CN111382921B - Task matching method, storage medium and computing device of offshore wind turbine generator system - Google Patents
Task matching method, storage medium and computing device of offshore wind turbine generator system Download PDFInfo
- Publication number
- CN111382921B CN111382921B CN201811634844.6A CN201811634844A CN111382921B CN 111382921 B CN111382921 B CN 111382921B CN 201811634844 A CN201811634844 A CN 201811634844A CN 111382921 B CN111382921 B CN 111382921B
- Authority
- CN
- China
- Prior art keywords
- task
- executor
- parameter
- information
- distribution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000013507 mapping Methods 0.000 claims abstract description 18
- 230000004044 response Effects 0.000 claims abstract description 10
- 238000012545 processing Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 6
- 238000010586 diagram Methods 0.000 claims description 4
- 230000008901 benefit Effects 0.000 abstract description 4
- 238000012423 maintenance Methods 0.000 description 4
- 238000001816 cooling Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011534 incubation Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Provided are a task matching method, a storage medium and a computing device of an offshore wind turbine generator system, wherein the method comprises the following steps: receiving an instruction comprising a request for determining an executor capability distribution and task type information; in response to receiving the instruction, acquiring an executor historical capacity distribution from the map according to the task type information as an executor initial capacity distribution; adjusting an initial capacity distribution of the executor based on the task history time-consuming information of the map; determining the adjusted initial capacity distribution of the executor as a final executor; wherein, the executor capability distribution is the information of a single executor about the proportion of different capabilities among all executors executing the task; the mapping graph comprises a mapping relation between task type information and historical capability distribution of an executor and a mapping relation between task type information and time-consuming information of the task history, so that the capability distribution of the executor is optimized, and the use benefit of task resources is improved.
Description
Technical Field
The invention relates to the technical field of offshore wind power, in particular to a task matching method of an offshore wind turbine generator set.
Background
The offshore wind turbine has the advantage of large single-machine capacity and abundant offshore wind resources, and has better development potential than that of the onshore wind turbine. As the conditions that tasks of executing the offshore wind turbine need to be considered are more than those of the onshore wind turbine, the operation window is greatly influenced by sea conditions, the task execution difficulty is high, and the lean execution of the tasks is very important to the development of the offshore wind turbine.
However, the existing offshore wind turbine usually places an optimization emphasis on only performance and subsystem failure early warning of the offshore wind turbine, and accurate assessment on operation and maintenance tasks of the offshore wind turbine is lacking.
Disclosure of Invention
The invention aims to provide a task matching method of an offshore wind turbine generator system.
According to an embodiment of the inventive concept, there is provided a mission matching method of an offshore wind turbine generator system, including: receiving an instruction comprising a request for determining an executor capability distribution and task type information; in response to receiving the instruction, acquiring an executor historical capacity distribution from the map according to the task type information as an executor initial capacity distribution; adjusting an initial capacity distribution of the executor based on the task history time-consuming information of the map; determining final executors according to the adjusted initial capacity distribution of the executors; wherein, the executor capability distribution is the information of a single executor about the proportion of different capabilities among all executors executing the task; the mapping graph comprises a mapping relation between task type information and executor historical capability distribution and a mapping relation between task type information and task historical time-consuming information.
Optionally, the method further comprises: establishing a map, wherein the step of establishing the map comprises: acquiring historical capability distribution of an executor and time-consuming information of task history according to the type of the task; and establishing a mapping relation diagram among the task type information, the executor historical capability distribution and the task historical time-consuming information.
Optionally, the step of establishing a map between the task type information, the executor historical capability distribution, and the task history time-consuming information includes: taking task type information as a first horizontal axis, executor historical capability distribution as a second horizontal axis, task history time-consuming information as a first vertical axis, and establishing a coordinate system; based on the map.
Optionally, the map further includes task history processing number information corresponding to the task type information.
Optionally, the step of adjusting the initial capability distribution of the performer based on the task history time-consuming information of the map comprises: in response to the task history time consuming information being greater than the first threshold time consuming, the initial capacity distribution of the executives is adjusted such that the proportion of the executives having capacities above the first threshold capacity is increased among all of the executives performing the task.
Optionally, the step of adjusting the initial capability distribution of the performer based on the task history time-consuming information of the map comprises: in response to the task history time consumption information being less than the second threshold time consumption, the initial capacity distribution of the executives is adjusted such that the proportion of the executives with capacities below the second threshold capacity is increased among all the executives performing the task.
