CN113449044A - Intelligent station equipment maintenance safety measure automatic generation method and device based on learning inference machine - Google Patents
Intelligent station equipment maintenance safety measure automatic generation method and device based on learning inference machine Download PDFInfo
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Abstract
The invention discloses an intelligent station equipment maintenance safety measure automatic generation method based on a learning inference machine. According to the selected maintenance equipment, searching the associated accompanying and stopping equipment and the associated operating equipment corresponding to the maintenance equipment from the associated accompanying and stopping library and the associated operating library, and collecting the maintenance equipment and the operating information of the corresponding associated accompanying and stopping equipment and the associated operating equipment; inputting the collected operation information into an inference engine generated by training of a neural network model to obtain inference results corresponding to various devices; the interpreter interprets the reasoning result based on the safety measure rule knowledge base to generate the maintenance equipment and corresponding safety measures of the associated accompanying equipment and the associated running equipment; and displaying the overhaul safety measure information in a graphical mode. The method can automatically generate the safety measure required by the equipment to be detected, and effectively reduces the equipment operation risk caused by incorrect safety measure made by maintenance testers.
Description
Technical Field
The invention belongs to the technical field of power system relay protection, and particularly relates to an intelligent station equipment maintenance safety measure automatic generation method based on a learning inference machine, and further relates to an intelligent station equipment maintenance safety measure automatic generation device based on the learning inference machine.
Background
The transformer substation is a key junction link in a power grid system. With the annual popularization and operation of the intelligent transformer substation, the regular maintenance and test of the intelligent transformer substation becomes an important task of a power grid company. Therefore, the safety problem of other related equipment in the transformer substation when the working process of the related equipment of the intelligent transformer substation is overhauled and tested is also an important aspect which must be considered when the overhaul personnel carry out operation. The intelligent transformer substation is greatly different from the traditional transformer substation in the formulation aspect, a general principle or technical requirement is formulated for the field work of the intelligent transformer substation at present, and in the field work, secondary maintenance personnel need to manually formulate a total station safety measure ticket according to the difference of the field operation mode, the equipment operation state and the working mode, so that the workload is huge, and the correctness cannot be guaranteed.
According to the summary of the mode of making safety measure tickets by substation maintenance workers at the present stage, the working method can be divided into two types: the first is that the worker makes safety measures by past experiences and professional abilities; and the second method is that based on a typical overhaul test safety measure ticket compiled in advance, corresponding to an object to be overhauled, an overhaul safety measure ticket is formulated by adopting a principle of consistent application, namely the same object uses the same past safety measure ticket, and when the overhaul test condition of the equipment to be operated is different from the past formulated condition, the operator still performs field change according to overhaul regulations at the moment to formulate a new safety measure ticket. The integrity of the safety measure ticket compiled by the first method has strong dependence on professional qualities of maintenance and test professionals, and the integrity of the safety measure ticket can be directly influenced by different professional levels of different operators. The safety measure ticket made by the second method lacks flexibility, and when some maintenance test conditions of the maintenance object change, a technician is still required to temporarily add the safety measure.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides an intelligent station equipment maintenance safety measure automatic generation method based on a learning inference machine, which is used for automatically generating a maintenance equipment and corresponding safety measures of an associated accompanying equipment and an associated running equipment based on the inference machine and an interpreter.
In order to solve the technical problems, the invention provides the following technical scheme.
In a first aspect, the invention provides an intelligent station equipment maintenance safety measure automatic generation method based on a learning inference machine, which comprises the following processes:
analyzing the structure and functional characteristics of the intelligent substation, determining the safety measure range and safety measure rules facing the equipment according to the standard safety measure of the intelligent substation, and forming a safety measure rule knowledge base;
determining the maintenance equipment and associated accompanying and stopping equipment and associated running equipment corresponding to the maintenance equipment according to the in-station equipment relationship to form an associated accompanying and stopping library and an associated running library corresponding to the maintenance equipment;
acquiring selected maintenance equipment, searching relevant accompanying and stopping equipment and relevant operating equipment corresponding to the maintenance equipment from the relevant accompanying and stopping library and the relevant operating library, and collecting the maintenance equipment and the operating information of the corresponding relevant accompanying and stopping equipment and the relevant operating equipment;
inputting the collected operation information into an inference engine generated by training of a neural network model to obtain inference results corresponding to various devices;
and the interpreter interprets the reasoning result based on the safety measure rule knowledge base to generate the safety measures of the overhaul equipment and the corresponding associated accompanying and stopping equipment and associated operating equipment.
