CN111079766A - Intelligent method for P & ID (peer-to-peer) graph - Google Patents
Intelligent method for P & ID (peer-to-peer) graph Download PDFInfo
- Publication number
- CN111079766A CN111079766A CN201911320281.8A CN201911320281A CN111079766A CN 111079766 A CN111079766 A CN 111079766A CN 201911320281 A CN201911320281 A CN 201911320281A CN 111079766 A CN111079766 A CN 111079766A
- Authority
- CN
- China
- Prior art keywords
- standard
- graphic
- attribute
- graph
- identifying
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 12
- 230000008569 process Effects 0.000 claims description 14
- 238000012549 training Methods 0.000 claims description 8
- 238000003909 pattern recognition Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 abstract description 26
- 238000012545 processing Methods 0.000 abstract description 2
- 238000013461 design Methods 0.000 description 5
- 238000002955 isolation Methods 0.000 description 4
- 238000007789 sealing Methods 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/14—Image acquisition
- G06V30/148—Segmentation of character regions
- G06V30/153—Segmentation of character regions using recognition of characters or words
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Multimedia (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Health & Medical Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Evolutionary Biology (AREA)
- Image Analysis (AREA)
Abstract
The invention relates to the technical field of graphic image processing, and discloses an intelligent method of a P & ID (peer-to-peer) graph, which comprises a graphic symbol and character code identification and attribute setting step, a connecting line identification and attribute setting step, a connection relation identification and attribute setting step, wherein the output ends of the graphic symbol and character code identification and attribute setting step, the connecting line identification and attribute setting step and the connecting line identification and attribute setting step are connected with the input ends of the connection relation identification and attribute setting step. The intelligent method of the P & ID diagram utilizes the convolutional neural network to identify the graphic symbols and the character codes on the P & ID diagram, leads all the graphic symbols to be provided with engineering attributes, establishes the physical and logical connection relationship between the devices, realizes the intelligence of the P & ID diagram, and can lay a foundation for the follow-up high-level intelligent explanation and analysis of the P & ID diagram, thereby greatly improving the application capability of the P & ID diagram in the actual engineering.
Description
Technical Field
The invention relates to the technical field of graphic image processing, in particular to an intelligent method for a P & ID (peer-to-peer) graph.
Background
In the field of petrochemical processes, P & ID diagrams are engineering drawings that use industry-standard-based graphical symbols and written codes to represent in detail all equipment, instruments, piping, valves, and connections and process flow relationships between them required by process plants. At present, the preservation forms of P & ID drawings mainly comprise: the vector drawing method comprises an image or PDF format file formed by scanning a manually drawn paper drawing and a vector drawing file drawn by CAD software.
The P & ID diagram can be repeatedly inquired and used in the works of design, construction, operation, maintenance, device transformation, safety and environmental protection management and the like of engineering projects. A substantial portion of which may be modified and re-used in the design and manufacture of other similar items at a later time. However, the P & ID map has problems such as the following in actual use:
for example, when a field device is technically modified or a new project is designed and developed, the existing design intent is known by referring to the existing drawing to a great extent, and the original design drawing is modified and reused to shorten the design period. However, for the drawings with the PDF or image format, the modification difficulty is large, and the query, the update and the reuse are inconvenient.
In addition, when the process equipment is periodically maintained, energy isolation work must be performed on each system before maintenance. At this time, the isolation range needs to be determined according to information such as connection between each device and process flow relation on the P & ID diagram, which parts need to be inserted with blind plates and which devices need to be locked or listed are marked on the P & ID diagram, and an energy isolation ledger is established.
Furthermore, according to the requirements of the ministry of environmental protection, when a process device performs leak detection and repair (LDAR), a P & ID diagram needs to be consulted, and a sealing point ledger including information such as the number of equipment, pipe components, and sealing points needs to be established.
Due to the complex process and numerous equipment of the petrochemical project, the number of P & ID drawings related to the whole project is huge. The various graphic symbols on the P & ID drawing of the existing file format are only used for geometrically representing various devices corresponding to the process equipment, and do not provide the engineering attributes applicable to the actual engineering; the graphical symbols, i.e. the connection relations between the devices are represented by the geometrical relations between the devices, and the physical and logical connection relations between the devices cannot be identified, and the P & ID diagram cannot be subsequently interpreted and analyzed with high-level intelligence. Therefore, when the P & ID diagram is applied to the above-mentioned actual engineering, the information such as the connection relation of the equipment, the isolation point, the sealing point, etc. is confirmed on the P & ID diagram all by manpower, which is very large in workload, low in efficiency and easy to make mistakes.
