CN118244378B - Zero point correction method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a zero point correction method, a zero point correction device, zero point correction equipment and a storage medium, which relate to the technical field of data processing, wherein the zero point correction method comprises the following steps: filtering the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data; calculating the zero offset of the gravity sensor of the vehicle according to the correction data; and correcting the zero position of the gravity sensor of the vehicle according to the zero offset of the gravity sensor to obtain the zero position of the standard gravity sensor. The method and the device can improve the accuracy and stability of zero position detection, and further improve the collision detection performance of the automobile data recorder.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a zero correction method, apparatus, device, and storage medium.
Background
With the rapid development of the automobile industry, the automobile data recorder is taken as an important component of automobile electronic equipment, and plays an increasingly important role in the field of automobile safety. The G-sensor (Gravitysensor gravity sensor) is used as one of key components of the automobile data recorder and bears an important task of collision detection. However, in practical applications, the zero position of the G-sensor tends to shift due to uncertainty of the installation position and interference of environmental noise, resulting in a decrease in accuracy of collision detection results.
The traditional zero point correction method mainly depends on fixed parameters or preset thresholds for correction, and is simple and easy to implement, but the correction effect is often unsatisfactory when facing complex and changeable application environments. On the one hand, due to the change of the installation position, the zero position can also change correspondingly, and the method for fixing parameters cannot adapt to the change; on the other hand, the interference of the environmental noise also affects the detection of the zero point position, resulting in inaccurate correction results.
In summary, how to realize adaptive zero position change and effectively inhibit zero correction of noise interference so as to improve the accuracy and stability of collision detection of a vehicle recorder is a technical problem that needs to be solved in the art.
Disclosure of Invention
The application mainly aims to provide a zero point correction method, a zero point correction device, zero point correction equipment and a storage medium, which aim to realize zero point correction which is adaptive to zero point position change and effectively inhibit noise interference so as to improve the collision detection accuracy and stability of a vehicle recorder.
In order to achieve the above object, the present application provides a zero point correction method, which includes:
filtering the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data;
calculating the zero offset of the gravity sensor of the vehicle according to the correction data;
And correcting the zero position of the gravity sensor of the vehicle according to the zero offset of the gravity sensor to obtain the zero position of the standard gravity sensor.
In an embodiment, when the preset filtering algorithm is a kalman filtering algorithm, the step of filtering the gravity sensor data of the vehicle by the preset filtering algorithm to obtain the correction data includes:
Determining a state vector and an observation vector based on the gravity sensor data of the vehicle;
obtaining a predicted state vector and a predicted error covariance matrix of the next moment according to the observed vector of the current moment and the state vector of the previous moment;
determining a Kalman gain based on the prediction error covariance matrix, the observation matrix, and the observation noise covariance;
And updating the prediction state vector and the prediction error covariance matrix based on the Kalman gain and the new observation vector respectively to obtain correction data, wherein the correction data is correction data.
In an embodiment, the method further comprises:
acquiring environmental parameters;
and adjusting the filtering parameters in the Kalman filtering algorithm according to the environment parameters so as to enable the Kalman filtering algorithm to adapt to environment changes.
In one embodiment, the step of calculating the gravity sensor zero-point offset amount of the vehicle from the correction data includes:
calculating the zero position of the current gravity sensor of the vehicle according to the correction data;
and carrying out difference value calculation on the current zero position of the gravity sensor and the zero position of the standard gravity sensor to obtain the zero offset of the gravity sensor of the vehicle.
In one embodiment, the step of calculating the current gravity sensor zero position of the vehicle from the correction data comprises:
When the vehicle is in a running state, carrying out compound operation analysis on the correction data, the GPS data of the vehicle and the road condition data to obtain the running data of the vehicle, wherein the running data comprises a moving distance, a speed and an acceleration;
And calculating the current gravity sensor zero position of the vehicle according to the driving data and the correction data by adopting a multi-source data fusion algorithm.
In an embodiment, the method further comprises:
Establishing a vehicle running model, wherein the vehicle running model comprises a vehicle state evaluation algorithm;
determining a first running state of the vehicle according to a vehicle state evaluation algorithm and running parameters of the vehicle;
comparing the first driving state with a second driving state determined according to the gravity sensor data;
And if the first running state and the second running state are different, executing a step of filtering the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data.
In one embodiment, the step of correcting the zero position of the gravity sensor of the vehicle according to the zero offset of the gravity sensor to obtain the zero position of the standard gravity sensor includes:
Comparing the zero offset of the gravity sensor with a preset offset threshold value to obtain a comparison result;
And if the comparison result is larger than the preset offset threshold value, correcting the zero position of the gravity sensor of the vehicle to obtain the zero position of the standard gravity sensor, wherein the zero offset of the zero position of the standard gravity sensor is zero.
In addition, in order to achieve the above object, the present application also provides a zero point correction device, including:
the filtering module is used for carrying out filtering processing on the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data;
the offset calculation module is used for calculating the zero offset of the gravity sensor of the vehicle according to the correction data;
The zero point correction module is used for correcting the zero point position of the gravity sensor of the vehicle according to the zero point offset of the gravity sensor to obtain the zero point position of the standard gravity sensor.