Optionally, the associated accompanying and stopping equipment and the associated running equipment corresponding to the overhaul equipment are searched by using the SCD equipment name fuzzy approximate matching technology.
Optionally, the network structure of the inference engine is determined according to different types of primary and secondary equipment of the overhaul equipment.
Optionally, the method further includes screening inference results:
(1) the sequence of executing safety measures is as follows: primary equipment, secondary equipment for associated operation, secondary equipment for associated halt, secondary equipment for overhaul and optical fiber pulling;
(2) the sequence of displaying similar equipment in the overhaul operation is as follows: the dispatching number is from small to large, the high voltage grade is firstly carried out, then the low voltage grade is carried out, and the next interval equipment is processed after the equipment in one interval is processed;
(3) if one equipment needs to be overhauled or has information interaction with other operations and needs to be associated and accompanied, all safety measures of the equipment are executed;
(4) the safety measure of the associated operation equipment comprises an input/output pressing plate which exits from the equipment with information interaction with the overhaul equipment;
(5) and the associated accompanying and stopping equipment pressing plates interacting with the information of the equipment to be overhauled need to be withdrawn.
Optionally, the method further includes: and graphically displaying the safety measure result.
Optionally, safety measures of the overhaul equipment, the associated accompanying and stopping equipment and the associated operation equipment are displayed in a classified mode.
In a second aspect, the present invention further provides an intelligent station equipment maintenance safety measure automatic generation apparatus based on a learning inference machine, including:
the knowledge base construction module is used for analyzing the structure and functional characteristics of the intelligent substation, determining the safety measure range and the safety measure rule facing the equipment according to the standard safety measure of the intelligent substation, and forming a safety measure rule knowledge base;
the associated equipment determining module is used for determining the maintenance equipment and associated accompanying and stopping equipment and associated running equipment corresponding to the maintenance equipment according to the in-station equipment relationship to form an associated accompanying and stopping library and an associated running library corresponding to the maintenance equipment;
the information acquisition module is used for acquiring the selected maintenance equipment, searching the associated accompanying and stopping equipment and the associated operating equipment corresponding to the maintenance equipment from the associated accompanying and stopping library and the associated operating library, and acquiring the operation information of the maintenance equipment and the corresponding associated accompanying and stopping equipment and the associated operating equipment;
the reasoning calculation module is used for inputting the collected operation information into a reasoning machine generated by the training of the neural network model to obtain reasoning results corresponding to various devices;
and the safety measure generation module is used for generating the safety measures of the overhaul equipment and the corresponding associated stopping equipment and associated operating equipment by the interpreter based on the safety measure rule knowledge base interpretation reasoning result.
Compared with the prior art, the invention has the following beneficial effects: according to the method, after the maintenance equipment and the maintenance tasks are selected according to the actual maintenance working scene, the data of the substation equipment is analyzed, and the associated accompanying and stopping equipment and the associated running equipment are obtained by fuzzy matching of the equipment association relation and the equipment name. A learning inference machine based on a double-layer neural network algorithm is constructed, safety measures are automatically generated and graphically displayed by utilizing a maintenance boundary concept, the safety measures can be compared with safety measure tickets issued by maintenance testing workers, and equipment operation risks caused by incorrect safety measures formulated by the maintenance testing workers are reduced.
Drawings
Fig. 1 is a schematic diagram of a functional association relationship of a line protection device;
FIG. 2 is a schematic diagram of a system architecture of an inference engine model;
FIG. 3 is a schematic diagram of a two-layer neural network structure;
FIG. 4 is a flow chart of the method of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The invention provides an intelligent station maintenance safety measure automatic generation method based on a learning inference machine. A learning inference machine based on a double-layer neural network algorithm is constructed, safety measures are automatically generated and graphically displayed by utilizing a maintenance boundary concept, the safety measures can be compared with safety measure tickets issued by maintenance testing workers, and equipment operation risks caused by incorrect safety measures formulated by the maintenance testing workers are reduced.