Optionally, the step of calculating the capabilities of the performer includes: calculating a first parameter P using equation (1) below, where n a For the number of times of the sea-going operation of the executor, n Total (S) For the total number of sea operations, n a And n Total (S) Is a positive integer;
calculating a second parameter L using equation (2) below, wherein m a The number of times the executor performs the task as a core executor, m Total (S) For the number of times the task is performed by the performer, m a And m Total (S) Is a positive integer;
calculating a third parameter C using equation (3) below, wherein k q Number of times of executing q-class task for executor S q For executing q-class tasks most times among all executors, h q Total man-hour for executing q-class task for executor, T q When the total job with the most q-class tasks is executed among all executors, the task types are divided according to subsystems, b is the total number of the subsystems, A, B, … … and M are the numbers of the task types, and k q 、S q 、h q 、T q Is a positive integer, q is A, B, … … and M;
calculating a fourth parameter E using equation (4) below, wherein d i On member ith number of days of sea operations, M ij The number of j-th tasks completed for the ith sea-going operation, a ij Weighting defined for the difficulty level of the j-th task and the number of executors of the j-th task according to the i-th offshore operationThe higher the difficulty coefficient, a ij The smaller the number of executives, the more, a ij The larger n is the number of times the executor goes out of sea, mi is the number of task types executed by the ith go out of sea job, M Standard of Day d for standard sea operation Standard of When the weighting coefficient of the task under the condition is 1, the number of the tasks to be completed, a ij 、M Standard of 、d Standard of I is a positive integer not greater than n, j is not greater than m i N, m i 、d i 、M ij Is a positive integer;
determining positions of the first parameter, the second parameter, the third parameter and the fourth parameter on corresponding coordinate axes respectively as vertexes in a coordinate system comprising four coordinate axes corresponding to the first parameter, the second parameter, the third parameter and the fourth parameter one by one; determining a quadrangle formed by the vertexes, wherein connecting lines connected with the vertexes on adjacent coordinate axes are used as edges of the quadrangle; the ability to calculate the area of the quadrilateral as an actor; wherein the out-of-sea job includes performing at least one task.
Optionally, the method further comprises: calculating the processing cost of the task; the step of calculating the processing cost of the task comprises the following steps: in the case where a weighting coefficient that differs depending on the type of task and the number of executors is used as a weighting coefficient for the task man-hours, a value obtained by multiplying the ratio of the weighted task man-hours to the weighted total task man-hours by the transportation cost is used as the transportation cost for the task; multiplying a ratio of the weighting coefficient of a task to a sum of the weighting coefficients of all tasks by a value obtained by dispatching the number of executors performing the task, the number of days of execution and the number of patches of the executors on a single day, as an executor cost of the task; the sum of the transport cost of the task and the cost of the executor of the task is taken as the processing cost of the task.
According to an embodiment of the inventive concept, there is provided a computer readable storage medium storing a computer program which, when executed by a processor, implements a task matching method of an offshore wind turbine generator set as described above.
According to one embodiment of the inventive concept, there is provided a computing device including: a processor; and the memory is used for storing a computer program, and when the computer program is executed by the processor, the task matching method of the offshore wind turbine generator set is realized.
The invention provides a method for adjusting the capacity distribution of the executor according to the task based on the actual record of the project task, and further provides a method for calculating the capacity of the executor and a method for calculating the task cost, thereby providing reliable reference for optimizing the capacity distribution of the executor, making an executor capacity culture plan and optimizing and matching the executor and the task, and improving the use benefit of task resources.
Drawings
The above and/or other aspects of the invention will become apparent from the following detailed description when taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart illustrating one example of a mission matching method for an offshore wind turbine generator system in accordance with an embodiment of the inventive concept;
fig. 2 is a flowchart illustrating a method of creating a map according to an embodiment of the inventive concept.
Detailed Description
Hereinafter, embodiments of the inventive concept will be described in detail with reference to the accompanying drawings.
Fig. 1 is a flowchart illustrating one example of a mission matching method of an offshore wind turbine generator system according to an embodiment of the inventive concept.
Referring to fig. 1, in step S10, an instruction including a request to determine an executive capability distribution and task type information is received.
