CN111191845A - Power communication network work monotony scheduling method based on DBSCAN algorithm and KMP mode matching method - Google Patents

Power communication network work monotony scheduling method based on DBSCAN algorithm and KMP mode matching method Download PDF

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CN111191845A
CN111191845A CN201911415413.5A CN201911415413A CN111191845A CN 111191845 A CN111191845 A CN 111191845A CN 201911415413 A CN201911415413 A CN 201911415413A CN 111191845 A CN111191845 A CN 111191845A
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work order
influence factors
worker
maintenance
maintenance work
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莫穗江
高国华
李瑞德
王�锋
张欣欣
温志坤
黄定威
杨玺
张欣
汤铭华
梁英杰
廖振朝
陈嘉俊
李伟雄
童捷
张天乙
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Guangdong Power Grid Co Ltd
Jiangmen Power Supply Bureau of Guangdong Power Grid Co Ltd
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Guangdong Power Grid Co Ltd
Jiangmen Power Supply Bureau of Guangdong Power Grid Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063112—Skill-based matching of a person or a group to a task
    • G06Q50/40—

Abstract

The invention relates to the technical field of power communication network work monotonicity, in particular to a power communication network work monotonicity method based on a DBSCAN algorithm and a KMP mode matching method, which comprises the following steps: s1, according to operation and maintenance personnel influence factors and operation and maintenance work order influence factors of field work order scheduling appearing in a worker database column, corresponding the operation and maintenance personnel influence factors and the operation and maintenance work order influence factors, and setting the mutually corresponding influence factors as the same attribute; s2, dividing a plurality of operation and maintenance personnel influence factors and operation and maintenance work order influence factors into a plurality of categories according to attributes by using a DBSCAN algorithm, then calculating the distance between the operation and maintenance work order influence factors and the operation and maintenance personnel influence factors, and prioritizing the operation and maintenance work order influence factors according to the distance; and S3, matching the operation and maintenance work order influence factors with information in a worker database according to the priority sequence of the operation and maintenance work order influence factors by using a KMP mode matching method, wherein the highest matching degree is an optimal solution. The invention can realize the reasonable dispatch of the work order and improve the quality and the efficiency of the field operation and maintenance operation of the power communication network.

Description

Power communication network work monotony scheduling method based on DBSCAN algorithm and KMP mode matching method
Technical Field
The invention relates to the technical field of power communication network work monotonicity, in particular to a power communication network work monotonicity method based on a DBSCAN algorithm and a KMP mode matching method.
Background
With the trend of networking, digitalization, integration and ecology of intelligent buildings in China, the power communication network is used as an important support and guarantee system for enterprise operation management and power grid safety production of a national power grid company, the construction of network infrastructure is rapidly developed, reliable foundation guarantee is provided for power grid production scheduling, enterprise operation and management modernization, and the safe operation of a power grid is effectively supported. The rapid development of power communication networks and the continuous improvement of communication guarantee requirements of power grid production and various services lead the operation and maintenance pressure of communication networks to be increasingly increased.
The field operation and maintenance work is the most important and basic component of the power communication operation and maintenance work, and the safety, quality and efficiency of the work are directly related to the work effect of the power communication operation and maintenance work, so that the field operation and maintenance work faces more strict requirements. The main objective of the power communication network field operation and maintenance work order scheduling is to reasonably schedule the maintenance work order on the premise of ensuring the normal operation of the communication service as much as possible. However, with the continuous expansion of the scale of the power communication network, the workload of field operation and maintenance is inevitably multiplied, and meanwhile, the operation surface of the power communication field is scattered in a plurality of places, and the work order scheduling process not only needs personnel skills, personnel utilization rate and execution time, but also needs to consider the emergency degree of a specific work order, the real-time position of personnel and the like, so that how to judge the influence degree of factors needing to be considered in dispatching the specific work order and how to select the most appropriate personnel according to the influence degree to efficiently and quickly complete the work order becomes a problem to be solved urgently in field operation and maintenance operation. Therefore, it is necessary to design a reasonable and efficient method for dispatching the on-site operation and maintenance work order.
At present, an intelligent power operation and maintenance dispatching work order management method based on a mobile data real-time transmission technology is available, but the method does not consider the problem of multi-task human resource scheduling, and cannot reasonably and effectively utilize the existing resources, so that the operation scheduling efficiency is low; at present, an intelligent power operation and maintenance dispatching work order management method based on an Anderson-Darling inspection clustering algorithm is provided, but the method also has the problem of low efficiency; at present, an operation and maintenance work order scheduling optimization method based on an improved virus genetic algorithm is provided, but the difference of human resources and the rationality of work order dispatching and manual matching are not considered in the method, and the quality and efficiency of operation and maintenance work cannot be guaranteed.
Disclosure of Invention
The invention aims to overcome the defect that the conventional work order scheduling method cannot be applied to a power communication network, provides a power communication network work order scheduling method based on a DBSCAN algorithm and a KMP mode matching method, can realize reasonable dispatching of work orders, and improves the quality and efficiency of field operation and maintenance work of the power communication network.
