CN117851839A - Full-period intelligent management and control system of photovoltaic equipment based on digital twin - Google Patents
Full-period intelligent management and control system of photovoltaic equipment based on digital twin Download PDFInfo
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Abstract
The invention discloses a full-period intelligent control system of photovoltaic equipment based on digital twinning, which relates to the field of photovoltaic equipment control and comprises a data processing unit; according to the full-period intelligent management and control system of the photovoltaic equipment based on digital twin, the photovoltaic equipment vectors and the photovoltaic demand vectors of the photovoltaic equipment in the photovoltaic equipment library are scored through the photovoltaic equipment matching scoring module, the photovoltaic equipment with the scoring meeting the working demand is selected, the photovoltaic equipment with the scoring meeting the non-working demand is replaced, the photovoltaic equipment with the scoring meeting the non-working demand is set to be convenient for better matching, the corresponding photovoltaic equipment is set, the updating state of the overlapping characteristics is determined through extracting the overlapping characteristics, and therefore the choosing and the rejecting of the overlapping characteristics are determined, useful measurement data can be selected according to the extracted characteristics of different photovoltaic equipment, more data are avoided, proper photovoltaic equipment data cannot be selected, and meanwhile due to the setting of the photovoltaic equipment matching scoring module, the matching of the photovoltaic equipment is selected under the same standard, and errors existing in manual selection of the photovoltaic equipment are avoided.
Description
Technical Field
The invention relates to a photovoltaic equipment management and control technology, in particular to a digital twin-based full-period intelligent management and control system for photovoltaic equipment.
Background
The digital twin photovoltaic station is an information technology system and a power grid information model based on digital identification, automatic perception, networking connection, general Hui Hua calculation, intelligent control and platform service, a digital photovoltaic station corresponding to physical power grid matching is reproduced in a digital space, the state of a power grid physical entity in a real environment is subjected to holographic simulation, dynamic monitoring, real-time diagnosis and accurate prediction, full-element digitization and virtualization, full-state real-time visualization and power grid operation management coordination and intellectualization are promoted, and the physical power grid and the digital power grid are realized to cooperatively interact and run in parallel.
But at present, under the condition that the photovoltaic station is required to be powered differently in a working period, energy is supplied through different photovoltaic equipment in order to reduce energy loss, so that errors can occur in the selection of the powered photovoltaic equipment due to manpower, and meanwhile, proper photovoltaic equipment cannot be selected for supplying energy according to the current working efficiency of the photovoltaic equipment.
Disclosure of Invention
The invention aims to provide a digital twin-based full-cycle intelligent management and control system for photovoltaic equipment, which aims to solve the defects in the prior art.
In order to achieve the above object, the present invention provides the following technical solutions: the utility model provides a photovoltaic equipment full cycle intelligence management and control system based on digit twin, includes data processing unit, information acquisition unit, includes multiple photovoltaic equipment's photovoltaic equipment storehouse, multisource information integration unit, photovoltaic equipment match scoring module and display element:
the information acquisition unit is used for acquiring measurement data of different photovoltaic devices;
the data processing unit is used for carrying out data adjustment on the data acquired by the information acquisition unit so that the data is convenient to use;
the multi-source information integration unit is used for integrating the data processed by the data processing unit, so that multiple data can be conveniently used in combination;
the photovoltaic equipment matching scoring module is used for scoring the photovoltaic vectors and the photovoltaic demand vectors of the photovoltaic equipment in the photovoltaic equipment library, wherein the photovoltaic equipment meeting the working demand is selected for use, the photovoltaic equipment not meeting the working demand is replaced, and the photovoltaic equipment matching scoring module comprises:
the photovoltaic demand vector analysis module is used for carrying out vector analysis on the photovoltaic demand;
the photovoltaic equipment vector analysis module is used for analyzing the photovoltaic vector under the current working condition of the photovoltaic equipment;
the scoring module is used for carrying out matching scoring on the finished product model vector analysis module and the photovoltaic equipment vector analysis module;
the display unit is used for displaying the data processed by the data processing unit.
Further, the data processing unit adjusts the data collected by the data collection unit including, but not limited to, abnormal data recovery, discarding, filling, replacement, and deduplication.
Further, the specific method for the data processing unit to perform data adjustment is as follows:
s1, data screening, wherein the data screening further comprises:
preliminary screening;
threshold value screening;
quality control screening, wherein if mutation occurs in measurement parameter data of a certain sampling interval, abnormal conditions of the measurement parameters are considered to occur;
s2, data recovery, wherein the data recovery method further comprises the following steps:
a time-series-based data recovery method, the time-series-based data prediction method being essentially suitable for recovery of measured data;
the data recovery method based on the space-time correlation is to establish a correlation model of any measured data and other measured data in space-time, and then recover abnormal data through the other measured data.
