CN109684861A - Multi-energy data storage method, system and data audit center based on block chain - Google Patents
Multi-energy data storage method, system and data audit center based on block chain Download PDFInfo
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- CN109684861A CN109684861A CN201910026328.3A CN201910026328A CN109684861A CN 109684861 A CN109684861 A CN 109684861A CN 201910026328 A CN201910026328 A CN 201910026328A CN 109684861 A CN109684861 A CN 109684861A
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- G06F21/60—Protecting data
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- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6227—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database where protection concerns the structure of data, e.g. records, types, queries
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Abstract
This application provides a kind of multi-energy data storage method based on block chain, system and data audit centers, receive multi-energy data caused by the multi-energy data source of acquisition equipment acquisition；According to preset rules, authenticity audit is carried out to the multi-energy data；When audit passes through, by multi-energy data storage into block chain.The present invention ensure that the authenticity of data source by acquiring multi-energy data using acquisition equipment, and by carrying out authenticity audit to multi-energy data, guarantee that the data in deposit block chain are authentic and valid.
Description
Technical field
The present invention relates to field of computer technology, more particularly to a kind of multi-energy data storage side based on block chain
Method, system and data audit center.
Background technique
Block chain become nearly 2 years hot topics, because its by Distributed Storage, point-to-point transmission, common recognition mechanism,
Integrating for the technologies such as Encryption Algorithm, can effectively solve fraud row during data circulate in system in conventional transaction mode
To make credible society to construct credible trading environment.
In the prior art, applications client sends transactions requests, is gone to handle this transactions requests by block catenary system, and will
Implementing result be written block chain, client, which is done, later confirms, again to block chain send request, do it is secondary enter chain.In this method
It mentions and processing request is issued by intelligent contract whereabouts external data provider, processing result is returned to by external data provider.By
It is possible to play tricks in external data provider, it is also possible to which external data provider is intercepted during feedback result by hacker
And distort, this process can not guarantee the authenticity of institute's reception result.
Summary of the invention
In view of this, the present invention provides the audits of a kind of multi-energy data storage method based on block chain, system and data
Center guarantees that storage is authentic and valid to the multi-energy data in block chain.
In order to achieve the above-mentioned object of the invention, specific technical solution provided by the invention is as follows:
A kind of multi-energy data storage method based on block chain is applied to data audit center, which comprises
Receive multi-energy data caused by the multi-energy data source of acquisition equipment acquisition；
According to preset rules, authenticity audit is carried out to the multi-energy data；
When audit passes through, by multi-energy data storage into block chain.
Optionally, the preset rules include format auditing rule, type auditing rule and value range auditing rule；Institute
It states according to preset rules, authenticity audit is carried out to the multi-energy data, comprising:
According to the format auditing rule, judge whether the format of the multi-energy data is readable；
If readable, according to the type auditing rule, judge the type of the multi-energy data quantity whether with set in advance
Fixed quantity is identical；
If they are the same, be based on the value range auditing rule, judge each type in the multi-energy data value whether
All in corresponding value range；
If determining to pass through the authenticity audit of the multi-energy data all in corresponding value range.
Optionally, the method also includes:
The historical data of the multi-energy data is obtained, and calculates each categorical data in the historical data of the multi-energy data
Mean value and standard deviation；
The mean value and standard deviation of each categorical data determine corresponding thereto in historical data according to the multi-energy data
Value range.
Optionally, the method also includes:
Obtain the influence data of the historical data of the multi-energy data and the historical data of the multi-energy data；
Machine learning model is instructed according to the historical data of the multi-energy data and the influence data of historical data
Practice, obtains multi-energy data prediction model；
The influence data of the multi-energy data are input in the multi-energy data prediction model, the multi-energy data is obtained
In each categorical data predicted value；
Calculate the mean value of each categorical data in the historical data of the multi-energy data, and going through according to the multi-energy data
The mean value, maximum value and minimum value of each categorical data in history data calculate the inclined of each categorical data in the multi-energy data
Margin；
According to each categorical data in the predicted value of each categorical data in the multi-energy data and the multi-energy data
The degree of deviation determine value range corresponding thereto.
Optionally, after the multi-energy data caused by the multi-energy data source for receiving acquisition equipment acquisition, the side
Method further include:
Obtain the record time of the multi-energy data and the mark of the acquisition equipment.