The invention discloses an intelligent station maintenance safety measure automatic generation method based on a learning inference machine, which is shown in a figure 4 and comprises the following steps:
step 1, analyzing the structural and functional characteristics of an intelligent substation, and constructing an equipment-oriented safety measure rule knowledge base according to the experience of practitioners and standard safety measures of the intelligent substation; the knowledge base comprises a primary equipment running state definition, a secondary equipment safety measure range, a GOOSE link safety measure rule and an SV link safety measure rule. This knowledge base is used to define the neural network input variable structure.
The secondary equipment running state definition comprises the following steps:
(1) the protection device sets three states of tripping, signal and deactivation, and the specific meaning is as follows:
tripping the protection device, namely switching the main protection and backup protection function soft pressing plate into a normal AC/DC loop of the protection device, switching the trip and start failure GOOSE soft pressing plate into the backup protection function soft pressing plate, and taking down the overhaul pressing plate;
the protection device signal means that the alternating current and direct current loop of the protection device is normal, the main protection and backup protection function soft pressing plate is put into, the trip and start failure GOOSE soft pressing plate is withdrawn, and the overhaul pressing plate is taken down;
the protection device is disabled, namely the protection device has a normal alternating current-direct current loop, the main protection and backup protection function soft pressing plate is withdrawn, the trip and start failure GOOSE soft pressing plate is withdrawn, and the maintenance pressing plate is put on;
(2) the intelligent terminal sets three states of tripping, signal and deactivation, and the specific meaning is as follows:
the intelligent terminal is tripped, namely the direct current circuit of the intelligent terminal is normal, a hard pressing plate at the outlet of a tripping and closing switch is placed, and a maintenance pressing plate is taken down;
an intelligent terminal signal means that a direct current circuit of the intelligent terminal is normal, a hard pressing plate at a tripping and closing outlet is taken down, and a maintenance pressing plate is taken down;
the intelligent terminal is stopped, namely the direct current circuit of the intelligent terminal is normal, the hard pressing plate at the tripping and closing outlet is taken down, and the maintenance pressing plate is put on;
(3) the merging unit device is provided with two states of tripping and stopping, and the specific meaning is as follows:
tripping the merging unit device, namely taking down the maintenance pressing plate when the direct current loop of the merging unit is normal;
when the merging unit device is stopped, the direct current loop of the merging unit is normal, and an overhaul pressure plate is placed;
(4) the circuit protection sets two states of tripping and signal, and the specific meaning is as follows:
the tripping operation refers to the switching-in of a circuit protection reclosing outlet soft pressing plate and the withdrawal of a disabled reclosing soft pressing plate;
the signal means that the circuit protection reclosing outlet soft pressing plate withdraws and the reclosing soft pressing plate is stopped to be put in;
the primary equipment running state comprises the following steps:
(1) the switch is cold standby, namely the switch in the switch interval and the knife switches on the two sides of the switch are all in the off position, and the bus differential protection GOOSE outlet soft pressing plate and the failure protection GOOSE receiving soft pressing plate of the switch interval are taken down;
(2) and (4) switch maintenance, namely, the switch and the switch blades on two sides of the switch in the switch interval are in an off position, the grounding switch blades are closed on two sides of the switch, and the bus differential protection GOOSE outlet soft pressing plate, the failure protection GOOSE receiving soft pressing plate and the SV receiving soft pressing plate of the switch interval are taken down.
The primary equipment safety measure comprises the following steps:
(1) safety measure for pulling operation power supply open
(2) Safety measure for controlling power supply by pulling open
(3) Safety measure for ground closing knife
(4) Safety measure for hanging ground wire
According to the regulations on the field work security of intelligent substation relay protection and automatic power grid safety devices proposed by the national grid company, the rules for making secondary equipment overhaul safety measures of the intelligent substation are as follows:
rule 1: and withdrawing the maintenance equipment protection function pressing plate.
