Mobile phone data security intelligent processing storage method based on block chain

Abstract

The invention discloses a block chain-based mobile phone data security intelligent processing and storage method, and belongs to the technical field of data processing. The method is used for solving the technical problem that in an existing scheme, active supervision analysis and storage prompting effects in different aspects of mobile phone data security processing are poor. Carrying out processing analysis and digital processing on local after-sales matching data sets of different upgrading keywords corresponding to mobile phone models, and carrying out upgrading reliable data analysis of a single keyword dimension on different upgrading keywords corresponding to the same mobile phone model by utilizing a plurality of security processing identifiers obtained by processing; the method comprises the following steps: performing single-keyword-dimension upgrading reliable data analysis on same upgrading keywords corresponding to different mobile phone models, performing multi-keyword-dimension upgrading reliable data analysis on the mobile phone models, performing integrated analysis on analysis results of different dimensions, determining upgrading risks corresponding to the upgrading keywords of mobile phone model processing and analysis, and performing multi-keyword-dimension upgrading reliable data analysis on the upgrading keywords of the mobile phone models. And a prompt is dynamically broadcasted in the upgrade block chain.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an intelligent processing and storage method for mobile phone data security based on blockchain. Background Art

[0002] The security processing and storage of mobile phone data refer to ensuring the privacy, integrity, and availability of mobile phone data through technical and management means, and preventing data from being leaked, tampered with, or illegally accessed during the processes of collection, transmission, storage, use, and destruction.

[0003] For existing mobile phones that have been used for a long time, users often choose to perform partial upgrades on the mobile phones, such as replacing the battery and upgrading the operating and storage memory of the mobile phone. However, due to the usage effects after the partial upgrades of different mobile phones, old users of other mobile phones of the same model cannot comprehensively understand the different defects and impacts corresponding to the partial upgrades that have been carried out, and thus are prone to repeatedly perform high-risk partial upgrades of the mobile phones, resulting in poor active supervision analysis and storage prompt effects in different aspects of mobile phone data security processing. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent processing and storage method for mobile phone data security based on blockchain, which is used to solve the technical problem of poor active supervision analysis and storage prompt effects in different aspects of mobile phone data security processing in the existing solutions.

[0005] The purpose of the present invention can be achieved by the following technical solutions: An intelligent processing and storage method for mobile phone data security based on blockchain includes: Obtain a local after-sales matching data set corresponding to different upgrade keywords in the after-sales database according to the mobile phone model, and perform data analysis and digital processing on the upgrade feedback data associated with different local after-sales matching sequences in different local after-sales matching data sets in sequence to obtain security processing identifiers with values of 0, 1, or 2 associated with different local after-sales matching sequences; Utilize the obtained several security processing identifiers to perform upgrade reliability data analysis on different upgrade keywords corresponding to the same mobile phone model in a single-keyword dimension, and upload the analysis results to the upgrade keyword storage directory corresponding to the mobile phone model in the upgrade blockchain for storage; Perform upgrade reliability data analysis on the same upgrade keywords corresponding to different mobile phone models in a single-keyword dimension according to the upgrade reliability data analysis results, and perform upgrade reliability data analysis on the mobile phone models in a multi-keyword dimension, integrate and analyze the analysis results of different dimensions, determine the upgrade risks corresponding to the upgrade keywords for mobile phone model processing analysis, and dynamically broadcast and prompt in the upgrade blockchain.

[0006] Preferably, traverse and match the mobile phone model in the after-sales database to obtain a partial after-sales matching data set corresponding to different upgrade keywords of the mobile phone model in the after-sales database; In addition, obtain the upgrade feedback data associated with different partial after-sales matching sequences in the partial after-sales matching data set corresponding to different upgrade keywords of the mobile phone model, and identify and analyze it.

[0007] Preferably, the upgrade keywords include battery replacement, running memory upgrade, and storage memory upgrade.

