CN117112849A - Description method and device for working condition data, storage medium and processor - Google Patents
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Abstract
The embodiment of the application provides a description method and device for working condition data, a storage medium and a processor. Comprising the following steps: determining a plurality of label levels for describing the working condition data; determining target working condition data to be described, and determining a first label value of each target working condition data; determining a second tag value of each target working condition data based on the anomaly detection model; determining a third tag value of each target working condition data through a preset classification algorithm; determining a fourth tag value of each target working condition data based on the membership function; according to the technical scheme, the working condition data can be described more objectively, more comprehensively and more accurately according to the first label value, the second label value, the third label value and the fourth label value of each target working condition data, and the working condition data can be described according to the four-level label values of the working condition data, so that the description of each working condition data is finer, and different requirements of users can be met.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a description method and device for working condition data, an engineering vehicle, a storage medium and a processor.
Background
Data tag technology has been widely used as a basis for portrait technology. However, the current data tag has a single structure and function, and is only applicable to a single scene, but cannot be applied in multiple scenes, multiple demands and multiple roles. Moreover, the existing data labeling technology either directly uses the original data as the corresponding label value, or performs simple statistical calculation on the original data to obtain the corresponding label value. The label values obtained through the technical scheme are low in precision, and data description is carried out on the basis of the label values, so that the data description result is inaccurate and fine, the data type description is single and incomplete, and different requirements of users cannot be met.
Disclosure of Invention
The embodiment of the application aims to provide a description method and device for working condition data, an engineering vehicle, a storage medium and a processor.
In order to achieve the above object, a first aspect of the present application provides a description method for operating condition data, including:
Determining a plurality of label levels for describing working condition data of the engineering vehicle, wherein the plurality of label levels comprise an initial label, a first-level label, a second-level label and a third-level label;
determining a plurality of target working condition data to be described based on service requirements, and determining an initial data value of each target working condition data as a first label value of an initial label of each target working condition data, wherein the initial data value comprises the oil consumption and the running speed of the engineering vehicle;
determining a second tag value of the first-level tag of each target working condition data based on the anomaly detection model;
dividing all target working condition data into a plurality of working condition data sets through a preset classification algorithm, and determining class labels and membership functions of each working condition data set;
determining a third tag value of the second-level tag of each target working condition data according to the class tag of each working condition data set;
determining a function value of each target working condition data based on the membership function of each working condition data set, and determining a fourth label value of the third-level label of each target working condition data according to the function value;
and describing each target working condition data according to the first label value, the second label value, the third label value and the fourth label value of each target working condition data.
In an embodiment of the present application, determining the second tag value of the first level tag of each target operating condition data based on the anomaly detection model includes: sequentially inputting each target working condition data into a constant detection model to output the probability of each target working condition data through an abnormal detection model; for any one target working condition data, determining a second label value as a first preset value under the condition that the probability of the target working condition data is larger than a probability threshold value corresponding to the target working condition data; and determining a second label value as a second preset value according to any one target working condition data under the condition that the probability of the target working condition data is smaller than a probability threshold value corresponding to the target working condition data.
In the embodiment of the present application, the preset classification algorithm is a clustering algorithm, and dividing all target working condition data into a plurality of working condition data sets by the preset classification algorithm includes: determining a plurality of clustering centers of a clustering algorithm aiming at all target working condition data; for any one of the cluster centers, determining the distance between each target working condition data and the cluster center, selecting the target working condition data with the distance within the preset range of the cluster center, and determining all the selected target working condition data as a working condition data set.
In an embodiment of the present application, determining class labels and membership functions for each working condition data set includes: for any working condition data set, determining the center point characteristics of the clustering center corresponding to the working condition data set, and determining the characteristic values of the center point characteristics as class labels; and generating a membership function of the working condition data set according to the distribution rule of all target working condition data of the working condition data set and the constraint condition of the working condition data set aiming at any working condition data set.
In the embodiment of the application, the description method further comprises the following steps: after describing each target working condition data according to the first label value, the second label value, the third label value and the fourth label value of each target working condition data, acquiring login information of a user; determining the data authority of the user according to the login information, and determining a target tag level corresponding to the data authority; and under the condition that a viewing instruction triggered by the user is received, returning the tag value of the target tag level of the target working condition data to the user according to the viewing instruction.
