CN112199464A - System for constructing binary fault tree diagnosis knowledge base - Google Patents
System for constructing binary fault tree diagnosis knowledge base Download PDFInfo
- Publication number
- CN112199464A CN112199464A CN202011180396.4A CN202011180396A CN112199464A CN 112199464 A CN112199464 A CN 112199464A CN 202011180396 A CN202011180396 A CN 202011180396A CN 112199464 A CN112199464 A CN 112199464A
- Authority
- CN
- China
- Prior art keywords
- fault tree
- binary
- branch
- data
- event
- 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
- 238000003745 diagnosis Methods 0.000 title abstract description 25
- 238000003860 storage Methods 0.000 claims abstract description 32
- 238000000034 method Methods 0.000 claims abstract description 24
- 238000004458 analytical method Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims abstract description 3
- 238000004519 manufacturing process Methods 0.000 claims description 25
- 238000001514 detection method Methods 0.000 claims description 12
- 238000007726 management method Methods 0.000 claims description 11
- 230000003993 interaction Effects 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 238000009826 distribution Methods 0.000 claims description 4
- 230000003213 activating effect Effects 0.000 claims description 2
- 238000011058 failure modes and effects analysis Methods 0.000 claims 2
- 238000013500 data storage Methods 0.000 abstract description 2
- 230000004048 modification Effects 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000009411 base construction Methods 0.000 description 3
- 238000007792 addition Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000037430 deletion Effects 0.000 description 2
- 238000012217 deletion Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 241000282414 Homo sapiens Species 0.000 description 1
- 230000001364 causal effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/31—Indexing; Data structures therefor; Storage structures
- G06F16/316—Indexing structures
- G06F16/322—Trees
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Test And Diagnosis Of Digital Computers (AREA)
Abstract
The invention discloses a method for constructing a binary fault tree diagnosis knowledge base, belongs to the technical field of knowledge data storage methods, and solves the technical problem that the prior art realizes the convenient storage and automatic acquisition of knowledge in a fault diagnosis expert system. The method comprises the following steps: s101, acquiring original data in a knowledge base; s102, determining multi-branch tree storage data, wherein the multi-branch tree storage data is determined by performing fault analysis algorithm processing according to the original data; and S103, determining binary tree storage data, which is determined according to the multi-branch tree storage data. The invention is used for improving the functions of the vector hydrophone and reducing the cost.
Description
Technical Field
The invention belongs to the technical field of knowledge data storage systems, and relates to a system for constructing a binary fault tree diagnosis knowledge base.
Background
Knowledge is the understanding of human beings on objective things and the laws thereof in practice, and the knowledge is used as a main component of a knowledge base and plays an important role in a fault maintenance and diagnosis system.
A Fault Tree Analysis (FTA) in the existing method is a common effective method for equipment Fault diagnosis. When system-level fault analysis is performed, a large number of system components and a complex fault propagation mechanism are required, and a large number of fault trees are often drawn and stored.
Although the graphical fault tree in the existing method can intuitively and clearly reflect the propagation mode of the fault and the causal relationship between fault events, a large amount of storage space is occupied during storage, and the complex logical relationship among nodes easily causes the problems of data redundancy and rule conflict in the reasoning process. The method realizes the convenient storage and automatic acquisition of knowledge in the fault diagnosis expert system.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
The invention aims to provide a method for constructing a binary fault tree diagnosis knowledge base, which solves the technical problem that the prior art realizes the convenient storage and automatic acquisition of knowledge in a fault diagnosis expert system. The technical scheme of the scheme has a plurality of technical beneficial effects, which are described as follows:
the present disclosure provides a system for constructing a binary fault tree diagnosis knowledge base, the system comprising:
a production rule automatic generation module: the fault tree creating module is used for creating a multi-branch fault tree according to the acquired FMEA, determining a binary fault tree according to the multi-branch fault tree and linking or storing data according to the binary fault tree;
a production rule management module: adding, deleting, modifying and searching operations for the linked or stored data of the binary fault tree;
a rule redundancy and anti-cycle detection module: ensure the integrity and consistency of the rule base and avoid redundancy and dead cycle
And (3) storage of a knowledge base: the system is used for carrying out ID distribution and storage on the binary fault tree link or storage data;
a human-computer interaction interface: the graphical interface enables human-computer interaction to perform operations of the production rule management module.
