CN117033705A - Data value-added service method for client side energy interconnection - Google Patents
Data value-added service method for client side energy interconnection Download PDFInfo
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Abstract
A data value-added service method for customer side energy interconnection includes such steps as recognizing the energy data set for judging and the energy data to be judged according to the stored energy data set and the preset percentages, randomly selecting the destination data from the judged energy data set, deriving the discrimination of the energy data to be judged according to the preset SVM model and the destination data, deriving the reconstruction of the energy data to be judged according to the preset reconstruction judging equation and the destination data, recognizing whether the energy data to be judged is high-quality energy data or not according to the discrimination and reconstruction of the energy data to be judged, deriving the discrimination and reconstruction of the energy data to be judged according to the preset SVM model and the preset reconstruction judging equation, and decreasing the consumption of the same energy data.
Description
Technical Field
The application belongs to the technical field of client-side energy interconnection, and particularly relates to a data value-added service method for client-side energy interconnection.
Background
In the client-side energy interconnection technology, a data processing center of the energy internet usually receives a large amount of energy data to perform processing (such as display, comparison or backup), an energy data set formed by a plurality of received energy data is critical to the processing performance, if some lightning identity data is included in the energy data set, the data non-uniformity is increased, so that the data processing performance is poor, therefore, how to reduce the consumption of the lightning identity data is the direction to be processed, and the current energy data consumption mode comprises arbitrary extraction, timing extraction, automatic extraction and the like, so that the number of the lightning identity energy data to be used cannot be truly reduced.
Disclosure of Invention
In order to solve the defects in the prior art, the application provides a data value-added service method for interconnection of client side energy, which is characterized by identifying an energy data set for judgment and energy data to be judged according to the stored energy data set and a preset percentage, randomly selecting target data in the energy data set for judgment, deducing the distinguishing degree of the energy data to be judged according to a preset SVM model and the target data, deducing the reconstruction degree of the energy data to be judged according to a preset reconstruction judgment equation and the target data, identifying whether the energy data to be judged is high-quality energy data according to the distinguishing degree and the reconstruction degree of the energy data to be judged, deducing whether the energy data to be judged is high-quality energy data or not through the preset SVM model and the preset reconstruction judgment equation of the energy data set, truly selecting the high-quality energy data and reducing the availability of low-quality similar energy data.
The application adopts the following technical scheme.
A data value-added service method facing to client side energy interconnection comprises the following steps:
step 1: the data acquisition device acquires energy data and transmits the energy data to the data processing center for storage, and a plurality of stored energy data form a stored energy data group;
step 2: the data processing center judges and processes the stored energy data;
the method for judging the stored energy data by the data processing center comprises the following steps:
step 2-1-1: identifying the energy data set for judgment and the energy data to be judged according to the stored energy data set and the preset percentage;
step 2-1-2: randomly selecting target data from the energy data set for judgment, and deducing the distinguishing degree of the energy data to be judged according to a preset SVM model and the target data;
step 2-1-3: deducing the reconstructable degree of the energy data to be judged according to a preset reconstruction judgment equation and target data;
step 2-1-4: and according to the distinguishing degree and the reconstruction degree of the energy data to be judged, judging whether the energy data to be judged is high-quality energy data or not.
Preferably, the step 2-1-2 specifically comprises:
deducing destination data A e And energy data A to be determined f non-Lei Tongxing of which the equation is as follows:
here, a->Representing destination data A e And energy data A to be determined f non-Lei Tongxing @ between>And->Respectively representing the feeding amount A f SVM model and feed quantity A e Is represented by G, and the target data A e Is the e-th energy data in the judging energy data group G, A f Is the f energy data to be determined, and the energy data A to be determined f Degree of distinction M of (2) f Is:the highest amount for discriminating ++>Is:，/>representative of the wholeThe lowest discrimination metric is selected from +.>Representing all->Arithmetic mean of>Representing all->Variance of->Representing the highest amount of energy data in the stored energy data set under the condition of the error probability range determined in advance when the variance alignment test is performed on the stored energy data set;
the discrimination between the energy data to be determined and the whole samples in the energy data set for determination is sequentially deduced through the above equations, a plurality of discrimination metrics are obtained, and the lowest discrimination metric is selected from the whole discrimination metrics to be used as the discrimination of the energy data to be determined.