In addition, to achieve the above object, the present application also proposes an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the zero point correction method as above.
In addition, in order to achieve the above object, the present application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the zero point correction method as above.
The application provides a zero point correction method, which comprises the steps of firstly, carrying out filtering treatment on original data collected by a gravity sensor of a vehicle through a preset filtering algorithm to eliminate noise and abnormal values in the data, thereby obtaining corrected gravity sensor data, namely correction data; then, calculating a zero offset of the gravity sensor based on the correction data, wherein the zero offset represents a difference value between an actual zero and an ideal zero; and finally, correcting the zero position of the gravity sensor according to the calculated zero offset, so as to ensure that the zero position of the gravity sensor is accurate, and further obtain the zero position of the standard gravity sensor.
In summary, the application can effectively remove noise signals in the gravity sensor data and retain real and effective data by adopting the preset filtering algorithm to process the gravity sensor data, thereby improving the accuracy of zero position detection, and simultaneously, the zero offset is calculated based on real-time data, so that the change of the installation position of the gravity sensor can be adaptively applied, the accurate zero position can be ensured under any condition, the problem that the traditional zero correction method cannot be suitable for complex and changeable environments is solved, the accuracy and the stability of zero position detection are also improved, and the collision detection performance of the automobile data recorder is further improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flow chart of a zero-point correction method according to a first embodiment of the present application;
fig. 2 is a schematic flow chart of another embodiment of the zero-point correction method according to the present application;
fig. 3 is a schematic flow chart of a second embodiment of the zero correction method according to the present application;
Fig. 4 is a schematic block diagram of a zero point correction device according to an embodiment of the present application;
fig. 5 is a schematic device structure diagram of a hardware operating environment related to a zero correction method in an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the technical solution of the present application and are not intended to limit the present application.
For a better understanding of the technical solution of the present application, the following detailed description will be given with reference to the drawings and the specific embodiments.
The main solutions of the embodiments of the present application are: filtering the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data; calculating the zero offset of the gravity sensor of the vehicle according to the correction data; and correcting the zero position of the gravity sensor of the vehicle according to the zero offset of the gravity sensor to obtain the zero position of the standard gravity sensor.
Because the traditional zero point correction method mainly depends on fixed parameters or preset thresholds for correction, the method is simple and easy to implement, but the correction effect is often unsatisfactory when facing complex and changeable application environments. On the one hand, due to the change of the installation position, the zero position can also change correspondingly, and the method for fixing parameters cannot adapt to the change; on the other hand, the interference of the environmental noise also affects the detection of the zero point position, resulting in inaccurate correction results.
The embodiment of the application provides a solution, the noise signals in the gravity sensor data can be effectively removed by adopting a preset filtering algorithm to process the gravity sensor data, and real and effective data are reserved, so that the accuracy of detecting the zero position is improved, meanwhile, the zero offset is calculated based on real-time data, so that the change of the installation position of the gravity sensor can be adaptively applied, the accurate zero position can be obtained under any condition, the problem that the traditional zero correction method cannot be suitable for complex and changeable environments is solved, the accuracy and the stability of detecting the zero position are improved, and the collision detection performance of the automobile data recorder is further improved.
It should be noted that, the execution body of the present embodiment may be a computing service device having functions of data processing, network communication, and program running, such as a tablet computer, a personal computer, a vehicle-mounted terminal, or an electronic device capable of implementing the above functions. The present embodiment and the following embodiments will be described below with reference to a vehicle control terminal.
Based on this, an embodiment of the present application provides a zero point correction method, and referring to fig. 1, fig. 1 is a schematic flow chart of a first embodiment of the zero point correction method of the present application.
In this embodiment, the zero correction method includes steps S10 to S30:
step S10, filtering the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data;
it should be noted that, the preset filtering algorithm may be a kalman filtering (KALMANFILTER), a complementary filtering (ComplementaryFilter) or a quaternion filtering (QuaternionFilter) algorithm, etc. for removing noise and abnormal values in the data, so as to improve accuracy and stability of the gravity sensor data.
Additionally, the gravity sensor data is data collected by a gravity sensor (GravitySensor) or an acceleration sensor (Accelerometer) installed in the vehicle, and the vehicle control terminal can detect the collision of the vehicle based on the gravity sensor data and trigger a safety device for protection, a brake auxiliary system or an automatic emergency brake function when the collision of the vehicle is judged, so that the driving safety and the life safety of drivers and passengers are ensured.
The gravity sensor data of the vehicle is subjected to filtering processing through a preset filtering algorithm to remove high-frequency noise and random errors in the original data, so as to obtain corrected data, wherein the filtering algorithm can be selected according to specific application scenes and precision requirements, and the method is not particularly limited in the embodiment.
