CN117933571B - Comprehensive management system and storage method for garbage power generation data - Google Patents
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Abstract
The invention relates to the technical field of data processing, in particular to a garbage power generation data integrated management system and a storage method, comprising the following steps: acquiring an oxygen content curve of the incinerator, and constructing a scatter diagram according to data points in the curve; clustering the scatter diagram to obtain a plurality of clusters; obtaining the aggregation degree of the class clusters according to the duty ratio of the area of the class clusters in the area of the minimum circumscribing circle, the distance between the data points and the mass centers in the class clusters and the distance between the mass centers of the adjacent class clusters; obtaining the abnormality degree of the cluster according to the aggregation degree difference; obtaining an abnormal aggregation degree cluster and a normal aggregation degree cluster according to the abnormal degree; thereby obtaining high-aggregation clusters and low-aggregation clusters; and obtaining coded data corresponding to the oxygen content curve, and storing the coded data. The invention reduces the problem of generating a large amount of redundant space due to the similarity of partial data during lossless compression, and improves the data compression efficiency.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a comprehensive management system and a storage method for garbage power generation data.
Background
Garbage power generation is generally a way to drive a steam turbine to generate electricity by thermal energy generated by burning garbage. The method not only can effectively reduce the volume of the garbage, but also can recycle energy, and is a comparatively environment-friendly garbage treatment technology. In the process of generating electricity by garbage incineration, the oxygen content in the furnace is a critical parameter, and the oxygen content directly influences the combustion efficiency, the generation of pollutants in the flue gas and the energy recovery efficiency. In order to realize efficient and environment-friendly incineration of garbage, the storage and analysis of oxygen content data in the furnace are particularly important.
At present, when oxygen content data in a furnace is stored in the garbage incineration process, specific monitoring values of the oxygen content are stored in a lossless manner one by one according to time sequence, but the oxygen content data in a plurality of incinerators in one incineration period can be similar in part of time period, and more redundant space can be occupied by using lossless compression storage, so that compression efficiency is reduced.
Disclosure of Invention
In order to solve the problems, the invention provides a garbage power generation data integrated management system and a storage method.
The invention relates to a garbage power generation data comprehensive management system and a storage method, which adopt the following technical scheme:
The embodiment of the invention provides a garbage power generation data integrated management and storage method, which comprises the following steps:
acquiring a plurality of oxygen content curves of a plurality of incinerators, wherein the oxygen content curves comprise a plurality of data points, and each data point corresponds to one incineration time and one oxygen content value;
Constructing a scatter diagram according to data points in all oxygen content curves, the incineration time corresponding to the data points and the oxygen content value; clustering the data points in the scatter diagram to obtain a plurality of clusters;
Acquiring the area, the mass center, the area of the minimum circumscribing circle and the adjacent clusters of each cluster; obtaining the aggregation degree of each cluster according to the ratio of the area of each cluster to the area of the minimum circumscribing circle, the distance between the data points and the mass centers in the cluster and the distance between the mass centers of the adjacent clusters;
obtaining the abnormality degree of each cluster according to the aggregation degree difference between different clusters; obtaining a plurality of abnormal aggregation degree clusters and a plurality of normal aggregation degree clusters according to the degree of the abnormality; obtaining the final clustering degree of each abnormal aggregation degree cluster and each normal aggregation degree cluster according to the clustering degree corresponding to the abnormal aggregation degree cluster and the normal aggregation degree cluster and the adjacent clusters;
Obtaining a plurality of high-aggregation class clusters and a plurality of low-aggregation class clusters according to the final clustering degree; and obtaining coded data corresponding to each oxygen content curve according to the high-aggregation class clusters and the low-aggregation class clusters, and storing the coded data corresponding to each oxygen content curve.
Further, the construction of a scatter diagram according to the data points in all the oxygen content curves, the incineration time and the oxygen content value corresponding to the data points, comprises the following specific steps:
Constructing a two-dimensional coordinate system, wherein the horizontal axis of the two-dimensional coordinate system is incineration time, the vertical axis of the two-dimensional coordinate system is oxygen content value, and according to the incineration time and the oxygen content value corresponding to the data points, mapping the data points in all the oxygen content curves into the two-dimensional coordinate system to obtain a scatter diagram containing a plurality of data points, and marking the scatter diagram as a first scatter diagram.
Further, the clustering of the data points in the scatter diagram to obtain a plurality of clusters comprises the following specific steps:
And performing DBSCAN clustering on the data points in the first scatter diagram to obtain a plurality of clusters.
