CN112001561A - Electric power industry risk prediction method and system - Google Patents
Electric power industry risk prediction method and system Download PDFInfo
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Abstract
The invention discloses a risk prediction method for the power industry, which comprises the following steps: acquiring initial parameters of each power device; acquiring corresponding operation prior knowledge of each power device according to the initial parameters of each power device; establishing a risk prediction model according to the operation priori knowledge of each power device; acquiring and sending real-time operation data of each circuit device; and importing the real-time operation data into a risk prediction model, and outputting a prediction result through the risk prediction model. The invention also discloses a risk prediction system for the power industry. According to the method and the system, risk factors of the power equipment in the power industry can be analyzed, and risk prediction can be timely and efficiently carried out on the power equipment, so that the power equipment can be timely and efficiently overhauled and maintained, and the maintenance cost is saved.
Description
Technical Field
The invention relates to the technical field of electric power industry risk prediction, in particular to an electric power industry risk prediction method and system.
Background
A plurality of power equipment are involved in the power industry, because the power equipment has the characteristics of long continuous operation period, severe equipment operation environment and the like, the power equipment needs to be overhauled and maintained in time, power line and station equipment need to be overhauled according to a plan, and serious defects exist, such as frequent temporary maintenance, insufficient maintenance or excessive maintenance, blind maintenance and the like, so that the cost of equipment maintenance is huge in all countries around the world every year, the power line and station equipment are powered off to cause the change of the distribution of the trend of the whole power grid, and meanwhile, the life of residents can be greatly influenced, therefore, the monitoring and risk prediction on the operation effect of the power equipment need to be influenced, so that the power equipment can be overhauled in time and efficiently, and the power utilization safety is ensured.
Disclosure of Invention
In order to overcome the above problems or at least partially solve the above problems, embodiments of the present invention provide a method and a system for predicting risk in the power industry, which can analyze risk factors of power equipment in the power industry and predict risk of the power equipment, so as to repair and maintain the power equipment in time and efficiently, and save maintenance cost.
The embodiment of the invention is realized by the following steps:
in a first aspect, an embodiment of the present invention provides a power industry risk prediction method, including the following steps:
acquiring initial parameters of each power device;
acquiring corresponding operation prior knowledge of each power device according to the initial parameters of each power device;
establishing a risk prediction model according to the operation priori knowledge of each power device;
acquiring and sending real-time operation data of each circuit device;
and importing the real-time operation data into a risk prediction model, and outputting a prediction result through the risk prediction model.
The electric power safety of the electric power industry is vital, the running states of a plurality of electric power equipment need to be monitored in real time, and risk prediction is carried out in time so as to carry out timely and efficient maintenance on the electric power equipment. When the risk prediction is carried out on the operation safety of the electric power equipment, firstly, the initial parameters of each electric power equipment related to the electric power industry are collected, the initial parameters comprise basic parameter information such as the model, name and type of the electric power equipment and information such as the installation position, then the operation priori knowledge of the corresponding electric power equipment is obtained according to the initial parameters of each electric power equipment, a risk prediction model is built according to the operation priori knowledge of each electric power equipment, the risk prediction model is a mathematical model for predicting the safe operation of the electric power equipment according to the equipment safe operation and dangerous operation data in the operation priori knowledge, after the risk prediction model is built, the real-time operation data of each circuit equipment is obtained and sent, the real-time operation data of each circuit equipment is sequentially led into the risk prediction model, and the safe operation of the electric power equipment is predicted and evaluated through the risk prediction model, and then outputting a prediction result, and carrying out early warning prompt in time.
According to the method, the risk factors of the power equipment in the power industry can be analyzed through the risk prediction model, and the risk prediction is performed on the power equipment, so that the power equipment can be overhauled and maintained timely and efficiently, and the maintenance cost is saved.
Based on the first aspect, in some embodiments of the present invention, a power industry risk prediction method, the operation priori knowledge includes safe operation data and dangerous condition data.
Based on the first aspect, in some embodiments of the present invention, a risk prediction method for power industry, a method for establishing a risk prediction model according to operation prior knowledge of power equipment includes the following steps:
determining equipment safety parameters according to the safety operation data of the power equipment;
determining equipment danger parameters according to the danger condition data of the power equipment;
and establishing a risk prediction model according to the equipment safety parameters and the equipment danger parameters.
