CN114970904B - Digital adjustment method for contact network operation and maintenance resources based on defect processing - Google Patents
Digital adjustment method for contact network operation and maintenance resources based on defect processing Download PDFInfo
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- 238000012423 maintenance Methods 0.000 title claims abstract description 97
- 230000007547 defect Effects 0.000 title claims abstract description 68
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000012545 processing Methods 0.000 title claims abstract description 8
- 230000036541 health Effects 0.000 claims abstract description 71
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 238000013468 resource allocation Methods 0.000 claims description 12
- 239000000126 substance Substances 0.000 claims description 11
- 238000007689 inspection Methods 0.000 claims description 7
- 238000007726 management method Methods 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 6
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- 238000010276 construction Methods 0.000 claims description 3
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- 230000007704 transition Effects 0.000 claims description 3
- 230000001575 pathological effect Effects 0.000 claims description 2
- 239000002994 raw material Substances 0.000 claims description 2
- 238000005485 electric heating Methods 0.000 claims 1
- 238000013439 planning Methods 0.000 claims 1
- 230000008569 process Effects 0.000 abstract description 4
- 230000008859 change Effects 0.000 abstract description 3
- 238000010252 digital analysis Methods 0.000 abstract description 2
- 230000003068 static effect Effects 0.000 description 11
- 230000008439 repair process Effects 0.000 description 7
- 238000011156 evaluation Methods 0.000 description 6
- 239000013078 crystal Substances 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
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- 230000009286 beneficial effect Effects 0.000 description 1
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- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention provides a defect processing-based digital adjustment method for contact network operation and maintenance resources, which comprises the following steps: counting N parts and equipment before the ranking of the historical defect record table, and M parts and equipment before the ranking of the historical maintenance record table; when a worker of a single maintenance plan is adjusted according to M parts and equipment before ranking; adjusting the allocation amount of maintenance resources according to a historical defect record table with an anchor section as a unit; and correcting the health state index of the contact network system by taking the defects of N parts and equipment before ranking as the quality index of the equipment, evaluating the health state of the contact network system, calculating the health degree calculation time point of the contact network at the sub-health state or below, and distributing the allocation amount of maintenance resources when the maintenance plan worker works. The digital analysis of time consumption and occurrence frequency based on key defects is combined with distribution and change of the health degree of the anchor section of the contact net, so that the digital process of operation and maintenance of the contact net can be obviously improved.
Description
Technical Field
The invention belongs to the field of operation, maintenance and management of a rail transit overhead line system, and particularly relates to a defect processing-based digital adjustment method for overhead line system operation and maintenance resources.
Background
By the end of 2021, a metro line is opened up at 8000km in China, related professional management and operation and maintenance personnel are established as doctors of the metro line to guarantee normal operation of the train, the operation and maintenance personnel carry out regular maintenance operation, after a defect is found and reported to an operation and maintenance management center, the operation and maintenance management center issues a work order to complete defect maintenance operation, and inspection is an effective inspection means for finding a fault or defect symptom.
The subway operation in China is carried out in a traditional operation mode of organizing operation in a mode of combining periodic maintenance and simultaneous maintenance, the occupied manpower inspection time is long, and the operation effect is general; the occupied material resource is large, and spare parts are not used up until the upper limit of the storage period is reached. Additionally, the performance calculation mode of the operation and maintenance personnel usually takes the number of the discovered defects and the processed defects as evaluation indexes, and usually does not process the potential defects or the defects which do not form a defect confirmation state.
The existing defect processing mode has the problems of lacking of a processing method for potential defects and low manpower and material resource efficiency in operation and maintenance resource management.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a defect-processing-based digital adjustment method for contact network operation and maintenance resources based on digital analysis of time consumption and occurrence frequency of key defects and combined with distribution and change of the contact network anchor section health degree, and obviously improves the digital process of contact network operation and maintenance.
