CN113283619A - Power failure maintenance planning auxiliary method and system based on knowledge graph - Google Patents
Power failure maintenance planning auxiliary method and system based on knowledge graph Download PDFInfo
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Abstract
The application relates to a power failure maintenance planning auxiliary method and system based on a knowledge graph, wherein the method comprises the following steps: and acquiring a power failure plan application file, and identifying whether the entity records in the power failure plan application file can be normally matched in the knowledge map one by one on the basis of the pre-constructed knowledge map. And if the power failure plan application file contains an abnormal entity record which cannot be normally matched in the knowledge graph, sending the abnormal entity record to a power dispatching person, reporting abnormal information, and asking the power dispatching person to confirm whether the application file is wrong. And if all the entity records in the power failure plan application file are normally matched in the knowledge map, judging whether the power failure plan application file meets the power failure rule or not based on the knowledge map and a pre-trained power failure rule algorithm model, and outputting a judgment result. And finally, generating an approval result of the power failure plan application file according to the judgment result, thereby timely discovering careless mistakes in the power failure maintenance plan.
Description
Technical Field
The application relates to the technical field of power grid operation modes, in particular to a power failure maintenance planning auxiliary method and system based on a knowledge graph.
Background
The maintenance of the power grid equipment is an important and fundamental work of a power dispatching and operating department, and the reasonable power failure maintenance plan ensures the safety of the power grid equipment and the normal operation of a power grid. The blackout maintenance plan needs to ensure the reliability and safety of the power system and consider the user benefit and the maintenance cost. In order to ensure safe and economic operation of the power grid, electric power equipment needs to be scheduled for maintenance periodically or according to the operation condition of the equipment. The power grid is a complex energy interconnection system, and the starting and stopping of each device can change the operation mode of the power grid, possibly causing potential safety hazards. Therefore, power dispatching personnel need to reasonably arrange a power failure maintenance plan according to the state of a power grid, and in the prior art, the power dispatching personnel in various regions need to manually arrange the maintenance plan according to factors such as the topological relation of the power grid, the capacity of power grid equipment, the load condition in a predicted planned maintenance time period and the like. From the aspect of data acquisition, the current equipment operation data, real-time network topology information, maintenance records of power equipment, historical load data and other information are respectively stored in a plurality of systems of a plurality of departments, the data are relatively isolated, a multi-department collaborative decision is needed when power failure planning is carried out, and the efficiency is relatively low. In the face of the complex situation, even the power failure maintenance plans organized by experienced power dispatching personnel are inevitable to generate careless mistakes, and hidden dangers are buried for safe and efficient operation of the power grid.
Disclosure of Invention
In order to overcome the problem that careless mistakes are easy to occur in the power failure maintenance plan in the related art at least to a certain extent, the application provides a power failure maintenance plan arrangement auxiliary method and system based on a knowledge graph.
The scheme of the application is as follows:
according to a first aspect of the embodiments of the present application, there is provided a power outage maintenance planning assistance method based on a knowledge graph, including:
acquiring a power failure plan application file;
identifying whether entity records in the power failure plan application file can be normally matched in a knowledge graph one by one on the basis of a pre-constructed knowledge graph; wherein the knowledge graph is constructed according to power grid data;
if the power failure plan application file contains an abnormal entity record which cannot be normally matched in the knowledge graph, the abnormal entity record is sent to power dispatching personnel, and abnormal information is reported;
if all entity records in the power failure plan application file are normally matched in the knowledge graph, judging whether the power failure plan application file meets a power failure rule or not based on the knowledge graph and a pre-trained power failure rule algorithm model, and outputting a judgment result;
and generating an approval result of the power failure plan application file according to the judgment result.
Preferably, in an implementation manner of the present application, the method further includes:
acquiring power grid data required for constructing the knowledge graph from a plurality of data sources; the grid data at least comprises: power grid model file data, OMS data, PMS data, EMS data and scheduling regulation data;
preprocessing the power grid data;
and inputting the processed power grid data into a map to generate the knowledge map.
Preferably, in an implementable manner of the present application, the preprocessing the grid data includes:
cleaning inaccurate power grid data which are irrelevant to final services in each data source;
performing entity alignment on the cleaned power grid data based on similarity calculation, edit distance calculation and automatic clustering technology;
and finally confirming the aligned power grid data.