Based on the first aspect, in some embodiments of the present invention, a power industry risk prediction method further includes the following steps:
acquiring and transmitting a scheduling plan;
comparing the scheduling plan with the prediction result, generating and sending a comparison result;
and generating and sending a prompt report according to the comparison result.
Based on the first aspect, in some embodiments of the present invention, a method for predicting risk in power industry, the method for comparing a scheduling plan with a prediction result and generating and sending the comparison result includes the following steps:
presetting a safety threshold;
comparing the scheduling plan with the prediction result, generating and sending an initial comparison result;
judging whether the initial comparison result exceeds a preset safety threshold value, and if so, generating a danger comparison result; if not, generating a safety comparison result.
In a second aspect, an embodiment of the present invention provides a risk prediction system for an electric power industry, including a parameter acquisition module, a priori knowledge acquisition module, a model establishment module, an operation data acquisition module, and a result output module, where:
the parameter acquisition module is used for acquiring initial parameters of each power device;
the prior knowledge acquisition module is used for acquiring the corresponding operation prior knowledge of each electric power device according to the initial parameters of each electric power device;
the model establishing module is used for establishing a risk prediction model according to the operation priori knowledge of each power device;
the operation data acquisition module is used for acquiring and transmitting real-time operation data of each circuit device;
and the result output module is used for importing the real-time operation data into the risk prediction model and outputting the prediction result through the risk prediction model.
The electric power safety of the electric power industry is vital, the running states of a plurality of electric power equipment need to be monitored in real time, and risk prediction is carried out in time so as to carry out timely and efficient maintenance on the electric power equipment. When the risk prediction is carried out on the operation safety of the electric power equipment, firstly, the initial parameters of each electric power equipment related to the electric power industry are collected through a parameter collecting module, the initial parameters comprise basic parameter information such as the model, name and type of the electric power equipment and information such as the installation position, then the prior knowledge acquisition module acquires the operation prior knowledge of the corresponding electric power equipment according to the initial parameters of each electric power equipment, the operation prior knowledge comprises safe operation data and dangerous condition data, a risk prediction model is established through a model establishing module according to the operation prior knowledge of each electric power equipment, the risk prediction model is a mathematical model for predicting the safe operation of the electric power equipment according to the safe operation data and the dangerous operation data of the equipment in the operation prior knowledge, and after the risk prediction model is established, the real-time operation data of each circuit equipment is acquired and sent through an operation data acquisition module, real-time operation data of each circuit device are sequentially imported into the risk prediction model through the result output module, safe operation of the power equipment is predicted and evaluated through the risk prediction model, then a prediction result is output, and early warning prompt is timely carried out.
According to the system, risk factors of the power equipment in the power industry can be analyzed through the risk prediction model, and the risk prediction is carried out on the power equipment, so that the power equipment can be overhauled and maintained efficiently in time, and the maintenance cost is saved.
Based on the second aspect, in some embodiments of the invention, an electric power industry risk prediction system, the operation priori knowledge comprises safe operation data and dangerous condition data.
Based on the second aspect, in some embodiments of the present invention, an electric power industry risk prediction system, the model building module includes a safety parameter sub-module, a risk parameter sub-module, and a prediction module sub-module, wherein:
the safety parameter submodule is used for determining equipment safety parameters according to the safety operation data of the power equipment;
the danger parameter submodule is used for determining equipment danger parameters according to the danger condition data of the power equipment;
and the prediction module submodule is used for establishing a risk prediction model according to the equipment safety parameters and the equipment danger parameters.
Based on the second aspect, in some embodiments of the present invention, an electric power industry risk prediction system further includes a plan obtaining module, a comparing module, and a prompting module, wherein:
the plan acquisition module is used for acquiring and transmitting a scheduling plan;
the comparison module is used for comparing the scheduling plan with the prediction result, generating and sending a comparison result;
and the prompt module is used for generating and sending a prompt report according to the comparison result.
