CN116756265A - Track data processing method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a track data processing method, a track data processing device, electronic equipment and a storage medium, and relates to the technical field of data processing, wherein the track data processing method comprises the following steps: acquiring original data of a target ship; extracting an initialization observation value from the original data; predicting the target ship at a second unit time point according to the initialized observed value to obtain the predicted state information of the target ship at the second unit time point; filtering the predicted state information and the state information matched with a second unit time point in the original data to obtain target state information of the target ship at the second unit time point; and updating the track data matched with the target ship according to the original data and the target data, wherein the target data comprises target state information of the target ship at the second unit time point. The invention can improve the accuracy of track data.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and apparatus for processing track data, an electronic device, and a storage medium.
Background
The ship track smoothing technology belongs to the track planning and control technology in the navigation field, and has important application in actual ship operation. In recent years, with the rapid development of the fields of aerospace, robots, autopilot and the like, the technology of smoothing the track of ships is also continuously developing.
In the prior art, control is generally performed by a proportional-integral-derivative (Proportion Integration Differentiation, PID) controller, a fuzzy controller or the like. However, these methods are prone to problems such as oscillations and oscillations in the case of uncertain ship motion models and complex environmental conditions, thereby affecting trajectory smoothness and stability of the ship.
Therefore, the prior art has a problem of low accuracy of the trajectory data.
Disclosure of Invention
The embodiment of the invention provides a track data processing method, a track data processing device, electronic equipment and a storage medium, which are used for solving the problem of low track data accuracy in the prior art.
In a first aspect, an embodiment of the present invention provides a method for processing track data, including:
acquiring original data of a target ship, wherein the original data comprises state information of the target ship at a plurality of unit time points, and the state information comprises position information and speed information;
Extracting an initialization observation value from the original data, wherein the initialization observation value is state information of a first unit time point in the plurality of unit time points in the original data;
predicting the target ship at a second unit time point according to the initialized observed value to obtain the predicted state information of the target ship at the second unit time point;
filtering the predicted state information and the state information matched with a second unit time point in the original data to obtain target state information of the target ship at the second unit time point;
and updating the track data matched with the target ship according to the original data and the target data, wherein the target data comprises target state information of the target ship at the second unit time point.
Optionally, the initialization observation further includes heading information;
the extracting the initialization observation value from the original data comprises the following steps:
classifying the original data to obtain a data set of the target ship, wherein the data set comprises: the position information, the speed information and the course information of the target ship in a preset time period;
And taking the position information, the speed information and the course information corresponding to the first unit time point in the data set as the initialization observation value.
Optionally, classifying the raw data to obtain a data set of the target ship, including:
acquiring an identifier of the target ship;
hashing data belonging to the target ship in the original data into subtasks according to the identifier by using a real-time computing framework (Flink);
generating a data set of the target ship according to the elements in the subtasks.
Optionally, the predicting the target ship at the second unit time point according to the initialized observed value, to obtain the predicted state information of the target ship at the second unit time point, includes:
converting the initialization observation value into a vector form, and acquiring a preset state transition matrix;
multiplying the vector corresponding to the initialization observation value by the preset state transition matrix to obtain first state information of the target ship in the form of a vector at a second unit time point;
and generating the prediction state information according to the first state information.
Optionally, after predicting the target ship at the second unit time point according to the initialized observed value, the method further includes:
Acquiring a first covariance matrix, a state transition matrix and a noise covariance matrix, wherein the first covariance matrix is matched with the first unit time point;
determining a second covariance matrix according to the first covariance matrix, wherein the second covariance matrix is obtained by adding a first part and a second part, and the first part is a transposed determinant of the state transition matrix multiplied by the first covariance matrix and multiplied by the state transition matrix;
the second portion is the noise covariance matrix.
Optionally, the filtering processing is performed on the state information matched with the second unit time point in the original data to obtain target state information of the target ship at the second unit time point, including:
acquiring a covariance matrix of measurement noise;
determining a Kalman gain according to the covariance matrix of the measurement noise and the second covariance matrix, wherein the Kalman gain is obtained by dividing a third part by a fourth part, the third part is the second covariance matrix, and the fourth part is the sum of the second covariance matrix and the covariance matrix of the measurement noise;
extracting state information matched with the second unit time point from the original data;
And determining the target state information according to the state information of the second unit time point, the Kalman gain and the predicted state information.