When a problem occurs in the offshore genset or a predetermined maintenance period is reached, an instruction to deal with the corresponding problem/perform the corresponding task is issued. To execute the question/task, the instruction needs to include information requesting a determination of the executor performing the task and send information related to the task. In general, an actor may be a robot or an operation and maintenance person capable of performing a related task, and the actor's information may include actor capability profiles, and the information related to the task may include task type information. Here, the executor capability distribution may be information about the proportion of executors of different capabilities among all executors performing a task, and the task type information may be different according to a specific task. In one example, the task type information may include, but is not limited to: mechanical problems, environmental control problems, water cooling system problems, main control system problems, communication system problems, air cooling system problems, hydraulic system problems, cabin system problems and pitch system problems of the wind generating set.
In step S20, in response to receiving the instruction, an actor historical capability profile is obtained from the map as an actor initial capability profile according to the task type information.
Specifically, the step of obtaining the actor historical capacity distribution from the map as the actor initial capacity distribution according to the task type information includes: and acquiring the historical capability distribution of the executor corresponding to the task type information from the mapping chart.
Here, the map is a mapping relationship between data established based on history data. In one example, the map includes a mapping relationship between task type information and an executor historical capability distribution and a mapping relationship between task type information and task history time consuming information. In another example, the map further includes task history processing number information corresponding to the task type information. A method of creating the map will be described in detail later with reference to fig. 2.
In step S30, the actor initial capability profile is adjusted based on the task history time-consuming information of the map.
Specifically, the step of adjusting the initial capacity distribution of the performer based on the task history time-consuming information of the map includes: responsive to the task history time consuming information being greater than a first threshold time consuming, adjusting an initial capacity distribution of the executors such that a proportion of the executors having capacities above the first threshold capacity among all the executors performing the task is increased; in response to the task history time consumption information being less than the second threshold time consumption, the initial capacity distribution of the executives is adjusted such that the proportion of the executives with capacities below the second threshold capacity is increased among all the executives performing the task.
That is, when the historical time-consuming information of the task indicates that the historical processing time of the task is longer, more performers with high capability can be selected to accelerate the execution speed of the task when the task is executed, so that the execution time of the task is shortened; when the historical time-consuming information of the task indicates that the historical processing time of the task is short, more performers with general capability or insufficient capability can be selected to restore the execution speed of the task to a reasonable level when the task is executed, and the number of performers with high capability needed for executing the task is reduced so as to allow the performers with high capability to execute other tasks.
In step S40, the final actor is determined from the adjusted actor initial competency distribution.
After adjusting the executor capability distribution, an end executor for executing the task may be determined from the adjusted executor capability distribution, thereby optimizing the time consumption of the task.
The method for evaluating the operation and maintenance tasks of the offshore wind turbine can further comprise the step of establishing a map, and the step of establishing the map will be described in detail with reference to fig. 2.
Fig. 2 is a flowchart illustrating a method of creating a map according to an embodiment of the inventive concept.
Referring to fig. 2, in step S51, according to the type of task, the executor history capability distribution and task history time-consuming information are acquired.
In one embodiment, the executor capability distribution for each task and the time-consuming data for the task may be stored in memory and extracted from the memory when the database is built. In another embodiment, the executor capability distribution data for executing the present task and the time-consuming data for executing the present task may also be used to update the executor historical capability distribution and task history time-consuming information automatically and/or manually each time after step S40 of fig. 1 is completed.
In step S52, a map of the relationship among the task type information, the executor history capability distribution, and the task history time-consuming information is established as a map.
Specifically, task type information is taken as a first horizontal axis, executor historical capability distribution is taken as a second horizontal axis, task history time-consuming information is taken as a first vertical axis, and a coordinate system is established; and establishing a mapping relation diagram based on the coordinate system. In a preferred embodiment, the map may characterize the mapping between task type information, actor historical capability profiles, and task history time consuming information in the form of a box plot. However, the specific type of map is not limited thereto, and other maps capable of characterizing the relationship therebetween are also possible.
Thus, the relationship between task type information, actor historical capability profiles, and task history time consuming information can be clearly understood from the map.
In one embodiment, the task matching method of the offshore wind turbine set as described above may further include: computing an ability of an actor; responsive to the capability of the actor being below the threshold capability, a targeted incubation of the actor is performed.
In one example, the step of calculating the capabilities of the performer may specifically include the following steps (1) to (7).