Preferably, in an implementation manner of the present application, the mapping the processed data to generate the knowledge-graph includes:
taking the power grid topological nodes in the power grid data as node entities of the knowledge graph, and taking connecting lines as the relation of the knowledge graph;
putting production management knowledge data in the power grid data into a graph; the production management knowledge data at least includes: and power failure maintenance of associated department and service personnel information.
Preferably, in an implementation manner of the present application, the method further includes:
acquiring sample data of a power failure plan application file;
and training the power failure rule algorithm model according to the sample data of the power failure plan application file.
Preferably, in an implementation manner of the present application, the training of the blackout rule algorithm model according to the sample data of the blackout plan application file includes:
identifying equipment needing to be overhauled, an application unit, overhaul starting time, overhaul ending time, grade and overhaul category in the sample data of the power failure plan application file;
carrying out maintenance and check, and judging the power failure rule met by the sample data of the power failure plan application file; the power outage rules at least include: security constraints, mutual exclusion constraints, simultaneous stopping constraints, resource constraints and certainty constraints;
and outputting a judgment result.
Preferably, in an implementation manner of the present application, the generating an approval result of the power outage plan application file according to the determination result includes:
if the power failure plan application file meets all power failure rules, the power failure plan application file is placed in a passing state, and the meeting conditions of all power failure rules are output;
and if the power failure plan application file does not meet all power failure rules, placing the power failure plan application file in a non-passing state, and outputting the power failure rules which are not met by the power failure plan application file.
Preferably, in an implementation manner of the present application, the method further includes:
and updating the knowledge graph in real time according to the data change condition of each data source.
Preferably, in an implementable manner of the present application, the knowledge-graph comprises at least: persistent state information, historical state information, current state information and future state information;
the persistent state information of the knowledge-graph comprises: a fixed device electrical parameter;
the historical state information of the knowledge graph at least comprises: equipment maintenance records and equipment historical loads;
the current state information of the knowledge-graph at least comprises: the service life of the current equipment, the connection state of the current equipment and the running state of the current equipment;
the future state information of the knowledge-graph at least comprises: prediction information of the future state of the device.
According to a second aspect of the embodiments of the present application, there is provided a power outage maintenance planning assistance system based on a knowledge graph, including:
the acquisition module is used for acquiring a power failure plan application file;
the identification module is used for identifying whether the entity records in the power failure plan application file can be normally matched in the knowledge map one by one based on a pre-constructed knowledge map; wherein the knowledge graph is constructed according to power grid data;
the processing module is used for sending the abnormal entity record to power dispatching personnel and reporting abnormal information when the abnormal entity record which cannot be normally matched in the knowledge graph is contained in the power failure plan application file; when all entity records in the power failure plan application file are normally matched in the knowledge graph, judging whether the power failure plan application file meets a power failure rule or not based on the knowledge graph and a pre-trained power failure rule algorithm model, and outputting a judgment result;
and the generating module is used for generating an approval result of the power failure plan application file according to the judgment result.
The technical scheme provided by the application can comprise the following beneficial effects: the power failure maintenance scheduling auxiliary method based on the knowledge graph comprises the following steps: and acquiring a power failure plan application file, and identifying whether the entity records in the power failure plan application file can be normally matched in the knowledge map one by one on the basis of the pre-constructed knowledge map. And if the power failure plan application file contains an abnormal entity record which cannot be normally matched in the knowledge graph, sending the abnormal entity record to a power dispatching person, reporting abnormal information, and asking the power dispatching person to confirm whether the application file is wrong. And if all the entity records in the power failure plan application file are normally matched in the knowledge map, judging whether the power failure plan application file meets the power failure rule or not based on the knowledge map and a pre-trained power failure rule algorithm model, and outputting a judgment result. And finally generating an approval result of the power failure plan application file according to the judgment result. Because the knowledge graph in the application is constructed according to the power grid data, the power failure plan application file is judged and verified based on the knowledge graph and the power failure rule algorithm model, careless mistakes in the power failure maintenance plan can be found in time, and the power dispatching personnel are assisted to arrange a more reasonable power failure maintenance plan.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic flow chart diagram illustrating a method for assisting power outage maintenance planning based on knowledge-graph according to an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating entity identification in a power outage maintenance planning assistance method based on a knowledge graph according to an embodiment of the present application;
FIG. 3 is a schematic diagram illustrating an examination and approval result output in an assistance method for power outage maintenance planning based on a knowledge graph according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a power outage maintenance planning assistance system based on a knowledge graph according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an outage maintenance planning assistance device based on a knowledge graph according to an embodiment of the present application.
