Vehicle state judgment method, device, equipment and readable storage medium

Abstract

The present application relates to a vehicle state judgment method, device, equipment and readable storage medium, and relates to the field of vehicle performance test technology, including obtaining GPS data and three-axis acceleration data at the current moment; calculating a first acceleration based on GPS data, and calculating a second acceleration based on three-axis acceleration data; when it is detected that the difference between the first acceleration and the second acceleration is greater than a preset difference threshold, the second acceleration is used as the actual acceleration of the vehicle at the current moment; filtering the GPS data at the current moment based on a Kalman filter algorithm to obtain an estimated vehicle speed at the current moment; determining the vehicle state at the current moment based on the actual acceleration, the estimated vehicle speed, a preset critical acceleration and a preset critical speed. The present application improves the accuracy of the acceleration and vehicle speed obtained based on GPS data through three-axis acceleration and a Kalman filter algorithm, thereby improving the accuracy of vehicle state judgment.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle performance testing, and in particular to a vehicle state determination method, device, equipment and readable storage medium. Background Art

[0002] With the rapid development of the economy and the continuous improvement of people's living standards, vehicles have become an indispensable means of transportation for every household. Therefore, the performance and safety of vehicles are closely related to every family. Therefore, in order to ensure the performance and safety of vehicles, relevant performance tests (such as speed test, consumption test, etc.) are required after the vehicles roll off the assembly line and leave the factory, and they can enter the market only after all performance requirements are met.

[0003] In the related art, VBox speed measuring equipment is often used to perform related performance tests, such as calculating the distance between two points based on the latitude and longitude of this GPS data and the latitude and longitude of the last GPS data, and then calculating the speed based on the time of the two points, and then judging the vehicle status based on the speed. Among them, GPS (Global Positioning System) is one of the most mature satellite navigation and positioning systems at present, so it is widely used in the field of automotive electronics. However, due to the high positioning requirements of the GPS navigation system, and its signal anti-interference ability and signal strength are relatively weak, it is often affected by high-rise buildings and trees, viaducts, metal, radio waves, weather, obstacles, etc. For example, high-rise buildings and trees will become reflectors, interfering with the reception of satellite signals, and when the GPS signal passes through the edge of an obstacle, the direct signal will bend to produce an extra path, and metal objects (such as lightning rods, metal brackets or power lines) will also have a greater impact on the GPS signal, that is, multiple factors will affect the accuracy of GPS positioning, so that the GPS data received by the VBox speed measuring device may drift, and thus the accuracy of the vehicle status judgment result cannot be guaranteed.

[0004] In addition, since GPS longitude and latitude are often converted to distances in decimal format, there is a certain error between the speed calculated by the VBox speed measuring device and the estimated speed. It can be seen that GPS data drift and the conversion of GPS longitude and latitude to approximate distances will have a significant impact on electronic devices that rely on GPS navigation and positioning systems when judging vehicle status, making it impossible to accurately judge the vehicle status. Summary of the invention

[0005] The present application provides a vehicle status determination method, device, equipment and readable storage medium to solve the problem in the related art that the vehicle status cannot be accurately determined.

[0006] In a first aspect, a vehicle state determination method is provided, comprising the following steps:

[0007] Get the current GPS data and the current three-axis acceleration data;

[0008] Calculate a first acceleration based on the GPS data at the current moment, and calculate a second acceleration based on the three-axis acceleration data at the current moment;

[0009] When it is detected that the difference between the first acceleration and the second acceleration is greater than a preset difference threshold, taking the second acceleration as the actual acceleration of the vehicle at the current moment;

[0010] Performing filtering processing on the GPS data at the current moment based on a Kalman filter algorithm to obtain an estimated value of the vehicle speed at the current moment;

[0011] The current state of the vehicle is determined based on the actual acceleration, the estimated vehicle speed, a preset critical acceleration and a preset critical speed.

[0012] In some embodiments, the preset critical acceleration is the acceleration corresponding to the critical point from the stationary state to the starting state or the acceleration corresponding to the critical point from the running state to the stationary state, and the preset critical speed is the speed corresponding to the critical point from the stationary state to the starting state or the speed corresponding to the critical point from the running state to the stationary state.

[0013] In some embodiments, when the preset critical acceleration is the acceleration corresponding to the critical point of the vehicle from a stationary state to a starting state, and the preset critical speed is the speed corresponding to the critical point of the vehicle from a stationary state to a starting state, the determining of the vehicle state at the current moment based on the actual acceleration, the estimated vehicle speed, the preset critical acceleration and the preset critical speed includes:

[0014] When the actual acceleration is greater than a preset critical acceleration and the estimated vehicle speed is greater than a preset critical speed, it is determined that the vehicle is currently in a start state;

[0015] When the actual acceleration is less than or equal to a preset critical acceleration or the estimated vehicle speed is less than or equal to a preset critical speed, it is determined that the vehicle is currently in a stationary state.

[0016] In some embodiments, when the preset critical acceleration is the acceleration corresponding to the critical point of the vehicle from the running state to the stationary state, and the preset critical speed is the speed corresponding to the critical point of the vehicle from the running state to the stationary state, the determining the state of the vehicle at the current moment based on the actual acceleration, the estimated vehicle speed, the preset critical acceleration and the preset critical speed includes:

[0017] When the actual acceleration is greater than a preset critical acceleration and the estimated vehicle speed is greater than a preset critical speed, it is determined that the vehicle is currently in a running state;

[0018] When the actual acceleration is less than or equal to a preset critical acceleration or the estimated vehicle speed is less than or equal to a preset critical speed, it is determined that the vehicle is currently in a stationary state.