Disclosure of Invention
Technical problem to be solved
In order to solve the technical problem, the P & ID diagram must be intelligentized, so that the computer can acquire semantic information from the P & ID diagram, bring attributes to each graphic symbol on the P & ID diagram, understand each graphic symbol on the P & ID diagram and the connection relationship between the graphic symbols on the P & ID diagram in physical and logical aspects, and lay a foundation for subsequent high-level intelligent interpretation and analysis of the P & ID diagram. However, there is no effective technical method for solving such problems.
(II) technical scheme
In order to achieve the purpose, the invention provides the following technical scheme: an intelligent method for P & ID diagram includes identifying code number of graphics symbol and character and setting its attribute, identifying connection line and setting its attribute, identifying connection relation and setting its attribute.
As a further aspect of the present invention,
s10, identifying the graph symbols on the graph surface by adopting a graph identification module based on a convolutional neural network for the input P & ID graph;
s11, adding corresponding attribute records to the graphic symbols identified in S10 in a database, wherein the attribute records are used for recording the attributes of the graphic symbols, and the attributes comprise information such as types, subclasses, sequence numbers, equipment position numbers and the like of the corresponding graphic symbols;
s12, calculating the center coordinates of the recognized graphic symbol in S10 to determine the position of the graphic symbol on the drawing surface, and recording the center coordinates in the attribute of the graphic symbol;
s13, recognizing the character code marked in the inner part or the nearby part of the graphic symbol area by the graphic symbol recognized by S10 by adopting a character recognition module based on a convolutional neural network, and recording the recognized character code information in the attribute of the graphic symbol;
s14, matching the graphic symbols recognized in S10 with standard graphics in a standard graphic library by a graphic matching module based on a graphic feature matching method, finding out standard graphics matched with the graphic symbols, copying standard attributes of the standard graphics into the attributes of the graphic symbols, and determining the categories and subclasses to which the graphic symbols belong according to category and subclass information contained in the standard attributes;
at S15, since there are a plurality of subclasses having the same symbols for the instrument type symbols obtained at S10 and S14, the subclass of the instrument type symbol cannot be determined by the pattern matching module at S14. Therefore, it is necessary to further perform character matching between the character code information containing the instrument class subclass information recognized in S13 and the standard attribute information of each standard subclass of the instrument class in the standard graphics library, find the standard subclass matching therewith, thereby determining the subclass of the instrument class graphics symbol, and copy the standard attribute of the standard subclass to the attribute of the subclass of the instrument class graphics symbol;
and S16, repeating the steps to complete the identification of all the graphic symbols and the relevant character codes on all the drawings in the process device, and enabling each graphic symbol to be in one-to-one correspondence with the attribute records in the database.
As a further scheme of the invention, the pattern recognition module is obtained by constructing a convolutional neural network in advance under a TensorFlow framework and training by using a large number of existing pattern symbols on P & ID pictures of various paper versions and CAD versions as a training set.
As a further scheme of the invention, the standard graph library is constructed in advance, the standard graphs stored in the standard graph library are created according to the specification of the national petrochemical industry standard SH/T3101-2017 on the P & ID graph, the standard graphs are classified according to the categories of equipment, instruments, valves and the like, each standard graph of the same category is further divided into a plurality of standard subclasses according to the local tiny difference of the graph, corresponding standard attributes are predefined for the standard graphs, and the standard attributes comprise the type, the subclass and other engineering information to which the corresponding standard graph belongs.
As a further scheme of the present invention, S20, identifying the connecting lines, i.e. the pipes or the signal lines, on the drawing of the input P & ID diagram by using a connecting line identification module based on a contour tracing algorithm;
s21, adding corresponding attribute records for the connecting lines identified in the S20 in a database, wherein the attribute records are used for recording the attributes of the connecting lines;
s22, calculating the center coordinates and the coordinates of the two end points of the connecting line recognized by the S20, and recording the coordinate values of the center point and the two end points of the connecting line in the attributes of the connecting line;
s23, repeating S20 to S22, and identifying all connecting lines on all drawings in the process device, wherein each connecting line corresponds to the attribute records in the database one by one.
