CN113011472A - Method and device for judging similarity of multi-section power quotation curves - Google Patents
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Abstract
The invention provides a method and a device for judging similarity of multi-section power quotation curves, wherein the method comprises the following steps: acquiring spot quoted price data of a market main body and performing per unit on the spot quoted price data to form a point set P; performing density clustering on the quoted price points in the point set P according to a preset optimization variable constraint condition, and outputting a density clustering result; performing optimization calculation on a preset objective function based on the density clustering result to obtain the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point; and screening out the quotation similar unit clusters according to the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point. According to the method, the clustering parameters are optimized, the situation that the number of unclassified points is too large can be avoided when the quotation point set is optimally clustered, and therefore the accuracy of judging the similarity of the multi-section power quotation curves is effectively improved.
Description
Technical Field
The invention relates to the technical field of power data analysis, in particular to a method and a device for judging similarity of multi-section power quotation curves.
Background
With the advance of a new round of electric power market reform, the trial run work is developed in the domestic trial-and-spot market. In the electric power spot market, a market main body combines market boundary conditions and pre-declaration multi-section quotation of game patterns according to the unit cost and the characteristics of the market main body, so that the spot quotation is high in complexity. In the process of adapting to the market for a long time, the spot quoted prices of all units gradually change from high differentiation to local differentiation, and collusion behavior that a plurality of market subjects form a substantial quotation alliance and aim to manipulate the market prices may occur. Quote behavior analysis and collusion identification will play an important role in electric power spot market monitoring.
The quotation curve similarity analysis of the electric power spot market is a basic work for judging whether collusion quotation exists or not, and machine groups with similar quotation and quotation similarity degrees of the machine groups are judged mainly through clustering analysis. In the collusion bidding behavior analysis based on density clustering, clustering parameters are the neighborhood radius and the minimum number of quoted points in the neighborhood. The setting of the parameter affects the number and the degree of similarity of clusters after density clustering. The similarity degree of the density clustering is generally judged by the sum of the distances from all classified points to the core point of the density clustering, but the method may cause the number of unclassified points to be too large, so that similar quotation behaviors among certain groups are ignored. How to reasonably set the clustering parameters and better classify the power quote points is the key to analyzing the quote mode in the power spot market by using clustering analysis.
Disclosure of Invention
The invention aims to provide a method and a device for judging similarity of multi-section power quotation curves, which are used for solving the technical problem and effectively improving the accuracy of judging the similarity of the multi-section power quotation curves.
In order to solve the technical problem, the invention provides a method for judging similarity of multi-segment power quotation curves, which comprises the following steps:
acquiring spot quoted price data of a market main body and performing per unit on the spot quoted price data to form a point set P;
performing density clustering on the quoted price points in the point set P according to a preset optimization variable constraint condition, and outputting a density clustering result; the density clustering result comprises all neighborhood radiuses meeting the optimization variable constraint condition and corresponding clusters, core points, classified points and unclassified points;
performing optimization calculation on a preset objective function based on the density clustering result to obtain the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point;
and screening out the quotation similar unit clusters according to the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point.
Further, the density clustering is performed on the quoted price points in the point set P according to a preset optimization variable constraint condition, and a density clustering result is output, which specifically includes:
performing core point judgment on the quotation points in the point set P according to the preset density clustering neighborhood radius and the preset minimum quotation point number in the neighborhood;
performing density clustering on the point set P based on each core point, calculating the Hausdorff distance between the core point and unprocessed points in the point set P, classifying the points meeting preset conditions into the same cluster according to each Hausdorff distance, and marking the points of the same cluster as processed points;
repeating the core point judgment and density clustering steps until all the points in the point set P are classified into clusters or judged to be non-core points, and classifying the points which do not belong to any clusters into unclassified points;
and outputting all neighborhood radiuses which accord with the optimization variable constraint conditions and corresponding clusters, core points, classified points and unclassified points.
Further, the classifying the points meeting the preset condition into the same cluster according to each hausdov distance specifically includes:
and if the Hausdorff distance between each unprocessed point and the core point is judged to belong to the conditions of direct density accessibility, density accessibility or density connection, classifying the core point and the corresponding unprocessed points into the same cluster.