Preferably, the steps 2-1-3 specifically include:
the f-th energy data to be determined can be reconstructed as W f ，The re-constructability of the e-th destination data is W e ，/>Here, a->Is a data identification code set for the f-th energy data to be determined,/for the energy data to be determined>Is a data identification code set for the e-th destination data, a data identification code set for the e-th destination data>Is a handle->As the input quantity, the derived quantity obtained after the operation of the encoder is used +.>Is a handle->The reconfigurable group is formed by the reconstruction of the whole target data in the determination energy data group as the derived quantity obtained by the calculation of the input quantity from the encoder>，/> The maximum amount for reconsideration can be determined again>Is: ->，/>And->Are respectively a reconfigurable group->Arithmetic mean and variance of each of the reconfigurable data in the database, and the reconfigurable W of the energy data to be determined is derived from the above equation f 。
Preferably, the steps 2-1-4 specifically include: if the discrimination of the energy data to be determined is higher than the maximum discrimination determination amount and the reconfigurability of the energy data to be determined is higher than the maximum reconfigurability determination amount, multiplying the discrimination of the energy data to be determined by the reconfigurability of the energy data to be determined, and taking the obtained amount after multiplication as the quality number of the energy data to be determined, if the quality number of the energy data to be determined is higher than a set amount, determining that the energy data to be determined is high-quality energy data; if any of the above requirements is met, the energy data to be determined is deemed to be non-high quality energy data.
Preferably, after the step 2-1-3 of deriving the renewable energy data to be determined, the method further comprises the steps of:
step 2-1-3-2: identifying the highest amount for distinguishing degree and the highest amount for reconstruction according to the preset first check equation, the preset second check equation and the energy data set for judgment;
step 2-1-3-3: if the discrimination of the energy data to be determined is higher than the maximum discrimination and the reconstructable amount of the energy data to be determined is higher than the maximum reconstructable amount, steps 2-1-4 are performed.
Preferably, after said step 2-1-4, further comprising:
step 2-1-5: if the energy data to be determined is high-quality energy data, adding the energy data to be determined into a target energy data group;
step 2-1-6: when the number of samples of the target energy data set is higher than a preset definition amount II, replacing the target energy data set with the energy data set for judgment;
step 2-1-7: and executing the step 2-1-2 until the stored energy data set does not contain the energy data to be judged, which is the high-quality energy data, so as to obtain the high-quality energy data set.
Preferably, after said step 2-1-7, further comprising:
step 2-1-8: and collecting and storing new energy data, and determining whether the new energy data is high-quality energy data or not according to the high-quality energy data group, the preset SVM model and the preset reconstruction judgment equation.
Preferably, the data processing center executes a processing method for stored energy data, including:
processing is performed on the energy data in the high quality energy data set.
A customer-side energy interconnection oriented data value-added service device, comprising:
the data acquisition device is arranged in the energy internet and is connected with the data processing center; the data processing center can be a server.
The data acquisition device is used for acquiring energy data to be transmitted to the data processing center for storage, and a plurality of stored energy data form a stored energy data group;
the data processing center is used for judging and processing the stored energy data.
The voltage sensor, the mobile communication module and the ammeter are all connected with the controller;
the controller is used for transmitting the energy data to the data processing center through the mobile communication module.
Preferably, the data acquisition device comprises a mobile communication module, a controller, a voltage sensor for acquiring voltage data of the distributed power supply in the energy internet and transmitting the voltage data to the controller, and an ammeter for acquiring electricity consumption of the distributed power supply in the energy internet and transmitting the electricity consumption to the controller, wherein the voltage of the distributed power supply and the electricity consumption of the distributed power supply form the energy data.
Compared with the prior art, the method has the advantages that the method identifies the energy data group for judgment and the energy data to be judged according to the stored energy data group and the preset percentage, then randomly selects the destination data in the energy data group for judgment, deduces the distinction degree of the energy data to be judged according to the preset SVM model and the destination data, deduces the reconstruction degree of the energy data to be judged according to the preset reconstruction judgment equation and the destination data, then identifies whether the energy data to be judged is high-quality energy data according to the distinction degree and the reconstruction degree of the energy data to be judged, deduces whether the energy data to be judged is high-quality energy data or not through the preset SVM model and the preset reconstruction judgment equation of the energy data group for judgment, truly selects the high-quality energy data and reduces the access of the low-quality identical energy data.