In order to solve the technical problems, the invention adopts the technical scheme that:
the utility model provides a power communication network worker monotony method based on DBSCAN algorithm and KMP mode matching method, including the workman database, the workman database includes workman's name, workman corresponds serial number, workman's professional skill proficiency, workman's performance appraisal condition, workman's work order condition of having accomplished on the same day, the condition of receiving the work order on the same day, the daily route of patrolling and examining, accomplish work order efficiency, workman's professional skill proficiency, workman's performance appraisal condition, workman's work order condition of having accomplished on the same day, the condition of receiving the work order on the same day, daily route of patrolling and examining, workman's efficiency of completing the work order, the information in the workman database all stores with the pinyin form, its characterized in:
s1, respectively listing a plurality of operation and maintenance personnel influence factors and operation and maintenance work order influence factors scheduled by a field work order according to the worker database, corresponding the operation and maintenance personnel influence factors and the operation and maintenance work order influence factors, and setting the operation and maintenance personnel influence factors and the operation and maintenance work order influence factors which correspond to each other as the same attributes;
s2, dividing a plurality of operation and maintenance personnel influence factors and operation and maintenance work order influence factors into a plurality of categories according to attributes by using a DBSCAN algorithm, then calculating the distance between the operation and maintenance work order influence factors and the operation and maintenance personnel influence factors in the same attribute, and prioritizing the operation and maintenance work order influence factors according to the distance value;
and S3, matching the operation and maintenance work order influence factors with information in a worker database according to the priority sequence of the operation and maintenance work order influence factors by using a KMP mode matching method, wherein the highest matching degree is an optimal solution.
The invention comprises a power communication network work monotonicity method based on a DBSCAN algorithm and a KMP mode matching method, wherein the DBSCAN is a clustering algorithm based on density, a cluster is defined as a maximum set of points connected by the density, an area with enough density can be divided into clusters, and clusters with any shapes can be found in a noise spatial database. KMP is a character string matching algorithm, and the core of the KMP is to utilize information after matching failure to reduce the matching times of a mode string and a main string as much as possible so as to achieve the purpose of quick matching, and the KMP is realized through a next () function. According to the method, the operation and maintenance staff influence factors and the operation and maintenance work order influence factors scheduled by a field work order are listed by combining the worker information in the worker database, each operation and maintenance staff influence factor can correspond to a plurality of operation and maintenance work order influence factors, and each operation and maintenance work order influence factor can also correspond to a plurality of operation and maintenance staff influence factors; and setting the same attribute for the operation and maintenance personnel influence factors and the operation and maintenance work order influence factors which correspond to each other. Dividing operation and maintenance staff influence factors and operation and maintenance work order influence factors into a plurality of categories according to attributes by using a DBSCAN algorithm, calculating the distance between the operation and maintenance work order influence factors in the same attribute, and arranging the operation and maintenance work order influence factors into priorities according to distance values, wherein the higher the priority is, the greater the influence on work order scheduling decision is; and finally, matching the operation and maintenance work order influence factors with the information in the worker database according to the sequence of the priority from high to low by using a KMP mode matching method, wherein the highest matching degree is an optimal solution, so that the reasonable dispatching of the work orders is realized, and the field operation and maintenance work quality and efficiency of the power communication network are improved.
Preferably, the specific steps of step S1 are as follows:
s11, establishing a set of operation and maintenance personnel influence factors and operation and maintenance work order influence factors;
and S12, setting the same attributes of the corresponding operation and maintenance personnel influence factors and the operation and maintenance work order influence factors, and forming a sample set.
Preferably, in step S11, the operation and maintenance personnel influence factors include the proficiency level of professional skills of workers, the performance assessment condition of workers, the condition that the workers have finished work orders on the same day, the condition that the workers receive the work orders on the same day, the routine routing inspection route and the efficiency of finishing the work orders.
Preferably, in step S11, the operation and maintenance work order influencing factors include work order difficulty, work order urgency, skill direction required for completing the work order, and time required for completing the work order.
Preferably, in step S11, the set of influencing factors of the operation and maintenance personnel is D1Said set D1＝{x1,x2,x3...xn-1}; the set of the operation and maintenance work order influence factors is D2Said set D2＝{xn,xn+1,...,xm}; in step S12, the sample set is represented by D, and the sample set D ═ x1,x2,...xn-1,xn,...,xm}。
Preferably, the specific steps of step S2 are as follows:
s21, inputting neighborhood parameters (Eps, MinPts);
wherein Eps represents a neighborhood distance threshold of a certain sample, and MinPts represents a threshold of the number of samples in a neighborhood of which the distance of the certain sample is Eps;
s22, dividing the objects in the sample set D into core points, boundary points and noise points according to a DBSCAN algorithm and neighborhood parameters (Eps, MinPts);
wherein the core point represents a point having a number exceeding MinPts within the radius Eps, the boundary point represents a point having a number smaller than MinPts within the radius Eps but falling within a neighborhood of the core point, and the noise point represents a point which is neither the core point nor the boundary point;
s23, classifying the sample set D based on a DBSCAN algorithm;
and S24, calculating the distance between the operation and maintenance work order influence factors and the operation and maintenance personnel influence factors for the same attribute in the sample set D, and prioritizing the operation and maintenance work order influence factors according to the distance value.
