CN116664028B - Cargo flow direction control method and device of transport vehicle and transport vehicle - Google Patents
Cargo flow direction control method and device of transport vehicle and transport vehicle Download PDFInfo
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Abstract
The invention provides a cargo flow direction control method and device of a transport vehicle and the transport vehicle, comprising the following steps: acquiring state data of the transport vehicle based on an inertial sensor, and acquiring position information of the transport vehicle based on a GPS sensor; wherein, the state data is vibration signal data of the transport vehicle; detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained by training; the state data of the transport vehicle, the current transport state of the transport vehicle and the position information corresponding to the transport vehicle are sent to a cloud server; the cloud server judges whether the transport vehicle is stolen or not based on the current transport state of the transport vehicle and position information corresponding to the transport vehicle. According to the invention, the inertial sensor is adopted to collect vibration signal data of the transport vehicle, so that the current transport state of the vehicle is detected, and the transport state comprises parking, transporting, loading and unloading, so that whether the cargo is stolen or not is judged, the accuracy is high, and the cost is low.
Description
Technical Field
The invention relates to the field of data processing, in particular to a cargo flow direction control method and device of a transport vehicle and the transport vehicle.
Background
Cement, sand, coal and the like are important basic raw materials for national economy construction, and no other materials can replace the materials at home and abroad at present. Along with the high-speed development of the economy in China, the roles of cement, sand and stone and coal in the national economy are increasingly larger. The use amount of cement, sand and stone and coal in the past ten years is always high, and the transportation mode of the cement, sand and stone and coal is land transportation and water transportation, and the building materials are transported by adopting four-axis, six-axis and other multi-axis dump trucks in general land transportation.
Based on the goods value of the cement and other materials, the cement can be transported more than the goods value if transported for a long distance, and the product competitiveness is completely lost, so that the land transportation with the common cement transportation radius is 200km, and the water transportation is 500km. Because of the strong dependence of building materials on areas, common transportation companies will divide different prices according to sales areas, and thus the actual flow of building materials to areas becomes particularly important, which is a focus of attention for various large manufacturers.
In the current state of the art, the flow direction control of transport vehicles is mainly represented by the variation of the vehicle weight.
For example: and measuring a vehicle load value by utilizing information such as torque, rotation speed and the like of the vehicle engine, and further determining that the current vehicle is in a loading or unloading state. The disadvantage of this technique is that the measured weight is gradually inaccurate as the engine ages, thereby affecting the weight calculation of the vehicle cargo.
Or, strain gauges are used to measure the deformation of the axle, so as to judge the cargo state of the vehicle according to the current change of the load capacity of the vehicle, however, the cost of such a technical scheme is high due to the problem that the on-board strain gauges are easy to damage due to high-frequency vibration.
Or, the tire pressure sensor is used for measuring the change of the loading capacity of the vehicle body so as to control the flow direction of the goods, but the tire pressure can change along with the change of the temperature, so that the loading capacity of the vehicle body is difficult to accurately monitor.
Or, the loading weight of the vehicle is measured through the deformation degree of the plate spring, and then the goods of the vehicle are monitored. This technique has the disadvantage that the linearity of the leaf springs is not so good that the measured weight tends to be less accurate, thereby affecting the monitoring of the cargo.
Therefore, the conventional method for judging the state of the vehicle by adopting the vehicle-mounted weight detection method has the defects of inaccuracy and high cost.
Disclosure of Invention
The invention mainly aims to provide a cargo flow direction control method and device of a transport vehicle and the transport vehicle, and aims to overcome the defects of inaccuracy and high cost of the existing vehicle state judging mode by adopting a vehicle weight detection mode.
In order to achieve the above purpose, the invention provides a cargo flow direction control method of a transport vehicle, comprising the following steps:
acquiring state data of the transport vehicle based on an inertial sensor, and acquiring position information of the transport vehicle based on a GPS sensor; wherein, the state data is vibration signal data of the transport vehicle;
detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained through training; the state classification model is obtained by training an XGBoost machine learning model trained on a decision tree in advance;
the state data of the transport vehicle, the current transport state of the transport vehicle and the position information corresponding to the transport vehicle are sent to a cloud server; the cloud server judges whether the transport vehicle is stolen or not based on the current transport state of the transport vehicle and position information corresponding to the transport vehicle.