Further, the multi-source information integrating unit includes:
the feature screening module is used for screening features in the measurement data;
the same feature extraction module is used for extracting the same features screened by the feature screening module;
the abnormal feature extraction module is used for extracting the abnormal features screened by the feature screening module;
the overlapped feature extraction module is used for extracting the overlapped features screened by the feature screening module;
and the feature fusion unit is used for carrying out feature fusion on the corresponding features extracted by the same feature extraction module, the abnormal feature extraction module and the overlapped feature extraction module.
Further, the specific method for extracting the overlapping features by the multi-source information integration unit comprises the following steps:
a1, calculating all measurement data j (j epsilon Z) in each target T (t=1, 2,.. t (k) Associated probability beta) with target t jt ；
A2, constructing a confirmation matrix, and judging whether the measurement is public measurement belonging to a plurality of targets according to the confirmation matrix, wherein the expression of the confirmation matrix is as follows:
；
a3, calculating the probability of the affiliation between the public echo and each target, and assuming a target set T M With a common echo j, calculate the echo j and each target T (T e T M ) Euclidean distance d between jt Because the larger the measurement distance from the target, the smaller the probability of the measurement belonging to the target set, the more the common echo and each target are, the more the probability of the assignment between them is inversely related to the distance, and the echoes are all assigned to their associated targets, the probability of the assignment is
；
A4, correcting the association probability of the public echo j obtained in the first step by utilizing the affiliation probability between the echo formula and each target, thereby obtaining a new association probability beta between the public echo and each related target ’ jt ，
；
A5, normalizing the interconnection probabilities of all measurements in the association gate where each target is located to obtain beta ’’ jt ，
;
A6, utilizing the associated probability beta ’’ jt The state is updated by weighting all the metrology data within each target.
Further, the working steps of the photovoltaic equipment matching scoring module are as follows:
e1, filtering useless features in the vector feature information of the finished product model, taking a sigmoid activation function as a gating state, performing dot multiplication on the useless features and the vector features of the finished product model, and performing a tanh activation function to obtain the screening of the gate unit on the features;
e2, use of the attention mechanism to photovoltaicThe key vector feature information in the equipment library is enhanced, and an embedded vector t and a feature representation of a text are obtained according to the vector feature typeThrough t T Scoring each feature in the text to perceive important information in the text, as shown in the following equation:
;
e3, obtaining the photovoltaic equipment library representation after evaluationThe following formula is shown: />
Wherein the method comprises the steps of,/>Is a vector of the attention of the person,is a vector matrix of a photovoltaic device library;
e4, obtaining the query representation by the vector feature information fusion moduleAnd the characterization of the photovoltaic device library +.>Then calculated by maximum similarity (MaxSim), by +.>And->The Score between the query and the photovoltaic device library can be calculated, i.e. the Score is the queryThe maximum similarity sum represented by each finished product model vector and each photovoltaic device work vector of the photovoltaic device library is shown as follows: 。
compared with the prior art, the full-period intelligent management and control system for the photovoltaic equipment based on the digital twin provided by the invention has the advantages that the photovoltaic equipment vector and the photovoltaic demand vector of the photovoltaic equipment in the photovoltaic equipment library are scored through the photovoltaic equipment matching scoring module, the photovoltaic equipment with the scoring meeting the working demand is selected, the photovoltaic equipment with the scoring not meeting the working demand is replaced, the corresponding photovoltaic equipment is conveniently matched better, the overlapping characteristics are extracted, the updating state of the overlapping characteristics is determined, the choice of the overlapping characteristics is determined, the useful measurement data can be selected according to the extracted characteristics of different photovoltaic equipment, the situation that the data are more, the proper photovoltaic equipment data cannot be selected is avoided, and meanwhile, the matching of the photovoltaic equipment is enabled to select the proper photovoltaic equipment under the same standard through the setting of the photovoltaic equipment matching scoring module, and the error existing in the manual selection of the photovoltaic equipment is avoided.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
Fig. 1 is a schematic diagram of an overall flow structure according to an embodiment of the present invention.
Detailed Description
In order to make the technical scheme of the present invention better understood by those skilled in the art, the present invention will be further described in detail with reference to the accompanying drawings.