Preferably, the specific steps of step S23 are as follows:
s231, marking all objects in the sample set D as 'unoccupied', and enabling i to be 1; wherein i represents a subscript used to create the cluster;
s232, randomly selecting an object p in the sample set D by using a DBSCAN algorithm for access, marking the object p as 'visited', and then checking whether an Eps neighborhood of the object p at least comprises MinPts objects; if yes, executing step S233, otherwise marking the object p as a noise point; deleting all noise points after all the objects in the sample set D are accessed, and then executing the step S236;
s233, establishing a candidate set N, and then creating a cluster C for the object piPutting all objects in the Eps neighborhood of the object p into a candidate set N;
s234, establishing other clusters, and adding objects which do not belong to other clusters in the candidate set N to the cluster C in an iterative mode by utilizing a DBSCAN algorithmiIn the process, marking an object p 'marked as "unvisited" in the candidate set N as "visited", and checking whether an Eps neighborhood of the object p' at least contains MinPts objects; if yes, adding all objects in the Eps neighborhood of the object p' into the candidate set N; otherwise, executing step S235;
s235, judging whether Eps neighborhoods of other objects marked as 'unused' in the candidate set N contain MinPts objects by using a DBSCAN algorithm, and if not, adding the objects to the cluster CiUntil the candidate set N is empty, let i be i +1, and then execute step S232;
s236, outputting a cluster dividing result C ═ C1,C2...Ck}。
Preferably, in step S24, the two-point distance is calculated using the euclidean distance formula:
d(A,B)＝sqrt[∑((a[i]＝b[i]^2](i＝1,2,...,n)；
in the formula, a [ i ] and b [ i ] represent i-dimensional coordinates of two points at a desired distance.
Preferably, the specific steps of step S3 are as follows:
s31, order ml＝0,n＝1，w l0; wherein, represents mlThe number of current matching items of the ith worker, n represents the priority of the current operation and maintenance work order influence factor, w represents the sum of the priorities of all the matching items, and l represents the corresponding number of the worker;
s32, setting the pinyin of a worker database to be matched as a main string s, and then finding out a pinyin mode string p of the highest-priority operation and maintenance work order influence factor which is not matched from the main string s;
s33, searching the longest prefix public element in the pattern string p; all elements in the whole pattern string P form a set P ═ P0p1...pj-1pjAnd, if present:
p0p1...pk-1pk＝pj-kpj-k+1...pj-1pj；
then contains pjThe mode string has the same prefix suffix with the maximum length of k + 1;
s34, solving a next array of the pattern string, shifting the value obtained in the step S33 backward by one bit to the right, and then assigning an initial value as-1; the character string with the length of next [ j ] after the 0 th bit of the pattern string is equal to the character with the length of next [ j ] before the jth bit;
s35, matching is carried out according to the next array; if the match is mismatched, the pattern string is shifted to the right by j-next [ j]Then let pkAnd siContinuing matching; if the matching is successful, continuing the matching of the next bit; wherein p iskThe next bit, s, representing the prefix of the pattern stringiIndicating a next bit of the main string corresponding to the prefix string;
s36, repeating the steps S31 to S35 until the matching reaches the tail of the main string; if all the pattern strings are matched, the pattern string is considered to exist in the main string, and m isl＝ml+1，wl＝wl+ n; when the matching of all the operation and maintenance work order influence factors is completed, executing step S37, otherwise, executing step S32;
s37, finding out the bestLarge mlThe value, the worker corresponding to the worker number l is the work order and sends the optimal solution; if there are at least two identical mlValue, then w is comparedlValue, minimum wlThe worker with the value corresponding to the worker number l sends the optimal solution to the work order; if ml、wlIf the values are the same, a plurality of optimal solutions exist, and any optimal solution is randomly selected for dispatching.
Preferably, the first and second electrodes are formed of a metal,
compared with the prior art, the invention has the beneficial effects that:
the DBSCAN algorithm is used for carrying out category division on a plurality of operation and maintenance personnel influence factors and operation and maintenance work order influence factors, the DBSCAN algorithm can be suitable for both a convex sample set and a non-convex sample set, category division is carried out by using the DBSCAN algorithm, and the method has the advantages of high clustering speed, capability of effectively processing noise and finding spatial clusters in any shapes. The invention also uses a KMP mode matching method, can match the operation and maintenance work order influence factors with the information in the worker database, realizes the reasonable dispatch of the work order, shortens the requirement of the operation and maintenance task duration time, and improves the field operation and maintenance work quality and efficiency of the power communication network.
Drawings
Fig. 1 is a flowchart of a power communication network work monotonicity method based on a DBSCAN algorithm and a KMP mode matching method according to the present invention.
Fig. 2 is a schematic diagram illustrating object classification based on DBSCAN algorithm according to the present invention.
FIG. 3 is a table of maximum common element lengths for strings of the present invention.