Further, the state data collected by the inertial sensor comprises signal data collected by a triaxial accelerometer and a triaxial gyroscope respectively;
the step of detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained by training comprises the following steps:
respectively carrying out characteristic calculation on signal data collected by the triaxial accelerometer and signal data collected by the triaxial gyroscope at the same moment to obtain corresponding acceleration energy value characteristics and gyroscope energy value characteristics;
carrying out combined acceleration characteristic calculation on signal data corresponding to the triaxial accelerometer at the same moment;
carrying out combined gyroscope characteristic calculation on signal data corresponding to the triaxial gyroscope at the same moment;
the acceleration energy value characteristic, the gyroscope energy value characteristic, the combined acceleration characteristic and the combined gyroscope characteristic are input into a state classification model obtained through training together for classification detection, and the current transportation state of the transport vehicle is obtained; the state classification model is obtained by training based on an XGBoost machine learning model in advance.
Further, before the step of calculating the characteristics of the signal data collected by the triaxial accelerometer and the signal data collected by the triaxial gyroscope at the same moment to obtain the corresponding acceleration energy value characteristics and gyroscope energy value characteristics, the method further includes:
respectively carrying out sliding average noise reduction and standardization processing on the signal data collected by the triaxial accelerometer and the signal data collected by the triaxial gyroscope at the same moment;
the sliding average value noise reduction calculation method comprises the following steps:
wherein x (i) is the data value at the i-th moment, x (i-3) is the data value at the i-3-th moment, x (i+3) is the data value at the i+3-th moment, and 7 is the size of the mean window;
the standardized processing comprises the following standardized calculation formula:
wherein,mean value of data axis, +.>Refers to the variance of the data axis.
Further, the calculation formula of the acceleration energy value characteristic is as follows:
the calculation formula of the gyroscope energy value characteristic is as follows:
acc_x_abs (i) refers to the acceleration energy value of the x-axis of the three-axis accelerometer after the absolute value of the moment i, gro_x_abs (i) refers to the gyroscope energy value characteristic of the x-axis of the three-axis gyroscope after the absolute value of the moment i, abs refers to the absolute value function;
the calculation formula of the combined acceleration characteristic is as follows:
the calculation formula of the synthetic gyroscope features is as follows:
wherein x, y, z are three axes of the tri-axis accelerometer and the tri-axis gyroscope;
acc_x (i), acc_y (i) and Acc_z (i) are respectively signal data of x, y and z axes of the triaxial accelerometer at the moment i;
gro_x (i), gro_y (i), gro_z (i) are signal data of the x, y, z axes of the three-axis gyroscope at the moment i respectively.
Further, the training process of the state classification model includes:
initializing an XGBoost machine learning model; the initialized XGBoost machine learning model is a decision tree model;
inputting samples in the training set into the initialized XGBoost machine learning model, and calculating a loss value between a predicted value and a true value of each sample;
calculating the gradient of each sample and a hessian matrix; wherein the gradient represents a first derivative of the loss value to the model parameter and the hessian matrix represents a second derivative of the loss value to the model parameter;
constructing a new decision tree according to the gradient and the hessian matrix, wherein leaf nodes of the new decision tree correspond to predicted values of the samples;
calculating the weight value of each leaf node of the decision tree;
and according to the new decision tree and the weight value of each leaf node, iteratively training and updating corresponding model parameters, and obtaining the state classification model after training.
Further, the calculation function of the loss value is:
l represents the loss value, y represents the true label,representing a predicted probability value;
the calculation formula of the hessian matrix is as follows:
h represents the hessian matrix,representing the true value of the ith sample, < +.>A probability value representing that the i-th sample belongs to the k-th class prediction.
Further, the cloud server is specifically configured to:
if the current transportation state of the transportation vehicle is a parking state, recording the position information at the moment, and marking the GPS position data as parking;
if the current transportation state of the transportation vehicle is a driving transportation state, recording the position information at the moment, and marking the GPS position data as in-transportation;
if the current transportation state of the transportation vehicle is a loading state, recording the position information at the moment, and indicating the loading place to start from the place;
if the current transportation state of the transportation vehicle is a unloading state, recording the position information at the moment, and comparing the position information with the preset unloading position information; if the two differ by more than a preset distance, the fact that the transport vehicle steals goods at the moment is indicated.
Further, the inertial sensor is mounted at the bottom of the middle of the carriage of the transport vehicle.
The invention also provides a cargo flow direction control device of the transport vehicle, which comprises:
the acquisition unit is used for acquiring state data of the transport vehicle based on the inertial sensor and acquiring position information of the transport vehicle based on the GPS sensor; wherein, the state data is vibration signal data of the transport vehicle;
the classification unit is used for detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained through training; the state classification model is obtained by training an XGBoost machine learning model trained on a decision tree in advance;
the sending unit is used for sending the state data of the transport vehicle, the current transport state of the transport vehicle and the position information corresponding to the transport vehicle to the cloud server; the cloud server judges whether the transport vehicle is stolen or not based on the current transport state of the transport vehicle and position information corresponding to the transport vehicle.