It is optionally, described to store the multi-energy data into block chain, comprising:
Multi-energy data described in key pair using the distribution of block chain is encrypted；
The multi-energy data, the record time of the multi-energy data, the mark of the acquisition equipment and block chain are distributed
Key as parameter, call the storage method in block chain in specific intelligence contract, the encrypted multi-energy data deposited
It stores up in block chain.
Optionally, when audit passes through, the method also includes:
The multi-energy data, the record time of the multi-energy data, the mark of the acquisition equipment and block chain are distributed
Key storage in local.
Optionally, it is described by the multi-energy data storage into block chain after, the method also includes:
By the multi-energy data being locally stored, the multi-energy data record the time, it is described acquisition equipment mark and
The key of block chain distribution calls the querying method in block chain in specific intelligence contract as parameter, obtains and deposits in block chain
The corresponding multi-energy data of storage；
The multi-energy data being locally stored is compared with the multi-energy data obtained from block, verifies multi-energy data
Whether actual stored is into block chain.
A kind of data audit center, comprising:
Multi-energy data receiving unit, for receiving multi-energy data caused by the multi-energy data source of acquisition equipment acquisition；
Authenticity audits unit, for carrying out authenticity audit to the multi-energy data according to preset rules；
Multi-energy data storage unit, for when audit passes through, the multi-energy data to be stored into block chain.
Optionally, the preset rules include format auditing rule, type auditing rule and value range auditing rule；Institute
Stating authenticity audit unit includes:
Format audits subelement, for judging that the format of the multi-energy data whether may be used according to the format auditing rule
It reads；
Type audits subelement, for according to the type auditing rule, sentencing when the format of the multi-energy data is readable
Break the multi-energy data type quantity it is whether identical as preset quantity；
Value audits subelement, for when the quantity of the type of the multi-energy data is identical as preset quantity,
Based on the value range auditing rule, judge the value of each type in the multi-energy data whether in corresponding value model
In enclosing；
Determine subelement, for when the value of type each in the multi-energy data is in corresponding value range,
Then determine to pass through the authenticity audit of the multi-energy data.
Optionally, the data audit center further include:
Value range determination unit for obtaining the historical data of the multi-energy data, and calculates the multi-energy data
The mean value and standard deviation of each categorical data in historical data；Each categorical data in historical data according to the multi-energy data
Mean value and standard deviation determine value range corresponding thereto.
Optionally, the data audit center further include:
Value range determination unit, for obtaining the historical data of the multi-energy data and going through for the multi-energy data
The influence data of history data；According to the historical data of the multi-energy data and the influence data of historical data to machine learning
Model is trained, and obtains multi-energy data prediction model；The influence data of the multi-energy data are input to the multi-energy data
In prediction model, the predicted value of each categorical data in the multi-energy data is obtained；Calculate the historical data of the multi-energy data
In each categorical data mean value, and according to mean value, the maximum value of categorical data each in the historical data of the multi-energy data
And minimum value, calculate the degree of deviation of each categorical data in the multi-energy data；According to each number of types in the multi-energy data
According to predicted value and the multi-energy data in the degree of deviation of each categorical data determine value range corresponding thereto.
Optionally, the data audit center further include:
Data capture unit, for obtaining the record time of the multi-energy data and the mark of the acquisition equipment.
Optionally, the multi-energy data storage unit, specifically for energy number described in the key pair using the distribution of block chain
According to being encrypted；
The multi-energy data, the record time of the multi-energy data, the mark of the acquisition equipment and block chain are distributed
Key as parameter, call the storage method in block chain in specific intelligence contract, the encrypted multi-energy data deposited
It stores up in block chain.
Optionally, the data audit center further include:
Local storage unit, for by the record time of the multi-energy data, the multi-energy data, the acquisition equipment
Mark and the key storage of block chain distribution are in local.
Optionally, the data audit center further include:
Inquiring and authenticating unit, for by record time of the multi-energy data being locally stored, the multi-energy data, described
The mark of equipment and the key of block chain distribution are acquired as parameter, calls the issuer in block chain in specific intelligence contract
Method obtains the corresponding multi-energy data stored in block chain；By the multi-energy data being locally stored and the energy obtained from block
Source data compares, and whether actual stored is into block chain for verifying multi-energy data.
A kind of multi-energy data storage system based on block chain, comprising: acquisition equipment, block chain and above-mentioned data are examined
Core center；
The acquisition equipment is sent for acquiring multi-energy data caused by multi-energy data source, and by the multi-energy data
To the data audit center；
The block chain, the multi-energy data sent for storing the data audit center.