Drawings
FIG. 1 is a flow chart of steps 1 to 2 of the present application;
FIG. 2 is a flow chart of steps 2-1-1 through 2-1-4 of the present application;
FIG. 3 is a flow chart of steps 2-1-5 through 2-1-8 described in the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely expressed with reference to the drawings in the embodiments of the present application. The embodiments of the application that are presented are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art without inventive faculty, are within the scope of the application.
As shown in fig. 1, the data value-added service method for client-side energy interconnection of the present application includes:
step 1: the data acquisition device acquires energy data and transmits the energy data to the data processing center for storage, and a plurality of stored energy data form a stored energy data group;
step 2: the data processing center judges and processes the stored energy data;
as shown in fig. 2, the method for determining the stored energy data by the data processing center includes:
step 2-1-1: identifying the energy data set for judgment and the energy data to be judged according to the stored energy data set and the preset percentage;
specifically, the energy data set for judgment with the functionality is selected from the huge stored energy data sets to be used for more useful processing; just like the stored energy data set has 10 6 The preset percentage of the energy data is seven percent, and the sample size of the energy data set for judgment is deduced to be 7 x 10 according to the total energy data in the stored energy data set and the preset percentage 3 The energy data to be determined can be the stored energy data set, namely, the energy data outside the energy data set for determination is removed from the stored energy data set, namely, the energy data is obtained by the method of 10 6 The energy data is selected from 7 x 10 3 The individual energy data was used as the determination energy data set, followed by the remaining 93 x 10 3 The energy data to be judged is arbitrarily selected from the energy data.
Step 2-1-2: randomly selecting target data from the energy data set for judgment, and deducing the distinction degree of the energy data to be judged according to a preset SVM model (SVM model is SVM algorithm) and the target data;
in a preferred but non-limiting embodiment of the present application, the steps 2-1-2 specifically include:
deducing destination data A e And energy data A to be determined f non-Lei Tongxing therebetween (characterized by quantization) and its equation is as follows:
here, a->Representing destination data A e And energy data A to be determined f non-Lei Tongxing @ between>And->Respectively representing the feeding amount A f SVM model and feed quantity A e Is represented by G, and the target data A e Is the e-th energy data in the judging energy data group G, A f Is the f energy data to be determined, and the energy data A to be determined f Degree of distinction M of (2) f Is:the highest amount for discriminating ++>Is:，/>representative of the wholeThe lowest discrimination metric is selected from +.>Representing all->Arithmetic mean of>Representing all->Variance of->Representing the highest amount of energy data in the stored energy data set under the condition of the error probability range determined in advance when the variance alignment test is performed on the stored energy data set;
the discrimination between the energy data to be determined and the whole samples in the energy data set for determination is sequentially deduced through the above equations, a plurality of discrimination metrics are obtained, and the lowest discrimination metric is selected from the whole discrimination metrics to be used as the discrimination of the energy data to be determined.
Step 2-1-3: deducing the reconstructable degree of the energy data to be judged according to a preset reconstruction judgment equation and target data;
in a preferred but non-limiting embodiment of the present application, the steps 2-1-3 specifically include:
the f-th energy data to be determined can be reconstructed as W f ，The re-constructability of the e-th destination data is W e ，/>Here, a->Is a data identification code set for the f-th energy data to be determined,/for the energy data to be determined>Is a data identification code set for the e-th destination data, a data identification code set for the e-th destination data>Is a handle->As the input quantity, the derived quantity obtained after the operation of the encoder is used +.>Is a handle->The reconfigurable group is formed by the reconstruction of the whole target data in the determination energy data group as the derived quantity obtained by the calculation of the input quantity from the encoder>，/> The maximum amount for reconsideration can be determined again>Is: ->，/>And->Are respectively a reconfigurable group->Arithmetic mean and variance of each of the reconfigurable data in the database, and the reconfigurable W of the energy data to be determined is derived from the above equation f 。
Step 2-1-4: and according to the distinguishing degree and the reconstruction degree of the energy data to be judged, judging whether the energy data to be judged is high-quality energy data or not.