Further, the step of obtaining the aggregation degree of each cluster according to the ratio of the area of each cluster to the area of the minimum circumscribing circle, the distance between the data point and the mass center in the cluster and the distance between the mass centers of adjacent clusters, includes the following specific steps:
marking any one class cluster as a target class cluster;
In the method, in the process of the invention, For the area of the target cluster,/>Is the area of the smallest circumcircle of the target cluster,/>For the number of data points in the target class cluster,/>For the/>, in the target class clusterEuclidean distance between data point and centroid of target class cluster,/>For the number of class clusters adjacent to the target class cluster,/>For the second/>, adjacent to the target class clusterEuclidean distance between centroid of individual cluster and centroid of target cluster,/>As an exponential function based on natural constants,/>Is the aggregation degree of the target class cluster.
Further, the method for obtaining the abnormality degree of each cluster according to the aggregation degree difference between different clusters comprises the following specific steps:
marking any one class cluster as a target class cluster;
In the method, in the process of the invention, For the aggregation degree of target class clusters,/>For the number of class clusters adjacent to the target class cluster,/>For the second/>, adjacent to the target class clusterAggregation degree of individual class clusters,/>To take absolute value,/>For the second/>, adjacent to the target class clusterThe degree of aggregation of the individual clusters of the class,For the second/>, adjacent to the target class clusterAggregation degree of individual class clusters,/>An anomaly factor of the target cluster;
obtaining the abnormal factors of each class cluster, and carrying out linear normalization processing on the abnormal factors of all class clusters to obtain a result which is used as the abnormal degree of each class cluster.
Further, according to the degree of anomaly, a plurality of abnormal aggregation degree clusters and a plurality of normal aggregation degree clusters are obtained, and the method comprises the following specific steps:
Presetting a first threshold value, and taking a class cluster with the anomaly degree larger than the first threshold value as an anomaly aggregation degree class cluster; and taking the class cluster with the anomaly degree smaller than or equal to the first threshold value as a normal aggregation degree class cluster.
Further, the step of obtaining the final clustering degree of each abnormal aggregation degree cluster and each normal aggregation degree cluster according to the clustering degree and the adjacent cluster corresponding to the abnormal aggregation degree cluster and the normal aggregation degree cluster comprises the following specific steps:
Marking any abnormal aggregation degree cluster as a target abnormal aggregation degree cluster; acquiring all adjacent clusters of the target abnormal aggregation degree clusters, and marking the adjacent clusters as target adjacent clusters; the average value of the aggregation degrees corresponding to all the target adjacent clusters is recorded as a first average value; replacing the aggregation degree corresponding to the target abnormal aggregation degree cluster with a first average value to obtain the final aggregation degree of the target abnormal aggregation degree cluster; marking any normal aggregation degree cluster as a target normal aggregation degree cluster; and taking the aggregation degree corresponding to the target normal aggregation degree cluster as the final clustering degree of the target normal aggregation degree cluster.
Further, according to the final clustering degree, a plurality of high-aggregation clusters and a plurality of low-aggregation clusters are obtained, which comprises the following specific steps:
Obtaining the final clustering degree of each abnormal clustering degree cluster and the final clustering degree of each normal clustering degree cluster, carrying out linear normalization processing on the final clustering degree of all abnormal clustering degree clusters, taking the obtained result as the normalized final clustering degree of each abnormal clustering degree cluster, carrying out linear normalization processing on the final clustering degree of all normal clustering degree clusters, taking the obtained result as the normalized final clustering degree of each normal clustering degree cluster, presetting a second threshold, and taking the class cluster with the normalized final clustering degree larger than or equal to the second threshold as the high-clustering class cluster; and taking the class cluster with the normalized final clustering degree smaller than the second threshold value as the low-aggregation class cluster.
Further, the method for obtaining the coded data corresponding to each oxygen content curve according to the high-aggregation cluster and the low-aggregation cluster comprises the following specific steps:
Recording any one oxygen content curve as a target oxygen content curve; marking any one high-aggregation cluster as a target high-aggregation cluster; acquiring an average value of oxygen content values corresponding to all data points in the target aggregation cluster, marking the average value as a second average value, replacing the oxygen content value corresponding to the data point belonging to the target aggregation cluster in the target oxygen content curve with the second average value, and replacing the oxygen content value corresponding to the data point belonging to the high aggregation cluster in the target oxygen content curve; maintaining the oxygen content value corresponding to the data points belonging to the low-aggregation cluster in the target oxygen content curve unchanged; and after replacing the oxygen content values corresponding to the data points in the target oxygen content curve, marking the obtained new curve as a first oxygen content curve, performing run-length coding on all the oxygen content values in the first oxygen content curve to obtain coded data of the first oxygen content curve, and marking the coded data as coded data corresponding to the target oxygen content curve.
The invention also provides a garbage power generation data integrated management system, which comprises a memory and a processor, wherein the processor executes a computer program stored in the memory to realize the steps of the method.