[0019] In some embodiments, after the step of determining that the vehicle is currently in a running state when the actual acceleration is greater than a preset critical acceleration and the estimated vehicle speed is greater than a preset critical speed, the step further includes:

[0020] Based on the Kalman filter algorithm, the current GPS data is filtered to obtain the optimal GPS data at the current moment;

[0021] Calculate a first driving distance based on the optimal state GPS data at the current moment and the optimal state GPS data at the previous moment;

[0022] Based on the median average filtering method, the driving distances corresponding to M historical moments and the first driving distance are filtered to obtain the arithmetic mean of the driving distances, and the arithmetic mean of the driving distances is used as the actual driving distance at the current moment, where M is a positive integer.

[0023] In some embodiments, after the step of taking the arithmetic mean of the driving distance as the actual driving distance at the current moment, the method further includes:

[0024] The basic data corresponding to each moment is stored in a preset storage order to obtain a storage list, wherein the basic data includes an estimated vehicle speed, an actual driving distance and time;

[0025] Receiving specific point information to be queried input by the user;

[0026] Traversing the storage list according to the specific point information to be queried to obtain the associated information of the specific point to be queried;

[0027] Calculate the state information to be displayed corresponding to the specific point to be queried based on the association information of the specific point to be queried, the state information to be displayed includes the estimated vehicle speed and / or the actual travel distance and / or time information;

[0028] The status information to be displayed is displayed.

[0029] In some embodiments, the preset storage order includes an increasing storage order and a decreasing storage order.

[0030] In a second aspect, a vehicle state determination device is provided, comprising:

[0031] An acquisition unit, which is used to acquire the current GPS data and the current three-axis acceleration data;

[0032] A calculation unit, configured to calculate a first acceleration based on the GPS data at the current moment, and to calculate a second acceleration based on the three-axis acceleration data at the current moment;

[0033] a detection unit, configured to use the second acceleration as the actual acceleration of the vehicle at a current moment when detecting that the difference between the first acceleration and the second acceleration is greater than a preset difference threshold;

[0034] A filtering unit, which is used to filter the GPS data at the current moment based on a Kalman filtering algorithm to obtain an estimated value of the vehicle speed at the current moment;

[0035] A determination unit is used to determine the current state of the vehicle based on the actual acceleration, the estimated vehicle speed, a preset critical acceleration and a preset critical speed.

[0036] In a third aspect, a vehicle state judgment device is provided, comprising: a memory and a processor, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the aforementioned vehicle state judgment method.

[0037] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer storage medium stores a computer program, and when the computer program is executed by a processor, the aforementioned vehicle state determination method is implemented.

[0038] The beneficial effects brought about by the technical solution provided by this application include: it can effectively improve the accuracy of vehicle status judgment.

[0039] The present application provides a vehicle state judgment method, device, equipment and readable storage medium, including obtaining GPS data and three-axis acceleration data at the current moment; calculating a first acceleration based on the GPS data at the current moment, and calculating a second acceleration based on the three-axis acceleration data at the current moment; when it is detected that the difference between the first acceleration and the second acceleration is greater than a preset difference threshold, the second acceleration is used as the actual acceleration of the vehicle at the current moment; filtering the GPS data at the current moment based on the Kalman filter algorithm to obtain the estimated vehicle speed at the current moment; determining the vehicle state at the current moment based on the actual acceleration, the estimated vehicle speed, the preset critical acceleration and the preset critical speed. The present application corrects the acceleration calculated based on the GPS data by the three-axis acceleration, and corrects the speed in the GPS data by the Kalman filter algorithm to improve the accuracy of the acceleration and vehicle speed obtained based on the GPS data, and finally accurately judges the current state of the vehicle according to the actual acceleration and the estimated vehicle speed. It can be seen that the present application can effectively improve the accuracy of vehicle state judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 A flow chart of a vehicle state determination method provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of the structure of a vehicle state determination device provided in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of the structure of a vehicle status judgment device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0045] The embodiments of the present application provide a vehicle status determination method, device, equipment and readable storage medium, which can solve the problem in the related art that the vehicle status cannot be accurately determined.

[0046] Figure 1 A vehicle state determination method provided in an embodiment of the present application comprises the following steps:

[0047] Step S10: Obtaining the current GPS data and the current three-axis acceleration data;

[0048] For example, in this embodiment, after the system equipment is powered on, the vehicle test can be controlled through the mobile terminal. When the tester conducts the test, he first needs to select the test mode and fill in the relevant parameters, and submit the changed configuration data to the server, that is, send the content of the request to change the configuration to the server, thereby triggering the trigger switch of the device and starting the vehicle test. The tester can view the real-time vehicle status, speed, driving distance, driving time, acceleration, GPS positioning status and other information through the mobile terminal.

[0049] However, when the vehicle is stationary, the vehicle's GPS longitude, latitude, altitude, speed and other values ​​should theoretically remain unchanged. However, due to GPS drift errors and vehicle jitter before starting, the speed received from the GPS server is not zero when the vehicle is stationary. Therefore, using a speed value of 0km / h as a judgment of the vehicle's movement or stationary state is not reasonable and reliable.