As a further aspect of the present invention, the connection relationship identifying step is to determine, by using a connection relationship identifying module, whether an end point of the connection line is located inside or on a boundary of an edge region of a certain graphic symbol, or on an end point of another connection line, for the connection line identified in S20, a connection relationship between the connection line and the graphic symbol or the another connection line, and record the connection relationship information in the attributes of the connection line and the graphic symbol.
(III) advantageous effects
Compared with the prior art, the invention has the following beneficial effects:
the invention utilizes the convolutional neural network to identify the graphic symbols and the character codes on the P & ID diagram, leads all the graphic symbols to be provided with engineering attributes, establishes the physical and logical connection relationship between the devices, realizes the intellectualization of the P & ID diagram, and can lay a foundation for the follow-up advanced intelligent explanation and analysis of the P & ID diagram, thereby greatly improving the application capability of the P & ID diagram in the actual engineering.
Drawings
FIG. 1 is a flow chart of an implementation of an embodiment of the present invention;
FIG. 2 is an example of the categories and subclasses to which the graphic symbols of the present invention belong;
FIG. 3 shows a plurality of subclasses of the instrument class of the present invention having identical graphic symbols.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1, an intelligent method for a P & ID diagram includes a step of identifying a graphic symbol and a character code and setting an attribute thereof, a step of identifying a connection line and setting an attribute thereof, and a step of judging a connection relationship; the identification of the graphic symbols and the character codes and the setting of the attributes of the graphic symbols and the character codes can be completed by the steps of intelligently identifying the graphic symbols and the character codes on the input P & ID picture and recording the attributes of the graphic symbols in a database; the connecting line identification and attribute setting step can finish intelligent identification of the connecting line on the input P & ID picture and record the attribute of the connecting line in a database; the connection relation judging step can finish the judgment of the physical and logical connection relation between the connecting line and the graphic symbol, and record the connection relation information between the connecting line and the graphic symbol in a database. And completing the intelligentization and output of the P & ID graph through the steps.
Example 2
Based on example 1, as shown in figures 1-3,
s10, identifying the graph symbols on the graph surface by adopting a graph identification module based on a convolutional neural network for the input P & ID graph;
the pattern recognition module is obtained by constructing a convolutional neural network in advance under a TensorFlow framework and training by using a large number of existing pattern symbols on P & ID pictures of various paper versions and CAD versions as a training set.
And S11, adding corresponding attribute records to the graphic symbols identified in S10 in a database, wherein the attribute records are used for recording the attributes of the graphic symbols, and the attributes must contain the types, subclasses and sequence numbers to which the graphic symbols belong. The serial number is the sequential number of the graphic symbol and is a number automatically generated by the system. The sequence number must be unique and non-repeatable and used to identify the correspondence of the graphical symbol to the attributes of the graphical symbol in the database. In addition, other information such as engineering attributes and the like can be flexibly defined according to engineering requirements;
s12, calculating the center coordinates of the recognized graphic symbol in S10 to determine the position of the graphic symbol on the drawing surface, and recording the center coordinates in the attribute of the graphic symbol;
and S13, carrying out character recognition on the character code marked in the inner part or the vicinity of the graphic symbol area by the graphic symbol recognized by the S10 by adopting a character recognition module based on a convolutional neural network, and recording the recognized character code information in the attribute of the graphic symbol. The literal code contains engineering information such as equipment type and equipment position number representing the graphic symbol. Particularly for the instrument class, the character code also contains subclass information representing the instrument class;
the character recognition module is obtained by constructing a convolutional neural network in advance under a TensorFlow framework and training by using a large number of character codes on P & ID pictures of various existing paper versions and CAD versions as a training set.
And S14, matching the graphic symbols recognized in the S10 with standard graphics in a standard graphic library by adopting a graphic matching module based on a graphic feature matching method, finding out standard graphics matched with the graphic symbols, copying standard attributes of the standard graphics into the attributes of the graphic symbols, and determining the categories and subclasses to which the graphic symbols belong according to category and subclass information contained in the standard attributes. FIG. 2 is an example of categories and subclasses to which graphical symbols belong;
wherein the standard graphic library is pre-constructed. The standard graphs stored in the standard graph library are created according to the specification of the national petrochemical industry standard SH/T3101-2017 on the P & ID graphs, the standard graphs are classified according to the categories of equipment, instruments, valves and the like, and each standard graph of the same category is further divided into a plurality of standard subclasses according to local tiny difference of the graph. Corresponding standard attributes are predefined for the standard graph. The standard attribute contains the type, subclass and other engineering information of the corresponding standard graph.
