CN116881768B - Data processing method, device, computer equipment and storage medium - Google Patents
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Abstract
The present application relates to a data processing method, apparatus, computer device, storage medium and computer program product. The method comprises the following steps: respectively acquiring a detection acceleration sequence acquired by a detection sensor and a reference acceleration sequence acquired by a reference sensor in a detection time window; the detection sensor and the reference sensor are distributed according to the running direction of the road to be detected; carrying out prediction processing on the detected acceleration sequence based on a prediction model to obtain a predicted acceleration sequence, and constructing a mapping relation between the detected sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence; and determining the damage level of the road to be detected according to the mapping relation and the damage level interval. By adopting the method, the accuracy of the data processing method can be improved.
Description
Technical Field
The present application relates to the field of road exploration technology, and in particular, to a data processing method, apparatus, computer device, storage medium, and computer program product.
Background
Along with the gradual increase of the mileage of expressways in China, the number of roads and bridges to be maintained in China is also continuously increased, and roads need to be monitored to obtain road monitoring data. And then, carrying out data processing on the road monitoring data to obtain a road damage result.
The current data processing method is to acquire road surface image data of a road, input the road surface image data into a convolutional neural network after training, and perform image processing on the road surface data through the convolutional neural network to obtain a road damage result of the road.
However, the current data processing method processes road surface image data, only determines whether the road surface is damaged, and cannot determine the actual damage degree of the interior of the road to be detected. The accuracy of the road damage result determined by the current data processing method is lower.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a data processing method, apparatus, computer device, computer readable storage medium, and computer program product.
In a first aspect, the present application provides a data processing method. The method comprises the following steps:
respectively acquiring a detection acceleration sequence acquired by a detection sensor and a reference acceleration sequence acquired by a reference sensor in a detection time window; the detection sensor and the reference sensor are distributed with reference to the driving direction of the road to be detected;
carrying out prediction processing on the detected acceleration sequence based on a prediction model to obtain a predicted acceleration sequence, and constructing a mapping relation between the detected sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence;
And determining the damage level of the road to be detected according to the mapping relation and the damage level interval.
In one embodiment, the detection sensor and the reference sensor are arranged on the same straight line or plate angle of the driving direction of the road to be detected according to the driving direction of the road to be detected.
In one embodiment, the constructing a mapping relationship between the detection sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence includes:
establishing a mapping relation between the predicted acceleration and the reference acceleration at the same time in the predicted acceleration sequence and the reference acceleration sequence;
according to a preset root mean square algorithm and the mapping relation, carrying out data processing on the predicted acceleration in the predicted acceleration sequence and the reference acceleration in the reference acceleration sequence to obtain a root mean square error;
the root mean square error is determined as a deviation value between the detection sensor and the reference sensor.
In one embodiment, the determining the damage level of the road to be detected according to the mapping relationship and the damage level interval includes:
According to a preset standard deviation algorithm, carrying out data processing on the reference acceleration sequence to obtain a standard deviation;
determining the damage value of the road to be detected according to the deviation value and the standard deviation corresponding to the mapping relation;
and determining the damage grade corresponding to the damage value of the road to be detected based on each damage grade interval.
In one embodiment, after determining the damage level of the road to be detected according to the mapping relationship and the damage level interval, the method further includes:
judging whether the damage level meets a preset reporting condition or not;
acquiring the position information of the road to be detected under the condition that the damage level meets the reporting condition;
and constructing reporting information according to the position information of the road to be detected and the damage level, and feeding back the reporting information to a target person.
In one embodiment, before the predicting the detected acceleration sequence based on the prediction model to obtain a predicted acceleration sequence, the method further includes:
acquiring a training data set, wherein the training data set comprises a detection training subset and a reference training subset, and the detection training subset and the reference training subset comprise acceleration data of a road in a normal state;
Training a preset long-short-period memory artificial neural network based on the training data set until the trained long-short-period memory artificial neural network meets a preset training stop condition, and taking the long-short-period memory artificial neural network meeting the training stop condition as a prediction model.
In one embodiment, the acquiring the training data set includes:
respectively acquiring a detection training set and a reference training set; the detection training set comprises a plurality of detection training data acquired by a detection sensor, and the reference training set comprises a plurality of reference training data acquired by a reference sensor;
dividing the detection training set and the reference training set according to the detection time window to obtain a plurality of detection training subsets corresponding to the detection training set and a plurality of reference training subsets corresponding to the reference training set;
and constructing a training data set according to each detection training subset and each reference training subset.