Reference numerals: an acquisition module-21; an identification module-22; a processing module-23; a generating module-24; a processor-31; a memory-32.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
A power failure maintenance planning auxiliary method based on a knowledge graph comprises the following steps:
s11: acquiring a power failure plan application file;
the power failure plan application file is written and reported by power dispatching personnel.
S12: identifying whether entity records in the power failure plan application file can be normally matched in the knowledge map one by one based on a pre-constructed knowledge map; the knowledge graph is constructed according to power grid data;
the manner of entity identification is described with reference to fig. 2.
The construction of the knowledge graph specifically comprises the following steps:
acquiring power grid data required for constructing a knowledge graph from a plurality of data sources; the grid data at least comprises: power grid model file data, OMS data, PMS data, EMS data and scheduling regulation data;
preprocessing the power grid data;
and inputting the processed power grid data into a map to generate a knowledge map.
OMS, PMS and EMS are three major systems of the national power grid. The OMS is an intelligent scheduling management system and comprises power failure application and the like; the PMS is a device management system of the operation inspection section, including a single line diagram; EMS is an energy management system that includes real-time data transmission.
Preprocessing the power grid data, comprising:
cleaning inaccurate power grid data which are irrelevant to final services in each data source;
performing entity alignment on the cleaned power grid data based on similarity calculation, edit distance calculation and automatic clustering technology;
and finally confirming the aligned power grid data.
Entity alignment aims to judge whether two or more entities with different information sources point to the same object in the real world or not, and is mainly used for ensuring the consistency of the entities in multi-source data. Technical means required to be used include similarity calculation, edit distance calculation, automatic clustering and the like. Because entity alignment is crucial to the quality of the whole knowledge map, in this stage, it is necessary to combine the knowledge of service personnel to write artificial rules and deliver the aligned result to the human for final confirmation.
Performing mapping on the processed data to generate a knowledge graph, wherein the mapping comprises the following steps:
taking power grid topological nodes in the power grid data as node entities of the knowledge graph, and taking connecting lines as the relation of the knowledge graph;
putting production management knowledge data in the power grid data into a graph; the production management knowledge data at least includes: and power failure maintenance of associated department and service personnel information.
Specifically, the entities of the grid topology node are represented as: the system node comprises a system node, a system node number, a node name, an operation parameter attribute, a power equipment attribute and a load attribute; the method comprises the following steps of (1) power equipment, power equipment name, equipment type, equipment number, operation information, historical fault record, rated voltage, rated current and rated power; connecting a line: start node-relationship name, relationship-end node.
The production management knowledge data mapping is to map information of related departments, service personnel and the like involved in power failure maintenance so as to quickly position related service personnel for collaborative manual arrangement when a maintenance application scheme cannot pass. The entities of the production management part are represented as: business department (department name), department responsibility task, responsible person, department member, contact phone; business personnel (employee name), personnel attributes (employee name, age, contact, job, skill, department of belonging); business relationships (department to which the employee belongs).
S13: if the power failure plan application file contains abnormal entity records which cannot be normally matched in the knowledge graph, the abnormal entity records are sent to power dispatching personnel, and abnormal information is reported;
if the entity in an entity record in the application file of the power failure plan cannot be normally matched or a plurality of records are matched in the knowledge graph, the entity record is taken as an abnormal entity record to be sent to the power dispatching personnel, abnormal information is reported, and the service personnel is asked to confirm whether the application file is wrong.
S14: if all entity records in the power failure plan application file are normally matched in the knowledge graph, judging whether the power failure plan application file meets the power failure rule or not based on the knowledge graph and a pre-trained power failure rule algorithm model, and outputting a judgment result;
the power failure rule training algorithm model specifically comprises the following steps:
acquiring sample data of a power failure plan application file;
training a power failure rule algorithm model according to the sample data of the power failure plan application file, which comprises the following steps:
identifying equipment needing to be overhauled, an application unit, overhaul starting time, overhaul ending time, grade and overhaul category in the sample data of the power failure plan application file;
carrying out maintenance and check, and judging a power failure rule met by the sample data of the power failure plan application file; the power outage rules at least include: security constraints, mutual exclusion constraints, simultaneous stopping constraints, resource constraints and certainty constraints;
and outputting a judgment result.