(1) The ratio of the number of times of the sea-going operation of the executor to the total number of times of the sea-going operation is calculated as a first parameter. Here, the number of the out-of-sea jobs may correspond to the number of out-sea times the executor performs the task out of sea, and the out-of-sea jobs may include performing at least one task. For example, the first parameter P may be calculated by the following equation 1.
[ Eq.1 ]
Wherein n is a For the number of sea-going times of the executor, n Total (S) For total number of sea-going out, n a And n Total (S) Is a positive integer.
(2) The ratio of the number of times the executor performs the task as the core executor to the number of times the executor performs the task is calculated as the second parameter. Here, the core executives may correspond to a task group leader of a task or a director of a task. For example, the second parameter L may be calculated by the following equation 2.
[ Eq.2 ]
Wherein m is a Take group leader number, n Total (S) For the number of times the task is performed by the performer, m a And m Total (S) Is a positive integer.
(3) The average value of the first average value and the second average value is calculated as a third parameter by calculating a first average value of a ratio of the number of times each task type is executed by an executor to the number of times the task type is executed most among all executors, and a second average value of a ratio of man-hours each task type is executed by an executor to the number of times the task type is executed most among all executors. For example, the third parameter C may be calculated by the following equation 3.
[ Eq.3 ]
Wherein k is q Number of times of executing q-class task for executor S q The q-class tasks are performed the most times among all the executives. h is a q Total man-hour for executing q-class task for executor, T q The total man-hour for executing q-class tasks among all executives is the greatest. The task types can be divided according to the unit subsystems, b is the total number of the subsystems, A, B, … … and M are the numbers of task categories, and k q 、S q 、h q 、T q Is a positive integer, q is A, B, … … and M.
(4) The fourth parameter is a quotient obtained by dividing the ratio of the standard execution days to the number of tasks to be completed under the standard execution days and under the weighting coefficient considering the standard task difficulty and the number of standard executors by the average of the ratio of the number of executors per execution days to the number of tasks to be actually completed per time under the weighting coefficient considering the actual task difficulty and the number of actual executors per time. For example, the fourth parameter E may be calculated by the following equation 4.
[ Eq.4 ]
Wherein d i On member ith number of days of sea operations, M ij The number of j-th tasks completed for the ith sea-going operation, a ij A, which is higher the difficulty coefficient defined for the difficulty level of the j-th task and the number of executors of the j-th task (or called task comprehensive difficulty weighting coefficient) according to the i-th offshore operation ij The smaller the number of executives, the more, a ij The larger n is the number of times the executor goes out of sea, m i Is the number of task types executed by the ith sea operation, M Standard of Day d for standard sea operation Standard of When the weighting coefficient is 1 under the condition, the number of tasks to be completed, a ij 、M Standard of 、d Standard of I is a positive integer not greater than n, j is not greater than m i N, m i 、d i 、M ij Is a positive integer.
Although steps (1) to (4) above are listed sequentially, one skilled in the art will appreciate that steps (1) to (4) may also be performed in parallel, or in other sequences.
(5) And determining the positions of the first parameter, the second parameter, the third parameter and the fourth parameter on the corresponding coordinate axes respectively as vertexes in a coordinate system comprising four coordinate axes which are in one-to-one correspondence with the first parameter, the second parameter, the third parameter and the fourth parameter.
(6) And determining a quadrangle formed by the vertexes, wherein connecting lines of the vertexes on adjacent coordinate axes are used as edges of the quadrangle.
(7) The area of the quadrangle is calculated as the ability of the executor.
In another example, before the above steps (5) to (7), step (4') may be further included. In step (4'), an average score of the performer for the various logistical support tasks is calculated as a fifth parameter. In one example, the score for the logistics work may be derived by scoring the executives after comparing them to each other. For example, the fifth parameter S may be calculated by the following equation 5.
[ Eq.5 ]
In U e For the class e logistic support work of the executor, if the executor does not participate in such logistic support work, the score is 0, and the A-K is assumed to be n in total e Class supports work types for all logistics of the project, U e Is a positive real number, e is A-K, n e Is a positive integer.
In this case, the above steps (5) to (7) can be adjusted accordingly to the steps (5 ') to (7') as follows.
(5') establishing a coordinate system using five coordinate axes on the plane that share an origin and are not collinear with each other, wherein the five coordinate axes correspond to the first parameter, the second parameter, the third parameter, the fourth parameter, and the fifth parameter, respectively.
