CN107122440A - Power network goods and materials air control early warning system based on public sentiment big data - Google Patents
Power network goods and materials air control early warning system based on public sentiment big data Download PDFInfo
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Abstract
The invention provides the power network goods and materials air control early warning system based on public sentiment big data, including handling of goods and materials module, the analysis of public opinion module and warning module, the handling of goods and materials module is managed by building power network materials supply chain to power network goods and materials, the analysis of public opinion module is analyzed the risk of power network handling of goods and materials based on public sentiment big data, and analysis result is exported, the warning module sends early warning according to the analysis result.Beneficial effects of the present invention are：Realize the effective management and air control early warning to power network goods and materials.
Description
Technical field
The present invention relates to electric power network technique field, and in particular to the power network goods and materials air control early warning system based on public sentiment big data.
Background technology
In the management functions of power grid enterprises, both including power generation, conveying and merchandise, again include power network capital construction and
Equipment operation maintenance.Fixed assets accounting is up to 70% in power grid enterprises, operation cost and assets height correlation.Power network goods and materials are not
Only it is the major part for constituting enterprise assets, while being also the material guarantee of enterprise safety operation, and then the economy of enterprise is imitated
Benefit has a direct impact.
Existing power network materials management technique falls behind, it is impossible to realize effective management air control early warning of power network goods and materials.
The content of the invention
In view of the above-mentioned problems, the present invention is intended to provide the power network goods and materials air control early warning system based on public sentiment big data.
The purpose of the present invention is realized using following technical scheme：
There is provided the power network goods and materials air control early warning system based on public sentiment big data, including handling of goods and materials module, the analysis of public opinion
Module and warning module, the handling of goods and materials module are managed by building power network materials supply chain to power network goods and materials, described
The analysis of public opinion module is analyzed the risk of power network handling of goods and materials based on public sentiment big data, and exports analysis result, described pre-
Alert module sends early warning according to the analysis result.
Beneficial effects of the present invention are：Realize the effective management and air control early warning to power network goods and materials.
Brief description of the drawings
Using accompanying drawing, the invention will be further described, but the embodiment in accompanying drawing does not constitute any limit to the present invention
System, for one of ordinary skill in the art, on the premise of not paying creative work, can also be obtained according to the following drawings
Other accompanying drawings.
Fig. 1 is the structural representation of the present invention；
Reference：
Handling of goods and materials module 1, the analysis of public opinion module 2, warning module 3.
Embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, the power network goods and materials air control early warning system based on public sentiment big data of the present embodiment, including handling of goods and materials mould
Block 1, the analysis of public opinion module 2 and warning module 3, the handling of goods and materials module 1 is by building power network materials supply chain to power network thing
Money is managed, and the analysis of public opinion module 2 is analyzed the risk of power network handling of goods and materials based on public sentiment big data, and is exported
Analysis result, the warning module 3 sends early warning according to the analysis result.
The present embodiment realizes the effective management and air control early warning to power network goods and materials.
It is preferred that, the power network material supply chain building is carried out using following steps：
(1) power network handling of goods and materials target is established, power network handling of goods and materials target is the shared power network of structure supply and demand stabilization, income
Materials supply chain；
(2) power network material supply business is selected, is carried out using following steps：
The first step：Power grid enterprises are according to power network handling of goods and materials target, and the ability to power network supplier claims, it is determined that closing
Make the initial ranges of member, specify selection standard, the index system that selection is carried out to power network material supply business is set up, according to index
The model that Establishing partner selection is evaluated；
Second step：The associated detailed information for the member that intends to cooperate is collected, binding model is evaluated, and finally determines partner；
(3) power network materials supply chain is implemented, with feeding back, after supplier member determines, i.e., tentatively to establish power network material supply
Chain, during power network materials supply chain is run, constantly collects information and carries out judgement decision-making, supplier member is adjusted
It is whole.
This preferred embodiment is by building power network materials supply chain, and the science for realizing supplier is chosen and adjusted.