In the embodiment of the application, determining the data authority of the user according to the login information and determining the target tag level corresponding to the data authority comprises the following steps: determining that the data authority is to view a target label level corresponding to the first identity information under the condition that the login information is the first identity information, and determining that the target label level comprises at least one of an initial label, a second-level label and a third-level label; determining that the data authority is to view a target tag level corresponding to the second identity information and determining that the target tag level comprises at least one of a first level tag and a third level tag when the login information is the second identity information; and under the condition that the login information is the third identity information, determining that the data authority is to view a target label level corresponding to the third identity information, and determining that the target label level is a second-level label.
In the embodiment of the application, the description method further comprises the following steps: after returning the label value of the target label level of the target working condition data to the user according to the checking instruction, visualizing the label value through a display device; and/or receiving an abnormality analysis instruction returned by the user under the condition that the tag value comprises a second preset value, wherein the abnormality analysis instruction is obtained by analyzing the second preset value by the user; and determining target working condition data with the tag value including a second preset value as target working condition data to be optimized according to the abnormality analysis instruction.
A second aspect of the present application provides a processor configured to perform the above description method for operating condition data.
The third aspect of the application provides a description device for working condition data, comprising the processor.
The fourth aspect of the application provides an engineering vehicle, comprising the description device for working condition data.
A fifth aspect of the application provides a machine-readable storage medium having stored thereon instructions that when executed by a processor cause the processor to be configured to perform the method of description for operating condition data described above.
According to the technical scheme, the various label levels for describing the working condition data of the engineering vehicle are determined, wherein the various label levels comprise an initial label, a first level label, a second level label and a third level label; determining a plurality of target working condition data to be described based on service requirements, and determining an initial data value of each target working condition data as a first label value of an initial label of each target working condition data, wherein the initial data value comprises the oil consumption and the running speed of the engineering vehicle; determining a second tag value of the first-level tag of each target working condition data based on the anomaly detection model; dividing all target working condition data into a plurality of working condition data sets through a preset classification algorithm, and determining class labels and membership functions of each working condition data set; determining a third tag value of the second-level tag of each target working condition data according to the class tag of each working condition data set; determining a function value of each target working condition data based on the membership function of each working condition data set, and determining a fourth label value of the third-level label of each target working condition data according to the function value; and describing each target working condition data according to the first label value, the second label value, the third label value and the fourth label value of each target working condition data. By adopting the technical scheme, the working condition data can be described more objectively, more comprehensively and more accurately, and the working condition data can be described according to the four-level label values of the working condition data, so that the description of each working condition data is finer, and different requirements of users can be met.
Additional features and advantages of embodiments of the application will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the embodiments of the application. In the drawings:
FIG. 1 schematically illustrates a first flow diagram of a description method for operating condition data according to an embodiment of the application;
FIG. 2 schematically illustrates a second flow diagram of a description method for operating condition data according to an embodiment of the application;
FIG. 3 schematically illustrates a third flow diagram of a description method for operating condition data according to an embodiment of the application;
fig. 4 schematically shows an internal structural view of a computer device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it should be understood that the detailed description described herein is merely for illustrating and explaining the embodiments of the present application, and is not intended to limit the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
FIG. 1 schematically illustrates a first flow diagram of a description method for operating condition data according to an embodiment of the application. As shown in fig. 1, in an embodiment of the present application, a description method for operating condition data is provided, including the following steps:
step 101, determining various label levels describing working condition data of the engineering vehicle, wherein the various label levels comprise an initial label, a first-level label, a second-level label and a third-level label.
Step 102, determining a plurality of target working condition data to be described based on service requirements, and determining an initial data value of each target working condition data as a first label value of an initial label of each target working condition data, wherein the initial data value comprises the oil consumption and the running speed of the engineering vehicle.
Step 103, determining a second tag value of the first-level tag of each target working condition data based on the anomaly detection model.
And 104, dividing all target working condition data into a plurality of working condition data sets through a preset classification algorithm, and determining class labels and membership functions of each working condition data set.
Step 105, determining a third tag value of the second-level tag of each target working condition data according to the class tag of each working condition data set.
And 106, determining a function value of each target working condition data based on the membership function of each working condition data set, and determining a fourth label value of the third-level label of each target working condition data according to the function value.
Step 107, describing each target working condition data according to the first label value, the second label value, the third label value and the fourth label value of each target working condition data.
