CN113987721A - Single line diagram layout checking method and device based on statistical anomaly point detection algorithm - Google Patents

Abstract

The invention discloses a single line diagram layout checking method and a device based on a statistical abnormal point detection algorithm, wherein the method comprises the following steps: step S1: learning a typical layout sample; step S2: detecting a layout example; step S3: and displaying and outputting the layout detection result. The method and the device of the invention take the typical single line diagram as the blue book from the machine learning angle, judge the layout rationality of other newly added and adjusted single line diagrams through the machine learning, form a virtuous circle through the experience learning mode, guide the layout detection of the single line diagram, and have good popularization and application values.

Description

Single line diagram layout checking method and device based on statistical anomaly point detection algorithm

Technical Field

The invention relates to the technical field of computer and distribution network single line diagram processing, in particular to a single line diagram layout checking method and device based on a statistical abnormal point detection algorithm.

Background

The single line diagram is one of important tools for operation and maintenance of power distribution network equipment, the difficulty and workload of two modes of manual drawing and drawing software utilization are high, the requirement for development and use of a smart power grid is difficult to meet under the condition that the complexity of a power grid structure is continuously increased, and a power grid company compiles a distribution line single line diagram drawing specification production and dispatching application detailed rule suitable for the unit on the basis of a unified power grid GIS platform based on the localization characteristics and refines the single line diagram.

However, the drawing specification aims at a single drawing person and a single line diagram, and the experience of the normative single line diagram cannot be passed and referred.

Disclosure of Invention

In view of the above, one of the objectives of the present invention is to provide a single line diagram layout checking method based on statistical outlier detection algorithm. The problems in the background art can be overcome.

One of the purposes of the invention is realized by the following technical scheme:

a single line diagram layout checking method based on a statistical outlier detection algorithm comprises the following steps:

step S1: learning a typical layout sample;

step S2: detecting a layout example;

step S3: and displaying and outputting the layout detection result.

Further, step S1 is to perform typical layout learning on the typical sample single line diagrams one by one, and write the result of statistical probability distribution of layout into the learning result library, where the method includes:

setting a selection range of appointed sample learning;

setting a threshold value of abnormal point out-of-limit;

analyzing the sample data one by one, and learning typical layout characteristics of the sample;

and counting the probability of the occurrence of the typical characteristics of the sample, and warehousing the learning result.

Further, in step S2, the example single line diagram is detected, a difference between the statistical probability of the example data and the statistical probability of the learning library is detected from the set abnormal point threshold, and the abnormal data is written into the abnormal result library after being found.

Further, the step S2 specifically includes

Selecting a detection range;

detecting a difference value between the statistical probability of the example data and the statistical probability of the learning database from a set abnormal point threshold according to the statistical probability data based on the learning result library, and if the statistical probability of the detection example data at a certain detection point is greater than the statistical probability of the learning database, determining the detection example data as abnormal data;

and writing the abnormal data into an abnormal detection result library.

Further, in step S3, the detection result in the abnormal result library is detected, positioned, and output.

Further, step S3 specifically includes

Listing an abnormal single line diagram list and a specific layout problem list in an abnormal detection result library;

positioning a specific layout problem;

the layout problem is derived.

The invention also aims to provide a single line diagram layout checking device based on the statistical abnormal point detection algorithm, and the system comprises a typical layout sample learning module, an actual layout example detection module and a layout detection result display output module;

the typical sample learning device comprises four submodules of sample selection, learning parameter setting, sample learning and learning result warehousing, the layout example detection module comprises three submodules of example selection, example detection and inspection result warehousing, and the layout detection result display output module comprises three submodules of a layout detection list, layout problem positioning and layout problem exporting.

Further, the sample selection submodule is responsible for setting a selection range of the appointed sample learning;

the learning parameter setting submodule is responsible for setting an abnormal point out-of-limit threshold;

the sample learning submodule is responsible for analyzing the sample data one by one and learning typical layout characteristics of the sample;

and the learning achievement warehousing submodule is responsible for counting the probability of the occurrence of the typical characteristics of the sample and warehousing the learning achievement.