[0008] Preferably, if the upgrade feedback data is empty, mark the corresponding partial after-sales matching sequence as the first matching sequence, and set its corresponding security processing identifier to 0; If the upgrade feedback data is not empty and the content is upgrade exception, mark the corresponding partial after-sales matching sequence as the second matching sequence, set the corresponding security processing identifier to 1, and mark the upgrade feedback data as the target exception feedback data; If the upgrade feedback data is not empty and the content is normal upgrade, mark the corresponding partial after-sales matching sequence as the third matching sequence, and set its corresponding security processing identifier to 2.

[0009] Preferably, sequentially obtain the total number of the first identifiers, the total number of the second identifiers, and the total number of the third identifiers corresponding to the security processing identifiers with values of 0, 1, and 2 associated with different upgrade keywords of the mobile phone model; When the total number of the first identifiers is 0 and the total number of the second identifiers is 0, then associate the upgrade keyword of the mobile phone model with the upgrade completely reliable label; When the total number of the first identifiers is not 0, generate an upgrade exception instruction, and perform processing and calculation on the total number of the first identifiers, the total number of the second identifiers, and the total number of the third identifiers according to the upgrade exception instruction to obtain the first upgrade exception recognition value SY1 and the second upgrade exception recognition value SY2 corresponding to the upgrade keyword of the mobile phone model.

[0010] Preferably, perform data analysis on the obtained SY1 and SY2 to obtain the upgrade highly reliable label, the upgrade low reliable label, or the upgrade unreliable label associated with the upgrade keyword of the mobile phone model; Upload the obtained upgrade completely reliable label, upgrade highly reliable label, upgrade low reliable label, or upgrade unreliable label to the storage directory corresponding to the upgrade keyword of the mobile phone model in the upgrade blockchain for storage.

[0011] Preferably, when performing upgrade reliable data analysis on the same upgrade keyword corresponding to different mobile phone models in terms of a single keyword dimension, obtain the total number of upgrade completely reliable labels corresponding to different mobile phone models according to the same upgrade keyword, and set it as the total number of the first labels; Calculate the total number of the first tags and the total number of all tags obtained by the corresponding analysis of the same upgrade keywords, and obtain the keyword upgrade supervision value GSJ corresponding to the same upgrade keywords for different mobile phone models; In addition, when performing reliable data analysis on the upgrade of multiple keyword dimensions for a mobile phone model, obtain the total number of completely reliable upgrade tags corresponding to different upgrade keywords associated with the mobile phone model, and set it as the total number of the second tags; Calculate the total number of the second tags and the total number of all tags obtained by the corresponding analysis of all upgrade keywords associated with the mobile phone model, and obtain the model upgrade supervision value XSJ corresponding to the mobile phone model.

[0012] Preferably, perform multi-dimensional data integration analysis on the upgrade security of the upgrade keywords of the mobile phone model according to the obtained keyword upgrade supervision value and model upgrade supervision value, and perform broadcast prompts of low upgrade risk, medium upgrade risk or high upgrade risk on the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain according to the analysis results, and perform visual display of the corresponding target abnormal feedback data.

[0013] Preferably, if both GSJ and XSJ are greater than or equal to 0, perform a broadcast prompt of low upgrade risk on the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain; If there is one of GSJ and XSJ less than 0, perform a broadcast prompt of medium upgrade risk on the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain; If both GSJ and XSJ are less than 0, perform a broadcast prompt of high upgrade risk on the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain.

[0014] Compared with the existing solution, the beneficial effects achieved by the present invention: By processing and analyzing the local after-sales matching data set corresponding to different upgrade keywords of the mobile phone model and performing digital processing, the present invention can provide reliable individual upgrade supervision data support for subsequent data security processing and storage analysis corresponding to different dimensions of different upgrade keywords, and improve the diversity of historical after-sales data processing and analysis of different mobile phone models in the after-sales database.

[0015] By using several security processing identifiers obtained by processing, the present invention performs reliable data analysis on the upgrade of a single keyword dimension for different upgrade keywords corresponding to the same mobile phone model. It can not only obtain the reliable upgrade tag data of different upgrade keywords corresponding to a single mobile phone model, but also provide reliable data support for subsequent corresponding multi-dimensional data processing and analysis of different upgrade keyword upgrade reliability analysis.