In a preferred but non-limiting embodiment of the present application, the steps 2-1-4 specifically include: if the discrimination of the energy data to be determined is higher than the maximum discrimination determination amount and the reconfigurability of the energy data to be determined is higher than the maximum reconfigurability determination amount, multiplying the discrimination of the energy data to be determined by the reconfigurability of the energy data to be determined, and taking the obtained amount after multiplication as the quality number of the energy data to be determined, if the quality number of the energy data to be determined is higher than a set amount, determining that the energy data to be determined is high-quality energy data; if any of the above requirements is met, the energy data to be determined is deemed to be non-high quality energy data.
That is, the detailed flow of the step 2-1-2 comprises:
step 2-1-2-1: the energy data to be judged and the target data are sent into a preset SVM model to obtain a degree of distinction;
step 2-1-2-2: replacing the target data, and repeatedly executing the step 2-1-2-1 to obtain a plurality of discriminators;
step 2-1-2-2: and taking the lowest quantity in the plurality of distinctions as the distinctions of the energy data to be determined.
After the determination of the energy data set, a target data can be arbitrarily selected from the determination energy data set, the non-Lei Tongxing value of the energy data to be determined and the target data can be deduced according to the preset SVM model, then a target data (the target data selected in the previous cycle can be cleaned) is arbitrarily selected from the fine determination energy data set, and thus the non-lightning similarity value (namely the discrimination) between the whole target data in the determination energy data set and the energy data to be determined can be obtained by repeatedly executing the steps, and a lowest discrimination is selected from the discrimination, so that the lowest discrimination is determined as the discrimination of the energy data to be determined.
That is, the detailed flow of the steps 2-1-3 includes:
step 2-1-3-1: and sending the energy data to be judged and the target data into a preset reconstruction judging equation comprising a preset verification model to obtain the reconstruction of the energy data to be judged.
After deriving the discrimination of the energy data to be determined, deriving the reconstructable degree of the energy data to be determined according to a reconstruction determination equation and target data set in advance, wherein the reconstructable degree of the f-th energy data to be determined is W f ，The re-constructability of the e-th destination data is W e ，/>Here, a->Is a data identification code set for the f-th energy data to be determined,/for the energy data to be determined>Is a data identification code set for the e-th destination data,is a handle->As the input quantity, the derived quantity obtained after the operation of the encoder is used +.>Is a handle->The reconfigurable group is formed by the reconstruction of the whole target data in the determination energy data group as the derived quantity obtained by the calculation of the input quantity from the encoder>，/> The maximum amount for reconsideration can be determined again>The method comprises the following steps:，/>and->Are respectively a reconfigurable group->Arithmetic mean and variance of each of the reconfigurable data in the database, and the reconfigurable W of the energy data to be determined is derived from the above equation f And the maximum amount for the reconfigurability determination, and then determines whether the energy data to be determined can pass the reconfigurability check.
In a preferred but non-limiting embodiment of the present application, the step 2-1-3 of deriving the renewable energy data to be determined further comprises the steps of:
step 2-1-3-2: identifying the highest amount for distinguishing degree and the highest amount for reconstruction according to the preset first check equation, the preset second check equation and the energy data set for judgment;
step 2-1-3-3: if the discrimination of the energy data to be determined is higher than the maximum discrimination and the reconstructable amount of the energy data to be determined is higher than the maximum reconstructable amount, steps 2-1-4 are performed.
The preset check equation is the highest quantitative equation for discriminating degree determination:the second preset check equation is the maximum equation for reconstruction identification>And (2) respectively identifying the highest difference and the highest reconstruction amount according to the preset first check equation, the preset second check equation and the set of the energy data for determination, and then identifying the difference between the highest difference and the highest reconstruction amount of the energy data for determination, and identifying whether the energy data for determination is high-quality energy data or not when the difference between the energy data for determination and the reconstruction is checked respectively.