Optionally, the updating the trajectory graph matched with the target ship according to the original data and the target data includes:
determining the position information of the first unit time point according to the original data, determining the first position information of the second unit time point according to the target data, and determining the second position information of the second unit time point according to the original data, wherein the first position information is the position information matched with the second unit time point after filtering correction, and the second position information is the position information matched with the second unit time point obtained by the acquisition equipment;
calculating a first navigational speed between the first unit time point and the second unit time point according to the position information of the first unit time point, the first position information of the second unit time point and the unit time;
calculating a second navigational speed between the first unit time point and the second unit time point according to the position information of the first unit time point, the second position information of the second unit time point and the unit time;
Under the condition that the first navigational speed and the second navigational speed meet preset conditions, the position information of the first unit time point is replaced with the position information of the second unit time point, so that a track diagram matched with the target ship is updated;
the preset condition is that the first navigational speed is greater than a preset speed and the second navigational speed is less than the preset speed; or (b)
The first navigational speed is greater than twice the second navigational speed and the second navigational speed is greater than the preset speed.
In a second aspect, an embodiment of the present invention provides a track data processing apparatus, including:
a first acquisition module for acquiring raw data of a target ship, the raw data including state information of the target ship at a plurality of unit time points, the state information including position information and speed information;
the first processing module is used for extracting an initialization observation value from the original data, wherein the initialization observation value is state information of a first unit time point in the plurality of unit time points in the original data;
the second processing module is used for predicting the target ship at a second unit time point according to the initialized observed value to obtain the predicted state information of the target ship at the second unit time point;
The third processing module is used for carrying out filtering processing on the predicted state information and the state information matched with a second unit time point in the original data to obtain target state information of the target ship at the second unit time point;
and the updating module is used for updating the track data matched with the target ship according to the original data and the target data, wherein the target data comprises target state information of the target ship at the second unit time point.
Optionally, the initialization observation further includes heading information;
the first processing module includes:
the first processing unit is used for classifying the original data to obtain a data set of the target ship, and the data set comprises: the position information, the speed information and the course information of the target ship in a preset time period;
and the second processing unit is used for taking the position information, the speed information and the course information corresponding to the first unit time point in the data set as the initialization observation value.
Optionally, the first processing unit includes:
acquiring an identifier of the target ship;
hashing data belonging to the target ship in the original data into subtasks according to the identifier by using a real-time computing framework (Flink);
Generating a data set of the target ship according to the elements in the subtasks.
Optionally, the second processing module includes:
the third processing unit is used for converting the initialization observed value into a vector form and acquiring a preset state transition matrix;
a fourth processing unit, configured to multiply a vector corresponding to the initialized observed value by the preset state transition matrix, so as to obtain first state information of the target ship in a vector form at a second unit time point;
the first generation unit is used for generating the prediction state information according to the first state information.
Optionally, the apparatus further comprises:
the second acquisition module is used for acquiring a first covariance matrix, a state transition matrix and a noise covariance matrix, wherein the first covariance matrix is matched with the first unit time point;
a fourth processing module, configured to determine a second covariance matrix according to the first covariance matrix, where the second covariance matrix is obtained by adding a first portion and a second portion, where the first portion is a transposed determinant of the state transition matrix multiplied by the first covariance matrix and multiplied by the state transition matrix;
the second portion is the noise covariance matrix.
Optionally, the third processing module includes:
a first acquisition unit configured to acquire a covariance matrix of measurement noise;
a fifth processing unit, configured to determine a kalman gain according to the covariance matrix of the measurement noise and the second covariance matrix, where the kalman gain is obtained by dividing a third portion by a fourth portion, where the third portion is the second covariance matrix, and the fourth portion is a sum of the second covariance matrix and the covariance matrix of the measurement noise;
a sixth processing unit, configured to extract state information matching the second unit time point from the original data;
and a seventh processing unit, configured to determine the target state information according to the state information of the second unit time point, the kalman gain, and the predicted state information.
Optionally, the updating module includes:
an eighth processing unit, configured to determine, according to the original data, location information of the first unit time point, determine, according to the target data, first location information of the second unit time point, determine, according to the original data, second location information of the second unit time point, where the first location information is location information matched with the second unit time point after filtering correction, and the second location information is location information matched with the second unit time point obtained by the acquisition device;
A first calculation unit configured to calculate a first navigational speed between the first unit time point and the second unit time point according to the position information of the first unit time point, the first position information of the second unit time point, and the unit time;
a second calculation unit configured to calculate a second navigational speed between the first unit time point and the second unit time point according to the position information of the first unit time point, the second position information of the second unit time point, and the unit time;
an updating unit, configured to replace the position information of the first unit time point with the position information of the second unit time point, so as to update a trajectory graph matched with the target ship, where the first navigational speed and the second navigational speed meet preset conditions;
the preset condition is that the first navigational speed is greater than a preset speed and the second navigational speed is less than the preset speed; or (b)
The first navigational speed is greater than twice the second navigational speed and the second navigational speed is greater than the preset speed.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of processing trace data according to the first aspect.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer readable storage medium storing computer instructions comprising:
the computer instructions are for causing the computer to perform the method of processing trajectory data according to the first aspect.