FIG. 4 is a table of values corresponding to the next array of the pattern string of the present invention.
Fig. 5 is a diagram illustrating the practical application of the KMP pattern matching method of the present invention.
Fig. 6 is a sample set example of the present invention.
FIG. 7 is a schematic view of a sample class of the present invention.
Detailed Description
The present invention will be further described with reference to the following embodiments. Wherein the showings are for the purpose of illustration only and are shown by way of illustration only and not in actual form, and are not to be construed as limiting the present patent; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", etc. based on the orientation or positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but it is not intended to indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present patent, and the specific meaning of the terms may be understood by those skilled in the art according to specific circumstances.
Example 1
Fig. 1 to fig. are diagrams showing a first embodiment of a power communication network work monotony degree method based on a DBSCAN algorithm and a KMP mode matching method according to the present invention, which includes a worker database, where information of the worker database includes a worker name, a worker corresponding number, a worker professional skill proficiency level, a worker performance assessment condition, a worker work order condition completed on the same day, a work order condition completed on the same day by the worker, a work order condition received on the same day by the worker, a daily routing inspection route, a work order efficiency completed on the same day by the worker, a worker professional skill proficiency level, a worker performance assessment condition, a work order condition completed on the same day by the worker, a work order receiving condition on the same day, a daily routing inspection route, and information in the worker database are stored in a pinyin form, that means that the above-mentioned contents are:
s1, respectively listing a plurality of operation and maintenance personnel influence factors and operation and maintenance work order influence factors scheduled by a field work order according to the worker database, corresponding the operation and maintenance personnel influence factors to the operation and maintenance work order influence factors, and setting the same attribute for the operation and maintenance personnel influence factors and the operation and maintenance work order influence factors corresponding to each other;
s2, dividing a plurality of operation and maintenance personnel influence factors and operation and maintenance work order influence factors into a plurality of categories according to attributes by using a DBSCAN algorithm, then calculating the distance between the operation and maintenance work order influence factors and the operation and maintenance personnel influence factors in the same attribute, and prioritizing the operation and maintenance work order influence factors according to the distance value;
and S3, matching the operation and maintenance work order influence factors with information in a worker database according to the priority sequence of the operation and maintenance work order influence factors by using a KMP mode matching method, wherein the highest matching degree is an optimal solution.
DBSCAN is a density-based clustering algorithm that defines clusters as the largest set of density-connected points, can divide areas of sufficient density into clusters, and can find arbitrarily shaped clusters in a spatial database of noise. KMP is a character string matching algorithm, and the core of the KMP is to utilize information after matching failure to reduce the matching times of a mode string and a main string as much as possible so as to achieve the purpose of quick matching, and the KMP is realized through a next () function. According to the method, the operation and maintenance staff influence factors and the operation and maintenance work order influence factors scheduled by a field work order are listed by combining the worker information in the worker database, each operation and maintenance staff influence factor can correspond to a plurality of operation and maintenance work order influence factors, and each operation and maintenance work order influence factor can also correspond to a plurality of operation and maintenance staff influence factors; and setting the same attribute for the operation and maintenance personnel influence factors and the operation and maintenance work order influence factors which correspond to each other. Dividing operation and maintenance staff influence factors and operation and maintenance work order influence factors into a plurality of categories according to attributes by using a DBSCAN algorithm, calculating the distance between the operation and maintenance work order influence factors in the same attribute, and arranging the operation and maintenance work order influence factors into priorities according to distance values, wherein the higher the priority is, the greater the influence on work order scheduling decision is; and finally, matching the operation and maintenance work order influence factors with the information in the worker database according to the sequence of the priority from high to low by using a KMP mode matching method, wherein the highest matching degree is an optimal solution, so that the reasonable dispatching of the work orders is realized, and the field operation and maintenance work quality and efficiency of the power communication network are improved.
The specific steps of step S1 are as follows:
s11, establishing a set of operation and maintenance personnel influence factors and operation and maintenance work order influence factors;
the operation and maintenance personnel influence factors comprise the professional skill proficiency of workers, the performance assessment condition of the workers, the condition that the workers finish work orders on the same day, the condition that the workers receive the work orders on the same day, a daily routing inspection route and the efficiency of finishing the work orders; the operation and maintenance work order influence factors comprise work order difficulty, work order emergency degree, skill direction required for finishing the work order and time required for finishing the work order. The set of the influence factors of the operation and maintenance personnel is D1Set D of1＝{x1,x2,x3...xn-1}; the set of the operation and maintenance work order influence factors is D2Set D of2＝{xn,xn+1,...,xm}; it should be noted that the operation and maintenance personnel influence factors and the content specifically included in the operation and maintenance work order influence factors may be adjusted according to the work order content and requirement to be scheduled in the actual site, and the conditions of the required operation and maintenance personnel and operation and maintenance resource information.
