CN103164749A - Relevance type load prediction system and method - Google Patents
Relevance type load prediction system and method Download PDFInfo
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- CN103164749A CN103164749A CN2011104240354A CN201110424035A CN103164749A CN 103164749 A CN103164749 A CN 103164749A CN 2011104240354 A CN2011104240354 A CN 2011104240354A CN 201110424035 A CN201110424035 A CN 201110424035A CN 103164749 A CN103164749 A CN 103164749A
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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
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Abstract
The invention discloses a relevance type load prediction system and a method. The system comprises an information reception module, an electrical load index data bank and an event prediction operation module. The information reception module obtains an event sequence which combines a plurality of events and corresponding event sequence electrical load wave mode information. The electrical load index data bank stores at least one prediction event sequence and prediction event sequence electrical load index information which corresponds to each prediction event sequence and an event prediction sample. The event prediction operation module compares the event sequence electrical load wave mode information and at least the prediction event sequence electrical load index information to produce at least one event prediction sample and an event prediction result which corresponds to the event sequence. Reliability of predication results is improved.
Description
Technical field
The present invention relates to a kind of correlation load estimation system and method, and particularly relate to a kind of correlation load estimation system and method that uses historical power information to calculate next imminent event.
Background technology
Because industrial society's electricity needs continues to increase, the situation benefit of rationing the power supply is all over the world seen frequently, when a certain regional electricity needs increases, must set up new power plant and distribution system.Each power generation system is unique system, is to produce the capacity grid because each power generation system comprises electric power, and they gather together, to supply the electricity needs of change in specific masses or zone.
In general, the electric power of most system produces capacity and can't meet the electricity needs that day by day increases in the world.This kind state must will have the correct electricity consumption deficiency of filling up to measure, as limiting program during the spike user load.Rationing the power supply to cause voltage drop, and frequency also can descend.The method also can only temporarily solve the problem of power shortage.At last, if power system capacity does not increase, system will be failed.Even now, ration the power supply and still can't solve the problem of power shortage.And the state that voltage and/or frequency descend, some can't suitably operate as sensing electronic installation or apparatus such as computer.
Aging power equipment is also a serious problem.There are at present many power plant must keep in repair and/or exist serious inefficiency.Multiple electric power produces equipment (as nuclear energy power plant) and operates under precarious position, therefore essential decommissioning.So existing multiple power produces the ability that in fact system supply electric power and shrinks.
Because considering and some temporal can't grasp when founding the factory economically, be not one to cover as scheduled surely new power plant, with the capacity of increasing electric power.If even increase electric power the generation capacity, it not yet determines can reach top efficiency at the electric power that where adds of system.Another speech, can determine so-called in each power generation system without any equipment now " faint zone ".The problem that let us not go into the question now not enough electric power measurement and new power capacity add, many systems still must be rationed the power supply.The reason of rationing the power supply now is industrial unpredictable or proofread and correct this faint zone.
Faint zone definitions can not put up with the generation grid position that adds load in power generation system.For example, answer can't be provided in calculating now as whether a specific bus can be stood given load increase, or the instantaneous increase of system's tolerable load on a bus, and keep increase in demand in another bus in grid.Collapse of voltage is generally caused by the system interference of two kinds of patterns: load change and incident.
Yet the present short-term load estimation algorithm that proposes all focuses on the historical load wave mode of analyzing indivedual ammeters, sets up model and predicts action, does not consider and ammeter data relation is each other included in.
Because the defective that above-mentioned existing load estimation technology exists, the inventor is based on being engaged in this type of product design manufacturing abundant practical experience and professional knowledge for many years, coordinate the utilization of scientific principle, positive research and innovation in addition, to founding a kind of new correlation load estimation system and method, can improve existing load estimation technology, make it have more practicality.Through constantly research, design, after repeatedly studying sample and improvement, finally create the present invention who has practical value.
Summary of the invention
The object of the invention is to, overcome the problem that existing load estimation technology exists, and a kind of correlation load estimation system and method is provided, technical matters to be solved is to improve the fiduciary level that predicts the outcome.
