CN110689452A - Clustering algorithm-based power market business center service center planning method - Google Patents
Clustering algorithm-based power market business center service center planning method Download PDFInfo
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Abstract
The invention relates to a method for planning a power market service center based on a clustering algorithm, which comprises the following steps: defining business boundary division and business capability description according to a modeling result of a business domain of the power market; respectively selecting more typical alternative service nodes from each service domain which is preliminarily set as initial central points (algorithm condensation points) to prepare for the next cluster analysis; according to three dimensional indexes of technology/service correlation, management/service correlation and market/user correlation, respectively scoring the alternative services related to the service one by one; calculating a central point closest to each alternative service in Euclidean distance, and classifying the alternative services correspondingly; and after the clustering calculation is finished, updating the central point according to the clustering condition, and performing clustering optimization until a relatively stable clustering model is obtained. By applying the method and the device, the reasonable attribution of each alternative service in the power market business can be determined, and the reasonability of the planning scheme of the service center can be verified.
Description
Technical Field
The invention relates to a service center planning and clustering algorithm, in particular to a service center planning method based on a clustering algorithm.
Background
With the deep advance of the Chinese electric power reform, the number of market subjects participating in the electric power market transaction and the transaction electric quantity are in a rapid growth state, and the national marketized transaction electric quantity is increased from less than 1 trillion kilowatt hour before 2016 to 2.1 trillion kilowatt hours in 2018, and accounts for nearly 40% of the national electricity selling proportion. And the method adapts to the requirement, and by using the development experience of the Internet industry for reference, the convergence of shared service capacity is realized by adopting the middle platform architecture, so that the full-range support is provided for the realization of long-term requirements, short-term requirements and temporary requirements of customers, the aim of quickly meeting the requirements of the customers and the market is achieved, and the method becomes a better choice for the construction of a power market service platform.
The continuous and quick response, exploration, mining and leading of the user demands are key factors for the survival and the continuous development of internet enterprises. Enterprises that truly honor users, even adjust themselves and subvert themselves in response to users, will survive and develop in this user-centric business war; on the contrary, the enterprises which hope the gambling psychology to hope that the users can keep following themselves as before can be eliminated by the users. Under the background, the digital enterprise is based on the customer center and guided by science and technology, and particularly needs to establish a real-time strategic mechanism and a life-style organization of agile ecology under the unified vision.
Therefore, in recent years, a concept and strategy of the middlebox have been proposed for quick response to foreground service change and innovation. The middle platform is relative to the foreground and the background, the foreground refers to a front-end platform of the system and is an application layer which is directly interacted with a terminal user; background refers to the backend platform of the system, generally speaking, the end user cannot perceive the existence of the background, and the value of the background mainly lies in storing and computing the core data of the enterprise. Along with the change of the user requirements, the foreground needs to quickly and iteratively respond to the user requirements, so that the requirements are also generated on the quick strain of the background, which is contrary to the original purpose of the background for improving the data security and the system management efficiency of the back end; the core of the method is to split the logic layer of the background to form a framework of "foreground (application layer) -middle station (logic layer) -background (data layer)".
The service middleboxes are an important component of the middlebox strategy. The business middle desk standardizes and uniformly provides services for shareable business rules and links in enterprises through an informatization means, and realizes business capability opening and sharing based on business datamation. The core value of the business center station is to convert the business capacity IT mode into the business capacity asset mode, so that the business agility and the market response speed are improved, and the goals of quality improvement, transformation, cost reduction and efficiency improvement of enterprises are achieved. Nowadays, many enterprises want to learn and create a proper middle station strategy, and adopt a management mode of 'big middle station, small front station' to perform their own digital transformation, but face a problem of how to scientifically and reasonably perform middle station planning in business in the process of executing the middle station strategy.