S12, setting the same attribute of the corresponding operation and maintenance personnel influence factor and the operation and maintenance work order influence factor, and forming a sample set; the sample set is represented using D, which is { x ═ x1,x2,...xn-1,xn,...,xm}。
The specific steps of step S2 are as follows:
s21, inputting neighborhood parameters (Eps, MinPts);
wherein Eps represents a neighborhood distance threshold of a certain sample, and MinPts represents a threshold of the number of samples in a neighborhood of which the distance of the certain sample is Eps;
s22, dividing the objects in the sample set D into core points, boundary points and noise points according to a DBSCAN algorithm and neighborhood parameters (Eps, MinPts);
wherein the core point represents a point having a number exceeding MinPts within the radius Eps, the boundary point represents a point having a number smaller than MinPts within the radius Eps but falling within a neighborhood of the core point, and the noise point represents a point which is neither the core point nor the boundary point; as shown in fig. 2, a point located in the center of the radius Eps is a core point, a point located in the radius Eps is a boundary point, and the rest are noise points;
s23, classifying the sample set D based on a DBSCAN algorithm;
and S24, calculating the distance between the operation and maintenance work order influence factors and the operation and maintenance personnel influence factors for the same attribute in the sample set D, and prioritizing the operation and maintenance work order influence factors according to the distance value.
Specifically, the specific steps of step S23 are as follows:
s231, marking all objects in the sample set D as 'unoccupied', and enabling i to be 1; wherein i represents a subscript used to create the cluster;
s232, randomly selecting an object p in the sample set D by using a DBSCAN algorithm for access, marking the object p as 'visited', and then checking whether an Eps neighborhood of the object p at least comprises MinPts objects; if yes, executing step S233, otherwise marking the object p as a noise point; deleting all noise points after all the objects in the sample set D are accessed, and then executing the step S236;
s233, establishing a candidate set N, and then creating a cluster C for the object piPutting all objects in the Eps neighborhood of the object p into a candidate set N;
s234, establishing other clusters, and adding objects which do not belong to other clusters in the candidate set N to the cluster C in an iterative mode by utilizing a DBSCAN algorithmiIn the process, marking an object p 'marked as "unvisited" in the candidate set N as "visited", and checking whether an Eps neighborhood of the object p' at least contains MinPts objects; if yes, adding all objects in the Eps neighborhood of the object p' into the candidate set N; otherwise, executing step S235;
s235, judging whether Eps neighborhoods of other objects marked as 'unused' in the candidate set N contain MinPts objects by using a DBSCAN algorithm, and if not, adding the objects to the cluster CiUntil the candidate set N is empty, let i be i +1, and then execute step S232;
s236, outputting a cluster dividing result C ═ C1,C2...Ck}. Specifically, in step S24, the two-point distance is calculated using the euclidean distance formula:
d(A,B)＝sqrt[∑((a[i]＝b[i]^2](i＝1,2,...,n)；
in the formula, a [ i ] and b [ i ] represent i-dimensional coordinates of two points at a desired distance.
Example 2
This embodiment is similar to embodiment 1, except that the specific steps of step S3 in this embodiment are as follows:
s31, order ml＝0,n＝1，w l0; wherein, represents mlThe number of current matching items of the ith worker, n represents the priority of the current operation and maintenance work order influence factor, w represents the sum of the priorities of all the matching items, and l represents the corresponding number of the worker;
s32, setting the pinyin of a worker database to be matched as a main string s, and then finding out a pinyin mode string p of the highest-priority operation and maintenance work order influence factor which is not matched from the main string s;
s33, searching the longest prefix public element in the pattern string p; all elements in the whole pattern string P form a set P ═ P0p1...pj-1pjFor set P, if present:
p0p1...pk-1pk＝pj-kpj-k+1...pj-1pj；
then contains pjThe mode string has the same prefix suffix with the maximum length of k + 1;
specifically, as shown in fig. 3, the maximum common element length table of the character strings is set to "abab" for the pattern string, which has the same prefix suffix of length 1 for the character string "aba", and 2 for the character string "abab", which has the same prefix suffix ab of length 2, where k +1 is 2 for the length of the same prefix suffix k + 1.
S34, solving a next array of the pattern string, shifting the value obtained in the step S33 backward by one bit to the right, and then assigning an initial value as-1; the character string with the length of next [ j ] after the 0 th bit of the pattern string is equal to the character with the length of next [ j ] before the jth bit (starting from 0 and except 0);
specifically, as shown in fig. 4, the value corresponding to the next array of the pattern string is shown, when the pattern string is "abab", for the character string "aba", the character ab before the third character a has the same prefix and suffix with the length of 0, so the next value corresponding to the third character a is 0; on the other hand, for the character "abab", since the same prefix suffix a having a length of 1 is present in the character string "aba" preceding the fourth character b, the next value corresponding to the fourth character b is 1, the length of the same prefix suffix is k, and k is 1.