Rule 2: and withdrawing the starting failure soft pressing plate of the maintenance equipment and the associated operation equipment.
Rule 3: and withdrawing the failed joint-jump soft pressing plate between the maintenance equipment and the associated operating equipment.
Rule 4: and (4) withdrawing the other tripping soft pressing plates between the maintenance equipment and the associated operation equipment.
Rule 5: and withdrawing the hard pressing plate at the outlet of the maintenance equipment.
Rule 6: and the SV of the maintenance equipment is removed from the associated operation equipment to receive the soft pressing plate.
Rule 7: and putting in maintenance pressing plates of all maintenance equipment and the like.
According to the rules, the safety measure range of the secondary equipment is as follows:
(1) the second equipment safety measure comprises:
a. disconnecting the soft and hard pressing plates and sending and receiving pressing plate information. The pressing plates comprise a GOOSE soft pressing plate, an SV receiving soft pressing plate, a functional pressing plate, an outlet hard pressing plate, an overhaul hard pressing plate and the like;
b. and (5) pulling the optical fiber.
(2) The secondary equipment maintenance safety measure is mainly to take an isolation measure aiming at a link of a process layer between maintenance equipment and operation equipment. And searching the process layer link relation between the equipment to be overhauled and the associated running equipment according to the SCD virtual terminal connection. Both ends of the link are maintenance equipment, corresponding safety isolation measures are not required to be formulated, one side of the link is maintained, the other side of the link runs (including associated accompanying and stopping and associated running), and the link needs to be isolated according to specific safety measure rules; and searching the connection relation of the process layer equipment to be isolated, and generating a corresponding safety measure link table. The safety measure link is divided into a GOOSE link and an SV link according to functions, wherein:
GOOSE link safety rule:
when the line protection device is overhauled, an output pressure plate of the protection device needs to be withdrawn, such as a protection tripping soft pressure plate, a starting failure soft pressure plate, a reclosing soft pressure plate, a three-phase inconsistent soft pressure plate, a permanent tripping soft pressure plate and the like, and a functional pressure plate of the protection device, such as differential protection, distance protection, zero sequence overcurrent, a stop reclosing and the like.
SV link safety rules:
(1) and (3) operating the equipment at the interval primary time, and if the interval merging unit is overhauled, receiving the ' associated operation secondary device ' of the SV link sent by the interval merging unit, and taking the ' associated operation secondary device ' needing to exit the operation ' as associated accompanying and stopping equipment.
(2) Stopping the operation of the interval primary equipment, and if the interval merging unit is overhauled, receiving a receiving pressing plate of an SV link, which is sent by the interval merging unit and needs to exit the SV link, of a 'related operation secondary device';
(3) when the bus merging unit is overhauled, the associated operation secondary device receiving the SV link of the bus merging unit needs to quit operation, the interval merging unit receiving the SV link of the bus merging unit sets an overhauling mark, and the merging unit (possibly a bus or an interval) receiving the overhauling mark sets the overhauling mark.
(4) The service device receives the SVs running the merging unit (which may be a bus or bay) without safety isolation measures.
Step 2, dividing different types of equipment according to the in-station equipment relationship, analyzing the contents in the file to summarize and construct in-station equipment configuration information, and establishing an equipment association accompanying and stopping library and an association running library according to the contents such as a wiring form, an interaction relationship and the like by analyzing an SSD (system configuration file) and an SCD (substation configuration file) file;
the device type is defined as:
(1) overhauling the test equipment:
and an electric device for performing a final operation when the maintenance plan is made.
(2) Associating the chaperone device:
in the maintenance test work, when a certain device is implemented according to a specified plan, the result generated in the implementation of the scheme may affect the functions of other normally operating devices in the substation, so that an unexpected result occurs, and a more serious result is caused. And in the working process, the equipment needs to be kept in the exit state all the time and cannot work on the equipment.
(3) Associated operating device
The two types of equipment have input and output transmission data association, and the equipment possibly affected by the function of the equipment caused by the maintenance of the associated accompanying and stopping equipment in the type (2) can be used. Thus, such devices can be classified into two categories according to the above description: an operational device interoperating with servicing secondary device presence information and interoperating with associated attendant secondary device presence information.