Based on the second aspect, in some embodiments of the present invention, an electric power industry risk prediction system includes a comparison module, a preset sub-module, an initial sub-module, and a judgment sub-module, where:
the preset submodule is used for presetting a safety threshold;
the initial submodule is used for comparing the scheduling plan with the prediction result, generating and sending an initial comparison result;
the judgment submodule is used for judging whether the initial comparison result exceeds a preset safety threshold value or not, and if so, generating a danger comparison result; if not, generating a safety comparison result.
The embodiment of the invention at least has the following advantages or beneficial effects:
the embodiment of the invention provides a risk prediction method for the power industry, which comprises the steps of collecting initial parameters of each power device related to the power industry, acquiring operation priori knowledge of the corresponding power device according to the initial parameters of each power device, establishing a risk prediction model according to the operation priori knowledge of each power device, acquiring and transmitting real-time operation data of each circuit device after the establishment of the risk prediction model is completed, sequentially importing the real-time operation data of each circuit device into the risk prediction model, performing prediction and evaluation on the safe operation of the power device through the risk prediction model, outputting a prediction result, and performing early warning prompt in time when the risk prediction is performed on the operation safety of the power device. According to the method, the risk factors of the power equipment in the power industry can be analyzed through the risk prediction model, and the risk prediction is performed on the power equipment, so that the power equipment can be overhauled and maintained timely and efficiently, and the maintenance cost is saved.
The embodiment of the invention also provides a risk prediction system for the power industry, when the risk prediction is carried out on the operation safety of the power equipment, firstly, the initial parameters of each power equipment related to the power industry are collected through the parameter collection module, then the operation priori knowledge of the corresponding power equipment is obtained according to the initial parameters of each power equipment through the priori knowledge acquisition module, the risk prediction model is established according to the operation priori knowledge of each power equipment through the model establishment module, after the establishment of the risk prediction model is finished, the real-time operation data of each circuit equipment is acquired and sent through the operation data acquisition module, the real-time operation data of each circuit equipment is sequentially imported into the risk prediction model through the result output module, the prediction evaluation is carried out on the safety operation of the power equipment through the risk prediction model, and then the prediction result is output, and carrying out early warning prompt in time. According to the system, risk factors of the power equipment in the power industry can be analyzed through the risk prediction model, and the risk prediction is carried out on the power equipment, so that the power equipment can be overhauled and maintained efficiently in time, and the maintenance cost is saved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a risk prediction method for the power industry according to an embodiment of the present invention;
FIG. 2 is a flow chart of modeling in a risk prediction method for the power industry according to an embodiment of the present invention;
FIG. 3 is a flowchart of plan comparison in a risk prediction method for the power industry according to an embodiment of the present invention;
fig. 4 is a schematic block diagram of a risk prediction system in the power industry according to an embodiment of the present invention.
Icon: 100. a parameter acquisition module; 200. a priori knowledge acquisition module; 300. a model building module; 301. a security parameter submodule; 302. a risk parameter submodule; 303. a prediction module sub-module; 400. operating a data acquisition module; 500. a result output module; 600. a plan acquisition module; 700. a comparison module; 701. presetting a submodule; 702. an initial submodule; 703. a judgment submodule; 800. and a prompt module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
In the description of the embodiments of the present invention, "a plurality" represents at least 2.
Examples
As shown in fig. 1, an embodiment of the present invention provides a method for predicting risk in power industry, including the following steps:
s1, collecting initial parameters of each power device;
s2, acquiring the operation prior knowledge of each corresponding power device according to the initial parameters of each power device;
s3, establishing a risk prediction model according to the operation priori knowledge of each power device;
s4, acquiring and transmitting real-time operation data of each circuit device;
and S5, importing the real-time operation data into the risk prediction model, and outputting a prediction result through the risk prediction model.
The electric power safety of the electric power industry is vital, the running states of a plurality of electric power equipment need to be monitored in real time, and risk prediction is carried out in time so as to carry out timely and efficient maintenance on the electric power equipment. When the risk prediction is carried out on the operation safety of the electric power equipment, firstly, the initial parameters of each electric power equipment related to the electric power industry are collected, the initial parameters comprise basic parameter information such as the model, name and type of the electric power equipment and information such as the installation position, then the operation priori knowledge of the corresponding electric power equipment is obtained according to the initial parameters of each electric power equipment, the operation priori knowledge comprises safe operation data and dangerous condition data, a risk prediction model is established according to the operation priori knowledge of each electric power equipment, the risk prediction model is a mathematical model for predicting the safe operation of the electric power equipment according to the equipment safe operation and dangerous operation data in the operation priori knowledge, after the establishment of the risk prediction model is finished, the real-time operation data of each circuit equipment is obtained and sent, and the real-time operation data of each circuit equipment is sequentially led into the risk prediction model, and performing prediction evaluation on the safe operation of the power equipment through the risk prediction model, outputting a prediction result and performing early warning prompt in time.
