parameters thereof into a preset regression model to obtain predicted values of the target operation parameters;
And the diagnosis and analysis module is used for judging whether the difference value between the target operation parameter and the target operation parameter predicted value is within a preset range, if so, judging that the detection result is normal, otherwise, judging that the detection result is abnormal and sending a prompt.
A third aspect of the application provides a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect of the invention when executing the computer program.
A fourth aspect of the present application provides a computer readable storage medium having stored thereon a computer program for performing, when executed by a processor, the steps of the method according to the first aspect of the present invention.
The application provides a method, a device, a computer device and a storage medium for detecting a cooling loop of a vehicle, which are characterized in that a target operation parameter and related operation parameters in the cooling loop are obtained, the target operation parameter and the related operation parameters are input into a preset regression model to obtain a target operation parameter predicted value, the difference value between the target operation parameter predicted value and the target operation parameter is compared with a preset range, whether the detection is normal or not is judged according to the comparison result, the technical scheme of the application establishes the regression model according to the target operation parameter and the related operation parameters in advance, an abnormity judgment mechanism is established according to the regression model, when the deviation between the predicted value and the actual value of the target operation parameter in the cooling loop is detected to be overlarge, an abnormity prompt is sent out, the change condition of the target operation parameter in the cooling loop is considered, and the related operation parameters related to the target operation parameter are also considered, compared with the prior art that the abnormity is judged through a single operation parameter, the abnormity can be more accurately judged.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a flow chart of a vehicle cooling circuit detection method in accordance with one embodiment of the present invention;
FIG. 2 is another flow chart of a method of vehicle cooling circuit detection in an embodiment of the present invention;
FIG. 3 is a fragmentary, truncated view of a regression residual map of raw data for a vehicle cooling circuit, in accordance with an embodiment of the present invention;
FIG. 4 is a graph of a fitted residual of coolant temperature for a vehicle cooling circuit in accordance with an embodiment of the present invention;
FIG. 5 is a fragmentary, schematic illustration of a regression model S2 for a vehicle cooling circuit, according to an embodiment of the present invention;
FIG. 6 is a fragmentary, truncated schematic view of another effect of the regression model S2 of a vehicle cooling circuit in accordance with an embodiment of the present invention;
FIG. 7 is a schematic structural diagram of a vehicle cooling circuit detection device according to an embodiment of the present invention;
FIG. 8 is another schematic diagram of a vehicle cooling circuit detection device according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a computer device according to an embodiment of the 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 some, not all, embodiments of the present invention. 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 vehicle cooling circuit detection method provided by the embodiment of the application can be applied to a cooling circuit detection system of an automobile or an electric vehicle, is used for jointly detecting the target operation parameters and the related operation parameters in the vehicle cooling circuit, and avoids the problem that the detection result has errors when only a single parameter is subjected to abnormal judgment during detection of the cooling circuit in the prior art.
In one embodiment, as shown in fig. 1, there is provided a vehicle cooling circuit detection method, including:
and S101, acquiring target operation parameters and relevant operation parameters in the cooling circuit.
Wherein, the cooling circuit is used for keeping the engine or the motor in the vehicle in a proper temperature range under all working conditions, the cooling circuit is used for preventing the engine or the motor from overheating and preventing the engine from overcooling in winter, the cooling circuit can be divided into an air cooling circuit and a water cooling circuit according to different cooling media, the circuit which directly radiates the heat of high-temperature parts in the engine or the motor into the atmosphere for cooling is called an air cooling circuit, the circuit which firstly transfers the heat to cooling water and then radiates the heat into the atmosphere for cooling is called a water cooling circuit, the operating parameters are acquired during operation of the cooling circuit by means of a parameter sensor in the cooling circuit, for example, for a water-cooled circuit, the operating parameters in the water-cooled circuit may include coolant temperature, retarder oil temperature, IGBT temperature, radiator temperature, and armature winding temperature.
The target operation parameters refer to operation parameters to be detected in the cooling circuit, the related operation parameters refer to operation parameters with high correlation with the target operation parameters, correlation exists between different operation parameters in the cooling circuit, the correlation between the operation parameters can be obtained according to a correlation matrix, when a correlation coefficient between the two operation parameters is larger than a preset value, the two operation parameters are considered to have strong correlation, for example, the correlation coefficient of the two operation parameters is larger than 0.7, and all the operation parameters with the target operation parameters can be obtained according to the correlation matrix, for example, the operation parameters of the reducer oil temperature, the IGBT temperature, the radiator temperature and the armature winding temperature in the water cooling circuit are related operation parameters of the cooling liquid temperature.