[0050] In this embodiment, by analyzing the physical model of vehicle motion, the vehicle is only subject to gravity in a stationary state, that is, when the vehicle starts, it will be subject to traction, so the vehicle speed and X-axis acceleration fusion algorithm can be used to analyze the vehicle state. Therefore, this embodiment obtains the current GPS data and the current three-axis acceleration data.

[0051] Step S20: Calculate a first acceleration based on the GPS data at the current moment, and calculate a second acceleration based on the three-axis acceleration data at the current moment;

[0052] Exemplarily, in this embodiment, the first acceleration of the vehicle at the current moment is calculated using the speed parameters and time intervals in the received GPS data; however, due to the offset of the GPS, the calculated first acceleration at the current moment may not be very accurate, so this embodiment uses a three-axis acceleration sensor to filter and correct the first acceleration data, so the three-axis acceleration sensor is first used to obtain the vehicle's acceleration in real time on the X-axis, Y-axis, and Z-axis, and the acceleration in the X-axis direction is used as the second acceleration at the current moment.

[0053] Step S30: when it is detected that the difference between the first acceleration and the second acceleration is greater than a preset difference threshold, taking the second acceleration as the actual acceleration of the vehicle at the current moment;

[0054] Exemplarily, in this embodiment, the first acceleration is filtered and corrected by three-axis acceleration, so whether the first acceleration is reasonable and accurate can be determined based on the difference between the first acceleration and the second acceleration. Specifically, whether the difference between the calculated first acceleration value and the second acceleration value is within an acceptable range, that is, if the difference between the first acceleration and the second acceleration is less than or equal to the preset difference threshold, the first acceleration calculated based on the GPS data is considered reasonable and accurate, and the first acceleration can be used as the actual acceleration of the vehicle at the current moment, and the first acceleration is involved in the judgment of the vehicle state; if the difference between the first acceleration and the second acceleration is greater than the preset difference threshold, it means that the first acceleration calculated based on the GPS data is unreasonable, and the second acceleration needs to be used as the actual acceleration of the vehicle at the current moment, and the second acceleration is involved in the judgment of the vehicle state.

[0055] Step S40: filtering the GPS data at the current moment based on a Kalman filter algorithm to obtain an estimated value of the vehicle speed at the current moment;

[0056] For example, since there is an error in the vehicle speed received from the GPS server, this embodiment uses Kalman filtering for optimization processing. Specifically, the state equation of the Kalman filtering system is: k =A*X k-1 +B*U k +W k , the observation equation is: Z k =H*X k +V k ; Among them, X k is the estimated value of the system state at time k, X k-1 represents the estimated value of the system state at time k-1, U k is the control quantity of the system at time k, A and B are system parameters. If the system is a multi-model system, A and B are matrices; Z k is the measured value of the driving distance at time k, H is the parameter of the measurement system, and for a multi-measurement system, H is a matrix; W k and V k represent prediction noise and state transition noise respectively. Since the filtering object in this embodiment is the vehicle speed, X k represents the estimated speed at time k, X k-1 Represents the optimal estimated value of the vehicle speed at time k-1.

[0057] The state equation and observation equation of the Kalman filter system can be used to further obtain the state prediction equation, covariance equation, mean wave gain equation, filter estimation equation and mean square error equation. Among them, the state prediction equation is: k|k-1 =A*X k-1|k-1 +B*U k , X k|k-1 represents the optimal estimated value of the vehicle speed at time k predicted by time k-1, X k-1|k-1 is the optimal estimated value of vehicle speed at time k-1, U k is the control quantity at time k; the covariance prediction equation is: P k|k-1 =A*P k-1|k-1 *A'+Q,P k|k-1 Yes X k|k-1 The corresponding system covariance matrix, P k-1|k-1 Yes X k-1|k-1 The corresponding system covariance matrix, A' represents the transposed matrix of A, Q is the process noise covariance; the mean wave gain equation is: K k =P k|k-1 *H' / (H*P k|k-1 *H'+R), K k is the Kalman gain, R is the observation noise covariance, H' represents the transposed matrix of H; the filter estimation equation is: X k|k =X k|k-1 +K k (Z k -H*X k|k-1 ), X k|k Represents the optimal estimate of the state variable at time k; mean square error equation: P k|k =(IK k *H)*P k|k-1 , I is a matrix. For a single model and single measurement, I = 1. To keep the Kalman filter system running until the system process ends, it is also necessary to update X at time k. k|k The covariance matrix P k|k , and when the system enters the k+1 moment, P k|k is the P of the prediction covariance equation k-1|k-1 .

[0058] For the above process, it is only necessary to determine the initial values ​​X of the Kalman system at two zero moments. 0|0 and P 0|0 , the estimated value of the vehicle speed at time k can be obtained through recursive calculation. As the Kalman system works, X will gradually converge, so X can be determined according to actual needs. 0|0 and P 0|0 However, for P, it is generally not advisable to take the value of 0, because 0|0 A value of 0 may cause the Kalman system to fully believe that the given X0|0 is the optimal value for the system, thus making the algorithm unable to converge.

[0059] Step S50: determining the current state of the vehicle based on the actual acceleration, the estimated vehicle speed, a preset critical acceleration and a preset critical speed.