In a second aspect, the present application also provides a data processing apparatus. The device comprises:
the acquisition module is used for respectively acquiring a detection acceleration sequence acquired by the detection sensor and a reference acceleration sequence acquired by the reference sensor in the detection time window; the detection sensor and the reference sensor are distributed with reference to the driving direction of the road to be detected;
The construction module is used for carrying out prediction processing on the detection acceleration sequence based on a prediction model to obtain a prediction acceleration sequence, and constructing a mapping relation between the detection sensor and the reference sensor according to the prediction acceleration sequence and the reference acceleration sequence;
and the determining module is used for determining the damage level of the road to be detected according to the mapping relation and the damage level interval.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
respectively acquiring a detection acceleration sequence acquired by a detection sensor and a reference acceleration sequence acquired by a reference sensor in a detection time window; the detection sensor and the reference sensor are distributed with reference to the driving direction of the road to be detected;
carrying out prediction processing on the detected acceleration sequence based on a prediction model to obtain a predicted acceleration sequence, and constructing a mapping relation between the detected sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence;
And determining the damage level of the road to be detected according to the mapping relation and the damage level interval.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
respectively acquiring a detection acceleration sequence acquired by a detection sensor and a reference acceleration sequence acquired by a reference sensor in a detection time window; the detection sensor and the reference sensor are distributed with reference to the driving direction of the road to be detected;
carrying out prediction processing on the detected acceleration sequence based on a prediction model to obtain a predicted acceleration sequence, and constructing a mapping relation between the detected sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence;
and determining the damage level of the road to be detected according to the mapping relation and the damage level interval.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
Respectively acquiring a detection acceleration sequence acquired by a detection sensor and a reference acceleration sequence acquired by a reference sensor in a detection time window; the detection sensor and the reference sensor are distributed with reference to the driving direction of the road to be detected;
carrying out prediction processing on the detected acceleration sequence based on a prediction model to obtain a predicted acceleration sequence, and constructing a mapping relation between the detected sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence;
and determining the damage level of the road to be detected according to the mapping relation and the damage level interval.
The data processing method, the data processing device, the computer equipment, the storage medium and the computer program product respectively acquire a detection acceleration sequence acquired by the detection sensor and a reference acceleration sequence acquired by the reference sensor in a detection time window; the detection sensor and the reference sensor are distributed with reference to the driving direction of the road to be detected; carrying out prediction processing on the detected acceleration sequence based on a prediction model to obtain a predicted acceleration sequence, and constructing a mapping relation between the detected sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence; and determining the damage level of the road to be detected according to the mapping relation and the damage level interval. By adopting the method, the change condition of the vibration signal when the damage occurs in the road is obtained by constructing the mapping relation between the acceleration of the detection sensor embedded in the road and the acceleration of the reference sensor. And the damage level of the road is determined based on the mapping relation and the damage level interval, so that the damage in the road is identified, the damage degree in the road is clarified, and the accuracy of the data processing method is improved.
Drawings
FIG. 1 is a flow diagram of a data processing method in one embodiment;
FIG. 2 is a flow chart of the steps for building a mapping relationship in one embodiment;
FIG. 3 is a flow chart illustrating the steps for determining a lesion level according to one embodiment;
FIG. 4 is a flowchart illustrating a feedback report message step in one embodiment;
FIG. 5 is a flow diagram of the steps for training a predictive model in one embodiment;
FIG. 6 is a flow diagram of the steps for constructing a training dataset in one embodiment;
FIG. 7 is a flow diagram of a method of data processing in one embodiment;
FIG. 8 is a flow chart of a data processing method according to another embodiment;
FIG. 9 is a block diagram of a data processing apparatus in one embodiment;
fig. 10 is an internal structural view of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a data processing method is provided, and the data processing method is applied to a computer device for illustration, and includes the following steps:
Step 102, acquiring a detection acceleration sequence acquired by a detection sensor and a reference acceleration sequence acquired by a reference sensor in a detection time window respectively.
Wherein the detection sensor and the reference sensor are arranged with reference to the driving direction of the road to be detected.
