CN110110801A - A kind of transmission line of electricity fire extinguishing necessity sentences knowledge method and system - Google Patents
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Abstract
The present invention relates to electrical engineering technical fields, disclose a kind of transmission line of electricity fire extinguishing necessity sentences knowledge method and system, it to efficiently use mountain fire environmental data and grid equipment data, sufficiently analyzes mountain fire sprawling and causes the environmental condition of calamity, while considering artificial control cause of fire element to make a policy；The method comprise the steps that choosing the history mountain fire related data in region to be analyzed, is pre-processed history mountain fire related data to obtain initial data set, initial data set is divided into training dataset and validation data set；Binary tree computation model is established, and whether verify binary tree computation model effective；Mountain fire related data input binary tree computation model of the transmission line of electricity to be analyzed in the following setting time is obtained, if the output valve of binary tree computation model belongs to first threshold range, sentences and knows the transmission line of electricity and do not put out a fire necessity；If the output valve of binary tree computation model belongs to second threshold range, sentence know the transmission line of electricity have fire extinguishing necessity.
Description
Technical field
The present invention relates to electrical engineering technical field more particularly to a kind of transmission line of electricity fire extinguishing necessity sentence knowledge method and
System.
Background technique
In recent years, electric power enterprise threatens to respond actively transmission line forest fire, has developed a variety of profession fire extinguishing dresses
It is standby.Whenever the mountain fire high-incidence season, mountain fire easily sends out province electric power enterprise all can dispose large-scale fire extinguishing equipment in advance, utmostly to be promoted
Transmission line forest fire fire-fighting efficiency reduces transmission line forest fire hidden danger to greatest extent.Realize the high effect of large-scale fire extinguishing equipment
With needing to consider simultaneously two critical issues, on the one hand, since transmission line forest fire is multi-point and wide-ranging, certain districts and cities are in the clear and bright, spring
More than ten possibly even occurred within the same period and plays mountain fire the mountain fires high-incidence seasons such as section, and limited amount is equipped in large-scale fire extinguishing, real
Existing route fire point takes into account that there are still bigger difficulty comprehensively；On the other hand, the developmenting spread of mountain fire fire point needs while expiring
The a variety of environmental conditions of foot, not all fire point can cause transmission line malfunction, and more and more local governments in recent years
A clear guidance and the fiery custom of the management masses, the initial mountain fire in some areas can be put out rapidly by governmental personnel, greatly reduce
Electric power enterprise fire extinguishing necessity.For the two, contradictory equipment is deployed to ensure effective monitoring and control of illegal activities problem each other, and traditional decision-making technique is often difficult to excellent
Change modeling.And once fire behavior occurs, and the troop that needs to put out a fire makes decisions rapidly, in order to avoid delay fire extinguishing opportunity.
Therefore, mountain fire environmental data and grid equipment data how are efficiently used, the ring that mountain fire sprawling causes calamity is sufficiently analyzed
Border condition, while considering that artificial control cause of fire element becomes a urgent problem to make a policy.
Summary of the invention
Knowledge method and system are sentenced it is an object of that present invention to provide a kind of transmission line of electricity fire extinguishing necessity, to efficiently use mountain
Fire environment data and grid equipment data sufficiently analyze mountain fire sprawling and cause the environmental condition of calamity, while considering artificial control cause of fire element
To make a policy.
To achieve the above object, knowledge method is sentenced the present invention provides a kind of transmission line of electricity fire extinguishing necessity, including following
Step:
S1: being chosen the history mountain fire related data in region to be analyzed by sets requirement, by the history mountain fire related data
It is pre-processed to obtain initial data set, the initial data set is divided into training dataset and validation data set；
S2: the output threshold range of binary decision tree is set as two, the training dataset is inputted into the y-bend and is determined
Whether plan tree establishes binary tree computation model, and effective using the validation data set verifying binary tree computation model, if
It is invalid then adjust the historical heat monitoring data and re-establish binary tree computation model, until the binary tree computation model has
Effect；
S3: it obtains mountain fire related data of the transmission line of electricity to be analyzed in the following setting time and inputs the binary tree
Computation model, if the output valve of the binary tree computation model belongs to first threshold range, sentence know the transmission line of electricity do not go out
Fiery necessity；If the output valve of binary tree computation model belongs to second threshold range, sentences and know the transmission line of electricity and have fire extinguishing necessary
Property.