Purpose of the present invention and technical matters to be solved are to realize by following technical scheme:
The present invention proposes a kind of correlation load estimation system, comprises an information receiving module, an electrical load indexing information storehouse and an event prediction computing module.information receiving module is obtained the sequence of events electrical load wave mode information in conjunction with a sequence of events of many events and corresponding sequence of events, electrical load indexing information storehouse stores at least one predeterminable event sequence, at least one event prediction sample of at least one predeterminable event sequence electrical load index information of corresponding each predeterminable event sequence and corresponding each predeterminable event sequence, event prediction computing module comparison sequence of events electrical load wave mode information and at least one predeterminable event sequence electrical load index information, to produce an event prediction result of at least one event prediction sample and corresponding sequence of events.
In one embodiment of this invention, correlation load estimation system more comprises an event training module, comprises a gather material unit, at least one intelligent meter unit, at least one electric equipment state recording unit and a central service unit.each intelligent meter unit is arranged on an electric equipment, to note down respectively an electric equipment load wave mode of electric equipment, and return electric equipment load wave mode to the gather material unit at a fixed cycle time, electric equipment state recording unit is arranged at respectively on electric equipment, the unlatching of each electric equipment and close and be considered as respectively an event, the event of corresponding electric equipment is detected and recorded to electric equipment state recording unary system, and be sent to the gather material unit, the central service unit connects the gather material unit by a network, electric equipment load wave mode and event with the operating state of obtaining corresponding at least one electric equipment, and set up respectively an event sample according to each electric equipment, set up again at least one event prediction sample of at least one sequence of events sample and corresponding each predeterminable event sequence according to above-mentioned event sample and above-mentioned electric equipment load wave mode.
In one embodiment of this invention, above-mentioned central service unit more comprises an event data bank, a load data bank and a processing unit, the event data bank stores above-mentioned event sample, and according at least one sequence of events sample of above-mentioned event Sample Establishing, the load data bank stores electric equipment load wave mode, and processing unit is set up the predeterminable event sequence electrical load index information of predeterminable event sequence, corresponding predeterminable event sequence and the event prediction sample of corresponding predeterminable event sequence according to sequence of events sample and electric equipment load wave mode.
In one embodiment of this invention, above-mentioned processing unit is more set up an electric equipment load wave mode index.
In one embodiment of this invention, store an ammeter code name, a real-time power and the time record of corresponding each electric equipment in above-mentioned load data bank.
In one embodiment of this invention, above-mentioned central service unit more comprises a plurality of predicting unit, each predicting unit is carried out computing according at least one event prediction sample of corresponding each predeterminable event sequence, with the level of confidence index of the event prediction sample of obtaining corresponding each predeterminable event sequence.
The present invention proposes a kind of correlation load predicting method, its step is as follows: at first, at least one event prediction sample that stores at least one predeterminable event sequence electrical load index information of at least one predeterminable event sequence, corresponding each predeterminable event sequence and corresponding each predeterminable event sequence is in an electrical load indexing information storehouse, then, obtain a sequence of events electrical load wave mode information in conjunction with a sequence of events and the corresponding sequence of events of many events by an information receiving module.By an event prediction computing module comparison sequence of events electrical load wave mode information, at least one predeterminable event sequence electrical load index information and at least one event prediction sample, last again, an event prediction result of the corresponding sequence of events of generation.
In of the present invention one implements, the correlation load predicting method more comprises an event training step, comprise: an intelligent meter unit and an electric equipment state recording unit first are set on an electric equipment, by an electric equipment load wave mode of intelligent meter unit record electric equipment, and in fixed cycle time passback electric equipment load wave mode to a gather material unit.Then, by the detecting of electric equipment state recording unit and record an event of corresponding electric equipment, and be sent to the gather material unit, event refers to the unlatching of electric equipment and closes, again by a network, make a connection gather material unit, central service unit, electric equipment load wave mode and event with the operating state of obtaining corresponding at least one electric equipment, at last, set up an event sample, set up an event prediction sample of a sequence of events sample and corresponding predeterminable event sequence according to event sample and electric equipment load wave mode.
In one embodiment of this invention, above-mentioned central service unit is set up in the step of sequence of events sample and event prediction sample, more comprises: set up an electric equipment load wave mode index.