The invention also provides a transport vehicle comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods when executing the computer program.
The invention provides a cargo flow direction control method and device of a transport vehicle and the transport vehicle, comprising the following steps: acquiring state data of the transport vehicle based on an inertial sensor, and acquiring position information of the transport vehicle based on a GPS sensor; wherein, the state data is vibration signal data of the transport vehicle; detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained through training; the state data of the transport vehicle, the current transport state of the transport vehicle and the position information corresponding to the transport vehicle are sent to a cloud server; the cloud server judges whether the transport vehicle is stolen or not based on the current transport state of the transport vehicle and position information corresponding to the transport vehicle. According to the invention, the inertial sensor is adopted to collect the vibration signal data of the transport vehicle, so that the current transport state of the vehicle is detected, and whether the cargo is stolen or not is judged, and the accuracy and the cost are high.
Drawings
FIG. 1 is a schematic diagram showing the steps of a cargo flow control method for a transport vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the installation of an inertial sensor in an embodiment of the invention;
FIG. 3 is a block diagram illustrating a cargo flow control device for a transport vehicle according to an embodiment of the present invention;
fig. 4 is a schematic block diagram of a transport vehicle according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, in one embodiment of the present invention, a cargo flow direction control method for a transport vehicle is provided, including the following steps:
step S1, acquiring state data of a transport vehicle based on an inertial sensor and acquiring position information of the transport vehicle based on a GPS sensor; wherein, the state data is vibration signal data of the transport vehicle;
step S2, detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained through training; the state classification model is obtained by training an XGBoost machine learning model trained on a decision tree in advance;
step S3, the state data of the transport vehicle, the current transport state of the transport vehicle and the position information corresponding to the transport vehicle are sent to a cloud server; the cloud server judges whether the transport vehicle is stolen or not based on the current transport state of the transport vehicle and position information corresponding to the transport vehicle.
In this embodiment, the above-mentioned scheme is applied to the cargo flow direction management and control of transport vechicle, is different from adopting weight detection's scheme in traditional scheme, and the scheme in this embodiment does not need weight detection, but through adopting inertial sensor to gather the vibration signal data of transport vechicle, and then detects the current transport state of vehicle, and then judges whether to steal goods, and the accuracy is high, and is with low costs.
As described in the above step S1, the inertial sensor is mounted at the bottom of the middle of the carriage of the carrier vehicle (refer to fig. 2), and can collect status data of the carrier vehicle, and the GPS sensor is mounted on the carrier vehicle for collecting position information of the carrier vehicle. In this embodiment, the system includes a sensing end (an inertial sensor, a GPS sensor, etc.), a host end, a cloud server, and a mobile end. Specifically, at the sensing end, inertial sensors (IMU, inertial Measurement Unit) and GPS sensors are mainly utilized. And the inertial sensor (model MPU 6050) is provided with a triaxial accelerometer and a triaxial gyroscope and is used for detecting vibration signal data generated by a frame when the transport vehicle is loaded and unloaded. When the existing transport vehicle is used for loading, two main modes exist, one is to pour cargoes into the carriage directly through a pipeline, and the other is to pour into the carriage through a forklift with a shovel, so that the loading mode can generate severe collision to the bottom of the carriage, and then the inertial sensor can acquire high-frequency vibration signals. When the transport vehicle unloads, the goods are often inclined to the ground through the lifting carriage, and at the moment, the inertial sensor can receive carriage movement state signals. In order for the inertial sensor to better receive the car status data, the sensor needs to be installed at the middle bottom of the car, only 1 sensor is installed and connected to the host. The GPS sensor is mainly used for detecting the position information of the transport vehicle in real time and recording the position coordinates of the state change of the transport vehicle.
As described in the above steps S2-S3, a processing chip (STM 32 chip) and a machine learning algorithm are provided on the host computer side to perform pattern recognition on the status data transmitted from the inertial sensor, and then output 4 status results of whether the current transport vehicle is in loading, unloading, stationary and transporting. And finally, sending the state data of the transport vehicle, the current transport state of the transport vehicle and the position information corresponding to the transport vehicle to a cloud server for storage, so that the current monitored cargo flow direction information of the transport vehicle can be queried on a mobile terminal such as an android or a computer webpage.