Furthermore, in one possible embodiment, the gravity sensor data is pre-processed prior to filtering the gravity sensor data. Specifically, the original data is cleaned to remove outliers and noise points. This may be achieved by setting a suitable threshold or by means of a sliding average or the like. The cleaned data can more truly reflect the motion state of the vehicle. Next, normalization processing is performed on the cleaned data. Normalization is a common data processing means, which can convert data of different dimensions into the same dimension, so that the comparison between the data is more fair and accurate. In the invention, the data can be processed by adopting methods such as maximum and minimum normalization and the like.
Step S20, calculating the zero offset of a gravity sensor of the vehicle according to the correction data;
the gravity sensor zero point offset refers to a non-zero value of an output of the sensor in a stationary state due to factors such as a sensor mounting position, a temperature change, equipment aging or a malfunction.
The zero-point offset of the gravity sensor of the vehicle is calculated according to the correction data, specifically, the zero-point position of the gravity sensor can be determined by calculating the average value or the median of the correction data in a period of time, and then the zero-point offset is calculated according to the determined zero-point position of the gravity sensor.
And step S30, correcting the zero position of the gravity sensor of the vehicle according to the zero offset of the gravity sensor to obtain the zero position of the standard gravity sensor.
And correcting the zero position of the gravity sensor of the vehicle according to the calculated zero offset of the gravity sensor to obtain the zero position of the standard gravity sensor, and outputting zero of corrected output data of the gravity sensor in a static state, so that the accuracy of a sensor measurement result is ensured, and the vehicle control terminal can perform subsequent processing and analysis according to the output data of the gravity sensor.
It should be noted that the detection and correction process of the zero point position should be dynamic, i.e. it should be adjusted in real time according to the change of the data. For this purpose, a threshold value may be set, and when it is detected that the gravity sensor zero-point offset amount exceeds the threshold value, the correction operation is triggered. Meanwhile, the threshold value can be adjusted according to the actual application environment so as to adapt to the requirements under different scenes.
When the preset filtering algorithm is a kalman filtering algorithm, as shown in fig. 2, step S10 may include steps S101 to S104:
Step S101, a state vector and an observation vector are determined based on gravity sensor data of a vehicle;
It can be appreciated that the kalman filter algorithm has significant advantages in the application scenario of the present embodiment compared to other data processing algorithms. Firstly, the Kalman filtering algorithm can fully utilize a dynamic model and observation data of a system, and estimate the state in a recursion mode, so that the Kalman filtering algorithm has higher calculation efficiency and instantaneity; secondly, the Kalman filtering algorithm can effectively process noise and errors in the sensor data, and accuracy of estimation is improved by fusing information of a plurality of data sources. In contrast, some simple filtering algorithms may not handle noise and errors well, resulting in instability of the estimation results.
The state vector and the observation vector at the present time are defined and determined based on data acquired by a gravity sensor loaded on the vehicle. The state vector typically contains key parameters describing the dynamic state of the vehicle, such as position, speed, acceleration, etc.; the observation vector represents vehicle motion state data obtained through direct measurement of the gravity sensor, and the state vector is iteratively updated through a Kalman filtering algorithm, so that accurate estimation of the vehicle state is realized.
Step S102, a predicted state vector and a predicted error covariance matrix of the next moment are obtained according to the observed vector of the current moment and the state vector of the previous moment;
Based on a Kalman filtering algorithm, a dynamic model of gravity sensor data is firstly established, the gravity sensor data can be expressed as a discrete time linear dynamic system, and a state equation and an observation equation of the gravity sensor data are respectively as follows:
The equation of state: x (k) =ax (k-1) +bu (k-1) +w (k-1);
Observation equation: z (k) =hx (k) +v (k);
Where X (k) is the state vector of the system at time k, A is the state transition matrix, B is the control input matrix, U (k-1) is the control input vector, W (k-1) is the process noise vector, Z (k) is the observation vector of the system at time k, H is the observation matrix, and V (k) is the observation noise vector.
Next, a kalman filtered prediction equation is determined, the prediction equation is used for obtaining a prediction state vector and a prediction error covariance matrix of a next moment according to an observation vector of a current moment and a state vector of a previous moment, and the prediction equation comprises:
Prediction state: hat { X } (k|k-1) = Ahat { X } (k-1|k-1) +BU (k-1);
prediction covariance: p (k|k-1) =AP (k-1|k-1) A++T;
Where hat { X } (k|k-1 is the predicted value of the state vector for time k, i.e., the predicted state vector for the next time, P (k|k-1) is the predicted covariance matrix, i.e., the predicted error covariance matrix for the next time, and Q is the process noise covariance matrix.
Step S103, determining Kalman gain based on the prediction error covariance matrix, the observation matrix and the observation noise covariance;
Step S104, updating the prediction state vector and the prediction error covariance matrix based on the Kalman gain and the new observation vector respectively to obtain correction data, wherein the correction data is correction data.
Determining an update equation of the Kalman filter, wherein the update equation is used for updating the state estimation value according to the observed value and the predicted value of the current moment, and the expression is as follows:
Kalman gain: k (K) =P (k|k-1) H≡T (HP (k|k-1) H≡T+R) ≡1;
updating the state: hat { X } (k|k) =hat { X } (k|k-1) +k (K) [ Z (K) -Hhat { X } (k|k-1) ];
Updating covariance: p (k|k) = (1-K (K) H) P (k|k-1);
Where K (K) is the kalman gain, R is the observed noise covariance matrix, hat { X } (k|k) is the updated estimate of the state vector at time K, i.e., the predicted state vector, and P (k|k) is the updated covariance matrix, i.e., the prediction error covariance matrix.