Referring to fig. 1, a digital twin-based full-cycle intelligent management and control system for photovoltaic devices comprises a data processing unit, an information acquisition unit, a photovoltaic device library comprising a plurality of photovoltaic devices, a multi-source information integration unit, a photovoltaic device matching scoring module and a display unit:
the information acquisition unit is used for acquiring measurement data of different photovoltaic devices;
the data processing unit is used for carrying out data adjustment on the data acquired by the information acquisition unit so that the data is convenient to use;
the multi-source information integration unit is used for integrating the data processed by the data processing unit, so that multiple data can be conveniently used in a combined mode;
the photovoltaic equipment matching scoring module is used for scoring the photovoltaic vectors and the photovoltaic demand vectors of the photovoltaic equipment in the photovoltaic equipment library, wherein the photovoltaic equipment meeting the working demand is selected for use, and the photovoltaic equipment not meeting the working demand is replaced, and the photovoltaic equipment matching scoring module comprises:
the photovoltaic demand vector analysis module is used for carrying out vector analysis on the photovoltaic demand;
the photovoltaic equipment vector analysis module is used for analyzing the photovoltaic vector under the current working condition of the photovoltaic equipment;
the scoring module is used for carrying out matching scoring on the finished product model vector analysis module and the photovoltaic equipment vector analysis module;
and the display unit is used for displaying the data processed by the data processing unit.
The measurement data of each photovoltaic device in the photovoltaic device library is collected through the information collection unit, the data collected by the information collection unit is subjected to data adjustment through the data processing unit, so that the data is convenient to use, the processed multi-source data is integrated through the multi-source information integration unit, the data of a plurality of photovoltaic devices in the photovoltaic device library is convenient to integrate and use, the data integrated by the multisource information integration unit are scored through a photovoltaic equipment matching scoring module for photovoltaic equipment vectors and photovoltaic demand vectors of photovoltaic equipment in a photovoltaic equipment library, wherein the photovoltaic equipment which meets the working demand is selected for use, the photovoltaic equipment which does not meet the working demand is replaced, the photovoltaic equipment which does not meet the working demand is matched for scoring is arranged, the photovoltaic equipment is matched with the corresponding photovoltaic equipment conveniently and better, the data processed by the data processing unit are displayed through the display unit, and the data monitoring is facilitated, and the data accuracy is guaranteed.
The data processing unit adjusts the data acquired by the data acquisition unit, including but not limited to abnormal data recovery, discarding, filling, replacing and deduplication, so that usable data can be selected for use.
The specific method for the data processing unit to adjust the data comprises the following steps:
s1, data screening, wherein the data screening further comprises:
preliminary screening;
threshold screening, which is to set certain data critical values and screen the measurement data exceeding the critical values, so as to ensure that the measurement data is within a reasonable range. The key of the threshold screening is to determine a proper critical threshold value which is closely related to different places, external environments and data sampling intervals, and the expression is that
,
Wherein x is max To measure the critical threshold of the data, x 0 For the basic limit of the measured data, T is the sampling interval of the data, and f () is the fitting function of the root mean square error of the measured data stream parameters and the sampling interval T.
And (3) quality control screening, wherein the measured data has a certain continuity in time, i.e. the measured data in a certain period of time does not have mutation. If the measured data of a certain sampling interval has mutation, the group of measured data can be considered to have abnormal conditions, and the abnormal value screening is carried out by combining the correlation among the measured data and the sampling multisource quality control method. The index formula for constructing the multi-source quality control is as follows
,
Wherein the formula is as follows: i is an index of multi-source data quality control, mq n 、mν n 、mo n Mean values of n sampling interval x-axis measurement data q, y-axis measurement data v and z-axis measurement data o, sq respectively n 、sν n 、so n The standard deviation of the x-axis measurement data q, the y-axis measurement data v and the z-axis measurement data o are respectively, and the index I is an ellipsoid of multi-source quality control established according to the 3 sigma principle. If a set of measurement data q, v and o is such that I is greater than 1, indicating that the point of the three-dimensional data vector of the measurement point in three-dimensional space falls outside the quality control ellipsoid, the set of data is abnormal and should be rejected, otherwise the data is normal.
The reason for data screening is that measurement data acquired by a detector is affected by external factors, and thus, there are abnormal cases such as deletion, mutation, and error, and thus, the measurement data cannot be directly used as data.