(6') connecting adjacent ones of the data points of the first parameter, the second parameter, the third parameter, the fourth parameter, and the fifth parameter to form a pentagon.
(7') calculating the area of the pentagon as the ability of the actor.
One example of computing executor capabilities is shown above. However, examples are not limited thereto, and other examples capable of calculating the performer's capabilities are possible.
In another embodiment, the task matching method of the offshore wind turbine generator set as described above may further include: calculating the processing cost of the task; in response to the processing cost of the task being above a threshold cost, the task is executed with other tasks. In one example, the step of calculating the processing cost of the task may specifically include the following steps (8) to (10).
(8) In the case where a weighting coefficient that differs depending on the type of task and the number of executors is used as the weighting coefficient for the task man-hours, a value obtained by multiplying the ratio of the weighted task man-hours to the weighted total task man-hours by the transportation cost is used as the transportation cost for the task. For example, the mission's transportation cost CostA may be calculated by equation 6 below g 。
[ Eq.6 ]
Wherein C is s The back and forth expense for a single sea going vessel; f (f) g The difficulty of the g task is the weighting coefficient of the personnel, the lower the difficulty is, the more the number of people is, and the smaller the coefficient is; i g Man-hour for the g-th task; the denominator is f of all tasks of the sea-going operation g ·I g Summation, g is positive integer, C s 、f g 、I g For positive real numbers, sum () represents the summation function.
(9) And multiplying the ratio of the weighting coefficient of the task to the sum of the weighting coefficients of all tasks by the number of executors executing the task, the number of days of the work out of sea and the value obtained by the single day subsidy of the executors as the cost of the executors of the task. For example, the task's actor cost CostB may be calculated by equation 7 below g 。
[ Eq.7 ]
Wherein n is h The number of people who go out of sea; c (C) h For people going out of the seaThe job ticket is patched on a daily basis; d is the number of days of the personnel going out of the sea; f (f) g The difficulty of the g-th task is the personnel weight coefficient, the lower the difficulty is, the more the number of people is, and the smaller the coefficient is; the denominator is f of all tasks in the sea-going operation g Sum, g, n h Is a positive integer, C h 、D、f g For positive real numbers, sum () represents the summation function.
(10) The sum of the transport cost of the task and the cost of the executor of the task is taken as the processing cost of the task.
Furthermore, it should be understood that various units in the device according to the exemplary embodiments of the present invention may be implemented as hardware components and/or as software components. The individual units may be implemented, for example, using a Field Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC), depending on the processing performed by the individual units as defined.
Further, the standby control method according to the exemplary embodiment of the present invention may be implemented as program instructions in a computer-readable storage medium. Those skilled in the art can implement the program instructions in light of the description of the above methods. The above-described method of the present invention is implemented when the program instructions are executed in a computer.
The invention provides a method for adjusting the capacity distribution of the executor according to the task based on the actual record of the project task, and also provides a method for calculating the capacity of the executor and a method for calculating the task cost, which provides reliable references for formulating the capacity distribution of the executor, the capacity culture plan of the executor and the optimization matching of the executor and the task, and improves the use benefit of task resources.
While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the following claims.
Claims (9)
1. A task matching method of an offshore wind generating set comprises the following steps:
receiving an instruction comprising a request for determining an executor capability distribution and task type information;
in response to receiving the instruction, acquiring an executor historical capacity distribution from the map according to the task type information as an executor initial capacity distribution;
adjusting an initial capacity distribution of the executor based on the task history time-consuming information of the map;
determining a final executor according to the adjusted initial capacity distribution of the executor;
wherein, the executor capability distribution is the information of a single executor about the proportion of different capabilities among all executors executing the task;
wherein the map includes a mapping relationship between task type information and executor history capability distribution and a mapping relationship between task type information and task history time-consuming information,
wherein the step of adjusting the initial capability distribution of the performer based on the task history time-consuming information of the map comprises:
in response to the task history time consumption information being less than the second threshold time consumption, the initial capacity distribution of the executives is adjusted such that the proportion of the executives with capacities below the second threshold capacity is increased among all the executives performing the task.
2. The method of claim 1, further comprising: a map is established and a map is set up,
wherein, the step of establishing the mapping chart comprises the following steps:
acquiring historical capability distribution of an executor and time-consuming information of task history according to the type of the task;
and establishing a mapping relation diagram among the task type information, the executor historical capability distribution and the task historical time-consuming information.