The technical scheme of the invention has the beneficial effects that: after a plurality of oxygen content curves of a plurality of incinerators are obtained, clustering is carried out on data points in the scatter diagram through constructing the scatter diagram to obtain a plurality of clusters, the concentration degree of each cluster is obtained through the ratio of the area of each cluster to the area of the minimum circumcircle, the distance between the data points and the mass centers of the clusters and the distance between the mass centers of the adjacent clusters, then the abnormality degree of each cluster is obtained through the concentration degree difference between the different clusters, the influence of different concentration degrees of the adjacent clusters is reduced, so that uniform compression is realized, local compression efficiency is improved, a plurality of abnormal concentration degree clusters and a plurality of normal concentration degree clusters are obtained according to the size of the abnormality degree, final clustering degree is obtained, a plurality of high concentration degree clusters and a plurality of low concentration degree clusters are obtained according to the size of the final clustering degree, and different adjustment methods are adopted for the oxygen content values of the data points in the high concentration degree clusters and the low concentration degree clusters, the similar oxygen content value distribution is more, a large amount of redundancy is generated due to the similarity of partial data in lossless compression, and the problem of storage space is solved, and the compression efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of a method for comprehensively managing and storing garbage power generation data according to an embodiment of the present invention;
fig. 2 is a flowchart of acquiring encoded data according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of a specific implementation, structure, characteristics and effects of the integrated management system and storage method for garbage power generation data according to the invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a comprehensive management system for garbage power generation data and a specific scheme of a storage method thereof, which are specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a method for comprehensively managing and storing garbage power generation data according to an embodiment of the present invention is shown, where the method includes the following steps:
And S001, acquiring a plurality of oxygen content curves of a plurality of incinerators.
Note that, the overall logic of this embodiment is: firstly, oxygen content change data of one incineration period in a plurality of incinerators are collected through related sensors, and a two-dimensional sample space is established. The method comprises the steps of processing sample data by using a DBSCAN algorithm, obtaining a plurality of clustering clusters according to the aggregation of the data, calculating the corresponding aggregation degree by analyzing the form of the clusters and the data distribution characteristics, adjusting the clusters with abnormal aggregation degree, determining whether the clusters use lossless compression storage or lossy compression storage according to different aggregation degrees and determining compression rules, so that the multisource oxygen content data can be efficiently stored, and acquiring the data before starting analysis.
Specifically, a plurality of oxygen content curves of a plurality of incinerators are obtained, and the method specifically comprises the following steps:
Respectively installing oxygen concentration sensors in a plurality of garbage incinerators, outputting an oxygen content value by the oxygen concentration sensors every 1 minute, and recording a two-dimensional time sequence curve formed by all oxygen content values of each garbage incinerator in the latest incineration period as an oxygen content curve of the corresponding garbage incinerator; it should be noted that the oxygen content curve includes a plurality of data points, each data point corresponds to one incineration time and one oxygen content value, and the time sequence lengths of the oxygen content curves of different garbage incinerators are the same.
It should be noted that, in the starting stage of the incinerator, due to the lower temperature in the incinerator, a higher oxygen content is required to promote ignition and combustion of the garbage. At this time, by controlling the oxygen supply amount, sufficient oxygen supply is ensured to rapidly raise the temperature in the furnace. After the incinerator reaches a certain operating temperature, the stable combustion stage is started in the incinerator. The ideal oxygen content should be maintained at a relatively stable level to ensure adequate combustion of the waste, but the calorific value, composition and feed rate of the waste may change during incineration, resulting in abrupt changes in oxygen content at certain times. During the period of stopping the incinerator or reducing load, the oxygen supply is gradually reduced to slow down the combustion speed until the combustion in the incinerator is completely stopped.
So far, the oxygen content curves of a plurality of garbage incinerators are obtained.
Step S002, constructing a scatter diagram according to data points in all oxygen content curves, the burning time corresponding to the data points and the oxygen content value; and clustering the data points in the scatter diagram to obtain a plurality of clusters.
It should be noted that, by analyzing the multi-source oxygen content curve, the oxygen content curve is first processed by using a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, and the distribution of the data points in the oxygen content curve is different, so that the oxygen content curve can be clustered into a plurality of clusters by Density clustering (DBSCAN). The class clusters with more concentrated data distribution are subjected to lossy compression storage by means of run-length coding, and the class clusters with more discrete data distribution are subjected to lossless storage according to the original values, so that the purposes of reducing redundancy and saving storage space are achieved. It is therefore first necessary to calculate the corresponding aggregation level index for all class clusters based on their data distribution. The corresponding compression mode is judged according to the degree of aggregation. Since there may be some cases where the degree of aggregation of the partial clusters is abnormal, resulting in a reduction in compression efficiency, it is necessary to appropriately adjust the abnormal degree of aggregation of the clusters so as to satisfy the partial satisfaction level. And finally, determining a rule of lossy compression, and performing efficient compression on the oxygen content value corresponding to the multisource oxygen content curve.