At S15, since there are a plurality of subclasses having the same symbols as those of the instrument type symbols obtained at S10 and S14, for example, as shown in fig. 3, the subclass of the instrument type symbol cannot be specified by the pattern matching module at S14. Therefore, it is necessary to further character-match the character code information containing the instrument class subclass information recognized at S13 with the standard attribute information of each standard subclass of the instrument class in the standard graphics library, find the standard subclass matching therewith, thereby determining the subclass of the instrument class graphics symbol, and copy the standard attribute of the standard subclass to the attribute of the subclass of the instrument class graphics symbol. In FIG. 3, TI, FI, LI, and PI denote a thermometer, a flowmeter, a liquid level gauge, and a pressure gauge, respectively;
and S16, repeating the steps to complete the identification of all the graphic symbols and the relevant character codes on all the drawings in the process device, and enabling each graphic symbol to be in one-to-one correspondence with the attribute records in the database.
Example 3
Based on example 1, as shown in figure 1,
s20, identifying the connecting lines on the picture of the input P & ID picture, namely pipelines or signal lines, by adopting a connecting line identification module based on a contour tracing algorithm;
s21, adding corresponding attribute records for the connecting lines identified in the S20 in a database, wherein the attribute records are used for recording the attributes of the connecting lines;
s22, calculating the center coordinates and the coordinates of the two end points of the connecting line recognized by the S20, and recording the coordinate values of the center point and the two end points of the connecting line in the attributes of the connecting line;
s23, repeating S20 to S22, and identifying all connecting lines on all drawings in the process device, wherein each connecting line corresponds to the attribute records in the database one by one.
Further, in the connection relationship identifying step, for the connection line identified in S20, the connection relationship identifying module is used to determine whether the end point of the connection line is located inside or on the boundary of an edge region of a certain graphic symbol, or is located on the end point of another connection line, so as to determine the connection relationship between the connection line and the graphic symbol or another connection line, and record the connection relationship information in the attributes of the connection line and the graphic symbol.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. An intelligent method of a P & ID graph is characterized in that:
the method comprises the steps of identifying the code number of the graph and the character and setting the attribute of the graph and the character, identifying the connecting line and setting the attribute of the connecting line, identifying the connecting relation and setting the attribute of the connecting line, wherein the output ends of the steps of identifying the code number of the graph and the character and setting the attribute of the connecting line and identifying the connecting relation and setting the attribute of the connecting line are connected with the input ends of the steps of identifying the connecting relation and setting the attribute of the connecting.
2. The intelligent method of a P & ID graph according to claim 1, characterized in that: the steps of identifying the code number of the graphic symbol and the character and setting the attribute of the graphic symbol and the character comprise the following steps:
s10, identifying the graph symbols on the graph surface by adopting a graph identification module based on a convolutional neural network for the input P & ID graph;
s11, adding corresponding attribute records to the graphic symbols identified in S10 in a database, wherein the attribute records are used for recording the attributes of the graphic symbols, and the attributes comprise information such as types, subclasses, sequence numbers, equipment position numbers and the like of the corresponding graphic symbols;
s12, calculating the center coordinates of the recognized graphic symbol in S10 to determine the position of the graphic symbol on the drawing surface, and recording the center coordinates in the attribute of the graphic symbol;
s13, recognizing the character code marked in the inner part or the nearby part of the graphic symbol area by the graphic symbol recognized by S10 by adopting a character recognition module based on a convolutional neural network, and recording the recognized character code information in the attribute of the graphic symbol;
s14, matching the graphic symbols recognized in S10 with standard graphics in a standard graphic library by a graphic matching module based on a graphic feature matching method, finding out standard graphics matched with the graphic symbols, copying standard attributes of the standard graphics into the attributes of the graphic symbols, and determining the categories and subclasses to which the graphic symbols belong according to category and subclass information contained in the standard attributes;
s15, since there are a plurality of subclasses having the same symbols for the instrument type symbols obtained in S10 and S14, the subclass of the instrument type symbol cannot be determined by the pattern matching module in S14;
therefore, it is necessary to further perform character matching between the character code information containing the instrument class subclass information recognized in S13 and the standard attribute information of each standard subclass of the instrument class in the standard graphics library, find the standard subclass matching therewith, thereby determining the subclass of the instrument class graphics symbol, and copy the standard attribute of the standard subclass to the attribute of the subclass of the instrument class graphics symbol;
and S16, repeating the steps to complete the identification of all the graphic symbols and the relevant character codes on all the drawings in the process device, and enabling each graphic symbol to be in one-to-one correspondence with the attribute records in the database.