In one embodiment of this invention, the above-mentioned central service unit collection step of electric loading data, comprise: record an electrical load information of current electric equipment according to a fixed time period via the intelligent meter unit, be back to data searching device, be back to a load data bank of central server.
In one embodiment of this invention, the step of above-mentioned central service unit Collection Events data comprises: the code name of setting an event of intelligent meter unit institute wish detecting by electric equipment state recording unit, utilize an ammeter wave mode recognition technology to detect the generation of each event, inform the gather material unit by network, record the historical events of intelligent meter unit, and be back to an event data bank of central server.
By technique scheme, correlation load estimation system and method for the present invention has following advantages and beneficial effect at least: the present invention is by coordinating the load historical summary, obtain every event rear interior load wave mode of a period of time occurs, and the event that cooperation detecting wish prediction ammeter may occur has recently improved with aid forecasting the fiduciary level that predicts the outcome.
In sum, the present invention has significant progress technically, and has significantly positive technique effect, becomes a new and innovative, progressive, practical new design.
Above-mentioned explanation is only the general introduction of technical solution of the present invention, for can clearer understanding technological means of the present invention, and can be implemented according to the content of instructions, and for above and other purpose of the present invention, feature and advantage can be become apparent, below especially exemplified by preferred embodiment, and the cooperation accompanying drawing, be described in detail as follows.
Description of drawings
Fig. 1 is the element schematic diagram of correlation load estimation of the present invention system.
Fig. 2 is the flow chart of steps of correlation load predicting method of the present invention.
Fig. 3 is the element schematic diagram of event training module of the present invention.
Fig. 4 is the flow chart of steps of event training method of the present invention.
Fig. 5 is the element schematic diagram of training of the present invention and operation system.
Fig. 6 is main execution in step process flow diagram of the present invention.
Fig. 7 is the flow chart of steps that the present invention carries out training and operation system.
Fig. 8 A is DAQ and the pre-process schematic diagram of one embodiment of the invention.
Fig. 8 B is the sequence samples schematic diagram of one embodiment of the invention.
Fig. 8 C is sequence of events and the wave mode schematic diagram of one embodiment of the invention.
Fig. 8 D is the event prediction sample schematic diagram of one embodiment of the invention.
[main element symbol description]
110 information receiving module 111 sequences of events
112 sequence of events electrical load wave mode information 120 electrical load indexing information storehouses
130 event prediction computing module 140 predeterminable event sequences
150 predeterminable event sequence electrical load index informations
160 event prediction sample 310 electric equipments
311 312 electric equipment state recording unit, intelligent meter unit
320 330 central service unit, gather material unit
331 event data bank 332 load data bank
333 processing unit S210～S240 steps flow charts
S410～S450 steps flow chart S610～S630 steps flow chart
S710～S790 steps flow chart
Embodiment
For further set forth the present invention be reach technological means that predetermined goal of the invention takes with and effect, below in conjunction with accompanying drawing and preferred embodiment, embodiment, structure, feature and the effect thereof of the correlation load estimation system and method that foundation the present invention is proposed are described in detail as follows.
Fig. 1 is the element schematic diagram of correlation load estimation of the present invention system.In Fig. 1, correlation load estimation system comprises an information receiving module 110, an electrical load indexing information storehouse 120 and an event prediction computing module 130.information receiving module 110 is obtained the sequence of events electrical load wave mode information 112 in conjunction with a sequence of events 111 of many events and corresponding sequence of events 111, electrical load indexing information storehouse 120 stores at least one predeterminable event sequence 140, at least one event prediction sample 160 of at least one predeterminable event sequence electrical load index information 150 of corresponding each predeterminable event sequence 140 and corresponding each predeterminable event sequence 140, event prediction computing module 130 comparison sequence of events electrical load wave mode information 112 and at least one predeterminable event sequence electrical load index informations 150, to produce an event prediction result of at least one event prediction sample 160 and corresponding sequence of events.
Fig. 2 is the flow chart of steps of correlation load predicting method of the present invention.In Fig. 2, comprising:
Step S210: at least one event prediction sample that stores at least one predeterminable event sequence electrical load index information of at least one predeterminable event sequence, corresponding each predeterminable event sequence and corresponding each predeterminable event sequence is in an electrical load indexing information storehouse.