Specifically, a scatter diagram is constructed according to data points in all oxygen content curves, the incineration time corresponding to the data points and the oxygen content value, and the scatter diagram is specifically as follows:
Constructing a two-dimensional coordinate system, wherein the horizontal axis of the two-dimensional coordinate system is incineration time, the vertical axis of the two-dimensional coordinate system is oxygen content value, and according to the incineration time and the oxygen content value corresponding to the data points, mapping the data points in all the oxygen content curves into the two-dimensional coordinate system to obtain a scatter diagram containing a plurality of data points, and marking the scatter diagram as a first scatter diagram.
Further, clustering the data points in the scatter diagram to obtain a plurality of clusters, which are specifically as follows:
Performing DBSCAN clustering on the data points in the first scatter diagram to obtain a plurality of clusters; it should be noted that, the present method that the data points in the scatter diagram are subjected to DBSCAN clustering to obtain several clusters as the DBSCAN clustering algorithm is not described in detail in this embodiment.
Thus, several classes of clusters are obtained.
Step S003, obtaining the area, the mass center and the area of the minimum circumscribing circle of each cluster and adjacent clusters; and obtaining the aggregation degree of each cluster according to the ratio of the area of each cluster to the area of the minimum circumscribing circle, the distance between the data points and the mass centers in the cluster and the distance between the mass centers of the adjacent clusters.
It should be noted that, the density clustering is completed to obtain a plurality of class clusters, and the aggregation index of each class cluster is established by analyzing the data distribution condition in the class clusters, and then the class clusters with local abnormal aggregation are adjusted.
It should be noted that each cluster represents a data point aggregation area, so that the morphological feature of each cluster and the aggregation of the data points in the cluster are analyzed to define the aggregation degree of the cluster. If the distribution of data points in the class clusters is more concentrated, and the distribution distance between the adjacent class clusters is larger, the aggregation degree of the class clusters is higher, and otherwise, the aggregation degree is lower. For the clusters with higher aggregation degree, the data points are distributed and concentrated, so that the distances of a plurality of data points are closer, namely the characteristics are more similar, the clusters can be compressed in a lossy compression mode, and for the clusters with lower aggregation degree, the data points are distributed and scattered relatively, a large amount of data precision can be lost in a lossy compression mode, so that the clusters can be compressed in a lossless compression mode; in the cluster-like morphology, the higher the aggregation degree of the cluster, the more uniform the morphology, and the cluster-like region does not have a significant extending tendency. The aggregation index of the class clusters is defined by the above features.
Specifically, the area, the mass center, the area of the minimum circumscribing circle and the adjacent clusters of each cluster are obtained, and the method specifically comprises the following steps:
Marking any one cluster as a target cluster, acquiring the area of the target cluster, acquiring the mass center of the target cluster, and acquiring the area of the minimum circumcircle of the target cluster; it should be noted that, the area, the centroid and the area of the minimum circumscribing circle of any cluster in the obtained clustering result are all existing methods, and the embodiment is not described again; acquiring a plurality of clusters adjacent to the target cluster, wherein the Euclidean distance between centroids of different clusters is smaller than Is used as the adjacent class cluster of the corresponding class cluster,/>For a preset distance parameter, in this embodiment, the distance parameter is defined as/>Description will be made.
Further, according to the ratio of the area of each cluster to the area of the minimum circumscribing circle, the distance between the data point and the mass center in the cluster and the distance between the mass centers of adjacent clusters, the aggregation degree of each cluster is obtained, and as an embodiment, the specific calculation method is as follows:
In the method, in the process of the invention, For the area of the target cluster,/>Is the area of the smallest circumcircle of the target cluster,/>For the number of data points in the target class cluster,/>For the/>, in the target class clusterEuclidean distance between data point and centroid of target class cluster,/>For the number of class clusters adjacent to the target class cluster,/>For the second/>, adjacent to the target class clusterEuclidean distance between centroid of individual cluster and centroid of target cluster,/>As an exponential function based on natural constants,/>Is the aggregation degree of the target class cluster.
It should be noted that the number of the substrates,Representing the ratio of the area of the cluster to the area of the smallest circumscribing circle, reflecting the uniformity of the cluster morphology, if the cluster morphology is more uniform, i.e. the more nearly the circle has no obvious extending trend,/>The larger the value of (2), the larger the aggregation degree of the cluster; /(I)The average distance between the data points in the same class cluster and the centroid is represented, the smaller the value is, the more concentrated the data point distribution in the class cluster is, the larger the aggregation degree of the class cluster is, and therefore the reciprocal is taken; /(I)The average concentration degree (average distance) between the target cluster and the adjacent clusters is represented, and the larger the value is, the more discrete the distribution of the target cluster and the adjacent clusters around is, and the larger the concentration degree of the target cluster is; finally mapping is carried out through an exponential function, so that aggregation degree of different clusters is prevented from being too close.