In practice, before the computer device executes the data processing method, the target person arranges the detection sensor and the reference sensor according to the driving direction of the road to be detected, and a certain distance is reserved between the detection sensor and the reference sensor. The sensor to be detected and the reference sensor respectively acquire acceleration data of the road to be detected according to a preset acquisition frequency, and the acceleration data are transmitted to the computer equipment through communication connection. The computer equipment respectively acquires a detection acceleration sequence acquired by the detection sensor and a reference acceleration sequence acquired by the reference sensor in the detection time window through communication connection.
Preferably, the acquisition frequency of the detection sensor and the reference sensor is 200Hz (hertz), which can reduce the data volume of the obtained acceleration data, since the response frequency of the road structure generally does not exceed 100Hz.
Alternatively, the detection time window may be, but not limited to, set to 30s (seconds), and the embodiment of the present application does not limit the length of the detection time window herein.
Alternatively, the road to be detected may be, but not limited to, an expressway, a bridge road, an asphalt road, or a cement road. The embodiment of the application does not limit the type of the road to be detected.
And 104, carrying out prediction processing on the detected acceleration sequence based on the prediction model to obtain a predicted acceleration sequence, and constructing a mapping relation between the detected sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence.
The mapping relation is used for reflecting vibration response changes of the road to be detected.
In practice, a predictive model is preset in the computer device. The computer equipment inputs the detected acceleration sequence into a prediction model according to the time sequence of the detected acceleration sequence, and predicts the detected acceleration sequence through the prediction model to obtain a predicted acceleration sequence. The computer device then builds a mapping between the detection sensor and the reference sensor based on the time of the predicted acceleration sequence and the time of the reference acceleration sequence.
In an alternative embodiment, if the number of detection sensors is 2 or more, the computer device obtains a plurality of detection acceleration sequences. The computer equipment integrates a plurality of detected accelerations in a plurality of detected acceleration sequences at the same moment into a detected acceleration vector to obtain a detected acceleration vector sequence. The computer equipment inputs the detected acceleration vector sequence into a prediction model according to the time sequence of the detected acceleration vector sequence, and predicts the detected acceleration vector sequence through the prediction model to obtain a predicted acceleration sequence. The computer device then builds a mapping between the detection sensor and the reference sensor based on the time of the predicted acceleration sequence and the time of the reference acceleration sequence.
Alternatively, the prediction model may be, but is not limited to, an LSTM neural network (Long Short-Term Memory network), which is not limited by the embodiments of the present application.
And 106, determining the damage level of the road to be detected according to the mapping relation and the damage level interval.
In implementation, the computer device determines the damage value of the road to be detected according to the deviation value corresponding to the mapping relation and the standard deviation corresponding to the reference acceleration sequence. Then, the computer equipment determines the damage grade corresponding to the damage value of the road to be detected based on each damage grade.
In the data processing method, the change condition of the vibration signal when the damage occurs in the road is obtained by constructing the mapping relation between the acceleration of the detection sensor embedded in the road and the acceleration of the reference sensor. And the damage level of the road is determined based on the mapping relation and the damage level interval, so that the damage in the road is identified, the damage degree in the road is clarified, and the accuracy of the data processing method is improved.
In one embodiment, the detection sensor and the reference sensor are arranged on the same straight line or at the same corner of the travel direction of the road to be detected, according to the travel direction of the road to be detected.
In practice, the number of detection sensors and reference sensors is 1. The reference sensor is arranged in front of the detection sensor with reference to the driving direction of the road to be detected and is in a straight line with the detection sensor. The detection sensor is positioned at the corner of the road to be detected, and the reference sensor can also be arranged in front of the detection sensor and positioned at the corner of the road to be detected.
In an alternative embodiment, the detection sensor comprises a first detection sensor and a second detection sensor. The reference sensor includes a first reference sensor and a second reference sensor. The first detection sensor and the second detection sensor are respectively arranged on two sides of the road. The first reference sensor and the second reference sensor are arranged in front of the detection sensor with reference to the driving direction of the road to be detected. The connecting lines of the positions of the first detection sensor, the second detection sensor, the first reference sensor and the second reference sensor are rectangular.
In an alternative embodiment, the detection sensor comprises a first detection sensor and a second detection sensor. The number of reference sensors is 1. The first detection sensor and the second detection sensor are respectively arranged on two sides of the road. The reference sensor is arranged in front of the first detection sensor in line with the first detection sensor with reference to the driving direction of the road to be detected. The connecting lines of the positions of the first detection sensor, the second detection sensor and the first reference sensor are right triangles.