Analyzing SSD (system configuration file) and SCD (substation configuration file) files, analyzing the internal content of the files to summarize and construct the configuration information of the equipment in the station, and establishing the associated accompanying and stopping library and the associated running library of the maintenance equipment according to the contents such as wiring form, interaction relation and the like.
Taking a 220kV line protection device as an example, a schematic diagram of an association relationship between substation equipment related to a line is shown in fig. 1. When the line protection device detects a fault, relevant information is sent to devices which have information interaction relation with the line protection device, and the devices comprise a line merging unit, a line-bus switch, a line-monitoring background connection switch and a line intelligent terminal. Therefore, when the protection device needs to be subjected to maintenance test, the protection device needs to be provided with associated accompanying and stopping equipment. And providing a basis for maintenance test boundary division and related equipment classification and safety measure formulation according to the relationship in the graph.
The structure of the related accompanying parking equipment library is as follows:
table 1 associated parking equipment library architecture
| Field(s) | Description of the invention |
| Servicing equipment description | Chinese descriptions of maintenance equipment, e.g. "line protection", "measurement and control devices" |
| Overhauling equipment balance | Symbolic descriptions of service equipment, e.g. "PL", "ML", support a variety of |
| Associating companion device descriptions | With servicing equipment description, here Chinese description of the associated accompanying equipment |
| Associated accompany-stop device alias | Alternative names for servicing equipment, here symbolic representations of associated accompanying equipment |
| Whether or not at the same interval | Indicating accompany-upWhether the equipment and the overhaul equipment have consistent serial numbers by default |
It is not possible to completely define the device model in the substation SCD when building the device association chaperone library. It can only be defined in terms of abstract device types, such as "220 kV line protection". Firstly, matching a corresponding equipment type in an associated accompanying and stopping library according to maintenance equipment; and then searching a corresponding associated accompanying and stopping device type according to the device type, and finally finding out specific associated accompanying and stopping devices from the SCD. The description and the alternative name of the overhaul equipment required by the 220kV intelligent station associated parking garage are listed as follows:
table 2220 kV intelligent station equipment association accompany, stop warehouse and examine and repair equipment table
| Description of the invention | Alternative name |
| 220kV line protection device A, B sleeve | PL22A、PL22B |
| 220kV merging unit A, B sleeve | ML22A、ML22B |
| 220kV intelligent terminal A, B set | IL22A、IL22B |
| A, B sleeve for 220kV bus protection | PM22A、PM22B |
| 220kV bus coupler protection A, B sleeve | PF22A、PF22B |
| 110kV line protection measurement and control device | SL11 |
| 110kV line closes integrative device of intelligence | ML11 |
| Main transformer protection device A, B sleeve | PTA、PTB |
And the matching problem of the SCD concrete equipment name and the abstract equipment name can be processed in a fuzzy matching mode. The minimum number of operands required to convert from the original character to the target character is first defined as N, and operations include delete, replace, insert, etc. If the longer of the original character and the target character is L (number of characters), the similarity between two character strings can be defined
According to the SCD equipment description, the calculation result of the similarity of the close keywords in the associated cosecant library is as follows:
table 3 device description similarity table
The similarity calculation results are as follows according to table 3:
TABLE 4 alternative name similarity table
| Alternative name character | Operand(s) | Degree of similarity (%) |
| PL22A | 2 | 71.4 |
| |
3 | 57.1 |
| |
3 | 57.1 |
| PT | 6 | 14.3 |
| |
3 | 57.1 |
The similarity of the equipment description and the similarity of the alternative call are compared with each other, and the one with higher similarity is matched with the 220kV line protection A sleeve.
Searching for the associated accompanying and stopping equipment by using an SCD equipment name fuzzy approximate matching technology, and comprising the following steps of:
a. matching the corresponding equipment type in the associated accompanying and stopping library according to the maintenance equipment;
b. searching for the associated accompany-stop equipment type according to the equipment type;
c. defining character similarity;
d. and searching for the specific associated accompany-stop equipment in the SCD according to the fuzzy approximate matching of the equipment name.
The same principle is used for the search of the associated operating device.