In step S101, target operating parameters and related operating parameters in the cooling circuit are obtained, which previously further includes:
and acquiring target operation parameters and related operation parameters of the cooling circuit in a normal working state, and acquiring a regression model according to the target operation parameters and the related operation parameters.
The regression model is constructed according to the operation parameters of the cooling circuit under normal conditions, and according to the target operation parameters and the correlation matrix between the target operation parameters, the fact that the correlation between the target operation parameters and the correlation operation parameters is strong can be known, and further the problem that multiple collinearity and autocorrelation exist between the target operation parameters and the correlation operation parameters, therefore, the multiple collinearity and autocorrelation need to be eliminated in the process of constructing the regression model.
And S102, inputting the target operation parameters and the relevant operation parameters into a preset regression model to obtain the predicted values of the target operation parameters.
As an implementation manner, for a preset regression model, obtaining the regression model according to the target operating parameters and the related operating parameters thereof includes:
and carrying out iterative operation and regression fitting on the target operation parameters and the related operation parameters to obtain a regression model.
Wherein, in order to eliminate multiple collinearity and autocorrelation, an iterative method and regression fitting can be adopted to obtain a regression model, the iteration is a process of solving a problem (generally solving an equation or an equation set) by searching a series of approximate solutions from initial estimation in numerical analysis, and a method used for realizing the process is generally called asIs an iterative method. For example: for a given system of linear equations xK+1＝BxK+ f (x, B, f are matrices, and any system of linear equations can be transformed into this form, xKRepresenting x obtained by iteration k times, and initially k being 0) is gradually substituted into a method for solving an approximate solution, which is called an iteration method, and a regression model can be obtained by performing regression fitting through a fitting equation after performing iteration operation for preset times.
In this embodiment, the regression model is obtained by performing iterative operation and regression fitting on the target operating parameters and the related operating parameters thereof, so that multiple collinearity and autocorrelation existing between the target operating parameters and the related operating parameters thereof can be eliminated.
As another embodiment, the obtaining a regression model according to the target operating parameters and the related operating parameters includes:
and carrying out differential transformation and regression fitting on the target operation parameters and the related operation parameters to obtain a regression model.
The target operation parameters and the related operation parameters can be converted by adopting a generalized difference method, wherein the generalized difference method is to convert an original model into a difference model meeting the least square method, then carry out the lowest multiplication estimation, and is provided with a unary linear model, yt＝β0+β1xt+μt；
There is a first order autocorrelation mut＝ρμt-1+Vt
Wherein, VtPerturbation terms to satisfy the underlying assumptions
The model is delayed for a period of time: y ist-1＝β0+β1x1，t-1+μt-1
Multiplying rho on both sides of the equation and subtracting from the original model to obtain:
yt-ρyt-1＝β0(1-ρ)+β1(xt-ρxt-1)+μt-1
finishing to obtain:
yt＝β0(1-ρ)+ρyt-1+β1xt-ρβ1xt-1+μt，
and carrying out least square method differential estimation on the model to obtain an estimated value of rho, carrying out generalized differential transformation on the original model by using the estimated value of rho, carrying out regression fitting to obtain a regression model, and inputting the obtained target operation parameters and related operation parameters into the regression model to obtain a target operation parameter predicted value.
And S103, judging whether the difference value between the target operation parameter and the target operation parameter predicted value is within a preset range, if so, executing the step S104, and if not, executing the step S105.
And S104, judging that the detection result is normal.
After the detection result is normal, step S101 may be executed again after a preset time interval, for example, repeatedly executed once after timing 2 minutes.
And S105, judging that the detection result is abnormal and sending a prompt.
Wherein, judge that the testing result is unusual and send the suggestion, include:
and when the detection result is judged to be abnormal, acquiring and storing abnormal information, and prompting the abnormal information through a preset early warning path.