[0060] Furthermore, the preset critical acceleration is the acceleration corresponding to the critical point from the stationary state to the starting state or the acceleration corresponding to the critical point from the running state to the stationary state, and the preset critical speed is the speed corresponding to the critical point from the stationary state to the starting state or the speed corresponding to the critical point from the running state to the stationary state.

[0061] Further, when the preset critical acceleration is the acceleration corresponding to the critical point of the vehicle from a stationary state to a starting state, and the preset critical speed is the speed corresponding to the critical point of the vehicle from a stationary state to a starting state, the current state of the vehicle is determined based on the actual acceleration, the estimated vehicle speed, the preset critical acceleration and the preset critical speed, including:

[0062] When the actual acceleration is greater than a preset critical acceleration and the estimated vehicle speed is greater than a preset critical speed, it is determined that the vehicle is currently in a start state;

[0063] When the actual acceleration is less than or equal to a preset critical acceleration or the estimated vehicle speed is less than or equal to a preset critical speed, it is determined that the vehicle is currently in a stationary state.

[0064] Further, when the preset critical acceleration is the acceleration corresponding to the critical point of the vehicle from the running state to the stationary state, and the preset critical speed is the speed corresponding to the critical point of the vehicle from the running state to the stationary state, the current state of the vehicle is determined based on the actual acceleration, the estimated vehicle speed, the preset critical acceleration and the preset critical speed, including:

[0065] When the actual acceleration is greater than a preset critical acceleration and the estimated vehicle speed is greater than a preset critical speed, it is determined that the vehicle is currently in a running state;

[0066] When the actual acceleration is less than or equal to a preset critical acceleration or the estimated vehicle speed is less than or equal to a preset critical speed, it is determined that the vehicle is currently in a stationary state.

[0067] For example, in this embodiment, when the preset critical acceleration is the acceleration corresponding to the critical point from the stationary state to the starting state of the vehicle, and the preset critical speed is the speed corresponding to the critical point from the stationary state to the starting state of the vehicle, it is found through experimental testing that the critical acceleration can be preset to 0.5g, g = 0.98m / s 2 , and the critical speed is preset to 0.5km / h. If the actual acceleration exceeds 0.5g and the current estimated vehicle speed is greater than 0.5km / h, it means that this time can be used as the critical point from the vehicle being stationary to moving, that is, the vehicle is judged to be in the starting state at the current moment, otherwise the vehicle is judged to be in the stationary state at the current moment. Similarly, when the preset critical acceleration is the acceleration corresponding to the critical point from the running state to the stationary state of the vehicle, and the preset critical speed is the speed corresponding to the critical point from the running state to the stationary state of the vehicle, the preset values ​​of the critical acceleration and critical speed can also be determined after experimental testing, and the vehicle state can be judged based on the actual acceleration and the estimated vehicle speed. For the sake of brevity of description, it will not be repeated here.

[0068] It can be seen that the present application corrects the acceleration calculated based on the GPS data through the three-axis acceleration, and corrects the speed in the GPS data through the Kalman filter algorithm to improve the accuracy of the acceleration and vehicle speed obtained based on the GPS data. Finally, the current state of the vehicle can be accurately judged based on the actual acceleration and vehicle speed estimation. Therefore, the present application can effectively improve the accuracy of vehicle state judgment.

[0069] Further, after the step of determining that the vehicle is currently in a running state when the actual acceleration is greater than the preset critical acceleration and the estimated vehicle speed is greater than the preset critical speed, the method further includes:

[0070] Based on the Kalman filter algorithm, the current GPS data is filtered to obtain the optimal GPS data at the current moment;

[0071] Calculate a first driving distance based on the optimal state GPS data at the current moment and the optimal state GPS data at the previous moment;

[0072] Based on the median average filtering method, the driving distances corresponding to M historical moments and the first driving distance are filtered to obtain the arithmetic mean of the driving distances, and the arithmetic mean of the driving distances is used as the actual driving distance at the current moment, where M is a positive integer.

[0073] Demonstration, due to the drift of GPS, the vehicle travel distance calculated based on GPS data has deviations during vehicle movement, so this embodiment realizes dynamic real-time statistics of the actual travel distance and speed of the vehicle based on dynamic Kalman filtering and median average filtering method. That is, the Kalman filter system in this embodiment will always process the position and speed in the received GPS data, constantly predict and correct new observations, and can calculate the optimal state GPS data at the current moment at any time, and make a well-founded prediction of the next direction of the vehicle. Even with various interferences, Kalman filtering can always point out the real situation. Therefore, it is very ideal to use Kalman filtering in a continuously changing system. It has the advantages of small memory usage and high speed, and is very suitable for real-time problems and embedded systems.

