CN112598052A - Mechanical attitude analysis method and system based on K-Means - Google Patents
Mechanical attitude analysis method and system based on K-Means Download PDFInfo
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Abstract
The invention discloses a mechanical attitude analysis method and a system based on K-Means, relating to the technical field of data mining; acquiring running state data of mechanical equipment, wherein the running state data comprises ground speed data, acceleration data and angular speed data, and performing clustering analysis on the running state data by using a K-Means clustering algorithm: the method comprises the steps of respectively randomly selecting ground speed data, acceleration data and angular speed data as initial center points, regarding other running state data as data points, calculating the distance from each other data point to the initial center point, iteratively replacing the data point closest to the center as the initial center point, carrying out data standardization on the running state data, distributing corresponding proportion coefficients of the ground speed data, the acceleration data and the angular speed data, distributing data labels according to the attributes of the running state data, forming a posture analysis model by combining cluster analysis, the proportion coefficients and the data labels, and carrying out mechanical posture analysis according to the running state data of mechanical equipment by using the model.
Description
Technical Field
The invention discloses a method and a system, relates to the technical field of data mining, and particularly relates to a mechanical attitude analysis method and a system based on K-Means.
Background
The existing method for judging the mechanical attitude mainly has the problems of complicated steps, more places needing manual intervention, large and inaccurate judgment results and the like.
The reason for this problem is that each mechanical device has its own characteristics, and even if the same device changes its own characteristics over time, there is no way to set a uniform rule to adapt to all types of vehicles, and human intervention is required to tag the monitoring data of each device in advance, which is complicated and inaccurate.
Disclosure of Invention
The invention provides a mechanical attitude analysis method and system based on K-Means, aiming at the problems in the prior art, the K-Means data mining algorithm is utilized, the attributes such as acceleration, angular velocity, angle and the like are deeply analyzed, the running state of equipment is judged, and the judgment accuracy, the operation steps are simplified and the working efficiency is improved according to the judgment state before the continuous running of the equipment is automatically corrected.
The specific scheme provided by the invention is as follows:
a mechanical attitude analysis method based on K-Means obtains the running state data of mechanical equipment, including ground speed data, acceleration data and angular speed data,
performing clustering analysis on the running state data by using a K-Means clustering algorithm: respectively randomly selecting ground speed data, acceleration data and angular speed data as initial center points, regarding other running state data as data points, calculating the distance from each other data point to the initial center point, iteratively replacing the data point closest to the center as the initial center point,
standardizing the data of the running state, distributing the corresponding proportion coefficients of the ground speed data, the acceleration data and the angular speed data,
distributing corresponding data labels according to the attributes of the operating state data, forming a posture analysis model by combining cluster analysis, a proportion coefficient and the data labels,
and analyzing the mechanical attitude according to the running state data of the mechanical equipment by utilizing the attitude analysis model.
Preferably, the mechanical attitude analysis method based on K-Means comprises the following steps:
step 1: setting the clustering number K to be 3,
step 2: respectively randomly taking ground speed data, acceleration data and angular speed data as initial central points,
and step 3: calculating the distance between each other data point and the initial central point, replacing the data point of the center closest to the initial central point with the initial central point,
and 4, step 4: and (5) iterating and replacing until the change value of the new and old initial center points is smaller than a set threshold value or the set iteration number is reached, and terminating the iteration.
Preferably, the mechanical attitude analysis method based on K-Means assigns corresponding data tags:
setting data labels and data label distribution rules according to the mechanical posture,
and automatically distributing the data labels to the formed running state data according to a data label distribution rule.
Preferably, in the mechanical attitude analysis method based on K-Means, the attitude analysis model is verified, and the improvement of the attitude analysis model is performed according to the verification result.
A mechanical attitude analysis system based on K-Means comprises an acquisition module, a clustering module, a model establishing module and an analysis module,
the acquisition module acquires the running state data of the mechanical equipment, including ground speed data, acceleration data and angular speed data,
the clustering module carries out clustering analysis on the running state data by using a K-Means clustering algorithm: respectively randomly selecting ground speed data, acceleration data and angular speed data as initial center points, regarding other running state data as data points, calculating the distance from each other data point to the initial center point, iteratively replacing the data point closest to the center as the initial center point,
the model building module carries out data standardization on the running state data, distributes corresponding proportion coefficients of ground speed data, acceleration data and angular speed data,
distributing corresponding data labels according to the attributes of the operating state data, forming a posture analysis model by combining cluster analysis, a proportion coefficient and the data labels,
and the analysis module performs mechanical attitude analysis according to the running state data of the mechanical equipment by using the attitude analysis model.