Optionally, the number of the detecting sensors is at least 1, and the number of the reference sensors is at least 1.
In one embodiment, as shown in fig. 2, the specific process of constructing the mapping relationship between the detection sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence in step 104 includes:
step 202, establishing a mapping relation between the predicted acceleration and the reference acceleration at the same time in the predicted acceleration sequence and the reference acceleration sequence.
In an implementation, the computer device determines a target predicted acceleration in the predicted acceleration sequence and a target reference acceleration in the reference acceleration sequence at the same time in a time sequence of the predicted acceleration sequence. The computer device then establishes a mapping between the target predicted acceleration and the target reference acceleration.
And 204, carrying out data processing on the predicted acceleration in the predicted acceleration sequence and the reference acceleration in the reference acceleration sequence according to a preset root mean square algorithm and a mapping relation to obtain a root mean square error.
In practice, the computer device is pre-configured with a root mean square algorithm. The computer device performs data processing on the predicted acceleration in the predicted acceleration sequence and the reference acceleration mapped by the predicted acceleration based on a preset root mean square algorithm to obtain root mean square error (RMSE, root Mean Squared Error).
In step 206, the root mean square error is determined as the offset value between the detection sensor and the reference sensor.
In an implementation, the computer device determines the root mean square error as a deviation value between the detection sensor and the reference sensor. When the deviation value is smaller, the mapping between the detection sensor and the reference sensor is not changed, the road to be detected is not damaged, when the deviation value is gradually increased, the mapping between the detection sensor and the reference sensor is gradually increased, the damage value of the road to be detected is continuously increased, and the damage degree of the road to be detected is also continuously increased.
In an alternative embodiment, the damage inside the roadway includes roadway void damage, roadway pushing, and the like. And under the condition that the road to be detected is empty, establishing a mapping relation between the detection sensor and the reference sensor. When a vehicle passes through a road to be detected, the internal structure of the road can vibrate and respond, and the difference between the reference acceleration obtained by the reference sensor and the reference acceleration obtained under normal road conditions can be gradually increased. Furthermore, the mapping change between the detection sensor and the reference sensor is gradually increased, the damage value of the road to be detected is continuously increased, and the damage degree of the road to be detected is also continuously increased.
In this embodiment, the mapping relationship between the acceleration of the detection sensor embedded in the road and the acceleration of the reference sensor is constructed by predicting the acceleration sequence and the reference acceleration sequence, so as to obtain the change condition of the vibration signal when the inside of the road is damaged.
In one embodiment, as shown in FIG. 3, the specific process of step 106 includes:
step 302, performing data processing on the reference acceleration sequence according to a preset standard deviation algorithm to obtain a standard deviation.
In practice, the standard deviation algorithm is preset in the computer device. And the computer equipment performs standard deviation operation on the reference acceleration sequence according to a preset standard deviation algorithm to obtain a standard deviation.
And step 304, determining the damage value of the road to be detected according to the deviation value and the standard deviation corresponding to the mapping relation.
In implementation, the computer equipment performs division operation on the deviation value and the standard deviation corresponding to the mapping relation according to a preset damage value formula to obtain a damage value of the road to be detected. The damage value formula is shown in the following formula (1):
T＝RMSE/σ(1)
wherein T is the damage value of the road to be detected, RMSE is the deviation value, and sigma is the standard deviation. The greater the standard deviation corresponding to the reference acceleration sequence, the heavier the vehicle that is traversing the road surface. The damage of the road to be detected is related to the mapping relation between the predicted acceleration sequence and the reference acceleration sequence and the whole size of the reference acceleration sequence. A larger RMSE may be generated when a heavier vehicle passes over a road surface to interfere with the determination. Therefore, it is necessary to eliminate the error based on the standard deviation of the reference acceleration sequence.
Step 306, determining the damage level corresponding to the damage value of the road to be detected based on each damage level interval.
In implementation, each damage level interval is set in the computer equipment, and the boundary value corresponding to each damage level interval is a damage value determined by multiple experiments. And the computer equipment determines the damage grade corresponding to the damage value of the road to be detected in each damage grade interval.
In an alternative embodiment, the multi-segment threshold values H1, H2 …, HN are defined as boundary values of the injury-class interval. The injury scale includes no injury, minor injury and severe injury. The computer equipment determines a target damage level interval in which the road damage value to be detected is located. Then, the computer equipment determines the target damage grade corresponding to the target damage grade interval as the damage grade corresponding to the road damage value to be detected.