On the cloud: the method is mainly used for storing and calculating whether the current monitored transport vehicle is loaded and unloaded at a preset position, and sending out an alarm when unloading is carried out at a non-preset position, so that the transport vehicle is illegally unloaded at the moment, and the transport vehicle is monitored.
In the embodiment, the inertial sensor is adopted to collect vibration signal data of the transport vehicle, so that the current transport state of the vehicle is detected, whether cargo theft occurs or not is judged, accuracy is high, and cost is low. The key point of the embodiment of the invention is that the mode of monitoring the vehicle by adopting the dimension of weight in the past flow direction monitoring is changed, and the state and the behavior of the vehicle body of the transport vehicle are accurately identified only by an inertial sensor, so that the flow direction information of goods is accurately monitored. Compared with other schemes, the invention has lower cost, more precision, more durability and durability, and is applicable to various vehicles and does not damage the vehicle body structure.
In an embodiment, the state data collected by the inertial sensor includes signal data collected by a tri-axis accelerometer and a tri-axis gyroscope respectively;
the step of detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained by training comprises the following steps:
respectively carrying out characteristic calculation on signal data collected by the triaxial accelerometer and signal data collected by the triaxial gyroscope at the same moment to obtain corresponding acceleration energy value characteristics and gyroscope energy value characteristics;
carrying out combined acceleration characteristic calculation on signal data corresponding to the triaxial accelerometer at the same moment;
carrying out combined gyroscope characteristic calculation on signal data corresponding to the triaxial gyroscope at the same moment;
the acceleration energy value characteristic, the gyroscope energy value characteristic, the combined acceleration characteristic and the combined gyroscope characteristic are input into a state classification model obtained through training together for classification detection, and the current transportation state of the transport vehicle is obtained; the state classification model is obtained by training based on an XGBoost machine learning model in advance.
In this embodiment, after the state data collected by the inertial sensor is obtained, it is required to perform feature engineering, which refers to extracting meaningful and usable features from the state data by using domain knowledge and data processing skills, and using the extracted meaningful and usable features for training and prediction of a machine learning model. Further characterization engineering was done here on 6 data points per time stamp in order to improve the performance of the model, reduce the risk of the model being over fitted in the training data, speed up model training, and enhance the interpretability of the model.
The method specifically comprises the following steps:
and calculating the energy value characteristics, namely the acceleration energy value characteristics and the gyroscope energy value characteristics, wherein the characteristics are obtained by taking data of the three-axis accelerometer and the three-axis gyroscope at the moment i as an absolute value.
Combined acceleration characteristics: and squaring and then squaring three signal data of the triaxial acceleration of each time stamp to obtain the combined acceleration characteristic.
Characteristics of the combined gyroscope: and squaring and then squaring three signal data of the three-axis gyroscope with each time stamp to obtain the characteristics of the combined gyroscope. After the feature engineering stage, 6 energy value features, 1 combined acceleration feature, 1 combined gyroscope feature and the original 6 data are extracted in total to obtain 14 features for model training.
In this embodiment, in order to better learn the feature data generated in the feature engineering, the state classification model is trained in advance based on an XGBoost machine learning model. The XGBoost model computes data derived in the feature engineering. XGBoost is an efficient machine learning model, is based on the realization of a gradient lifting tree (Gradient Boosting Tree) algorithm, adopts a method similar to a decision tree, and improves the prediction precision by continuously iterating and adjusting a prediction model. Specifically, the acceleration energy value characteristic, the gyroscope energy value characteristic, the combined acceleration characteristic and the combined gyroscope characteristic are input into a state classification model obtained through training together for classification detection, so that the current transportation state of the transport vehicle is obtained, and the current state of loading, unloading, resting and transportation of the transport vehicle can be predicted.
The inertial sensor collects state data of mainly 1 inertial sensor data, which obtains values of 3 accelerometers and values of 3 gyroscopes every second, considering that the collection frequency is 1 Hz. The values of the three axes of the three-axis accelerometer thus obtained are respectively designated as Acc_x, acc_y, acc_z, and the values of the three axes of the three-axis gyroscope thus obtained are designated as Gro_x, gro_y, gro_z, and the data size of the inertial sensor obtained every second is [1,6]. Because the data acquired by the inertial sensor belongs to time sequence data, the original data is required to be obtained by adopting a sliding window, wherein the size of the sliding window is 50, the sliding step length is 25, and the size of the IMU data in the window is [50,6].