The technical scheme adopted by the invention is as follows: a digital adjustment method for contact network operation and maintenance resources based on defect handling comprises the following steps:
step 1: counting N parts and equipment before the ranking of the historical defect record table in one period, counting M parts and equipment before the ranking of the historical maintenance record table in one period,
step 2: for M parts and equipment before ranking, respectively calculating the working hours of a single maintenance plan
：
Wherein the content of the first and second substances,
the number of the personnel invested in a single operation is shown,
indicating the time at which the actual job is completed,
indicating a registration job time;
and step 3: digital utilization rate of single maintenance plan
%：
Wherein, the first and the second end of the pipe are connected with each other,
indicates the number of times that the maintenance plan was implemented,
is as follows
When the maintenance worker is in secondary operation;
if it is
If the number of the maintenance plan workers is larger than the preset value, the number of the maintenance plan workers is increased;
if it is
If the maintenance plan is not changed, the maintenance plan is not changed;
if it is
The number of the single maintenance plan workers is reduced;
wherein the content of the first and second substances,
in order to manage the desired upper threshold value,
a lower threshold desired for management;
and 4, step 4: counting the historical defect record table of one period according to the unit of the anchor segment, and calculating the risk coefficient of one period of the anchor segment
：
Wherein the content of the first and second substances,
is the number of defect classes occurring within the anchor segment,
is shown as
The defect of the seed crystal is that the seed crystal,
is shown as
The number of the seed defects is increased,
is shown as
The risk degree of the seed defect is the ratio of the number of the type of defects in the space W in the time T range to the number of the type of defects in the whole line,
and 5: calculating maintenance resource utilization
，
if it is
The maintenance resource allocation is reduced;
if it is
If the maintenance resource allocation amount is not changed;
if it is
Then the maintenance resource allocation is increased;
step 6: taking N defects of parts and equipment before ranking as equipment quality indexes of the health state indexes of the contact network system, obtaining the corrected health state indexes of the contact network system, evaluating the health state of the contact network system to obtain a health value and a health grade, wherein S (t) is the health value of the contact network system at the time t, H (t) represents the health grade corresponding to the health value at the time t,
table 1 table of correspondence between health degree value and health grade of indexes of health state of contact network system
| Health grade | Health value |
| Health care | (t 1 ,100] |
| Sub-health | (t 2 , t 1 ] |
| Pathological state | (0, t 2 ] |
| Fail to work | 0 |
Determining a hidden state transition probability matrix using a hidden Markov model construction method
，
Wherein m is the number of health values in the history, and P is
Of time of day
In the state of the device, the device is in a closed state,
is shifted in time to
The probability of a state is determined by the probability of the state,
the current health value of the contact net is S (0), then
The distribution probability of the contact net health degree of each health degree calculation time point can be calculated by the following formula:
when is coming into contact with
In the matrix, the extracted health value is lower than
When the sum of probabilities is greater than
In the meantime, the health status of the catenary is sub-healthy and below
The high contact net inspection frequency on the premise of calculating the time point of the individual health degree, and distribution and maintenanceScheduling staff and repair resource allocations.
Further, in step 1, ranking is counted from most to least according to the number of records.
Further, in step 3, when the adjusted single maintenance schedule is worked
：
Further, in step 4, T is taken for 3 weeks and W is taken for 2 anchor segments.
Further, in step 6, t 1 Get 90,t 2 Taking the weight of the sample to be 82,
taking 50 percent.
Further, in the above-mentioned case,
taking out 70 percent of the raw materials,
taking 50 percent.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, through dynamic change and probability estimation of the health degree of the anchor section of the contact network, maintenance resources are reasonably distributed, and the operation and maintenance efficiency is improved.
2. The invention calculates the maintenance resource utilization rate and the digital health degree historical record to be used as an operation and maintenance performance and resource management mode, and considers potential defects and predicted defects together, thereby being an important ring for converting regular repair into state repair.
3. The invention is a dynamic comprehensive adjustment, which is accompanied with the accumulated growth of actual operation management and operation experience, and obviously improves the matching of the operation and maintenance strategy and the characteristics of the operation line.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a flow chart of a health status according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The embodiment of the invention provides a defect processing-based digital adjustment method for contact network operation and maintenance resources, which comprises the following steps as shown in figures 1-2
Step 1: and counting N parts and equipment before the historical defect record list ranking in one period, counting M parts and equipment before the historical maintenance record list ranking in one period, and counting the ranking according to the number of recording times. One cycle is 2 years. Taking several items before the ranking, defects and repairs with small occurrence probability can be omitted, for example, defects and repairs which occur only 1-2 times within 2 years can be omitted.