Wherein the exception information includes: abnormal content, abnormal position, abnormal time, possible consequences, guidance measures and the like, wherein the early warning path comprises a short message reminding path, a PC page reminding path and a work order distribution path, and the prompting mode comprises the following steps: the method comprises the following steps of short message reminding, PC page early warning detail and icon reminding, automatic generation and distribution of APP maintenance work orders, and abnormal reason verification and vehicle maintenance are carried out by maintenance personnel according to the early warning reminding and the work order contents.
The application provides a method for detecting a cooling circuit of a vehicle, which comprises the steps of obtaining target operation parameters and relevant operation parameters in the cooling circuit, inputting the target operation parameters and the relevant operation parameters into a preset regression model to obtain target operation parameter predicted values, comparing the difference value between the target operation parameter predicted values and the target operation parameters with a preset range, and judging whether the detection is normal or not according to the comparison result, wherein the technical scheme comprises the steps of constructing the regression model according to the target operation parameters and the relevant operation parameters in advance, establishing an abnormity judgment mechanism according to the regression model, sending an abnormity prompt when the deviation between the predicted values and the actual values of the target operation parameters in the cooling circuit is detected to be overlarge, considering not only the change condition of the target operation parameters in the cooling circuit, but also considering the relevant operation parameters relevant to the target operation parameters, compared with the prior art that the abnormity is judged through a single operation parameter, the abnormity can be more accurately judged.
The vehicle cooling circuit detection method provided by another embodiment of the application can be applied to a cooling circuit detection system of a rubber-tyred tramcar and used for jointly detecting the target operation parameters and the related operation parameters in the vehicle cooling circuit, and the problem that errors occur in detection results when only single parameters are judged abnormally when the cooling circuit is detected in the prior art is solved.
In one embodiment, as shown in fig. 2, there is provided a vehicle cooling circuit detection method, including:
and S201, obtaining the temperature of cooling liquid, the temperature of oil of the speed reducer, the temperature of oil temperature of the speed reducer, the temperature of IGBT (insulated gate bipolar translator), the temperature of a radiator and the temperature of an armature winding in the cooling loop.
Wherein the target operating parameter is coolant temperature, as shown in table 1 below, and is a correlation matrix of operating parameters in the cooling circuit, coolant temperature, reducer oil temperature, IGBT temperature, radiator temperature, and armature winding temperature, and Y in table 1 represents coolant temperature, and X represents1Representing the oil temperature of the retarder, X2Representing the oil temperature of the reducer, X3Representing IGBT temperature, X4Representing the radiator temperature, X5The correlation coefficient between the coolant temperature and the oil temperature of the reducer is 0.979, the correlation coefficient between the coolant temperature and the oil temperature of the reducer is 0.994, the correlation coefficient between the coolant temperature and the IGBT temperature is 0.986, the correlation coefficient between the coolant temperature and the radiator temperature is 0.761, and the correlation coefficient between the coolant temperature and the radiator temperature is 0.97, respectively 0.902, according to the correlation coefficient value, the related operation parameters of the oil temperature of the speed reducer, the temperature of the IGBT, the temperature of the radiator and the temperature of the armature winding which are the temperature of the cooling liquid can be determined.
| Y | X1 | X2 | X3 | X4 | X5 | |
| Y | 1.000 | 0.979 | 0.994 | 0.986 | 0.761 | 0.902 |
| X1 | 0.979 | 1.000 | 0.974 | 0.973 | 0.728 | 0.884 |
| X2 | 0.994 | 0.974 | 1.000 | 0.983 | 0.789 | 0.884 |
| X3 | 0.986 | 0.973 | 0.983 | 1.000 | 0.746 | 0.900 |
| X4 | 0.761 | 0.728 | 0.789 | 0.746 | 1.000 | 0.518 |
| X5 | 0.902 | 0.884 | 0.884 | 0.900 | 0.518 | 1.000 |
TABLE 1 correlation matrix table of operating parameters in cooling circuits
And S202, inputting the temperature of the cooling liquid, the temperature of the oil of the speed reducer, the temperature of the IGBT (insulated gate bipolar transistor), the temperature of the radiator and the temperature of the armature winding into a preset regression model to obtain a predicted value of the temperature of the cooling liquid.
The regression model is obtained by calculating the temperature of cooling liquid, the temperature of oil of the speed reducer, the temperature of IGBT (insulated gate bipolar translator), the temperature of a radiator and the temperature of an armature winding of the cooling circuit in a normal working state.