The working condition data refer to basic physical information, dynamic behavior information and static behavior information of the engineering vehicle, such as meter section, delivery year and month, chassis type, vehicle age, accumulated pumping capacity, oil consumption and the like of the pump truck. The processor may determine a variety of tag levels describing operating condition data of the work vehicle. The plurality of label levels includes an initial label, a first level label, a second level label, and a third level label. After determining the plurality of tag levels, the processor may determine a plurality of target operating condition data to be described based on the traffic demand, and determine an initial data value of each target operating condition data as a first tag value of an initial tag of each target operating condition data. The initial data value comprises the oil consumption and the running speed of the engineering vehicle. After determining the first tag value, the processor may determine a second tag value for the first level tag for each target operating condition data based on the anomaly detection model. After determining the second tag value, the processor may divide all the target working condition data into a plurality of working condition data sets through a preset classification algorithm, and determine class tags and membership functions of each working condition data set. After determining the class label for each set of operating condition data, the processor may determine a third label value for the second level label for each target operating condition data based on the class label for each set of operating condition data. After determining the membership function for each set of operating condition data, the processor may determine a function value for each target operating condition data based on the membership function for each set of operating condition data. And determining a fourth tag value of the third-level tag of each target working condition data according to the function value. The processor may describe each target operating condition data based on the first, second, third, and fourth tag values for each target operating condition data.
Through the technical scheme, each working condition data can be described more objectively, more comprehensively and more accurately, and the four-level label values of the working condition data can be described according to the working condition data, so that the description of each data is finer, and different requirements of users can be met.
In one embodiment, determining the second tag value of the first level tag for each target operating condition data based on the anomaly detection model includes: sequentially inputting each target working condition data into an anomaly detection model so as to output the probability of each target working condition data through the anomaly detection model; for any one target working condition data, determining a second label value as a first preset value under the condition that the probability of the target working condition data is larger than a probability threshold value corresponding to the target working condition data; and determining a second label value as a second preset value according to any one target working condition data under the condition that the probability of the target working condition data is smaller than a probability threshold value corresponding to the target working condition data.
The processor may determine a second tag value for the first level tag for each target operating condition data based on the anomaly detection model. Specifically, the processor may sequentially input each target operating condition data to the anomaly detection model to output a probability of each target operating condition data through the anomaly detection model. After obtaining the probability of each target operating condition data, the processor may compare the probability of each target operating condition data to a probability threshold corresponding to the target operating condition data. For any one target working condition data, the processor can determine the second label value of the target working condition data as the first preset value under the condition that the probability of the target working condition data is larger than the probability threshold value corresponding to the target working condition data. And under the condition that the probability of the target working condition data is smaller than the probability threshold value corresponding to the target working condition data, the processor can determine that the second label value of the target working condition data is a second preset value.
For example, the processor may construct the anomaly detection model by a gaussian distribution algorithm, a depth method, a distance method, or the like. Taking a Gaussian distribution abnormal detection model constructed by a Gaussian distribution algorithm as an example, the first preset value represents normal, and the second preset value represents abnormal. The processor may sequentially input the target operating condition data a and the target operating condition data B to the gaussian distribution anomaly detection model to output the probability P (a) of the target operating condition data a and the probability P (B) of the target operating condition data B through the gaussian distribution anomaly detection model. The processor may compare the probability P (A) of the target operating condition data A to a probability threshold ε corresponding to the target operating condition data A A A comparison is made. P (A) > ε A The processor may determine that the second tag value of the target operating condition data a is normal. The processor may compare the probability P (B) of the target operating condition data B to a probability threshold ε corresponding to the target operating condition data B B A comparison is made. P (B)<ε B The processor may determine that the second tag value of the target operating condition data B is abnormal. And whether the data of each working condition is normal or not can be determined through the second tag value, so that the state of the engineering vehicle can be judged.
In one embodiment, the preset classification algorithm is a clustering algorithm, and dividing all the target working condition data into a plurality of working condition data sets by the preset classification algorithm includes: determining a plurality of clustering centers of a clustering algorithm aiming at all target working condition data; for any one of the cluster centers, determining the distance between each target working condition data and the cluster center, selecting the target working condition data with the distance within the preset range of the cluster center, and determining all the selected target working condition data as a working condition data set.
The processor may divide all the target operating condition data into a plurality of operating condition data sets through a preset classification algorithm. Specifically, the preset classification algorithm may be a clustering algorithm, a decision tree, a support vector machine, and the like. Taking a clustering algorithm as an example, the processor may determine a plurality of cluster centers for all target operating condition data for the clustering algorithm. For any one cluster center, the processor can determine the distance between each target working condition data and the cluster center, and select the target working condition data with the distance within the preset range of the cluster center. And determining all selected target working condition data as a working condition data set.