Preferably, the clustering module in the K-Means-based mechanical attitude analysis system performs the clustering analysis step:
step 1: setting the clustering number K to be 3,
step 2: respectively randomly taking ground speed data, acceleration data and angular speed data as initial central points,
and step 3: calculating the distance between each other data point and the initial central point, replacing the data point of the center closest to the initial central point with the initial central point,
and 4, step 4: and (5) iterating and replacing until the change value of the new and old initial center points is smaller than a set threshold value or the set iteration number is reached, and terminating the iteration.
Preferably, the model building module in the K-Means based mechanical attitude analysis system assigns corresponding data tags:
setting data labels and data label distribution rules according to the mechanical posture,
and automatically distributing the data labels to the formed running state data according to a data label distribution rule.
Preferably, in the mechanical attitude analysis system based on K-Means, the attitude analysis model is verified, and the improvement of the attitude analysis model is performed according to the verification result.
A K-Means based mechanical attitude analysis device comprising: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is configured to invoke the machine readable program to perform the K-Means based mechanical pose analysis method.
The invention has the advantages that:
compared with the prior art, the system has the advantages that the efficient data mining algorithm is used for supporting, the model is trained automatically, the data are corrected automatically, the running state of mechanical equipment is calculated more accurately, the mechanical state discrimination readiness is improved, the operation of project personnel is simplified greatly, and the system plays an important role in improving the working efficiency and reducing the workload of the personnel.
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FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic illustration of a sampling point in the application of the method of the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
The invention provides a mechanical attitude analysis method based on K-Means, which is used for acquiring running state data of mechanical equipment, including ground speed data, acceleration data and angular speed data,
performing clustering analysis on the running state data by using a K-Means clustering algorithm: respectively randomly selecting ground speed data, acceleration data and angular speed data as initial center points, regarding other running state data as data points, calculating the distance from each other data point to the initial center point, iteratively replacing the data point closest to the center as the initial center point,
standardizing the data of the running state, distributing the corresponding proportion coefficients of the ground speed data, the acceleration data and the angular speed data,
distributing corresponding data labels according to the attributes of the operating state data, forming a posture analysis model by combining cluster analysis, a proportion coefficient and the data labels,
and analyzing the mechanical attitude according to the running state data of the mechanical equipment by utilizing the attitude analysis model.
Each mechanical device has the characteristics of the mechanical device, even if the characteristics of the same device change along with the time, a unified rule cannot be set to adapt to all types of vehicles, the data collected in advance cannot be labeled and then distinguished by using a classification algorithm, the method collects the running state data, clustering analysis is carried out based on a data mining algorithm K-Means, self-training is carried out, namely, running is efficient, distinguishing is accurate, the whole process is carried out automatically, and personnel participation is reduced.
In some embodiments of the invention, the process is performed as follows:
the running state data of the mechanical equipment, including ground speed, acceleration, angular velocity, angle and other parameters, is obtained through the sensor. The form of the data collected can be seen in table 1,
performing clustering analysis on the running state data based on a K-Means clustering algorithm:
setting the clustering number K to 3;
randomly taking three data as initial central points;
calculating the distance from other points to the three points, and assigning the point to the center of the nearest distance;
recalculating a new data point as a new central point according to the distributed class;
and terminating the iteration if the change value of the new and old central points is less than the set threshold value or reaches the set iteration times.
In the above steps, the distance between two points needs to be calculated, which is equivalent to calculating the similarity between two data, and the ground speed is different from the acceleration, the angular velocity and the angle type, so that data is firstly normalized, and different ratio coefficients are set for different parameters.
After finishing the preliminary clustering, automatically setting data labels, such as attitude data labels of static, idling, working and the like, according to the attributes of the final three central points, wherein the three types have larger differences in multiple dimensions, and automatically allocating the formed attributes of the three types by setting a data label allocation rule.
And distributing corresponding data labels according to the attributes of the running state data, and forming a posture analysis model by combining cluster analysis, the proportion coefficient and the data labels.