Further, when the optimal clustering is carried out, only the other quotation points except the quotation points of the first several declaration sections are optimally clustered.
Further, the screening out the quotation similar unit cluster according to the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point specifically comprises:
and selecting a unit corresponding to the core point of each cluster and units corresponding to other quotation points in the neighborhood of the core point according to the neighborhood radius of the optimal cluster and the corresponding cluster, the core point, the classified point and the unclassified point, so as to obtain the quotation similar unit cluster.
In order to solve the same technical problem, the invention also provides a device for judging the similarity of the multi-section power quotation curves, which comprises:
the point set acquisition module is used for acquiring spot quoted price data of a market main body and conducting per-unit on the spot quoted price data to form a point set P;
the density clustering module is used for performing density clustering on the quoted price points in the point set P according to a preset optimization variable constraint condition and outputting a density clustering result; the density clustering result comprises all neighborhood radiuses meeting the optimization variable constraint condition and corresponding clusters, core points, classified points and unclassified points;
the optimization calculation module is used for carrying out optimization calculation on a preset objective function based on the density clustering result and obtaining the neighborhood radius of the optimal clustering and the corresponding cluster class, core point, classified point and unclassified point;
and the similar quotation screening module is used for screening out quotation similar unit clusters according to the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point.
Further, the density clustering module is specifically configured to:
performing core point judgment on the quotation points in the point set P according to the preset density clustering neighborhood radius and the preset minimum quotation point number in the neighborhood;
performing density clustering on the point set P based on each core point, calculating the Hausdorff distance between the core point and unprocessed points in the point set P, classifying the points meeting preset conditions into the same cluster according to each Hausdorff distance, and marking the points of the same cluster as processed points;
repeating the core point judgment and density clustering steps until all the points in the point set P are classified into clusters or judged to be non-core points, and classifying the points which do not belong to any clusters into unclassified points;
and outputting all neighborhood radiuses which accord with the optimization variable constraint conditions and corresponding clusters, core points, classified points and unclassified points.
Further, the classifying the points meeting the preset condition into the same cluster according to each hausdov distance specifically includes:
and if the Hausdorff distance between each unprocessed point and the core point is judged to belong to the conditions of direct density accessibility, density accessibility or density connection, classifying the core point and the corresponding unprocessed points into the same cluster.
Further, when the optimal clustering is carried out, only the other quotation points except the quotation points of the first several declaration sections are optimally clustered.
Further, the similar offer filtering module is specifically configured to: and selecting a unit corresponding to the core point of each cluster and units corresponding to other quotation points in the neighborhood of the core point according to the neighborhood radius of the optimal cluster and the corresponding cluster, the core point, the classified point and the unclassified point, so as to obtain the quotation similar unit cluster.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a method and a device for judging similarity of multi-section power quotation curves, wherein the method comprises the following steps: acquiring spot quoted price data of a market main body and performing per unit on the spot quoted price data to form a point set P; performing density clustering on the quoted price points in the point set P according to a preset optimization variable constraint condition, and outputting a density clustering result; the density clustering result comprises all neighborhood radiuses meeting the optimization variable constraint condition and corresponding clusters, core points, classified points and unclassified points; performing optimization calculation on a preset objective function based on the density clustering result to obtain the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point; and screening out the quotation similar unit clusters according to the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point. According to the method, the clustering parameters are optimized, the situation that the number of unclassified points is too large can be avoided when the quotation point set is optimally clustered, and therefore the accuracy of judging the similarity of the multi-section power quotation curves is effectively improved.
Drawings
Fig. 1 is a schematic flow chart illustrating a method for determining similarity of multi-segment power quoting curves according to an embodiment of the invention;
fig. 2 is another schematic flow chart of a method for determining similarity of multi-segment power pricing curves according to an embodiment of the invention;
fig. 3 is a schematic structural diagram of a device for determining similarity of multi-segment power pricing curves according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a method for determining similarity of multi-segment power quoting curves, including:
s1, acquiring spot quoted price data of the market main body and performing per unit on the spot quoted price data to form a point set P.