S2, data recovery, wherein the data recovery method further comprises the following steps:
the data recovery method based on the time sequence is essentially suitable for recovering the measured data, but takes the real-time property, the randomness and the mass property of the measured data into consideration, and needs strong on-line processing capacity, so the specific calculation mode is as follows
,
Wherein the method comprises the steps ofThe value is the recovery value of abnormal data in the formula, x is the detection value of the first several sampling intervals, beta is a weight coefficient, and Σbeta=1; k is the sampling interval width adopted by smooth recovery, the method is mainly suitable for recovery of isolated abnormal data, and for a plurality of continuous abnormal data, the error of the method can be greatly increased. Therefore, when the continuous abnormal data is too much, the method is not applicable;
the data recovery method based on the space-time correlation is to establish a data recovery formula by establishing a correlation model of any detector data and other detector data in space-time, recovering abnormal data by the other detector data and considering binary regression and median robustness according to a data regression model
,
Wherein the method comprises the steps ofValues are recovered for the j-position data for the pairs of detectors m and n associated with the j-position. Gamma ray 1 、γ 2 And gamma 3 For regression equation coefficients, x i (m) and x i (n) is the actual detection value of m and n positions,/->And for the predicted value of the missing detector j, a plurality of regression equations are obtained by establishing a correlation model among a plurality of detector data, the median value of the recovery values of the plurality of regression equation data is used as final recovery data, and the model with the median robust characteristic is adopted, so that the influence of the abnormality and the loss of part of detector data on the final recovery result can be avoided, and the anti-interference capability of the method is improved.
The multi-source information integration unit includes:
the feature screening module is used for screening features in the measurement data;
the same feature extraction module is used for extracting the same features screened by the feature screening module;
the abnormal feature extraction module is used for extracting the abnormal features screened by the feature screening module;
the overlapped feature extraction module is used for extracting the overlapped features screened by the feature screening module;
and the feature fusion unit is used for carrying out feature fusion on the corresponding features extracted by the same feature extraction module, the abnormal feature extraction module and the overlapped feature extraction module.
The method comprises the steps of setting the same feature selected in a feature screening module through the same feature extraction module, extracting the abnormal feature selected in the feature screening module through the abnormal feature extraction module, extracting the overlapped feature selected in the feature screening module through the overlapped feature extraction module, fusing the same feature through a feature fusion unit, eliminating the abnormal feature, and determining the updating state of the overlapped feature through extracting the overlapped feature, so that the choice of the overlapped feature is determined, and therefore, useful measurement data can be selected according to the extracted features of different photovoltaic devices, and the situation that the data are more to cause that proper photovoltaic device data cannot be selected is avoided.
The specific method for extracting the overlapping characteristics by the multi-source information integration unit comprises the following steps:
a1, calculating all measurement data j (j epsilon Z) in each target T (t=1, 2,.. t (k) Associated probability beta) with target t jt ；
A2, constructing a confirmation matrix, and judging whether the measurement is public measurement belonging to a plurality of targets according to the confirmation matrix, wherein the expression of the confirmation matrix is as follows:
,
if in the above formula:;
then it is explained that the measurement j is a common echo, the element equal to 1 in each row of the validation matrix is judged, and the target label t is recorded 1 ，t 2 .. the measurement j is the t of each of the mouthpieces 1 ，t 2 ... Public measurements, all measurements are judged to obtain all public echoes, and targets corresponding to these echoes, respectively;
a3, calculating the probability of the affiliation between the public echo and each target, and assuming a target set T M With a common echo j, calculate the echo j and each target T (T e T M ) Euclidean distance d between jt Because the larger the measurement distance target is, the smaller the probability of the measurement belonging to the target set is, the probability of the affiliation between the common echo and each target is inversely related to the distance, andthe waves are all distributed to the targets associated with the waves, and the probability of the affiliation is;
;
a4, correcting the association probability of the public echo j obtained in the first step by utilizing the affiliation probability between the echo formula and each target, thereby obtaining a new association probability beta between the public echo and each related target ’ jt ，
;
A5, normalizing the interconnection probabilities of all measurements in the association gate where each target is located to obtain beta ’’ jt ：
;
A6, utilizing the associated probability beta ’’ jt The update state of all the measurement data in each target is carried out through the weighting process, the update state of the overlapped features is determined through the extraction of the overlapped features, and therefore the choice of the overlapped features is determined, and therefore useful measurement data can be selected according to the extracted features, and the situation that proper data cannot be selected due to more data is avoided.