And the subsequent data uploaded only needs to calculate the distance from the three central points, select the central point closest to the central point, distribute a label to the central point, and analyze the mechanical posture by using a posture analysis model according to the running state data of the mechanical equipment.
On the basis of the embodiment, the attitude analysis model is verified, and the judgment result before self-correction can be obtained according to the subsequent monitoring data, and the process is as follows: when the nearest distance calculated by the newly collected operation state data and the three central points exceeds 1.5 times of the radius or other thresholds, if the data is not less than a preset threshold, the data does not belong to discrete points, and the data represents that the current attitude analysis model needs to be improved. After the newly collected data reaches a certain quantity value, iteration is carried out again by using the original three central points to replace the central points, and then the processes of establishing the model and analyzing the data by using the model are executed.
The method of the invention is used for monitoring the data of the mechanical equipment, and each machine is automatically subjected to independent model training by using a K-Means algorithm; distributing labels to the trained models according to the attributes of each type; finding out a corresponding class according to an algorithm for the subsequently received monitoring data, and labeling the data; if the training model is monitored to be optimized, the model training is carried out again until the model is effective; the accuracy of analyzing various states of the machine and the utilization rate of data are improved.
TABLE 1
| Ground speed | Acceleration of a vehicle | Angular velocity | Angle of rotation |
| 17.46 km/h | X：-0.049316406 Y：0.084472656 Z：1.0161133 | X：-0.30517578 Y：-0.4272461 Z：-0.12207031 | X：4.0594482 Y：2.9608154 Z：-2.6696777 |
| 17.08 km/h | X：-0.05908203 Y：-0.011230469 Z：1.0390625 | X：-0.79345703 Y：0.061035156 Z：-0.36621094 | X：3.0377197 Y：2.5982666 Z：-2.5598145 |
| 8.33 km/h | X：-0.0034179688 Y：0.047851562 Z：0.9790039 | X：-0.61035156 Y：-0.24414062 Z：0.61035156 | X：2.9003906 Y：1.1315918 Z：-5.196533 |
| 4.31 km/h | X：-0.049604688 Y：0.028808594 Z：0.99316406 | X：-0.48828125 Y：0.0 Z：-0.5493164 | X：3.213501 Y：2.4169922 Z：-0.20324707 |
| 3.24 km/h | X：-0.025878906 Y：-0.038085938 Z：0.9873047 | X：3.0517578 Y：-1.7089844 Z：-5.493164 | X：0.0 Y：0.74157715 Z：-38.485107 |
| 0.0 km/h | X：-0.060058594 Y：0.0390625 Z：0.9770508 | X：0.12207031 Y：-0.061035156 Z：0.0 | X：2.0214844 Y：2.7410889 Z：-120.2124 |
| 0.0 km/h | X：-0.041992188 Y：0.038085938 Z：0.97802734 | X：0.0 Y：0.0 Z：0.0 | X：2.2576904 Y：2.4279785 Z：-120.00366 |
Meanwhile, the invention provides a mechanical attitude analysis system based on K-Means, which comprises an acquisition module, a clustering module, a model building module and an analysis module,
the acquisition module acquires the running state data of the mechanical equipment, including ground speed data, acceleration data and angular speed data,
the clustering module carries out clustering analysis on the running state data by using a K-Means clustering algorithm: respectively randomly selecting ground speed data, acceleration data and angular speed data as initial center points, regarding other running state data as data points, calculating the distance from each other data point to the initial center point, iteratively replacing the data point closest to the center as the initial center point,
the model building module carries out data standardization on the running state data, distributes corresponding proportion coefficients of ground speed data, acceleration data and angular speed data,
distributing corresponding data labels according to the attributes of the operating state data, forming a posture analysis model by combining cluster analysis, a proportion coefficient and the data labels,
and the analysis module performs mechanical attitude analysis according to the running state data of the mechanical equipment by using the attitude analysis model.
The contents of information interaction, readable program execution process and the like among the modules in the system are based on the same concept as the method embodiment of the present invention, and specific contents can be referred to the description in the method embodiment of the present invention, and are not described herein again.
The invention also provides a mechanical attitude analysis device based on K-Means, which comprises: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is configured to invoke the machine readable program to perform the K-Means based mechanical pose analysis method.