Compared with the existing technology, beneficial effects of the present invention are as follows:
Multi-energy data storage method based on block chain, system and data audit center disclosed by the invention, pass through acquisition
Equipment acquires multi-energy data caused by multi-energy data source, acquires equipment for multi-energy data and is sent to data audit center, herein
It is participated in the process without artificial, avoids the data fabrication problem manually participated in, ensure that the authenticity of data source.Data audit
Center carries out authenticity audit to multi-energy data according to preset rules, only when audit passes through just by multi-energy data storage to block
In chain, guarantee that storage is authentic and valid to the multi-energy data in block chain.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of invention for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of flow diagram of the multi-energy data storage method based on block chain disclosed by the embodiments of the present invention；
Fig. 2 is the process signal for the method that a kind of pair of multi-energy data disclosed by the embodiments of the present invention carries out authenticity audit
Figure；
Fig. 3 is disclosed by the embodiments of the present invention a kind of by statistical method combination machine learning algorithm setting value range
The flow diagram of method；
Fig. 4 is a kind of structural schematic diagram of data audit center disclosed by the embodiments of the present invention；
Fig. 5 is a kind of structural schematic diagram of the multi-energy data storage system based on block chain disclosed by the embodiments of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Present embodiment discloses a kind of multi-energy data storage methods based on block chain, are applied to data audit center, should
Data audit center can be disposed in the server, referring to Fig. 1, the multi-energy data based on block chain disclosed in the present embodiment is deposited
Method for storing specifically includes the following steps:
S101: multi-energy data caused by the multi-energy data source of acquisition equipment acquisition is received；
Specifically, acquisition equipment can be senser element, multi-energy data source such as inverter, header box, energy-storage battery, intelligence
Ammeter etc., multi-energy data such as voltage, electric current, active power, reactive power, degree electricity etc..
Wireless transmission method can be used by acquiring between equipment and data audit center, such as radio frequency, laser, infrared
Communication etc., prevents network attack.
S102: according to preset rules, authenticity audit is carried out to multi-energy data；
Preset rules are the whether true rule of audit multi-energy data, and preset rules may include: format auditing rule, class
Type auditing rule and value range auditing rule etc..
Multi-energy data is carried out authenticity to audit it being substantially according to format auditing rule, type auditing rule and value
Range auditing rule etc. judges whether the format of multi-energy data meets format auditing rule, whether the type of multi-energy data meets
Whether type auditing rule, the value of multi-energy data meet value range auditing rule.
Specifically, referring to Fig. 2, to multi-energy data carry out authenticity audit specifically includes the following steps:
S201: according to format auditing rule, judge whether the format of multi-energy data is readable；
It is understood that data audit center can read the data format that acquisition equipment is sent, illegal data lattice
Formula data audit center can not be read.
If unreadable, S202: determine not pass through the authenticity audit of multi-energy data；
If readable, S203: according to type auditing rule, judge the type of multi-energy data quantity whether with it is preset
Quantity is identical；
If not identical, S202 is executed；
If they are the same, S204: be based on value range auditing rule, judge each type in multi-energy data value whether
In corresponding value range；
If executing S202 not all in corresponding value range；
If S205: determining to pass through the authenticity audit of multi-energy data all in corresponding value range.
Wherein, the value range of each type can be set or be passed through by statistical method in multi-energy data
Statistical method combination machine learning algorithm is set.
It can be with specifically, setting value range by statistical method are as follows: obtain the historical data of multi-energy data, and calculate energy
The mean value and standard deviation of each categorical data in the historical data of source data, according to each type in the historical data of multi-energy data
The mean value and standard deviation of data determine value range corresponding thereto, such as when the type of multi-energy data is that photovoltaic plant is sent out daily
Electricity, then the value range of the every daily generation of photovoltaic plant is [the every daily generation mean value -3* standard deviation of photovoltaic plant, photovoltaic electric
It stands every daily generation mean value+3* standard deviation].
Referring to Fig. 3, one of method for setting value range by statistical method combination machine learning algorithm is specific
The following steps are included:
S301: the influence data of the historical data of multi-energy data and the historical data of multi-energy data are obtained；
The influence data of the historical data of multi-energy data, for the ambient data for influencing Energy output, such as solar irradiation
Intensity, environment temperature, ambient humidity, wind speed etc..
S302: machine learning model is carried out according to the historical data of multi-energy data and the influence data of historical data
Training, obtains multi-energy data prediction model；
Specifically, by the influence data of the historical data of multi-energy data and historical data be divided into training set data and
Test set data are trained machine learning model using training set data, and using test set data to machine learning mould
The accuracy rate of type is verified, and when accuracy rate reaches preset threshold value, machine learning model training terminates to obtain the energy
Data prediction model.