S2, performing density clustering on the quoted points in the point set P according to a preset optimization variable constraint condition, and outputting a density clustering result; and the density clustering result comprises all neighborhood radiuses meeting the optimization variable constraint condition and corresponding cluster classes, core points, classified points and unclassified points.
Further, step S2 specifically includes:
s201, according to the preset density clustering neighborhood radius and the preset minimum quotation point number in the neighborhood, core point judgment is conducted on quotation points in the point set P.
S202, density clustering is carried out on the point set P based on each core point, the Hausdorff distance between the core point and unprocessed points in the point set P is calculated, the points meeting the preset conditions are classified into the same cluster according to each Hausdorff distance, and meanwhile, the points of the same cluster are marked as processed.
Further, the classifying the points meeting the preset condition into the same cluster according to each hausdov distance specifically includes:
and if the Hausdorff distance between each unprocessed point and the core point is judged to belong to the conditions of direct density accessibility, density accessibility or density connection, classifying the core point and the corresponding unprocessed points into the same cluster.
S203, repeating the steps S201-S202 until all the points in the point set P are classified into clusters or judged to be non-core points, and classifying the points which do not belong to any clusters into unclassified points.
And S204, outputting all neighborhood radiuses meeting the optimized variable constraint conditions and corresponding clusters, core points, classified points and unclassified points.
S3, performing optimization calculation on a preset objective function based on the density clustering result, and obtaining the neighborhood radius of the optimal clustering and the corresponding cluster class, core point, classified point and unclassified point.
S4, screening out the quotation similar unit clusters according to the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point.
Further, step S4 is specifically:
and selecting a unit corresponding to the core point of each cluster and units corresponding to other quotation points in the neighborhood of the core point according to the neighborhood radius of the optimal cluster and the corresponding cluster, the core point, the classified point and the unclassified point, so as to obtain the quotation similar unit cluster.
Further, when the optimal clustering is carried out, only the other quotation points except the quotation points of the first several declaration sections are optimally clustered.
Referring to fig. 2, based on the above scheme, in order to better understand the method for determining similarity of multi-segment power pricing curves provided by the embodiment of the present invention, the following detailed description is provided:
1. reading the spot quoted price data of the market main body in the statistical period, and performing per unit to form quoted price point data (a plurality of quoted prices form a multidimensional vector point), and counting a point set P as
wherein 
The declared price and the declared amount of the r-th declaration section of the market subject represented by the n-th point. As the price game in the spot market is mainly concentrated on the quoted prices in the last sections, the quoted prices in the first sections of the power generation manufacturers are possibly lower than the quoted prices in the power generation cost so as to extract the probability of winning the bid, and therefore, other quoted points except the quoted prices in the first two sections are selected for optimal clustering.
2. Carrying out optimal clustering on the N quotation points except the previous two quotation points, wherein the optimization variable is the neighborhood radius d of density clustering, and the objective function is as follows:
wherein M represents the number of classified quote points, PmFor the m-th classified point
Respectively the declaration price and the declaration amount of the r declaration section of the market main body represented by the m classification point,
core point of cluster in which m-th classified point is
Represents PmTo
The Hausdorff (Hausdorff) distance of (C) is calculated as shown in equation (6). The optimization variable d of the optimal clustering model has the following constraint conditions:
min{H(Pi,Pj)|Pi∈P,Pj∈P,i≠j}<d<max{H(Pi,Pj)|Pi∈P,Pj∈P,i≠j} (2)
in the formula, P is a set for optimally clustering all N points; d is the neighborhood radius of density clustering, and k effective numbers are reserved, so that the dispersion of the objective function is ensured, and the optimization model has a certain solution. H (P)i,Pj) Is the hausdorff distance between the quoted multidimensional vectors.
3. When the optimization model objective function is calculated in step 2, density clustering needs to be performed on all N points in the point set P, so as to obtain clusters, core points, classified points and unclassified points corresponding to d, which meet the constraint condition in formula (2). The density clustering process is as follows:
3.1 inputting the radius d of the density clustering neighborhood of the optimization iteration, and setting the minimum quotation point number MinNum in the neighborhood. The value of MinNum can be set according to the number of units in the spot market and the requirement on density clustering. For example, there are 50 units in the market, MinNum may be set to 5, that is, in each cluster after density clustering, there are 5 other units in the unit corresponding to the core point that are judged to be similar to the quoted price.