That is, the detailed flow of the steps 2-1-4 includes:
step 2-1-4-1: deducing the quality number of the energy data to be judged according to the distinguishing degree and the reconstruction degree of the energy data to be judged;
step 2-1-4-2: if the mass number is higher than a preset definition amount, the energy data to be judged is determined to be high-quality energy data;
step 2-1-4-3: and if the mass number is not higher than the preset defined amount, identifying that the energy data to be judged is not the high-quality energy data.
The detailed flow of identifying whether the energy data to be determined is high quality energy data or not according to the discrimination degree and the reconstruction degree of the energy data to be determined is as follows: if the discrimination of the energy data to be determined is higher than the maximum discrimination determination amount and the reconfigurability of the energy data to be determined is higher than the maximum reconfigurability determination amount, multiplying the discrimination of the energy data to be determined by the reconfigurability of the energy data to be determined, and taking the obtained amount after multiplication as the quality number of the energy data to be determined, if the quality number of the energy data to be determined is higher than a set amount, determining that the energy data to be determined is high-quality energy data; if any of the above requirements is met, the energy data to be determined is deemed to be non-high quality energy data.
The method comprises the steps of identifying the energy data set for judgment and the energy data to be judged according to the stored energy data set and the preset percentage, randomly selecting target data in the energy data set for judgment, deducing the distinction degree of the energy data to be judged according to the preset SVM model and the target data, deducing the reconstruction degree of the energy data to be judged according to the preset reconstruction judgment equation and the target data, identifying whether the energy data to be judged is high-quality energy data according to the distinction degree and the reconstruction degree of the energy data to be judged, deducing and obtaining whether the energy data to be identified is high-quality energy data or not through the preset SVM model and the preset reconstruction judgment equation of the energy data set for judgment, and truly selecting high-quality energy data to reduce the taking of low-quality identical energy data.
In a preferred but non-limiting embodiment of the present application, as shown in FIG. 3, after the steps 2-1-4, further comprising:
step 2-1-5: if the energy data to be determined is high-quality energy data, adding the energy data to be determined into a target energy data group;
step 2-1-6: when the number of samples of the target energy data set is higher than a preset definition amount II, replacing the target energy data set with the energy data set for judgment;
step 2-1-7: and executing the step 2-1-2 until the stored energy data set does not contain the energy data to be judged, which is the high-quality energy data, so as to obtain the high-quality energy data set.
After the energy data to be judged is identified as high-quality energy data, the energy data to be judged can be added into the target energy data group for storing the identified high-quality energy data, when the number of the high-quality energy data in the target energy data group is higher than a preset definition amount II, the target energy data group with the high-quality energy data is replaced by the judging energy data group, the identification of the energy data to be judged is continuously executed, the target data is arbitrarily selected from the target energy data group, the distinguishing degree of the energy data to be judged is deduced according to a preset SVM model and the target data, the flow is equivalent to that in the step 2-1-1, the identified judging energy data group is the target energy data group with the high-quality energy data which is obtained in advance, and the steps 2-1-1 to 2-1-4 are repeatedly executed until the stored energy data group does not have the high-quality energy data, and then the high-quality energy data group is obtained.
In a preferred but non-limiting embodiment of the present application, after said steps 2-1-7, further comprising:
step 2-1-8: and collecting and storing new energy data, and determining whether the new energy data is high-quality energy data or not according to the high-quality energy data group, the preset SVM model and the preset reconstruction judgment equation.
After the high-quality energy data set is obtained, new energy data outside the stored energy data set can be collected, whether the new energy data is the high-quality energy data or not is determined according to the data value-added service method facing the client-side energy interconnection, so that the high-quality energy data set can be continuously refreshed, and the high-quality energy data can be better taken.
Therefore, the high-quality energy data can be truly obtained by refreshing the high-quality energy data, so that the obtaining of the low-quality radon energy data is reduced.
In a preferred but non-limiting embodiment of the present application, the data processing center performs a method for processing stored energy data, including:
processing is performed on the energy data in the high quality energy data set. Thus, the processed energy data can be ensured to be not low-quality. The processing includes displaying the energy data in the high quality energy data set or comparing the energy data in the high quality energy data set with a predetermined reasonable energy data range to determine whether the energy data in the high quality energy data set is in the reasonable energy data range.