The service plan should consider the following issues:
(1) safety restraint: the cross-section tide during equipment maintenance is considered, and the damage to the power grid equipment caused by the out-of-limit voltage of the power grid equipment and the out-of-limit line tide is avoided.
(2) And (3) determinacy constraint: and the essential overhaul of important tasks of capital construction and technical improvement projects is carried out by matching with the instructions of a superior power dispatching department.
(3) And (4) overhauling resource constraint: under the general condition, the power grid coverage is large, the geographical intervals of all power equipment are far away, the maintenance workers are limited, the time problem is considered, and the maintenance of too many equipment in the same time period is avoided.
(4) Mutual exclusion constraint: the safe and stable operation of the power grid is ensured, and some devices cannot be overhauled simultaneously. For example, lines with double circuit lines as backup lines, or some equipment overhaul may cause overload of other equipment, or overhaul may cause generation of an electrical island.
(5) And simultaneous maintenance constraint, namely, in order to meet the principle of no repeated power failure, if the A equipment fails, the B equipment fails, and if the AB equipment and the AB equipment are both in the maintenance plan, the AB equipment and the AB equipment are considered to be maintained simultaneously.
Aiming at the problems, when a power failure rule algorithm model is trained, equipment needing to be overhauled, an application unit, overhaul starting time, overhaul ending time, grade, overhaul category and the like in the sample data of a power failure plan application file need to be identified firstly. Then, an attempt is made to perform maintenance and check, and whether safety constraint (whether the peripheral equipment is out of limit) is met, mutual exclusion constraint (whether the electric island is generated), simultaneous stopping constraint (whether a plurality of pieces of equipment can be arranged to be maintained simultaneously), resource constraint (whether maintenance staff are enough during maintenance time period) and deterministic constraint (the maintenance must be executed, and if the maintenance staff are not met, the maintenance staff are returned to the business staff for rearrangement until the maintenance is met) are judged.
Safety constraints check whether equipment which has a one-degree relationship with planned blackout equipment can generate out-of-limit or not. Specifically, for bus maintenance, assuming that the AA station bus 1 has power failure for maintenance, the device having a one-degree relationship with the AA station bus 1 needs to be queried in the knowledge graph, for example, the AA station bus 2 calculates whether the load transfer from the bus 1 to the bus 2 will be out of limit, if the bus 2 is out of limit, the arranging failure information is returned, and other devices having a one-degree relationship with the AA station bus 1 are traversed in the same manner. And if no abnormal information exists after the traversal is completed, the security constraint is passed.
The mutual exclusion constraint mainly inquires whether the first-degree equipment has power supply after the connection relation is broken due to overhaul (only inquiring whether all the first-degree connection equipment has incoming edges). And if the isolated subgraph is generated after the connection relation is broken, the mutual exclusion constraint is passed, and if the isolated subgraph is generated, the mutual exclusion constraint is not passed.
The simultaneous stopping constraint main inquiry meter causes whether all other power-off equipment is in the maintenance plan application form or not when a certain power-off plan passes, and if the power-off plan passes, the equipment causing the power-off is tried to be scheduled to be maintained in the time period.
Resource constraints primarily query whether schedulable service personnel are sufficient. Before each attempt of arranging the power failure plan, whether the maintainers are enough or not needs to be calculated, if so, the resource constraint is passed, and if not, the resource constraint is not passed.
S15: and generating an approval result of the power failure plan application file according to the judgment result.
Specifically, if the power failure plan application file meets all power failure rules, the power failure plan application file is placed in a passing state, and the meeting conditions of all the power failure rules are output;
and if the power failure plan application file does not meet all power failure rules, the power failure plan application file is placed in a non-passing state, and the power failure rules which are not met by the power failure plan application file are output.
Preferably, referring to fig. 3, the approval result is also displayed at the front end.
Preferably, the power failure plan passed through is also recorded in a map.
Preferably, the constraint condition is also searched for the power failure rule algorithm model through the knowledge graph.
For the power failure plan application file which only meets part of power failure rules or does not meet the power failure rules, the service personnel can carry out plan adjustment according to the returned examination and approval results and report the adjusted result again.
The blackout maintenance planning auxiliary method based on the knowledge graph in some embodiments further comprises:
and updating the knowledge graph in real time according to the data change condition of each data source.
In the embodiment, the knowledge graph is updated in real time according to the data change condition of each data source, so that the entity in the knowledge graph is in the latest state at any time, and the error in the judgment of the power failure plan application file is avoided.