3. The method of claim 2, wherein the step of creating a map of task type information, executor historical capability distribution, and task history time consuming information comprises:
taking task type information as a first horizontal axis, executor historical capability distribution as a second horizontal axis, task history time-consuming information as a first vertical axis, and establishing a coordinate system; and establishing a mapping relation diagram based on the coordinate system.
4. The method of claim 1, wherein the map further includes task history processing number information corresponding to the task type information.
5. The method of claim 1, wherein adjusting the actor initial capability profile based on the task history time-consuming information of the map comprises:
in response to the task history time consuming information being greater than the first threshold time consuming, the initial capacity distribution of the executives is adjusted such that the proportion of the executives having capacities above the first threshold capacity is increased among all of the executives performing the task.
6. The method of claim 1, further comprising: the ability of the practitioner to calculate the performance of the practitioner,
wherein the step of calculating the capabilities of the performer comprises:
calculating a first parameter using the following equation (1), wherein n a For the number of times of the sea-going operation of the executor, n Total (S) For the total number of sea operations, n a And n Total (S) Is a positive integer;
calculating a second parameter using equation (2) below, wherein m a The number of times the executor performs the task as a core executor, m Total (S) For the number of times the task is performed by the performer, m a And m Total (S) Is a positive integer;
calculating a third parameter using equation (3) below, wherein k q Number of times of executing q-class task for executor S q For executing q-class tasks most times among all executors, h q Total man-hour for executing q-class task for executor, T q When the total job with the most q-class tasks is executed among all executors, the task types are divided according to subsystems, b is the total number of the subsystems, A, B, … … and M are the numbers of the task types, and k q 、S q 、h q 、T q Is a positive integer, q is A, B, … … and M;
calculating a fourth parameter using equation (4) below, wherein d i On member ith number of days of sea operations, M ij The number of j-th tasks completed for the ith sea-going operation, a ij A, defining a weight coefficient according to the difficulty level of the j-th task and the number of executors of the j-th task, wherein the higher the difficulty coefficient is, the a ij The smaller the number of executives, the more, a ij The larger n is the number of times the executor goes out of sea, m i Is the number of task types executed by the ith sea operation, M Standard of Day d for standard sea operation Standard of When the weighting coefficient of the task under the condition is 1, the number of the tasks to be completed, a ij 、M Standard of 、d Standard of I is a positive integer not greater than n, j is not greater than m i N, m i 、d i 、M ij Is a positive integer;
determining positions of the first parameter, the second parameter, the third parameter and the fourth parameter on corresponding coordinate axes respectively as vertexes in a coordinate system comprising four coordinate axes corresponding to the first parameter, the second parameter, the third parameter and the fourth parameter one by one;
determining a quadrangle formed by the vertexes, wherein connecting lines connected with the vertexes on adjacent coordinate axes are used as edges of the quadrangle;
the ability to calculate the area of the quadrilateral as an actor; wherein the out-of-sea job includes performing at least one task.
7. The method of claim 1, further comprising:
calculating the processing cost of the task; the step of calculating the processing cost of the task comprises the following steps:
in the case where a weighting coefficient that differs depending on the type of task and the number of executors is used as a weighting coefficient for the task man-hours, a value obtained by multiplying the ratio of the weighted task man-hours to the weighted total task man-hours by the transportation cost is used as the transportation cost for the task;
multiplying the ratio of the weighting coefficient of a task to the sum of the weighting coefficients of all tasks by the number of executors of the offshore operation, the number of days of the offshore operation and the value obtained by the single-day subsidy of the executors as the cost of the executors of the task;
the sum of the transport cost of the task and the cost of the executor of the task is taken as the processing cost of the task.
8. A computer readable storage medium storing a computer program, characterized in that the task matching method of an offshore wind park according to any of claims 1-7 is implemented when the computer program is executed by a processor.