And 3, establishing a double-layer neural network algorithm inference machine, inputting parameters of the equipment to be overhauled, and outputting parameters of the overhauling safety measure.
The reasoning machine learning training and reasoning maintenance equipment safety measure process is shown in figure 2. Firstly, the number of neurons in the input layer and the output layer of the neural network is defined based on the rules of the knowledge base in the step 1, so as to establish the input and output vector structure of the neural network. And training an inference machine by taking the example of the problem solved by the domain expert and the guidance suggestion of the on-site maintenance safety measure of the intelligent substation relay protection and the safety automatic device as samples for the safety measure samples of the devices in the substation, so that the neural network obtains the output which is as same as the expert answers as possible under the condition of the same input. The neural network weight is obtained through learning and training, and the threshold parameter is obtained through experience. After the maintenance equipment is selected, searching the associated accompanying and stopping equipment and the associated operating equipment corresponding to the maintenance equipment from the associated accompanying and stopping library and the associated operating library, and collecting the maintenance equipment and the operating information of the corresponding associated accompanying and stopping equipment and the associated operating equipment;
inputting the inference engine to carry out inference, and generating a specific safety measure by an interpreter according to an output result. And finally, graphically displaying the safety measure information on the interface.
In order to avoid overlarge connection weight of the neural network and the combination number of the samples, the equipment to be detected is used for constructing a neural network structure according to different subclasses. These subclasses include, but are not limited to:
1. ground knife
2. Switch with a switch body
3. Main transformer
4. Bus bar
5. Merging unit
6. Integrated device for protection and measurement
7. Protective device
8. Measuring and controlling device
9. Intelligent terminal
10. Integrated device for intelligence
Wherein 1-4 are primary devices, the number of neurons in the output layer of the neural network can be defined to be 4 according to the safety measure rule in the knowledge base in the step 1, and the output vector structure is as follows:
TABLE 5 Primary device safety measure output vector structure
And 5-10 are secondary equipment, the number of the output layers of the neural network can be defined to be 6 according to the safety measure rule in the knowledge base in the step 1, and the output vector structure is as follows:
TABLE 6 Secondary equipment safety measure output vector structure
Taking the protection device as an example, the constructed double-layer neural network structure is shown in fig. 3. Defining the number of input layer neurons as 9, the number of hidden layer neurons as 6, and the number of output layer neurons as 6. In order to transform an input logic concept into an input vector of a fixed mode, a transformation rule is determined according to the characteristics of a domain, and then a sample state is changed into the input vector according to the rule.
The protection device searches the process layer link relation between the equipment to be overhauled and the associated operation equipment, the process layer equipment connection relation to be isolated and the like according to the input and output relation between the equipment, and the equipment operation state definition in the knowledge base in the step 1, and establishes the following table of the input vector structure of the protection device:
TABLE 7 line protection sample input vector Structure
The neural network can perform a self-learning process like neurons of biological nerves, and the output of each layer is determined by the weight w of each path leading to the layer and a corresponding function. The structure is shown in fig. 3.
The hidden layer neuron outputs are:
the output layer neuron output is:
the path weight w is learned through an error back propagation algorithm, and the threshold value theta is selected according to experience. The trained parameter values are stored in the system. The inference engine is used for reasoning the circuit protection, and the reasoning result is as follows:
| Y1 | Y2 | Y3 | Y4 | Y5 | Y6 |
| 0.891 | 0.357 | 0.788 | 0.693 | 0.126 | 0.238 |
the correct safety measure ticket can be generated by the interpreter only if the inference result meets the reference rules, and the reference rules include but are not limited to:
(1) the sequence of executing safety measures is as follows: primary equipment, secondary equipment for associated operation, secondary equipment for associated halt, secondary equipment for overhaul and optical fiber pulling;
(2) the sequence of displaying similar equipment in the overhaul operation is as follows: the dispatching number (the unified number of the equipment accessed to the power grid) is from small to large, the high voltage grade is firstly carried out, then the low voltage grade is carried out, and the next interval equipment is processed after the equipment in one interval is processed;
(3) if one equipment needs to be overhauled or has information interaction with other operations and needs to be associated and accompanied, all safety measures of the equipment are executed;
(4) the safety measure of the associated operation equipment comprises an input/output pressing plate which exits from the equipment with information interaction with the overhaul equipment;
(5) and the associated accompanying and stopping equipment pressing plates interacting with the information of the equipment to be overhauled need to be withdrawn.