Thus, the aggregation degree of each cluster is obtained.
Step S004, obtaining the degree of abnormality of each cluster according to the aggregation degree difference between different clusters; obtaining a plurality of abnormal aggregation degree clusters and a plurality of normal aggregation degree clusters according to the degree of the abnormality; and obtaining the final clustering degree of each abnormal aggregation degree cluster and each normal aggregation degree cluster according to the clustering degree corresponding to the abnormal aggregation degree cluster and the normal aggregation degree cluster and the adjacent clusters.
It should be noted that, the foregoing obtaining the aggregation degree of each cluster, the final objective of this embodiment is to perform efficient compression on the oxygen content value corresponding to the multi-source oxygen content curve, and if the cluster surrounding with high aggregation degree is a cluster with relatively low aggregation degree or the cluster surrounding with low aggregation degree is a cluster with relatively high aggregation degree, the compression efficiency will be relatively low due to data compression, so that it is necessary to adjust the local abnormal aggregation condition, and adjust the local abnormal aggregation condition to the aggregation degree similar level of the surrounding clusters, thereby implementing consistent compression and improving the local compression efficiency.
It should be noted that, first, abnormal clusters are screened out, and the abnormal clusters and the clusters adjacent to each other around the abnormal clusters are different in the aggregation degree, so that the abnormal clusters and the clusters adjacent to each other around the abnormal clusters can be screened out by comparing the aggregation degree of the clusters.
Specifically, the anomaly degree of each cluster is obtained according to the aggregation degree difference between different clusters, and the anomaly degree is specifically as follows:
In the method, in the process of the invention, For the aggregation degree of target class clusters,/>For the number of class clusters adjacent to the target class cluster,/>For the second/>, adjacent to the target class clusterAggregation degree of individual class clusters,/>To take absolute value,/>For the second/>, adjacent to the target class clusterThe degree of aggregation of the individual clusters of the class,For the second/>, adjacent to the target class clusterAggregation degree of individual class clusters,/>Is an anomaly factor of the target class cluster.
Obtaining the abnormal factors of each class cluster, and carrying out linear normalization processing on the abnormal factors of all class clusters to obtain a result as the abnormality degree of each class cluster; it should be noted that, in this embodiment, for simplicity and clarity of description, the case where the denominator of the above formula is 0 is not dealt with; in the implementation, the molecular denominator is added by one at the same time, so that the non-implementation condition that the denominator is 0 can be avoided.
It should be noted that the number of the substrates,The aggregation degree difference between the target cluster and all adjacent clusters is represented, the larger the difference is, the larger the aggregation degree difference between the target cluster and the adjacent clusters is, the more the target cluster is likely to be the cluster with abnormal aggregation degree, and the larger the abnormality degree is; /(I)The smaller the value of the aggregation degree difference between all adjacent clusters of the target cluster is, the larger the aggregation degree difference between the target cluster and the adjacent clusters is, and the more likely the target cluster is an abnormal aggregation degree cluster, namely the greater the abnormal degree is.
Further, according to the degree of anomaly, a plurality of abnormal aggregation degree clusters and a plurality of normal aggregation degree clusters are obtained, and the method specifically comprises the following steps:
presetting a first threshold, describing the embodiment by using the first threshold as 0.85, and taking the cluster with the anomaly degree larger than the first threshold as an anomaly aggregation cluster; and taking the class cluster with the anomaly degree smaller than or equal to the first threshold value as a normal aggregation degree class cluster.
In addition, in order to improve compression efficiency, the aggregation degree of the abnormal aggregation degree clusters needs to be adjusted so that the aggregation degree of the abnormal aggregation degree clusters is similar to that of surrounding clusters, thereby realizing a consistent compression mode.
Specifically, according to the clustering degree and the adjacent clustering degree corresponding to the abnormal clustering degree clusters and the normal clustering degree clusters, the final clustering degree of each abnormal clustering degree cluster and each normal clustering degree cluster is obtained, and the method specifically comprises the following steps:
Marking any abnormal aggregation degree cluster as a target abnormal aggregation degree cluster; acquiring all adjacent clusters of the target abnormal aggregation degree clusters, and marking the adjacent clusters as target adjacent clusters; the average value of the aggregation degrees corresponding to all the target adjacent clusters is recorded as a first average value; replacing the aggregation degree corresponding to the target abnormal aggregation degree cluster with a first average value to obtain the final aggregation degree of the target abnormal aggregation degree cluster; marking any normal aggregation degree cluster as a target normal aggregation degree cluster; and taking the aggregation degree corresponding to the target normal aggregation degree cluster as the final clustering degree of the target normal aggregation degree cluster.