The working steps of the photovoltaic equipment matching scoring module are as follows:
e1, filtering useless features in the vector feature information of the finished product model, taking a sigmoid activation function as a gating state, performing dot multiplication on the useless features and the vector features of the finished product model, and performing a tanh activation function to obtain the screening of the gate unit on the features;
e2, reinforcing key vector feature information in the photovoltaic equipment library by using an attention mechanism, and obtaining an embedded vector t and a feature representation of a text according to the vector feature typeThrough t T Scoring each feature in the text to perceive itImportant information in the text is shown in the following formula:
;
e3, obtaining the photovoltaic equipment library representation after evaluationThe following formula is shown:
wherein the method comprises the steps of,/>Is a vector of the attention of the person,is a vector matrix of a photovoltaic device library;
e4, obtaining the query representation by the vector feature information fusion moduleAnd the characterization of the photovoltaic device library +.>Then calculated by maximum similarity (MaxSim), by +.>And->The Score between the query and the photovoltaic device library can be calculated, namely the maximum similarity sum represented by each finished product model vector of the query and each photovoltaic device working vector of the photovoltaic device library is represented by the following formula:
。
working principle: during the use, the measurement data of each photovoltaic device in the photovoltaic device library is collected through the information collection unit, data adjustment is carried out on the data collected by the information collection unit through the data processing unit, so that the data are convenient to use, the processed multi-source data are integrated through the multi-source information integration unit, the data of a plurality of photovoltaic devices in the photovoltaic device library are convenient to integrate and use, the data integrated through the multi-source information integration unit are scored through the photovoltaic device matching scoring module, the photovoltaic device vectors and the photovoltaic demand vectors of the photovoltaic devices in the photovoltaic device library are selected for use on the photovoltaic devices with the scoring meeting the working demand, the photovoltaic devices with the scoring not meeting the working demand are replaced, the data processed by the data processing unit are displayed through the display unit, the data are convenient to monitor, the data accuracy is guaranteed, meanwhile, the update state of the characteristics is determined through the multi-source information integration unit, the selection of the characteristics is determined, the photovoltaic devices with the overlapping characteristics can be selected according to the characteristics of the extracted different photovoltaic devices, the fact that the photovoltaic devices with the matching scoring module cannot be selected for the photovoltaic devices is more, and the matching error of the photovoltaic devices cannot be avoided due to the fact that the matching photovoltaic devices are matched with the photovoltaic devices is properly selected.
While certain exemplary embodiments of the present invention have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the invention, which is defined by the appended claims.
Claims (6)
1. The full-period intelligent management and control system for the photovoltaic equipment based on digital twinning is characterized by comprising a data processing unit, an information acquisition unit, a photovoltaic equipment library comprising various photovoltaic equipment, a multi-source information integration unit, a photovoltaic equipment matching scoring module and a display unit:
the information acquisition unit is used for acquiring measurement data of different photovoltaic devices;
the data processing unit is used for carrying out data adjustment on the data acquired by the information acquisition unit so that the data is convenient to use;
the multi-source information integration unit is used for integrating the data processed by the data processing unit, so that multiple data can be conveniently used in combination;
the photovoltaic equipment matching scoring module is used for scoring the photovoltaic vectors and the photovoltaic demand vectors of the photovoltaic equipment in the photovoltaic equipment library, wherein the photovoltaic equipment meeting the working demand is selected for use, the photovoltaic equipment not meeting the working demand is replaced, and the photovoltaic equipment matching scoring module comprises:
the photovoltaic demand vector analysis module is used for carrying out vector analysis on the photovoltaic demand;
the photovoltaic equipment vector analysis module is used for analyzing the photovoltaic vector under the current working condition of the photovoltaic equipment;
the scoring module is used for carrying out matching scoring on the finished product model vector analysis module and the photovoltaic equipment vector analysis module;
the display unit is used for displaying the data processed by the data processing unit.
2. The full-cycle intelligent management and control system of the photovoltaic equipment based on digital twinning according to claim 1, wherein the data processing unit adjusts the data collected by the data collection unit, including but not limited to abnormal data recovery, discarding, filling, replacement and deduplication.
3. The full-period intelligent management and control system of the photovoltaic equipment based on digital twinning according to claim 2, wherein the specific method for carrying out data adjustment by the data processing unit is as follows:
s1, data screening, wherein the data screening further comprises:
preliminary screening;
threshold value screening;
quality control screening, wherein if mutation occurs in measurement parameter data of a certain sampling interval, abnormal conditions of the measurement parameters are considered to occur;
s2, data recovery, wherein the data recovery method further comprises the following steps:
a time-series-based data recovery method, the time-series-based data prediction method being essentially suitable for recovery of measured data;
the data recovery method based on the space-time correlation is to establish a correlation model of any measured data and other measured data in space-time, and then recover abnormal data through the other measured data.