According to the method, the risk factors of the power equipment in the power industry can be analyzed through the risk prediction model, and the risk prediction is performed on the power equipment, so that the power equipment can be overhauled and maintained timely and efficiently, and the maintenance cost is saved.
In one embodiment, as shown in fig. 2, the method for establishing a risk prediction model according to the operation prior knowledge of the power equipment includes the following steps:
s31, determining equipment safety parameters according to the safety operation data of the power equipment;
s32, determining equipment danger parameters according to the danger condition data of the power equipment;
and S33, establishing a risk prediction model according to the equipment safety parameters and the equipment danger parameters.
When the risk prediction model is established, firstly, the equipment safety parameters of each electric power equipment are determined according to the safety operation data in the operation priori knowledge of each electric power equipment, the equipment danger parameters of each electric power equipment are determined according to the danger condition data in the operation priori knowledge of each electric power equipment, then the equipment safety parameters and the equipment danger parameters of all the electric power equipment are sorted to obtain integrated data, and the risk prediction model is established according to the integrated data of the equipment safety parameters and the equipment danger parameters, so that the risk prediction is carried out on the operation safety of the electric power equipment in the subsequent process.
In one embodiment, as shown in fig. 3, the method for predicting risk in power industry further includes the following steps:
s6, acquiring and sending a scheduling plan;
s7, comparing the scheduling plan with the prediction result, generating and sending a comparison result;
and S8, generating and sending a prompt report according to the comparison result.
When the electric power industry overhauls the power equipment, generally overhaul and maintain each power equipment according to the scheduling plan, can judge the rationality of the scheduling plan through comparing the scheduling plan with the prediction result, avoid blindly maintaining to reduce cost of maintenance. Firstly, a scheduling plan is obtained and sent, then information such as maintenance time and maintenance equipment in the scheduling plan is compared with maintenance time and maintenance equipment in a prediction result, the rationality of the scheduling plan is judged, a comparison result is generated and sent, and a prompt report is generated and sent to relevant equipment maintenance personnel according to the comparison result.
In one embodiment, the method for comparing the scheduling plan with the prediction result and generating and sending the comparison result comprises the following steps:
presetting a safety threshold;
comparing the scheduling plan with the prediction result, generating and sending an initial comparison result;
judging whether the initial comparison result exceeds a preset safety threshold value, and if so, generating a danger comparison result; if not, generating a safety comparison result.
When the rationality of the scheduling plan is judged, firstly, a safety threshold (safety range value) is preset, then the maintenance time and the maintenance equipment in the scheduling plan are compared with the maintenance time and the maintenance equipment in the prediction result to generate and send an initial comparison result, then whether the comparison difference value in the initial comparison result exceeds the preset safety threshold is judged, and if so, a danger comparison result is generated; if not, generating a safety comparison result.
As shown in fig. 4, an embodiment of the present invention provides a risk prediction system for power industry, including a parameter acquisition module 100, a priori knowledge acquisition module 200, a model establishment module 300, an operation data acquisition module 400, and a result output module 500, where:
a parameter collecting module 100, configured to collect initial parameters of each power device;
a priori knowledge acquisition module 200, configured to acquire, according to initial parameters of each electrical device, operation priori knowledge of each corresponding electrical device;
the model establishing module 300 is used for establishing a risk prediction model according to the operation priori knowledge of each power device;
an operation data acquiring module 400, configured to acquire and send real-time operation data of each circuit device;
and a result output module 500, configured to import the real-time operation data into the risk prediction model, and output a prediction result through the risk prediction model.