So far, the final clustering degree of each abnormal clustering degree cluster and each normal clustering degree cluster is obtained.
Step S005, obtaining a plurality of high-aggregation clusters and a plurality of low-aggregation clusters according to the final clustering degree; and obtaining coded data corresponding to each oxygen content curve according to the high-aggregation class clusters and the low-aggregation class clusters, and storing the coded data corresponding to each oxygen content curve.
The final clustering degree of each abnormal clustering degree cluster and each normal clustering degree cluster is obtained, the clusters are divided into high-clustering-degree clusters and low-clustering-degree clusters according to the final clustering degree, and the low-clustering-degree clusters are stored one by one in a lossless compression mode according to the original value of the data because the spatial distribution of the data is discrete and the data is relatively large in difference. The high-aggregation cluster has higher similarity of data due to more aggregation of data distribution, and larger redundant space can be generated if lossless storage is adopted, so that the data in the cluster is subjected to the same value substitution after being averaged, and then is subjected to the coding compression.
Specifically, according to the final clustering degree, a plurality of high-aggregation clusters and a plurality of low-aggregation clusters are obtained, and the method specifically comprises the following steps:
Obtaining the final clustering degree of each abnormal clustering degree cluster and the final clustering degree of each normal clustering degree cluster, carrying out linear normalization processing on the final clustering degree of all abnormal clustering degree clusters, taking the obtained result as the normalized final clustering degree of each abnormal clustering degree cluster, carrying out linear normalization processing on the final clustering degree of all normal clustering degree clusters, taking the obtained result as the normalized final clustering degree of each normal clustering degree cluster, and presetting a second threshold value, wherein the second threshold value is 0.75 for description, and taking the cluster with the normalized final clustering degree larger than or equal to the second threshold value as the high clustering cluster; it should be noted that, the cluster with the normalized final cluster degree greater than or equal to the second threshold value may be an abnormal cluster degree cluster or a normal cluster degree cluster; and taking the class cluster with the normalized final clustering degree smaller than the second threshold value as the low-aggregation class cluster.
Further, according to the high aggregation class cluster and the low aggregation class cluster, the coded data corresponding to each oxygen content curve is obtained, and the coded data corresponding to each oxygen content curve is stored, specifically as follows:
Recording any one oxygen content curve as a target oxygen content curve; marking any one high-aggregation cluster as a target high-aggregation cluster; acquiring an average value of oxygen content values corresponding to all data points in the target aggregation cluster, marking the average value as a second average value, replacing the oxygen content value corresponding to the data point belonging to the target aggregation cluster in the target oxygen content curve with the second average value, and replacing the oxygen content value corresponding to the data point belonging to the high aggregation cluster in the target oxygen content curve; when oxygen content values corresponding to data points in different high-aggregation class clusters are replaced, the average value corresponding to each high-aggregation class cluster is required to be calculated respectively, and is not replaced by a second average value, wherein the second average value is the average value corresponding to the target high-aggregation class cluster; maintaining the oxygen content value corresponding to the data points belonging to the low-aggregation cluster in the target oxygen content curve unchanged; after replacing oxygen content values corresponding to data points in a target oxygen content curve, marking the obtained new curve as a first oxygen content curve, performing run-length coding on all oxygen content values in the first oxygen content curve to obtain coded data of the first oxygen content curve, and marking the coded data as coded data corresponding to the target oxygen content curve; it should be noted that, run-length encoding is performed on all values in the curve, and the obtained encoded data of the curve is the existing method, which is not described in detail in this embodiment; acquiring coded data corresponding to each oxygen content curve, and respectively storing the coded data corresponding to each oxygen content curve; referring to fig. 2, fig. 2 is a flowchart of the method for acquiring encoded data according to the present embodiment, and the method for acquiring each feature in fig. 2 is as follows:
Acquiring a plurality of oxygen content curves of a plurality of incinerators, wherein the oxygen content curves comprise a plurality of data points, and each data point corresponds to one incineration time and one oxygen content value; constructing a scatter diagram according to data points in all oxygen content curves, the incineration time corresponding to the data points and the oxygen content value; clustering the data points in the scatter diagram to obtain a plurality of clusters; acquiring the area, the mass center, the area of the minimum circumscribing circle and the adjacent clusters of each cluster; obtaining the aggregation degree of each cluster according to the ratio of the area of each cluster to the area of the minimum circumscribing circle, the distance between the data points and the mass centers in the cluster and the distance between the mass centers of the adjacent clusters; obtaining the abnormality degree of each cluster according to the aggregation degree difference between different clusters; obtaining a plurality of abnormal aggregation degree clusters and a plurality of normal aggregation degree clusters according to the degree of the abnormality; obtaining the final clustering degree of each abnormal aggregation degree cluster and each normal aggregation degree cluster according to the clustering degree corresponding to the abnormal aggregation degree cluster and the normal aggregation degree cluster and the adjacent clusters; obtaining a plurality of high-aggregation class clusters and a plurality of low-aggregation class clusters according to the final clustering degree; and obtaining coded data corresponding to each oxygen content curve according to the high-aggregation clusters and the low-aggregation clusters.