[0016] The present invention performs reliable data analysis on the same upgrade keywords corresponding to different mobile phone models in a single-keyword dimension, and performs reliable data analysis on mobile phone models in a multi-keyword dimension. The analysis results of different dimensions are integrated and analyzed to determine the upgrade risks corresponding to the upgrade keywords for mobile phone model processing and analysis, and dynamic broadcast prompts are sent in the upgrade blockchain, realizing diversified supervision and data mining analysis of the upgrade risks corresponding to different upgrade keywords for mobile phone models, improving the active supervision analysis and storage prompt effects in different aspects of mobile phone data security processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 It is a flowchart of a method for intelligent processing and storage of mobile phone data security based on blockchain according to the present invention.

[0019] Figure 2 It is a flowchart of the process for obtaining the security processing identifier analysis in the present invention.

[0020] Figure 3 It is a flowchart of the process for performing data analysis on the keyword upgrade supervision value and model upgrade supervision value obtained by processing in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] As Figure 1 shown, the present invention is a method for intelligent processing and storage of mobile phone data security based on blockchain, including: Obtain a local after-sales matching data set corresponding to different upgrade keywords in the after-sales database according to the mobile phone model, and perform data analysis and digital processing on the upgrade feedback data associated with different local after-sales matching sequences in different local after-sales matching data sets in turn to obtain security processing identifiers with values of 0, 1, or 2 associated with different local after-sales matching sequences; including: Traverse and match the mobile phone model in the after-sales database to obtain the local after-sales matching data set corresponding to different upgrade keywords of the mobile phone model in the after-sales database; It should be noted that the after-sales database is constructed from all official after-sales data corresponding to different mobile phone models of a mobile phone brand. There are several main directories of upgrade keywords in the mobile phone database, and several mobile phone models of the same mobile phone brand and local after-sales matching data sets are associated with the several upgrade keywords; Among them, several local after-sales matching sequences are associated with the local after-sales matching data set, and different local after-sales matching sequences are associated with a corresponding upgrade feedback data; The local after-sales matching sequence specifically includes an upgrade hardware brand, an upgrade hardware code, and upgrade hardware parameters; The upgrade hardware code is used to digitally represent the model of the upgrade hardware to which it belongs; The upgrade hardware parameters are determined according to the existing design parameters associated with the model of the corresponding upgrade hardware, and the specific content is not limited; The upgrade feedback data is obtained by the user to whom the mobile phone corresponding to the local upgrade belongs through active filling or passive filling; The upgrade feedback data can also be empty, indicating that the user to whom the mobile phone corresponding to the local upgrade belongs has not filled in the feedback; In addition, the upgrade keywords include but are not limited to battery replacement, running memory upgrade, and storage memory upgrade; It can be understood that for a mobile phone with a long usage time, even if it is locally upgraded, due to the aging of internal components, other negative effects may occur. For example, when replacing a battery with a health degree less than 70%, there are several upgrade negative effects such as the mobile phone running stuck, overheating severely, and the screen being abnormal after the battery replacement; And, obtain the upgrade feedback data associated with different local after-sales matching sequences in the local after-sales matching data sets corresponding to different upgrade keywords of the mobile phone model and identify and analyze it; As Figure 2 shown, if the upgrade feedback data is empty, mark the local after-sales matching sequence to which it belongs as the first matching sequence, and set its corresponding security processing identifier to 0; If the upgrade feedback data is not empty and the content is upgrade anomaly, mark the local after-sales matching sequence to which it belongs as the second matching sequence, set the corresponding security processing identifier to 1, and mark the upgrade feedback data as the target anomaly feedback data. The target anomaly feedback data is used to provide a visual display of the specific content of the subsequent upgrade risks of different upgrade keywords of the mobile phone model to which it belongs; If the upgrade feedback data is not empty and the content is upgrade normal, mark the local after-sales matching sequence to which it belongs as the third matching sequence, and set its corresponding security processing identifier to 2; In the embodiments of the present invention, through processing, analyzing, and digitally processing the local after-sales matching data sets corresponding to different upgrade keywords of the mobile phone model, it can provide reliable individual upgrade supervision data support for subsequent data security processing and storage analysis of different dimensions corresponding to different upgrade keywords, and improve the diversity of processing and analysis of historical after-sales data of different mobile phone models in the after-sales database.