The Kalman filtering algorithm can estimate and update the vehicle state in each time step continuously, the Kalman filtering algorithm can gradually approach to a real state value to obtain correction data, the correction data is corrected gravity sensor data, the real-time performance of data processing is guaranteed, and the Kalman filtering algorithm can obtain more accurate state estimation than single observed data by comprehensively considering current observed data and historical state information and quantizing observed noise and system noise, so that noise interference in the data is inhibited to a certain extent, and more accurate and stable gravity sensor data is obtained.
In a possible embodiment, the method may further include steps S40 to S50:
step S40, obtaining environmental parameters;
And S50, adjusting the filtering parameters in the Kalman filtering algorithm according to the environment parameters so as to enable the Kalman filtering algorithm to adapt to the environment change.
In the zero point correction process of the gravity sensor of the vehicle, current environmental parameters including road conditions, terrains, temperatures, humidity, illumination intensity, noise level and the like are actively acquired, after the environmental parameters are acquired, the filtering parameters in the Kalman filtering algorithm are dynamically adjusted according to the parameters, the adjustment of the filtering parameters comprises adjustment of parameters such as noise covariance, measurement noise covariance and system noise covariance, and the like, and the Kalman filtering algorithm can respond to environmental changes in real time under different environmental conditions through parameter adjustment, so that reliable and accurate filtering results can be provided in various complex environments.
In this embodiment, taking an autopilot car as an example, a gravity sensor mounted on the autopilot car is used for monitoring acceleration changes of the car in real time, in the autopilot process, the car needs to determine its own motion state according to gravity sensor data to implement accurate path planning and driving control, however, due to sensor aging and external environment effects, noise and zero offset may exist in the raw data of the gravity sensor, so that the autopilot control terminal cannot accurately determine the motion state of the car.
In order to solve the problem, the automatic driving control terminal adopts a Kalman filtering algorithm to carry out filtering processing on the original output data of the gravity sensor data so as to remove noise and random errors in the gravity sensor data and obtain higher-quality and more accurate correction data; and then, the terminal calculates the zero offset according to the correction data, corrects the zero position of the gravity sensor according to the zero offset, and the corrected gravity sensor data is more accurate and reliable, so that accurate acceleration information is provided for the automatic driving control terminal.
In the automatic driving process, the corrected gravity sensor data are used for monitoring the acceleration change of the vehicle in real time, when the vehicle accelerates or decelerates, the automatic driving control terminal can accurately judge the motion state of the vehicle and adjust the driving speed and path planning according to the motion state, and meanwhile, when the vehicle encounters an emergency condition and needs emergency braking or triggers a safety device, the accurate gravity sensor data can help the terminal to rapidly judge and react, so that the driving safety is ensured. Therefore, filtering and zero correction of gravity sensor data is critical to the performance and safety of an automatic driving automobile.
In summary, it can be known that by adopting the preset filtering algorithm to process the gravity sensor data, the embodiment of the application can effectively remove noise signals in the gravity sensor data and retain real and effective data, thereby improving the accuracy of detecting the zero position.
In the second embodiment of the present application, the same or similar content as in the first embodiment of the present application may be referred to the above description, and will not be repeated. On this basis, step S20 may include steps S201 to S202:
Step S201, calculating the zero point position of the current gravity sensor of the vehicle according to the correction data;
in order to ensure the accuracy of the gravity sensor data, the current gravity sensor zero point position of the vehicle is calculated according to the obtained correction data.
And step S202, calculating a difference value between the current zero position of the gravity sensor and the zero position of the standard gravity sensor to obtain the zero offset of the gravity sensor of the vehicle.
And carrying out difference value calculation on the calculated zero position of the current gravity sensor and a preset zero position of the standard gravity sensor to obtain the zero offset of the gravity sensor of the vehicle, wherein the zero offset reflects the deviation degree between the zero position of the current gravity sensor and the standard position.
In a possible embodiment, step S201 may include steps S2011 to S2012:
step S2011, when the vehicle is in a running state, carrying out compound operation analysis on the correction data, the GPS data of the vehicle and the road condition data to obtain the running data of the vehicle, wherein the running data comprises a moving distance, a speed and an acceleration;
When the vehicle is in a running state, GPS data, road condition data and correction data of the vehicle are collected in real time, and the three types of data are subjected to compound operation analysis so as to obtain more accurate and comprehensive running data.
In the composite operation analysis, the GPS data is firstly analyzed to obtain real-time position information of the vehicle, then the GPS data is corrected by combining road condition data such as road type, traffic condition, weather condition and the like, and then the GPS data and the road condition data are comprehensively operated by combining the correction data so as to calculate and obtain accurate key driving data such as moving distance, speed, acceleration and the like of the vehicle.