In an embodiment, before the step of calculating the features of the signal data collected by the triaxial accelerometer and the signal data collected by the triaxial gyroscope at the same time to obtain the corresponding acceleration energy value feature and the gyroscope energy value feature, the method further includes:
respectively carrying out sliding average noise reduction and standardization processing on the signal data collected by the triaxial accelerometer and the signal data collected by the triaxial gyroscope at the same moment; for the obtained state data of the inertial sensor, because the obtained state data contains other noise, preliminary noise reduction is needed, and a sliding average noise reduction method is adopted to process the state data.
The sliding average value noise reduction calculation method comprises the following steps:
wherein x (i) is the data value at the i-th moment, x (i-3) is the data value at the i-3-th moment, x (i+3) is the data value at the i+3-th moment, and 7 is the size of the mean window; the output size of the 6 time sequence data of the inertial sensor is still [50,6] after mean value filtering and noise reduction.
The standardized processing comprises the following standardized calculation formula:
wherein,mean value of data axis, +.>Refers to the variance of the data axis.
In an embodiment, the calculation formula of the acceleration energy value characteristic is:
the calculation formula of the gyroscope energy value characteristic is as follows:
acc_x_abs (i) refers to the acceleration energy value of the x-axis of the three-axis accelerometer after the absolute value of the moment i, gro_x_abs (i) refers to the gyroscope energy value characteristic of the x-axis of the three-axis gyroscope after the absolute value of the moment i, abs refers to the absolute value function;
the calculation formula of the combined acceleration characteristic is as follows:
the calculation formula of the synthetic gyroscope features is as follows:
wherein x, y, z are three axes of the tri-axis accelerometer and the tri-axis gyroscope;
acc_x (i), acc_y (i) and Acc_z (i) are respectively signal data of x, y and z axes of the triaxial accelerometer at the moment i;
gro_x (i), gro_y (i), gro_z (i) are signal data of the x, y, z axes of the three-axis gyroscope at the moment i respectively.
In an embodiment, the training process of the state classification model includes:
initializing an XGBoost machine learning model; the initialized XGBoost machine learning model is a decision tree model; in this embodiment, the depth of the decision tree model is set to 6, and the number of decision trees is 200.
Inputting samples in the training set into the initialized XGBoost machine learning model, and calculating a loss value between a predicted value and a true value of each sample; among them, the usual loss function is the softmax loss function. In calculating the loss function, XGBoost will accumulate the loss function value of each sample as the total loss function value of the current model for subsequent model optimization and updating. At the same time, XGBoost also adds a regularization term to prevent overfitting.
Calculating the gradient of each sample and a hessian matrix; wherein the gradient represents a first derivative of the loss value to the model parameter and the hessian matrix represents a second derivative of the loss value to the model parameter for describing the local curvature and convexity of the function; the specific formula of the hessian matrix is calculated from the second derivative of the loss function.
Constructing a new decision tree according to the gradient and the hessian matrix, wherein leaf nodes of the new decision tree correspond to predicted values of the samples;
calculating the weight value of each leaf node of the decision tree; the weight value represents the sum of gradients of the samples corresponding to the leaf nodes divided by the square of the sum of gradients, and then a regularization term is added to prevent overfitting.
And according to the new decision tree and the weight value of each leaf node, iteratively training and updating the corresponding model parameters until the appointed iteration times are reached or the performance of the model is not improved any more, and obtaining the state classification model after training.
In an embodiment, the loss value calculation function is:
l represents the loss value, y represents the true label,representing a predicted probability value;
the calculation formula of the hessian matrix is as follows:
h represents the hessian matrix,representing the true value of the ith sample, < +.>A probability value representing that the i-th sample belongs to the k-th class prediction.
In an embodiment, the cloud server is specifically configured to:
if the current transportation state of the transportation vehicle is a parking state, recording the position information at the moment, and marking the GPS position data as parking; wherein the GPS position data refers to position information of a parking state stage;
if the current transportation state of the transportation vehicle is a driving transportation state, recording the position information at the moment, and marking the GPS position data as in-transportation; the GPS position data refers to position information in a driving transportation state stage;
if the current transportation state of the transportation vehicle is a loading state, recording the position information at the moment, and indicating the loading place to start from the place;
if the current transportation state of the transportation vehicle is a unloading state, recording the position information at the moment, and comparing the position information with the preset unloading position information; if the two differ by more than a preset distance (for example, 2 km), the fact that the transport vehicle steals goods at the moment is indicated; when the theft phenomenon occurs, an alarm can be sent out.