In the embodiment, the damage value is determined based on the deviation value corresponding to the mapping relation and the standard deviation of the reference acceleration sequence, so that the influence of the vehicle weight on the damage value is eliminated. And the damage level of the road is determined based on the damage value and the damage level interval, so that the damage in the road is identified, the damage degree in the road is defined, and the accuracy of the data processing method is improved.
In one embodiment, after determining the damage level of the road to be detected, the report information is constructed according to the damage level of the road to be detected, and the report information is fed back to the target person. As shown in fig. 4, after the step 106 is performed, the specific processing procedure of the data processing method further includes:
step 402, determining whether the damage level satisfies a preset reporting condition.
Wherein, the reporting condition is that the injury grade is not injury-free.
In implementation, the reporting condition is preset in the computer device. The computer device determines whether the damage level is damage free. If the damage level is no damage, the computer equipment determines that the damage level does not meet the preset reporting condition. If the damage level is not damage-free, the computer equipment determines that the damage level meets the preset reporting condition.
And step 404, acquiring the position information of the road to be detected under the condition that the damage level meets the reporting condition.
In implementation, the computer device determines an alarm level corresponding to the damage level when the damage level satisfies the reporting condition. If the damage level is more serious, the corresponding alarm level is higher. Meanwhile, the computer equipment acquires the position information of the road to be detected.
And step 406, constructing report information according to the position information and the damage level of the road to be detected, and feeding back the report information to the target personnel.
In the implementation, the computer equipment constructs reporting information according to the position information, the damage level and the alarm level of the road to be detected. The computer device then feeds the reported information back to the target person.
In an alternative embodiment, the computer device constructs the report information according to the position information, the damage level and the alarm level of the road to be detected. The computer device then feeds the reported information back to the cloud. The cloud receives a plurality of pieces of report information in real time. And then, the cloud end sequentially sorts all the reported information according to the order from high to low of the alarm level to obtain a maintenance information set. And then, the cloud end feeds back all the reported information to the target personnel according to the sequence of the maintenance information set.
Optionally, the target person may be a road maintenance person or a road construction person, and the embodiment of the present application does not limit the target person.
In the embodiment, the report information is constructed and fed back to the target personnel, so that the target personnel can maintain the road to be detected conveniently, and the safety of the road to be detected is improved.
In one embodiment, the long and short engineering memory neural network needs to be trained to obtain the predictive model before the detected acceleration sequence is input into the predictive model. As shown in fig. 5, the data processing method further includes, before the step 104 is performed:
step 502, a training data set is acquired.
The training data set comprises a detection training subset and a reference training subset, and the detection training subset and the reference training subset comprise acceleration data of a road in a normal state. The training dataset comprises a training set, a validation set, and a test set.
In practice, the target person lays the detection sensor and the reference sensor in the road under normal conditions before acquiring the training data set. And the detection sensor and the reference sensor are arranged with reference to the traveling direction of the road to be detected. The computer equipment acquires acceleration data of the road under the normal state, which are acquired by the detection sensor and the reference sensor. The computer device then constructs a training data set based on the detection time window and the acceleration data of the road.
And step 504, training the preset long-short-period memory artificial neural network based on the training data set until the trained long-short-period memory artificial neural network meets the preset training stop condition, and taking the long-short-period memory artificial neural network meeting the training stop condition as a prediction model.
In the implementation, the computer equipment is provided with a long-term and short-term memory artificial neural network in advance. The computer equipment inputs a training set in the training data set into a preset long-short-period memory artificial neural network, and the training set is predicted through the long-short-period memory artificial neural network to obtain a training result. The computer equipment judges whether the training result reaches a preset training stop condition. If the training result reaches the preset training stopping condition, the computer equipment determines that the trained long-period memory artificial neural network meets the preset training stopping condition. Then, the computer device uses the long-term and short-term memory artificial neural network meeting the training stop condition as a prediction model. If the training result reaches the preset training stop condition, the computer equipment continues training the long-term memory artificial neural network until the training result meets the preset training stop condition.
In an alternative embodiment, during the training process of the long-short-term memory artificial neural network, the computer device continuously changes the magnitude of each parameter in the LSTM neural network according to a gradient descent method, the activation function of the LSTM neural network is a Relu activation function (Linear rectification function ), and the optimization method is an Adam method (an adaptive motion estimation algorithm), and the optimization method can adaptively adjust the learning rate in the gradient descent process and avoid convergence to local extremum as much as possible.