The planning of the service center station comprises two parts of service domain modeling for roughly decomposing the service of the required service related to the service center station, roughly dividing the service boundary into main working contents, and planning the service center for attributing the service range of the alternative service to the corresponding service domain as the main working contents. The problem encountered in practical work is that since the existence of alternative services is often generated by interleaving the needs in terms of business, management and technology, there is no quantitative method to evaluate the planning scheme of the service center and the proper attribution scheme of the alternative services to the service center.
Therefore, it is necessary to provide a quantitative method for solving the planning problem of the service center of the central station in the business, so as to determine the reasonable attribution of each alternative service of the central station in the power market business and verify the rationality of the planning scheme of the service center, thereby improving the planning efficiency and the planning scientificity of the central station in the business.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for planning a power market service center based on a clustering algorithm, which is used for improving the planning efficiency and the planning scientificity of the service center.
The method for planning the power market service center based on the clustering algorithm comprises the following steps:
s1, dividing the initial scheme of the service center planning in the service middle range according to the service center which can reflect the clear service boundary division and the service capability description of the service domain modeling result, and acquiring and storing the service center division data and the alternative service list data;
s2, selecting alternative services from each service center as algorithm condensation points of a clustering algorithm, wherein a category cluster formed by clustering according to the algorithm condensation points corresponds to the service center, and member points in the category cluster are all the alternative services;
s3, respectively scoring the alternative services in the service middlebox range one by one according to the indexes of multiple dimensions to form alternative service scoring data, and establishing a dimension index matrix related to an alternative service list;
s4, calculating the Euclidean distance between each alternative service and each algorithm condensation point to obtain the algorithm condensation point closest to the Euclidean distance of each alternative service, and classifying the alternative services correspondingly to obtain an initial clustering result;
s5, calculating the gravity center of each class as an updated algorithm condensation point according to the initial clustering result, repeatedly calculating the Euclidean distance between each alternative service and the updated algorithm condensation point, judging the algorithm condensation point with the Euclidean distance being the closest to the alternative service, and classifying the alternative services correspondingly; until the Euclidean distance between each candidate service and the condensation point of the algorithm and the service center identification closest to the candidate service do not change any more.
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Fig. 1 is a flowchart of a service center planning method in a business based on a clustering algorithm according to an embodiment of the present invention;
Detailed Description
The planning method of the present invention will be described in detail and fully with reference to the accompanying drawings and examples, it is to be understood that the described examples are only a part of the examples, and not all of the examples. 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.
In the planning method of this embodiment, a big data analysis method is used to cluster the candidate services within the range of the station in the service that is primarily identified: dividing an initial scheme according to service centers which can reflect definite service boundary division and service capability description of a service domain modeling result, selecting typical alternative services from each service center as algorithm aggregation points, and preparing for next clustering analysis; then, according to three dimensional indexes of technology/service correlation, management/service correlation and market/user correlation, respectively scoring the alternative services in the service middle station range one by one, and establishing a dimensional index matrix related to an alternative service list; calculating a central point closest to each alternative service in Euclidean distance by adopting a K-means clustering (K-means) algorithm, and classifying the alternative services correspondingly; updating the central point according to the clustering calculation result, and performing clustering optimization until a relatively stable clustering model is obtained; and if the clustering fails, adjusting the planning scheme of the service center, and reselecting the corresponding algorithm condensation points for clustering again until the clustering succeeds.
The technical solution of the embodiments of the present invention is described in detail below with reference to the accompanying drawings. The flow of the business center service planning method based on the clustering algorithm provided by the embodiment of the invention is shown in figure 1, and the method comprises the following steps:
s101, data preparation, namely dividing an initial scheme of service center planning in a service middle station range according to a service center which can reflect definite service boundary division and service capability description of a service domain modeling result, and acquiring and storing service center division data and alternative service list data;
table 1 shows service center partition data; table 2 shows alternative service list data. The service center of the embodiment comprises a user center, a message center, a contract center and the like; the alternative services provided by the service center comprise operation information publishing, message sending service, contract management service, plan declaration service, user registration service, market operation analysis and the like.