In an embodiment, before the step of detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the trained state classification model, the method further includes:
acquiring the type, the identification code and the current time stamp of the transport vehicle; wherein, the identification code is a character string;
calling a database; the database comprises a plurality of folders, each folder is provided with a unique identification number, and each folder is stored with a plurality of corresponding pre-selected state classification models;
according to the type of the transport vehicle, matching a corresponding decoding table in a database; decoding the identification codes based on the matched decoding tables respectively to obtain corresponding decoding character strings;
sequentially inputting each character in the decoded character string into a first set; wherein each character is an element in the first set;
for each folder, sequentially inputting identification characters in the unique identification number of each folder into a second set to obtain second sets respectively corresponding to the folders; wherein each identification character is an element in the second set;
respectively calculating intersection elements of a first set and each second set, acquiring the second set with the largest intersection elements of the first set as a target set, and taking folders corresponding to the target set as target folders;
acquiring the update time of each pre-selected state classification model stored in a target folder, and acquiring a pre-selected state classification model with the update time closest to the current timestamp as the state classification model; the state classification model is used for detecting the current transportation state of the transportation vehicle.
In this embodiment, before detecting the current transportation state of the transportation vehicle, it is first necessary to acquire information related to the transportation vehicle. Including the type of transporter (possibly using different models and decoding tables depending on the type of vehicle), an identification code (typically a string of characters for uniquely identifying the vehicle), and a current timestamp (for subsequent comparison with the update time of the pre-selected state classification model).
Calling a database: for the detected needs of the transporter, a database is accessed that contains a plurality of folders, each folder having a unique identification number, and each folder having a corresponding plurality of pre-selected state classification models stored therein. By accessing the database, a pre-selected state classification model associated with the transporter may be obtained.
According to the type of the transport vehicle, matching a corresponding decoding table in a database: according to the type of the transport vehicle, the system can match a decoding table corresponding to the type of the transport vehicle in a database. The decoding table is typically a rule or map for decoding characters in the identification code into a particular string. This decoding process is to obtain a more readable identification code for subsequent processing.
Decoding the identification code to obtain a decoded character string: after matching to the required decoding table, the system will use the decoding table to decode the identification code. The purpose of decoding is to convert the characters in the identification code into a string form that is easy to understand, which can provide more convenient data for subsequent computation and comparison.
Constructing a first set and a second set: after decoding the string, the system will use each character as an element of the first set and the unique identifying character of each folder as an element of the second set. In this way, the first set and the second set will contain the decoded string of the transporter and the identification characters in the database.
Calculating intersection elements of the first set and the second set, and finding a target set and a target folder: the system calculates intersection elements of the first set and each second set respectively, and finds the second set with the most intersection elements of the first set as a target set. From this target set, the system may determine the corresponding folder as the target folder for use by subsequent steps.
Acquiring the update time of each pre-selected state classification model in the target folder: after determining the target folder, the system will obtain the update time of each pre-selected state classification model stored in the folder. This update time is used to compare with the current timestamp to find the preselected state classification model whose update time is closest to the current timestamp.
Acquiring a preselected state classification model with the closest update time as a state classification model: by comparing the update time of the preselected state classification model with the current timestamp, the system can find the model with the closest update time as the state classification model. The state classification model can be used for detecting the current transportation state of the transportation vehicle and carrying out state identification and prediction according to actual requirements.
Referring to fig. 3, in an embodiment of the present invention, there is further provided a cargo flow direction control device of a transport vehicle, including:
the acquisition unit is used for acquiring state data of the transport vehicle based on the inertial sensor and acquiring position information of the transport vehicle based on the GPS sensor; wherein, the state data is vibration signal data of the transport vehicle;
the classification unit is used for detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained through training; the state classification model is obtained by training an XGBoost machine learning model trained on a decision tree in advance;
the sending unit is used for sending the state data of the transport vehicle, the current transport state of the transport vehicle and the position information corresponding to the transport vehicle to the cloud server; the cloud server judges whether the transport vehicle is stolen or not based on the current transport state of the transport vehicle and position information corresponding to the transport vehicle.
In this embodiment, for specific implementation of each unit in the above embodiment of the apparatus, please refer to the description in the above embodiment of the method, and no further description is given here.
Referring to fig. 4, there is further provided a transporter according to an embodiment of the present invention, which may be a server, and an internal structure thereof may be as shown in fig. 4. The transport vehicle comprises a processor, a memory, a display screen, an input device, a network interface and a database which are connected through a system bus. Wherein the computer is configured to provide computing and control capabilities. The memory of the transport vehicle comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the transport vehicle is used for storing the corresponding data in the embodiment. The network interface of the transport vehicle is used for communicating with an external terminal through network connection. Which computer program, when being executed by a processor, carries out the above-mentioned method.
It will be appreciated by those skilled in the art that the structure shown in fig. 4 is merely a block diagram of a portion of the structure associated with the present invention and is not intended to limit the transportation vehicle to which the present invention is applied.