It is preferred that, the index system that selection is carried out to power network material supply business of setting up to evaluation index including carrying out just
Step screening, be specially：
Different classes of with n if M is one group of supplier's sample, M includes miIndividual Yi, then any one supplier belong to Yi
Probability be：Wherein, m is all total sample numbers in set M；
Equipment selects evaluation index B can value { b1,b2,…,ba, corresponding supplier is divided into q parts：{m1,m2,…,
mq}；
Define alternative evaluation index B screening function Z (B)：
In formula,
mijRepresent supplier mjIn belong to miNumber；
Given threshold, rejects to the index that screening function is less than threshold value, completes evaluation index preliminary screening.
Power network material supply business has as the key link of power network materials supply chain in whole power network materials supply chain
Very important effect, the selective goal that this preferred embodiment screens function pair power network material supply business by setting up is sieved
Choosing, the index of correlation difference is rejected, the efficiency of selection of supplier is improved, specifically, by integrally entering to supplier
Row is classified and supplier is divided using each evaluation index, supplier information is more fully obtained, to index
More science when being screened.
It is preferred that, the index system that selection is carried out to power network material supply business of setting up is also including to the index after screening
Handled, be specially：
Increase for positive index, the i.e. contribution rate to general objective with the increase of evaluation result, in the following ways
Handled：
In formula, G (xk) expression processing after index value, xkRepresent before processing index value, xkminAnd xkmaxRepresent to refer to respectively
The desirable minimum value of mark and maximum；
Reduce for negative sense index, the i.e. contribution rate to general objective with the increase of evaluation result, in the following ways
Managed：
In formula, G (xk) expression processing after index value, xkRepresent before processing index value, xkminAnd xkmaxRepresent to refer to respectively
The desirable minimum value of mark and maximum；
For appropriate class index, i.e., it is monotonic increase to general objective contribution when index value is less than appropriateness value；Work as finger
When marking numerical value more than appropriateness value, it is monotone decreasing to general objective contribution, is handled in the following ways：
In formula, G (xk) expression processing after index value, xkRepresent before processing index value, xkshiRepresent that the optimal of index takes
Value, xkminAnd xkmaxRepresent that index can use minimum value and maximum respectively.
Because each evaluation index form of expression differs, there is absolute number index, there is relative indicatrix, in the mistake selected supplier
Act on also different in journey, index is divided into positive index, negative sense index by this preferred embodiment in the index after processing is screened
With appropriate class index, handled, overcome in conventional index processing procedure using different functions for different type index
Defect, enormously simplify computing, the desired value of acquisition is more directly perceived.
It is preferred that, the analysis of public opinion module 2 regard the public sentiment risk of power network handling of goods and materials as power network handling of goods and materials risk
The foundation of analysis, including public sentiment Risk Calculation unit and public sentiment Risk Governance unit, the public sentiment Risk Calculation unit is to power network
The public sentiment risk of handling of goods and materials is calculated, and the public sentiment Risk Governance unit is controlled to the public sentiment risk of power network handling of goods and materials
System.
The public sentiment Risk Calculation of the power network handling of goods and materials is carried out in the following ways：
The first step, each public sentiment is propagated individual as a node, uses IijRepresent two public sentiment letters between node i and j
Total amount is ceased, public feelings information is transmitted among the nodes, if front public feelings information is then Iij(zh)If negative public feelings information is then
For Iij(fu)；
Second step, defines some node i and its network that the node that is joined directly together therewith is constituted is individual net, and calculating should
The public sentiment risk index of node：
In formula,Represent the public sentiment risk index of node i, diRepresent the number of node being joined directly together with node i, PiTable
Show negative public feelings information accounting in all public feelings informations directly related with node i；
3rd step, it is the negative public sentiment letter that can be adversely affected to power network handling of goods and materials to define integral net public sentiment risk
Cease what is integrated after being propagated between the node of concern power network handling of goods and materials, calculate the public sentiment risk index of integral net：
In formula, R represents the public sentiment risk index of integral net, and W represents node total number,Represent individual node public sentiment wind
Dangerous index maximum；
The public sentiment risk index of integral net is bigger, then the public sentiment risk of power network handling of goods and materials is bigger, power network handling of goods and materials
Risk is also bigger；
The public sentiment Risk Governance unit is by reducing diAnd PiTo realize public sentiment risk control.