Optionally, the training stopping condition may be the number of training rounds, or may be the accuracy of the training result.
In the embodiment, the long-period memory artificial neural network reaching the preset training stop condition is used as the prediction model, so that the accuracy of the prediction model is improved.
In one embodiment, as shown in FIG. 6, the specific process of step 502 includes:
step 602, a detection training set and a reference training set are acquired respectively.
The detection training set comprises a plurality of detection training data acquired by the detection sensor, and the reference training set comprises a plurality of reference training data acquired by the reference sensor.
In practice, the target person lays the detection sensor and the reference sensor in the road in a normal state before detecting the training set and the reference training set. And the detection sensor and the reference sensor are arranged with reference to the traveling direction of the road to be detected. The detection sensor acquires a plurality of detection accelerations of the road in a normal state according to a preset acquisition frequency to obtain a detection training set. The computer device obtains a detection training set through communication connection with the detection sensor. The reference sensor acquires a plurality of reference accelerations of the road in a normal state according to a preset acquisition frequency to obtain a reference training set. The computer device obtains a reference training set through communication connection with the reference sensor.
Step 604, according to the detection time window, the detection training set and the reference training set are respectively segmented, so as to obtain a plurality of detection training subsets corresponding to the detection training set and a plurality of reference training subsets corresponding to the reference training set.
In an implementation, the computer device segments the detection training set according to the detection time window to obtain a plurality of detection training subsets corresponding to the detection training set. Meanwhile, the computer equipment divides the reference training set according to the detection time window to obtain a plurality of reference training subsets corresponding to the reference training set.
In an alternative embodiment, the detection time window is 30s. The computer equipment divides the detection training set according to the time period of 30s to obtain a plurality of detection training subsets corresponding to the detection training set. Meanwhile, the computer equipment divides the reference training set according to the time period of 30s to obtain a plurality of reference training subsets corresponding to the reference training set.
Step 606, a training data set is constructed from each of the sounding training subsets and each of the reference training subsets.
In an implementation, a computer device constructs a training data set based on each detection training subset and each reference training subset. The training data set does not need to consider the problem of discarding the data without the vehicle passing, because the mapping between the detection sensor and the reference sensor can be constructed based on the prediction model when the vehicle passes, and the data without the vehicle passing is input into the prediction model in the follow-up process and cannot be alarmed.
In an alternative embodiment, after obtaining the training data set, the computer device segments the training data set according to a preset segmentation scale to obtain a training set, a validation set, and a test set.
Alternatively, the division ratio may be, but is not limited to, a ratio of training set, validation set, and test set of 0.9:0.09:0.01, the dividing ratio is not limited in the embodiment of the present application.
In this embodiment, the detection training set and the reference training set are segmented based on the detection time window, and the training data set is constructed based on each segmented detection training subset and each segmented reference training subset, so that data for training the long-short-term memory artificial neural network is obtained, and the long-short-term memory artificial neural network is convenient to train subsequently.
In an exemplary embodiment, as shown in fig. 7, the computer device acquires a detected acceleration sequence and a reference acceleration sequence through the detection sensor and the reference sensor. Then, the computer equipment predicts the detected acceleration sequence based on the LSTM model to obtain a predicted acceleration sequence. The computer device constructs a mapping relation between the detection sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence. And then, the computer equipment determines the damage degree of the road to be detected according to the mapping relation and the damage level interval.
In an alternative embodiment, as shown in fig. 8, the detection sensor collects real-time detection acceleration data to obtain a detection acceleration sequence. The reference sensor acquires real-time reference acceleration data to obtain a reference acceleration sequence. The computer equipment acquires the detected acceleration sequence and the reference acceleration sequence, and inputs the detected acceleration sequence into a trained LSTM model (predictive model) to obtain a predicted acceleration sequence. The predicted acceleration sequence is a predicted acceleration sequence of a reference sensor. The computer device determines a deviation value of the detection sensor and the reference sensor based on the predicted acceleration sequence and the reference acceleration sequence. Then, the computer device calculates standard deviation of the reference acceleration sequence to obtain standard deviation. The computer device determines a damage value (T value) based on the deviation value and the standard deviation. And the computer equipment determines the damage grade corresponding to the damage value of the road to be detected according to the damage grade interval where the damage value is located.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a data processing device for realizing the above related data processing method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation of one or more embodiments of the data processing device provided below may refer to the limitation of the data processing method hereinabove, and will not be repeated herein.