In order to solve the problems, the method combines the FTA and the Fault Mode with the theory of Fault Mode and Effect Analysis (FMEA), establishes an accurate, comprehensive and clear multi-branch Fault tree, effectively converts the multi-branch Fault tree into a binary Fault tree, realizes portable storage of the Fault tree and automatic generation of production rule knowledge, and improves the diagnosis efficiency of a Fault diagnosis expert system.
The diagnostic knowledge base construction method based on the binary fault tree comprises the following steps:
(1) establishing a multi-branch fault tree based on FMEA;
(2) converting the multi-branch fault tree into a binary fault tree;
(3) and automatically generating a production rule from the binary fault tree and performing knowledge redundancy and anti-circulation detection on the knowledge base.
Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects:
because the binary fault tree has the characteristic of closeness, the rules are closely related to each other, and the knowledge data is conveniently stored and automatically acquired. When the management operations such as rule addition, deletion, modification and the like are performed on the knowledge base, in order to avoid redundancy and conflict and ensure the integrity and consistency of the rules, knowledge redundancy detection needs to be performed on the knowledge base. Meanwhile, in order to prevent the occurrence of dead cycle of inference in the inference machine of the expert system during fault diagnosis, which leads to the failure of rapid diagnosis conclusion, anti-cycle detection is required.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a diagram of a diagnostic knowledge base;
FIG. 2 is a flow chart of a diagnostic knowledge base construction method based on a binary fault tree;
FIG. 3 is a schematic diagram of a multi-pronged fault tree;
FIG. 4 is a schematic diagram of a transformed binary fault tree;
FIG. 5 is a production rule expression;
FIG. 6 is a diagram of a binary fault tree chain storage structure.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. 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.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than the number, shape and size of the components in practical implementation, and the type, quantity and proportion of the components in practical implementation can be changed freely, and the layout of the components can be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that aspects may be practiced without these specific details. In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments. The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
Watch 1
The first table is a stored table of knowledge, and records problems and maintenance methods corresponding to format conditions, and the traditional method is very troublesome in data calling, and is inconvenient in automatic acquisition and the knowledge cannot be stored conveniently.
A system for building a binary fault tree diagnostic knowledge base as shown in fig. 1, the system comprising:
a production rule automatic generation module: the fault tree creating method is used for creating a multi-branch fault tree according to the acquired FMEA, determining a binary fault tree according to the multi-branch fault tree and carrying out data linkage or storage according to the binary fault tree. FMEA is used to analyze each latent failure mode in a product and to classify each latent failure mode by its severity, and the possible impact it may have on the product. Items of the FMEA analysis include "failure mode", "failure cause", "failure influence", "detection method", "failure rate", and the like. Based on the 'fault mode', 'fault reason' and 'fault influence' in the FMEA, a multi-branch fault tree is established in an auxiliary mode, and objectivity and rationality of the fault tree are enhanced;
a production rule management module: adding, deleting, modifying and searching operations for the linked or stored data of the binary fault tree;
a rule redundancy and anti-cycle detection module: ensure the integrity and consistency of the rule base and avoid redundancy and dead cycle
And (3) storage of a knowledge base: the system is used for carrying out ID distribution and storage on the binary fault tree link or storage data;
a human-computer interaction interface: the graphical interface enables human-computer interaction to perform operations of the production rule management module.
As a specific implementation manner provided in the present application, the production rule automatic generation module is further configured to determine the multi-branch fault tree through a fault analysis algorithm using the obtained FMEA data, and the specific method includes:
s301, storing the top event in the multi-branch fault tree as the root node of the binary fault tree;
s302, starting from the top event of the multi-branch fault tree, inquiring downwards layer by layer, adding a first event in the current search layer of the multi-branch fault tree as a left branch node of a corresponding result event in the binary fault tree, and activating the left branch node as the current node of the binary fault tree;
s303, searching the rest events in the same layer in the multi-branch fault tree one by one,
if the intermediate event data and the basic event data exist in the same layer of event at the same time, processing the intermediate event first;
if the logical relationship between the events at the same layer in the multi-branch fault tree is 'OR', the event is stored as the right branch node of the current node in the binary fault tree, and if the logical relationship is 'AND', the event is stored as the left branch node of the current node in the binary fault tree, and the newly added branch node is activated as the current node of the binary fault tree;
and S304, repeating the methods of S303 and S304 until all fault tree nodes complete conversion, and determining the binary fault tree link or storage data.