4. The digital twinning-based photovoltaic device full cycle intelligent control system of claim 1, wherein the multi-source information integration unit comprises:
the feature screening module is used for screening features in the measurement data;
the same feature extraction module is used for extracting the same features screened by the feature screening module;
the abnormal feature extraction module is used for extracting the abnormal features screened by the feature screening module;
the overlapped feature extraction module is used for extracting the overlapped features screened by the feature screening module;
and the feature fusion unit is used for carrying out feature fusion on the corresponding features extracted by the same feature extraction module, the abnormal feature extraction module and the overlapped feature extraction module.
5. The digital twin-based photovoltaic equipment full-cycle intelligent control system according to claim 4, wherein the specific method for extracting overlapping features by the multi-source information integration unit is as follows:
a1, calculating all measurement data j (j epsilon Z) in each target T (t=1, 2,.. t (k) Associated probability beta) with target t jt ；
A2, constructing a confirmation matrix, and judging whether the measurement is public measurement belonging to a plurality of targets according to the confirmation matrix, wherein the expression of the confirmation matrix is as follows:
;
a3, calculating the probability of the affiliation between the public echo and each target, and assuming a target set T M With a common echo j, calculate the echo j and each target T (T e T M ) Euclidean distance d between jt Because the larger the measurement distance target is, the smaller the probability that the measurement belongs to the target set is, the probability of the affiliation between the common echo and each target is inversely related to the distance, and the echoes are all distributed to the targets associated with the common echo, the probability of the affiliation is:
;
a4, correcting the association probability of the public echo j obtained in the first step by utilizing the affiliation probability between the echo formula and each target, thereby obtaining a new association probability beta between the public echo and each related target ’ jt ，
;
A5, normalizing the interconnection probabilities of all measurements in the association gate where each target is located to obtain beta ’’ jt ，
;
A6, utilizing the association probabilityβ ’’ jt The state is updated by weighting all the metrology data within each target.
6. The full-period intelligent management and control system of the photovoltaic equipment based on digital twinning according to claim 1, wherein the working steps of the photovoltaic equipment matching scoring module are as follows:
e1, filtering useless features in the vector feature information of the finished product model, taking a sigmoid activation function as a gating state, performing dot multiplication on the useless features and the vector features of the finished product model, and performing a tanh activation function to obtain the screening of the gate unit on the features;
e2, reinforcing key vector feature information in the photovoltaic equipment library by using an attention mechanism, and obtaining an embedded vector t and a feature representation of a text according to the vector feature typeThrough t T Scoring each feature in the text to perceive important information in the text, as shown in the following equation:
;
e3, obtaining the photovoltaic equipment library representation after evaluationThe following formula is shown: />;
Wherein the method comprises the steps of,/>Is a vector of the attention of the person,is a vector matrix of a photovoltaic device library;
e4, obtaining the query representation by the vector feature information fusion moduleAnd the characterization of the photovoltaic device library +.>Then calculated by maximum similarity (MaxSim), by +.>Andthe Score between the query and the photovoltaic device library can be calculated, namely the maximum similarity sum represented by each finished product model vector of the query and each photovoltaic device working vector of the photovoltaic device library is represented by the following formula:
。
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112087041A (en) * | 2020-08-30 | 2020-12-15 | 盘锦京联石油技术有限公司 | Photovoltaic full-electric kitchen and energy management optimization system |
| CN117407939A (en) * | 2023-10-17 | 2024-01-16 | 中铁四局集团有限公司 | Digital twinning-based photovoltaic system design model system and application method thereof |
| CN117464420A (en) * | 2023-12-28 | 2024-01-30 | 江苏新贝斯特智能制造有限公司 | Digital twin control cutter self-adaptive matching system suitable for numerical control machine tool |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112087041A (en) * | 2020-08-30 | 2020-12-15 | 盘锦京联石油技术有限公司 | Photovoltaic full-electric kitchen and energy management optimization system |
| CN117407939A (en) * | 2023-10-17 | 2024-01-16 | 中铁四局集团有限公司 | Digital twinning-based photovoltaic system design model system and application method thereof |
| CN117464420A (en) * | 2023-12-28 | 2024-01-30 | 江苏新贝斯特智能制造有限公司 | Digital twin control cutter self-adaptive matching system suitable for numerical control machine tool |
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