[0074] In addition, since the earth is a nearly standard ellipsoid, assuming that the earth is a perfect sphere, its radius is the average radius of the earth, recorded as R; if the 0 degree longitude is used as the reference, then the surface distance between any two points on the earth's surface can be calculated based on the longitude and latitude of the two points. Therefore, the vehicle's driving distance can be calculated based on the difference between the current optimal state GPS data and the optimal state GPS data at the previous moment, the earth's WGS-84 coordinate system and the Pythagorean theorem. Among them, X in the state equation of the Kalman filter system k is the estimated driving distance at time k, X k-1 represents the optimal estimate of the travel distance at time k-1, U k is the control quantity of the system at time k, A and B are system parameters. If the system is a multi-model system, A and B are matrices; Z k is the measured value of the driving distance at time k, H is the parameter of the measurement system, and for a multi-measurement system, H is a matrix; W k and V k Represent the prediction noise and state transition noise respectively; after determining the initial values ​​X of the two zero moments of the Kalman filter system 0|0 and P 0|0 , the vehicle travel distance at time k can be obtained by recursive calculation. Since the filtering process of travel distance is similar to the filtering process of vehicle speed, it will not be described here for the sake of brevity.

[0075] Then, the driving distance obtained after Kalman filtering is smoothed based on the median average filtering method; among them, the median average filtering method is equivalent to the combination of "median filtering method" and "arithmetic mean filtering method", for example: first save 10 historical driving distances sampled continuously, discard the driving distance corresponding to the moment with the largest time difference with the current moment, assign the remaining 9 driving distances to the array, and then add the driving distance at the current moment to the array, and then remove the maximum and minimum values ​​in the array, and calculate the arithmetic mean of all the data in the array, and finally store the calculated arithmetic mean in the array as the actual driving distance value at the current moment, that is, the actual driving distance of the vehicle at the current moment.

[0076] Furthermore, after the step of taking the arithmetic mean of the driving distances as the actual driving distance at the current moment, the method further includes:

[0077] The basic data corresponding to each moment is stored in a preset storage order to obtain a storage list, wherein the basic data includes an estimated vehicle speed, an actual driving distance and a time; wherein the preset storage order includes an increasing storage order and a decreasing storage order;

[0078] Receiving specific point information to be queried input by the user;

[0079] Traversing the storage list according to the specific point information to be queried to obtain the associated information of the specific point to be queried;

[0080] Calculate the state information to be displayed corresponding to the specific point to be queried based on the association information of the specific point to be queried, the state information to be displayed includes the estimated vehicle speed and / or the actual travel distance and / or time information;

[0081] The status information to be displayed is displayed.

[0082] For example, in vehicle testing, vehicle testers often want to find the time, longitude and latitude, and travel distance corresponding to all points with 5km / h as the starting point, 50km / h as the ending point, and the value 2 as the cumulative value, that is, the points with speeds equal to 5km / h, 7km / h, 9km / h, ..., 47km / h, 49km / h, and 50km / h. These points are referred to as specific points below, that is, the specific points include the starting point, the ending point, and the specific speed, specific travel distance, specific time, and specific spatial position. However, since the data transmitted by the actual GPS is non-integer value and non-continuous data, there are errors in the specific point search results. Therefore, this embodiment obtains the speed integer value, distance integer value, accurate time, etc. of the specific point by performing function derivation according to the GPS speed curve or the travel distance curve.

[0083] In this embodiment, the judgment and display of specific points are realized through QT analysis software (QT analysis software is a message analysis tool). Its main function is to first read the test data files such as acceleration mode, uniform speed mode, deceleration mode, etc. saved by the remote data acquisition terminal, and filter and smooth the data to calculate the relative time of each point; then write the basic data (such as vehicle speed estimation, actual driving distance and time, etc.) into the database, and then analyze and process the data according to the user's data screening, and finally present a series of processed data to the user in a list so that the user can see the required data.

[0084] Among them, the main interface of the QT analysis software can be designed from top to bottom as a prompt line for users to query files, a file path display box and a file import button; the query conditions for data are time, vehicle speed and driving distance, the time selection has input boxes for start time and end time, the vehicle speed has start speed and end speed, and the driving distance is divided into start driving speed and end driving speed; the input box for specific points has vehicle speed and distance, and the user enters the specific point value of speed or distance to find the related information with the specific point; the list display button, list clear button, speed curve button and distance curve button can also be set, the corresponding function of the list display button is to display the queried data in the form of a list, the corresponding function of the speed curve button is to display the filtered data in the relationship between speed and time, and the corresponding function of the distance curve button is to display the filtered data in the relationship between speed and time.

[0085] Specifically, the QT parsing software first reads the data file imported by the user. The content of the data file includes data such as table header name, time, longitude and latitude, vehicle speed, driving distance, and running state. Then, the data is smoothed and filtered, and the filtered data is written to the database. When the user submits the specific speed or driving distance to be queried, the QT parsing software will use time as the query primary key to traverse all the data from the database, obtain the associated information of the specific speed or driving distance to be queried, and calculate the status information to be displayed corresponding to the specific speed or driving distance to be queried. Among them, if the data file saves the data in acceleration mode, the speed is calculated according to the increasing relationship, and if the data file saves the data in deceleration mode, the speed is calculated according to the decreasing law; however, for the driving distance, no matter which mode the data file is used to save the data, the driving distance is calculated according to the increasing relationship.

[0086] Taking a specific point as the starting point as an example, according to the increasing or decreasing relationship, traverse to two points between the specific point in the database and extract data records. Since the time interval between each data record is 20ms, the function derivation method can be used for numerical calculation: first calculate the slope between the two points, then calculate the position of the starting point between the two points, and calculate the time corresponding to the starting point, relative time, vehicle speed, driving distance and other values ​​through the slope relationship. In addition, if the speed of the specific point as the end point is set to zero, if the calculated speed of 10 consecutive records is less than 0.5km / h at the same time, this point can be used as the stopping point of the vehicle.