Further, the example selection submodule is responsible for selecting a detection range;

the instance detection submodule is responsible for detecting the difference value of the statistical probability of the instance data and the statistical probability of the learning base from the set abnormal point threshold crossing value according to the statistical probability data based on the learning result base, and if the statistical probability of the detection instance data at a certain detection point is greater than the statistical probability of the learning database, the detection instance data are considered to be abnormal data;

and the inspection result storage submodule writes the abnormal data into an abnormal detection result base.

Further, the layout detection list submodule is used for listing an abnormal single line diagram list and a specific layout problem list in an abnormal detection result library;

the layout problem positioning submodule is used for positioning a specific layout problem;

the layout problem derivation submodule is used for realizing derivation of the layout problem.

The invention has the beneficial effects that: the method and the device of the invention take the typical single line diagram as the blue book from the machine learning angle, judge the layout rationality of other newly added and adjusted single line diagrams through the machine learning, form a virtuous circle through the experience learning mode, guide the layout detection of the single line diagram, and have good popularization and application values.

Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the present invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:

FIG. 1 is a schematic flow diagram of the process of the present invention;

FIG. 2 is a schematic diagram of the device architecture of the present invention.

Detailed Description

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.

As shown in fig. 1, a single line diagram layout checking method based on a statistical outlier detection algorithm of the present invention includes:

step S1: learning a typical layout sample; specifically, typical layout learning is performed on a typical sample single line diagram one by one, and a layout statistical probability distribution result is written into a learning result library, and the method comprises the following steps:

setting a selection range of appointed sample learning;

setting a threshold value of abnormal point out-of-limit;

analyzing the sample data one by one, and learning typical layout characteristics of the sample;

and counting the probability of the occurrence of the typical characteristics of the sample, and warehousing the learning result.

Step S2: detecting a layout example; specifically, the example single line diagram is detected, the difference value between the statistical probability of the example data and the statistical probability of the learning library is detected from the set abnormal point threshold, and the abnormal data is written into the abnormal result library after being found. Further, the method specifically comprises the following steps:

selecting a detection range;

detecting a difference value between the statistical probability of the example data and the statistical probability of the learning database from a set abnormal point threshold according to the statistical probability data based on the learning result library, and if the statistical probability of the detection example data at a certain detection point is greater than the statistical probability of the learning database, determining the detection example data as abnormal data;

and writing the abnormal data into an abnormal detection result library.

Step S3: and displaying and outputting the layout detection result. Specifically, the detection result in the abnormal result library is detected, positioned and output. Further, the method specifically comprises the following steps:

listing an abnormal single line diagram list and a specific layout problem list in an abnormal detection result library;

positioning a specific layout problem;

the layout problem is derived.

As shown in fig. 2, based on the design idea of the above method, the present invention further provides a single line diagram layout inspection apparatus based on statistical outlier detection algorithm, which includes a typical layout sample learning module, an actual layout example detection module, and a layout detection result display output module; the properties and composition are detailed below:

(1) the typical sample learning module: comprises four sub-modules of sample selection, learning parameter setting, sample learning and learning result storage, wherein,

the sample selection submodule is responsible for setting a selection range of appointed sample learning;

the learning parameter setting submodule is responsible for setting an abnormal point out-of-limit threshold;

the sample learning submodule is responsible for analyzing the sample data one by one and learning typical layout characteristics of the sample;

and the learning achievement warehousing submodule is responsible for counting the probability of the occurrence of the typical characteristics of the sample and warehousing the learning achievement.

(2) Layout example detection module: comprises three submodules of instance selection, instance detection and inspection result warehousing, wherein,

the example selection sub-module is responsible for selecting a detection range;

the instance detection submodule is responsible for detecting the difference value of the statistical probability of the instance data and the statistical probability of the learning database from the set abnormal point threshold crossing value according to the statistical probability data based on the learning result database, and if the statistical probability of the detection instance data at a certain detection point is greater than the statistical probability of the learning database, the detection instance data is considered to be abnormal data;

and the inspection result storage submodule writes the abnormal data into an abnormal detection result library.