The electric power safety of the electric power industry is vital, the running states of a plurality of electric power equipment need to be monitored in real time, and risk prediction is carried out in time so as to carry out timely and efficient maintenance on the electric power equipment. When the risk prediction is performed on the operation safety of the electric power equipment, firstly, the parameter acquisition module 100 acquires initial parameters of each electric power equipment related to the electric power industry, the initial parameters include basic parameter information such as the model, name and type of the electric power equipment and information such as the installation position, then the priori knowledge acquisition module 200 acquires the operation priori knowledge of the corresponding electric power equipment according to the initial parameters of each electric power equipment, the operation priori knowledge comprises safe operation data and dangerous condition data, the model establishment module 300 establishes a risk prediction model according to the operation priori knowledge of each electric power equipment, the risk prediction model is a mathematical model for predicting the safe operation of the electric power equipment according to the equipment safe operation and dangerous operation data in the operation priori knowledge, and after the risk prediction model is established, the operation data acquisition module 400 acquires and transmits the real-time operation data of each circuit equipment, real-time operation data of each circuit device is sequentially imported into the risk prediction model through the result output module 500, safe operation of the power device is predicted and evaluated through the risk prediction model, and then a prediction result is output to give an early warning prompt in time.
According to the system, risk factors of the power equipment in the power industry can be analyzed through the risk prediction model, and the risk prediction is carried out on the power equipment, so that the power equipment can be overhauled and maintained efficiently in time, and the maintenance cost is saved.
In one embodiment, as shown in FIG. 4, the model building module 300 includes a safety parameter sub-module 301, a risk parameter sub-module 302, and a prediction module sub-module 303, wherein:
the safety parameter submodule 301 is configured to determine an apparatus safety parameter according to the safety operation data of the electrical apparatus;
the danger parameter submodule 302 is used for determining equipment danger parameters according to the danger condition data of the power equipment;
and the prediction module submodule 303 is used for establishing a risk prediction model according to the equipment safety parameters and the equipment danger parameters.
When the risk prediction model is established, firstly, the safety parameter submodule 301 determines the equipment safety parameter of each electric power equipment according to the safety operation data in the operation priori knowledge of each electric power equipment, the danger parameter submodule 302 determines the equipment danger parameter of each electric power equipment according to the danger condition data in the operation priori knowledge of each electric power equipment, then the prediction module submodule 303 arranges the equipment safety parameters and the equipment danger parameters of all the electric power equipment to obtain integrated data, and the risk prediction model is established according to the integrated data of the equipment safety parameters and the equipment danger parameters, so that the risk prediction is carried out on the operation safety of the electric power equipment in the subsequent process.
In one embodiment, as shown in fig. 4, the power industry risk prediction system further includes a plan obtaining module 600, a comparing module 700, and a prompting module 800, wherein:
a plan obtaining module 600, configured to obtain and send a scheduling plan;
a comparison module 700, configured to compare the scheduling plan with the prediction result, generate and send a comparison result;
and the prompt module 800 is configured to generate and send a prompt report according to the comparison result.
Firstly, a scheduling plan is obtained and sent through a plan obtaining model, then information such as maintenance time and maintenance equipment in the scheduling plan is compared with maintenance time and maintenance equipment in a prediction result through a comparison module 700, the rationality of the scheduling plan is judged, a comparison result is generated and sent, and a prompt report is generated and sent to relevant equipment maintenance personnel through a prompt module 800 according to the comparison result. When the electric power industry overhauls the power equipment, generally overhaul and maintain each power equipment according to the scheduling plan, can judge the rationality of the scheduling plan through comparing the scheduling plan with the prediction result, avoid blindly maintaining to reduce cost of maintenance.
In one embodiment, as shown in fig. 4, the comparison module 700 includes a preset sub-module 701, an initial sub-module 702, and a determination sub-module 703, wherein:
a preset sub-module 701, configured to preset a safety threshold;
an initial sub-module 702, configured to compare the scheduling plan with the prediction result, generate and send an initial comparison result;
a judging submodule 703, configured to judge whether the initial comparison result exceeds a preset safety threshold, and if so, generate a dangerous comparison result; if not, generating a safety comparison result.
