CN117540435A - Heavy truck power battery information tracing system and method based on block chain - Google Patents
Heavy truck power battery information tracing system and method based on block chain Download PDFInfo
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Abstract
The invention discloses a heavy truck power battery information tracing system and method based on a blockchain, which relate to the technical field of battery information management.
Description
Technical Field
The invention belongs to the technical field of battery information management, and particularly relates to a heavy truck power battery information tracing system and method based on a block chain.
Background
A battery industry traceability management system traces a quality traceability record of a product, and a seller and a maintainer of cooperation of an automobile production enterprise. The battery leasing enterprises need to report information such as sales, change, maintenance, replacement information and the like to the automobile production enterprises, and apply for merchant codes and record coding rules.
When the battery information is modified by illegal personnel, the related management personnel may not know and perform data analysis on the modified data, and the authenticity of the data analysis is reduced at the moment.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art; therefore, the invention provides a heavy truck power battery information tracing system and method based on a block chain, which are used for solving the technical problems.
To achieve the above object, an embodiment according to a first aspect of the present invention proposes a heavy truck power battery information tracing system based on a blockchain, including:
the regional information analysis end is used for analyzing the received data information, selecting one regional information in the data information, marking the regional information as target information, extracting the replacement reason of the power battery in the target information, processing the replacement reason, the total driving mileage and the service cycle of the power battery to obtain the service factor of each power battery, processing the service factors to obtain a dispersion value, and comparing the dispersion value with a dispersion threshold value to obtain the abnormal factor of the power battery;
the integrated data analysis end is used for comparing the abnormal factors of all the area information, selecting the abnormal factor of any target area, and comparing the abnormal factor with the abnormal factors of the rest target areas to obtain a tracing area;
the abnormal data tracing end is used for searching the modified information in the tracing area to obtain information sources after each modification, comparing the modified information between adjacent information sources to obtain data types, and then generating a tamper signal according to the data types.
As a further aspect of the present invention, the method for obtaining the usage factor includes:
s1: optionally, taking the regional information QYi as target information, acquiring a replacement reason of the power battery in the target information, inquiring the replacement reason in a maintenance rule comparison table to obtain a fault score GFj, marking the total driving mileage of the power battery as LIj, and marking the using period as ZQj;
s2: using the formulaThe usage factors SYj of each power battery are obtained, alpha 1, alpha 2 and alpha 3 are respectively weight coefficients, and Cj is the maintenance frequency of the power battery in the using process.
As a further scheme of the invention, the method for acquiring the abnormal factors comprises the following steps:
the mean SYa of the usage factor SYj is obtained byObtaining a dispersion value Uf, comparing the dispersion value Uf with a dispersion threshold value, when the dispersion value Uf is smaller than the dispersion threshold value, at the moment, representing that the use factor of a power battery of a heavy truck in a target area is in a stable state, otherwise, obtaining a distance value SC by adopting SC= | SYj-SYa|, arranging the distance values SC in a descending order, sequentially obtaining corresponding use factors, firstly marking the first use factor as an abnormal factor, simultaneously calculating the rest use factors in the above way, obtaining the dispersion value Uf, comparing the dispersion value Uf with the dispersion threshold value, and when the dispersion value Uf exceeds the dispersion threshold value, marking the second use factor as the abnormal factor in the arrangement order, calculating the rest use factors, and directly calculating the rest use factorsUntil the dispersion value Uf is smaller than the dispersion threshold value.
As a further scheme of the invention, the method for acquiring the tracing area comprises the following steps:
ST1: firstly, acquiring an abnormal factor of any target area, comparing the abnormal factor with the abnormal factor of the residual target area, and when the residual target area has abnormal data identical to the target area, acquiring the residual target area which does not have the identical abnormal data, marking the residual target area as a tracing area and generating a tracing signal;
ST2: when the residual target area does not have the same abnormal factor as the target area, the target area is marked as a tracing area at the moment, and a tracing signal is generated.
As a further aspect of the present invention, a tamper signal acquisition method includes:
f1: retrieving the modified information of the trace back area, marking the data information stored after each modification as information sources k, k=1, 2, … … and A according to the modified information, wherein the presence of A information sources is indicated, and the sequence of the information sources k is sequenced according to the time of the modified information;
f2: comparing the information source (k+1) with the information source k by taking the information source k as standard data, and when data different from the information source k exists in the information source (k+1), judging data types corresponding to the different data, wherein the data types comprise newly added data, changed data and deleted data, the newly added data is the data information of a newly stored power battery in the information source (k+1), the changed data is that one piece of data information in the information source (k+1) is inconsistent with the one piece of data information in the information source k, and the deleted data is the missing data information of the information source (k+1) compared with the information source k;
f3: when new data is detected, the data information is not processed at this time, when changed data and deleted data are detected, a tamper signal is generated at this time, and then the abnormal data tracing end tamper the signal.