S303: the influence data of multi-energy data are input in multi-energy data prediction model, are obtained each in multi-energy data
The predicted value of categorical data；
S304: the mean value of each categorical data in the historical data of multi-energy data is calculated, and according to the history of multi-energy data
The mean value, maximum value and minimum value of each categorical data in data calculate the degree of deviation of each categorical data in multi-energy data；
There are many kinds of the calculation methods of the degree of deviation, wherein a kind of optional degree of deviation calculation method is max { (maximum value-
Mean value)/mean value, (mean value-minimum value)/mean value }.
Also, with the migration of time, data are constantly updated, constantly can be superimposed by new data, in real time or fixed
Phase calculates the degree of deviation.
S305: according to each categorical data in the predicted value and multi-energy data of each categorical data in multi-energy data
The degree of deviation determines value range corresponding thereto.
If value range can be [predicted value * (the 1- degree of deviation), predicted value * (the 1+ degree of deviation)].
S103: when audit passes through, by multi-energy data storage into block chain.
Certificate center in block chain network is data audit center distribution key in advance, and data audit center is by the energy
Before data storage is into block chain, the key of block chain distribution is read, and is encrypted using the key pair multi-energy data.
The key storage of multi-energy data, the record time of multi-energy data, the mark and the distribution of block chain that acquire equipment is existed
It is local.
When audit passes through, encrypted multi-energy data is stored by way of specific intelligence contract calling in block chain
Into block chain, wherein in the storage method in specific intelligence contract include a structural array, can large capacity storage acquisition
The mark of equipment, the record time of multi-energy data and multi-energy data call specific intelligence contract specifically: by multi-energy data, energy
The key of the record time of source data, the mark for acquiring equipment and the distribution of block chain call specific intelligence in block chain as parameter
Storage method in energy contract successfully calls specific intelligence in block chain when the key recorded in key and block chain matches
Storage method in energy contract, by the storage of encrypted multi-energy data into block chain.
For block chain, block chain accounting nodes carry out multi-energy data to be packed into a transaction, and in the whole network
It builds consensus, when common recognition passes through, storage method successful execution in specific intelligence contract, multi-energy data is by authentic and valid storage
It is obstructed out-of-date when knowing together into block chain, common recognition failure is fed back to accounting nodes and feeding back unsuccessful reason, multi-energy data cannot be by
Authentic and valid storage is into block chain.
Preferably, after data audit center stores multi-energy data to block chain, user can also be special by calling
Querying method in fixed intelligence contract, inquires stored multi-energy data in block chain, to verify whether multi-energy data is really deposited
It stores up in block chain.
Specifically, by the mark and block chain for recording the time, acquiring equipment of the multi-energy data being locally stored, multi-energy data
The key of distribution calls the querying method in block chain in specific intelligence contract as parameter, obtains the phase stored in block chain
It should be able to source data；The multi-energy data being locally stored is compared with the multi-energy data obtained from block, verifies multi-energy data
Whether actual stored is into block chain, specifically, when the multi-energy data being locally stored and the multi-energy data obtained from block chain
Unanimously, then multi-energy data actual stored is into block chain, when the multi-energy data being locally stored and the energy obtained from block chain
Data are inconsistent, then the multi-energy data stored in block chain is then labeled as nothing into block chain by the non-actual stored of multi-energy data
Effect, again by the multi-energy data being locally stored storage into block chain.
Multi-energy data storage method based on block chain disclosed in the present embodiment acquires multi-energy data source by acquisition equipment
Generated multi-energy data acquires equipment for multi-energy data and is sent to data audit center, participates in the process without artificial,
The data fabrication problem manually participated in is avoided, ensure that the authenticity of data source.Data audit center is according to preset rules pair
Multi-energy data carries out authenticity audit, only when audit is by guaranteeing that area is arrived in storage just by multi-energy data storage into block chain
Multi-energy data in block chain is authentic and valid.
The disclosed multi-energy data storage method based on block chain based on the above embodiment, the present embodiment correspondence disclose one
Kind data audit center, referring to Fig. 4, the data audit center, comprising:
Multi-energy data receiving unit 401, for receiving multi-energy data caused by the multi-energy data source of acquisition equipment acquisition；
Authenticity audits unit 402, for carrying out authenticity audit to the multi-energy data according to preset rules；
Multi-energy data storage unit 403, for when audit passes through, the multi-energy data to be stored into block chain.