Step S220: obtain a sequence of events electrical load wave mode information in conjunction with a sequence of events and the corresponding sequence of events of many events by an information receiving module.
Step S230: by an event prediction computing module comparison sequence of events electrical load wave mode information, at least one predeterminable event sequence electrical load index information and at least one event prediction sample.
Step S240: an event prediction result that produces corresponding sequence of events.
Fig. 3 is the element schematic diagram of event training module of the present invention.The event training module comprises an electric equipment 310, a gather material unit 320 and a central service unit 330.Each electric equipment 310 is provided with an intelligent meter unit 311 and an electric equipment state recording unit 312, one electric equipment load wave mode of intelligent meter unit 311 records, and in a fixed cycle time passback electric equipment load wave mode to the gather material unit 320, the unlatching of each electric equipment 310 and close and be considered as respectively an event, the event of corresponding electric equipment 310 is detected and recorded in electric equipment state recording unit 312, and be sent to gather material unit 320.
In the present embodiment, processing unit is more set up an electric equipment load wave mode index.
Fig. 4 is the flow chart of steps of event training method of the present invention.Comprise:
Step S410: an intelligent meter unit and an electric equipment state recording unit are set on an electric equipment.
Step S420: by an electric equipment load wave mode of this electric equipment of intelligent meter unit record, and in fixed cycle time passback electric equipment load wave mode to a gather material unit.
Step S430: by the detecting of electric equipment state recording unit and record an event of corresponding electric equipment, and be sent to the gather material unit, event refers to the unlatching of electric equipment and closes.
Step S440: by a network, make a central service unit connect the gather material unit, with electric equipment load wave mode and the event of the operating state of obtaining corresponding at least one electric equipment.
In the present embodiment, comprise and record an electrical load information of current electric equipment via the intelligent meter unit according to a fixed time period, be back to data searching device, be back to a load data bank of central server.
In the present embodiment, comprise the code name of setting an event of intelligent meter unit institute wish detecting by electric equipment state recording unit, the for example unlatching of indivedual electrical equipment or close, utilize an ammeter wave mode recognition technology to detect the generation of each event, inform the gather material unit by network, record the historical events of intelligent meter unit, and be back to an event data bank of central server.
Step S450: set up an event sample, set up a sequence of events sample and to an event prediction sample of predeterminable event sequence according to the electric equipment load wave mode of event sample.
In the present embodiment, more comprise and set up an electric equipment load wave mode index.
Fig. 5 is the element schematic diagram of training of the present invention and operation system.Mainly that correlation load estimation system is combined with the event training module in Fig. 5, particularly in central server.As shown in Figure 5, the sequence of events 111 of collecting by gather material unit 320 and a sequence of events electrical load wave mode information 112 of corresponding sequence of events can be sent in event prediction computing module 130 and load data bank 332 by the event of the information receiving module 110 in central service unit 330 with electric equipment load wave mode and electric equipment.
In the present embodiment, store an ammeter code name, a real-time power and the time record of corresponding each electric equipment in load data bank.
In the present embodiment, the central service unit more comprises a plurality of predicting unit, each predicting unit is carried out computing according at least one event prediction sample of corresponding each predeterminable event sequence, with the level of confidence index of the event prediction sample of obtaining corresponding each predeterminable event sequence.
Fig. 6 is main execution in step process flow diagram of the present invention.Comprise:
Step S610: DAQ and pre-process.
Step S620: find out sequence samples.
Step S630: predict.
Fig. 7 is the flow chart of steps that the present invention carries out training and operation system.Comprise:
Step S710: a sequence of events electrical load wave mode information that obtains sequence of events and corresponding sequence of events.
Step S720: a sequence of events electrical load wave mode information of pre-service sequence of events and corresponding sequence of events.
Step S730: query event sequence samples.
Step S740: utilize sequence of events, sequence of events electrical load wave mode information and sequence of events sample to predict.
Step S751: obtain the event data.
Step S752: find out the event sample.
Step S761: obtain the load data.
Step S762: pre-service load wave mode data.
Step S770: set up the corresponding of event and load wave mode.