In the embodiment of the invention, the original data of the target ship is firstly obtained, the initialization observation value is extracted from the original data, wherein the initialization observation value is the state information of the corresponding starting point in the original data, namely the state information of a first unit time point, then the state information of the target ship at a second unit time point is predicted according to the initialization observation value to obtain the predicted state information, then the state information of the second unit time point is filtered according to the predicted state information and the original data, so that the target state information of the target ship at the second unit time point is obtained, finally the original data and the target data are taken as judgment basis, the track data are updated, the error of the data can be reduced by the method, the data is smoothed, and the accuracy of the position and the speed of the target point, namely the accuracy of the track data is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a track data processing method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a track data processing device according to an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device for implementing a method of processing trajectory data according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms "first," "second," and the like in embodiments of the present invention are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, fig. 1 is a flowchart of a track data processing method according to an embodiment of the present invention, as shown in fig. 1, including the following steps:
step 101, obtaining original data of a target ship, wherein the original data comprise state information of the target ship at a plurality of unit time points, and the state information comprises position information and speed information;
102, extracting an initialization observation value from the original data, wherein the initialization observation value is state information of a first unit time point in the plurality of unit time points in the original data;
Step 103, predicting the target ship at a second unit time point according to the initialized observed value to obtain the predicted state information of the target ship at the second unit time point;
104, filtering the predicted state information and the state information matched with a second unit time point in the original data to obtain target state information of the target ship at the second unit time point;
and step 105, updating the track data matched with the target ship according to the original data and target data, wherein the target data comprises target state information of the target ship at the second unit time point.
The steps 101, 102, 103, 104, and 105 included in the above-mentioned power bill management method may be executed by an electronic computer or a virtual machine, which is not limited to the embodiment of the present invention.
In step 101, the raw data may be obtained by a data acquisition device such as a device or a radar provided on the ship, and the raw data may be understood as a data set, that is, the raw data may include data of a plurality of target ships, specifically, may include real-time complete track data of the target ships, the track data may further include status information of a plurality of unit time points, and the status information may be understood as position information and speed information of the target ships at the unit time points.
In addition, the location information may be longitude and latitude information, that is, specific longitude and latitude of the target ship at a certain moment, or may be coordinates of the target ship at a certain moment, which is not limited to the embodiment of the present invention.
It should be noted that, the above raw data may further include heading information of the target ship.
In step 102, before extracting the initialization observation from the above raw data, the following steps may be further performed: since the raw data may be data including a plurality of target ships, the raw data may be classified according to individual target ships, for example: and classifying the original data according to the identifiers of the target ships, and if the original data comprises the data of three target ships, obtaining three parts of data after classification, wherein the three parts of data correspond to the three target ships respectively. Of course, the original data may be classified in other manners, and it is sufficient to collect the data belonging to the same target ship.
It should be noted that, in the embodiment of the present invention, the trajectory data of a single target ship is taken as the object of processing, and then the initial observed value is extracted from the raw data, and it is understood that the position information (longitude and latitude) and the speed information (ship navigation speed) of the starting point of the target ship in the raw data are taken as the initial observed value, where the starting point is the first unit time point, and the initial observed value may also include heading information of the target ship.
In step 103, the target ship is predicted at a second unit time point according to the initialized observed value, so as to obtain predicted status information of the target ship at the second unit time point, where the second unit time point may be understood as a point separated from the first unit time point by one unit time, and of course, the second unit time point may include several points, for example: if the original data only comprises data of two points of the matched target ship, the second unit time point represents one point, and if the original data comprises data of N points of the matched target ship, the second unit time point represents N-1 points.
The predicted status information indicates status information of the target ship at the second unit time, for example: speed information, location information, and heading information.
In the embodiment of the present invention, the predicted state information may be obtained by a state prediction equation in the filtering process, and since the state of the target ship is changed with time, it is necessary to define a state transfer function to describe how the state vector is changed with time.
In step 104, filtering the predicted state information and the state information of the original data, which matches the second unit time point, to obtain target state information of the target ship at the second unit time point, where the target state information may be represented as a speed, a position, or a heading of the target ship at the second unit time point, and may further include information such as an acceleration of the target ship at the second unit time point.