So far, the comprehensive management and storage method for the garbage power generation data is completed by storing the coded data corresponding to the oxygen content curve.
Another embodiment of the present invention provides a garbage power generation data integrated management system, the system including a memory and a processor, the processor executing a computer program stored in the memory, performing the following operations:
Acquiring a plurality of oxygen content curves of a plurality of incinerators, wherein the oxygen content curves comprise a plurality of data points, and each data point corresponds to one incineration time and one oxygen content value; constructing a scatter diagram according to data points in all oxygen content curves, the incineration time corresponding to the data points and the oxygen content value; clustering the data points in the scatter diagram to obtain a plurality of clusters; acquiring the area, the mass center, the area of the minimum circumscribing circle and the adjacent clusters of each cluster; obtaining the aggregation degree of each cluster according to the ratio of the area of each cluster to the area of the minimum circumscribing circle, the distance between the data points and the mass centers in the cluster and the distance between the mass centers of the adjacent clusters; obtaining the abnormality degree of each cluster according to the aggregation degree difference between different clusters; obtaining a plurality of abnormal aggregation degree clusters and a plurality of normal aggregation degree clusters according to the degree of the abnormality; obtaining the final clustering degree of each abnormal aggregation degree cluster and each normal aggregation degree cluster according to the clustering degree corresponding to the abnormal aggregation degree cluster and the normal aggregation degree cluster and the adjacent clusters; obtaining a plurality of high-aggregation class clusters and a plurality of low-aggregation class clusters according to the final clustering degree; and obtaining coded data corresponding to each oxygen content curve according to the high-aggregation class clusters and the low-aggregation class clusters, and storing the coded data corresponding to each oxygen content curve.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (5)
1. The comprehensive management and storage method for the garbage power generation data is characterized by comprising the following steps of:
acquiring a plurality of oxygen content curves of a plurality of incinerators, wherein the oxygen content curves comprise a plurality of data points, and each data point corresponds to one incineration time and one oxygen content value;
Constructing a scatter diagram according to data points in all oxygen content curves, the incineration time corresponding to the data points and the oxygen content value; clustering the data points in the scatter diagram to obtain a plurality of clusters;
Acquiring the area, the mass center, the area of the minimum circumscribing circle and the adjacent clusters of each cluster; obtaining the aggregation degree of each cluster according to the ratio of the area of each cluster to the area of the minimum circumscribing circle, the distance between the data points and the mass centers in the cluster and the distance between the mass centers of the adjacent clusters;
obtaining the abnormality degree of each cluster according to the aggregation degree difference between different clusters; obtaining a plurality of abnormal aggregation degree clusters and a plurality of normal aggregation degree clusters according to the degree of the abnormality; obtaining the final clustering degree of each abnormal aggregation degree cluster and each normal aggregation degree cluster according to the clustering degree corresponding to the abnormal aggregation degree cluster and the normal aggregation degree cluster and the adjacent clusters;
Obtaining a plurality of high-aggregation class clusters and a plurality of low-aggregation class clusters according to the final clustering degree; according to the high-aggregation class clusters and the low-aggregation class clusters, obtaining coded data corresponding to each oxygen content curve, and storing the coded data corresponding to each oxygen content curve;
The aggregation degree of each cluster is obtained according to the ratio of the area of each cluster to the area of the minimum circumscribing circle, the distance between the data points and the mass centers in the cluster and the distance between the mass centers of the adjacent clusters, and the method comprises the following specific steps:
marking any one class cluster as a target class cluster;
In the method, in the process of the invention, For the area of the target cluster,/>Is the area of the smallest circumcircle of the target cluster,/>For the number of data points in the target class cluster,/>For the/>, in the target class clusterEuclidean distance between data point and centroid of target class cluster,/>For the number of class clusters adjacent to the target class cluster,/>For the second/>, adjacent to the target class clusterEuclidean distance between centroid of individual cluster and centroid of target cluster,/>As an exponential function based on natural constants,/>The aggregation degree of the target cluster;
obtaining the degree of abnormality of each cluster according to the aggregation degree difference between different clusters, comprising the following specific steps:
marking any one class cluster as a target class cluster;
In the method, in the process of the invention, For the aggregation degree of target class clusters,/>For the number of class clusters adjacent to the target class cluster,/>For the second/>, adjacent to the target class clusterAggregation degree of individual class clusters,/>To take absolute value,/>For the second/>, adjacent to the target class clusterAggregation degree of individual class clusters,/>For the second/>, adjacent to the target class clusterAggregation degree of individual class clusters,/>An anomaly factor of the target cluster;