In some embodiments, the blackout maintenance planning assistance method based on the knowledge graph at least includes: persistent state information, historical state information, current state information and future state information;
the persistent state information of the knowledge-graph comprises: a fixed device electrical parameter;
historical state information of the knowledge graph at least comprises the following steps: equipment maintenance records and equipment historical loads;
the current state information of the knowledge-graph at least comprises: the service life of the current equipment, the connection state of the current equipment and the running state of the current equipment;
the future state information of the knowledge-graph at least comprises: prediction information of the future state of the device.
The relevant requirements of the constructed knowledge graph are fed back as follows:
firstly, identifying the topology of adjacent elements of the equipment:
1. a switching element to which the line information and the identification output are connected;
example one：
find the line connected with "220 kV Nansha II":
1) "220 kV Nansha II line 205 switch (220kV Shatian station side)";
2) '220kV Nansha II line xxx switch (500kV Nanning station side)'
And (3) query statement:
MATCH p ═ (n1: line segment) - [ r1] - (n2: end) - [ r2] - (n3: connecting node) - [ r3] - (n4: end) - [ r4] - (n5: handcart) - [ r5] - (n6: end) - [ r6] - (n7: connecting node) - [ r7] - (n8: end) - [ r8] - (n9: switch)
WHERE n1. describes CONTAINS 'NanShaII line'
RETURN n9
2. A switching element for inputting bus information and identifying the bus connection output;
example two：
searching for a bus connected with a 220kV sand field station 220kV #1 bus:
1) "220 kV sand five line 207 switch";
2) "220 kV Nansha I line 204 switch";
3) "220 kV Nansha II line 205 switch";
4) 220kV fine sand line 206 switch "
And (3) query statement:
MATCH p ═ or (n1: voltage class limit) - [ r1] - (n2: bus) - [ r2] - (n3: end) - [ r3] - (n4: connecting node) - [ r4] - (n5: end) - [ r5] - (n6: handcart) - [ r6] - (n7: end) - [ r7] - (n8: connecting node) - [ r8] - (n9: end) - [ r9] - (n10: switch)
WHERE n1. describes CONTAINS '220kV Shatian station'
AND n2. describes CONTAINS '220kV'
AND n2. describes CONTAINS '#1'
RETURN n10
Secondly, mutually exclusive overhaul constraint set:
1. for all buses of 220kV and 110kV, all other buses with the same voltage class which output the same station with a certain bus can be identified, and mutual exclusion constraint among the buses is formed.
example three：
1) Searching a bus with the same voltage class as the station where the 220kV sandfield station 220kV #1 bus is located: 220kV sand field station 220kV # 2 bus "
And (3) query statement:
MATCH p ═ (n1: voltage class limit) - [ r1] - (n2: bus bar)
WHERE n1. describes CONTAINS '220kV Shatian station'
AND n2. describes CONTAINS '220kV'
RETURN n2
2. For the bus, the homonymous double-circuit line connected with a certain bus can be identified and output, and the mutual exclusion constraint among the lines is formed.
example four：
Searching for a bus connected with a 220kV sand field station 220kV #1 bus: 220kV south sand I line and 220kV south sand II line
And (3) query statement:
MATCH p ═ n: voltage class limit) - [ r ] - (n: bus) - [ r ] - (n: end) - [ r ] - (n: connecting node) - [ r ] - (n: end) - [ r ] - (n: handcart) - [ r ] - (n: end) - [ r ] - (n: connecting node) - [ r ] - (n: end) - [ r ] - (n: switch) - [ r ] - (n: end) - [ r ] - (n: connecting node) - [ r ] - (n: end) - [ r ] - (n: handcart) - [ r ] - (n: end) - [ r ] - (n: connecting node) - [ r ] - (n: end) - [ r ] - (n: line segment).