As a further scheme of the invention, the data information is acquired by the regional information acquisition end for the data in each server in the block chain and is transmitted to the regional data analysis end.
The invention further provides an information display terminal for receiving the tampered signal and displaying the information corresponding to the tampered signal on the information display terminal.
The heavy truck power battery information tracing method based on the block chain specifically comprises the following steps:
step one: firstly, collecting data information in each server according to a block chain, and dividing the data information into a plurality of area information according to the information stored in each server;
step two: selecting one piece of area information as target information, extracting the replacement reason of the power battery in the target information, and simultaneously processing the replacement reason, the total driving mileage and the service cycle of the power battery to obtain the service factor of each power battery;
step three: processing the use factors to obtain a dispersion value, and comparing the dispersion value with a dispersion threshold value to obtain abnormal factors of the power battery;
step four: comparing the abnormal factors of all the region information, firstly obtaining the abnormal factor of any target region, and comparing the abnormal factor with the abnormal factors of the rest target regions to obtain a tracing region;
step five: and searching the modified information in the traceability area to obtain information sources after each modification, comparing the modified information between adjacent information sources to obtain a data type, and generating a tamper signal according to the data type.
Compared with the prior art, the invention has the beneficial effects that: the data in each server are acquired, the data in each server are analyzed to respectively obtain the abnormal factors in each target area, the abnormal factors in one target area are selected, the abnormal factors in one target area are compared with the rest target areas to obtain a tracing area, then the data in the tracing area are analyzed to obtain a falsified signal, and the authenticity judgment of the data stored in the server is improved, so that the data analysis result is more accurate.
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FIG. 1 is a schematic diagram of a system frame of the present invention;
fig. 2 is a schematic diagram of a flow frame of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
referring to fig. 1 and 2, the present application provides a heavy truck power battery information tracing system based on a blockchain, which comprises a regional information acquisition end, a regional data analysis end, an integrated data analysis end, an abnormal data tracing end and an information display terminal;
the regional information acquisition end is used for acquiring data information in each server in the blockchain, and marking the data information stored in the same server as regional information QYi according to the data information stored in each server, wherein i=1, 2, 3, … … and n represent that n servers exist, and then the regional information acquisition end transmits the acquired regional information to the regional information analysis end, wherein the data information refers to the use data of the power battery of the heavy truck, and the specific use data refers to the power battery replacement reason, the corresponding use period, the driving mileage and the maintenance times of the power battery in the use process;
the regional data analysis end is used for analyzing the received information to obtain the abnormal factors of each region, and the specific analysis method comprises the following steps:
s1: optionally, taking the regional information QYi as target information, firstly acquiring the replacement reason of each power battery in the target information, inquiring a maintenance rule comparison table for the replacement reason to obtain a fault score GFj, marking the total driving mileage of the power battery as LIj and marking the service cycle as ZQj, wherein j represents different power batteries, j=1, 2, … … and m, and representing that the service data of the power batteries of j heavy trucks are stored in the regional information QYi, wherein the maintenance rule comparison table comprises a plurality of power battery replacement reasons, meanwhile, the fault score is arranged corresponding to each battery replacement reason, and a specific value method of the fault score is calculated and valued according to big data by a person skilled in the art;
s2: using the formulaObtaining a use factor SYj of each power battery, wherein alpha 1, alpha 2 and alpha 3 are weight coefficients respectively, and Cj is the maintenance frequency of the power battery in the use process;
s3: the mean SYa of the usage factor SYj is obtained and then adoptedObtaining a dispersion value Uf, comparing the dispersion value Uf with a dispersion threshold value, when the dispersion value Uf is smaller than the dispersion threshold value, at the moment, representing that the use factor of a power battery of a heavy truck in a target area is in a stable state, otherwise, when the dispersion value Uf exceeds the dispersion threshold value, obtaining a distance value SC by adopting SC= | SYj-SYa|, then arranging the distance values SC in a descending order, sequentially obtaining corresponding use factors, then marking the first use factor as an abnormal factor, simultaneously calculating the rest use factors in the above way to obtain the dispersion value Uf, comparing the dispersion value Uf with the dispersion threshold value, when the dispersion value Uf exceeds the dispersion threshold value, marking the second use factor as an abnormal factor according to the arrangement order, and calculating the rest use factors until the dispersion value Uf is smaller than the dispersion threshold value;
processing the data in the rest servers according to the steps S1 to S3 respectively to obtain abnormal factors in each server;
s4: then acquiring power batteries and use data corresponding to the abnormal factors of the target area, and transmitting the power batteries and the use data to an integrated data analysis end;