Step 2: for M parts and equipment before ranking, respectively calculating the working hours of a single maintenance plan
：
Wherein, the first and the second end of the pipe are connected with each other,
the number of the staffs invested in a single operation is shown,
indicating the time at which the actual job is completed,
indicating a registration job time;
and 3, step 3: digital utilization rate of single maintenance plan
%：
Wherein the content of the first and second substances,
indicates the number of times the maintenance plan was implemented,
is as follows
When the maintenance worker is in secondary operation;
if it is
If the number of the maintenance plan workers is larger than the preset value, the number of the maintenance plan workers is increased;
if it is
If the maintenance plan is not changed, the maintenance plan is not changed;
if it is
The number of the single maintenance plan workers is reduced;
wherein the content of the first and second substances,
in order to manage the desired upper threshold value,
in order to manage the desired lower threshold value,
in particular, when the adjusted single maintenance schedule is worked
：
For all adjusted single maintenance schedule workers
Summing to obtain the sum of the maintenance schedule workers of the M parts and equipment before the ranking
；
And 4, step 4: counting the historical defect record table of one period according to the unit of the anchor segment, and calculating the risk coefficient of one period of the anchor segment
：
Wherein the content of the first and second substances,
is the number of defect classes occurring within the anchor segment,
is shown as
The defect of the seed crystal is that the seed crystal,
denotes the first
The number of the seed defects is increased,
is shown as
The risk degree of the defect is the ratio of the defect occurrence number of the space W in the time T range to the defect occurrence number of the whole line, and for calculation, T can be taken for 3 weeks, and W can be taken for 2 anchor sections.
And 5: calculating maintenance resource utilization
，
If it is
The maintenance resource allocation is reduced;
if it is
If so, maintaining the resource allocation quantity unchanged;
if it is
The repair resource allocation increases.
Step 6: and taking the defects of N parts and equipment before ranking as the equipment quality index of the health state index of the overhead line system, and obtaining the corrected health state index of the overhead line system, wherein the index is shown in a table 2.
Table 2 index and weight table of health status index of contact network system
The health state index of the contact network system is a first-level index, is formed by four second-level indexes which are respectively a dynamic and static detection evaluation index
Quality index of equipment
Index of maintenance degree
And availability index
。
Dynamic and static detection evaluation index
The method is established by two three-level indexes, namely static operation quality CQI and dynamic operation quality CDI. The static operation quality CQI is constructed by four levels of indexes, namely a static pull value CQI of an anchor point S Fixed point static contact line height CQI H And the contact line height smoothness CQI of the adjacent positioning points D And contact line height smoothness in span CQI Hα . The dynamic operation quality CDI is constructed by four levels of indexes which are respectively dynamic pull-out values CDI of positioning points S Positioning point dynamic contact line height CDI H Bow net contact force component CDI F Arc rate CQI Aα 。
Quality index of equipment
The method is characterized by comprising six three-level indexes, namely contact suspension defects, positioning support device defects, single-phase equipment defects, other cause defects, additional suspension defects, support columns, stay wires and foundation defects.
Index of maintenance degree
The three-level indexes are established and respectively mean man-hour maintenance, mean maintenance duration and repair rate.
The availability index consists of first-level defects, and common first-level defects comprise tripping, bowing, wire breakage, water seepage, insulation faults, driving accidents, pillar breakage, natural disasters and the like.
The index values of the static operation quality CQI and the dynamic operation quality CDI are respectively the sum of the product of the index values of all four levels of indexes corresponding to the static operation quality CQI and the product of four levels of index weights.
Taking static running quality CQI as an example, the calculation process of the index value is described, referring to table 2, CQI =0.17 × CQI S +0.17*CQI H +0.33*CQI D +0.33*CQI Hα
The calculation method and data source of the index values of the three-level indexes corresponding to the equipment quality indexes are shown in table 3:
TABLE 3 index value calculation and data Source Table for three-level index corresponding to Equipment quality index
The calculation method and data source of the index values of the three-level indexes corresponding to the maintenance degree indexes are as shown in table 4:
table 4 index value calculation and data source table of three-level indexes corresponding to the maintenance degree index
And the index values of the dynamic and static detection evaluation index, the equipment quality index and the maintenance degree index are respectively the sum of the products of the index values of all the corresponding three-level indexes and the three-level index weight.
Availability index
∈[0,1]When the first-order defect occurs,
=0; when the first-order defect does not occur,
=1。
the index value of the health state index of the contact network system is
，
Respectively weighing secondary indexes corresponding to the dynamic and static detection evaluation index, the equipment quality index and the maintenance degree index; referring to table 1, according to the characteristics of the subway line,
are set to 0.4, 0.5 and 0.1, respectively.