[0087] See also Figure 2 As shown, the embodiment of the present application also provides a vehicle state judgment device, including:

[0088] An acquisition unit, which is used to acquire the current GPS data and the current three-axis acceleration data;

[0089] A calculation unit, configured to calculate a first acceleration based on the GPS data at the current moment, and to calculate a second acceleration based on the three-axis acceleration data at the current moment;

[0090] a detection unit, configured to use the second acceleration as the actual acceleration of the vehicle at a current moment when detecting that the difference between the first acceleration and the second acceleration is greater than a preset difference threshold;

[0091] A filtering unit, which is used to filter the GPS data at the current moment based on a Kalman filtering algorithm to obtain an estimated value of the vehicle speed at the current moment;

[0092] A determination unit is used to determine the current state of the vehicle based on the actual acceleration, the estimated vehicle speed, a preset critical acceleration and a preset critical speed.

[0093] It can be seen that the present application corrects the acceleration calculated based on GPS data by three-axis acceleration, and corrects the speed in GPS data by Kalman filter algorithm to improve the accuracy of acceleration and vehicle speed obtained based on GPS data, and finally accurately judges the current state of the vehicle according to the actual acceleration and vehicle speed estimation value. It can be seen that the present application can effectively improve the accuracy of vehicle state judgment, and has the advantages of good portability, simple operation, and low cost.

[0094] Furthermore, the preset critical acceleration is the acceleration corresponding to the critical point from the stationary state to the starting state or the acceleration corresponding to the critical point from the running state to the stationary state, and the preset critical speed is the speed corresponding to the critical point from the stationary state to the starting state or the speed corresponding to the critical point from the running state to the stationary state.

[0095] Further, when the preset critical acceleration is the acceleration corresponding to the critical point of the vehicle from the stationary state to the starting state, and the preset critical speed is the speed corresponding to the critical point of the vehicle from the stationary state to the starting state, the determining unit is specifically used to:

[0096] When the actual acceleration is greater than a preset critical acceleration and the estimated vehicle speed is greater than a preset critical speed, it is determined that the vehicle is currently in a start state;

[0097] When the actual acceleration is less than or equal to a preset critical acceleration or the estimated vehicle speed is less than or equal to a preset critical speed, it is determined that the vehicle is currently in a stationary state.

[0098] Further, when the preset critical acceleration is the acceleration corresponding to the critical point from the running state to the stationary state of the vehicle, and the preset critical speed is the speed corresponding to the critical point from the running state to the stationary state of the vehicle, the determining unit is specifically used to:

[0099] When the actual acceleration is greater than a preset critical acceleration and the estimated vehicle speed is greater than a preset critical speed, it is determined that the vehicle is currently in a running state;

[0100] When the actual acceleration is less than or equal to a preset critical acceleration or the estimated vehicle speed is less than or equal to a preset critical speed, it is determined that the vehicle is currently in a stationary state.

[0101] Furthermore, the filtering unit is also used to: filter the GPS data at the current moment based on the Kalman filtering algorithm to obtain the optimal state GPS data at the current moment; the calculation unit is also used to: calculate the first driving distance based on the optimal state GPS data at the current moment and the optimal state GPS data at the previous moment; filter the driving distances corresponding to M historical moments and the first driving distance based on the median average filtering method to obtain the arithmetic mean of the driving distances, and use the arithmetic mean of the driving distances as the actual driving distance at the current moment, wherein M is a positive integer.

[0102] Furthermore, the device also includes a processing unit, which is used to:

[0103] The basic data corresponding to each moment is stored in a preset storage order to obtain a storage list, wherein the basic data includes an estimated vehicle speed, an actual driving distance and a time; wherein the preset storage order includes an increasing storage order and a decreasing storage order;

[0104] Receiving specific point information to be queried input by the user;

[0105] Traversing the storage list according to the specific point information to be queried to obtain the associated information of the specific point to be queried;

[0106] Calculate the state information to be displayed corresponding to the specific point to be queried based on the association information of the specific point to be queried, the state information to be displayed includes the estimated vehicle speed and / or the actual travel distance and / or time information;

[0107] The status information to be displayed is displayed.

[0108] Exemplarily, this application can be composed of a remote data acquisition terminal, a mobile terminal, QT parsing software, etc. Among them, the mobile terminal is mainly used to display the relevant data display and configuration of various test items of the whole vehicle, so as to facilitate the test operator to better conduct the test. Its main functions include three parts: braking test, consumption test, and vehicle speed test.

[0109] The interface corresponding to the brake test function will display the relevant data required for the test, such as braking speed, average deceleration, maximum pedal force, measured braking distance, measured braking distance, GPS positioning status, etc. The user clicks the "Change Configuration" button to enter the configuration change interface under the brake mode, which can be configured for different vehicle speed modes. The vehicle speed mode is divided into two types: default test and interval test; click the "Submit" button on the interface to submit the changed configuration data to the server.

[0110] The interface corresponding to the consumption test function will display the relevant data required for the test, such as vehicle speed, driving distance, driving time, fuel consumption, fuel consumption and GPS positioning status. There are three vehicle speed modes in the consumption test: acceleration, idling and constant speed. After the user modifies the relevant data, click the "Submit" button, which will send a request to the server to change the configuration content.