the integrated data analysis end is used for receiving all data information transmitted by the target area and carrying out integrated analysis, and meanwhile, when the data of one server is used as the target area, the data of the rest servers are marked as the rest target areas, and the specific integrated analysis method comprises the following steps:
ST1: firstly, acquiring an abnormal factor of any target area, comparing the abnormal factor with the abnormal factor of the residual target area, and when the residual target area has abnormal data identical to the target area, acquiring the residual target area which does not have the identical abnormal data, marking the residual target area as a tracing area and generating a tracing signal;
ST2: when the residual target area does not have the same abnormal factor as the target area, marking the target area as a tracing area at the moment, and generating a tracing signal;
ST3: then the integrated data analysis end transmits the tracing area and the tracing signal to the abnormal data tracing end;
the abnormal data tracing end is used for receiving tracing signals and acquiring change information of data information of a tracing area, wherein the change information refers to each time of modification information of data in a corresponding server, and the specific modification information comprises addition, deletion and data modification, and the specific data tracing method comprises the following steps:
f1: retrieving the modified information of the trace back area, marking the data information stored after each modification as information sources k, k=1, 2, … … and A according to the modified information, wherein the presence of A information sources is indicated, and the sequence of the information sources k is sequenced according to the time of the modified information;
f2: comparing the information source k with the information source k by taking the information source k as standard data, wherein the initial value of k is 1, when the information source k is different from the information source k, judging the data types corresponding to the different data, wherein the data types comprise newly added data, changed data and deleted data, the newly added data is the data information of a newly stored power battery in the information source k+1, the changed data is that one piece of used data in one piece of data information in the information source k+1 is not matched with the data in the information source k, and the deleted data is the missing data information of the information source k+1 compared with the information source k;
f3: when new data is detected, the data information is not processed at the moment, when changed data and deleted data are detected, a tamper signal is generated at the moment, and then the abnormal data tracing end transmits the tamper signal to the information display terminal;
the information display terminal is used for receiving the tampering signal, displaying information corresponding to the tampering signal on the information display terminal, and then restoring the data information by the staff.
Embodiment two:
the heavy truck power battery information tracing method based on the block chain specifically comprises the following steps:
step one: firstly, collecting data information in each server according to a block chain, and dividing the data information into a plurality of area information according to the information stored in each server;
step two: selecting one piece of area information as target information, extracting the replacement reason of the power battery in the target information, and simultaneously processing the replacement reason, the total driving mileage and the service cycle of the power battery to obtain the service factor of each power battery;
step three: processing the use factors to obtain a dispersion value, and comparing the dispersion value with a dispersion threshold value to obtain abnormal factors of the power battery;
step four: comparing the abnormal factors of all the region information, firstly obtaining the abnormal factor of any target region, and comparing the abnormal factor with the abnormal factors of the rest target regions to obtain a tracing region;
step five: and searching the modified information in the traceability area to obtain information sources after each modification, comparing the modified information between adjacent information sources to obtain data types, generating a tamper signal according to the data types, and restoring the data by staff according to the tamper signal.
The partial data in the formula are all obtained by removing dimension and taking the numerical value for calculation, and the formula is a formula closest to the real situation obtained by simulating a large amount of collected data through software; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or are obtained through mass data simulation.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (8)
1. Heavy truck power battery information traceability system based on block chain, its characterized in that includes:
the regional information analysis end is used for analyzing the received data information, selecting one regional information in the data information, marking the regional information as target information, extracting the replacement reason of the power battery in the target information, processing the replacement reason, the total driving mileage and the service cycle of the power battery to obtain the service factor of each power battery, processing the service factors to obtain a dispersion value, and comparing the dispersion value with a dispersion threshold value to obtain the abnormal factor of the power battery;
the integrated data analysis end is used for comparing the abnormal factors of all the area information, selecting the abnormal factor of any target area, and comparing the abnormal factor with the abnormal factors of the rest target areas to obtain a tracing area;
the abnormal data tracing end is used for searching the modified information in the tracing area to obtain information sources after each modification, comparing the modified information between adjacent information sources to obtain data types, and then generating a tamper signal according to the data types.