In step 104, an extended kalman filter may be used, that is, the kalman filter may be used to smooth the ship track data to obtain the target state information, and the specific steps may include: calculation of the kalman gain and input of the target state information.
In the process of calculating the kalman gain, the covariance needs to be calculated, and the covariance can be understood as a reference for calculating the confidence of the data.
In addition, the above steps can be performed by using unscented kalman Filter (Unscented Kalman Filter, UKF) or Particle Filter (Particle Filter) during the filtering process to process nonlinear and non-gaussian distribution systems.
It should be understood that in the process of smoothing the ship track data using the kalman filter, parameters of the kalman filter need to be defined, and the kalman filter parameters include, but are not limited to, a state covariance matrix, a process noise covariance matrix, an observation noise covariance matrix and an initial state vector.
Specifically, the adjustment method of the self-adaptive threshold can be changed according to specific requirements, for example, a fuzzy logic control method or a neural network control method can be adopted, and more refined and intelligent tracking threshold adjustment can be realized.
In step 105, the track data matched with the target ship is updated according to the original data and the target data, where the target data includes the target state information of the target ship at the second unit time point, and it should be understood that the updating operation may be to replace the track point in the track data matched with the target ship, so as to achieve the purpose of updating, so that a preset condition may be set to determine whether the track point in the track data can be replaced.
The preset conditions may be set according to the speed of the ship, for example: and determining the navigational speed from the first unit time point to the second unit time point in the original data, determining the navigational speed from the second unit time point to the first unit time point corresponding to the target state information, and replacing the original track point under the condition that the two navigational speeds meet a preset relational expression so as to update the track data.
It should be noted that, related personnel can adjust navigation according to the updated track data to improve the security of the heading.
In this embodiment, first, the original data of the target ship is obtained, and an initialization observation value is extracted from the original data, wherein the initialization observation value is state information of a corresponding starting point in the original data, namely, state information of a first unit time point, then the state information of the target ship at a second unit time point is predicted according to the initialization observation value to obtain predicted state information, then the state information of the target ship at the second unit time point is subjected to filtering processing according to the predicted state information and the original data, so as to obtain target state information of the target ship at the second unit time point, finally, the original data and the target data are taken as judgment basis, and the trace data are updated.
Optionally, the initialization observation further includes heading information;
the extracting the initialization observation value from the original data comprises the following steps:
classifying the original data to obtain a data set of the target ship, wherein the data set comprises: the position information, the speed information and the course information of the target ship in a preset time period;
And taking the position information, the speed information and the course information corresponding to the first unit time point in the data set as the initialization observation value.
In this embodiment, the raw data is first classified to obtain the position information, the speed information and the heading information of the target ship in the preset time period, and the position information, the speed information and the heading information corresponding to the starting point in the dataset are used as the initialization observation values.
Optionally, classifying the raw data to obtain a data set of the target ship, including:
acquiring an identifier of the target ship;
hashing data belonging to the target ship in the original data into subtasks according to the identifier by using a real-time computing framework (Flink);
generating a data set of the target ship according to the elements in the subtasks.
In this embodiment, all data matching the target ship are acquired according to the identifier of the target ship, specifically, a real-time computing frame Flink is used, key by operation is performed according to the unique identifier of the target ship, so that data or elements of the same key are divided into an operator, and finally, a data set of the target ship is generated.
Optionally, the predicting the target ship at the second unit time point according to the initialized observed value, to obtain the predicted state information of the target ship at the second unit time point, includes:
converting the initialization observation value into a vector form, and acquiring a preset state transition matrix;
multiplying the vector corresponding to the initialization observation value by the preset state transition matrix to obtain first state information of the target ship in the form of a vector at a second unit time point;
and generating the prediction state information according to the first state information.
In this embodiment, an extended kalman filter is adopted, the initialized observed value can be converted into a vector form, a preset state transition matrix is obtained, the first state information in the vector form can be directly obtained through a state prediction equation, specifically, the vector corresponding to the initialized observed value is multiplied by the preset state transition matrix, so that the first state information in the vector form of the target ship at a second unit time point is obtained, and finally, the predicted state information is generated.
Specifically, the determination of the above-mentioned prediction state information may also be obtained by the following state prediction equation:
x(k) = F * x(k-1) + B * u(k-1)
wherein x (k) represents a state vector representing the target ship at the second unit time point, that is, the predicted state information, the state vector may be obtained according to the state information, F represents a state transition matrix for transitioning the state of the previous time point to the current time point, and x (k-1) and u (k-1) may both be represented as the state vector of the target ship at the first unit time point, B represents an input matrix representing the influence of external input on the state.