Preferably, it in the S1, is pre-processed the history mountain fire related data to obtain initial data set, it is specific to wrap
Include following steps:
The history mountain fire related data is divided to obtain tripping and do not trip two according to whether mountain fire tripping occurs
Class data；
The two classes data are arranged according to chronological order respectively, by all nonnumeric data in data
Quantization obtains initial data set.
Preferably, the S2 specifically includes the following steps:
S21: assuming that x is input variable, y is output variable, and an input variable is considered as a region, establishes formula:
In formula, j is each feature in region, and s is the value of each feature, R1For the first sub-regions, R2It is second
Subregion, c1For section R1Interior output average value, c2For section R2Interior output average value；
Wherein:
In formula, x ∈ Rm, m=1,2, RmFor m-th of region of division, cmFor the output average value in m-th of region；
The each value s for successively traversing each feature j calculates the error of each current possible cut-off, selection
Make the smallest cut-off of error as optimal cut-off s, the corresponding variable of the optimal cut-off be considered as optimal cutting variable j,
Selection is to (j, s)；
S22: with it is selected be two sub-regions by region division to (j, s), and determine corresponding output valve, establish y-bend
Decision tree formula are as follows:
R1(j, s)=and x | x(j)≤s},R2(j, s)=and x | x(j)> s } (3)
S23: above-mentioned S21-S22 is repeated, continues to divide two sub-regions, the input space is divided into m region
R1, R2, R3 ..., Rm generate binary tree computation model, calculation formula are as follows:
In formula, I is weight coefficient, and M can value range for m's.
Preferably, the S3 specifically includes the following steps:
Validation data set is inputted into binary tree computation model, the identifying result of binary tree computation model and practical tripping are tied
Fruit is compared, if be more than 85% judging result it is consistent with actual result, then it is assumed that binary tree computation model is effective.
Preferably, it is further comprised the steps of: after the S3
S4: mountain fire related data described in S3 and practical mountain fire situation are included into historical data, update current y-bend
Computation model is set, the binary tree computation model after being optimized calculates for next time；
S5: repeating S3-S4, realizes and updates to the iteration of binary tree computation model.
Preferably, the mountain fire related data include the voltage class data of transmission line of electricity, it is transmission line of electricity density data, defeated
One of electric line head room data and electric transmission line channel width data or several any combination.
Preferably, it is line information, fire behavior space time information, ring that the sets requirement, which is the information that mountain fire related data includes,
Border information and fire extinguishing information.
The inventive concept total as one sentences knowledge system, packet the present invention also provides a kind of transmission line of electricity fire extinguishing necessity
It includes memory, processor and is stored in the computer program that can be run on the memory and on the processor, the place
Manage the step of realizing the above method when device executes described program.
The invention has the following advantages:
What the present invention provided a kind of transmission line of electricity fire extinguishing necessity sentences knowledge method and system, treats the history mountain of analyzed area
Fiery related data carries out calculating analysis, establishes binary tree computation model, then will need to sentence the real-time mountain fire of the transmission line of electricity of knowledge
Related data inputs the binary tree computation model, can fast and accurately obtain identifying result, can efficiently use mountain fire environment
Data and grid equipment data sufficiently analyze mountain fire sprawling and cause the environmental condition of calamity, while considering artificial control cause of fire element to make
Decision.
Below with reference to accompanying drawings, the present invention is described in further detail.
Detailed description of the invention
The attached drawing constituted part of this application is used to provide further understanding of the present invention, schematic reality of the invention
It applies example and its explanation is used to explain the present invention, do not constitute improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is that the grid equipment of the preferred embodiment of the present invention melts fire extinguishing necessity and sentences knowledge method flow diagram.
Specific embodiment
The embodiment of the present invention is described in detail below in conjunction with attached drawing, but the present invention can be defined by the claims
Implement with the multitude of different ways of covering.
Unless otherwise defined, all technical terms used hereinafter and the normally understood meaning of those skilled in the art
It is identical." first ", " second " used in present patent application specification and claims and similar word are simultaneously
Any sequence, quantity or importance are not indicated, and are intended merely to facilitate and corresponding components are distinguished.Equally, " one
It is a " or the similar word such as " one " do not indicate that quantity limits, but indicate that there are at least one.