Optionally, the preset rules include format auditing rule, type auditing rule and value range auditing rule；Institute
Stating authenticity audit unit 402 includes:
Format audits subelement, for judging that the format of the multi-energy data whether may be used according to the format auditing rule
It reads；
Type audits subelement, for according to the type auditing rule, sentencing when the format of the multi-energy data is readable
Break the multi-energy data type quantity it is whether identical as preset quantity；
Value audits subelement, for when the quantity of the type of the multi-energy data is identical as preset quantity,
Based on the value range auditing rule, judge the value of each type in the multi-energy data whether in corresponding value model
In enclosing；
Determine subelement, for when the value of type each in the multi-energy data is in corresponding value range,
Then determine to pass through the authenticity audit of the multi-energy data.
Optionally, the data audit center further include:
Value range determination unit for obtaining the historical data of the multi-energy data, and calculates the multi-energy data
The mean value and standard deviation of each categorical data in historical data；Each categorical data in historical data according to the multi-energy data
Mean value and standard deviation determine value range corresponding thereto.
Optionally, the data audit center further include:
Value range determination unit, for obtaining the historical data of the multi-energy data and going through for the multi-energy data
The influence data of history data；According to the historical data of the multi-energy data and the influence data of historical data to machine learning
Model is trained, and obtains multi-energy data prediction model；The influence data of the multi-energy data are input to the multi-energy data
In prediction model, the predicted value of each categorical data in the multi-energy data is obtained；Calculate the historical data of the multi-energy data
In each categorical data mean value, and according to mean value, the maximum value of categorical data each in the historical data of the multi-energy data
And minimum value, calculate the degree of deviation of each categorical data in the multi-energy data；According to each number of types in the multi-energy data
According to predicted value and the multi-energy data in the degree of deviation of each categorical data determine value range corresponding thereto.
Optionally, the data audit center further include:
Data capture unit, for obtaining the record time of the multi-energy data and the mark of the acquisition equipment.
Optionally, the multi-energy data storage unit 403, specifically for the energy described in the key pair using the distribution of block chain
Data are encrypted；
The multi-energy data, the record time of the multi-energy data, the mark of the acquisition equipment and block chain are distributed
Key as parameter, call the storage method in block chain in specific intelligence contract, the encrypted multi-energy data deposited
It stores up in block chain.
Optionally, the data audit center further include:
Local storage unit, for by the record time of the multi-energy data, the multi-energy data, the acquisition equipment
Mark and the key storage of block chain distribution are in local.
Optionally, the data audit center further include:
Inquiring and authenticating unit, for by record time of the multi-energy data being locally stored, the multi-energy data, described
The mark of equipment and the key of block chain distribution are acquired as parameter, calls the issuer in block chain in specific intelligence contract
Method obtains the corresponding multi-energy data stored in block chain；By the multi-energy data being locally stored and the energy obtained from block
Source data compares, and whether actual stored is into block chain for verifying multi-energy data.
Disclosed data audit center based on the above embodiment, present embodiment discloses a kind of energy numbers based on block chain
According to storage system, referring to Fig. 5, the multi-energy data storage system based on block chain disclosed in the present embodiment specifically includes: acquisition
Equipment 501, block chain 502 and above-mentioned data audit center 503；
The acquisition equipment 501, sends out for acquiring multi-energy data caused by multi-energy data source, and by the multi-energy data
It send to the data audit center 503；
Acquiring equipment 501 can be senser element, and nothing can be used between equipment 501 and data audit center 503 by acquiring
Line transmission mode, such as radio frequency, laser, infrared communication, prevent network attack.Key in combining wireless sensing network
Technology such as power management, simultaneous techniques, Data fusion technique, network security technology and Topology Control, to guarantee
The authenticity of data is obtained via acquisition equipment.
The block chain 502, the multi-energy data sent for storing the data audit center 503.
For hardware, the multi-energy data storage system based on block chain further includes power supply, network adapter, central processing
Device, block chain interface and memory, wherein external power adapter can be used for power supply or savings battery is powered, network
Adapter can be Ethernet or wireless network, provide the connection of block chain network, and central processing unit provides acquisition equipment and number
According to the program code performing environment of audit center, block chain interface provides the channel interface of access block chain, and memory is for depositing
The multi-energy data and all program codes of storage after the approval.