For example, the processor may divide all target operating condition data into a plurality of operating condition data sets using a K-means clustering algorithm. Specifically, the processor may determine target operating condition data A 1 、A 2 、A 3 、A 4 、A 5 K clustering is carried out to obtain m aiming at all target working condition data i And clustering centers. Where i=1, 2. For the cluster center m 1 The processor may determine target operating condition data A 1 、A 2 、A 3 、A 4 、A 5 And cluster center m 1 Is a distance of (3). The processor can select the distance to be in the cluster center m 1 Target condition data a within a preset range of (2) 1 、A 2 、A 3 And the selected target working condition data A 1 、A 2 、A 3 And determining a working condition data set. For the cluster center m 2 The processor may determine target operating condition data A 1 、A 2 、A 3 、A 4 、A 5 And cluster center m 2 Is a distance of (3). The processor can select the distance to be in the cluster center m 2 Target condition data a within a preset range of (2) 4 、A 5 And the selected target working condition numberAccording to A 4 、A 5 And determining a working condition data set.
In one embodiment, determining class labels and membership functions for each set of operating condition data includes: for any working condition data set, determining the center point characteristics of the clustering center corresponding to the working condition data set, and determining the characteristic values of the center point characteristics as class labels; and generating a membership function of the working condition data set according to the distribution rule of all target working condition data of the working condition data set and the constraint condition of the working condition data set aiming at any working condition data set.
The processor may determine class labels and membership functions for each set of operating condition data. Specifically, for any one working condition data set, the processor may determine a center point feature of a cluster center corresponding to the working condition data set. And determining the characteristic value of the central point characteristic as a class label of the working condition data set. For any one working condition data set, the processor can generate a membership function of the working condition data set according to the distribution rule of all target working condition data of the working condition data set and the constraint condition of the working condition data set.
For example, the processor may divide all target operating condition data into a plurality of operating condition data sets using a K-means clustering algorithm. Specifically, the processor may determine target operating condition data A 1 、A 2 、A 3 、A 4 、A 5 K clustering is carried out to obtain m aiming at all target working condition data i And clustering centers. Where i=1, 2. For the cluster center m 1 The processor may determine target operating condition data A 1 、A 2 、A 3 、A 4 、A 5 And cluster center m 1 Is a distance of (3). The processor can select the distance to be in the cluster center m 1 Target condition data a within a preset range of (2) 1 、A 2 、A 3 And the selected target working condition data A 1 、A 2 、A 3 And determining a working condition data set. For the cluster center m 2 The processor may determine target operating condition data A 1 、A 2 、A 3 、A 4 、A 5 And cluster center m 2 Is a distance of (3). The processor can select the distance to be in the cluster center m 2 Target condition data a within a preset range of (2) 4 、A 5 And the selected target working condition data A 4 、A 5 And determining a working condition data set.
For the cluster center m 1 The processor may refine the cluster center point m 1 And determining the feature value of the center point feature as a cluster center m 1 Class labels of the corresponding working condition data sets. After determining the class label of the working condition data set, the processor can determine the class label as target working condition data A included in the working condition data set 1 、A 2 、A 3 A third tag value of a second level tag of the (c).
For the cluster center m 2 The processor may refine the cluster center point m 2 And determining the feature value of the center point feature as a cluster center m 2 Class labels of the corresponding working condition data sets. After determining the class label of the working condition data set, the processor can determine the class label as target working condition data A included in the working condition data set 4 、A 5 A third tag value of a second level tag of the (c).
For the cluster center m 1 The processor is used for processing the target working condition data A according to the target working condition data A which can be obtained by the working condition data set 1 、A 2 、A 3 Generating a membership function f of the working condition data set according to the distribution rule of the working condition data set and the constraint condition of the working condition data set 1 (x) A. The invention relates to a method for producing a fibre-reinforced plastic composite In generating membership function f 1 (x) Then, the processor can sequentially compare the target working condition data A in the working condition data set 1 、A 2 、A 3 Input to membership function f 1 (x) To obtain membership functions f respectively 1 (x) Output target operating mode data A 1 、A 2 、A 3 The corresponding scores in the percentile evaluation set V. The processor may compare the target operating condition data A 1 、A 2 、A 3 Respectively determined as target working conditionsData A 1 、A 2 、A 3 A fourth tag value of the third level tag of (c).
For the cluster center m 2 The processor is used for processing the target working condition data A according to the target working condition data A which can be obtained by the working condition data set 4 、A 5 Generating a membership function f of the working condition data set according to the distribution rule of the working condition data set and the constraint condition of the working condition data set 2 (x) A. The invention relates to a method for producing a fibre-reinforced plastic composite In generating membership function f 2 (x) Then, the processor can sequentially compare the target working condition data A in the working condition data set 4 、A 5 Input to membership function f 2 (x) To obtain membership functions f respectively 2 (x) Output target operating mode data A 4 、A 5 The corresponding scores in the percentile evaluation set V. The processor may compare the target operating condition data A 4 、A 5 Respectively determined as target working condition data A 4 、A 5 A fourth tag value of the third level tag of (c).