The binary fault tree completes the logical relation of AND and OR of the multiple-branch fault tree through the positions of the left and right branch nodes, and can complete the AND and OR operation of the nodes by judging whether the current fault node belongs to the left and right positions of the binary fault tree during fault diagnosis, so that the redundancy and conflict of knowledge are effectively reduced.
The acquisition of knowledge is a key link of the fault diagnosis expert system. At present, in a fault diagnosis expert system, the acquisition of knowledge is mainly based on expert experience and manual input. The method specifically comprises the following steps:
(1) starting from a root node of the binary fault tree, and expanding search in a mode of firstly 'left' and then 'right' from top to bottom;
(2) if the current node of the binary fault tree has a left branch node, taking an intermediate event represented by the current node as a rule front piece, taking an intermediate event or a basic event represented by the left branch node as a rule back piece, automatically forming a rule, and storing the rule in a knowledge base;
(3) if the current node of the binary fault tree has a right branch node, taking the logical negation of the intermediate event represented by the current node as a rule front piece, taking the intermediate event or basic event represented by the right branch node as a rule back piece, automatically forming a rule, and storing the rule in a knowledge base;
(4) if the current node of the binary fault tree has no branch node, the search is terminated, and the automatic extraction of the rule knowledge is completed.
Because the binary fault tree has the characteristic of closeness, the rules are closely related to each other, and when management operations such as rule addition, deletion and modification are performed on the knowledge base, the knowledge redundancy detection needs to be performed on the knowledge base in order to avoid redundancy and conflict and ensure the integrity and consistency of the rules. Meanwhile, in order to prevent the occurrence of dead cycle of inference in the inference machine of the expert system during fault diagnosis, which leads to the failure of rapid diagnosis conclusion, anti-cycle detection is required.
In the embodiment S304 provided in this application, the data converted by all the fault tree nodes determines the binary fault tree linking or storage data according to a conditional function, and the production rule automatic generation module is further configured to link the basic event data as the last level data to the corresponding intermediate event data root node after all the intermediate event data are linked and stored.
The working principle is as follows:
in fig. 1, in order to improve maintainability of the diagnosis knowledge base and facilitate management by a knowledge engineer, the diagnosis knowledge base is divided into five modules, namely a production rule automatic generation module, a production rule management module, a rule redundancy and anti-circulation detection module, a knowledge base storage module, a human-computer interaction interface and the like.
The automatic generation module of the production rule comprises three submodules, namely multi-branch fault tree establishment, binary fault tree conversion and automatic rule generation, and is used for realizing the establishment and simplification of the fault tree and automatically generating the production rule according to the binary fault tree; the production rule management module comprises the operations of adding, deleting, modifying and searching the rule and is used for realizing the manual intervention on the rule base; the rule redundancy and anti-circulation detection module is used for ensuring the integrity and consistency of the rule base and avoiding redundancy and dead circulation; the knowledge base storage is used for carrying out ID distribution and storage on the formed rule; the human-computer interaction interface mainly utilizes a good graphical interface to realize human-computer interaction operation.
Taking the construction of a diagnosis knowledge base of a certain weapon launching and controlling device as an example, table 1 shows FMEA when the normal weapon launching and controlling function is lost. According to the diagnostic knowledge base construction method flow shown in fig. 2, firstly, a multi-branch fault tree is established in an auxiliary manner based on the 'fault mode', 'fault cause' and 'fault influence' in the FMEA, as shown in fig. 3. Then, the multi-branch fault tree provided by the present application is converted into a binary fault tree quickly, as shown in fig. 4.
The complex corresponding relation between the nodes of the multi-branch fault tree is simplified through the structure of the binary fault tree, and the browsing is simple and clear. Because the binary fault tree only has the left and right branch nodes, unlike the multi-branch fault tree which has a plurality of branch nodes, when the knowledge base is established, only the current node and the information of the left and right nodes thereof need to be stored, and a large amount of storage space can be saved.