S35, matching is carried out according to the next array; if the match is mismatched, the pattern string is shifted to the right by j-next [ j]Then let pkAnd siContinuing matching; if the matching is successful, continuing the matching of the next bit; wherein p iskThe next bit, s, representing the prefix of the pattern stringiIndicating a next bit of the main string corresponding to the prefix string;
s36, repeating the steps S31 to S35 until the matching reaches the tail of the main string; if all the pattern strings are matched, the pattern string is considered to exist in the main string, and m isl＝ml+1，wl＝wl+ n; when the matching of all the operation and maintenance work order influence factors is completed, executing step S37, otherwise, executing step S32;
s37, finding out the largest mlThe value, the worker corresponding to the worker number l is the work order and sends the optimal solution; if there are at least two identical mlValue, then w is comparedlValue, minimum wlThe worker with the value corresponding to the worker number l sends the optimal solution to the work order; if ml、wlIf the values are the same, a plurality of optimal solutions exist, and any optimal solution is randomly selected for dispatching.
As shown in fig. 5, which shows the KMP pattern matching method, the bold font part indicates that a mismatch event occurs, and the part with gray scale indicates matching.
When in use, the specific work order scheduling flow is as follows:
the erection needs to complete one-time operation and maintenance operation scheduling of the power communication site at present, and the conditions are as follows: a certain project group comprises a plurality of operation and maintenance personnel, each operation and maintenance personnel has different skill proficiency, historical work order completion conditions, daily routing inspection routes and the like, each project comprises a plurality of task sequences, the time of each work sequence is also known, and the task work sequences of different work orders may need workers with different skills;
(1) confirming the content and the requirement of the work order needing to be scheduled at present, and the information of operation and maintenance personnel and operation and maintenance resources, listing the operation and maintenance personnel influence factors influencing the dispatching of the current work order and the operation and maintenance work order influence factors x1, x2.
(2) And dividing the sample set into a plurality of categories according to the attributes by using a DBSCAN algorithm, wherein each operation and maintenance personnel influence factor is a category center, and the operation and maintenance work order influence factors in the specific work order are correspondingly prioritized from high to low according to the distance from the category center.
Specifically, the method comprises the following steps:
(2-1) let the sample set as shown in FIG. 6, input: sample set D ═ x1,x2...,xmAnd (4) field parameters (Eps, MinPts), wherein Eps is 3 and MinPts is 3. And (3) outputting: dividing of clusters C ═ C1,C2...Ck}；
(2-2) initializing core Point set
Figure BDA0002351073360000101
Initializing cluster sequence number k as 0, initializing sample point set Γ as D, and partitioning clusters
Figure BDA0002351073360000102
(2-3) for j ═ 1,2.. m all core points are found according to the following steps:
determining a sample point x by means of Euclidean distancejEps-Domain N ofEps(xj), n-dimensional Euclidean space is a set of points, each point X of which can be represented as (X [1 ])],x[2]...,x[n]) Wherein x [ i ]](i ═ 1,2.., n) is a real number, called the ith coordinate of X, and two points a ═ a [1 ·, 1 ]],a[2]...,x[n]) And B ═ B [1],b[2],...,b[n]) D (A, B) betweenDefined as the formula: d (A, B) ═ sqrt [ Sigma ((a [ i) [ ]]＝b[i]^2](i ═ 1,2,. n); number of points in Eps-Domain | NEps(p) | is not less than MinPts, then xjAdding into a core point set omega: q ═ Ω Y { xj}；
(2-4) if core Point set
Figure BDA0002351073360000103
If the sample point x9 in fig. 7 is found, ending the calculation, otherwise, proceeding to step (2-5);
(2-5) randomly selecting a core point o in the core point set omega, and initializing the core point queue omega of the current clustercurUpdating class sequence number k to k +1, initializing current cluster sample set ckUpdating the set of unaccessed samples Γ ═ Γ - { o };
(2-6) core point queue if current cluster
Figure BDA0002351073360000111
The current cluster Ck is generated, and the partition C ═ C of the updated cluster is completed1,C2...CkAnd f, updating a point set omega-CkThen entering the step (2-4);
(2-7) core queue Ω in the current clustercurRandomly taking out a core point o' from the neighborhood distance threshold value Eps, and drawing out all Eps-neighborhood NEps(o') making Δ ═ NEps(o') Γ, updating the sample set C of the current clusterk＝CkY delta, updating an unvisited sample set gamma-delta, and updating a core point queue; if omegacur＝ΩcurY (delta I omega) -o', and transferring to the step (2-6); after the final execution is finished, the division result C of the output cluster is equal to { C ═ C1,C2...Ck}: c1 ═ x1, x2, x3, x4, x13}, C2 ═ x5, x6, x7, x8}, C3 ═ x10, x11, x12}, and x9 is a noise point, as shown in fig. 7.