Embodiment 1
Referring to Fig. 1, knowledge method is sentenced the present embodiment provides a kind of transmission line of electricity fire extinguishing necessity, comprising the following steps:
S1: being chosen the history mountain fire related data in region to be analyzed by sets requirement, and history mountain fire related data is carried out
Pretreatment obtains initial data set, and initial data set is divided into training dataset and validation data set；
S2: the output threshold range of binary decision tree is set as two, training dataset input binary decision tree is established
Binary tree computation model, and it is whether effective using validation data set verifying binary tree computation model, history heat is adjusted if invalid
Point monitoring data re-establish binary tree computation model, until binary tree computation model is effective；
S3: it obtains mountain fire related data input binary tree of the transmission line of electricity to be analyzed in the following setting time and calculates mould
Type is sentenced if the output valve of binary tree computation model belongs to first threshold range and knows the transmission line of electricity and do not put out a fire necessity；If
The output valve of binary tree computation model belongs to second threshold range, then sentence know the transmission line of electricity have fire extinguishing necessity.
Above-mentioned transmission line of electricity fire extinguishing necessity sentences knowledge method, and the history mountain fire related data for treating analyzed area carries out
Calculate analysis, establish binary tree computation model, then will need to sentence knowledge transmission line of electricity real-time mountain fire related data input should
Binary tree computation model can fast and accurately obtain identifying result, can efficiently use mountain fire environmental data and grid equipment
Data sufficiently analyze mountain fire sprawling and cause the environmental condition of calamity, while considering artificial control cause of fire element to make a policy.
Specifically, the mountain fire related data of Hunan Province's transmission line of electricity in the latest 20 years is chosen, it is desirable that the data are that route is attached
Mountain fire closely has occurred but power department whole process does not carry out the fire point information rescued on a large scale, when including route essential information, fire behavior
Empty information, environmental information, fire extinguishing information etc..Specifically include voltage class, the mountain fire generation area route that route is influenced by mountain fire
Density (every square kilometre of number of lines), route head room (vertical range of route and the highest vegetation top in lower section), line
Road distance, the mountain fire of paths width (horizontal distance of route and the nearest vegetation in lower section), large-scale fire extinguishing equipment and fire point
Generation area vegetation pattern, landform, mountain fire date of occurrence (containing whether the mountain fire high-incidence season), the mountain fire period of right time (containing whether night
Between), wind speed of mountain fire when occurring, wind direction, burnt area, re-ignition situation (whether re-ignition), mountain fire spot municipal government control firepower
Degree (stringent control, is not managed local control) information etc..In case of mountain fire tripping, then the data information is trip position
Information, if mountain fire tripping does not occur, which is the letter according to 3 nearest base shaft towers of mountain fire fire point and its route
Breath.
Further, history mountain fire related data is divided to obtain tripping and do not jump according to whether mountain fire tripping occurs
Two class data of lock.Two class data are arranged according to chronological order respectively, by all nonnumeric data in data
Quantization obtains initial data set.In the present embodiment, when carrying out data quantization, for example, vegetation pattern coniferous forest is set as 1, broad-leaved
Woods is set as 2, and coppice is set as 3, and the raw shrubbery of drought is set as 4, and grassland is set as 5, and grassy marshland is set as 6, and landform Plain is set as 1, and plateau is set as 2,
Mountainous region is set as 3, and hills is set as 4, and basin is set as 5, is to be denoted as 1 the mountain fire high-incidence season, the non-mountain fire high-incidence season is denoted as 0, is to be denoted as at night
1, non-night is denoted as 0, and wind direction east wind is set as 1, and southeaster is set as 2, and south wind is set as 3, and southwester is set as 4, and west wind is set as 5, northwest
Wind is set as 6, and north wind is set as 7, and northeaster is set as 8, strictly manages in control firepower degree and is set as 2, local control is set as 1, does not manage and sets
It is 0, re-ignition is set as 1 in re-ignition situation, and non-re-ignition is set as 0, and finally adds column " whether a tripping " item in all data, will send out
The data for having given birth to mountain fire tripping are assigned a value of 1, and the data that mountain fire tripping does not occur are assigned a value of 0.
70% that above-mentioned primary data is concentrated is used as training set, and in addition 30% as verifying collection.It is built according to the training set
Vertical binary decision tree, specific step is as follows.It should be noted that, when establishing binary decision tree, mainly being used in the present embodiment
Information include road distance with fire point of route head room, line channel width, large-scale fire extinguishing equipment, mountain fire generation area
Firepower degree is controlled in vegetation pattern, mountain fire date of occurrence, mountain fire period of right time, mountain fire spot municipal government.From mountain fire, route, environment
Analysis fire extinguishing necessity in data, takes full advantage of various information when mountain fire occurs, avoids the blindness artificially judged.