The binary fault tree can clearly show various fault reasons causing the fault top event, but the fault tree must be stored in a knowledge base for convenient application, so that the storage of the fault tree is important. Since the binary tree is a special non-linear data structure, the binary tree is stored by adopting a one-way chained storage structure, and the storage structure is shown in fig. 6.
The representation of the knowledge is to enable a computer to identify a representation form of the knowledge, and a generative rule representation is selected to represent the knowledge by combining the characteristics of the binary fault tree. The production rule representation method shows knowledge through an IF-THEN structure mode and is widely applied to fault diagnosis systems. The rules shown in fig. 5 are obtained by using the method for automatically generating the production rule from the binary fault tree proposed in the present application. And forming a diagnosis knowledge base by performing knowledge redundancy and anti-circulation detection on the knowledge base.
Finally, it should be noted that the above examples are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above examples, it should be understood by those skilled in the art that the present invention can be modified or replaced with equivalents without departing from the spirit and scope of the present invention, which should be covered by the claims of the present invention.
The products provided by the present invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the core concepts of the present invention. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the invention without departing from the inventive concept, and those improvements and modifications also fall within the scope of the claims of the invention.
Claims (5)
1. A system for constructing a binary fault tree diagnostic knowledge base, the system comprising:
a production rule automatic generation module: the fault tree creating module is used for creating a multi-branch fault tree according to the acquired FMEA, determining a binary fault tree according to the multi-branch fault tree and linking or storing data according to the binary fault tree;
a production rule management module: adding, deleting, modifying and searching operations for the linked or stored data of the binary fault tree;
a rule redundancy and anti-cycle detection module: ensure the integrity and consistency of the rule base and avoid redundancy and dead cycle
And (3) storage of a knowledge base: the system is used for carrying out ID distribution and storage on the binary fault tree link or storage data;
a human-computer interaction interface: the graphical interface enables human-computer interaction to perform operations of the production rule management module.
2. The system of claim 1, wherein the production rule automatic generation module is further configured to:
and determining the multi-branch fault tree by the acquired FMEA data through a fault analysis algorithm.
3. The system of claim 1, wherein the production rule automatic generation module is further configured to:
s301, storing the top event in the multi-branch fault tree as the root node of the binary fault tree;
s302, starting from the top event of the multi-branch fault tree, inquiring downwards layer by layer, adding a first event in the current search layer of the multi-branch fault tree as a left branch node of a corresponding result event in the binary fault tree, and activating the left branch node as the current node of the binary fault tree;
s303, searching the rest events in the same layer in the multi-branch fault tree one by one,
if the intermediate event data and the basic event data exist in the same layer of event at the same time, processing the intermediate event first;
if the logical relationship between the events at the same layer in the multi-branch fault tree is 'OR', the event is stored as the right branch node of the current node in the binary fault tree, and if the logical relationship is 'AND', the event is stored as the left branch node of the current node in the binary fault tree, and the newly added branch node is activated as the current node of the binary fault tree;
and S304, repeating the methods of S303 and S304 until all fault tree nodes complete conversion, and determining the binary fault tree link or storage data.
4. The system according to claim 3, wherein the production rule automatic generation module is further configured to determine the binary fault tree link or storage data as a conditional function from data in S304 in which all fault tree nodes complete conversion.