3.2 taking a point from the Point set P
It is determined whether the point is a core point.
If the neighborhood d of the quote point at least comprises MinNum quote points, the quote point is the core point. All unprocessed points in the point set are collected and the unprocessed points and the core point are calculated
The hastelloff distance of.
And if the quoted price point is not the core point, jumping out of the cycle, and judging the core point of the next point in the point set P.
The hausdorff distance calculation method is as follows:
H(Pi,Pj)＝max{h(Pi,Pj),h(Pj,Pi)} (3)
in the formula ,h(Pi,Pj) Is a point set
Arrival set
One-way Hausdorff distance of h (P)j,Pi) As a set of points PjArrival set PiThe one-way Hausdorff distance of (1) is calculated as follows
h(Pi,Pj)＝max((pi,qi)∈Pi)min((pj,qj)∈Pj)||(pi,qi)-(pj,qj)|| (4)
h(Pj,Pi)＝max((pj,qj)∈Pj)min((pi,qi)∈Pi)||(pj,qj)-(pi,qi)|| (5)
Wherein, | | (p)i,qi)-(pj,qj) I represents a two-dimensional point (p)i,qi) And point (p)j,qj) Euclidean distance of (a):
in the formula ,h(Pi,Pj) Means that for the point set PiEach point (r in total) in the set of points PjAll points (s in total) obtain Euclidean distances, the minimum value is obtained in the s Euclidean distances, and then the maximum value is obtained in all r minimum Euclidean distances.
3.3 judging whether the Housdov distance between the unprocessed point and the core point belongs to the conditions of direct density reachable, density reachable and density connection, if so, classifying the points meeting the conditions into the same cluster, and marking the points (including the core point) in the same cluster as processed. The judgment method for the direct density accessibility, the density accessibility and the density connection is as follows:
1) the direct density can reach: if quote point x is within d neighborhood of some core point y (also including boundary points), then x and y are considered directly density reachable.
2) The density can reach: if there are quote points x, y, z, where x and y are directly dense, y and z are directly dense, but z is not in the d neighborhood of x. In this case, x and z cannot be directly density reachable, but are reachable directly density to z point by y point in its d-neighborhood, then x and z are defined as density reachable.
3) Density connection: if there is an ask point w that cannot be density-reached directly with the core point x or density-reached, but within d-neighborhood of the ask point whose density is reached, then x and w density are defined to be connected.
3.4 repeating step 3.2 and step 3.3 until all N points in the point set P are classified into clusters or judged to be non-core points, and classifying the points not belonging to any cluster into unclassified points. And outputting all cluster types, core points, classified points and unclassified points corresponding to the density clustering neighborhood radius d of the optimization iteration.
4. And performing optimization calculation on the objective function to obtain the neighborhood radius d of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point. When the method is applied to collusion analysis, the core point of each cluster class
Corresponding unit and other quotation points P in neighborhood thereofmThe corresponding unit is the selected unit cluster with highly similar spot quoted price, and the possibility of colluding quoted price exists. In addition, the method can also be used for analyzing the quotation mode of a specific market main body in a certain time period, or carrying out induction and arrangement on the price and capacity characteristics of the quotation points of each cluster to form a general quotation strategy library and the like.
It should be noted that the above method or flow embodiment is described as a series of acts or combinations for simplicity, but those skilled in the art should understand that the present invention is not limited by the described acts or sequences, as some steps may be performed in other sequences or simultaneously according to the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are exemplary embodiments and that no single embodiment is necessarily required by the inventive embodiments.
It should be noted that, in the embodiment of the present invention, similarity determination is performed on the multi-segment power offer curves through the optimal clustering model on the basis of spot offer per unit. When the power quotation curves are classified based on density clustering, the method provided by the invention originally optimizes the clustering parameters. The neighborhood radius in the density cluster is used as an optimization variable in the optimization model, and the sum of the distances from all points to the core point is minimum to serve as an objective function. The neighborhood radius only takes k significant digits, so that the optimal clustering model is converted into a discrete optimization model, the fact that the optimal clustering model has extreme values is guaranteed, and the infinite optimization process is avoided. The method provided by the invention meets the optimization solution under a certain precision condition, and meets the engineering requirements.