Step S2012, calculating the current gravity sensor zero position of the vehicle according to the driving data and the correction data by adopting a multi-source data fusion algorithm.
After accurate running data are obtained, calculating the current gravity sensor zero position of the vehicle by adopting a multi-source data fusion algorithm, wherein the multi-source data fusion algorithm comprehensively considers parameters such as speed, acceleration and the like in the running data and error values in the correction data.
Firstly, the algorithm pre-processes the running data to eliminate possible noise and abnormal values, then the algorithm iteratively calculates the zero position of the gravity sensor according to the speed and acceleration changes in the running data and the error values in the correction data, and finally, the algorithm outputs a relatively accurate value of the zero position of the gravity sensor.
In an exemplary embodiment, during the driving process of the vehicle, the terminal may collect GPS data, road condition data, and correction data, where the GPS data provides real-time location information of the vehicle, the road condition data includes road conditions, traffic conditions, weather conditions, and the like of the expressway, and the correction data is gravity sensor data after noise and error are eliminated. The terminal performs compound operation analysis on the GPS data and the road condition data to acquire running data such as moving distance, speed, acceleration and the like of the vehicle. For example, when a vehicle encounters an uphill road section, the system judges that the vehicle is uphill according to road condition data and GPS data, adjusts speed and acceleration values in running data, and then the terminal calculates the current gravity sensor zero position of the vehicle according to the running data and correction data by adopting a multi-source data fusion algorithm, so that the terminal can acquire and analyze the running data of the vehicle in real time, accurately calculate the gravity sensor zero position, and further ensure the running safety and stability of the vehicle.
In one possible embodiment, as shown in fig. 3, the method may further include steps a10 to a40:
step A10, a vehicle running model is established, wherein the vehicle running model comprises a vehicle state evaluation algorithm;
a vehicle driving model is built that will include a vehicle state assessment algorithm that is capable of assessing the current state of the vehicle based on a variety of factors.
Step A20, determining a first running state of the vehicle according to a vehicle state evaluation algorithm and running parameters of the vehicle;
According to the established vehicle running model and the vehicle state evaluation algorithm, the first running state of the vehicle is further determined by using the running parameters of the vehicle, wherein the running parameters comprise mileage, speed, GPS positioning data, environmental parameters (such as weather and road conditions) and the like of the vehicle, and the terminal comprehensively analyzes the data to evaluate the current running state of the vehicle, wherein the running state can be normal running, acceleration, deceleration, turning and the like.
Step A30, comparing the first running state with a second running state determined according to the gravity sensor data;
After determining the first running state of the vehicle, comparing the first running state with a second running state determined according to the gravity sensor data of the vehicle, wherein the gravity sensor data provides real-time information of acceleration of the vehicle so as to reflect the dynamic running state of the vehicle, and comparing the first running state with the second running state to check whether a difference exists between the first running state and the second running state, so as to determine whether the current data of the gravity sensor data has a large deviation.
And step A40, if the first running state and the second running state are different, executing a step of filtering the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data.
If the first running state and the second running state are different, it is determined that the gravity sensor data is affected by certain interference or errors, and in order to eliminate the interference and errors, a preset filtering algorithm is executed to perform filtering processing on the gravity sensor data so as to remove noise and abnormal values in the data, and more accurate and reliable gravity sensor data are obtained.
Therefore, the running state of the vehicle can be more comprehensively estimated, the accuracy of the gravity sensor data is optimized through filtering processing and data correction, and the running safety and stability of the vehicle can be improved.
In one possible embodiment, step S30 may include steps S301 to S302:
step S301, comparing the zero offset of the gravity sensor with a preset offset threshold value to obtain a comparison result;
Comparing the obtained zero offset of the gravity sensor with a preset offset threshold, wherein the preset offset threshold is a value determined according to factors such as the model of the vehicle, the performance of the sensor, the actual use requirement and the like, and is used for judging whether the zero offset of the gravity sensor exceeds an acceptable range, and the value of the preset offset threshold is not particularly limited in the embodiment.
If the zero offset of the gravity sensor is smaller than or equal to a preset offset threshold value, determining that the zero position of the gravity sensor is in a normal range, and no further processing is needed; if the gravity sensor zero-point offset amount is greater than the preset offset threshold value, step S302 is performed.
And step S302, if the comparison result is larger than the preset offset threshold value, correcting the zero position of the gravity sensor of the vehicle to obtain the zero position of the standard gravity sensor, wherein the zero offset of the zero position of the standard gravity sensor is zero.
And under the condition that the zero offset of the gravity sensor exceeds a preset threshold value, correcting the zero position of the gravity sensor of the vehicle to obtain a zero position of the standard gravity sensor, wherein the zero offset of the position is zero, and then, the terminal can apply the corrected zero position of the gravity sensor to vehicle control so as to update relevant parameters and settings, thereby ensuring that dynamic control and safety monitoring can be carried out based on accurate data of the gravity sensor, and improving the performance and safety of the vehicle.