WHERE n1. describes CONTAINS '220kV Shatian station'
AND n2. describes CONTAINS '220kV'
AND n2. describes CONTAINS '#1'
AND (n6. for CONTAINS 'I' OR n6. for CONTAINS 'II')
AND (n10. description CONTAINS 'I' OR n10. description CONTAINS 'II')
AND (n18. description CONTAINS 'I' OR n18. description CONTAINS 'II')
RETURN n18
Thirdly, simultaneously stopping adjacent lines:
1. and for a transformer substation node, identifying and outputting all lines connected with the transformer substation, and forming the simultaneous stop relationship between the high-voltage grade line and the low-voltage grade line of the transformer substation.
example five：
Find the one connected to "220 kV Shatian station":
220kV high voltage class circuit:
1) "220 kV sand five line";
2) "220 kV Nansha I line";
3) "220 kV Nansha II line";
4) '220kV fine sand line
110kV low-voltage class line':
1) "110 kV Shaji song line";
2) "110 kV Shaji line";
3) "110 kV sand body line";
4) "110 kV sand white line";
5) "110 kV desertification line";
6) "110 kV Sha-Yoppon line";
7) "110 kV silver wire;
8) "110 kV Shayu airline";
9) "110 kV Shayuwu line";
query statement (220 kV):
MATCH p ═ (n1: voltage class limit) - [ r1] - (n2: connecting node) - [ r2] - (n3: end) - [ r3] - (n4: line section)
WHERE n1. name CONTAINS '220kV'
AND n1. describes CONTAINS 'Shatian station'
AND n4. describes CONTAINS 'line'
RETURN n4
Query statement (110 kV):
MATCH p ═ (n1: voltage class limit) - [ r1] - (n2: connecting node) - [ r2] - (n3: end) - [ r3] - (n4: line section)
WHERE n1. name CONTAINS '110kV'
AND n1. describes CONTAINS 'Shatian station'
AND n4. describes CONTAINS 'line'
RETURN n4
2. For a line node, all buses connected with the line are identified and output, and the simultaneous stop relationship of adjacent lines and buses is formed.
example six：
Find the one connected to "220 kV south sand I line":
and (3) query statement:
MATCH p ═ line section) - [ r ] - (n: end) - [ r ] - (n: connecting node) - [ r ] - (n: end) - [ r ] - (n: handcart) - [ r ] - (n: end) - [ r ] - (n: connecting node) - [ r ] - (n: end) - [ r ] - (n: switch) - [ r ] - (n: end) - [ r ] - (n: connecting node) - [ r ] - (n: end) - [ r ] - (n: handcart) - [ r ] - (n: end) - [ r ] - (n: connecting node) - [ r ] - (n: end) - [ r ] - (n: bus) - [ r ] - (n: voltage level limit).
WHERE n1. describes CONTAINS 'NanShaI line'
RETURN n17,n18
3. And for one bus node, identifying all lines connected with the bus and outputting the lines to form the simultaneous stop relationship between adjacent lines and the bus.
example seven：
Find the one connected to "220 kV south sand I line":
1) 220kV sand field station 220kV #1 bus "
2) "500 kV Nanning station: 220kV # x bus
And (3) query statement:
MATCH p ═ n: voltage class limit) - [ r ] - (n: bus) - [ r ] - (n: end) - [ r ] - (n: connecting node) - [ r ] - (n: end) - [ r ] - (n: handcart) - [ r ] - (n: end) - [ r ] - (n: connecting node) - [ r ] - (n: end) - [ r ] - (n: switch) - [ r ] - (n: end) - [ r ] - (n: connecting node) - [ r ] - (n: end) - [ r ] - (n: handcart) - [ r ] - (n: end) - [ r ] - (n: connecting node) - [ r ] - (n: end) - [ r ] - (n: line segment).
WHERE n1. describes CONTAINS '220kV Shatian station'
AND n2. describes CONTAINS '220kV'
AND n2. describes CONTAINS '#1'
RETURN n18
An outage maintenance planning assistance system based on knowledge graph, referring to fig. 4, includes:
the acquisition module is used for acquiring a power failure plan application file;
the identification module is used for identifying whether the entity records in the power failure plan application file can be normally matched in the knowledge map one by one based on the pre-constructed knowledge map; the knowledge graph is constructed according to power grid data;
the processing module is used for sending the abnormal entity record to the power dispatching personnel and reporting abnormal information when the abnormal entity record which cannot be normally matched in the knowledge graph is contained in the power failure plan application file; when all entity records in the power failure plan application file are normally matched in the knowledge graph, judging whether the power failure plan application file meets the power failure rule or not based on the knowledge graph and a pre-trained power failure rule algorithm model, and outputting a judgment result;
and the generating module is used for generating an approval result of the power failure plan application file according to the judgment result.