It should be noted that u (k-1) and B may be used as optional parameters, and thus, the above state prediction equation may be expressed as follows:
x(k) = F * x(k-1)
it will be appreciated that the state vector of the target vessel at the first unit point in time can be represented by x (k-1).
In addition, for the above prediction state information, the following state transfer function may be expressed as the following formula:
x(k) = f(x(k-1), u(k-1), dt)
where x (k) represents a state vector of the target ship at the second unit time point, that is, the predicted state information, the state vector may be obtained according to the state information, and x (k-1) and u (k-1) may both be represented as a state vector of the target ship at the first unit time point, for example: the speed and position of the target ship at the first unit time point, dt represents the time interval, which can be generally calculated through a time stamp, and finally, the function f is a state transfer function, which can be defined according to the ship kinematics principle, and will not be described herein.
Optionally, after predicting the target ship at the second unit time point according to the initialized observed value, the method further includes:
acquiring a first covariance matrix, a state transition matrix and a noise covariance matrix, wherein the first covariance matrix is matched with the first unit time point;
determining a second covariance matrix according to the first covariance matrix, wherein the second covariance matrix is obtained by adding a first part and a second part, and the first part is a transposed determinant of the state transition matrix multiplied by the first covariance matrix and multiplied by the state transition matrix;
the second portion is the noise covariance matrix.
It should be noted that, the covariance may represent the confidence of the data, and therefore it is necessary to use the second covariance matrix in the embodiment of the present invention, specifically, the second covariance matrix may be obtained by the following formula:
P(k) = F * P(k-1) * F^T + Q
wherein P (k) represents a second covariance matrix, F represents a state transition matrix for transferring the state of the previous moment to the current moment, P (k-1) represents a first covariance matrix, and Q represents a covariance matrix estimation of system process noise.
In this embodiment, the introduction of covariance is mainly used to measure the overall error of two variables, first, a first covariance matrix, a state transition matrix and a noise covariance matrix are obtained, where the first covariance matrix matches the first unit time point, then, a second covariance matrix is determined according to the first covariance matrix, by this method, the error of track data processing is reduced, and the embodiment of the invention has the characteristics of high precision, high efficiency and robustness, so that the method is suitable for practical radar target tracking application.
Optionally, the filtering processing is performed on the state information matched with the second unit time point in the original data to obtain target state information of the target ship at the second unit time point, including:
acquiring a covariance matrix of measurement noise;
determining a Kalman gain according to the covariance matrix of the measurement noise and the second covariance matrix, wherein the Kalman gain is obtained by dividing a third part by a fourth part, the third part is the second covariance matrix, and the fourth part is the sum of the second covariance matrix and the covariance matrix of the measurement noise;
Extracting state information matched with the second unit time point from the original data;
and determining the target state information according to the state information of the second unit time point, the Kalman gain and the predicted state information.
In this embodiment, the kalman gain is first determined according to the obtained covariance matrix of the measurement noise and the second covariance matrix, and the specific formula is as follows:
K(k) = P(k) * H^T * (H * P(k) * H^T + R)^(-1)
where K (K) represents a kalman gain for combining the measured value and the estimated value, P (K) represents a covariance matrix corresponding to the second unit time point, H represents an observation matrix for mapping the state to the measurement space, and R represents a covariance matrix of the measurement noise.
In general, the observation matrix may be set as an identity matrix, and thus, the above formula may be converted into:
K（k）=P（k）/P（k）+R
then, extracting state information matching the second unit time point from the original data, wherein the state information can be acquired by an acquisition device, and determining the target state information according to the state information of the second unit time point, the kalman gain and the predicted state information, and the target state information can be expressed by the following formula:
x(k’) = x(k) + K(k) * (z(k) - H * x(k))
Where x (K ') represents the target state information, x (K) represents the predicted state information, K (K) represents a kalman gain, z (K) represents the state information of the second unit time point, H represents an observation matrix for mapping the state to the measurement space, and x (K') may be understood as the state information at the second unit time point including speed information, position information, and heading information.
It should be noted that, if the second unit time point includes a plurality of track points, after the calculation is completed, the covariance needs to be updated, and then iteration is continuously performed, where the covariance update may be represented by the following formula:
P(k+1) = (I - K(k) * H) * P(k)
where P (k+1) represents the covariance matrix estimate of the third locus point, K (K) represents the Kalman gain, H represents the observation matrix, and P (K) represents the covariance matrix estimate of the corresponding second locus point.
In addition, the extended kalman Filter may be replaced by other nonlinear filters, such as unscented kalman Filter (Unscented Kalman Filter, UKF) or Particle Filter (Particle Filter), to process nonlinear and non-gaussian distributed systems, thereby completing the steps of the embodiments of the invention.