The interpreter generates specific maintenance safety measures of the line protection device according to the rules of the knowledge base in the step 1 as follows:
table 8 line protection device overhaul test safety measure
| Sequence of events | Secondary equipment isolation safety measure |
| 1 | 220kV line protection deviceSV receiving soft pressure exit |
| 2 | Soft pressing plate exit of 220kV line protection device A set starting |
| 3 | Hard pressing plate exit of trip outlet of A sleeve of 220kV line protection device |
| 4 | Hard clamp plate investment for overhauling A set of 220kV line protection device |
And 4, determining a maintenance boundary and graphically displaying maintenance safety measure information.
And the overhaul boundary is an information interaction boundary of the overhaul equipment, the associated accompanying and stopping equipment and the associated operating equipment. All maintenance safety measures act on the information interaction points on the maintenance boundary. Determining a maintenance boundary between the devices according to the maintenance library, the associated accompanying and stopping library, the associated operation library and the reasoning result; the method is used for graphical display of safety measure tickets, safety measure classified display of overhaul equipment, associated accompanying and stopping equipment and associated running equipment, and the boundary is clear.
The safety measure information graph display function comprises the following steps:
(1) automatically generating maintenance equipment safety measures and displaying the maintenance equipment, the associated accompanying equipment and the associated running equipment in three categories;
(2) the generated maintenance safety measure displays the safety measure regulations to be executed in the current operation;
(3) and displaying the maintenance equipment safety measure and the corresponding maintenance boundary in a logic graph mode, wherein the image display safety measure information and the character display safety measure information have a linkage corresponding relation.
On the overhaul safety measure information graphical interface, when a certain protection device in secondary equipment is selected, corresponding equipment appears in an overhaul equipment list, an accompanying and stopping equipment list and an associated equipment list.
And clicking a check and verification button to switch to a safety measure verification interface. The system graphically presents the safety measure of the device to be overhauled and the corresponding overhaul boundary, and converts the logic model into an image model.
The safety measure information of the overhaul boundary displayed by the interface image model and the overhaul safety measure displayed by the characters have a one-to-one correspondence relationship, namely when the overhaul device is changed or other safety measures are changed, the corresponding display content is changed along with the change. By the method, the overhaul operator can visually judge the reasonability of the formulated safety measure according to the image.
Example 2
Based on the same inventive concept as embodiment 1, the invention provides an intelligent station equipment maintenance safety measure automatic generation device based on a learning inference machine, which comprises:
the knowledge base construction module is used for analyzing the structure and functional characteristics of the intelligent substation, determining the safety measure range and the safety measure rule facing the equipment according to the standard safety measure of the intelligent substation, and forming a safety measure rule knowledge base;
the associated equipment determining module is used for determining the maintenance equipment and associated accompanying and stopping equipment and associated running equipment corresponding to the maintenance equipment according to the in-station equipment relationship to form an associated accompanying and stopping library and an associated running library corresponding to the maintenance equipment;
the information acquisition module is used for acquiring the selected maintenance equipment, searching the associated accompanying and stopping equipment and the associated operating equipment corresponding to the maintenance equipment from the associated accompanying and stopping library and the associated operating library, and acquiring the operation information of the maintenance equipment and the corresponding associated accompanying and stopping equipment and the associated operating equipment;
the reasoning calculation module is used for inputting the collected operation information into a reasoning machine generated by the training of the neural network model to obtain reasoning results corresponding to various devices;
and the safety measure generation module is used for generating the safety measures of the overhaul equipment and the corresponding associated stopping equipment and associated operating equipment by the interpreter based on the safety measure rule knowledge base interpretation reasoning result.