2. The heavy truck power battery information traceability system based on block chain according to claim 1, wherein the method for obtaining the usage factor is as follows:
s1: optionally, taking the regional information QYi as target information, acquiring a replacement reason of the power battery in the target information, inquiring the replacement reason in a maintenance rule comparison table to obtain a fault score GFj, marking the total driving mileage of the power battery as LIj, and marking the using period as ZQj;
s2: using the formulaThe usage factors SYj of each power battery are obtained, alpha 1, alpha 2 and alpha 3 are respectively weight coefficients, and Cj is the maintenance frequency of the power battery in the using process.
3. The heavy truck power battery information traceability system based on block chain according to claim 1, wherein the method for obtaining the anomaly factor is as follows:
the mean SYa of the usage factor SYj is obtained byObtaining a dispersion value Uf, comparing the dispersion value Uf with a dispersion threshold, when the dispersion value Uf is smaller than the dispersion threshold, at the moment, representing that the use factor of the power battery of the heavy truck in the target area is in a stable state, otherwise, obtaining a distance value SC by adopting SC= | SYj-SYa|, arranging the distance values SC in a descending order, sequentially obtaining corresponding use factors, marking the first use factor as an abnormal factor, simultaneously calculating the rest use factors in the above way, obtaining the dispersion value Uf, comparing the dispersion value Uf with the dispersion threshold, and when the dispersion value Uf exceeds the dispersion threshold, marking the second use factor as the abnormal factor in the arrangement order, and calculating the rest use factors until the dispersion value Uf is smaller than the dispersion threshold.
4. The heavy truck power battery information traceability system based on block chain according to claim 1, wherein the traceability area acquisition method is as follows:
ST1: firstly, acquiring an abnormal factor of any target area, comparing the abnormal factor with the abnormal factor of the residual target area, and when the residual target area has abnormal data identical to the target area, acquiring the residual target area which does not have the identical abnormal data, marking the residual target area as a tracing area and generating a tracing signal;
ST2: when the residual target area does not have the same abnormal factor as the target area, the target area is marked as a tracing area at the moment, and a tracing signal is generated.
5. The heavy truck power battery information traceability system based on block chain according to claim 1, wherein the tamper signal acquisition method is as follows:
f1: retrieving the modified information of the trace back area, marking the data information stored after each modification as information sources k, k=1, 2, … … and A according to the modified information, wherein the presence of A information sources is indicated, and the sequence of the information sources k is sequenced according to the time of the modified information;
f2: comparing the information source (k+1) with the information source k by taking the information source k as standard data, and when data different from the information source k exists in the information source (k+1), judging data types corresponding to the different data, wherein the data types comprise newly added data, changed data and deleted data, the newly added data is the data information of a newly stored power battery in the information source (k+1), the changed data is that one piece of data information in the information source (k+1) is inconsistent with the one piece of data information in the information source k, and the deleted data is the missing data information of the information source (k+1) compared with the information source k;
f3: when new data is detected, the data information is not processed at this time, when changed data and deleted data are detected, a tamper signal is generated at this time, and then the abnormal data tracing end tamper the signal.
6. The heavy truck power battery information traceability system based on the blockchain of claim 1, wherein the data information is collected by the regional information collection terminal for the data in each server in the blockchain and transmitted to the regional data analysis terminal.
7. The heavy truck power battery information traceability system based on the blockchain according to claim 1, further comprising an information display terminal, wherein the information display terminal is used for receiving the tamper signal and displaying information corresponding to the tamper signal on the information display terminal.
8. The method is applied to the information tracing system of any one of the claims 1-7, and is characterized by comprising the following steps:
step one: firstly, collecting data information in each server according to a block chain, and dividing the data information into a plurality of area information according to the information stored in each server;
step two: selecting one piece of area information as target information, extracting the replacement reason of the power battery in the target information, and simultaneously processing the replacement reason, the total driving mileage and the service cycle of the power battery to obtain the service factor of each power battery;
step three: processing the use factors to obtain a dispersion value, and comparing the dispersion value with a dispersion threshold value to obtain abnormal factors of the power battery;
step four: comparing the abnormal factors of all the region information, firstly obtaining the abnormal factor of any target region, and comparing the abnormal factor with the abnormal factors of the rest target regions to obtain a tracing region;
step five: and searching the modified information in the traceability area to obtain information sources after each modification, comparing the modified information between adjacent information sources to obtain a data type, and generating a tamper signal according to the data type.
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