Optionally, the updating the trajectory graph matched with the target ship according to the original data and the target data includes:
Determining the position information of the first unit time point according to the original data, determining the first position information of the second unit time point according to the target data, and determining the second position information of the second unit time point according to the original data, wherein the first position information is the position information matched with the second unit time point after filtering correction, and the second position information is the position information matched with the second unit time point obtained by the acquisition equipment;
calculating a first navigational speed between the first unit time point and the second unit time point according to the position information of the first unit time point, the first position information of the second unit time point and the unit time;
calculating a second navigational speed between the first unit time point and the second unit time point according to the position information of the first unit time point, the second position information of the second unit time point and the unit time;
under the condition that the first navigational speed and the second navigational speed meet preset conditions, the position information of the first unit time point is replaced with the position information of the second unit time point, so that a track diagram matched with the target ship is updated;
The preset condition is that the first navigational speed is greater than a preset speed and the second navigational speed is less than the preset speed; or (b)
The first navigational speed is greater than twice the second navigational speed and the second navigational speed is greater than the preset speed.
In this embodiment, the first speed and the second speed are calculated respectively, and then the first speed and the second speed are compared, if the first speed is greater than a preset speed, and the second speed is less than the preset speed; or the first navigational speed is greater than twice of the second navigational speed, and the second navigational speed is greater than the preset speed, so that the position information of the second unit time point in the original data is determined to have errors, the position information of the first unit time point is replaced by the position information of the second unit time point, the track map matched with the target ship is updated, the accuracy of track data can be further improved through the track smoothing method, and the problems of target track jitter, error accumulation and the like are avoided.
It should be noted that, the track smoothing method may use other smoothing algorithms, such as kalman filter smoothing, bezier curve smoothing or polynomial fitting smoothing, so as to achieve a better smoothing effect.
In addition, the preset speed may be set according to actual situations, for example: specific values of 2 sear/hr or 2.5 sear/hr are set by the relevant personnel, and the embodiments of the present invention are not limited.
As an alternative embodiment, for example: according to the current longitude and latitude (target state information) and the longitude and latitude time difference (original data) of the previous point, calculating the actual navigational speed of the two points, comparing the actual navigational speed with the navigational speed in the original data, if the navigational speed in the original data is 0, using the longitude and latitude of the previous point to cover the current longitude and latitude, if the navigational speed in the original data is more than 0 and less than 2 seas/hour and the actual navigational speed is more than 2, considering the current point as an abnormal point, using the previous point to cover, if the navigational speed in the original data is more than 2 seas/hour and the actual navigational speed is more than 2 times of the navigational speed in the original data, judging the abnormal point, using the previous point to cover, and otherwise, using the normal point without change.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a track data processing apparatus according to an embodiment of the present invention, and as shown in fig. 2, a track data processing apparatus 200 includes:
a first acquisition module 201 for acquiring raw data of a target ship, the raw data including state information of the target ship at a plurality of unit time points, the state information including position information and speed information;
A first processing module 202, configured to extract an initialization observation from the raw data, where the initialization observation is state information of a first unit time point in the plurality of unit time points in the raw data;
a second processing module 203, configured to predict the target ship at a second unit time point according to the initialized observed value, so as to obtain predicted state information of the target ship at the second unit time point;
a third processing module 204, configured to perform filtering processing on the predicted state information and state information in the raw data, where the state information matches a second unit time point, to obtain target state information of the target ship at the second unit time point;
and the updating module 205 is configured to update the trajectory data matched with the target ship according to the original data and target data, where the target data includes target state information of the target ship at the second unit time point.
Optionally, the initialization observation further includes heading information;
the first processing module 202 includes:
the first processing unit is used for classifying the original data to obtain a data set of the target ship, and the data set comprises: the position information, the speed information and the course information of the target ship in a preset time period;
And the second processing unit is used for taking the position information, the speed information and the course information corresponding to the first unit time point in the data set as the initialization observation value.
Optionally, the first processing unit includes:
acquiring an identifier of the target ship;
hashing data belonging to the target ship in the original data into subtasks according to the identifier by using a real-time computing framework (Flink);
generating a data set of the target ship according to the elements in the subtasks.
Optionally, the second processing module 203 includes:
the third processing unit is used for converting the initialization observed value into a vector form and acquiring a preset state transition matrix;
a fourth processing unit, configured to multiply a vector corresponding to the initialized observed value by the preset state transition matrix, so as to obtain first state information of the target ship in a vector form at a second unit time point;
the first generation unit is used for generating the prediction state information according to the first state information.