When the rationality of the scheduling plan is judged, firstly, a safety threshold (safety range value) is preset through the preset submodule 701, then the maintenance time and the maintenance equipment in the scheduling plan are compared with the maintenance time and the maintenance equipment in the prediction result through the initial submodule 702 to generate and send an initial comparison result, then the judgment submodule 703 judges whether the comparison difference value in the initial comparison result exceeds the preset safety threshold, and if so, a danger comparison result is generated; if not, generating a safety comparison result.
In summary, embodiments of the present invention provide a method and a system for predicting risk in the power industry, when performing risk prediction on operation safety of power devices, first, acquiring initial parameters of each power device related to the power industry, where the initial parameters include basic parameter information such as a model, a name, and a type of the power device, and information such as an installation location, and then acquiring a priori operation knowledge of the corresponding power device according to the initial parameters of each power device, where the a priori operation knowledge includes safe operation data and dangerous condition data, when establishing a risk prediction model, first, determining a device safety parameter of each power device according to safe operation data in the priori operation knowledge of each power device, determining a device dangerous parameter of each power device according to dangerous condition data in the priori operation knowledge of each power device, and then, sorting the device safety parameters and the device dangerous parameters of all power devices, the risk prediction model is a mathematical model for predicting the safe operation of the power equipment according to the safe operation and the dangerous operation data of the equipment in the operation priori knowledge, after the risk prediction model is built, the real-time operation data of each circuit equipment is obtained and sent, the real-time operation data of each circuit equipment is sequentially imported into the risk prediction model, the safe operation of the power equipment is predicted and evaluated through the risk prediction model, then the prediction result is output, and early warning prompt is timely carried out. Risk factors of the power equipment in the power industry can be analyzed through the risk prediction model, risk prediction is carried out on the power equipment, and therefore the power equipment can be overhauled and maintained efficiently in time, and maintenance cost is saved. When the electric power industry overhauls the power equipment, generally overhaul and maintain each power equipment according to the scheduling plan, can judge the rationality of the scheduling plan through comparing the scheduling plan with the prediction result, avoid blindly maintaining to reduce cost of maintenance. Firstly, a scheduling plan is obtained and sent, then information such as maintenance time and maintenance equipment in the scheduling plan is compared with maintenance time and maintenance equipment in a prediction result, the rationality of the scheduling plan is judged, a comparison result is generated and sent, and a prompt report is generated and sent to relevant equipment maintenance personnel according to the comparison result.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. A power industry risk prediction method is characterized by comprising the following steps:
acquiring initial parameters of each power device;
acquiring corresponding operation prior knowledge of each power device according to the initial parameters of each power device;
establishing a risk prediction model according to the operation priori knowledge of each power device;
acquiring and sending real-time operation data of each circuit device;
and importing the real-time operation data into a risk prediction model, and outputting a prediction result through the risk prediction model.
2. The electric power industry risk prediction method of claim 1, wherein the operational prior knowledge comprises safe operational data and hazardous condition data.
3. The electric power industry risk prediction method of claim 2, wherein the method of establishing a risk prediction model based on the operational prior knowledge of the electric power equipment comprises the steps of:
determining equipment safety parameters according to the safety operation data of the power equipment;
determining equipment danger parameters according to the danger condition data of the power equipment;
and establishing a risk prediction model according to the equipment safety parameters and the equipment danger parameters.
4. The electric power industry risk prediction method of claim 1, further comprising the steps of:
acquiring and transmitting a scheduling plan;
comparing the scheduling plan with the prediction result, generating and sending a comparison result;
and generating and sending a prompt report according to the comparison result.
5. The electric power industry risk prediction method according to claim 4, wherein the method for comparing the scheduling plan with the prediction result and generating and sending the comparison result comprises the following steps:
presetting a safety threshold;
comparing the scheduling plan with the prediction result, generating and sending an initial comparison result;
judging whether the initial comparison result exceeds a preset safety threshold value, and if so, generating a danger comparison result; if not, generating a safety comparison result.
6. The utility model provides an electric power industry risk prediction system which characterized in that, includes parameter acquisition module, priori knowledge acquisition module, model establishment module, operation data acquisition module and result output module, wherein:
the parameter acquisition module is used for acquiring initial parameters of each power device;
the prior knowledge acquisition module is used for acquiring the corresponding operation prior knowledge of each electric power device according to the initial parameters of each electric power device;
the model establishing module is used for establishing a risk prediction model according to the operation priori knowledge of each power device;
the operation data acquisition module is used for acquiring and transmitting real-time operation data of each circuit device;
and the result output module is used for importing the real-time operation data into the risk prediction model and outputting the prediction result through the risk prediction model.