(2-8) calculating the Euclidean distance of the operation and maintenance work order influence factors in the specific work order and the corresponding class in the sample set, and judging the priority: d-sqrt (∑ (x)i1-xi2) Wherein xi1I-dimensional coordinate, x, representing the first pointi2An ith coordinate representing the second point;
(2-9) outputting operation and maintenance work order influence factors corresponding to different priorities;
(3) inquiring a worker database, wherein the worker database comprises personnel skill categories, capability levels, historical finished work order quantity, historical finished work order quality, historical finished work order time, daily routing inspection routes and the like, and taking the worker capability items with the same quantity as the operation and maintenance work order influence factors in the step (1) from high to low to be matched;
(4) matching the operation and maintenance work order influence factors in the divided sample set with a worker database from high to low according to the priority by using a KMP mode matching method;
(4-1) order ml＝0,n＝1，wl＝0；
(4-2) setting character strings to be matched, namely, taking a worker database as a main string, and finding a character string of a certain operation and maintenance work order influence factor in the main string as a mode string;
(4-3) finding a next array of pattern strings, the meaning of the array being: the character string with the length of next [ j ] after the 0 th bit of the pattern string is equal to the character with the length of next [ j ] before the j th bit (except 0) and the value of the next [ j ] is as large as possible, but the two characters can not be overlapped. Specific examples are as follows: the next value of 0 bit is-1;
(4-4) letting pos be i at the ith position of the main string, matching the main string pos bit with the pattern string next [ j ] bit if the character at the position of the main string pos is not equal to the pattern string j bit, and if not, going to next [ next [ j ] ]. Until the next value equals-1, the pattern string slides to main string pos +1 bit to be re-matched;
(4-5) if equal, continuing to match down until the end of the pattern string is matched, indicating that the pattern string exists in the main string, ml＝ml+1,wl＝wl+ n. If all the operation and maintenance work order influence factors are matched, the step (4-6) is carried out; otherwise, turning to the step (4-2) if n is n + 1;
(4-6) matching is completed, and the largest m is foundlThe value, the worker l corresponding to the value is the optimal solution sent by the work order; if the same m existslA value indicating that there are multiple workers meeting the dispatch condition, w is comparedlValue, corresponding to wlThe smaller the value, the higher the priority corresponding to the matching factor, the optimal solution, so the minimum wlThe worker with the value corresponding to the worker number l sends the optimal solution to the work order; if ml、wlIf the values are the same, a plurality of optimal solutions exist, and the system randomly selects the optimal solutions to dispatch.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. A power communication network work monotonicity method based on a DBSCAN algorithm and a KMP mode matching method comprises a worker database, wherein the information of the worker database comprises the name of a worker, the corresponding number of the worker, the proficiency of the professional skill of the worker, the performance assessment condition of the worker, the condition that the worker completes a work order on the same day, the condition that the worker receives the work order on the same day, a daily routing inspection route, the efficiency of completing the work order, the proficiency of the professional skill of the worker, the performance assessment condition of the worker, the condition that the worker completes the work order on the same day, the condition that the work order receives the work order on the same day, the daily routing inspection route and the efficiency of completing the work order by the worker, and the information in the worker:
s1, respectively listing a plurality of operation and maintenance personnel influence factors and operation and maintenance work order influence factors scheduled by a field work order according to the worker database, corresponding the operation and maintenance personnel influence factors to the operation and maintenance work order influence factors, and setting the same attribute for the operation and maintenance personnel influence factors and the operation and maintenance work order influence factors corresponding to each other;
s2, dividing a plurality of operation and maintenance personnel influence factors and operation and maintenance work order influence factors into a plurality of categories according to attributes by using a DBSCAN algorithm, then calculating the distance between the operation and maintenance work order influence factors and the operation and maintenance personnel influence factors in the same attribute, and prioritizing the operation and maintenance work order influence factors according to the distance value;
and S3, matching the operation and maintenance work order influence factors with information in a worker database according to the priority sequence of the operation and maintenance work order influence factors by using a KMP mode matching method, wherein the highest matching degree is an optimal solution.
2. The method for power communication network work monotony based on the DBSCAN algorithm and the KMP mode matching method according to claim 1, wherein the specific steps of the step S1 are as follows:
s11, establishing a set of operation and maintenance personnel influence factors and operation and maintenance work order influence factors;
and S12, setting the same attributes of the corresponding operation and maintenance personnel influence factors and the operation and maintenance work order influence factors, and forming a sample set.
3. The method for power communication network work monotony based on the DBSCAN algorithm and the KMP mode matching method according to claim 2, wherein in step S11, the operation and maintenance personnel influence factors include skill level of professional skills of workers, performance assessment condition of workers, condition that workers have finished work orders on the same day, condition that workers receive orders on the same day, routine routing inspection route, and efficiency of finished work orders.
4. The method for power communication network work order measurement based on the DBSCAN algorithm and the KMP mode matching method as claimed in claim 3, wherein in step S11, the operation and maintenance work order influencing factors include work order difficulty, work order urgency, skill direction required for completing the work order, and time required for completing the work order.