[0023] Using a number of security processing identifiers obtained through processing, perform reliable data analysis on different upgrade keywords corresponding to the same mobile phone model in terms of a single keyword dimension, and upload the analysis results to the storage directory of the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain for storage; including: Among them, the upgrade blockchain presets storage directories for several mobile phone models, and the specific mobile phone models are determined according to the historical after-sales data of the mobile phone brands to which they belong; Successively obtain the first identifier total number, the second identifier total number, and the third identifier total number corresponding to the security processing identifiers with all values of 0, 1, and 2 associated with different upgrade keywords of the mobile phone model; When the first identifier total number is 0 and the second identifier total number is 0, then associate the upgrade keyword of the mobile phone model with the upgrade completely reliable label; When the first identifier total number is not 0, generate an upgrade exception instruction, and perform processing calculations on the first identifier total number, the second identifier total number, and the third identifier total number according to the upgrade exception instruction to obtain the first upgrade exception recognition value SY1 and the second upgrade exception recognition value SY2 corresponding to the upgrade keyword of the mobile phone model; Among them, the processing calculations of the first upgrade exception recognition value SY1 and the second upgrade exception recognition value SY2 can be implemented in different ways; Specifically, Method 1: Through the formula Calculate to obtain; in the formula, k = 1, 2; NZ = N1 + N2 + N3; N1, N2, and N3 are respectively the first identifier total number, the second identifier total number, and the third identifier total number corresponding to the upgrade keyword of the mobile phone model; Ak is A1, A2, which are respectively the first exception standard value and the second exception standard value, and the specific values are not limited, and can be determined according to the operating design requirements of the actual upgrade risk prompt, or can be determined according to all the And Median value of, the unit of the supervision period is month, and the specific value is not limited; Method 2: Input the first identifier total number, the second identifier total number, and the third identifier total number associated with the upgrade keyword of the mobile phone model into the local upgrade recognition model for data analysis, and respectively set the output values in the output order as the first upgrade exception recognition value and the second upgrade exception recognition value; Among them, the local upgrade recognition model is established based on an artificial intelligence model, and the artificial intelligence model includes a BP neural network model or an RBF neural network model; Specifically, obtain standard training data; among them, the standard training data includes standard input data with the same attributes as the first identifier total number, the second identifier total number, and the third identifier total number, and standard output data representing the fault type; An artificial intelligence model constructed by training with standard training data is marked as a fault judgment model after training is completed. Training the standard training data to construct an artificial intelligence model is an existing conventional technical solution, and the specific implementation steps will not be elaborated here. The standard training data includes standard input data consistent with the attributes of the power supply quality data and standard output data representing the upgrade anomaly recognition status. The standard training data is obtained by integrating and extracting historical monitoring big data or obtained through laboratory simulation. It should be noted that by processing and calculating the total number of different identifiers obtained by preprocessing the upgrade keywords of the mobile phone model, the upgrade reliability corresponding to the upgrade keywords of the mobile phone model is digitally represented from different aspects. In addition, the processing and calculation of the first upgrade anomaly recognition value SY1 and the second upgrade anomaly recognition value SY2 in the embodiments of the present invention are carried out respectively from the aspect that the upgrade feedback data is empty and the aspect that the upgrade feedback data is not empty and the content is an upgrade anomaly. In the embodiments of the present invention, the total proportion when the upgrade feedback data is empty is used to determine the corresponding different impacts. When SY1≤0, it is determined that the upgrade with all upgrade feedback data being empty has no impact; otherwise, it is determined that the upgrade with all upgrade feedback data being empty has a negative impact. The calculation and analysis result weight of SY2 is greater than the calculation and analysis result weight of SY1. Data analysis is performed on the processed SY1 and SY2 to obtain an upgrade highly reliable label, an upgrade low reliable label, or an upgrade unreliable label associated with the upgrade keywords of the mobile phone model. Among them, if SY1≤0 and SY2≤0, an upgrade highly reliable label is associated with the upgrade keywords of the mobile phone model. If SY1>0 and SY2≤0, an upgrade low reliable label is associated with the upgrade keywords of the mobile phone model. If SY2>0, an upgrade unreliable label is associated with the upgrade keywords of the mobile phone model. It should be noted that in the embodiments of the present invention, SY1 and SY2 are combined and analyzed with the numerical value 0. In actual application scenarios, SY1 and SY2 can be combined and analyzed with other numerical values according to actual application requirements, and the analysis range of SY1 and SY2 is not limited. The obtained upgrade completely reliable label, upgrade highly reliable label, upgrade low reliable label, or upgrade unreliable label is uploaded and stored in the upgrade keyword storage directory corresponding to the mobile phone model in the upgrade blockchain. In the embodiments of the present invention, by using a number of security processing identifiers obtained through processing, reliable data analysis of upgrade for different upgrade keywords corresponding to the same mobile phone model is performed in terms of a single keyword dimension. It is possible to obtain the upgrade reliable label data of different upgrade keywords corresponding to a single mobile phone model, and at the same time, it can provide reliable data support for subsequent data processing and analysis in multiple dimensions for the upgrade reliable analysis of different upgrade keywords.