Obtaining the abnormal factors of each class cluster, and carrying out linear normalization processing on the abnormal factors of all class clusters to obtain a result as the abnormality degree of each class cluster;
According to the degree of anomaly, a plurality of abnormal aggregation degree clusters and a plurality of normal aggregation degree clusters are obtained, and the method comprises the following specific steps:
Presetting a first threshold value, and taking a class cluster with the anomaly degree larger than the first threshold value as an anomaly aggregation degree class cluster; taking the class cluster with the anomaly degree smaller than or equal to a first threshold value as a normal aggregation degree class cluster;
The final clustering degree of each abnormal aggregation degree cluster and each normal aggregation degree cluster is obtained according to the clustering degree corresponding to the abnormal aggregation degree cluster and the normal aggregation degree cluster and the adjacent clusters, and the method comprises the following specific steps:
Marking any abnormal aggregation degree cluster as a target abnormal aggregation degree cluster; acquiring all adjacent clusters of the target abnormal aggregation degree clusters, and marking the adjacent clusters as target adjacent clusters; the average value of the aggregation degrees corresponding to all the target adjacent clusters is recorded as a first average value; replacing the aggregation degree corresponding to the target abnormal aggregation degree cluster with a first average value to obtain the final aggregation degree of the target abnormal aggregation degree cluster; marking any normal aggregation degree cluster as a target normal aggregation degree cluster; taking the aggregation degree corresponding to the target normal aggregation degree cluster as the final clustering degree of the target normal aggregation degree cluster;
according to the final clustering degree, a plurality of high-aggregation clusters and a plurality of low-aggregation clusters are obtained, and the method comprises the following specific steps:
Obtaining the final clustering degree of each abnormal clustering degree cluster and the final clustering degree of each normal clustering degree cluster, carrying out linear normalization processing on the final clustering degree of all abnormal clustering degree clusters, taking the obtained result as the normalized final clustering degree of each abnormal clustering degree cluster, carrying out linear normalization processing on the final clustering degree of all normal clustering degree clusters, taking the obtained result as the normalized final clustering degree of each normal clustering degree cluster, presetting a second threshold, and taking the class cluster with the normalized final clustering degree larger than or equal to the second threshold as the high-clustering class cluster; and taking the class cluster with the normalized final clustering degree smaller than the second threshold value as the low-aggregation class cluster.
2. The method for comprehensively managing and storing garbage power generation data according to claim 1, wherein the construction of a scatter diagram according to data points in all oxygen content curves, incineration time and oxygen content values corresponding to the data points comprises the following specific steps:
Constructing a two-dimensional coordinate system, wherein the horizontal axis of the two-dimensional coordinate system is incineration time, the vertical axis of the two-dimensional coordinate system is oxygen content value, and according to the incineration time and the oxygen content value corresponding to the data points, mapping the data points in all the oxygen content curves into the two-dimensional coordinate system to obtain a scatter diagram containing a plurality of data points, and marking the scatter diagram as a first scatter diagram.
3. The method for comprehensively managing and storing the garbage power generation data according to claim 2, wherein the clustering of the data points in the scatter diagram to obtain a plurality of clusters comprises the following specific steps:
And performing DBSCAN clustering on the data points in the first scatter diagram to obtain a plurality of clusters.
4. The method for comprehensively managing and storing the garbage power generation data according to claim 1, wherein the method for obtaining the coded data corresponding to each oxygen content curve according to the high-aggregation clusters and the low-aggregation clusters comprises the following specific steps:
Recording any one oxygen content curve as a target oxygen content curve; marking any one high-aggregation cluster as a target high-aggregation cluster; acquiring an average value of oxygen content values corresponding to all data points in the target aggregation cluster, marking the average value as a second average value, replacing the oxygen content value corresponding to the data point belonging to the target aggregation cluster in the target oxygen content curve with the second average value, and replacing the oxygen content value corresponding to the data point belonging to the high aggregation cluster in the target oxygen content curve; maintaining the oxygen content value corresponding to the data points belonging to the low-aggregation cluster in the target oxygen content curve unchanged; and after replacing the oxygen content values corresponding to the data points in the target oxygen content curve, marking the obtained new curve as a first oxygen content curve, performing run-length coding on all the oxygen content values in the first oxygen content curve to obtain coded data of the first oxygen content curve, and marking the coded data as coded data corresponding to the target oxygen content curve.
5. A garbage power generation data integrated management system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program when executed by the processor implements the steps of a garbage power generation data integrated management storage method as claimed in any one of claims 1 to 4.
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