Wherein, according to the correlation matrix between the cooling liquid temperature and the oil temperature of the reducer, the IGBT temperature, the radiator temperature and the armature winding temperature in the normal working condition of the cooling circuit in the table 1, it can be seen that the correlation between the coolant temperature and the oil temperature of the reducer, the IGBT temperature and the armature winding temperature is strong, the correlation coefficients are all above 0.9, the correlation coefficient between the coolant temperature and the radiator temperature is also above 0.7, according to the working principle of the cooling circuit, the temperatures of all components have stronger correlation, the condition number K value can be accurately calculated to be 322 by adopting a characteristic root judgment method for judging multiple collinearity, stronger multiple collinearity exists among independent variables, and if an accurate correlation relationship or a high correlation relationship exists between the interpretation variables in the linear regression model, the model estimation is distorted or difficult to estimate accurately.
As shown in fig. 3, the temperature of the cooling liquid in the water tank of the cooling circuit of the rubber-tyred tramcar is intercepted from the image segment of the regression residual error of the original data of the temperature of each component, and it can be seen that after a plurality of positive values, a plurality of negative values appear in the residual error, that is, the residual error does not change its sign frequently with time, and it can be seen that a certain positive autocorrelation exists in the variables in the model. The autocorrelation generally leads to consequences: (1) autocorrelation does not affect the linearity and unbiased performance of the least squares estimate, but makes it less effective. That is, the variance of the least squares estimate is not the minimum, and the estimate is not the optimal linear unbiased estimate; (2) the coefficient estimators of the autocorrelation will have considerable variance; (3) the results of the T test of the autocorrelation coefficients and the F test of the regression equation are not credible; (4) the predictive function of the model fails.
Due to the multiple collinearity and autocorrelation problems of the operating parameters in the cooling circuit, an iterative method may be adopted to eliminate the multiple collinearity and autocorrelation, and as an implementation manner, an iterative model is obtained after two iterations and regression fitting, where the two iterations are as follows:
the first iteration formula is:
wherein, ytThe coolant temperature of the current cycle, y t-1The coolant temperature of the previous cycle, yt' first iteration value of coolant temperature, xi,tThe oil temperature of the reducer, the IGBT temperature, the radiator temperature and the armature winding temperature, x, of the current periodi',tAnd respectively carrying out first iteration values on the oil temperature of the speed reducer, the IGBT temperature, the radiator temperature and the armature winding temperature.
The second iteration formula is:
wherein, y ″)tIs the second iteration value, x ″, of the coolant temperaturei,tAnd respectively carrying out second iteration values on the oil temperature of the speed reducer, the IGBT temperature, the radiator temperature and the armature winding temperature.
Let the dependent variable be y, and k independent variables be X respectively1、X2、……XkDescribing how the dependent variable y depends on the independent variable X1、X2、XkThe equation for the sum error term is called a multiple regression model, which can be expressed in general form as y ═ B0+ B1X1+B2X2+……+BkXk+ E, where BO, B1, B2, … … Bk are parameters of the model; and E is an error term.
Determining a regression model:
firstly, after two times of iteration processing, historical vehicle operation parameter data are input into a regression equation, a normal equation set is obtained through a least square method, the equation set is solved, values of B0, B1, B2, … and Bk are obtained, and a primary expression of the regression equation is obtained.
Secondly, carrying out the following significance test on the regression equation, namely 1) carrying out multiple correlation coefficient test and testing the closeness degree of the linear relationship; 2) testing the goodness of fit, and testing the fitting degree of the regression equation to the observed value; 3) f, checking, namely checking the significance of the regression equation, namely whether the model parameters are all 0; 4) and (5) t-test, wherein the significance of the regression coefficient is tested, namely whether a certain model parameter is 0. 5) Performing self-correlation inspection; 6) and (4) performing multiple collinearity tests. If all the tests pass, a regression model is obtained.
The regression model S1 obtained after two iterations and regression fitting is:
the goodness of fit of the iterative model is calculated to be 0.856, but each prediction of the iterative model depends on the observed values of the first two times.
As another embodiment, a regression model is obtained by performing difference transformation and regression fitting on the target operating parameters and the related operating parameters thereof.