It should be noted that the foregoing examples are only for understanding the present application, and are not to be construed as limiting the zero point correction method of the present application, and that many simple variations based on this technical concept are possible within the scope of the present application.
The embodiment of the present application further provides a zero correction device, referring to fig. 4, where the zero correction device includes:
The filtering module 10 is used for filtering the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data;
an offset calculation module 20 for calculating a gravity sensor zero offset of the vehicle from the correction data;
The zero correction module 30 is configured to correct the zero position of the gravity sensor of the vehicle according to the zero offset of the gravity sensor, so as to obtain the zero position of the standard gravity sensor.
Optionally, the filtering module 10 is further configured to:
Determining a state vector and an observation vector based on the gravity sensor data of the vehicle;
obtaining a predicted state vector and a predicted error covariance matrix of the next moment according to the observed vector of the current moment and the state vector of the previous moment;
determining a Kalman gain based on the prediction error covariance matrix, the observation matrix, and the observation noise covariance;
And updating the prediction state vector and the prediction error covariance matrix based on the Kalman gain and the new observation vector respectively to obtain correction data, wherein the correction data is correction data.
Optionally, the zero correction device is further configured to:
acquiring environmental parameters;
and adjusting the filtering parameters in the Kalman filtering algorithm according to the environment parameters so as to enable the Kalman filtering algorithm to adapt to environment changes.
Optionally, the offset calculation module 20 is further configured to:
calculating the zero position of the current gravity sensor of the vehicle according to the correction data;
and carrying out difference value calculation on the current zero position of the gravity sensor and the zero position of the standard gravity sensor to obtain the zero offset of the gravity sensor of the vehicle.
Optionally, the offset calculation module 20 is further configured to:
When the vehicle is in a running state, carrying out compound operation analysis on the correction data, the GPS data of the vehicle and the road condition data to obtain the running data of the vehicle, wherein the running data comprises a moving distance, a speed and an acceleration;
And calculating the current gravity sensor zero position of the vehicle according to the driving data and the correction data by adopting a multi-source data fusion algorithm.
Optionally, the zero correction device is further configured to:
Establishing a vehicle running model, wherein the vehicle running model comprises a vehicle state evaluation algorithm;
determining a first running state of the vehicle according to a vehicle state evaluation algorithm and running parameters of the vehicle;
comparing the first driving state with a second driving state determined according to the gravity sensor data;
And if the first running state and the second running state are different, executing a step of filtering the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data.
Optionally, the zero correction module 30 is further configured to:
Comparing the zero offset of the gravity sensor with a preset offset threshold value to obtain a comparison result;
And if the comparison result is larger than the preset offset threshold value, correcting the zero position of the gravity sensor of the vehicle to obtain the zero position of the standard gravity sensor, wherein the zero offset of the zero position of the standard gravity sensor is zero.
The zero point correction device provided by the embodiment of the application can solve the technical problem of reduced vehicle collision detection accuracy caused by zero point position deviation of the gravity sensor by adopting the zero point correction method in the embodiment. Compared with the prior art, the zero point correction device provided by the embodiment of the application has the same beneficial effects as the zero point correction method provided by the embodiment, and other technical features in the zero point correction device are the same as the features disclosed by the zero point correction method of the embodiment, and are not repeated herein.
The embodiment of the application provides electronic equipment, which comprises: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the zero point correction method in the first embodiment.
Referring now to fig. 5, a schematic diagram of an electronic device suitable for use in implementing embodiments of the present application is shown. The electronic device in the embodiment of the present application may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (PortableApplicationDescription: a tablet), a PMP (PortableMediaPlayer: a portable multimedia player), an in-vehicle terminal (e.g., an in-vehicle navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The zero point correction device shown in fig. 5 is only one example, and should not impose any limitation on the function and use range of the embodiment of the present application.
As shown in fig. 5, the electronic apparatus may include a processing device 1001 (e.g., a central processor, a graphics processor, etc.) that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage device 1003 into a Random Access Memory (RAM) 1004. In the RAM1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, the ROM1002, and the RAM1004 are connected to each other by a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. In general, the following systems may be connected to the I/O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, and the like; an output device 1008 including, for example, a liquid crystal display (LCD: liquidCrystalDisplay), a speaker, a vibrator, and the like; storage device 1003 including, for example, a magnetic tape, a hard disk, and the like; and communication means 1009. The communication means 1009 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While electronic devices having various systems are shown in the figures, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. The above-described functions defined in the method of the disclosed embodiment of the application are performed when the computer program is executed by the processing device 1001.
The electronic equipment provided by the embodiment of the application can solve the technical problem of reduced accuracy of vehicle collision detection caused by zero position offset of the gravity sensor by adopting the zero correction method in the embodiment. Compared with the prior art, the electronic device provided by the embodiment of the application has the same beneficial effects as the zero point correction method provided by the embodiment, and other technical features in the electronic device are the same as the features disclosed by the zero point correction method of the previous embodiment, and are not described in detail herein.
It should be understood that portions of the disclosure of embodiments of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present application, and the application should be covered. Therefore, the protection scope of the application is subject to the protection scope of the claims.