[0024] According to the results of the reliable data analysis of upgrade, reliable data analysis of upgrade for the same upgrade keyword corresponding to different mobile phone models is performed in terms of a single keyword dimension, and reliable data analysis of upgrade for the mobile phone model is performed in terms of multiple keyword dimensions. The analysis results of different dimensions are integrated and analyzed to determine the upgrade risk corresponding to the upgrade keyword for the processing and analysis of the mobile phone model, and a dynamic broadcast prompt is given in the upgrade blockchain; including: When performing reliable data analysis of upgrade for the same upgrade keyword corresponding to different mobile phone models in terms of a single keyword dimension, the total number of completely reliable upgrade labels corresponding to different mobile phone models is obtained according to the same upgrade keyword, and it is set as the first total number of labels, and through the formula calculate the keyword upgrade supervision value GSJ for the same upgrade keyword corresponding to different mobile phone models; in the formula, NW1 is the first total number of labels; NB is the total number of all labels obtained by analyzing the same upgrade keyword; B is the keyword upgrade supervision standard value for the same upgrade keyword corresponding to different mobile phone models, and the specific value is not limited. It can be determined according to the operation design requirements of the upgrade keyword, or it can be determined according to the median of all obtained by processing all upgrade keywords; is the floor function; And when performing reliable data analysis of upgrade for the mobile phone model in terms of multiple keyword dimensions, the total number of completely reliable upgrade labels corresponding to different upgrade keywords associated with the mobile phone model is obtained, and it is set as the second total number of labels, and through the formula calculate the model upgrade supervision value XSJ corresponding to the mobile phone model; in the formula, NW2 is the second total number of labels; NX is the total number of all labels obtained by analyzing all upgrade keywords associated with the mobile phone model; C is the model upgrade supervision standard value for all upgrade keywords corresponding to the calculated mobile phone model, and the specific value is not limited. It can be determined according to the operation design requirements of the calculated mobile phone model, or it can be determined according to the median of all obtained by processing all mobile phone models; It should be noted that in the embodiments of the present invention, by expanding and calculating the local upgrade supervision processing data of different upgrade keywords of previous mobile phone models from the vertical keyword dimension and the horizontal mobile phone model dimension, diversified expansion analysis of the previous local upgrade supervision processing data is realized, and the diversity and reliability of the overall dimension upgrade risk analysis of different upgrade keywords of subsequent mobile phone models are improved; In addition, for the formula calculations involved in the embodiments of the present invention, standardization processing is performed on the data involved in the calculations. The standardization processing includes, but is not limited to, extracting numerical values for calculations of different types of data, or performing dimensionalization processing on different types of data; Perform multi-dimensional data integration analysis on the upgrade security of the upgrade keywords of the mobile phone model, and according to the analysis results, broadcast prompts of low upgrade risk, medium upgrade risk, or high upgrade risk for the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain, and perform visual display of the corresponding target abnormal feedback data; Specifically, perform data analysis on the keyword upgrade supervision value and model upgrade supervision value obtained by processing the upgrade keywords of the mobile phone model corresponding to different dimensions; As Figure 3 shown, if both GSJ and XSJ are greater than or equal to 0, then broadcast a low upgrade risk prompt for the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain; If there is one of GSJ and XSJ less than 0, then broadcast a medium upgrade risk prompt for the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain, and perform visual display of the corresponding risk content set for the medium upgrade risk; If both GSJ and XSJ are less than 0, then broadcast a high upgrade risk prompt for the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain, and perform visual display of the corresponding risk content set for the high upgrade risk; the risk content set includes several target abnormal feedback data associated with the upgrade keywords of the corresponding mobile phone model.