Optionally, the apparatus 200 further includes:
the second acquisition module is used for acquiring a first covariance matrix, a state transition matrix and a noise covariance matrix, wherein the first covariance matrix is matched with the first unit time point;
A fourth processing module, configured to determine a second covariance matrix according to the first covariance matrix, where the second covariance matrix is obtained by adding a first portion and a second portion, where the first portion is a transposed determinant of the state transition matrix multiplied by the first covariance matrix and multiplied by the state transition matrix;
the second portion is the noise covariance matrix.
Optionally, the third processing module includes:
a first acquisition unit configured to acquire a covariance matrix of measurement noise;
a fifth processing unit, configured to determine a kalman gain according to the covariance matrix of the measurement noise and the second covariance matrix, where the kalman gain is obtained by dividing a third portion by a fourth portion, where the third portion is the second covariance matrix, and the fourth portion is a sum of the second covariance matrix and the covariance matrix of the measurement noise;
a sixth processing unit, configured to extract state information matching the second unit time point from the original data;
and a seventh processing unit, configured to determine the target state information according to the state information of the second unit time point, the kalman gain, and the predicted state information.
Optionally, the updating module includes:
an eighth processing unit, configured to determine, according to the original data, location information of the first unit time point, determine, according to the target data, first location information of the second unit time point, determine, according to the original data, second location information of the second unit time point, where the first location information is location information matched with the second unit time point after filtering correction, and the second location information is location information matched with the second unit time point obtained by the acquisition device;
a first calculation unit configured to calculate a first navigational speed between the first unit time point and the second unit time point according to the position information of the first unit time point, the first position information of the second unit time point, and the unit time;
a second calculation unit configured to calculate a second navigational speed between the first unit time point and the second unit time point according to the position information of the first unit time point, the second position information of the second unit time point, and the unit time;
an updating unit, configured to replace the position information of the first unit time point with the position information of the second unit time point, so as to update a trajectory graph matched with the target ship, where the first navigational speed and the second navigational speed meet preset conditions;
The preset condition is that the first navigational speed is greater than a preset speed and the second navigational speed is less than the preset speed; or (b)
The first navigational speed is greater than twice the second navigational speed and the second navigational speed is greater than the preset speed.
As shown in fig. 3, the apparatus 300 includes a computing unit 301 that can perform various appropriate actions and processes according to a computer program stored in a Read-Only Memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access Memory (Random Access Memory, RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 may also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other by a bus 304. An Input/Output (I/O) interface 305 is also connected to bus 304.
Various components in device 300 are connected to I/O interface 305, including: an input unit 306 such as a keyboard, a mouse, etc.; an output unit 307 such as various types of displays, speakers, and the like; a storage unit 308 such as a magnetic disk, an optical disk, or the like; and a communication unit 309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 301 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 301 include, but are not limited to, a central processing unit (Central processing unit, CPU), a graphics processing unit (Graphics processing unit, GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processors, controllers, microcontrollers, etc. The computing unit 301 performs the respective methods and processes described above, for example, the processing method of the trajectory data.
Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuitry, field programmable gate arrays (Field Programmable Gate Array, FPGAs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), application specific standard products (Application Specific Standard Parts, ASSPs), system On Chip (SOC), complex programmable logic devices (Complex Programmable logic device, CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present invention may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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 (Erasable Programmable Read-Only Memory) 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.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: display means for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area networks, wide area networks, and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of processing trajectory data, comprising:
acquiring original data of a target ship, wherein the original data comprises state information of the target ship at a plurality of unit time points, and the state information comprises position information and speed information;
extracting an initialization observation value from the original data, wherein the initialization observation value is state information of a first unit time point in the plurality of unit time points in the original data;
predicting the target ship at a second unit time point according to the initialized observed value to obtain the predicted state information of the target ship at the second unit time point;
filtering the predicted state information and the state information matched with a second unit time point in the original data to obtain target state information of the target ship at the second unit time point;
and updating the track data matched with the target ship according to the original data and the target data, wherein the target data comprises target state information of the target ship at the second unit time point.
2. The method of claim 1, wherein the initialization observations further comprise heading information;
The extracting the initialization observation value from the original data comprises the following steps:
classifying the original data to obtain a data set of the target ship, wherein the data set comprises: the position information, the speed information and the course information of the target ship in a preset time period;
and taking the position information, the speed information and the course information corresponding to the first unit time point in the data set as the initialization observation value.