The application relates to a data value-added service device facing to client side energy interconnection, which comprises:
the data acquisition device is arranged in the energy internet and is connected with the data processing center; the data processing center can be a server.
The data acquisition device is used for acquiring energy data to be transmitted to the data processing center for storage, and a plurality of stored energy data form a stored energy data group;
in a preferred but non-limiting embodiment of the present application, the data acquisition device includes a mobile communication module, a controller, a voltage sensor for acquiring voltage data of a distributed power supply in the energy internet and transmitting the voltage data to the controller, and an ammeter for acquiring electricity consumption of the distributed power supply in the energy internet and transmitting the electricity consumption to the controller, where the voltage of the distributed power supply and the electricity consumption of the distributed power supply form the energy data; the controller can be a single-chip microcomputer. The mobile communication module can be a 4G module.
The voltage sensor, the mobile communication module and the ammeter are all connected with the controller;
the controller is used for transmitting the energy data to the data processing center through the mobile communication module.
The data processing center is used for judging and processing the stored energy data.
Compared with the prior art, the method has the advantages that the method identifies the energy data group for judgment and the energy data to be judged according to the stored energy data group and the preset percentage, then randomly selects the destination data in the energy data group for judgment, deduces the distinction degree of the energy data to be judged according to the preset SVM model and the destination data, deduces the reconstruction degree of the energy data to be judged according to the preset reconstruction judgment equation and the destination data, then identifies whether the energy data to be judged is high-quality energy data according to the distinction degree and the reconstruction degree of the energy data to be judged, deduces whether the energy data to be judged is high-quality energy data or not through the preset SVM model and the preset reconstruction judgment equation of the energy data group for judgment, truly selects the high-quality energy data and reduces the access of the low-quality identical energy data.
The present disclosure can be a system, method, and/or computer program product. The computer program product can include a computer-readable backup medium having computer-readable program instructions embodied thereon for causing a processor to perform the various aspects of the disclosure.
The computer readable backup medium can be a tangible power grid line capable of holding and backing up instructions for execution of the power grid line exercise by the instructions. The computer readable backup medium can be, but is not limited to, an electrical backup power grid line, a magnetic backup power grid line, an optical backup power grid line, an electromagnetic backup power grid line, a semiconductor backup power grid line, or any suitable combination of the foregoing. Still further examples (non-enumerated list) of the computer-readable backup medium include: portable computer disk, hard disk, random access backup (RAM), read-only backup (ROM), erasable programmable read-only backup (EPROM or flash memory), static random access backup (SRAM), portable compact disk read-only backup (HD-ROM), digital versatile disk (DXD), memory stick, floppy disk, mechanical coded electrical wiring, punch card like with instructions backed up thereon, or bump structures in grooves, optionally in combination with the above. The computer-readable backup medium as used herein is not to be construed as a transitory signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (as in the case of an optical pulse through a transmission line cable), or an electrical signal transmitted through an electrical wire.
The computer readable program instructions expressed herein can be downloaded from a computer readable backup medium to the respective extrapolated/processed power grid lines or downloaded to an external computer or external backup power grid line via a network, like the internet, a local area network, a wide area network, and/or a wireless network. The network can include copper transfer cables, transmission line transfer, wireless transfer, routers, firewalls, switches, gateway computers and/or edge servers. The network interface or network adapter card in each of the extrapolated/processed power grid lines receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable backup medium in each of the extrapolated/processed power grid lines.
The computer program instructions for performing the operations of the present disclosure can be assembler instructions, instruction set architecture (lSA) instructions, machine-related instructions, microcode, firmware instructions, conditional setting values, or source or destination code written in a random convergence of one or more programming languages, including an object oriented programming language such as Sdalltala, H++ or the like, as opposed to conventional procedural programming languages, such as the "H" language or similar programming languages. The computer readable program instructions can be executed entirely on the client computer, partly on the client computer, as a stand-alone software package, partly on the client computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer can be connected to the client computer through a random network, including a local area network (LAb) or a wide area network (WAb), or can be connected to an external computer (as if an internet service provider were employed to connect through the internet). In some embodiments, the various aspects of the disclosure are addressed by personalizing an electronic circuit, like a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with status values of computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, and any modifications and equivalents are intended to be encompassed within the scope of the claims.