[0025] In the embodiments of the present invention, through performing upgrade reliability data analysis on the same upgrade keywords corresponding to different mobile phone models in a single keyword dimension, and performing upgrade reliability data analysis on the mobile phone models in a multi-keyword dimension, integrating and analyzing the analysis results of different dimensions, determining the upgrade risks corresponding to the upgrade keywords processed and analyzed by the mobile phone models, and dynamically broadcasting prompts in the upgrade blockchain, realizing diversified supervision and data mining analysis of the upgrade risks corresponding to different upgrade keywords of the mobile phone models, and improving the active supervision analysis and storage prompt effects in different aspects of mobile phone data security processing.

[0026] In several embodiments provided by the present invention, it should be understood that the disclosed method can be implemented in other ways. For example, the described embodiments of the invention are merely illustrative. For example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0027] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0028] In addition, in each embodiment of the present invention, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0029] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0030] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for intelligent processing and storage of mobile phone data security based on blockchain, characterized in that, Including: Obtain local after-sales matching data sets corresponding to different upgrade keywords in the after-sales database according to the mobile phone model, and perform data analysis and digital processing on the upgrade feedback data associated with different local after-sales matching sequences in different local after-sales matching data sets in turn, to obtain security processing identifiers with values of 0, 1, or 2 associated with different local after-sales matching sequences; Utilize a number of security processing identifiers obtained through processing to perform upgrade reliability data analysis on different upgrade keywords corresponding to the same mobile phone model in terms of a single keyword dimension, and upload the analysis results to the storage directory of the upgrade keyword corresponding to the mobile phone model in the upgrade blockchain for storage; Perform upgrade reliability data analysis on the same upgrade keyword corresponding to different mobile phone models in terms of a single keyword dimension according to the upgrade reliability data analysis results, and perform upgrade reliability data analysis on the mobile phone model in terms of a multi-keyword dimension. Integrate and analyze the analysis results of different dimensions to determine the upgrade risks corresponding to the upgrade keywords for the mobile phone model processing analysis, and dynamically broadcast and prompt in the upgrade blockchain.

2. The mobile phone data security intelligent processing and storage method based on blockchain according to claim 1, wherein Traverse and match the mobile phone model in the after-sales database to obtain local after-sales matching data sets corresponding to different upgrade keywords of the mobile phone model in the after-sales database; And obtain and identify and analyze the upgrade feedback data associated with different local after-sales matching sequences in the local after-sales matching data sets corresponding to different upgrade keywords of the mobile phone model.

3. A method for intelligent processing and storage of mobile phone data security based on blockchain according to claim 2, characterized in that, The upgrade keywords include battery replacement, running memory upgrade, and storage memory upgrade.