3. The method of processing trajectory data according to claim 2, wherein classifying the raw data to obtain a dataset of the target ship comprises:
acquiring an identifier of the target ship;
hashing data belonging to the target ship in the original data into subtasks according to the identifier by using a real-time computing framework (Flink);
generating a data set of the target ship according to the elements in the subtasks.
4. The method for processing trajectory data according to claim 1, wherein predicting the target ship at the second unit time point according to the initialization observation value, to obtain the predicted state information of the target ship at the second unit time point, includes:
Converting the initialization observation value into a vector form, and acquiring a preset state transition matrix;
multiplying the vector corresponding to the initialization observation value by the preset state transition matrix to obtain first state information of the target ship in the form of a vector at a second unit time point;
and generating the prediction state information according to the first state information.
5. The method according to claim 4, wherein after predicting the target ship at the second unit time point based on the initialization observation value, the method further comprises:
acquiring a first covariance matrix, a state transition matrix and a noise covariance matrix, wherein the first covariance matrix is matched with the first unit time point;
determining a second covariance matrix according to the first covariance matrix, wherein the second covariance matrix is obtained by adding a first part and a second part, and the first part is a transposed determinant of the state transition matrix multiplied by the first covariance matrix and multiplied by the state transition matrix;
the second portion is the noise covariance matrix.
6. The method for processing track data according to claim 5, wherein filtering the state information of the original data, which matches the second unit time point, to obtain the target state information of the target ship at the second unit time point, includes:
acquiring a covariance matrix of measurement noise;
determining a Kalman gain according to the covariance matrix of the measurement noise and the second covariance matrix, wherein the Kalman gain is obtained by dividing a third part by a fourth part, the third part is the second covariance matrix, and the fourth part is the sum of the second covariance matrix and the covariance matrix of the measurement noise;
extracting state information matched with the second unit time point from the original data;
and determining the target state information according to the state information of the second unit time point, the Kalman gain and the predicted state information.
7. The method for processing the trajectory data according to claim 1, wherein updating the trajectory graph matched with the target ship according to the original data and the target data comprises:
determining the position information of the first unit time point according to the original data, determining the first position information of the second unit time point according to the target data, and determining the second position information of the second unit time point according to the original data, wherein the first position information is the position information matched with the second unit time point after filtering correction, and the second position information is the position information matched with the second unit time point obtained by the acquisition equipment;
Calculating a first navigational speed between the first unit time point and the second unit time point according to the position information of the first unit time point, the first position information of the second unit time point and the unit time;
calculating a second navigational speed between the first unit time point and the second unit time point according to the position information of the first unit time point, the second position information of the second unit time point and the unit time;
under the condition that the first navigational speed and the second navigational speed meet preset conditions, the position information of the first unit time point is replaced with the position information of the second unit time point, so that a track diagram matched with the target ship is updated;
the preset condition is that the first navigational speed is greater than a preset speed and the second navigational speed is less than the preset speed; or (b)
The first navigational speed is greater than twice the second navigational speed and the second navigational speed is greater than the preset speed.
8. A track data processing apparatus, comprising:
a first acquisition module for acquiring raw data of a target ship, the raw data including state information of the target ship at a plurality of unit time points, the state information including position information and speed information;
The first processing module is used for extracting an initialization observation value from the original data, wherein the initialization observation value is state information of a first unit time point in the plurality of unit time points in the original data;
the second processing module is used for predicting the target ship at a second unit time point according to the initialized observed value to obtain the predicted state information of the target ship at the second unit time point;
the third processing module is used for carrying out filtering processing on the predicted state information and the state information matched with a second unit time point in the original data to obtain target state information of the target ship at the second unit time point;
and the updating module is used for updating the track data matched with the target ship according to the original data and the target data, wherein the target data comprises target state information of the target ship at the second unit time point.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of processing trace data as claimed in any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the trajectory data processing method according to any one of claims 1 to 7.
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| CN110070565A (en) * | 2019-03-12 | 2019-07-30 | 杭州电子科技大学 | A kind of ship trajectory predictions method based on image superposition |
| WO2020142850A1 (en) * | 2019-01-11 | 2020-07-16 | Maerospace Corporation | System and method for tracking vessels |
| CN114861725A (en) * | 2022-05-09 | 2022-08-05 | 中国第一汽车股份有限公司 | Post-processing method, device, equipment and medium for perception and tracking of target |
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| CN110070565A (en) * | 2019-03-12 | 2019-07-30 | 杭州电子科技大学 | A kind of ship trajectory predictions method based on image superposition |
| CN114861725A (en) * | 2022-05-09 | 2022-08-05 | 中国第一汽车股份有限公司 | Post-processing method, device, equipment and medium for perception and tracking of target |
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