An embodiment of the present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon for performing the zero point correction method in the above-described embodiment.
The computer readable storage medium according to the embodiments of the present application may be, for example, a usb disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM: erasableProgrammableReadOnlyMemory or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The above-described computer-readable storage medium may be contained in an electronic device; or may exist alone without being assembled into an electronic device.
The computer-readable storage medium carries one or more programs that, when executed by an electronic device, cause the electronic device to: filtering the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data; calculating the zero offset of the gravity sensor of the vehicle according to the correction data; and correcting the zero position of the gravity sensor of the vehicle according to the zero offset of the gravity sensor to obtain the zero position of the standard gravity sensor.
Computer program code for carrying out operations for embodiments of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN: localAreaNetwork) or a wide area network (WAN: wideAreaNetwork), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present application may be implemented in software or in hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.
The readable storage medium provided by the embodiment of the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (namely a computer program) for executing the zero point correction method, so that the technical problem that the accuracy of vehicle collision detection is reduced due to zero point position deviation of a gravity sensor can be solved. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the embodiment of the application are the same as those of the zero point correction method provided by the above embodiment, and are not described herein.
The foregoing is only a part of embodiments of the present application, and is not intended to limit the scope of the present application, and all the equivalent structural changes made by the description and drawings of the present application or the direct/indirect application in other related technical fields are included in the scope of the present application.
Claims (7)
1. A zero correction method, characterized in that the method comprises:
filtering the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data;
Calculating the zero offset of a gravity sensor of the vehicle according to the correction data;
correcting the zero position of the gravity sensor of the vehicle according to the zero offset of the gravity sensor to obtain the zero position of the standard gravity sensor;
the step of calculating the gravity sensor zero point offset amount of the vehicle from the correction data includes:
Calculating the current gravity sensor zero point position of the vehicle according to the correction data;
Performing difference value calculation on the current gravity sensor zero position and the standard gravity sensor zero position to obtain the gravity sensor zero offset of the vehicle;
the step of calculating the current gravity sensor zero point position of the vehicle according to the correction data comprises the following steps:
when the vehicle is in a running state, carrying out compound operation analysis on the correction data, the GPS data of the vehicle and the road condition data to obtain running data of the vehicle, wherein the running data comprises moving distance, speed and acceleration;
calculating the current gravity sensor zero position of the vehicle according to the driving data and the correction data by adopting a multi-source data fusion algorithm;
When the preset filtering algorithm is a kalman filtering algorithm, the step of filtering the gravity sensor data of the vehicle through the preset filtering algorithm to obtain correction data comprises the following steps:
Determining a state vector and an observation vector based on the gravity sensor data of the vehicle;
Obtaining a predicted state vector and a predicted error covariance matrix of the next moment according to the observed vector of the current moment and the state vector of the previous moment;
based on Kalman filtering algorithm, a dynamic model of gravity sensor data is established, the gravity sensor data is expressed as a discrete time linear dynamic system, and a state equation and an observation equation of the gravity sensor data are respectively as follows:
The equation of state: x (k) =ax (k-1) +bu (k-1) +w (k-1);
Observation equation: z (k) =hx (k) +v (k);
Wherein X (k) is the state vector of the system at time k, A is the state transition matrix, B is the control input matrix, U (k-1) is the control input vector, W (k-1) is the process noise vector, Z (k) is the observation vector of the system at time k, H is the observation matrix, and V (k) is the observation noise vector;
Obtaining a predicted state vector and a predicted error covariance matrix of the next moment according to the observed vector of the current moment and the state vector of the previous moment, wherein a prediction equation comprises:
Prediction state: hat { X } (k|k-1) = Ahat { X } (k-1|k-1) +BU (k-1);
prediction covariance: p (k|k-1) =AP (k-1|k-1) A++T;
wherein hat { X } (k|k-1) is a predicted value of a state vector of time k, i.e., a predicted state vector of the next time, P (k|k-1) is a predicted covariance matrix, i.e., a predicted error covariance matrix of the next time, and Q is a process noise covariance matrix;
determining a kalman gain based on the prediction error covariance matrix, the observation matrix, and the observation noise covariance;
updating the prediction state vector and the prediction error covariance matrix based on the Kalman gain and the new observation vector respectively to obtain correction data, wherein the correction data is correction data;
Determining an update equation of the Kalman filtering, wherein the update equation is used for updating the state estimation value according to the observed value and the predicted value of the current moment, and the expression of the update equation is as follows:
Kalman gain: k (K) =P (k|k-1) H≡T (HP (k|k-1) H≡T+R) ≡1;
updating the state: hat { X } (k|k) =hat { X } (k|k-1) +k (K) [ Z (K) -Hhat { X } (k|k-1) ];
Updating covariance: p (k|k) = (1-K (K) H) P (k|k-1);
Wherein K (K) is Kalman gain, R is observation noise covariance matrix, hat { X } (k|k) is update estimation value of state vector of time K, namely prediction state vector, and P (k|k) is updated covariance matrix, namely prediction error covariance matrix;
And estimating and updating the vehicle state in each time step by circularly carrying out prediction and updating operation, so as to obtain correction data, wherein the correction data is correction data.