[0111] The interface corresponding to the speed test function will display the relevant data required for the test, such as acceleration, driving distance, driving time, average speed, real-time speed, test status, GPS positioning status, etc. The speed modes in the speed test are divided into three modes: manual mode, automatic mode and acceleration mode. When the user clicks the "Submit" button on this interface, the changed configuration data or calibration coefficient change request instruction will be submitted to the server.

[0112] The remote data acquisition terminal integrates the acquisition unit, calculation unit, detection unit, filtering unit and determination unit, and is mainly used for data acquisition, reception, calculation, transmission and storage. The function of data acquisition is mainly to collect vehicle GPS data every 20ms; the function of data reception is mainly to receive the working mode and related configuration parameters sent by the mobile terminal; the function of data calculation is mainly to calculate the vehicle's driving distance, speed and acceleration data based on the collected vehicle GPS data and three-axis acceleration data; the data storage function is mainly to save the real-time longitude and latitude, speed, driving distance and acceleration of each test in the form of CSV (Comma-Separated Values) file; the data transmission function is mainly to transmit the calculated speed, longitude and latitude, driving distance, acceleration and other data to the server, and the mobile terminal can read the vehicle speed, acceleration, driving distance and other data by accessing the server.

[0113] After the remote data acquisition terminal saves the data in CSV files, the test personnel can only view individual files, which is not convenient for viewing data and analyzing and organizing experimental data. For this reason, this embodiment performs data processing based on QT parsing software (QT parsing software integrates a processing unit), which can parse the data files saved by the vehicle in acceleration mode, uniform speed mode, deceleration mode, etc.; and through the curve chart, the corresponding vehicle speed, driving distance and state at each moment and the relationship between driving distance, speed and time can be found, and the time, vehicle speed, driving distance and other data of relevant specific points can also be found through the list chart.

[0114] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device and each unit can refer to the corresponding process in the aforementioned vehicle state judgment method embodiment, and will not be repeated here.

[0115] The apparatus provided in the above embodiment may be implemented in the form of a computer program. The computer program may be Figure 3 The vehicle status shown is run on the judgment device.

[0116] An embodiment of the present application also provides a vehicle status judgment device, including: a memory, a processor and a network interface connected through a system bus, at least one instruction is stored in the memory, and at least one instruction is loaded and executed by the processor to implement all or part of the steps of the aforementioned vehicle status judgment method.

[0117] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0118] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc. The processor is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0119] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a video playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as video data, image data, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device or other volatile solid-state storage device.

[0120] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, all or part of the steps of the aforementioned vehicle state judgment method are implemented.

[0121] The embodiment of the present application implements all or part of the aforementioned process, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of each of the above methods when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0122] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, servers or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0123] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0124] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0125] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A vehicle state determination method, characterized in that: The following steps are involved: Get the current GPS data and the current three-axis acceleration data; The first acceleration is calculated based on the GPS data at the current moment, and the second acceleration is calculated based on the three-axis acceleration data at the current moment; the specific steps of calculating the second acceleration based on the three-axis acceleration data at the current moment are: firstly, the acceleration of the vehicle in the X-axis, Y-axis and Z-axis is obtained in real time through the three-axis acceleration sensor, and the acceleration in the X-axis direction is used as the second acceleration at the current moment; When it is detected that the difference between the first acceleration and the second acceleration is greater than a preset difference threshold, taking the second acceleration as the actual acceleration of the vehicle at the current moment; Performing filtering processing on the GPS data at the current moment based on a Kalman filter algorithm to obtain an estimated value of the vehicle speed at the current moment; Determining the current state of the vehicle based on the actual acceleration, the estimated vehicle speed, a preset critical acceleration, and a preset critical speed; The preset critical acceleration is the acceleration corresponding to the critical point from the stationary state to the starting state of the vehicle or the acceleration corresponding to the critical point from the running state to the stationary state of the vehicle, and the preset critical speed is the speed corresponding to the critical point from the stationary state to the starting state of the vehicle or the speed corresponding to the critical point from the running state to the stationary state of the vehicle; When the preset critical acceleration is the acceleration corresponding to the critical point when the vehicle changes from a stationary state to a starting state, and the preset critical speed is the speed corresponding to the critical point when the vehicle changes from a stationary state to a starting state, the determining of the state of the vehicle at the current moment based on the actual acceleration, the estimated vehicle speed, the preset critical acceleration, and the preset critical speed includes: When the actual acceleration is greater than a preset critical acceleration and the estimated vehicle speed is greater than a preset critical speed, it is determined that the vehicle is currently in a start state; When the actual acceleration is less than or equal to a preset critical acceleration or the estimated vehicle speed is less than or equal to a preset critical speed, it is determined that the vehicle is currently stationary; When the preset critical acceleration is the acceleration corresponding to the critical point of the vehicle from the running state to the stationary state, and the preset critical speed is the speed corresponding to the critical point of the vehicle from the running state to the stationary state, the determining of the state of the vehicle at the current moment based on the actual acceleration, the estimated vehicle speed, the preset critical acceleration and the preset critical speed includes: When the actual acceleration is greater than a preset critical acceleration and the estimated vehicle speed is greater than a preset critical speed, it is determined that the vehicle is currently in a running state; When the actual acceleration is less than or equal to a preset critical acceleration or the estimated vehicle speed is less than or equal to a preset critical speed, it is determined that the vehicle is currently stationary; After the step of determining that the vehicle is currently in a running state when the actual acceleration is greater than the preset critical acceleration and the estimated vehicle speed is greater than the preset critical speed, the method further includes: Based on the Kalman filter algorithm, the current GPS data is filtered to obtain the optimal GPS data at the current moment; Calculate a first driving distance based on the optimal state GPS data at the current moment and the optimal state GPS data at the previous moment; Performing filtering processing on the driving distances corresponding to M historical moments and the first driving distance based on the median average filtering method to obtain an arithmetic mean of the driving distances, and taking the arithmetic mean of the driving distances as the actual driving distance at the current moment, wherein M is a positive integer; After the step of taking the arithmetic mean of the driving distance as the actual driving distance at the current moment, the method further includes: The basic data corresponding to each moment is stored in a preset storage order to obtain a storage list, wherein the basic data includes an estimated vehicle speed, an actual driving distance and time; Receiving specific point information to be queried input by the user; Traversing the storage list according to the specific point information to be queried to obtain the associated information of the specific point to be queried; Calculate the state information to be displayed corresponding to the specific point to be queried based on the association information of the specific point to be queried, the state information to be displayed includes the estimated vehicle speed and / or the actual travel distance and / or time information; Displaying the status information to be displayed; The preset storage order includes an increasing storage order and a decreasing storage order.