This preferred embodiment the analysis of public opinion module regard the public sentiment risk of power network handling of goods and materials as power network handling of goods and materials risk
The foundation of analysis, effect of the people during power network handling of goods and materials can be played to greatest extent, so as to preferably carry
High power network handling of goods and materials level, during the public sentiment Risk Calculation of power network handling of goods and materials, has considered the public sentiment of each node
Risk index and the overall public sentiment risk index of network, improve the public sentiment risk reliability of power network handling of goods and materials.
Vehicle reliability is estimated using power network goods and materials air control early warning system of the present invention based on public sentiment big data, when
When node total number W takes different value, the efficiency of management and managing risk to power network goods and materials are counted, with not using phase of the present invention
Than having the beneficial effect that shown in table for, generation：
| W | Power network handling of goods and materials efficiency is improved | Power network handling of goods and materials risk is reduced |
| 100 | 36% | 10% |
| 110 | 32% | 15% |
| 120 | 30% | 20% |
| 130 | 25% | 24% |
| 140 | 20% | 31% |
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than to present invention guarantor
The limitation of scope is protected, although being explained with reference to preferred embodiment to the present invention, one of ordinary skill in the art should
Work as understanding, technical scheme can be modified or equivalent substitution, without departing from the reality of technical solution of the present invention
Matter and scope.
Claims (6)
1. the power network goods and materials air control early warning system based on public sentiment big data, it is characterized in that, including handling of goods and materials module, the analysis of public opinion
Module and warning module, the handling of goods and materials module are managed by building power network materials supply chain to power network goods and materials, described
The analysis of public opinion module is analyzed the risk of power network handling of goods and materials based on public sentiment big data, and exports analysis result, described pre-
Alert module sends early warning according to the analysis result.
2. the power network goods and materials air control early warning system according to claim 1 based on public sentiment big data, it is characterized in that, the electricity
Net material supply chain building is carried out using following steps：
(1) power network handling of goods and materials target is established, power network handling of goods and materials target is the shared power network goods and materials of structure supply and demand stabilization, income
Supply chain；
(2) power network material supply business is selected, is carried out using following steps：
The first step：Power grid enterprises are according to power network handling of goods and materials target, and the ability to power network supplier claims, determine cooperation into
The initial ranges of member, specify selection standard, the index system that selection is carried out to power network material supply business are set up, according to index system
Set up the model of partner selection evaluation；
Second step：The associated detailed information for the member that intends to cooperate is collected, binding model is evaluated, and finally determines partner；
(3) power network materials supply chain is implemented, with feeding back, after supplier member determines, i.e., tentatively to establish power network materials supply chain,
During power network materials supply chain is run, constantly collect information and carry out judgement decision-making, supplier member is adjusted.
3. the power network goods and materials air control early warning system according to claim 2 based on public sentiment big data, it is characterized in that, it is described to build
The vertical index system that selection is carried out to power network material supply business includes carrying out preliminary screening to evaluation index, is specially：
Different classes of with n if M is one group of supplier's sample, M includes miIndividual Yi, then any one supplier belong to YiIt is general
Rate is：Wherein, m is all total sample numbers in set M；
Equipment selects evaluation index B can value { b1,b2,…,ba, corresponding supplier is divided into q parts：{m1,m2,…,mq}；
Define alternative evaluation index B screening function Z (B)：
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mijRepresent supplier mjIn belong to miNumber；
Given threshold, rejects to the index that screening function is less than threshold value, completes evaluation index preliminary screening.