TABLE 1
TABLE 2
S102, selecting an initial algorithm condensation point;
the step is used for selecting a more typical alternative service from each service center as an algorithm aggregation point of a k-means clustering algorithm, wherein the more typical alternative service refers to that the alternative service belongs to a core or basic function service of the corresponding service center, and the attribution problem of the service center is clear and has no objection. Taking the user center as an example, considering that the user registration service belongs to the basic function service of the user center, the home user center is clear in the service center home problem, so the user center is considered to belong to a more typical user center alternative service, and the user center is selected as an initial algorithm aggregation point, at this time, the user center has the first and only one determined member, namely the algorithm aggregation point, and the current center of gravity of the user center is also the algorithm aggregation point naturally. A category cluster formed by clustering according to the algorithm aggregation points corresponds to the service center, and the member points in the category cluster are all the alternative services; clustering forms a plurality of pieces of algorithm aggregate point data, as shown in table 3, each of which includes: the service center records comprise alternative service names, alternative service identifiers, service center identifiers and the service centers.
Therefore, the algorithm aggregation point data associates the alternative service with the service center to which the alternative service belongs, and correspondingly identifies the alternative service and the service center to which the alternative service belongs.
TABLE 3
| Alternative service name | Alternate service identification | Belonging service center | Service center identification |
| Messaging service | 000002 | Message center | C1 |
| Contract management service | 000003 | Contract center | C2 |
| User registration service | 000005 | User center | C3 |
S103, scoring alternative services within the range of the service middleboxes;
according to the indexes of multiple dimensions, respectively scoring the alternative services in the service middlebox range one by one to form alternative service scoring data, and establishing a dimension index matrix related to an alternative service list; the indexes of the multiple dimensions comprise dimension indexes of three aspects of technology/business relevance, management/service relevance and market/user relevance. Each alternative service scoring data comprises: alternative service names, alternative service identifiers, service dimension scores, management dimension scores and technology dimension scores, and a plurality of alternative service score data are shown in table 4.
TABLE 4
It should be noted that, in scoring, a correlation evaluation criterion needs to be specifically determined according to an actual situation, and the granularity of scoring (i.e., scoring) is grasped according to the principle that the expressions of different alternative services in corresponding scoring dimensions can be effectively distinguished. In the alternative service scoring example shown in table 4 above, the scoring criteria for "market/user relevance" is: the scoring area is 0-100 points, and the scoring is judged by comparing the correlation between the functional service and the electric power market (an electric power trading platform operator such as a trading center and a supervisor) and the correlation between the functional service and the user (an electric power market main body). Considering that the two functional services of 'operation information release' and 'message sending service' can be used for both the electric power market and the user, judging that the market/user correlation performance is neutral, and the score is 50; the two functions of contract management service and plan declaration service are mainly used for providing service functions for an operator and a supervisor of the electric power trading platform, but are slightly related to main members of the electric power market, and the contract management service is stronger than the plan declaration service in the related degree with the main members of the electric power market, so that the scores of the contract management service and the plan declaration service are respectively 80 scores and 90 scores; the two functions of 'user registration service' and 'market operation analysis' are focused on providing service functions for users (electric power market main bodies), wherein the 'user registration service' function does not relate to an electric power trading platform operator and a supervisor, so the score is 0, and the 'market operation analysis' function slightly relates to the electric power trading platform operator and the supervisor, and the score is 20. The number of the alternative services in the above table is small, and the granularity grasp of the scores is correspondingly thicker.
When scoring, there may be a plurality of dimensions in some aspect, for example, if it is desired to perform relevance evaluation from two dimensions of the message mechanism and the log processing mechanism in the aspect of technical relevance, scoring of two dimensions of "message mechanism/service relevance" and "log processing/service relevance" may occur in the aspect of "technical/service relevance". The same is true for other aspects of business, management, etc. that are parallel to the technology. In addition, in order to reduce subjectivity, a plurality of experts can be asked to participate in scoring, and then the average score of the experts is taken.