In one embodiment, the description method further comprises: after describing each target working condition data according to the first label value, the second label value, the third label value and the fourth label value of each target working condition data, acquiring login information of a user; determining the data authority of the user according to the login information, and determining a target tag level corresponding to the data authority; and under the condition that a viewing instruction triggered by the user is received, returning the tag value of the target tag level of the target working condition data to the user according to the viewing instruction.
After describing each target operating condition data according to the first, second, third and fourth tag values of each target operating condition data, the processor may obtain login information of the user. After the login information of the user is obtained, the processor may determine the data authority of the user according to the login information, and determine a target tag level corresponding to the data authority. The processor may receive a user-triggered viewing instruction. After receiving the user-triggered viewing instruction, the processor may return the tag value of the target tag hierarchy of the target operating condition data to the user according to the viewing instruction.
In one embodiment, determining the data rights of the user according to the login information and determining the target tag level corresponding to the data rights includes: determining that the data authority is to view a target label level corresponding to the first identity information under the condition that the login information is the first identity information, and determining that the target label level comprises at least one of an initial label, a second-level label and a third-level label; determining that the data authority is to view a target tag level corresponding to the second identity information and determining that the target tag level comprises at least one of a first level tag and a third level tag when the login information is the second identity information; and under the condition that the login information is the third identity information, determining that the data authority is to view a target label level corresponding to the third identity information, and determining that the target label level is a second-level label.
The processor may determine the user's data rights based on the login information and determine a target tag hierarchy corresponding to the data rights. Specifically, the processor may determine whether the login information is first identity information. In the case that the login information is the first identity information, the processor may determine that the data right of the user is to view a target tag level corresponding to the first identity information, and determine that the target tag level includes at least one of an initial tag, a second level tag, and a third level tag. The processor may determine whether the login information is second identity information. In the case where the login information is the second identity information, the processor may determine that the user's data right is to view a target tag hierarchy corresponding to the second identity information, and determine that the target tag hierarchy includes at least one of a first hierarchy tag and a third hierarchy tag. The processor may determine whether the login information is third identity information. In the case that the login information is the third identity information, the processor may determine that the data authority of the user is to view a target tag level corresponding to the third identity information, and determine that the target tag level is a second level tag.
In one embodiment, the description method further comprises: after returning the label value of the target label level of the target working condition data to the user according to the checking instruction, visualizing the label value through a display device; and/or receiving an abnormality analysis instruction returned by the user under the condition that the tag value comprises a second preset value, wherein the abnormality analysis instruction is obtained by analyzing the second preset value by the user; and determining target working condition data with the tag value including a second preset value as target working condition data to be optimized according to the abnormality analysis instruction.
After returning the tag value of the target tag hierarchy of the target operating condition data to the user according to the viewing instruction, the processor may visualize the tag value through a display device. The processor may also determine whether the returned tag value includes a second preset value. And under the condition that the returned label value comprises a second preset value, the processor can receive an abnormality analysis instruction returned by the user. The abnormality analysis instruction is obtained by analyzing a second preset value by a user. After the abnormality analysis instruction is received, the processor can determine that target working condition data, the tag value of which comprises a second preset value, is target working condition data to be optimized according to the abnormality analysis instruction, so that a user can optimize and improve the working condition vehicle, and iterative upgrading of the engineering vehicle is facilitated.
In one embodiment, as shown in FIG. 2, the processor may determine a first tier tag, a second tier tag, a third tier tag, and a fourth tier tag that are described by the operating condition data of the device. For data in the cloud platform database, the processor may first perform fourth level tag acquisition. Specifically, the processor may obtain data related to the device parameters or operating conditions. After the related data is obtained, the processor may clean and preprocess the obtained data to obtain the corresponding index. For the indexes obtained by preprocessing, the processor can screen out the indexes to be labeled according to the service requirement, and determine the index value of each index as the label value of the fourth-level label of each index.
After the fourth level tag acquisition, the processor may perform the first level tag acquisition. Specifically, the processor may determine whether each index is abnormal based on the gaussian distribution abnormality detection model M. For any one index, the processor may input the index to the gaussian distribution abnormality detection model M to output the probability of the index through the gaussian distribution abnormality detection model M. The processor may compare the probability of the indicator to a probability threshold corresponding to the indicator. The processor may determine that the first level tag of the indicator is normal if the probability of the indicator is greater than a probability threshold corresponding to the indicator. The processor may determine that the first level tag of the indicator is abnormal if the probability of the indicator is less than a probability threshold corresponding to the indicator.