Specifically, it is assumed that x is input variable, and y is output variable, and an input variable is considered as a region, is established public
Formula:
In formula, j is each feature in region, and s is the value of each feature, R1For the first sub-regions, R2It is second
Subregion, c1For section R1Interior output average value, c2For section R2Interior output average value；
Wherein:
In formula, x ∈ Rm, m=1,2, RmFor m-th of region of division, cmFor the output average value in m-th of region.
The each value s for successively traversing each feature j calculates the error of each current possible cut-off, selection
Make the smallest cut-off of error as optimal cut-off s, the corresponding variable of the optimal cut-off be considered as optimal cutting variable j,
Selection is to (j, s)；
With it is selected be two sub-regions by region division to (j, s), and determine corresponding output valve, in the present embodiment,
The output threshold range of binary decision tree is set as two, respectively includes first threshold range 0~0.3, second threshold range 0.7
If~1 output threshold range shows to threaten without tripping in the first threshold range, no fire extinguishing is necessary, if output threshold range
Within the scope of the second threshold, show there is tripping to threaten, has fire extinguishing necessary.
Establish binary decision tree formula are as follows:
R1(j, s)=and x | x(j)≤s},R2(j, s)=and x | x(j)> s } (3)
Continue to divide two sub-regions according to above-mentioned partiting step, the input space be divided into m region R1,
R2, R3 ..., Rm generate binary tree computation model, calculation formula are as follows:
In formula, I is weight coefficient, and M can value range for m's.
Further, by validation data set input binary tree computation model, by the identifying result of binary tree computation model with
Practical trip condition is compared, if be more than 85% judging result it is consistent with actual result, then it is assumed that binary tree computation model
Effectively.
It in the present embodiment, is verified using 92 groups of data, wherein by 12 groups of non-Tripping datas in binary tree computation model
In output result be greater than 0.7, that is, be judged as and tripped, sentence knowledge mistake, other with assessment situation it is consistent, sentencing knowledge just
Really.To the disaster-stricken identifying result in verification data are as follows: it is correct to sentence knowledge all greater than 0.7 for output result.Therefore sentence knowledge success rate
87%, it is believed that the binary tree computation model is effective.
Specifically, with 14 points to 17 points of afternoon on April 5th, 2018, it is deployed in the one large-scale fire extinguishing equipment periphery in certain city
6 in the 60 kilometer ranges fire points not put out judge one by one.Transmission line of electricity head room, line channel width, large size are gone out
Fire equipment and road distance, mountain fire generation area vegetation pattern, mountain fire date of occurrence, mountain fire period of right time, the mountain fire of fire point are sent out
Binary tree computation model is stated in the inputs such as Radix Rehmanniae municipal government control firepower degree, and it is as shown in table 1 below to obtain fire extinguishing necessity judging result:
1 transmission line forest fire related data of table and judging result
According to above-mentioned table 1 it is found that the transmission line of electricity of serial number 1 has the necessity of fire extinguishing, remaining transmission line of electricity does not have
The necessity of fire extinguishing.
Verified, No. 1 fire point causes the tripping of route mountain fire, it was demonstrated that it is necessary to be implicitly present in fire extinguishing.Other fire points are because each
Kind reason is extinguished, and does not impact to route.
As the present embodiment preferred embodiment, the historical data that above-mentioned 6 data is included into this area is updated current
Binary tree computation model, binary tree computation model after being optimized calculates for next time, and is using the binary tree every time
When computation model is calculated, S3-S4 is repeated, realizes and the iteration of binary tree computation model is updated.By calculating binary tree
Model is automatically updated, it is ensured that binary tree computation model can maintain validity and accuracy, avoid the history chosen
It is inaccurate as a result, improving automation and validity that transmission line of electricity fire extinguishing necessity sentences knowledge caused by data are remote.
Embodiment 2
With above method embodiment correspondingly, the present embodiment provides a kind of transmission line of electricity fire extinguishing necessity sentence knowledge system
System, including memory, processor and is stored in the computer program that can be run on the memory and on the processor, institute
State the step of realizing the above method when processor executes described program.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (9)
1. a kind of transmission line of electricity fire extinguishing necessity sentences knowledge method, which comprises the following steps:
S1: being chosen the history mountain fire related data in region to be analyzed by sets requirement, and the history mountain fire related data is carried out
Pretreatment obtains initial data set, and the initial data set is divided into training dataset and validation data set；
S2: the output threshold range of binary decision tree is set as two, the training dataset is inputted into the binary decision tree
Binary tree computation model is established, and whether effective using the validation data set verifying binary tree computation model, if in vain
It then adjusts the historical heat monitoring data and re-establishes binary tree computation model, until the binary tree computation model is effective；
S3: it obtains mountain fire related data of the transmission line of electricity to be analyzed in the following setting time and inputs the binary tree calculating
Model is sentenced if the output valve of the binary tree computation model belongs to first threshold range and knows the transmission line of electricity do not put out a fire must
The property wanted；If the output valve of binary tree computation model belongs to second threshold range, sentence know the transmission line of electricity have fire extinguishing necessity.