In summary, the method and device for controlling cargo flow direction of a transport vehicle and the transport vehicle provided in the embodiments of the present invention include: acquiring state data of the transport vehicle based on an inertial sensor, and acquiring position information of the transport vehicle based on a GPS sensor; wherein, the state data is vibration signal data of the transport vehicle; detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained through training; the state data of the transport vehicle, the current transport state of the transport vehicle and the position information corresponding to the transport vehicle are sent to a cloud server; the cloud server judges whether the transport vehicle is stolen or not based on the current transport state of the transport vehicle and position information corresponding to the transport vehicle. According to the invention, the inertial sensor is adopted to collect the vibration signal data of the transport vehicle, so that the current transport state of the vehicle is detected, and whether the cargo is stolen or not is judged, and the accuracy and the cost are high.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided by the present invention and used in embodiments may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes using the descriptions and drawings of the present invention or direct or indirect application in other related technical fields are included in the scope of the present invention.
Claims (8)
1. The cargo flow direction control method of the transport vehicle is characterized by comprising the following steps of:
acquiring state data of the transport vehicle based on an inertial sensor, and acquiring position information of the transport vehicle based on a GPS sensor; wherein, the state data is vibration signal data of the transport vehicle;
detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained through training; the state classification model is obtained by training an XGBoost machine learning model trained on a decision tree in advance;
the state data of the transport vehicle, the current transport state of the transport vehicle and the position information corresponding to the transport vehicle are sent to a cloud server; the cloud server judges whether the transport vehicle is stolen or not based on the current transport state of the transport vehicle and position information corresponding to the transport vehicle;
the training process of the state classification model comprises the following steps:
initializing an XGBoost machine learning model; the initialized XGBoost machine learning model is a decision tree model;
inputting samples in the training set into the initialized XGBoost machine learning model, and calculating a loss value between a predicted value and a true value of each sample;
calculating the gradient of each sample and a hessian matrix; wherein the gradient represents a first derivative of the loss value to the model parameter and the hessian matrix represents a second derivative of the loss value to the model parameter;
constructing a new decision tree according to the gradient and the hessian matrix, wherein leaf nodes of the new decision tree correspond to predicted values of the samples;
calculating the weight value of each leaf node of the decision tree; the weight value represents the sum of gradients of samples corresponding to leaf nodes, divided by the square of the sum of gradients, and then a regularization term is added for preventing overfitting;
according to the new decision tree and the weight value of each leaf node, iteratively training and updating corresponding model parameters, and obtaining the state classification model after training is completed;
the state data collected by the inertial sensor comprises signal data collected by a triaxial accelerometer and a triaxial gyroscope respectively;
the step of detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained by training comprises the following steps:
respectively carrying out characteristic calculation on signal data collected by the triaxial accelerometer and signal data collected by the triaxial gyroscope at the same moment to obtain corresponding acceleration energy value characteristics and gyroscope energy value characteristics;
carrying out combined acceleration characteristic calculation on signal data corresponding to the triaxial accelerometer at the same moment;
carrying out combined gyroscope characteristic calculation on signal data corresponding to the triaxial gyroscope at the same moment;
and commonly inputting the acceleration energy value characteristic, the gyroscope energy value characteristic, the combined acceleration characteristic and the combined gyroscope characteristic into a state classification model obtained through training for classification detection, so as to obtain the current transportation state of the transportation vehicle.
2. The method for controlling cargo flow of a transport vehicle according to claim 1, wherein before the step of performing feature computation on signal data collected by the triaxial accelerometer and signal data collected by the triaxial gyroscope at the same time to obtain the corresponding acceleration energy value feature and gyroscope energy value feature, the method further comprises:
respectively carrying out sliding average noise reduction and standardization processing on the signal data collected by the triaxial accelerometer and the signal data collected by the triaxial gyroscope at the same moment;
the sliding average value noise reduction calculation method comprises the following steps:
wherein x (i) is the data value at the i-th moment, x (i-3) is the data value at the i-3-th moment, x (i+3) is the data value at the i+3-th moment, and 7 is the size of the mean window;
the standardized processing comprises the following standardized calculation formula:
wherein,mean value of data axis, +.>Refers to the variance of the data axis.