In one embodiment, as shown in FIG. 9, there is provided a data processing apparatus 900 comprising: an acquisition module 901, a construction module 902 and a determination module 903, wherein:
the acquisition module 901 is used for respectively acquiring a detection acceleration sequence acquired by the detection sensor and a reference acceleration sequence acquired by the reference sensor in the detection time window; the detection sensor and the reference sensor are arranged with reference to the driving direction of the road to be detected.
The construction module 902 is configured to perform prediction processing on the detected acceleration sequence based on the prediction model to obtain a predicted acceleration sequence, and construct a mapping relationship between the detected sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence.
The determining module 903 is configured to determine a damage level of the road to be detected according to the mapping relationship and the damage level interval.
In an exemplary embodiment, the detection sensor and the reference sensor are arranged at the same straight line or at the same corner of the travel direction of the road to be detected according to the travel direction of the road to be detected.
In an exemplary embodiment, the build module 902 includes a first processing sub-module and a first build sub-module. Wherein the first building sub-module comprises:
the first building sub-module is used for building a mapping relation between the predicted acceleration and the reference acceleration at the same moment in the predicted acceleration sequence and the reference acceleration sequence.
And the second processing sub-module is used for carrying out data processing on the predicted acceleration in the predicted acceleration sequence and the reference acceleration in the reference acceleration sequence according to a preset root mean square algorithm and the mapping relation to obtain root mean square error.
A first determination submodule for determining the root mean square error as a deviation value between the detection sensor and the reference sensor.
In an exemplary embodiment, the determination module 903 includes:
and the second processing sub-module is used for carrying out data processing on the reference acceleration sequence according to a preset standard deviation algorithm to obtain a standard deviation.
And the second determining submodule is used for determining the damage value of the road to be detected according to the deviation value corresponding to the mapping relation and the standard deviation.
And the third determining submodule is used for determining the damage grade corresponding to the road damage value to be detected based on each damage grade interval.
In an exemplary embodiment, the data processing apparatus 900 further includes:
and the judging module is used for judging whether the damage grade meets a preset reporting condition.
And the second acquisition module is used for acquiring the position information of the road to be detected under the condition that the damage level meets the reporting condition.
And the second construction module is used for constructing report information according to the position information of the road to be detected and the damage level and feeding the report information back to the target personnel.
In an exemplary embodiment, the data processing apparatus 900 further includes:
and the third acquisition module is used for acquiring a training data set, wherein the training data set comprises a detection training subset and a reference training subset, and the detection training subset and the reference training subset comprise acceleration data of a road in a normal state.
The training module is used for training the preset long-short-period memory artificial neural network based on the training data set until the trained long-short-period memory artificial neural network meets the preset training stop condition, and the long-short-period memory artificial neural network meeting the training stop condition is used as a prediction model.
In an exemplary embodiment, the third acquisition module includes:
the first acquisition sub-module is used for respectively acquiring a detection training set and a reference training set; the detection training set comprises a plurality of detection training data acquired by a detection sensor, and the reference training set comprises a plurality of reference training data acquired by a reference sensor.
And the segmentation sub-module is used for respectively segmenting the detection training set and the reference training set according to the detection time window to obtain a plurality of detection training subsets corresponding to the detection training set and a plurality of reference training subsets corresponding to the reference training set.
And the second construction submodule is used for constructing a training data set according to each detection training subset and each reference training subset.