2. The method of claim 1, wherein the method further comprises:
acquiring environmental parameters;
And adjusting the filtering parameters in the Kalman filtering algorithm according to the environment parameters so as to enable the Kalman filtering algorithm to adapt to environment changes.
3. The method of claim 1, wherein the method further comprises:
Establishing a vehicle running model, wherein the vehicle running model comprises a vehicle state evaluation algorithm;
Determining a first driving state of the vehicle according to the vehicle state evaluation algorithm and driving data of the vehicle;
comparing the first travel state with a second travel state determined from the gravity sensor data;
And if the first running state and the second running state are different, executing a step of filtering the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data.
4. The method of claim 1, wherein the step of correcting the gravity sensor zero point position of the vehicle based on the gravity sensor zero point offset amount to obtain a standard gravity sensor zero point position comprises:
comparing the zero offset of the gravity sensor with a preset offset threshold value to obtain a comparison result;
And if the comparison result shows that the zero offset of the gravity sensor is larger than the preset offset threshold, correcting the zero position of the gravity sensor of the vehicle to obtain a zero position of the standard gravity sensor, wherein the zero offset of the zero position of the standard gravity sensor is zero.
5. A zero point correction device, characterized in that the device comprises:
the filtering module is used for carrying out filtering processing on the gravity sensor data of the vehicle through a preset filtering algorithm to obtain correction data;
An offset amount calculating module for calculating a gravity sensor zero offset amount of the vehicle according to the correction data;
the zero point correction module is used for correcting the zero point position of the gravity sensor of the vehicle according to the zero point offset of the gravity sensor to obtain the zero point position of the standard gravity sensor;
the offset calculation module is further configured to:
calculating the zero position of the current gravity sensor of the vehicle according to the correction data;
Calculating the difference value between the current zero position of the gravity sensor and the zero position of the standard gravity sensor to obtain the zero offset of the gravity sensor of the vehicle;
the offset calculation module is further configured to:
When the vehicle is in a running state, carrying out compound operation analysis on the correction data, the GPS data of the vehicle and the road condition data to obtain the running data of the vehicle, wherein the running data comprises a moving distance, a speed and an acceleration;
Calculating the zero position of a current gravity sensor of the vehicle according to the driving data and the correction data by adopting a multisource data fusion algorithm;
The filtering module is further configured to:
Determining a state vector and an observation vector based on the gravity sensor data of the vehicle;
Obtaining a predicted state vector and a predicted error covariance matrix of the next moment according to the observed vector of the current moment and the state vector of the previous moment;
based on Kalman filtering algorithm, a dynamic model of gravity sensor data is established, the gravity sensor data is expressed as a discrete time linear dynamic system, and a state equation and an observation equation of the gravity sensor data are respectively as follows:
The equation of state: x (k) =ax (k-1) +bu (k-1) +w (k-1);
Observation equation: z (k) =hx (k) +v (k);
Wherein X (k) is the state vector of the system at time k, A is the state transition matrix, B is the control input matrix, U (k-1) is the control input vector, W (k-1) is the process noise vector, Z (k) is the observation vector of the system at time k, H is the observation matrix, and V (k) is the observation noise vector;
Obtaining a predicted state vector and a predicted error covariance matrix of the next moment according to the observed vector of the current moment and the state vector of the previous moment, wherein a prediction equation comprises:
Prediction state: hat { X } (k|k-1) = Ahat { X } (k-1|k-1) +BU (k-1);
prediction covariance: p (k|k-1) =AP (k-1|k-1) A++T;
wherein hat { X } (k|k-1) is a predicted value of a state vector of time k, i.e., a predicted state vector of the next time, P (k|k-1) is a predicted covariance matrix, i.e., a predicted error covariance matrix of the next time, and Q is a process noise covariance matrix;
determining a kalman gain based on the prediction error covariance matrix, the observation matrix, and the observation noise covariance;
updating the prediction state vector and the prediction error covariance matrix based on the Kalman gain and the new observation vector respectively to obtain correction data, wherein the correction data is correction data;
Determining an update equation of the Kalman filtering, wherein the update equation is used for updating the state estimation value according to the observed value and the predicted value of the current moment, and the expression of the update equation is as follows:
Kalman gain: k (K) =P (k|k-1) H≡T (HP (k|k-1) H≡T+R) ≡1;
updating the state: hat { X } (k|k) =hat { X } (k|k-1) +k (K) [ Z (K) -Hhat { X } (k|k-1) ];
Updating covariance: p (k|k) = (1-K (K) H) P (k|k-1);
Wherein K (K) is Kalman gain, R is observation noise covariance matrix, hat { X } (k|k) is update estimation value of state vector of time K, namely prediction state vector, and P (k|k) is updated covariance matrix, namely prediction error covariance matrix;
And estimating and updating the vehicle state in each time step by circularly carrying out prediction and updating operation, so as to obtain correction data, wherein the correction data is correction data.
6. An electronic device, the device comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the method of any one of claims 1 to 4.
7. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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