3. The intelligent method of a P & ID graph according to claim 2, characterized in that: the pattern recognition module is obtained by constructing a convolutional neural network in advance under a TensorFlow framework and training by taking a large number of existing pattern symbols on P & ID pictures of various paper versions and CAD versions as a training set.
4. The intelligent method of a P & ID graph according to claim 2, characterized in that: the standard graph library is constructed in advance, the standard graphs stored in the standard graph library are created according to the specification of the national petrochemical industry standard SH/T3101-2017 on a P & ID graph, the standard graphs are classified according to the classes of equipment, instruments, valves and the like, each standard graph in the same class is further divided into a plurality of standard subclasses according to local small graph differences, corresponding standard attributes are predefined for the standard graphs, and the standard attributes contain types, subclasses and other engineering information to which the corresponding standard graphs belong.
5. The intelligent method of a P & ID graph according to claim 2, characterized in that: the connecting line identification and attribute setting step comprises the following steps:
s20, identifying the connecting lines on the picture of the input P & ID picture, namely pipelines or signal lines, by adopting a connecting line identification module based on a contour tracing algorithm;
s21, adding corresponding attribute records for the connecting lines identified in the S20 in a database, wherein the attribute records are used for recording the attributes of the connecting lines;
s22, calculating the center coordinates and the coordinates of the two end points of the connecting line recognized by the S20, and recording the coordinate values of the center point and the two end points of the connecting line in the attributes of the connecting line;
s23, repeating S20 to S22, and identifying all connecting lines on all drawings in the process device, wherein each connecting line corresponds to the attribute records in the database one by one.
6. An intelligent method for P & ID graph, according to claims 1 and 5, characterized by that: in the connection relation identifying step, for the connection line identified in S20, a connection relation identifying module is used to determine whether an end point of the connection line is located inside or on a boundary of an edge region of a graphic symbol, or is located on an end point of another connection line, thereby determining a connection relation between the connection line and the graphic symbol or the another connection line, and recording the connection relation information in the connection line and the attribute of the graphic symbol.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911320281.8A CN111079766A (en) | 2019-12-19 | 2019-12-19 | Intelligent method for P & ID (peer-to-peer) graph |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911320281.8A CN111079766A (en) | 2019-12-19 | 2019-12-19 | Intelligent method for P & ID (peer-to-peer) graph |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111079766A true CN111079766A (en) | 2020-04-28 |
Family
ID=70315940
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911320281.8A Pending CN111079766A (en) | 2019-12-19 | 2019-12-19 | Intelligent method for P & ID (peer-to-peer) graph |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111079766A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111798649A (en) * | 2020-06-23 | 2020-10-20 | 深圳市富思源智慧消防股份有限公司 | Fire-fighting component numbering method and system for automatic fire alarm plan, intelligent terminal and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101030258A (en) * | 2006-02-28 | 2007-09-05 | 浙江工业大学 | Dynamic character discriminating method of digital instrument based on BP nerve network |
| CN106599375A (en) * | 2016-11-21 | 2017-04-26 | 北京中科辅龙科技股份有限公司 | P&ID-based seal point automatic identification system and method |
| CN107832765A (en) * | 2017-09-13 | 2018-03-23 | 百度在线网络技术（北京）有限公司 | Picture recognition to including word content and picture material |
| CN109389050A (en) * | 2018-09-19 | 2019-02-26 | 陕西科技大学 | A kind of flow chart connection relationship recognition methods |
| CN109446689A (en) * | 2018-11-07 | 2019-03-08 | 国网江苏省电力有限公司电力科学研究院 | DC converter station electrical secondary system drawing recognition methods and system |