After the first level tag acquisition, the processor may perform a second level tag acquisition. Specifically, the processor may cluster the historical data (i.e., metrics) using the K-means method to obtain K class G i And its center point m i Obtaining K index sets G i And each index set G i Is the corresponding m i . For each center point m i The processor may summarize the center point feature to obtain class labels. For any one G i The processor may compare the G i The distance between the index value and the center point, and selecting the nearest class label as G i A second level tag for each of the metrics.
After the second level tag acquisition, the processor may perform a third level tag acquisition. Specifically, for the K class G obtained i For each G i The processor may construct membership functions based on data distribution and expert advice, respectively. And putting the G i And substituting the index value into the membership function to obtain the score of each index as a third-level label of each index.
As shown in fig. 3, the processor may determine a fourth level tag of the raw operating condition data. After determining the fourth level tag of the original operating condition data, the processor may perform anomaly detection on the original operating condition data to determine a detection result: the normal or abnormal condition is determined as a first level tag of the original operating condition data. After determining the first level tag, the processor may perform a cluster analysis on the raw operating condition data to obtain a second level tag of the raw operating condition data. After obtaining the second level tag of the original operating condition data, the processor may construct a score by fuzzy evaluation to determine a third level tag of the original operating condition data.
After the fourth-level tag, the first-level tag, the second-level tag, and the third-level tag of the original working condition data are determined, the processor may determine the data authority of the user according to the login information of the user.
For example, in the case where the login information of the user is a developer, the processor may determine that the data authority of the user is at least one of a fourth-level tag, a second-level tag, and a third-level tag that can view the original operating condition data. The fourth-level label is helpful for research and development personnel to monitor the working condition of equipment or trace the label. The second-level label is beneficial to the fine management of the equipment by research personnel, and the overall quality control of the equipment is realized. The third-level label is helpful for research personnel to timely identify the health condition of the equipment so as to manage and optimize the equipment.
In the event that the user's login information is at least one of customer, after-market service, quality personnel, and management, the processor may determine that the user's data rights are at least one of a first tier tag and a third tier tag that may view the original operating condition data. The first-level tag is beneficial to abnormal monitoring of customers, after-sales service, quality personnel and management on equipment, equipment abnormal conditions are rapidly acquired, and equipment problems are timely solved. The third-level label is beneficial to clients, after-sales service, quality personnel and management to quantitatively score or predict the trend of the working condition of the equipment, and can clearly and objectively master the development situation of the equipment.
In the case where the user's login information is marketing, the processor may determine that the user's data rights are a second level tag that may view the original operating condition data. The second-level label is helpful for marketing personnel to conduct inter-cluster analysis and comparison so as to obtain a proper marketing strategy.
According to the technical scheme, the various label levels for describing the working condition data of the engineering vehicle are determined, wherein the various label levels comprise an initial label, a first level label, a second level label and a third level label; determining a plurality of target working condition data to be described based on service requirements, and determining an initial data value of each target working condition data as a first label value of an initial label of each target working condition data, wherein the initial data value comprises the oil consumption and the running speed of the engineering vehicle; determining a second tag value of the first-level tag of each target working condition data based on the anomaly detection model; dividing all target working condition data into a plurality of working condition data sets through a preset classification algorithm, and determining class labels and membership functions of each working condition data set; determining a third tag value of the second-level tag of each target working condition data according to the class tag of each working condition data set; determining a function value of each target working condition data based on the membership function of each working condition data set, and determining a fourth label value of the third-level label of each target working condition data according to the function value; and describing each target working condition data according to the first label value, the second label value, the third label value and the fourth label value of each target working condition data. By adopting the technical scheme, each working condition data can be described more objectively, more comprehensively and more accurately, and the working condition data can be distributed according to the four-level label values of the working condition data, so that the description of each data is finer, and different requirements of users can be met.
FIGS. 1, 2, and 3 are flow diagrams of a method for describing operating condition data in one embodiment. It should be understood that, although the steps in the flowcharts of fig. 1, 2, and 3 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as 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 of fig. 1, 2, 3 may comprise a plurality of sub-steps or phases, which are not necessarily performed at the same time, but may be performed at different times, nor does the order of execution of the sub-steps or phases necessarily follow one another, but may be performed alternately or alternately with at least some of the other steps or sub-steps of other steps.
The embodiment of the application provides a processor, which is used for running a program, wherein the description method for working condition data is executed when the program runs.