5. The method for power communication network work monotony based on DBSCAN algorithm and KMP mode matching method of claim 4, wherein in step S11, the set of influencing factors of operation and maintenance personnel is D1Said set D1＝{x1,x2,x3...xn-1}; the set of the operation and maintenance work order influence factors is D2Said set D2＝{xn,xn+1,...,xm}。
6. The method for power communication network work monotonicity based on the DBSCAN algorithm and the KMP mode matching method according to claim 5, wherein in step S12, the sample set is represented by D, and the sample set D ═ x1,x2,...xn-1,xn,...,xm}。
7. The method for power communication network work monotony based on the DBSCAN algorithm and the KMP mode matching method according to claim 6, wherein the specific steps of the step S2 are as follows:
s21, inputting neighborhood parameters (Eps, MinPts);
wherein Eps represents a neighborhood distance threshold of a certain sample, and MinPts represents a threshold of the number of samples in a neighborhood of which the distance of the certain sample is Eps;
s22, dividing the objects in the sample set D into core points, boundary points and noise points according to a DBSCAN algorithm and neighborhood parameters (Eps, MinPts);
wherein the core point represents a point having a number exceeding MinPts within the radius Eps, the boundary point represents a point having a number smaller than MinPts within the radius Eps but falling within a neighborhood of the core point, and the noise point represents a point which is neither the core point nor the boundary point;
s23, classifying the sample set D based on a DBSCAN algorithm;
and S24, calculating the distance between the operation and maintenance work order influence factors and the operation and maintenance personnel influence factors for the same attribute in the sample set D, and prioritizing the operation and maintenance work order influence factors according to the distance value.
8. The method for power communication network work monotony based on the DBSCAN algorithm and the KMP mode matching method according to claim 7, wherein the specific steps of the step S23 are as follows:
s231, marking all objects in the sample set D as 'unoccupied', and enabling i to be 1; wherein i represents a subscript used to create the cluster;
s232, randomly selecting an object p in the sample set D by using a DBSCAN algorithm for access, marking the object p as 'visited', and then checking whether an Eps neighborhood of the object p at least comprises MinPts objects; if yes, executing step S233, otherwise marking the object p as a noise point; deleting all noise points after all the objects in the sample set D are accessed, and then executing the step S236;
s233, establishing a candidate set N, and then creating a cluster C for the object piPutting all objects in the Eps neighborhood of the object p into a candidate set N;
s234, establishing other clusters, and adding objects which do not belong to other clusters in the candidate set N to the cluster C in an iterative mode by utilizing a DBSCAN algorithmiIn the process, marking an object p 'marked as "unvisited" in the candidate set N as "visited", and checking whether an Eps neighborhood of the object p' at least contains MinPts objects; if yes, adding all objects in the Eps neighborhood of the object p' into the candidate set N; otherwise, executing step S235;
s235, judging whether Eps neighborhoods of other objects marked as 'unused' in the candidate set N contain MinPts objects by using a DBSCAN algorithm, and if not, adding the objects to the cluster CiUntil the candidate set N is empty, let i be i +1, and then execute step S232;
s236, outputting a cluster dividing result C ═ C1,C2...Ck}。
9. The power communication network work monotonicity method based on the DBSCAN algorithm and the KMP mode matching method as claimed in claim 8, wherein in step S24, the distance between two points is calculated by using the euclidean distance formula:
d(A,B)＝sqrt[∑((a[i]＝b[i]^2](i＝1,2,...,n)；
in the formula, a [ i ] and b [ i ] represent i-dimensional coordinates of two points at a desired distance.
10. The method for power communication network work monotony based on the DBSCAN algorithm and the KMP mode matching method according to claim 9, wherein the specific steps of the step S3 are as follows:
s31, order ml＝0,n＝1，wl0; wherein, represents mlThe number of current matching items of the ith worker, n represents the priority of the current operation and maintenance work order influence factor, w represents the sum of the priorities of all the matching items, and l represents the corresponding number of the worker;
s32, setting the pinyin of a worker database to be matched as a main string s, and then finding out a pinyin mode string p of the highest-priority operation and maintenance work order influence factor which is not matched from the main string s;
s33, searching the longest prefix public element in the pattern string p; all elements in the whole pattern string P form a set P ═ P0p1...pj-1pjAnd, if present:
p0p1...pk-1pk＝pj-kpj-k+1...pj-1pj；
then contains pjThe mode string has the same prefix suffix with the maximum length of k + 1;
s34, solving a next array of the pattern string, shifting the value obtained in the step S33 backward by one bit to the right, and then assigning an initial value as-1; the character string with the length of next [ j ] after the 0 th bit of the pattern string is equal to the character with the length of next [ j ] before the jth bit;
s35, matching is carried out according to the next array; if the match is mismatched, the pattern string is shifted to the right by j-next [ j]Then let pkAnd siContinuing matching; if the matching is successful, continuing the matching of the next bit; wherein p iskThe next bit, s, representing the prefix of the pattern stringiIndicating a next bit of the main string corresponding to the prefix string;
s36, repeating the steps S31 to S35 until the matching reaches the tail of the main string; if all the pattern strings are matched, the pattern string is considered to exist in the main string, and m isl＝ml+1，wl＝wl+ n; when the matching of all the operation and maintenance work order influence factors is completed, executing step S37, otherwise, executing step S32;
s37, finding out the largest mlThe value, the worker corresponding to the worker number l is the work order and sends the optimal solution; if there are at least two identical mlValue, then w is comparedlValue, minimum wlThe worker with the value corresponding to the worker number l sends the optimal solution to the work order; if ml、wlIf the values are the same, a plurality of optimal solutions exist, and any optimal solution is randomly selected for dispatching.
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