S104, calculating Euclidean distance between each alternative service and each algorithm condensation point, and classifying the alternative services according to the calculation result to obtain an initial clustering result;
in the step, an algorithm condensation point (also called a central point) closest to each alternative service in Euclidean distance is calculated, and the alternative services are classified correspondingly; table 5 shows the euclidean distance between each candidate service and the condensation point (i.e., the center point) of the algorithm, and the service center identification with the closest euclidean distance.
TABLE 5
S105, updating the central point according to the initial clustering result, and performing clustering optimization;
and calculating the gravity center of each class as an updated central point according to the initial clustering result, repeating Euclidean distance calculation of each alternative service and the updated central point and discrimination of the central point closest to the Euclidean distance of the alternative service, and classifying the alternative services correspondingly until the Euclidean distance between each alternative service and an algorithm condensation point (namely the central point) and the service center identification closest to the Euclidean distance are not changed any more.
In the example of candidate service classification and initial clustering result shown in table 4 above, the centers of gravity of C1, C2, and C3 are all the first and only one determined member of the initial clustering, i.e., the clustering point of the algorithm. In table 5, the euclidean distances between 6 candidate services such as "operation information distribution", "message sending service", "contract management service", "plan declaration service", "user registration service", "market operation analysis", and the like and 3 algorithm aggregation points (i.e., central points) are calculated, and the corresponding service center attributions of the candidate services are identified as C1, C1, C2, C2, C3, and C3, respectively, according to the algorithm aggregation point closest to the euclidean distance to each candidate service, and then the centers of gravity in C1, C2, and C3 are calculated as new algorithm aggregation points to prepare for the next calculation. Taking C1 as an example, at this time, there are two candidate service members "operation information distribution" and "messaging service" in C1, the coordinates of the two members are (30,50,50), (70,50,50), the C1 gravity center is the middle point (50,50,50) of the two members, and this C1 gravity center value is filled into the row "C1 gravity center" in table 6 for the next calculation.
Table 6 shows the euclidean distance between each candidate service and the updated algorithmic condensation point and the nearest service center identification.
Updating a central point according to a clustering calculation result, and performing clustering optimization until a relatively stable clustering model is obtained; and if the clustering fails, adjusting the planning scheme of the service center, and reselecting the corresponding algorithm condensation points for clustering again.
TABLE 6
In the cluster optimization example shown in table 6 above, the C1, C2, C3 centroids are updated according to the results of the initial clustering. In table 6, the euclidean distances between the centers of gravity of 6 candidate services, such as "operation information distribution", "message sending service", "contract management service", "plan declaration service", "user registration service", "market operation analysis", and the like, and C1, C2, and C3 are calculated, and the corresponding service center attributes of each candidate service are identified as C1, C1, C2, C2, C3, and C3, respectively, so that it can be determined that the service center identifier closest to each candidate service distance does not change any more, and therefore, it is considered that a relatively stable clustering model has been obtained, and clustering calculation is successfully completed.
An Elbow rule (Elbow Method) and an outline Coefficient (Silhouette Coefficient) Method which are commonly used for carrying out clustering model optimization in a k-means clustering algorithm can be used for clustering optimization in the step so as to judge the rationality of a service center planning scheme. Different k values corresponding to different service center planning scheme quantities can be selected, a plurality of k-means models are repeatedly trained, and relatively proper clustering categories are obtained through comparison and judgment.
The k-means clustering algorithm uses the minimized sample-to-particle squared error as an objective function, and the sum of squared distance errors of member points in each class cluster and their center points is called the degree of distortion. Lower distortion of a class cluster means more compact members in the cluster, and higher distortion means more loose structures in the cluster. The distortion degree is reduced along with the increase of the category, but for the data with a certain degree of discrimination, the distortion degree is greatly improved when reaching a certain critical point, and then is slowly reduced, and the critical point is generally used as a point with better clustering performance. The rule is an elbow rule for carrying out optimal K value identification in a K-means clustering algorithm; and the contour coefficient is an evaluation index of the density and the dispersion degree of the category cluster.