The regression model obtained by generalized differential transformation has the advantages that the estimation value of the autocorrelation parameter can be adjusted for multiple times until a satisfactory result is obtained without losing sample data, the rho value is continuously adjusted, and the rho is 0.888435 to carry out differential transformation and then carry out regression fitting,
Determining a regression model:
firstly, the vehicle historical operation parameter data is subjected to difference processing of 0.888435 and then is input into a regression equation, a normal equation set is obtained through a least square method, the equation set is solved, values of BO, B1, B2 and Bk are obtained, and a primary expression of the regression equation is obtained.
Secondly, carrying out the following significance test on the regression equation, namely 1) carrying out multiple correlation coefficient test and testing the closeness degree of the linear relationship; 2) testing the goodness of fit, and testing the fitting degree of the regression equation to the observed value; 3) f, checking, namely checking the significance of the regression equation, namely whether the model parameters are all 0; 4) t, testing, namely testing the significance of the regression coefficient, namely whether a certain model parameter is 0; 5) performing self-correlation inspection; 6) and (4) performing multiple collinearity tests. If all the tests pass, a final regression model is obtained.
The optimized regression model S2 is obtained as:
as shown in fig. 4, it can be seen that the autocorrelation of the regression model S2 of the present embodiment is more significantly eliminated than that of the regression model S1 of the previous embodiment, and the goodness of fit of the regression model S2 is 0.9619, which is higher than that of the regression model S1, and the regression model S2 does not need to obtain the observed values of the first two times, which is simpler and easier to use than the regression model S1, as compared with the regression model S3578, after ρ is 0.814325 for each temperature of the cooling circuit of the rubber-tyred tramcar.
As shown in fig. 5 and 6, the effect segment of the regression model S2 of the cooling circuit of the rubber-tyred tramcar is cut out, and the predicted values and the actual values of the coolant temperatures in the training set and the test set are substantially coincident with each other, so that it is known that the model has a superior prediction effect, and therefore, a cooling circuit abnormality diagnosis mechanism is formulated based on the regression model S2.
And S203, judging whether the difference value between the coolant temperature and the predicted coolant temperature is within a preset range, if so, executing S204, otherwise, executing S205.
And S204, judging that the detection result is normal.
And S205, judging that the detection result is abnormal and sending a prompt.
And judging the deviation between the predicted value and the actual value of the cooling liquid temperature, finishing the diagnosis if the difference between the predicted value and the actual value is within 2, otherwise, writing the information of the name, the abnormal position, the occurrence time, the duration, the abnormal content and the like of the current vehicle into a database early warning table t _ car _ pre _ war _ last _ v1 for recording if the diagnosis result is abnormal. The abnormal information is written into the database, meanwhile, the short message system is triggered, the early warning information is directionally sent to a responsible engineer, meanwhile, the PC end carries out alternate broadcasting of the early warning information, the APP end automatically generates a maintenance work order and directionally distributes the maintenance work order according to the personnel setting. The above process steps are performed by the computer timing task every 2 minutes.
According to the scheme, a multiple regression model is constructed according to the temperature data of the cooling liquid and the temperature data of each working component under the historical normal working condition of the rubber-tyred tramcar, a cooling circuit abnormity diagnosis mechanism is established according to a model result, when the deviation between a predicted value and a true value of the temperature of the cooling liquid obtained by the real-time temperature of each component of the cooling circuit is too large, the current operation parameter of the cooling circuit is diagnosed to be abnormal, and the function and the service life of each component of the cooling circuit are guaranteed to a certain extent by prompting in a short message mode, a PC mode and an APP mode at the first time.
Another embodiment of the present application provides a vehicle cooling circuit detection apparatus, as shown in fig. 7, including:
a data acquisition module 10 for acquiring target operating parameters and related operating parameters thereof in the cooling circuit;
the data prediction module 20 is used for inputting the target operation parameters and the related operation parameters thereof into a preset regression model to obtain predicted values of the target operation parameters;
and the diagnosis and analysis module 30 is configured to determine whether a difference between the target operation parameter and the target operation parameter predicted value is within a preset range, if so, determine that the detection result is normal, otherwise, determine that the detection result is abnormal, and send a prompt.
Further, as shown in fig. 8, the vehicle cooling circuit detecting apparatus further includes a data modeling module 40, where the data modeling module 40 is configured to:
and acquiring target operation parameters and related operation parameters of the cooling circuit in a normal working state, and acquiring a regression model according to the target operation parameters and the related operation parameters.