(3) The layout detection result display output module: the layout problem detection method comprises three submodules of layout detection list, layout problem positioning and layout problem derivation. Wherein the content of the first and second substances,

the layout detection list submodule is used for listing an abnormal single line diagram list and a specific layout problem list in the abnormal detection result library;

the layout problem positioning submodule is used for positioning a specific layout problem;

and the layout problem derivation submodule is used for realizing the derivation of the layout problem.

Any process or method descriptions in flow charts or otherwise herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, and the program may be stored in a computer readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (10)

1. A single line diagram layout checking method based on a statistical anomaly detection algorithm is characterized by comprising the following steps: the method comprises the following steps:

step S1: learning a typical layout sample;

step S2: detecting a layout example;

step S3: and displaying and outputting the layout detection result.

2. The single line diagram layout checking method based on the statistical outlier detection algorithm according to claim 1, wherein: step S1 is to perform typical layout learning on the typical sample single line diagrams one by one, and write the result of statistical probability distribution of layout into the learning result library, including:

setting a selection range of appointed sample learning;

setting a threshold value of abnormal point out-of-limit;

analyzing the sample data one by one, and learning typical layout characteristics of the sample;

and counting the probability of the occurrence of the typical characteristics of the sample, and warehousing the learning result.

3. The single line diagram layout checking method based on the statistical outlier detection algorithm according to claim 1, wherein: in step S2, the example single line diagram is detected, a difference between the statistical probability of the example data and the statistical probability of the learning base is detected from the set abnormal point threshold, and the abnormal data is written into the abnormal result base after being found.

4. The single line diagram layout checking method based on the statistical outlier detection algorithm according to claim 3, wherein: the step S2 specifically includes

Selecting a detection range;

detecting a difference value between the statistical probability of the example data and the statistical probability of the learning database from a set abnormal point threshold according to the statistical probability data based on the learning result library, and if the statistical probability of the detection example data at a certain detection point is greater than the statistical probability of the learning database, determining the detection example data as abnormal data;

and writing the abnormal data into an abnormal detection result library.

5. The single line diagram layout checking method based on the statistical outlier detection algorithm according to claim 1, wherein: in step S3, the detection result in the abnormal result library is detected, positioned, and output.

6. The single line diagram layout checking method based on the statistical outlier detection algorithm according to claim 5, wherein: step S3 specifically includes

Listing an abnormal single line diagram list and a specific layout problem list in an abnormal detection result library;

positioning a specific layout problem;

the layout problem is derived.

7. A single line diagram layout checking device based on a statistical anomaly detection algorithm is characterized in that: the device comprises a typical layout sample learning module, an actual layout sample detection module and a layout detection result display output module;

the typical sample learning device comprises four submodules of sample selection, learning parameter setting, sample learning and learning result warehousing, the layout example detection module comprises three submodules of example selection, example detection and inspection result warehousing, and the layout detection result display output module comprises three submodules of a layout detection list, layout problem positioning and layout problem exporting.

8. The single line diagram layout checking apparatus based on statistical outlier detection algorithm of claim 7, wherein: the sample selection submodule is responsible for setting a selection range of appointed sample learning;

the learning parameter setting submodule is responsible for setting an abnormal point out-of-limit threshold;

the sample learning submodule is responsible for analyzing the sample data one by one and learning typical layout characteristics of the sample;

and the learning achievement warehousing submodule is responsible for counting the probability of the occurrence of the typical characteristics of the sample and warehousing the learning achievement.

9. The single line diagram layout checking method based on the statistical outlier detection algorithm according to claim 7, wherein:

the example selection submodule is responsible for selecting a detection range;

the instance detection submodule is responsible for detecting the difference value of the statistical probability of the instance data and the statistical probability of the learning base from the set abnormal point threshold crossing value according to the statistical probability data based on the learning result base, and if the statistical probability of the detection instance data at a certain detection point is greater than the statistical probability of the learning database, the detection instance data are considered to be abnormal data;

and the inspection result storage submodule writes the abnormal data into an abnormal detection result base.

10. The single line diagram layout checking method based on the statistical outlier detection algorithm according to claim 7, wherein:

the layout detection list submodule is used for listing an abnormal single line diagram list and a specific layout problem list in an abnormal detection result library;

the layout problem positioning submodule is used for positioning a specific layout problem;

the layout problem derivation submodule is used for realizing derivation of the layout problem.

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