4. the power network goods and materials air control early warning system according to claim 3 based on public sentiment big data, it is characterized in that, it is described to build
The vertical index system that selection is carried out to power network material supply business also includes handling the index after screening, is specially：
Increase for positive index, the i.e. contribution rate to general objective with the increase of evaluation result, carry out in the following ways
Processing：
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Take minimum value and maximum；
Reduce for negative sense index, the i.e. contribution rate to general objective with the increase of evaluation result, carry out in the following ways
Reason：
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For appropriate class index, i.e., it is monotonic increase to general objective contribution when index value is less than appropriateness value；When index number
When value is more than appropriateness value, it is monotone decreasing to general objective contribution, is handled in the following ways：
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<mtd>
<mrow>
<mn>2</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>k</mi>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>k</mi>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mi>k</mi>
</msub>
<mo>-</mo>
<mfrac>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>k</mi>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>k</mi>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
</mrow>
<mn>2</mn>
</mfrac>
<mo>)</mo>
</mrow>
</mrow>
</msup>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>k</mi>
<mi>m</mi>
<mi>e</mi>
<mi>d</mi>
</mrow>
</msub>
<mo><</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>k</mi>
<mi>s</mi>
<mi>h</mi>
<mi>i</mi>
</mrow>
</msub>
<mo><</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>k</mi>
<mi>max</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
In formula, G (xk) expression processing after index value, xkRepresent before processing index value, xkshiThe optimal value of index is represented,
xkminAnd xkmaxRepresent that index can use minimum value and maximum respectively.
5. the power network goods and materials air control early warning system according to claim 4 based on public sentiment big data, it is characterized in that, the carriage
Feelings analysis module regard the public sentiment risk of power network handling of goods and materials as the foundation of power network handling of goods and materials risk analysis, including public sentiment risk
Computing unit and public sentiment Risk Governance unit, the public sentiment Risk Calculation unit are counted to the public sentiment risk of power network handling of goods and materials
Calculate, the public sentiment Risk Governance unit is controlled to the public sentiment risk of power network handling of goods and materials.
6. the power network goods and materials air control early warning system according to claim 5 based on public sentiment big data, it is characterized in that, the electricity
The public sentiment Risk Calculation of net handling of goods and materials is carried out in the following ways：
The first step, each public sentiment is propagated individual as a node, uses IijRepresent that two public feelings informations between node i and j are total
Amount, public feelings information is transmitted among the nodes, if front public feelings information is then Iij(zh)If negative public feelings information is then
Iij(fu)；
Second step, defines some node i and its network that the node that is joined directly together therewith is constituted is individual net, calculates the node
Public sentiment risk index：
<mrow>
<msub>
<mi>F</mi>
<msub>
<mi>d</mi>
<mi>i</mi>
</msub>
</msub>
<mo>=</mo>
<msub>
<mi>d</mi>
<mi>i</mi>
</msub>
<mo>&times;</mo>
<mfrac>
<mrow>
<msup>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mn>2</mn>
</msup>
</mrow>
<msup>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mfrac>
<mo>+</mo>
<msup>
<mi>e</mi>
<mrow>
<msub>
<mi>d</mi>
<mi>i</mi>
</msub>
<mo>&times;</mo>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
</mrow>
</msup>
</mrow>
In formula,Represent the public sentiment risk index of node i, diRepresent the number of node being joined directly together with node i, PiRepresent with
Negative public feelings information accounting in the directly related all public feelings informations of node i；
3rd step, it is that the negative public feelings information that can be adversely affected to power network handling of goods and materials exists to define integral net public sentiment risk
Integrated after being propagated between the node of concern power network handling of goods and materials, calculate the public sentiment risk index of integral net：
<mrow>
<mi>R</mi>
<mo>=</mo>
<mi>l</mi>
<mi>n</mi>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>+</mo>
<msub>
<mi>F</mi>
<msub>
<mi>d</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mfrac>
<mrow>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>W</mi>
</msubsup>
<mrow>
<mo>(</mo>
<msub>
<mi>F</mi>
<msub>
<mi>d</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</msub>
<mo>-</mo>
<msub>
<mi>F</mi>
<msub>
<mi>d</mi>
<mi>i</mi>
</msub>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mi>W</mi>
</mfrac>
</mrow>
In formula, R represents the public sentiment risk index of integral net, and W represents node total number,Represent individual node public sentiment risk index
Maximum；
The public sentiment risk index of integral net is bigger, then the public sentiment risk of power network handling of goods and materials is bigger, the risk of power network handling of goods and materials
Also it is bigger；
The public sentiment Risk Governance unit is by reducing diAnd PiTo realize public sentiment risk control.
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