The method of the invention sets the distance from the unclassified point to the core point as the mean value of the distances from all the points in other classified clusters to the core point of the cluster where the points are located, instead of 0 in the traditional density clustering. The inventive arrangement may weigh the number of classified points and unclassified points, avoiding the situation where there are too many unclassified points in order to minimize the sum of all points to the center point distance. When the distance from the core point to other points is calculated, the method uses the Hausdorff distance instead of the Euclidean distance, and the accuracy of the similarity judgment of the quotation curve of the method can be exerted under the condition of multi-section quotation (five sections or more).
Referring to fig. 3, in order to solve the same technical problem, the present invention further provides a device for determining similarity of multi-segment power quoting curves, including:
the system comprises a point set acquisition module 1, a point set generation module and a point set processing module, wherein the point set acquisition module is used for acquiring spot quoted price data of a market main body and performing per-unit treatment on the spot quoted price data to form a point set P;
the density clustering module 2 is used for performing density clustering on the quoted price points in the point set P according to a preset optimization variable constraint condition and outputting a density clustering result; the density clustering result comprises all neighborhood radiuses meeting the optimization variable constraint condition and corresponding clusters, core points, classified points and unclassified points;
the optimization calculation module 3 is used for performing optimization calculation on a preset objective function based on the density clustering result and obtaining the neighborhood radius of the optimal clustering and the corresponding cluster class, core point, classified point and unclassified point;
and the similar quotation screening module 4 is used for screening out quotation similar unit clusters according to the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point.
Further, the density clustering module 2 is specifically configured to:
performing core point judgment on the quotation points in the point set P according to the preset density clustering neighborhood radius and the preset minimum quotation point number in the neighborhood;
performing density clustering on the point set P based on each core point, calculating the Hausdorff distance between the core point and unprocessed points in the point set P, classifying the points meeting preset conditions into the same cluster according to each Hausdorff distance, and marking the points of the same cluster as processed points;
repeating the core point judgment and density clustering steps until all the points in the point set P are classified into clusters or judged to be non-core points, and classifying the points which do not belong to any clusters into unclassified points;
and outputting all neighborhood radiuses which accord with the optimization variable constraint conditions and corresponding clusters, core points, classified points and unclassified points.
Further, the classifying the points meeting the preset condition into the same cluster according to each hausdov distance specifically includes:
and if the Hausdorff distance between each unprocessed point and the core point is judged to belong to the conditions of direct density accessibility, density accessibility or density connection, classifying the core point and the corresponding unprocessed points into the same cluster.
Further, when the optimal clustering is carried out, only the other quotation points except the quotation points of the first several declaration sections are optimally clustered.
Further, the similar offer filtering module 4 is specifically configured to: and selecting a unit corresponding to the core point of each cluster and units corresponding to other quotation points in the neighborhood of the core point according to the neighborhood radius of the optimal cluster and the corresponding cluster, the core point, the classified point and the unclassified point, so as to obtain the quotation similar unit cluster.
It can be understood that the above device item embodiments correspond to the method item embodiments of the present invention, and the device for judging similarity of multi-segment power quoting curves provided by the embodiments of the present invention can implement the method for judging similarity of multi-segment power quoting curves provided by any one of the method item embodiments of the present invention.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A method for judging similarity of multi-segment power quotation curves is characterized by comprising the following steps:
acquiring spot quoted price data of a market main body and performing per unit on the spot quoted price data to form a point set P;
performing density clustering on the quoted price points in the point set P according to a preset optimization variable constraint condition, and outputting a density clustering result; the density clustering result comprises all neighborhood radiuses meeting the optimization variable constraint condition and corresponding clusters, core points, classified points and unclassified points;
performing optimization calculation on a preset objective function based on the density clustering result to obtain the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point;
and screening out the quotation similar unit clusters according to the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point.