Each of the modules in the above-described data processing apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and an internal structure diagram thereof may be as shown in fig. 10. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a data processing method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 10 is merely a block diagram of a portion of the structure associated with the present application and is not intended to limit the computer device to which the present application is applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
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, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of data processing, the method comprising:
respectively acquiring a detection acceleration sequence acquired by a detection sensor and a reference acceleration sequence acquired by a reference sensor in a detection time window; the detection sensor and the reference sensor are distributed with reference to the driving direction of the road to be detected;
carrying out prediction processing on the detected acceleration sequence based on a prediction model to obtain a predicted acceleration sequence, and constructing a mapping relation between the detected sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence; the mapping relation is used for reflecting vibration response changes of the road to be detected;
Determining the damage level of the road to be detected according to the mapping relation and the damage level interval; the damage level is determined based on a loss level interval of the damage value obtained by the deviation value corresponding to the mapping relation and the standard deviation of the reference acceleration sequence; the standard deviation of the reference acceleration sequence is used for eliminating errors;
the constructing a mapping relationship between the detection sensor and the reference sensor according to the predicted acceleration sequence and the reference acceleration sequence comprises the following steps:
establishing a mapping relation between the predicted acceleration and the reference acceleration at the same time in the predicted acceleration sequence and the reference acceleration sequence;
according to a preset root mean square algorithm and the mapping relation, carrying out data processing on the predicted acceleration in the predicted acceleration sequence and the reference acceleration in the reference acceleration sequence to obtain a root mean square error;
determining the root mean square error as a deviation value between the detection sensor and the reference sensor; the deviation value is increased, and the damage degree of the road to be detected is increased.
2. The method according to claim 1, characterized in that the detection sensor and the reference sensor are arranged at the same straight line or corner of the travel direction of the road to be detected as the travel direction of the road to be detected.
3. The method according to claim 1, the reference sensor being arranged in front of the detection sensor with reference to the direction of travel of the road to be detected.
4. The method of claim 1, wherein determining the damage level of the road to be detected according to the mapping relation and the damage level interval comprises:
according to a preset standard deviation algorithm, carrying out data processing on the reference acceleration sequence to obtain a standard deviation;
determining the damage value of the road to be detected according to the deviation value and the standard deviation corresponding to the mapping relation;
and determining the damage grade corresponding to the damage value of the road to be detected based on each damage grade interval.
5. The method according to claim 1, wherein after determining the damage level of the road to be detected according to the mapping relation and the damage level interval, the method further comprises:
judging whether the damage level meets a preset reporting condition or not;
acquiring the position information of the road to be detected under the condition that the damage level meets the reporting condition;
and constructing reporting information according to the position information of the road to be detected and the damage level, and feeding back the reporting information to a target person.
6. The method according to claim 1, wherein before the predicting the detected acceleration sequence based on the prediction model, the method further comprises:
acquiring a training data set, wherein the training data set comprises a detection training subset and a reference training subset, and the detection training subset and the reference training subset comprise acceleration data of a road in a normal state;
training a preset long-short-period memory artificial neural network based on the training data set until the trained long-short-period memory artificial neural network meets a preset training stop condition, and taking the long-short-period memory artificial neural network meeting the training stop condition as a prediction model.
7. The method of claim 6, wherein the acquiring a training data set comprises:
respectively acquiring a detection training set and a reference training set; the detection training set comprises a plurality of detection training data acquired by a detection sensor, and the reference training set comprises a plurality of reference training data acquired by a reference sensor;
dividing the detection training set and the reference training set according to the detection time window to obtain a plurality of detection training subsets corresponding to the detection training set and a plurality of reference training subsets corresponding to the reference training set;
And constructing a training data set according to each detection training subset and each reference training subset.
8. A data processing apparatus, the apparatus comprising:
the acquisition module is used for respectively acquiring a detection acceleration sequence acquired by the detection sensor and a reference acceleration sequence acquired by the reference sensor in the detection time window; the detection sensor and the reference sensor are distributed with reference to the driving direction of the road to be detected;
the construction module is used for carrying out prediction processing on the detection acceleration sequence based on a prediction model to obtain a prediction acceleration sequence, and constructing a mapping relation between the detection sensor and the reference sensor according to the prediction acceleration sequence and the reference acceleration sequence; the mapping relation is used for reflecting vibration response changes of the road to be detected;
the determining module is used for determining the damage level of the road to be detected according to the mapping relation and the damage level interval; the damage level is determined based on a loss level interval of the damage value obtained by the deviation value corresponding to the mapping relation and the standard deviation of the reference acceleration sequence; the standard deviation of the reference acceleration sequence is used for eliminating errors;
The first building sub-module of the building modules is used for: establishing a mapping relation between the predicted acceleration and the reference acceleration at the same time in the predicted acceleration sequence and the reference acceleration sequence; according to a preset root mean square algorithm and the mapping relation, carrying out data processing on the predicted acceleration in the predicted acceleration sequence and the reference acceleration in the reference acceleration sequence to obtain a root mean square error;
determining the root mean square error as a deviation value between the detection sensor and the reference sensor; the deviation value is increased, and the damage degree of the road to be detected is increased.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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