7. The electric power industry risk prediction system of claim 6, wherein the operational prior knowledge comprises safe operational data and hazardous conditions data.
8. The electric power industry risk prediction system of claim 7, wherein the model building module comprises a safety parameter sub-module, a risk parameter sub-module, and a prediction module sub-module, wherein:
the safety parameter submodule is used for determining equipment safety parameters according to the safety operation data of the power equipment;
the danger parameter submodule is used for determining equipment danger parameters according to the danger condition data of the power equipment;
and the prediction module submodule is used for establishing a risk prediction model according to the equipment safety parameters and the equipment danger parameters.
9. The electric power industry risk prediction system of claim 6, further comprising a plan acquisition module, a comparison module, and a prompt module, wherein:
the plan acquisition module is used for acquiring and transmitting a scheduling plan;
the comparison module is used for comparing the scheduling plan with the prediction result, generating and sending a comparison result;
and the prompt module is used for generating and sending a prompt report according to the comparison result.
10. The electric power industry risk prediction system of claim 9, wherein the comparison module comprises a preset submodule, an initial submodule, and a judgment submodule, wherein:
the preset submodule is used for presetting a safety threshold;
the initial submodule is used for comparing the scheduling plan with the prediction result, generating and sending an initial comparison result;
the judgment submodule is used for judging whether the initial comparison result exceeds a preset safety threshold value or not, and if so, generating a danger comparison result; if not, generating a safety comparison result.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112818515A (en) * | 2021-01-05 | 2021-05-18 | 北京经纬信息技术有限公司 | Gate leakage detection system and method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105447657A (en) * | 2015-12-30 | 2016-03-30 | 国网新疆电力公司经济技术研究院 | Risk-based operation and maintenance management system of 750KV power equipment |
| KR20160093119A (en) * | 2014-12-31 | 2016-08-08 | 주식회사 효성 | Asset management system and method for electric power apparatus |
| CN105956789A (en) * | 2016-05-24 | 2016-09-21 | 国网四川省电力公司 | Quantitative risk evaluation method for power equipment based on state evaluation |
| CN108008332A (en) * | 2017-11-29 | 2018-05-08 | 国网山东省电力公司电力科学研究院 | A kind of new energy Remote testing device method for diagnosing faults based on data mining |
| US20190101908A1 (en) * | 2016-05-04 | 2019-04-04 | Doosan Heavy Industries & Construction Co., Ltd. | Plant abnormality detection method and system |
| CN110516848A (en) * | 2019-07-31 | 2019-11-29 | 国网江苏省电力有限公司 | A kind of electric equipment maintenance Cost Optimization Approach based on survival analysis model |
-
2020
- 2020-09-01 CN CN202010905419.7A patent/CN112001561A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160093119A (en) * | 2014-12-31 | 2016-08-08 | 주식회사 효성 | Asset management system and method for electric power apparatus |
| CN105447657A (en) * | 2015-12-30 | 2016-03-30 | 国网新疆电力公司经济技术研究院 | Risk-based operation and maintenance management system of 750KV power equipment |
| US20190101908A1 (en) * | 2016-05-04 | 2019-04-04 | Doosan Heavy Industries & Construction Co., Ltd. | Plant abnormality detection method and system |
| CN105956789A (en) * | 2016-05-24 | 2016-09-21 | 国网四川省电力公司 | Quantitative risk evaluation method for power equipment based on state evaluation |
| CN108008332A (en) * | 2017-11-29 | 2018-05-08 | 国网山东省电力公司电力科学研究院 | A kind of new energy Remote testing device method for diagnosing faults based on data mining |
| CN110516848A (en) * | 2019-07-31 | 2019-11-29 | 国网江苏省电力有限公司 | A kind of electric equipment maintenance Cost Optimization Approach based on survival analysis model |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112818515A (en) * | 2021-01-05 | 2021-05-18 | 北京经纬信息技术有限公司 | Gate leakage detection system and method |
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