Claims (10)
1. The data value-added service method for client-side energy interconnection is characterized by comprising the following steps of:
step 1: the data acquisition device acquires energy data and transmits the energy data to the data processing center for storage, and a plurality of stored energy data form a stored energy data group;
step 2: the data processing center judges and processes the stored energy data;
the method for judging the stored energy data by the data processing center comprises the following steps:
step 2-1-1: identifying the energy data set for judgment and the energy data to be judged according to the stored energy data set and the preset percentage;
step 2-1-2: randomly selecting target data from the energy data set for judgment, and deducing the distinguishing degree of the energy data to be judged according to a preset SVM model and the target data;
step 2-1-3: deducing the reconstructable degree of the energy data to be judged according to a preset reconstruction judgment equation and target data;
step 2-1-4: and according to the distinguishing degree and the reconstruction degree of the energy data to be judged, judging whether the energy data to be judged is high-quality energy data or not.
2. The method for data value-added service for client-side energy interconnection according to claim 1, wherein the step 2-1-2 specifically comprises:
deducing destination data A e And energy data A to be determined f non-Lei Tongxing of which the equation is as follows:
here, a->Representing destination data A e And energy data A to be determined f non-Lei Tongxing @ between>And->Respectively representing the feeding amount A f SVM model and feed quantity A e Is represented by G, and the target data A e Is the e-th energy data in the judging energy data group G, A f Is the f energy data to be determined, and the energy data A to be determined f Degree of distinction M of (2) f Is:the highest amount for discriminating ++>Is:，/>representative of the wholeThe lowest discrimination metric is selected from +.>Representing all->Arithmetic mean of>Representing all->Variance of->Representing the energy data stored in pairsWhen the group executes the variance alignment test, the highest energy data amount in the stored energy data group under the condition of the error probability range is determined in advance;
the discrimination between the energy data to be determined and the whole samples in the energy data set for determination is sequentially deduced through the above equations, a plurality of discrimination metrics are obtained, and the lowest discrimination metric is selected from the whole discrimination metrics to be used as the discrimination of the energy data to be determined.
3. The method for data value-added service for client-side energy interconnection according to claim 1, wherein the steps 2-1-3 specifically include:
the f-th energy data to be determined can be reconstructed as W f ，The re-constructability of the e-th destination data is W e ，/>Here, a->Is a data identification code set for the f-th energy data to be determined,/for the energy data to be determined>Is a data identification code set for the e-th destination data, a data identification code set for the e-th destination data>Is a handle->As the input quantity, the derived quantity obtained after the operation of the encoder is used +.>Is a handle->The reconfigurable group is formed by the reconstruction of the whole target data in the determination energy data group as the derived quantity obtained by the calculation of the input quantity from the encoder>，/> The maximum amount for reconsideration can be determined again>Is: ->，/>And->Are respectively a reconfigurable group->Arithmetic mean and variance of each of the reconfigurable data in the database, and the reconfigurable W of the energy data to be determined is derived from the above equation f 。
4. The method for data value-added service for client-side energy interconnection according to claim 1, wherein the steps 2-1-4 specifically comprise: if the discrimination of the energy data to be determined is higher than the maximum discrimination determination amount and the reconfigurability of the energy data to be determined is higher than the maximum reconfigurability determination amount, multiplying the discrimination of the energy data to be determined by the reconfigurability of the energy data to be determined, and taking the obtained amount after multiplication as the quality number of the energy data to be determined, if the quality number of the energy data to be determined is higher than a set amount, determining that the energy data to be determined is high-quality energy data; if any of the above requirements is met, the energy data to be determined is deemed to be non-high quality energy data.
5. The method for data value-added service for customer-side energy interconnection according to claim 1, wherein after deriving the energy data to be determined in the step 2-1-3, the method further comprises the steps of:
step 2-1-3-2: identifying the highest amount for distinguishing degree and the highest amount for reconstruction according to the preset first check equation, the preset second check equation and the energy data set for judgment;
step 2-1-3-3: if the discrimination of the energy data to be determined is higher than the maximum discrimination and the reconstructable amount of the energy data to be determined is higher than the maximum reconstructable amount, steps 2-1-4 are performed.