And obtaining the health grade of the contact network system by cumulatively distributing index values of the health state indexes of the contact network system. The health grade of the contact network system is divided into four grades of health, sub-health, morbidity and failure, the evaluation boundaries t1 and t2 are selected to be values of the cumulative distribution function of the health state indexes of the contact network system in the evaluated line, the t1 interception probability value is 90%, and the t2 interception probability value is 82%. The conditions of the catenary system corresponding to each grade of the catenary system health grade are shown in table 1.
And evaluating the health state of the contact network system according to the corrected health state index of the contact network system to obtain a health value and a health grade, wherein S (t) is the health value of the contact network system at the time t, and H (t) represents the health grade corresponding to the health value at the time t.
Determining a hidden state transition probability matrix using a hidden Markov model construction method
，
Wherein m is the number of health values in the history record, and P is
Of time of day
In the state of the device, the device is in a closed state,
is shifted in time to
Probability of state.
The current health value of the contact net is S (0), then
The distribution probability of the contact net health degree of each health degree calculation time point can be calculated by the following formula:
when is coming into contact with
In the matrix, the extracted health value is lower than
When the sum of probabilities is greater than
In the meantime, the health status of the catenary is sub-healthy and below
And (4) calculating the high contact network patrol inspection frequency on the premise of a time point by the individual health degree, and reasonably distributing the maintenance resource allocation amount of the maintenance plan workers adjusted in the step (3) and the maintenance resource allocation amount adjusted in the step (5).
In FIG. 2, the state of sub-health and the following health at time 3The probability of state is 0.55, greater than
Before time point 3, the frequency of inspection tour of the overhead line system needs to be increased.
The present invention has been described in detail with reference to the embodiments, but the description is only illustrative of the present invention and should not be construed as limiting the scope of the present invention. The scope of protection of the invention is defined by the claims. The technical solutions of the present invention or those skilled in the art, based on the teaching of the technical solutions of the present invention, should be considered to be within the scope of the present invention, and all equivalent changes and modifications made within the scope of the present invention or equivalent technical solutions designed to achieve the above technical effects are also within the scope of the present invention.
Claims (6)
1. A digital adjustment method for contact network operation and maintenance resources based on defect processing is characterized by comprising the following steps: the method comprises the following steps:
step 1: counting N parts and equipment before the ranking of the historical defect record table in one period, and counting M parts and equipment before the ranking of the historical maintenance record table in one period;
and 2, step: for M parts and equipment before ranking, respectively calculating the working hours of a single maintenance plan
：
Wherein the content of the first and second substances,
the number of the personnel invested in a single operation is shown,
indicating the time at which the actual job is completed,
indicating a registration job time;
and step 3: digital utilization rate of single maintenance plan
%：
Wherein the content of the first and second substances,
indicates the number of times the maintenance plan was implemented,
is as follows
When the maintenance worker is in secondary operation;
if it is
If the number of the maintenance plan workers is larger than the preset value, the number of the maintenance plan workers is increased;
if it is
If the maintenance plan is not changed, the maintenance plan is not changed;
if it is
If the number of the maintenance plan workers is less than the preset value, the number of the maintenance plan workers is increased;
wherein the content of the first and second substances,
for managing desired upperThe threshold value is set to a value that is,
a lower threshold desired for management;
and 4, step 4: counting the historical defect record table of one period according to the unit of the anchor segment, and calculating the risk coefficient of one period of the anchor segment
：
Wherein the content of the first and second substances,
is the number of defect classes occurring within the anchor segment,
is shown as
The number of the seed defects is increased,
is shown as
The risk degree of the defects is planted, and the risk degree of a single defect is the ratio of the number of the defects in the space W in the time T range to the number of the defects in the whole line;
and 5: calculating maintenance resource utilization
，
Wherein the content of the first and second substances,
the total number of the M parts and maintenance planning workers of the equipment before the name is given;
if it is
The maintenance resource allocation is reduced;
if it is
If so, maintaining the resource allocation quantity unchanged;
if it is
Then the maintenance resource allocation is increased;
step 6: taking N defects of parts and equipment before ranking as an equipment quality index of the health state index of the contact network system, obtaining the corrected health state index of the contact network system, evaluating the health state of the contact network system, obtaining a health value and a health grade, wherein S (t) is the health value of the contact network system at the time t, H (t) represents the health grade corresponding to the health value at the time t,
table 1 table of correspondence between health degree value and health grade of indexes of health state of contact network system
Determining a hidden state transition probability matrix using a hidden Markov model construction method
，
Wherein m is the number of health values in the history record, and P is
Of time of day
In the state of the electric heating furnace,
is shifted in time to
The probability of a state is determined by the probability of the state,
the current health value of the contact net is S (0), then
The distribution probability of the contact net health degree of each health degree calculation time point can be calculated by the following formula:
when is coming into contact with
In the matrix, the extracted health value is lower than
When the sum of probabilities is greater than
In the meantime, the health status of the catenary is sub-healthy and below
The individual health degree calculates the high contact net inspection frequency on the premise of time point, and allocates the maintenance schedule workers and the maintenance resource ration.