| CN109858409A (en) * | 2019-01-18 | 2019-06-07 | 深圳壹账通智能科技有限公司 | Manual figure conversion method, device, equipment and medium |
-
2019
- 2019-12-19 CN CN201911320281.8A patent/CN111079766A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101030258A (en) * | 2006-02-28 | 2007-09-05 | 浙江工业大学 | Dynamic character discriminating method of digital instrument based on BP nerve network |
| CN106599375A (en) * | 2016-11-21 | 2017-04-26 | 北京中科辅龙科技股份有限公司 | P&ID-based seal point automatic identification system and method |
| CN107832765A (en) * | 2017-09-13 | 2018-03-23 | 百度在线网络技术（北京）有限公司 | Picture recognition to including word content and picture material |
| CN109389050A (en) * | 2018-09-19 | 2019-02-26 | 陕西科技大学 | A kind of flow chart connection relationship recognition methods |
| CN109446689A (en) * | 2018-11-07 | 2019-03-08 | 国网江苏省电力有限公司电力科学研究院 | DC converter station electrical secondary system drawing recognition methods and system |
| CN109858409A (en) * | 2019-01-18 | 2019-06-07 | 深圳壹账通智能科技有限公司 | Manual figure conversion method, device, equipment and medium |
Non-Patent Citations (3)
| Title |
|---|
| MOHAMAD FAIZAL AB. JABAL: "A Comparative Study on Extraction and Recognition Method of CAD Data from CAD Drawings" * |
| 凌元锦: "化工过程安全智能技术系统集成" * |
| 张琪: "基于对象图例及其拓扑关系识别的二维工程CAD图纸矢量化方法" * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111798649A (en) * | 2020-06-23 | 2020-10-20 | 深圳市富思源智慧消防股份有限公司 | Fire-fighting component numbering method and system for automatic fire alarm plan, intelligent terminal and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2019202743A1 (en) | Systems and methods for automating information extraction from piping and instrumentation diagrams | |
| US9916398B2 (en) | Laser scan re-engineering of 3D CAD models | |
| CN109934227A (en) | System for recognizing characters from image and method | |
| CN110704880B (en) | Correlation method of engineering drawings | |
| CN101719127A (en) | Quick systemic checking method of data quality of geological and mineral spatial database | |
| CN115510525A (en) | Automatic labeling method and system for pipeline three-dimensional building information model | |
| CN111079766A (en) | Intelligent method for P & ID (peer-to-peer) graph | |
| Agapaki et al. | CLOI: A shape classification benchmark dataset for industrial facilities | |
| CN110619128B (en) | Construction method of digital factory | |
| JP2006236299A (en) | Integrated knowledge based system | |
| Edwards et al. | Digital twin development through auto-linking to manage legacy assets in nuclear power plants | |
| CN115171145A (en) | Drawing primitive processing method and device | |
| KR102236625B1 (en) | Apparatus and method for generating training data for deep learning to recognize the design information of symbol-based engineering drawings | |
| CN115265923A (en) | Leakage detection method and device and terminal | |
| Vasin et al. | Increasing the effectiveness of intelligent information technology for producing digital graphic documents with weakly formalized description of objects | |
| Vasin et al. | Geometric modeling of raster images of documents with weakly formalized description of objects | |
| CN111583383A (en) | Three-dimensional visual auxiliary method for high-pressure container inspection | |
| Yang et al. | Design and Implementation of License Plate Recognition System Based on Android | |
| CN108920749B (en) | Pipeline two-dimensional and three-dimensional data updating method and device and computer readable storage medium | |
| Villena Toro et al. | Automated and customized cad drawings by utilizing machine learning algorithms: A case study | |
| CN110853016A (en) | Automatic checking method and device for topographic map publishing quality | |
| CN110929060B (en) | Storage, refinery sealing point account generation and management method and device | |
| Kang et al. | Feature Template–Based Parametric Swept Geometry Generation from Point Cloud Data: Pipeline Design in Building Systems | |
| US20240152661A1 (en) | Systems, methods, and media for automatically transforming textual data, representing an image, into p&id components | |
| Song et al. | Discovering geometric theorems from scanned and photographed images of diagrams |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200428 |
|
| WD01 | Invention patent application deemed withdrawn after publication |