5. The system according to claim 4, wherein the production rule automatic generation module is further configured to link the basic event data as the last level data on the corresponding root node of the intermediate event data after all the intermediate event data are linked and stored.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011180396.4A CN112199464A (en) | 2020-10-29 | 2020-10-29 | System for constructing binary fault tree diagnosis knowledge base |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011180396.4A CN112199464A (en) | 2020-10-29 | 2020-10-29 | System for constructing binary fault tree diagnosis knowledge base |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112199464A true CN112199464A (en) | 2021-01-08 |
Family
ID=74011908
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011180396.4A Pending CN112199464A (en) | 2020-10-29 | 2020-10-29 | System for constructing binary fault tree diagnosis knowledge base |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112199464A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07271590A (en) * | 1994-03-30 | 1995-10-20 | Fuji Heavy Ind Ltd | Fault diagnostic device |
| CN101877075A (en) * | 2009-10-29 | 2010-11-03 | 北京航空航天大学 | Fault diagnosis knowledge acquiring system |
| CN109270458A (en) * | 2018-11-08 | 2019-01-25 | 国电联合动力技术有限公司 | Intelligent failure diagnosis method, system, Wind turbines and storage medium |
| CN110517369A (en) * | 2019-08-23 | 2019-11-29 | 中国航空无线电电子研究所 | Fault tree construction method and system based on mind map |
| CN111080144A (en) * | 2019-12-20 | 2020-04-28 | 西安靖轩航空科技有限公司 | Intelligent perception airport guarantee capability real-time evaluation system and evaluation method |
-
2020
- 2020-10-29 CN CN202011180396.4A patent/CN112199464A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07271590A (en) * | 1994-03-30 | 1995-10-20 | Fuji Heavy Ind Ltd | Fault diagnostic device |
| CN101877075A (en) * | 2009-10-29 | 2010-11-03 | 北京航空航天大学 | Fault diagnosis knowledge acquiring system |
| CN109270458A (en) * | 2018-11-08 | 2019-01-25 | 国电联合动力技术有限公司 | Intelligent failure diagnosis method, system, Wind turbines and storage medium |
| CN110517369A (en) * | 2019-08-23 | 2019-11-29 | 中国航空无线电电子研究所 | Fault tree construction method and system based on mind map |
| CN111080144A (en) * | 2019-12-20 | 2020-04-28 | 西安靖轩航空科技有限公司 | Intelligent perception airport guarantee capability real-time evaluation system and evaluation method |
Non-Patent Citations (1)
| Title |
|---|
| 唐立堃: "基于知识库的发射机故障诊断辅助系统设计", 《中国优秀硕士学位论文全文数据库》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112069726A (en) | Risk analysis and evaluation method and device based on Bayesian network | |
| CN103646104A (en) | Hard real-time fault diagnosis method and system | |
| CN112070213A (en) | Neural network model optimization method, device, equipment and storage medium | |
| CN114666204A (en) | Fault root cause positioning method and system based on cause and effect reinforcement learning | |
| US20230394330A1 (en) | A method and system for designing ai modeling processes based on graph algorithms | |
| CN115170344A (en) | Intelligent processing method and device, medium and equipment for operation events of regulation and control system | |
| CN112288397A (en) | Flow template configuration method, flow execution method and device and electronic equipment | |
| CN113935487A (en) | Visual satellite fault diagnosis knowledge generation method, device and system | |
| CN116205627A (en) | Fault diagnosis guiding method, electronic device and storage medium | |
| CN112199464A (en) | System for constructing binary fault tree diagnosis knowledge base | |
| Shou-Qiang et al. | Research and design of hybrid collaborative filtering algorithm scalability reform based on genetic algorithm optimization | |
| CN111159203B (en) | Data association analysis method, platform, electronic equipment and storage medium | |
| CN114329896A (en) | Design change driven simulation change fast iteration method and device | |
| CN115796461A (en) | Multidimensional real-time data state diagnosis and analysis method and system under cloud environment | |
| CN107515867A (en) | The generation method and device that data storage, querying method and the device and a kind of rowKey of a kind of NoSQL databases combine entirely | |
| CN112751334A (en) | Power grid online modeling method and system based on memory computing architecture | |
| CN110069747A (en) | Ontology logical contradiction processing method based on integral linear programming | |
| CN116957170B (en) | Constraint intensive reduction method and system for power system optimization problem | |
| CN116383454B (en) | Data query method of graph database, electronic equipment and storage medium | |
| CN118363988A (en) | Database filter execution method, device, equipment, medium and product | |
| CN111953614B (en) | Data transmission method, device, processing equipment and medium | |
| WO2024189722A1 (en) | Question answering device, question answering method, and recording medium | |
| CN117455214A (en) | Civil aircraft demand demonstration flow model construction method, device, equipment and medium | |
| CN118093949A (en) | Structure tree generation method, device, computer equipment and storage medium | |
| CN115878162A (en) | IT operation and maintenance root cause analysis design method and device |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210108 |
|
| RJ01 | Rejection of invention patent application after publication |