2. A vehicle state determination device, characterized in that: include: An acquisition unit, which is used to acquire the current GPS data and the current three-axis acceleration data; A calculation unit, which is used to calculate the first acceleration based on the GPS data at the current moment, and calculate the second acceleration based on the three-axis acceleration data at the current moment; the specific steps of calculating the second acceleration based on the three-axis acceleration data at the current moment are: firstly obtaining the acceleration of the vehicle in the X-axis, Y-axis and Z-axis in real time through the three-axis acceleration sensor, and using the acceleration in the X-axis direction as the second acceleration at the current moment; a detection unit, configured to use the second acceleration as the actual acceleration of the vehicle at a current moment when detecting that the difference between the first acceleration and the second acceleration is greater than a preset difference threshold; A filtering unit, which is used to filter the GPS data at the current moment based on a Kalman filtering algorithm to obtain an estimated value of the vehicle speed at the current moment; a determination unit, configured to determine a current state of the vehicle based on the actual acceleration, the estimated vehicle speed, a preset critical acceleration, and a preset critical speed; The preset critical acceleration is the acceleration corresponding to the critical point from the stationary state to the starting state of the vehicle or the acceleration corresponding to the critical point from the running state to the stationary state of the vehicle, and the preset critical speed is the speed corresponding to the critical point from the stationary state to the starting state of the vehicle or the speed corresponding to the critical point from the running state to the stationary state of the vehicle; When the preset critical acceleration is the acceleration corresponding to the critical point of the vehicle from the stationary state to the starting state, and the preset critical speed is the speed corresponding to the critical point of the vehicle from the stationary state to the starting state, the determining unit is specifically used to: When the actual acceleration is greater than a preset critical acceleration and the estimated vehicle speed is greater than a preset critical speed, it is determined that the vehicle is currently in a start state; When the actual acceleration is less than or equal to a preset critical acceleration or the estimated vehicle speed is less than or equal to a preset critical speed, it is determined that the vehicle is currently stationary; When the preset critical acceleration is the acceleration corresponding to the critical point of the vehicle from the running state to the stationary state, and the preset critical speed is the speed corresponding to the critical point of the vehicle from the running state to the stationary state, the determining unit is specifically used to: When the actual acceleration is greater than a preset critical acceleration and the estimated vehicle speed is greater than a preset critical speed, it is determined that the vehicle is currently in a running state; When the actual acceleration is less than or equal to a preset critical acceleration or the estimated vehicle speed is less than or equal to a preset critical speed, it is determined that the vehicle is currently stationary; The filtering unit is also used to: filter the GPS data at the current moment based on the Kalman filtering algorithm to obtain the optimal state GPS data at the current moment; The calculation unit is also used to: calculate a first driving distance based on the optimal state GPS data at the current moment and the optimal state GPS data at the previous moment; Performing filtering processing on the driving distances corresponding to M historical moments and the first driving distance based on the median average filtering method to obtain an arithmetic mean of the driving distances, and taking the arithmetic mean of the driving distances as the actual driving distance at the current moment, wherein M is a positive integer; The device also includes a processing unit, which is configured to: The basic data corresponding to each moment is stored in a preset storage order to obtain a storage list, wherein the basic data includes an estimated vehicle speed, an actual driving distance and a time; wherein the preset storage order includes an increasing storage order and a decreasing storage order; Receiving specific point information to be queried input by a user; traversing the storage list according to the specific point information to be queried to obtain associated information of the specific point to be queried; Calculate the state information to be displayed corresponding to the specific point to be queried based on the association information of the specific point to be queried, the state information to be displayed includes the estimated vehicle speed and / or the actual travel distance and / or time information; The status information to be displayed is displayed.

3. A vehicle state judgment device, characterized in that: include: A memory and a processor, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the vehicle state judgment method according to claim 1.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the vehicle state determination method according to claim 1 is implemented.

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