3. The method for controlling cargo flow of a transport vehicle according to claim 1, wherein the calculation formula of the acceleration energy value characteristic is:
the calculation formula of the gyroscope energy value characteristic is as follows:
acc_x_abs (i) refers to the acceleration energy value of the x-axis of the three-axis accelerometer after the absolute value of the moment i, gro_x_abs (i) refers to the gyroscope energy value characteristic of the x-axis of the three-axis gyroscope after the absolute value of the moment i, abs refers to the absolute value function;
the calculation formula of the combined acceleration characteristic is as follows:
the calculation formula of the synthetic gyroscope features is as follows:
wherein x, y, z are three axes of the tri-axis accelerometer and the tri-axis gyroscope;
acc_x (i), acc_y (i) and Acc_z (i) are respectively signal data of x, y and z axes of the triaxial accelerometer at the moment i;
gro_x (i), gro_y (i), gro_z (i) are signal data of the x, y, z axes of the three-axis gyroscope at the moment i respectively.
4. The cargo flow direction control method of a transport vehicle according to claim 1, wherein the calculation function of the loss value is:
l represents the loss value, y represents the true label,representing a predicted probability value;
the calculation formula of the hessian matrix is as follows:
h represents a hessian matrix，Representing the true value of the ith sample, < +.>A probability value representing that the i-th sample belongs to the k-th class prediction.
5. The cargo flow direction management method of a transport vehicle according to claim 1, wherein the cloud server is specifically configured to:
if the current transportation state of the transportation vehicle is a parking state, recording the position information at the moment, and marking the GPS position data as parking;
if the current transportation state of the transportation vehicle is a driving transportation state, recording the position information at the moment, and marking the GPS position data as in-transportation;
if the current transportation state of the transportation vehicle is a loading state, recording the position information at the moment, and indicating the loading place to start from the place;
if the current transportation state of the transportation vehicle is a unloading state, recording the position information at the moment, and comparing the position information with the preset unloading position information; if the two differ by more than a preset distance, the fact that the transport vehicle steals goods at the moment is indicated.
6. The method of claim 1, wherein the inertial sensor is mounted to a bottom portion of a middle of a compartment of the transporter.
7. A cargo flow direction management and control device for a transport vehicle, comprising:
the acquisition unit is used for acquiring state data of the transport vehicle based on the inertial sensor and acquiring position information of the transport vehicle based on the GPS sensor; wherein, the state data is vibration signal data of the transport vehicle;
the classification unit is used for detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained through training; the state classification model is obtained by training an XGBoost machine learning model trained on a decision tree in advance;
the sending unit is used for sending the state data of the transport vehicle, the current transport state of the transport vehicle and the position information corresponding to the transport vehicle to the cloud server; the cloud server judges whether the transport vehicle is stolen or not based on the current transport state of the transport vehicle and position information corresponding to the transport vehicle;
the training process of the state classification model comprises the following steps:
initializing an XGBoost machine learning model; the initialized XGBoost machine learning model is a decision tree model;
inputting samples in the training set into the initialized XGBoost machine learning model, and calculating a loss value between a predicted value and a true value of each sample;
calculating the gradient of each sample and a hessian matrix; wherein the gradient represents a first derivative of the loss value to the model parameter and the hessian matrix represents a second derivative of the loss value to the model parameter;
constructing a new decision tree according to the gradient and the hessian matrix, wherein leaf nodes of the new decision tree correspond to predicted values of the samples;
calculating the weight value of each leaf node of the decision tree; the weight value represents the sum of gradients of samples corresponding to leaf nodes, divided by the square of the sum of gradients, and then a regularization term is added for preventing overfitting;
according to the new decision tree and the weight value of each leaf node, iteratively training and updating corresponding model parameters, and obtaining the state classification model after training is completed;
the state data collected by the inertial sensor comprises signal data collected by a triaxial accelerometer and a triaxial gyroscope respectively;
the step of detecting the current transportation state of the transportation vehicle based on the state data of the transportation vehicle and the state classification model obtained by training comprises the following steps:
respectively carrying out characteristic calculation on signal data collected by the triaxial accelerometer and signal data collected by the triaxial gyroscope at the same moment to obtain corresponding acceleration energy value characteristics and gyroscope energy value characteristics;
carrying out combined acceleration characteristic calculation on signal data corresponding to the triaxial accelerometer at the same moment;
carrying out combined gyroscope characteristic calculation on signal data corresponding to the triaxial gyroscope at the same moment;
and commonly inputting the acceleration energy value characteristic, the gyroscope energy value characteristic, the combined acceleration characteristic and the combined gyroscope characteristic into a state classification model obtained through training for classification detection, so as to obtain the current transportation state of the transportation vehicle.
8. A transporter comprising a memory and a processor, the memory having stored therein a computer program, characterized in that the processor, when executing the computer program, carries out the steps of the method according to any one of claims 1 to 6.
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