2. The method for judging similarity of multi-segment power quotation curves according to claim 1, wherein density clustering is performed on quotation points in a point set P according to a preset optimization variable constraint condition, and a density clustering result is output, specifically comprising:
performing core point judgment on the quotation points in the point set P according to the preset density clustering neighborhood radius and the preset minimum quotation point number in the neighborhood;
performing density clustering on the point set P based on each core point, calculating the Hausdorff distance between the core point and unprocessed points in the point set P, classifying the points meeting preset conditions into the same cluster according to each Hausdorff distance, and marking the points of the same cluster as processed points;
repeating the core point judgment and density clustering steps until all the points in the point set P are classified into clusters or judged to be non-core points, and classifying the points which do not belong to any clusters into unclassified points;
and outputting all neighborhood radiuses which accord with the optimization variable constraint conditions and corresponding clusters, core points, classified points and unclassified points.
3. The method for determining similarity of multi-segment power pricing curves according to claim 2, wherein the classifying points satisfying the predetermined condition into the same cluster according to each hausdov distance includes:
and if the Hausdorff distance between each unprocessed point and the core point is judged to belong to the conditions of direct density accessibility, density accessibility or density connection, classifying the core point and the corresponding unprocessed points into the same cluster.
4. The method for judging the similarity of the multi-segment power quotation curves according to claim 1, characterized in that, when optimal clustering is performed, optimal clustering is performed only on quotation points other than the last several declared segments.
5. The method for judging similarity of multi-segment power supply quotation curves according to claim 1, wherein the quotation similarity unit cluster is screened out according to the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point, and specifically comprises the following steps:
and selecting a unit corresponding to the core point of each cluster and units corresponding to other quotation points in the neighborhood of the core point according to the neighborhood radius of the optimal cluster and the corresponding cluster, the core point, the classified point and the unclassified point, so as to obtain the quotation similar unit cluster.
6. The utility model provides a multistage electric power quotation curve similarity judgement device which characterized in that includes:
the point set acquisition module is used for acquiring spot quoted price data of a market main body and conducting per-unit on the spot quoted price data to form a point set P;
the density clustering module is used for performing density clustering on the quoted price points in the point set P according to a preset optimization variable constraint condition and outputting a density clustering result; the density clustering result comprises all neighborhood radiuses meeting the optimization variable constraint condition and corresponding clusters, core points, classified points and unclassified points;
the optimization calculation module is used for carrying out optimization calculation on a preset objective function based on the density clustering result and obtaining the neighborhood radius of the optimal clustering and the corresponding cluster class, core point, classified point and unclassified point;
and the similar quotation screening module is used for screening out quotation similar unit clusters according to the neighborhood radius of the optimal cluster and the corresponding cluster class, core point, classified point and unclassified point.
7. The device for determining similarity of multi-segment power pricing curves according to claim 6, wherein the density clustering module is specifically configured to:
performing core point judgment on the quotation points in the point set P according to the preset density clustering neighborhood radius and the preset minimum quotation point number in the neighborhood;
performing density clustering on the point set P based on each core point, calculating the Hausdorff distance between the core point and unprocessed points in the point set P, classifying the points meeting preset conditions into the same cluster according to each Hausdorff distance, and marking the points of the same cluster as processed points;
repeating the core point judgment and density clustering steps until all the points in the point set P are classified into clusters or judged to be non-core points, and classifying the points which do not belong to any clusters into unclassified points;
and outputting all neighborhood radiuses which accord with the optimization variable constraint conditions and corresponding clusters, core points, classified points and unclassified points.
8. The multi-segment power supply curve similarity determination apparatus according to claim 7, wherein the classifying the points satisfying the predetermined condition into the same cluster according to each hausdorff distance includes:
and if the Hausdorff distance between each unprocessed point and the core point is judged to belong to the conditions of direct density accessibility, density accessibility or density connection, classifying the core point and the corresponding unprocessed points into the same cluster.
9. The multi-segment power quotation curve similarity judgment device of claim 6, wherein in optimal clustering, optimal clustering is performed only on quotation points other than the first several declared segment quotations.
10. The device for determining similarity of multi-segment power offer curves according to claim 6, wherein the similar offer screening module is specifically configured to: and selecting a unit corresponding to the core point of each cluster and units corresponding to other quotation points in the neighborhood of the core point according to the neighborhood radius of the optimal cluster and the corresponding cluster, the core point, the classified point and the unclassified point, so as to obtain the quotation similar unit cluster.
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