The embodiment of the application provides a description device for working condition data, which comprises the processor.
The embodiment of the application provides an engineering vehicle, which comprises the description device for working condition data.
The embodiment of the application provides a storage medium, on which a program is stored, which when executed by a processor, implements the above description method for operating mode data.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor a01, a network interface a02, a memory (not shown) and a database (not shown) connected by a system bus. Wherein the processor a01 of the computer device is adapted to provide computing and control capabilities. The memory of the computer device includes internal memory a03 and nonvolatile storage medium a04. The nonvolatile storage medium a04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory a03 provides an environment for the operation of the operating system B01 and the computer program B02 in the nonvolatile storage medium a04. The database of the computer device is used for storing data of label levels, label values and function values. The network interface a02 of the computer device is used for communication with an external terminal through a network connection. The computer program B02, when executed by the processor a01, implements a description method for operating mode data.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The embodiment of the application provides equipment, which comprises a processor, a memory and a program stored in the memory and capable of running on the processor, wherein the processor realizes the following steps when executing the program: determining a plurality of label levels for describing working condition data of the engineering vehicle, wherein the plurality of label levels comprise an initial label, a first-level label, a second-level label and a third-level label; determining a plurality of target working condition data to be described based on service requirements, and determining an initial data value of each target working condition data as a first label value of an initial label of each target working condition data, wherein the initial data value comprises the oil consumption and the running speed of the engineering vehicle; determining a second tag value of the first-level tag of each target working condition data based on the anomaly detection model; dividing all target working condition data into a plurality of working condition data sets through a preset classification algorithm, and determining class labels and membership functions of each working condition data set; determining a third tag value of the second-level tag of each target working condition data according to the class tag of each working condition data set; determining a function value of each target working condition data based on the membership function of each working condition data set, and determining a fourth label value of the third-level label of each target working condition data according to the function value; and describing each target working condition data according to the first label value, the second label value, the third label value and the fourth label value of each target working condition data.
In one embodiment, determining the second tag value of the first level tag for each target operating condition data based on the anomaly detection model includes: sequentially inputting each target working condition data into an anomaly detection model so as to output the probability of each target working condition data through the anomaly detection model; for any one target working condition data, determining a second label value as a first preset value under the condition that the probability of the target working condition data is larger than a probability threshold value corresponding to the target working condition data; and determining a second label value as a second preset value according to any one target working condition data under the condition that the probability of the target working condition data is smaller than a probability threshold value corresponding to the target working condition data.
In one embodiment, the preset classification algorithm is a clustering algorithm, and dividing all the target working condition data into a plurality of working condition data sets by the preset classification algorithm includes: determining a plurality of clustering centers of a clustering algorithm aiming at all target working condition data; for any one of the cluster centers, determining the distance between each target working condition data and the cluster center, selecting the target working condition data with the distance within the preset range of the cluster center, and determining all the selected target working condition data as a working condition data set.
In one embodiment, determining class labels and membership functions for each set of operating condition data includes: for any working condition data set, determining the center point characteristics of the clustering center corresponding to the working condition data set, and determining the characteristic values of the center point characteristics as class labels; and generating a membership function of the working condition data set according to the distribution rule of all target working condition data of the working condition data set and the constraint condition of the working condition data set aiming at any working condition data set.
In one embodiment, the description method further comprises: after describing each target working condition data according to the first label value, the second label value, the third label value and the fourth label value of each target working condition data, acquiring login information of a user; determining the data authority of the user according to the login information, and determining a target tag level corresponding to the data authority; and under the condition that a viewing instruction triggered by the user is received, returning the tag value of the target tag level of the target working condition data to the user according to the viewing instruction.
In one embodiment, determining the data rights of the user according to the login information and determining the target tag level corresponding to the data rights includes: determining that the data authority is to view a target label level corresponding to the first identity information under the condition that the login information is the first identity information, and determining that the target label level comprises at least one of an initial label, a second-level label and a third-level label; determining that the data authority is to view a target tag level corresponding to the second identity information and determining that the target tag level comprises at least one of a first level tag and a third level tag when the login information is the second identity information; and under the condition that the login information is the third identity information, determining that the data authority is to view a target label level corresponding to the third identity information, and determining that the target label level is a second-level label.
In one embodiment, the description method further comprises: after returning the label value of the target label level of the target working condition data to the user according to the checking instruction, visualizing the label value through a display device; and/or receiving an abnormality analysis instruction returned by the user under the condition that the tag value comprises a second preset value, wherein the abnormality analysis instruction is obtained by analyzing the second preset value by the user; and determining target working condition data with the tag value including a second preset value as target working condition data to be optimized according to the abnormality analysis instruction.