6. The method for data value-added service for customer-side energy interconnection according to claim 1, further comprising, after the step 2-1-4:
step 2-1-5: if the energy data to be determined is high-quality energy data, adding the energy data to be determined into a target energy data group;
step 2-1-6: when the number of samples of the target energy data set is higher than a preset definition amount II, replacing the target energy data set with the energy data set for judgment;
step 2-1-7: and executing the step 2-1-2 until the stored energy data set does not contain the energy data to be judged, which is the high-quality energy data, so as to obtain the high-quality energy data set.
7. The method for data value-added service for customer side energy interconnection according to claim 6, further comprising, after said step 2-1-7:
step 2-1-8: and collecting and storing new energy data, and determining whether the new energy data is high-quality energy data or not according to the high-quality energy data group, the preset SVM model and the preset reconstruction judgment equation.
8. The customer side energy interconnection oriented data value added service method according to claim 7, wherein the data processing center performs a method for processing stored energy data, comprising:
processing is performed on the energy data in the high quality energy data set.
9. A client-side energy interconnection oriented data value-added service device, comprising:
the data acquisition device is arranged in the energy internet and is connected with the data processing center; the data processing center can be a server;
the data acquisition device is used for acquiring energy data to be transmitted to the data processing center for storage, and a plurality of stored energy data form a stored energy data group;
the data processing center is used for judging and processing the stored energy data;
the voltage sensor, the mobile communication module and the ammeter are all connected with the controller;
the controller is used for transmitting the energy data to the data processing center through the mobile communication module.
10. The customer-side energy interconnection-oriented data value-added service device according to claim 9, wherein the data acquisition device comprises a mobile communication module, a controller, a voltage sensor for acquiring voltage data of a distributed power supply in the energy internet and transmitting the voltage data to the controller, and an ammeter for acquiring electricity consumption of the distributed power supply in the energy internet and transmitting the electricity consumption of the distributed power supply to the controller, wherein the voltage of the distributed power supply and the electricity consumption of the distributed power supply form the energy data.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103983845A (en) * | 2014-05-06 | 2014-08-13 | 浙江大学 | Distributed power supply connecting user double-direction metering and monitoring system and method |
| US9311386B1 (en) * | 2013-04-03 | 2016-04-12 | Narus, Inc. | Categorizing network resources and extracting user interests from network activity |
| CN110658725A (en) * | 2019-08-19 | 2020-01-07 | 周口师范学院 | Energy supervision and prediction system and method based on artificial intelligence |
| JP2021149506A (en) * | 2020-03-19 | 2021-09-27 | 株式会社エヌ・ティ・ティ・データ | Information processor, information processing method and program |
| CN114499895A (en) * | 2022-04-06 | 2022-05-13 | 国网浙江省电力有限公司电力科学研究院 | Data trusted processing method and system fusing trusted computing and block chain |
| CN116796271A (en) * | 2023-06-19 | 2023-09-22 | 天津三源电力信息技术股份有限公司 | Resident energy abnormality identification method |
-
2023
- 2023-10-10 CN CN202311300202.3A patent/CN117033705B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9311386B1 (en) * | 2013-04-03 | 2016-04-12 | Narus, Inc. | Categorizing network resources and extracting user interests from network activity |
| CN103983845A (en) * | 2014-05-06 | 2014-08-13 | 浙江大学 | Distributed power supply connecting user double-direction metering and monitoring system and method |
| CN110658725A (en) * | 2019-08-19 | 2020-01-07 | 周口师范学院 | Energy supervision and prediction system and method based on artificial intelligence |
| JP2021149506A (en) * | 2020-03-19 | 2021-09-27 | 株式会社エヌ・ティ・ティ・データ | Information processor, information processing method and program |
| CN114499895A (en) * | 2022-04-06 | 2022-05-13 | 国网浙江省电力有限公司电力科学研究院 | Data trusted processing method and system fusing trusted computing and block chain |
| CN116796271A (en) * | 2023-06-19 | 2023-09-22 | 天津三源电力信息技术股份有限公司 | Resident energy abnormality identification method |
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