Step S780: set up load wave mode index.
Step S790: utilize the many groups of algorithm training predicting machine.
Please consult simultaneously Fig. 8 A～Fig. 8 D, Fig. 8 A is DAQ and the pre-process schematic diagram of one embodiment of the invention.Fig. 8 B is the sequence samples schematic diagram of one embodiment of the invention.Fig. 8 C is sequence of events and the wave mode schematic diagram of one embodiment of the invention.Fig. 8 D is the event prediction sample schematic diagram of one embodiment of the invention.
In Fig. 8 A, be that explanation is synthetic with electric equipment and its load wave mode, form an event.For example: load wave mode A and electric equipment A form an event A (as shown in step S810), load wave mode B and electric equipment B forms an event B (as shown in step S820) and load wave mode C and electric equipment C formation one event C (as shown in step S830).
In Fig. 8 B, after being the permutation and combination of each event of explanation, can infer next imminent event.For example: event A, event B and event C can carry out event B after forming a sequence again, or are can carry out event C after event B, event D and event E form a sequence.
In Fig. 8 C, after explanation sequence of events electrical load wave mode information is analyzed by the event prediction computing module, this sequence of events electrical load wave mode information is combined by load wave mode A, load wave mode B and load wave mode C as can be known, that is this sequence of events electrical load wave mode information illustrates that this sequence of events is formed by event A, event B and event C.
In Fig. 8 D, accept Fig. 8 C, illustrate that event B might then occur for sequence of events that event A, event B and event C form confidence index is 60%, the confidence index of event E be 30% and the confidence index of event A be 10%.And along with the result of true generation, can adjust the confidence index of each event that continues.
the above, it is only preferred embodiment of the present invention, be not that the present invention is done any pro forma restriction, although the present invention discloses as above with preferred embodiment, yet be not to limit the present invention, any those skilled in the art, within not breaking away from the technical solution of the present invention scope, when the method that can utilize above-mentioned announcement and technology contents are made a little change or be modified to the equivalent embodiment of equivalent variations, in every case be the content that does not break away from technical solution of the present invention, any simple modification that foundation technical spirit of the present invention is done above embodiment, equivalent variations and modification, all still belong in the scope of technical solution of the present invention.
Claims (12)
1. correlation load estimation system is characterized in that comprising:
One information receiving module is obtained in conjunction with a sequence of events of many events and to a sequence of events electrical load wave mode information that should sequence of events;
One electrical load indexing information storehouse stores at least one predeterminable event sequence electrical load index information of at least one predeterminable event sequence, corresponding each this predeterminable event sequence and at least one event prediction sample of corresponding each this predeterminable event sequence; And
One event prediction computing module is compared this sequence of events electrical load wave mode information and this at least one predeterminable event sequence electrical load index information, reaches an event prediction result that should sequence of events to produce this at least one event prediction sample.
2. correlation load estimation as claimed in claim 1 system, is characterized in that more comprising an event training module, and this event training module comprises:
One gather material unit;
At least one intelligent meter unit is arranged at least one electric equipment, noting down respectively an electric equipment load wave mode of this at least one electric equipment, and in this at least one electric equipment load wave mode of fixed cycle time passback to this gather material unit;
At least one electric equipment state recording unit, be arranged at respectively on this at least one electric equipment, the unlatching of each above-mentioned electric equipment and close and be considered as respectively an event, this at least one electric equipment state recording unit is detected and is recorded this event that should at least one electric equipment, and is sent to this gather material unit; And
One central service unit, connect this gather material unit by a network, to obtain this electric equipment load wave mode and this event to operating state that should at least one electric equipment, and set up respectively an event sample according to this electric equipment respectively, then set up at least one event prediction sample of at least one sequence of events sample and corresponding each this predeterminable event sequence according to above-mentioned event sample and above-mentioned electric equipment load wave mode.
3. correlation load estimation as claimed in claim 2 system, it is characterized in that wherein this central service unit more comprises an event data bank, one load data bank and a processing unit, this event data bank stores above-mentioned event sample, and according to this at least one sequence of events sample of above-mentioned event Sample Establishing, this load data bank stores this at least one electric equipment load wave mode, this processing unit is set up this at least one predeterminable event sequence according to this at least one sequence of events sample and this at least one electric equipment load wave mode, at least one event prediction sample of at least one predeterminable event sequence electrical load index information of corresponding each this predeterminable event sequence and corresponding each this predeterminable event sequence.