The disclosed multi-energy data storage system and data audit center based on block chain of the present embodiment, software and hardware combining are protected
Multi-energy data caused by multi-energy data source is demonstrate,proved by authentic and valid storage into block chain, in the process without artificial ginseng
With avoid the data fabrication problem manually participated in, ensure that the authenticity of data source.Wherein, data audit center is according to pre-
If rule carries out authenticity audit to multi-energy data, only when audit is by guaranteeing just by multi-energy data storage into block chain
The multi-energy data stored in block chain is authentic and valid.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (10)
1. a kind of multi-energy data storage method based on block chain, which is characterized in that be applied to data audit center, the method
Include:
Receive multi-energy data caused by the multi-energy data source of acquisition equipment acquisition；
According to preset rules, authenticity audit is carried out to the multi-energy data；
When audit passes through, by multi-energy data storage into block chain.
2. the method according to claim 1, wherein the preset rules include format auditing rule, type examine
Core rule and value range auditing rule；The foundation preset rules, carry out authenticity audit to the multi-energy data, comprising:
According to the format auditing rule, judge whether the format of the multi-energy data is readable；
If readable, according to the type auditing rule, judge the type of the multi-energy data quantity whether with it is preset
Quantity is identical；
If they are the same, be based on the value range auditing rule, judge each type in the multi-energy data value whether
In corresponding value range；
If determining to pass through the authenticity audit of the multi-energy data all in corresponding value range.
3. according to the method described in claim 2, it is characterized in that, the method also includes:
The historical data of the multi-energy data is obtained, and calculates the equal of each categorical data in the historical data of the multi-energy data
Value and standard deviation；
The mean value and standard deviation of each categorical data determine taking corresponding thereto in historical data according to the multi-energy data
It is worth range.
4. according to the method described in claim 2, it is characterized in that, the method also includes:
Obtain the influence data of the historical data of the multi-energy data and the historical data of the multi-energy data；
Machine learning model is trained according to the historical data of the multi-energy data and the influence data of historical data,
Obtain multi-energy data prediction model；
The influence data of the multi-energy data are input in the multi-energy data prediction model, are obtained every in the multi-energy data
The predicted value of a categorical data；
The mean value of each categorical data in the historical data of the multi-energy data is calculated, and according to the history number of the multi-energy data
The mean value, maximum value and minimum value of each categorical data, calculate the degree of deviation of each categorical data in the multi-energy data in；
According in the predicted value of each categorical data in the multi-energy data and the multi-energy data each categorical data it is inclined
Margin determines value range corresponding thereto.
5. the method according to claim 1, wherein in the multi-energy data source institute for receiving acquisition equipment acquisition
After the multi-energy data of generation, the method also includes:
Obtain the record time of the multi-energy data and the mark of the acquisition equipment.
6. according to the method described in claim 5, it is characterized in that, it is described by the multi-energy data storage into block chain, packet
It includes:
Multi-energy data described in key pair using the distribution of block chain is encrypted；
By the close of the multi-energy data, the record time of the multi-energy data, the mark of the acquisition equipment and the distribution of block chain
Key calls the storage method in block chain in specific intelligence contract, the encrypted multi-energy data storage is arrived as parameter
In block chain.
7. according to the method described in claim 5, it is characterized in that, when audit passes through, the method also includes:
By the close of the multi-energy data, the record time of the multi-energy data, the mark of the acquisition equipment and the distribution of block chain
Key is stored in local.
8. the method according to the description of claim 7 is characterized in that the multi-energy data is stored into block chain it described
Afterwards, the method also includes:
By the multi-energy data being locally stored, the record time of the multi-energy data, the mark and block for acquiring equipment
The key of chain distribution calls the querying method in block chain in specific intelligence contract, stores in acquisition block chain as parameter
Corresponding multi-energy data；
The multi-energy data being locally stored is compared with the multi-energy data obtained from block, whether verifying multi-energy data
Actual stored is into block chain.
9. a kind of data audit center characterized by comprising
Multi-energy data receiving unit, for receiving multi-energy data caused by the multi-energy data source of acquisition equipment acquisition；
Authenticity audits unit, for carrying out authenticity audit to the multi-energy data according to preset rules；
Multi-energy data storage unit, for when audit passes through, the multi-energy data to be stored into block chain.
10. a kind of multi-energy data storage system based on block chain characterized by comprising acquisition equipment, block chain and
Data audit center as claimed in claim 9；
The acquisition equipment is sent to institute for acquiring multi-energy data caused by multi-energy data source, and by the multi-energy data
State data audit center；
The block chain, the multi-energy data sent for storing the data audit center.
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