4. The intelligent processing and storage method for mobile phone data security based on blockchain according to claim 2, characterized in that, If the upgrade feedback data is empty, mark the local after-sales matching sequence to which it belongs as the first matching sequence, and set the corresponding security processing identifier to 0; If the upgrade feedback data is not empty and the content is upgrade anomaly, mark the local after-sales matching sequence to which it belongs as the second matching sequence, set the corresponding security processing identifier to 1, and mark the upgrade feedback data as the target anomaly feedback data; If the upgrade feedback data is not empty and the content is normal upgrade, mark the local after-sales matching sequence to which it belongs as the third matching sequence, and set the corresponding security processing identifier to 2.

5. A method for intelligent processing and storage of mobile phone data security based on blockchain according to claim 4, characterized in that, Obtain the total number of the first identifiers, the total number of the second identifiers, and the total number of the third identifiers corresponding to all security processing identifiers with values of 0, 1, and 2 associated with different upgrade keywords of the mobile phone model in turn; When the total number of the first identifiers is 0 and the total number of the second identifiers is 0, associate the upgrade keyword of the mobile phone model to which it belongs with the upgrade completely reliable label; When the total number of the first identifiers is not 0, generate an upgrade anomaly instruction, and perform processing and calculation on the total number of the first identifiers, the total number of the second identifiers, and the total number of the third identifiers according to the upgrade anomaly instruction to obtain the first upgrade anomaly recognition value SY1 and the second upgrade anomaly recognition value SY2 corresponding to the upgrade keyword of the mobile phone model.

6. A method for intelligent processing and storage of mobile phone data security based on blockchain according to claim 5, characterized in that, Perform data analysis on the obtained SY1 and SY2 to obtain the upgrade highly reliable label, the upgrade low reliable label, or the upgrade unreliable label associated with the upgrade keyword of the mobile phone model to which it belongs; Upload the obtained upgrade completely reliable label, upgrade highly reliable label, upgrade low reliable label, or upgrade unreliable label to the storage directory of the upgrade keyword corresponding to the mobile phone model in the upgrade blockchain for storage.

7. A method for intelligent processing and storage of mobile phone data security based on blockchain according to claim 6, characterized in that, When performing reliable data analysis on the same upgrade keyword corresponding to different mobile phone models in terms of a single keyword dimension, obtain the total number of completely reliable upgrade tags corresponding to different mobile phone models based on the same upgrade keyword, and set it as the first total number of tags; Calculate the first total number of tags and all the total number of tags obtained by analyzing the same upgrade keyword to obtain the keyword upgrade supervision value GSJ corresponding to the same upgrade keyword for different mobile phone models; Moreover, when performing reliable data analysis on the multi-keyword dimension of mobile phone models, obtain the total number of completely reliable upgrade tags corresponding to different upgrade keywords associated with the mobile phone model, and set it as the second total number of tags; Calculate the second total number of tags and all the total number of tags obtained by analyzing all the upgrade keywords associated with the mobile phone model to obtain the model upgrade supervision value XSJ corresponding to the mobile phone model.

8. A method for intelligent processing and storage of mobile phone data security based on blockchain according to claim 7, characterized in that, Conduct multi-dimensional data integration analysis on the upgrade security of the upgrade keywords of the mobile phone model based on the obtained keyword upgrade supervision value and model upgrade supervision value, and according to the analysis results, conduct broadcast prompts of low upgrade risk, medium upgrade risk, or high upgrade risk for the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain, and conduct visual display of the corresponding target abnormal feedback data.

9. A method for intelligent processing and storage of mobile phone data security based on blockchain according to claim 8, characterized in that, If both GSJ and XSJ are greater than or equal to 0, conduct a broadcast prompt of low upgrade risk for the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain; If either GSJ or XSJ is less than 0, conduct a broadcast prompt of medium upgrade risk for the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain; If both GSJ and XSJ are less than 0, conduct a broadcast prompt of high upgrade risk for the upgrade keywords of the corresponding mobile phone model in the upgrade blockchain.

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