The specific implementation scheme of each module in the device is shown in the processing procedures of each step in the method of the embodiment 1.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (7)
1. An intelligent station equipment maintenance safety measure automatic generation method based on a learning inference machine is characterized by comprising the following processes:
analyzing the structure and functional characteristics of the intelligent substation, determining the safety measure range and safety measure rules facing the equipment according to the standard safety measure of the intelligent substation, and forming a safety measure rule knowledge base;
determining the maintenance equipment and associated accompanying and stopping equipment and associated running equipment corresponding to the maintenance equipment according to the in-station equipment relationship to form an associated accompanying and stopping library and an associated running library corresponding to the maintenance equipment;
acquiring selected maintenance equipment, searching relevant accompanying and stopping equipment and relevant operating equipment corresponding to the maintenance equipment from the relevant accompanying and stopping library and the relevant operating library, and collecting the maintenance equipment and the operating information of the corresponding relevant accompanying and stopping equipment and the relevant operating equipment;
inputting the collected operation information into an inference engine generated by training of a neural network model to obtain inference results corresponding to various devices;
and the interpreter interprets the reasoning result based on the safety measure rule knowledge base to generate the safety measures of the overhaul equipment and the corresponding associated accompanying and stopping equipment and associated operating equipment.
2. The intelligent station equipment maintenance safety measure automatic generation method based on the learning inference machine as claimed in claim 1, wherein an SCD equipment name fuzzy approximate matching technology is used for searching for associated stop accompanying equipment and associated running equipment corresponding to the maintenance equipment.
3. The intelligent station equipment maintenance safety measure automatic generation method based on the learning inference engine as claimed in claim 1, wherein the network structure of the inference engine is determined according to different types of primary and secondary equipment of maintenance equipment.
4. The intelligent station equipment overhaul safety measure automatic generation method based on the learning inference machine as claimed in claim 1, characterized by further comprising screening inference results:
(1) the sequence of executing safety measures is as follows: primary equipment, secondary equipment for associated operation, secondary equipment for associated halt, secondary equipment for overhaul and optical fiber pulling;
(2) the sequence of displaying similar equipment in the overhaul operation is as follows: the dispatching number is from small to large, the high voltage grade is firstly carried out, then the low voltage grade is carried out, and the next interval equipment is processed after the equipment in one interval is processed;
(3) if one equipment needs to be overhauled or has information interaction with other operations and needs to be associated and accompanied, all safety measures of the equipment are executed;
(4) the safety measure of the associated operation equipment comprises an input/output pressing plate which exits from the equipment with information interaction with the overhaul equipment;
(5) and the associated accompanying and stopping equipment pressing plates interacting with the information of the equipment to be overhauled need to be withdrawn.
5. The intelligent station equipment overhaul safety measure automatic generation method based on the learning inference machine as claimed in claim 1, characterized by further comprising: and graphically displaying the safety measure result.
6. The intelligent station equipment maintenance safety measure automatic generation method based on the learning inference machine is characterized in that safety measures of maintenance equipment, associated accompanying and stopping equipment and associated operation equipment are displayed in a classified mode.
7. The utility model provides an intelligence station equipment overhauls automatic generation device of safety measure based on learning inference machine, characterized by includes:
the knowledge base construction module is used for analyzing the structure and functional characteristics of the intelligent substation, determining the safety measure range and the safety measure rule facing the equipment according to the standard safety measure of the intelligent substation, and forming a safety measure rule knowledge base;
the associated equipment determining module is used for determining the maintenance equipment and associated accompanying and stopping equipment and associated running equipment corresponding to the maintenance equipment according to the in-station equipment relationship to form an associated accompanying and stopping library and an associated running library corresponding to the maintenance equipment;
the information acquisition module is used for acquiring the selected maintenance equipment, searching the associated accompanying and stopping equipment and the associated operating equipment corresponding to the maintenance equipment from the associated accompanying and stopping library and the associated operating library, and acquiring the operation information of the maintenance equipment and the corresponding associated accompanying and stopping equipment and the associated operating equipment;
the reasoning calculation module is used for inputting the collected operation information into a reasoning machine generated by the training of the neural network model to obtain reasoning results corresponding to various devices;
and the safety measure generation module is used for generating the safety measures of the overhaul equipment and the corresponding associated stopping equipment and associated operating equipment by the interpreter based on the safety measure rule knowledge base interpretation reasoning result.
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