The application also provides a computer program product adapted to perform a program initialized with the steps of the description method as for operating mode data when executed on a data processing device.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (11)
1. A description method for operating condition data, the description method comprising:
determining a plurality of label levels for describing working condition data of the engineering vehicle, wherein the plurality of label levels comprise an initial label, a first-level label, a second-level label and a third-level label;
Determining a plurality of target working condition data to be described based on service requirements, and determining an initial data value of each target working condition data as a first label value of an initial label of each target working condition data, wherein the initial data value comprises the oil consumption and the running speed of the engineering vehicle;
determining a second tag value of the first-level tag of each target working condition data based on the anomaly detection model;
dividing all target working condition data into a plurality of working condition data sets through a preset classification algorithm, and determining class labels and membership functions of each working condition data set;
determining a third tag value of the second-level tag of each target working condition data according to the class tag of each working condition data set;
determining a function value of each target working condition data based on the membership function of each working condition data set, and determining a fourth label value of the third-level label of each target working condition data according to the function value;
and describing each target working condition data according to the first label value, the second label value, the third label value and the fourth label value of each target working condition data.
2. The description method for operating condition data according to claim 1, wherein the determining the second tag value of the first-level tag of each target operating condition data based on the abnormality detection model includes:
Sequentially inputting each target working condition data into the anomaly detection model to output the probability of each target working condition data through the anomaly detection model;
for any one target working condition data, determining the second label value as a first preset value under the condition that the probability of the target working condition data is larger than a probability threshold value corresponding to the target working condition data;
and determining the second label value as a second preset value according to any one target working condition data when the probability of the target working condition data is smaller than a probability threshold value corresponding to the target working condition data.
3. The method for describing working condition data according to claim 1, wherein the preset classification algorithm is a clustering algorithm, and the dividing all target working condition data into a plurality of working condition data sets by the preset classification clustering algorithm includes:
determining a plurality of clustering centers of the clustering algorithm aiming at all target working condition data;
for any one clustering center, determining the distance between each target working condition data and the clustering center, selecting the target working condition data with the distance within the preset range of the clustering center, and determining all selected target working condition data as a working condition data set.
4. A description method for operating condition data according to claim 3, wherein determining class labels and membership functions for each set of operating condition data comprises:
for any working condition data set, determining the center point characteristic of a clustering center corresponding to the working condition data set, and determining the characteristic value of the center point characteristic as the class label;
and generating a membership function of any working condition data set according to the distribution rule of all target working condition data of the working condition data set and the constraint condition of the working condition data set.
5. The description method for operating condition data according to claim 1, characterized in that the description method further comprises:
after describing each target working condition data according to the first label value, the second label value, the third label value and the fourth label value of each target working condition data, acquiring login information of a user;
determining the data authority of the user according to the login information, and determining a target label level corresponding to the data authority;
and under the condition that a viewing instruction triggered by the user is received, returning the tag value of the target tag level of the target working condition data to the user according to the viewing instruction.
6. The method for describing operating mode data according to claim 5, wherein determining the data authority of the user according to the login information, and determining a target tag level corresponding to the data authority, comprises:
determining that the data authority is a target label level corresponding to the first identity information when the login information is the first identity information, and determining that the target label level comprises at least one of the initial label, the second-level label and the third-level label;
if the login information is second identity information, determining that the data authority is a target tag level corresponding to the second identity information, and determining that the target tag level comprises at least one of the first level tag and the third level tag;
and under the condition that the login information is third identity information, determining that the data authority is a target label level corresponding to the third identity information, and determining that the target label level is the second-level label.
7. The description method for operating condition data according to claim 5, characterized in that the description method further comprises:
After returning the label value of the target label level of the target working condition data to the user according to the checking instruction, visualizing the label value through a display device; and/or
Receiving an abnormality analysis instruction returned by the user under the condition that the tag value comprises a second preset value, wherein the abnormality analysis instruction is obtained by analyzing the second preset value by the user;
and determining that the target working condition data of which the label value comprises the second preset value is target working condition data to be optimized according to the abnormality analysis instruction.
8. A processor configured to perform the description method for operating condition data according to any one of claims 1 to 7.
9. A description device for operating condition data, characterized in that it comprises a processor according to claim 8.
10. An engineering vehicle, characterized by comprising the description device for operating condition data according to claim 9.
11. A machine-readable storage medium having instructions stored thereon, which when executed by a processor cause the processor to be configured to perform the description method for operating condition data according to any one of claims 1 to 7.
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