As described above, the present invention can be preferably realized.
Claims (7)
1. A method for planning a power market service center based on a clustering algorithm is characterized by comprising the following steps:
s1, dividing the initial scheme of the service center planning in the service middle range according to the service center which can reflect the clear service boundary division and the service capability description of the service domain modeling result, and acquiring and storing the service center division data and the alternative service list data;
s2, selecting alternative services from each service center as algorithm condensation points of a clustering algorithm, wherein a category cluster formed by clustering according to the algorithm condensation points corresponds to the service center, and member points in the category cluster are all the alternative services;
s3, respectively scoring the alternative services in the service middlebox range one by one according to the indexes of multiple dimensions to form alternative service scoring data, and establishing a dimension index matrix related to an alternative service list;
s4, calculating the Euclidean distance between each alternative service and each algorithm condensation point to obtain the algorithm condensation point closest to the Euclidean distance of each alternative service, and classifying the alternative services correspondingly to obtain an initial clustering result;
s5, calculating the gravity center of each class as an updated algorithm condensation point according to the initial clustering result, repeatedly calculating the Euclidean distance between each alternative service and the updated algorithm condensation point, judging the algorithm condensation point with the Euclidean distance being the closest to the alternative service, and classifying the alternative services correspondingly; until the Euclidean distance between each candidate service and the condensation point of the algorithm and the service center identification closest to the candidate service do not change any more.
2. The method for planning center service in power market business based on clustering algorithm according to claim 1, wherein step S2 clustering forms a plurality of algorithm aggregate point data, each algorithm aggregate point data comprising: the service center comprises an alternative service name, an alternative service identifier, a service center identifier and a record of the service center.
3. The method for planning a center service of an electric power market business based on a clustering algorithm according to claim 1, wherein the service center comprises a user center, a message center and a contract center; the alternative services comprise operation information publishing, message sending service, contract management service, plan declaration service, user registration service and market operation analysis.
4. The method for planning a service center in an electric power market service based on a clustering algorithm according to claim 1, wherein in step S3, the indexes of multiple dimensions include three dimensional indexes of technology/service relevance, management/service relevance and market/user relevance.
5. The method for planning a service center in an electric power market service based on a clustering algorithm according to claim 1, wherein in step S3, each candidate service scoring data comprises a candidate service name, a candidate service identifier, a service dimension score, a management dimension score and a technology dimension score.
6. The method for planning a service center in an electric power market service based on a clustering algorithm according to claim 1, wherein in step S2, the selected alternative service belongs to a core or basic function service of a corresponding service center, and there is no objection clearly in the service center attribution problem.
7. The method for planning the power market service center based on the clustering algorithm according to claim 4, wherein the correlation evaluation criteria is specifically determined in combination with the actual situation when scoring is performed in step S3, and the granularity of scoring is grasped so as to effectively distinguish the performances of different alternative services in corresponding scoring dimensions.
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| CN112085366A (en) * | 2020-09-01 | 2020-12-15 | 远光软件股份有限公司 | Device and method for performing mesoTaiization processing on enterprise organization index data |
| CN112712881A (en) * | 2020-12-29 | 2021-04-27 | 智慧神州(北京)科技有限公司 | Construction method, construction device and service system of business middle station |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112085366A (en) * | 2020-09-01 | 2020-12-15 | 远光软件股份有限公司 | Device and method for performing mesoTaiization processing on enterprise organization index data |
| CN112712881A (en) * | 2020-12-29 | 2021-04-27 | 智慧神州(北京)科技有限公司 | Construction method, construction device and service system of business middle station |
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