4. correlation load estimation as claimed in claim 3 system, is characterized in that wherein this processing unit is more set up an electric equipment load wave mode index.
5. correlation load estimation as claimed in claim 3 system is characterized in that storing in this event data bank wherein the time record of an ammeter code name and the corresponding above-mentioned event sample of corresponding each above-mentioned electric equipment.
6. correlation load estimation as claimed in claim 3 system is characterized in that storing in this load data bank wherein an ammeter code name, a real-time power and the time record of corresponding each above-mentioned electric equipment.
7. correlation load estimation as claimed in claim 2 system, it is characterized in that wherein this central service unit more comprises a plurality of predicting unit, each above-mentioned predicting unit is carried out computing according at least one event prediction sample of corresponding each this predeterminable event sequence, with the level of confidence index of each above-mentioned event prediction sample of obtaining corresponding each this predeterminable event sequence.
8. correlation load predicting method is characterized in that comprising:
At least one event prediction sample that stores at least one predeterminable event sequence electrical load index information of at least one predeterminable event sequence, corresponding each this predeterminable event sequence and corresponding each this predeterminable event sequence is in an electrical load indexing information storehouse;
Obtain in conjunction with a sequence of events of many events and to a sequence of events electrical load wave mode information that should sequence of events by an information receiving module;
Compare this sequence of events electrical load wave mode information, this at least one predeterminable event sequence electrical load index information and this at least one event prediction sample by an event prediction computing module; And
Generation is to an event prediction result that should sequence of events.
9. correlation load predicting method as claimed in claim 8, is characterized in that more comprising an event training step, comprising:
One intelligent meter unit and an electric equipment state recording unit are set on an electric equipment;
By an electric equipment load wave mode of this this electric equipment of intelligent meter unit record, and return this electric equipment load wave mode to gather material unit at a fixed cycle time;
To a event that should electric equipment, and be sent to this gather material unit by the unit detecting of this electric equipment state recording and record, this event refers to the unlatching of this electric equipment and closes;
By a network, make a central service unit connect this gather material unit, to obtain this electric equipment load wave mode and this event to operating state that should at least one electric equipment; And
Set up an event sample, set up a sequence of events sample and to an event prediction sample that should the predeterminable event sequence according to this event sample and this electric equipment load wave mode.
10. correlation load predicting method as claimed in claim 9 is characterized in that wherein this central service unit sets up in the step of this sequence of events sample and this event prediction sample, more comprises:
Set up an electric equipment load wave mode index.
11. correlation load predicting method as claimed in claim 9 is characterized in that wherein this central service unit collection comprises with the step of electric loading data:
Via this intelligent meter unit recording an electrical load information of current this electric equipment according to a fixed time period;
Be back to this data searching device; And
Be back to a load data bank of this central server.
12. correlation load predicting method as claimed in claim 9 is characterized in that wherein this central service unit collects the step of this event data and comprise:
Set the code name of an event of this intelligent meter unit institute wish detecting by this electric equipment state recording unit;
Utilize an ammeter wave mode recognition technology to detect the generation of each this event; And
Inform this gather material unit by this network, record the historical events of this intelligent meter unit, and be back to an event data bank of this central server.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113837480A (en) * | 2021-09-29 | 2021-12-24 | 河北工业大学 | Impact load prediction method based on improved GRU and differential error compensation |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1665088A (en) * | 2004-03-05 | 2005-09-07 | 株式会社Kd动力 | Digital diagrammatic view switch apparatus system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1665088A (en) * | 2004-03-05 | 2005-09-07 | 株式会社Kd动力 | Digital diagrammatic view switch apparatus system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113837480A (en) * | 2021-09-29 | 2021-12-24 | 河北工业大学 | Impact load prediction method based on improved GRU and differential error compensation |
| CN113837480B (en) * | 2021-09-29 | 2023-11-07 | 河北工业大学 | Impact load prediction method based on improved GRU and differential error compensation |
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