An assistance apparatus for monthly blackout maintenance planning based on knowledge graph, referring to fig. 5, includes:
a processor and a memory;
the processor and the memory are connected through a communication bus:
the processor is used for calling and executing the program stored in the memory;
the memory is used for storing a program, and the program is at least used for executing the monthly blackout maintenance planning auxiliary method based on the knowledge graph in any one of the above embodiments.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A power failure maintenance planning auxiliary method based on a knowledge graph is characterized by comprising the following steps:
acquiring a power failure plan application file;
identifying whether entity records in the power failure plan application file can be normally matched in a knowledge graph one by one on the basis of a pre-constructed knowledge graph; wherein the knowledge graph is constructed according to power grid data;
if the power failure plan application file contains an abnormal entity record which cannot be normally matched in the knowledge graph, the abnormal entity record is sent to power dispatching personnel, and abnormal information is reported;
if all entity records in the power failure plan application file are normally matched in the knowledge graph, judging whether the power failure plan application file meets a power failure rule or not based on the knowledge graph and a pre-trained power failure rule algorithm model, and outputting a judgment result;
and generating an approval result of the power failure plan application file according to the judgment result.
2. The method of claim 1, further comprising:
acquiring power grid data required for constructing the knowledge graph from a plurality of data sources; the grid data at least comprises: power grid model file data, OMS data, PMS data, EMS data and scheduling regulation data;
preprocessing the power grid data;
and inputting the processed power grid data into a map to generate the knowledge map.
3. The method of claim 2, wherein the pre-processing the grid data comprises:
cleaning inaccurate power grid data which are irrelevant to final services in each data source;
performing entity alignment on the cleaned power grid data based on similarity calculation, edit distance calculation and automatic clustering technology;
and finally confirming the aligned power grid data.
4. The method of claim 2, wherein the charting the processed data to generate the knowledge-graph comprises:
taking the power grid topological nodes in the power grid data as node entities of the knowledge graph, and taking connecting lines as the relation of the knowledge graph;
putting production management knowledge data in the power grid data into a graph; the production management knowledge data at least includes: and power failure maintenance of associated department and service personnel information.
5. The method of claim 1, further comprising:
acquiring sample data of a power failure plan application file;
and training the power failure rule algorithm model according to the sample data of the power failure plan application file.
6. The method of claim 5, wherein said training said blackout rule algorithm model according to said blackout plan application file sample data comprises:
identifying equipment needing to be overhauled, an application unit, overhaul starting time, overhaul ending time, grade and overhaul category in the sample data of the power failure plan application file;
carrying out maintenance and check, and judging the power failure rule met by the sample data of the power failure plan application file; the power outage rules at least include: security constraints, mutual exclusion constraints, simultaneous stopping constraints, resource constraints and certainty constraints;
and outputting a judgment result.
7. The method of claim 1, wherein generating the approval result of the blackout plan application file according to the determination result comprises:
if the power failure plan application file meets all power failure rules, the power failure plan application file is placed in a passing state, and the meeting conditions of all power failure rules are output;
and if the power failure plan application file does not meet all power failure rules, placing the power failure plan application file in a non-passing state, and outputting the power failure rules which are not met by the power failure plan application file.
8. The method of claim 2, further comprising:
and updating the knowledge graph in real time according to the data change condition of each data source.
9. The method of claim 1, wherein the knowledge-graph comprises at least: persistent state information, historical state information, current state information and future state information;
the persistent state information of the knowledge-graph comprises: a fixed device electrical parameter;
the historical state information of the knowledge graph at least comprises: equipment maintenance records and equipment historical loads;
the current state information of the knowledge-graph at least comprises: the service life of the current equipment, the connection state of the current equipment and the running state of the current equipment;
the future state information of the knowledge-graph at least comprises: prediction information of the future state of the device.
10. The utility model provides a power failure maintenance scheduling auxiliary system based on knowledge map which characterized in that includes:
the acquisition module is used for acquiring a power failure plan application file;
the identification module is used for identifying whether the entity records in the power failure plan application file can be normally matched in the knowledge map one by one based on a pre-constructed knowledge map; wherein the knowledge graph is constructed according to power grid data;
the processing module is used for sending the abnormal entity record to power dispatching personnel and reporting abnormal information when the abnormal entity record which cannot be normally matched in the knowledge graph is contained in the power failure plan application file; when all entity records in the power failure plan application file are normally matched in the knowledge graph, judging whether the power failure plan application file meets a power failure rule or not based on the knowledge graph and a pre-trained power failure rule algorithm model, and outputting a judgment result;
and the generating module is used for generating an approval result of the power failure plan application file according to the judgment result.
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