2. transmission line of electricity fire extinguishing necessity according to claim 1 sentences knowledge method, which is characterized in that, will in the S1
The history mountain fire related data is pre-processed to obtain initial data set, specifically includes the following steps:
The history mountain fire related data is divided to obtain tripping and the two class numbers that do not trip according to whether mountain fire tripping occurs
According to；
The two classes data are arranged according to chronological order respectively, by all nonnumeric data quantizations in data
Obtain initial data set.
3. transmission line of electricity fire extinguishing necessity according to claim 1 sentences knowledge method, which is characterized in that the S2 is specifically wrapped
Include following steps:
S21: assuming that x is input variable, y is output variable, and an input variable is considered as a region, establishes formula:
In formula, j is each feature in region, and s is the value of each feature, R1For the first sub-regions, R2For second sub-district
Domain, c1For section R1Interior output average value, c2For section R2Interior output average value；
Wherein:
In formula, x ∈ Rm, m=1,2, RmFor m-th of region of division, cmFor the output average value in m-th of region；
The each value s for successively traversing each feature j, calculates the error of each current possible cut-off, and selection makes to miss
The smallest cut-off of difference is considered as optimal cutting variable j as optimal cut-off s, by the corresponding variable of the optimal cut-off, selects
To (j, s)；
S22: with it is selected be two sub-regions by region division to (j, s), and determine corresponding output valve, establish Binary decision
Set formula are as follows:
R1(j, s)=and x | x(j)≤s},R2(j, s)=and x | x(j)> s } (3)
S23: repeating above-mentioned S21-S22, continue to divide two sub-regions, the input space is divided into m region R1,
R2, R3 ..., Rm generate binary tree computation model, calculation formula are as follows:
In formula, I is weight coefficient, and M can value range for m's.
4. transmission line of electricity fire extinguishing necessity according to claim 1 sentences knowledge method, which is characterized in that in the S2, if
The output threshold range for determining binary decision tree is two, respectively includes first threshold range 0~0.3, second threshold range 0.7~
1。
5. transmission line of electricity fire extinguishing necessity according to claim 1 sentences knowledge method, which is characterized in that the S3 is specifically wrapped
Include following steps:
Validation data set is inputted into binary tree computation model, by the identifying result of binary tree computation model and practical tripping result into
Row compare, if be more than 85% judging result it is consistent with actual result, then it is assumed that binary tree computation model is effective.
6. transmission line of electricity fire extinguishing necessity according to claim 1 sentences knowledge method, which is characterized in that after the S3 also
Comprising steps of
S4: mountain fire related data described in S3 and practical mountain fire situation are included into historical data, update current binary tree meter
Model is calculated, the binary tree computation model after being optimized calculates for next time；
S5: repeating S3-S4, realizes and updates to the iteration of binary tree computation model.
7. transmission line of electricity fire extinguishing necessity according to claim 1 sentences knowledge method, which is characterized in that in the S1, institute
State the voltage class data, transmission line of electricity density data, transmission line of electricity free height degree that mountain fire related data includes transmission line of electricity
According to and one of electric transmission line channel width data or several any combination.
8. transmission line of electricity fire extinguishing necessity according to claim 1 sentences knowledge method, which is characterized in that the sets requirement
The information for including for mountain fire related data is line information, fire behavior space time information, environmental information and fire extinguishing information.
9. a kind of transmission line of electricity fire extinguishing necessity sentences knowledge system, including memory, processor and it is stored on the memory
And the computer program that can be run on the processor, which is characterized in that the processor is realized when executing described program
The step of stating claim 1-8 any described method.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110490487A (en) * | 2019-08-27 | 2019-11-22 | 国网湖南省电力有限公司 | A kind of mountain fire high-incidence season dynamic fire extinguishing sort algorithm and system |
| CN111062488A (en) * | 2019-12-09 | 2020-04-24 | 北京国网富达科技发展有限责任公司 | Method and system for early warning of waving track |
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