The contents of information interaction, readable program process execution and the like of the processor in the device are based on the same concept as the method embodiment of the present invention, and specific contents can be referred to the description in the method embodiment of the present invention, and are not described herein again.
It should be noted that not all steps and modules in the processes and system and device structures of the above preferred embodiments are necessary, and some steps or modules may be omitted according to actual needs. The execution order of the steps is not fixed and can be adjusted as required. The system structure described in the above embodiments may be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by a plurality of physical entities, or some components in a plurality of independent devices may be implemented together.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (9)
1. A mechanical attitude analysis method based on K-Means is characterized in that the running state data of mechanical equipment is obtained, including ground speed data, acceleration data and angular speed data,
performing clustering analysis on the running state data by using a K-Means clustering algorithm: respectively randomly selecting ground speed data, acceleration data and angular speed data as initial center points, regarding other running state data as data points, calculating the distance from each other data point to the initial center point, iteratively replacing the data point closest to the center as the initial center point,
standardizing the data of the running state, distributing the corresponding proportion coefficients of the ground speed data, the acceleration data and the angular speed data,
distributing corresponding data labels according to the attributes of the operating state data, forming a posture analysis model by combining cluster analysis, a proportion coefficient and the data labels,
and analyzing the mechanical attitude according to the running state data of the mechanical equipment by utilizing the attitude analysis model.
2. The method for analyzing mechanical attitude of claim 1, wherein the step of cluster analysis comprises:
step 1: setting the clustering number K to be 3,
step 2: respectively randomly taking ground speed data, acceleration data and angular speed data as initial central points,
and step 3: calculating the distance between each other data point and the initial central point, replacing the data point of the center closest to the initial central point with the initial central point,
and 4, step 4: and (5) iterating and replacing until the change value of the new and old initial center points is smaller than a set threshold value or the set iteration number is reached, and terminating the iteration.
3. A K-Means based mechanical pose analysis method according to claim 1 or 2, characterized by assigning respective data tags:
setting data labels and data label distribution rules according to the mechanical posture,
and automatically distributing the data labels to the formed running state data according to a data label distribution rule.
4. A method according to any of claims 1-3, characterized in that the attitude analysis model is verified, and the improvement of the attitude analysis model is performed on the basis of the verification result.
5. A mechanical attitude analysis system based on K-Means is characterized by comprising an acquisition module, a clustering module, a model establishing module and an analysis module,
the acquisition module acquires the running state data of the mechanical equipment, including ground speed data, acceleration data and angular speed data,
the clustering module carries out clustering analysis on the running state data by using a K-Means clustering algorithm: respectively randomly selecting ground speed data, acceleration data and angular speed data as initial center points, regarding other running state data as data points, calculating the distance from each other data point to the initial center point, iteratively replacing the data point closest to the center as the initial center point,
the model building module carries out data standardization on the running state data, distributes corresponding proportion coefficients of ground speed data, acceleration data and angular speed data,
distributing corresponding data labels according to the attributes of the operating state data, forming a posture analysis model by combining cluster analysis, a proportion coefficient and the data labels,
and the analysis module performs mechanical attitude analysis according to the running state data of the mechanical equipment by using the attitude analysis model.
6. The K-Means based mechanical attitude analysis system of claim 5, wherein the clustering module clustering step:
step 1: setting the clustering number K to be 3,
step 2: respectively randomly taking ground speed data, acceleration data and angular speed data as initial central points,
and step 3: calculating the distance between each other data point and the initial central point, replacing the data point of the center closest to the initial central point with the initial central point,
and 4, step 4: and (5) iterating and replacing until the change value of the new and old initial center points is smaller than a set threshold value or the set iteration number is reached, and terminating the iteration.
7. A K-Means based mechanical pose analysis system according to claim 5 or 6, wherein the creation model module assigns corresponding data tags:
setting data labels and data label distribution rules according to the mechanical posture,
and automatically distributing the data labels to the formed running state data according to a data label distribution rule.
8. A K-Means based mechanical attitude analysis system according to any one of claims 5 to 6, wherein the attitude analysis model is verified and the improvement of the attitude analysis model is carried out on the basis of the verification result.
9. A mechanical attitude analysis device based on K-Means is characterized by comprising: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor, configured to invoke the machine readable program to perform a method of K-Means based mechanical pose analysis of any of claims 1 to 4.
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