2. The defect handling-based digital adjustment method for contact network operation and maintenance resources as claimed in claim 1, wherein: in step 1, ranking is counted from most to least according to the recording times.
3. The digital adjustment method for the operation and maintenance resources of the overhead line system based on the defect handling of claim 1, which is characterized in that: in step 3, when the adjusted single maintenance plan worker works
：
4. The defect handling-based digital adjustment method for contact network operation and maintenance resources as claimed in claim 1, wherein: in step 4, T is taken for 3 weeks and W is taken for 2 anchor segments.
5. The defect handling-based digital adjustment method for contact network operation and maintenance resources as claimed in claim 1, wherein: in step 6, t 1 Get 90，
Taking 50 percent.
6. The digital adjustment method for the operation and maintenance resources of the overhead line system based on the defect handling of claim 1, which is characterized in that:
taking out 70 percent of the raw materials,
taking 50 percent.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108182485A (en) * | 2017-12-05 | 2018-06-19 | 中国电力科学研究院有限公司 | A kind of power distribution network maintenance opportunity optimization method and system |
| CN110880062A (en) * | 2019-10-31 | 2020-03-13 | 广西电网有限责任公司电力科学研究院 | Method for determining state maintenance time of power distribution equipment |
| CN110929986A (en) * | 2019-10-25 | 2020-03-27 | 贵州电网有限责任公司 | Power distribution equipment state evaluation method based on standing book, real-time and historical information |
| CN110955649A (en) * | 2019-10-30 | 2020-04-03 | 广西电网有限责任公司电力科学研究院 | Power distribution network production decision system |
| CN113888013A (en) * | 2021-10-21 | 2022-01-04 | 北京京港地铁有限公司 | Method and system for determining static quality evaluation index of rail transit contact network |
| CN114205349A (en) * | 2020-09-18 | 2022-03-18 | 英特尔公司 | Elastic power scaling |
| CN114666876A (en) * | 2020-12-23 | 2022-06-24 | 英特尔公司 | Adaptive power management for edge devices |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112297958B (en) * | 2020-11-02 | 2022-05-17 | 中国铁建电气化局集团第五工程有限公司 | Method for replacing whole dropper in whole anchor section of 350km/h high-speed rail contact network |
| CN114444798A (en) * | 2022-01-27 | 2022-05-06 | 成都唐源电气股份有限公司 | Contact network operation and maintenance method and device based on space-time distribution technology |
-
2022
- 2022-07-26 CN CN202210880858.6A patent/CN114970904B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108182485A (en) * | 2017-12-05 | 2018-06-19 | 中国电力科学研究院有限公司 | A kind of power distribution network maintenance opportunity optimization method and system |
| CN110929986A (en) * | 2019-10-25 | 2020-03-27 | 贵州电网有限责任公司 | Power distribution equipment state evaluation method based on standing book, real-time and historical information |
| CN110955649A (en) * | 2019-10-30 | 2020-04-03 | 广西电网有限责任公司电力科学研究院 | Power distribution network production decision system |
| CN110880062A (en) * | 2019-10-31 | 2020-03-13 | 广西电网有限责任公司电力科学研究院 | Method for determining state maintenance time of power distribution equipment |
| CN114205349A (en) * | 2020-09-18 | 2022-03-18 | 英特尔公司 | Elastic power scaling |
| CN114666876A (en) * | 2020-12-23 | 2022-06-24 | 英特尔公司 | Adaptive power management for edge devices |
| CN113888013A (en) * | 2021-10-21 | 2022-01-04 | 北京京港地铁有限公司 | Method and system for determining static quality evaluation index of rail transit contact network |
Non-Patent Citations (1)
| Title |
|---|
| 电气化铁路接触网故障识别及其状态感知研究;赵健;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20220515;第C033-292页 * |
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