The data modeling module 40 is further configured to:
and carrying out iterative operation and regression fitting on the target operation parameters and the related operation parameters to obtain a regression model.
The data modeling module 40 is further configured to:
and carrying out differential transformation and regression fitting on the target operation parameters and the related operation parameters to obtain a regression model.
When the target operating parameter is coolant temperature, the data acquisition module 30 is configured to:
and acquiring the temperature of cooling liquid in the cooling loop, the temperature of speed reducer oil, the temperature of IGBT (insulated gate bipolar translator), the temperature of a radiator and the temperature of an armature winding.
The data modeling module 40 is further configured to:
carrying out the following two iterations on the temperature of the cooling liquid, the temperature of the oil of the speed reducer, the temperature of the IGBT, the temperature of the radiator and the temperature of the armature winding to obtain an iteration model;
wherein, the first iteration formula is as follows:
the second iteration formula is:
the iterative model is:
The data modeling module is also used for carrying out difference transformation and regression fitting on the target operation parameters and the related operation parameters to obtain a regression model as follows:
for specific limitations of the vehicle cooling circuit detection device, reference may be made to the above limitations of the vehicle cooling circuit detection method, which are not described in detail herein. The respective modules in the vehicle cooling circuit detection apparatus described above may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store data used in the vehicle cooling circuit detection method of the above-described embodiment. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a vehicle cooling circuit detection method.
In one embodiment, a computer device is provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the vehicle cooling circuit detection method in the above embodiments when executing the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, carries out the vehicle cooling circuit detection method in the above-mentioned embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A vehicle cooling circuit detection method, comprising:
acquiring target operating parameters and related operating parameters thereof in the cooling circuit;
Inputting the target operation parameters and relevant operation parameters thereof into a preset regression model to obtain target operation parameter predicted values;
and judging whether the difference value between the target operation parameter and the target operation parameter predicted value is within a preset range, if so, judging that the detection result is normal, otherwise, judging that the detection result is abnormal and sending a prompt.
2. The vehicle cooling circuit detection method as set forth in claim 1, wherein the obtaining of the target operating parameter and its associated operating parameter in the cooling circuit further comprises:
and acquiring target operation parameters and related operation parameters of the cooling circuit in a normal working state, and acquiring a regression model according to the target operation parameters and the related operation parameters.
3. The vehicle cooling circuit detection method of claim 2, wherein said deriving a regression model from said target operating parameters and associated operating parameters comprises:
and carrying out iterative operation and regression fitting on the target operation parameters and the related operation parameters to obtain a regression model.
4. The vehicle cooling circuit detection method of claim 2, wherein said deriving a regression model from said target operating parameters and associated operating parameters comprises:
And carrying out differential transformation and regression fitting on the target operation parameters and the related operation parameters to obtain a regression model.
5. The vehicle cooling circuit detection method according to claim 3 or 4, wherein when the target operation parameter is the coolant temperature, the obtaining of the target operation parameter and the relevant operation parameter in the cooling circuit includes:
and acquiring the temperature of cooling liquid, the temperature of oil of the speed reducer, the temperature of IGBT (insulated gate bipolar translator), the temperature of a radiator and the temperature of an armature winding in the cooling loop.
6. The vehicle cooling circuit detection method according to claim 1, wherein the determining that the detection result is abnormal and issuing a prompt includes:
and when the detection result is judged to be abnormal, acquiring and storing abnormal information, and prompting the abnormal information through a preset early warning path.
7. A vehicle cooling circuit detection device, comprising:
the data acquisition module is used for acquiring target operation parameters and relevant operation parameters in the cooling circuit;
the data prediction module is used for inputting the target operation parameters and the related operation parameters thereof into a preset regression model to obtain predicted values of the target operation parameters;
And the diagnosis and analysis module is used for judging whether the difference value between the target operation parameter and the target operation parameter predicted value is within a preset range, if so, judging that the detection result is normal, otherwise, judging that the detection result is abnormal and sending a prompt.
8. The vehicle cooling circuit detection device according to claim 7, further comprising:
and the data modeling module is used for acquiring target operation parameters and relevant operation parameters of the cooling circuit in a normal working state and acquiring a regression model according to the target operation parameters and the relevant operation parameters.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 6 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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