9. A computing device, the computing device comprising:
a processor;
memory storing a computer program which, when executed by a processor, implements a method for mission matching of an offshore wind turbine generator set according to any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811634844.6A CN111382921B (en) | 2018-12-29 | 2018-12-29 | Task matching method, storage medium and computing device of offshore wind turbine generator system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811634844.6A CN111382921B (en) | 2018-12-29 | 2018-12-29 | Task matching method, storage medium and computing device of offshore wind turbine generator system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111382921A CN111382921A (en) | 2020-07-07 |
| CN111382921B true CN111382921B (en) | 2023-05-23 |
Family
ID=71216420
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811634844.6A Active CN111382921B (en) | 2018-12-29 | 2018-12-29 | Task matching method, storage medium and computing device of offshore wind turbine generator system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111382921B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112101897B (en) * | 2020-08-25 | 2022-03-22 | 华能(浙江)能源开发有限公司清洁能源分公司 | Intelligent management method, device and system for offshore wind power |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103595076A (en) * | 2013-11-29 | 2014-02-19 | 湘潭大学 | Method for active power distribution for improving fatigue evenness of wind generating sets |
| CN105184438A (en) * | 2015-07-16 | 2015-12-23 | 陈飞 | System for managing human resources based on capacities and method of system |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130238900A1 (en) * | 2011-12-12 | 2013-09-12 | Cleversafe, Inc. | Dispersed storage network secure hierarchical file directory |
| CN103488691A (en) * | 2013-09-02 | 2014-01-01 | 用友软件股份有限公司 | Task scheduling device and task scheduling method |
| CN105162875B (en) * | 2015-09-23 | 2019-03-12 | 四川师范大学 | Big data group method for allocating tasks and device |
| CN107291767B (en) * | 2016-04-11 | 2020-07-17 | 西门子工厂自动化工程有限公司 | Optimization processing method and device for task execution time |
| CN107797851A (en) * | 2016-09-05 | 2018-03-13 | 展讯通信（上海）有限公司 | Method for scheduling task, device and mobile terminal |
-
2018
- 2018-12-29 CN CN201811634844.6A patent/CN111382921B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103595076A (en) * | 2013-11-29 | 2014-02-19 | 湘潭大学 | Method for active power distribution for improving fatigue evenness of wind generating sets |
| CN105184438A (en) * | 2015-07-16 | 2015-12-23 | 陈飞 | System for managing human resources based on capacities and method of system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111382921A (en) | 2020-07-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110490415B (en) | Building progress assessment method for visual coordination of multiple unmanned aerial vehicles | |
| CN109685152B (en) | Image target detection method based on DC-SPP-YOLO | |
| US20220309201A1 (en) | Artificial-intelligence-assisted method for providing urban design form and layout with improved wind environment | |
| CN112347550B (en) | Coupling type indoor three-dimensional semantic graph building and modeling method | |
| CN107862416B (en) | Emergency material storage warehouse site selection optimization method based on box uncertainty set | |
| CN112001110A (en) | Structural damage identification monitoring method based on vibration signal space real-time recursive graph convolutional neural network | |
| CN113793333A (en) | Defect picture generation method and device applied to industrial quality inspection | |
| CN111382921B (en) | Task matching method, storage medium and computing device of offshore wind turbine generator system | |
| CN110222824B (en) | Intelligent algorithm model autonomous generation and evolution method, system and device | |
| CN111539401B (en) | Lane line detection method, device, terminal and storage medium based on artificial intelligence | |
| KR101792702B1 (en) | Apparatus for determining work order | |
| CN115311396B (en) | Automatic extraction method and system for roof contour line of super-high building | |
| CN113779116B (en) | Object ordering method, related equipment and medium | |
| JP2023168193A (en) | learning model | |
| Kulikova et al. | Optimization algorithms to solve problems of comprehensive electrification based on renewable energy sources | |
| CN113822441A (en) | Decision model training method and device, terminal equipment and storage medium | |
| JP2023167502A (en) | Method of generating learning model, information processing apparatus, information processing method, and computer program | |
| CN115272594A (en) | Iso-surface generation method based on geotools | |
| CN113408895A (en) | Ecological quality index construction method and system based on pixel scale | |
| CN112729275A (en) | Satellite inversion chart gravity adaptation area selection method utilizing factor analysis | |
| CN112348062A (en) | Meteorological image prediction method, meteorological image prediction device, computer equipment and storage medium | |
| JP7332068B1 (en) | Automatic calculation system, automatic calculation method, machine learning model, and method for generating machine learning model | |
| CN111192363A (en) | Cloud computing-based user power distribution room design generation method | |
| CN113935549B (en) | Comprehensive intelligent energy optimization scheduling system | |
| CN116957414B (en) | Village planning analysis method and device based on artificial intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |