CN115879139B - User data management method based on edge calculation - Google Patents
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Abstract
The invention relates to the technical field of edge calculation, in particular to a user data management method based on edge calculation, which comprises the following steps of S1, collecting and segmenting user data in real time; step S2, hiding the matching part by using the first feature set, and encrypting the matching part by using the second feature set; step S3, marking the real-time data segment or deleting the real-time data segment according to the hidden part occupation ratio of the real-time data segment; and S4, selecting real-time data segments from the edge database for extraction. According to the invention, the data acquisition module is arranged in the client side to be subjected to edge calculation to segment and acquire the real-time data generated by the client side, and the real-time data is respectively hidden and encrypted, so that the deletion or marking of the real-time data segment is determined according to the proportion of the hidden part, the security level of the user data is effectively divided, the influence of the support of the edge calculation user data is reduced, and the security of the user data of the edge side is improved.
Description
Technical Field
The invention relates to the technical field of edge computing, in particular to a user data management method based on edge computing.
Background
Edge computing refers to providing nearest service nearby by adopting an open platform integrating network, computing, storage and application core capabilities on one side close to an object or data source; the application program is initiated at the edge side to generate faster network service response, so that the basic requirements of the industry on real-time service, application intelligence, security, privacy protection and the like are met, and meanwhile, the data processing pressure of the cloud computing end is effectively relieved, but the edge computing needs to generate and execute an edge computing task according to the equipment data of the edge end, so that the security problems of data loss, leakage and the like of the user equipment of the edge end are easily caused.
Chinese patent publication No.: CN111339554a discloses a method for protecting user data privacy based on mobile edge calculation, which is technically characterized in that a vehicle information data set is trained into model parameters, the model parameters are publicly shared, and leakage of the vehicle information data set is avoided, so that in the prior art, although the training conversion mode can be used for converting the user data training of an edge end into the model parameters for edge calculation, the training conversion mode is not irreversible, and the result output is directly carried out on the disclosed model parameters, the output data of the method is also close to the user data of the edge end, the task data privacy of a user cannot be protected, and the problem of poor safety of the user data of the edge calculation is caused.
Disclosure of Invention
Therefore, the invention provides a user data management method based on edge calculation, which is used for solving the problem of poor user data security of an edge end in the edge calculation in the prior art.
To achieve the above object, the present invention provides a user data management method based on edge computation, including,
step S1, a data acquisition module is arranged at a client side of edge calculation, user data of the client side are acquired in real time, the acquired user data are segmented in real time through a data processing module to form real-time data segments, and each real-time data segment is stored in an edge database;
step S2, performing feature matching on the real-time data segment stored in the edge database through a first feature set arranged in the feature encryption module, hiding a matching part in the real-time data segment, performing feature matching on the real-time data segment through a second feature set arranged in the feature encryption module, and encrypting the matching part in the real-time data segment;
s3, judging the hidden part duty ratio of the real-time data segment through a characteristic encryption module, and determining whether to mark the real-time data segment or delete the real-time data segment in the edge database according to the hidden part duty ratio of the real-time data segment;
and S4, receiving edge calculation information through an edge calculation module, and selecting real-time data segments from the edge database according to the edge calculation information for extraction.
Further, the data processing module is internally provided with a standard segment character length Lb, when the data acquisition module acquires the user data A of the client in real time, the data processing module acquires the real-time acquisition character length Ls of the user data A and compares the real-time acquisition character length Ls with the standard segment character length Lb,
when Ls is smaller than Lb, the data processing module judges that the real-time acquisition character length of the user data A is lower than the standard segmentation character length, and the data processing module judges the real-time acquisition time length of the user data A so as to determine whether to segment and store the user data A;
when Ls is more than or equal to Lb, the data processing module judges that the real-time acquisition character length of the user data A reaches the standard segmentation character length, the data processing module intercepts the user data A acquired in the data acquisition module to form a real-time data segment Au of the user data A, the real-time data segment Au is stored in the edge database, the data acquisition module does not interrupt the real-time acquisition of the user data A of the client, and the data processing module judges, intercepts and stores the next real-time data segment.
Further, the data processing module is internally provided with a maximum acquisition segmentation duration Tz, when the data processing module judges that the real-time acquisition character length of the user data A is lower than the standard segmentation character length, the data processing module acquires the real-time acquisition duration Ts of the data acquisition module to the user data A and compares the real-time acquisition duration Ts with the maximum acquisition segmentation duration Tz,
when Ts is smaller than Tz, the data processing module judges that the real-time acquisition time length does not reach the maximum acquisition segmentation time length, and the data processing module does not intercept the user data A acquired in the data acquisition module;
when Ts is more than or equal to Tz, the data processing module judges that the real-time acquisition time length reaches the maximum acquisition segmentation time length, the data processing module intercepts the acquired user data A in the data acquisition module to form a real-time data segment Au of the user data A, the real-time data segment Au is stored in the edge database, the data acquisition module does not interrupt the real-time acquisition of the user data A of the client, and the data processing module clears the real-time acquisition time length of the user data A and judges, intercepts and stores the next real-time data segment.
Further, a first feature set is provided in the feature encryption module, the first feature set includes a plurality of hidden feature data, when the data processing module stores the real-time data segment into the edge database, the feature encryption module performs feature matching on the real-time data segment according to the first feature set, wherein,
if no matching part of any hidden characteristic data in the first characteristic set exists in the real-time data segment, the characteristic encryption module does not hide the real-time data segment, and performs characteristic matching on the real-time data segment according to the second characteristic set so as to determine whether to encrypt the real-time data segment;
if any matching part of the hidden characteristic data in the first characteristic set exists in the real-time data segment, the characteristic encryption module hides the matching part of the real-time data segment and the hidden characteristic data, and judges the occupation ratio of the hidden part to determine whether to delete the real-time data segment in the edge database.
Further, a first preset duty ratio Y1 and a second preset duty ratio Y2 are set in the feature encryption module, wherein Y1 is smaller than Y2, when the feature encryption module conceals the portion of the real-time data segment, which is matched with the concealed feature data, the feature encryption module obtains the total character length Lz and the concealed character length Lc of the real-time data segment, calculates the concealed portion duty ratio Ye of the real-time data segment, ye=lc/Lz, compares the concealed portion duty ratio Ye with the first preset duty ratio Y1 and the second preset duty ratio Y2,
when Ye is smaller than Y1, the characteristic encryption module judges that the hidden part duty ratio of the real-time data segment is lower than a first preset duty ratio, and the characteristic encryption module performs characteristic matching on the real-time data segment according to a second characteristic set so as to determine whether to encrypt the real-time data segment;
when Y1 is more than or equal to Ye and less than or equal to Y2, the characteristic encryption module judges that the hidden part duty ratio of the real-time data segment is between a first preset duty ratio and a second preset duty ratio, the characteristic encryption module marks the real-time data segment, and performs characteristic matching on the real-time data segment according to a second characteristic set so as to determine whether to encrypt the real-time data segment;
and when Ye is more than Y2, the characteristic encryption module judges that the hidden part ratio of the real-time data segment is higher than a second preset ratio, and deletes the real-time data segment in the edge database.
Further, a second feature set is arranged in the feature encryption module, the second feature set includes a plurality of encrypted feature data, the feature encryption module performs feature matching on the non-hidden part of the real-time data segment according to the second feature set when performing feature matching on the real-time data segment according to the second feature set, wherein,
if the non-hidden part of the real-time data segment does not have any matching part of the encrypted characteristic data in the second characteristic set, the characteristic encryption module does not encrypt the real-time data segment;
and if the unhidden part in the real-time data segment has a matching part of any encrypted characteristic data in the second characteristic set, the characteristic encryption module encrypts the matching part of the real-time data segment and the encrypted characteristic data.
Further, the edge calculation module is internally provided with standard selection similarity Nb, the edge calculation module can receive the edge calculation information G and calculate data segment similarity Ni according to the edge calculation information G and any real-time data segment stored in the edge database, the edge calculation module compares the data segment similarity Ni with the standard selection similarity Nb,
when Ni is less than Nb, the edge calculation module judges that the similarity of the data segments does not reach the standard selection similarity, and the edge calculation module does not extract the corresponding real-time data segments;
when Ni is more than or equal to Nb, the edge calculation module judges that the similarity of the data segments reaches the standard selection similarity, and the edge calculation module judges the mark of the corresponding real-time data segment to determine whether to extract the real-time data segment.
Further, when the edge computing module determines that the similarity of the data segments has reached the standard selection similarity, the edge computing module determines the corresponding mark of the real-time data segment,
if the corresponding real-time data segment does not have the mark, the edge calculation module extracts the real-time data segment;
if the corresponding real-time data segment has a mark, the edge calculation module reserves the real-time data segment and judges according to the number of the reserved real-time data segments to determine whether extraction is performed or not.
Further, a first preset reserved quantity Q1 and a second preset reserved quantity Q2 are arranged in the edge calculation module, wherein Q1 is smaller than Q2, when the edge calculation module finishes the comparison of the similarity of all the real-time data segments stored in the edge database to the similarity of standard selection, the edge calculation module obtains the reserved quantity Qc of the real-time data segments and compares the reserved quantity Qc of the real-time data segments with the first preset reserved quantity Q1 and the second preset reserved quantity Q2,
when Qc is smaller than Q1, the edge calculation module judges that the number of reserved real-time data segments is lower than a first preset reserved number, and the edge calculation module extracts the reserved real-time data segments;
when Q1 is less than or equal to Qc is less than or equal to Q2, the edge calculation module judges that the number of reserved real-time data segments is between a first preset reserved number and a second preset reserved number, and the edge calculation module judges the total encryption character length in the reserved real-time data segments to determine whether to extract the reserved real-time data segments;
when Qc is more than Q2, the edge calculation module judges that the number of the reserved real-time data segments is higher than a second preset reserved number, does not extract the reserved real-time data segments, and marks the edge calculation information G as invalid calculation information.
Further, the edge calculation module is also provided with a marked encryption character length Lx, when the edge calculation module judges that the number of reserved real-time data segments is between the first preset reserved number and the second preset reserved number, the edge calculation module obtains the total encryption character length Lm in all reserved real-time data segments and compares the total encryption character length Lm with the marked encryption character length Lx,
when Lm is less than or equal to Lx, the edge calculation module judges that the total encryption character length of the reserved real-time data segment does not exceed the marked encryption character length, and the edge calculation module extracts the reserved real-time data segment;
when Lm is larger than Lx, the edge calculation module judges that the total encryption character length of the reserved real-time data segment exceeds the marked encryption character length, and the edge calculation module does not extract the reserved real-time data segment.
Compared with the prior art, the method has the advantages that the data acquisition module is arranged in the client side to be subjected to edge calculation to segment and acquire the real-time data generated by the client side, the acquired real-time data segments are stored in the edge database, the matching part in each real-time data is hidden according to the first characteristic set by the characteristic encryption module, the matching part in each real-time data is encrypted according to the second characteristic set, the use influence of the edge calculation of the real-time data segments is reduced, the safety of the user data base of the client side is improved, the hidden part proportion of the real-time data segments is judged by the characteristic encryption module, the deletion or marking of any real-time data segment is determined, the safety level of the user data is effectively divided, the corresponding real-time data segment is selected by the edge calculation module according to the externally input edge calculation information, the influence of the edge calculation user data support is reduced, and the safety of the user data of the edge side is improved.
In particular, by arranging the standard segmentation character length in the data processing module and segmenting the real-time data acquired by the data acquisition module by taking the standard segmentation character length as a segmentation reference, the uniformity of occupation of each real-time data segment is guaranteed, the storage and the operation of each real-time data segment are convenient, and meanwhile, the acquired real-time data are segmented, so that the sequence of the acquired user data can be adjusted to a certain extent, and the safety of the user data in edge calculation is guaranteed.
Further, by setting the maximum acquisition segmentation duration in the data processing module, the segmentation processing of the interval duration is carried out on the real-time data acquisition which does not reach the standard segmentation character length, so that the normal operation of the real-time data segment segmentation is ensured, meanwhile, the damage to the continuity of user data acquisition is reduced, and the data use influence of the edge end is reduced on the basis of ensuring the safety of the user data.
Further, by setting a first feature set with a plurality of hidden feature data in the feature encryption module, feature matching is performed on the real-time data segment stored in real time by the feature data in the first feature set, and the matching part in the real-time data segment is subjected to hiding processing, so that leakage of basic data in user data is avoided, and the overall safety of the user data in edge calculation is improved.
In particular, the first preset duty ratio and the second preset duty ratio are set in the characteristic encryption module, the security level of each real-time data segment is divided according to the hidden part duty ratio of the real-time data segment, when the hidden part duty ratio is higher than the second preset duty ratio, the divided security level is higher, so that the real-time data segment is deleted in the edge database, the data segment is not used as data support for edge calculation, and when the hidden part duty ratio is between the first preset duty ratio and the second preset duty ratio, the divided security level is moderately represented, so that the real-time data segment is marked, and double-level data hiding protection is adopted, so that the security of the user data of the edge segment is improved.
Further, feature matching of the second feature set is performed on the real-time data segment which is hidden by the feature encryption module, part of the real-time data segment is encrypted according to the matching result of the feature data, step-by-step hiding and encryption processing are performed on any real-time data segment by the feature encryption module through the first feature set and the second feature set respectively, multi-level protection is performed on the user data, and the use safety of the user data is further improved.
In particular, the edge computing module is arranged to perform similarity matching in the edge database according to the edge computing information input from the outside, and the direct extraction and use or the retention judgment processing is performed according to the marking condition of the real-time data segment of the matching result, so that the safety of participating in the edge computing of the user data is further ensured through the judgment processing of extracting the user data in the use of the user data.
Further, similarity matching in an edge database is completed through an edge calculation module according to edge calculation information, the number of reserved real-time data segments is controlled through setting a first preset reserved number and a second preset reserved number, the situation that user data are subjected to a large number of splicing and restoration after extraction, so that user data are leaked is avoided, and the safety of the user data in edge calculation is further improved through judging the total encryption character length in all reserved real-time data segments.
Drawings
FIG. 1 is a flow chart of a user data management method based on edge computing according to the present embodiment;
fig. 2 is a schematic structural diagram of an edge computing user management system according to the present embodiment.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, which is a flow diagram of a user data management method based on edge computing according to the present embodiment, the present embodiment discloses a user data management method based on edge computing, which includes,
step S1, a data acquisition module is arranged at a client side of edge calculation, user data of the client side are acquired in real time, the acquired user data are segmented in real time through a data processing module to form real-time data segments, and each real-time data segment is stored in an edge database;
step S2, performing feature matching on the real-time data segment stored in the edge database through a first feature set arranged in the feature encryption module, hiding a matching part in the real-time data segment, performing feature matching on the real-time data segment through a second feature set arranged in the feature encryption module, and encrypting the matching part in the real-time data segment;
s3, judging the hidden part duty ratio of the real-time data segment through a characteristic encryption module, and determining whether to mark the real-time data segment or delete the real-time data segment in the edge database according to the hidden part duty ratio of the real-time data segment;
and S4, receiving edge calculation information through an edge calculation module, and selecting real-time data segments from the edge database according to the edge calculation information for extraction.
The method comprises the steps that a data acquisition module is arranged in a client to be subjected to edge calculation to segment and acquire real-time data generated by the client, all acquired real-time data segments are stored in an edge database, a feature encryption module is used for hiding matching parts in all real-time data according to a first feature set, encrypting the matching parts in all real-time data according to a second feature set, the safety of a user data base of the client is improved while the influence of edge calculation on the real-time data segments is reduced, the hidden part proportion of the real-time data segments is judged through the feature encryption module, the deletion or marking of any real-time data segment is determined, the safety level of the user data is effectively divided, the corresponding real-time data segment is selected through an edge calculation module according to edge calculation information input from the outside, the influence of edge calculation user data support is reduced, and meanwhile the safety of the user data of the edge is improved.
Fig. 2 is a schematic structural diagram of an edge computing user management system according to the present embodiment, where the edge computing user management system includes a data acquisition module, a data processing module, an edge database, a feature encryption module, and an edge computing module.
Specifically, the data processing module is internally provided with a standard segment character length Lb, when the data acquisition module acquires the user data A of the client in real time, the data processing module acquires the real-time acquisition character length Ls of the user data A and compares the real-time acquisition character length Ls with the standard segment character length Lb,
when Ls is smaller than Lb, the data processing module judges that the real-time acquisition character length of the user data A is lower than the standard segmentation character length, and the data processing module judges the real-time acquisition time length of the user data A so as to determine whether to segment and store the user data A;
when Ls is more than or equal to Lb, the data processing module judges that the real-time acquisition character length of the user data A reaches the standard segmentation character length, the data processing module intercepts the user data A acquired in the data acquisition module to form a real-time data segment Au of the user data A, the real-time data segment Au is stored in the edge database, the data acquisition module does not interrupt the real-time acquisition of the user data A of the client, and the data processing module judges, intercepts and stores the next real-time data segment.
The standard segmentation character length is arranged in the data processing module, the standard segmentation character length is used as a segmentation reference to segment the real-time data acquired by the data acquisition module, so that the uniformity of occupation of each formed real-time data segment is ensured, the storage and the operation of each real-time data segment are convenient, the acquired real-time data are segmented, the sequence of the acquired user data can be adjusted to a certain extent, and the safety of the user data in edge calculation is ensured.
Specifically, the data processing module is internally provided with a maximum acquisition segmentation duration Tz, when the data processing module judges that the real-time acquisition character length of the user data A is lower than the standard segmentation character length, the data processing module acquires the real-time acquisition duration Ts of the data acquisition module to the user data A and compares the real-time acquisition duration Ts with the maximum acquisition segmentation duration Tz,
when Ts is smaller than Tz, the data processing module judges that the real-time acquisition time length does not reach the maximum acquisition segmentation time length, and the data processing module does not intercept the user data A acquired in the data acquisition module;
when Ts is more than or equal to Tz, the data processing module judges that the real-time acquisition time length reaches the maximum acquisition segmentation time length, the data processing module intercepts the acquired user data A in the data acquisition module to form a real-time data segment Au of the user data A, the real-time data segment Au is stored in the edge database, the data acquisition module does not interrupt the real-time acquisition of the user data A of the client, and the data processing module clears the real-time acquisition time length of the user data A and judges, intercepts and stores the next real-time data segment.
By setting the maximum acquisition segmentation duration in the data processing module, the segmentation processing of the interval duration is carried out on the real-time data acquisition which does not reach the standard segmentation character length, so that the normal operation of the real-time data segment segmentation is ensured, meanwhile, the damage to the continuity of user data acquisition is reduced, and the data use influence of the edge end is reduced on the basis of ensuring the safety of the user data.
Specifically, a first feature set is set in the feature encryption module, the first feature set includes a plurality of hidden feature data, when the data processing module stores the real-time data segment into the edge database, the feature encryption module performs feature matching on the real-time data segment according to the first feature set, wherein,
if no matching part of any hidden characteristic data in the first characteristic set exists in the real-time data segment, the characteristic encryption module does not hide the real-time data segment, and performs characteristic matching on the real-time data segment according to the second characteristic set so as to determine whether to encrypt the real-time data segment;
if any matching part of the hidden characteristic data in the first characteristic set exists in the real-time data segment, the characteristic encryption module hides the matching part of the real-time data segment and the hidden characteristic data, and judges the occupation ratio of the hidden part to determine whether to delete the real-time data segment in the edge database.
By setting a first feature set with a plurality of hidden feature data in the feature encryption module, feature matching is carried out on the real-time data segment stored in real time by the feature data in the first feature set, and the matching part in the real-time data segment is subjected to hiding processing, so that leakage of basic data in user data is avoided, and the overall safety of the user data in edge calculation is improved.
Specifically, a first preset duty ratio Y1 and a second preset duty ratio Y2 are set in the feature encryption module, wherein Y1 is smaller than Y2, when the feature encryption module conceals the portion of the real-time data segment, which is matched with the concealed feature data, the feature encryption module obtains the total character length Lz and the concealed character length Lc of the real-time data segment, calculates the concealed portion duty ratio Ye of the real-time data segment, ye=lc/Lz, compares the concealed portion duty ratio Ye with the first preset duty ratio Y1 and the second preset duty ratio Y2,
when Ye is smaller than Y1, the characteristic encryption module judges that the hidden part duty ratio of the real-time data segment is lower than a first preset duty ratio, and the characteristic encryption module performs characteristic matching on the real-time data segment according to a second characteristic set so as to determine whether to encrypt the real-time data segment;
when Y1 is more than or equal to Ye and less than or equal to Y2, the characteristic encryption module judges that the hidden part duty ratio of the real-time data segment is between a first preset duty ratio and a second preset duty ratio, the characteristic encryption module marks the real-time data segment, and performs characteristic matching on the real-time data segment according to a second characteristic set so as to determine whether to encrypt the real-time data segment;
and when Ye is more than Y2, the characteristic encryption module judges that the hidden part ratio of the real-time data segment is higher than a second preset ratio, and deletes the real-time data segment in the edge database.
The first preset duty ratio and the second preset duty ratio are set in the characteristic encryption module, the security level of each real-time data segment is divided according to the hidden part duty ratio of the real-time data segment, when the hidden part duty ratio is higher than the second preset duty ratio, the divided security level is higher, therefore, the real-time data segment is deleted in the edge database, the data segment is not used as data support for edge calculation, when the hidden part duty ratio is between the first preset duty ratio and the second preset duty ratio, the divided security level is moderately represented, therefore, the real-time data segment is marked, and double-level data hiding protection is adopted, so that the security of the user data of the edge segment is improved.
Specifically, a second feature set is set in the feature encryption module, the second feature set includes a plurality of encrypted feature data, and when the feature encryption module performs feature matching on the real-time data segment according to the second feature set, the feature encryption module performs feature matching on an unhidden portion of the real-time data segment according to the second feature set, wherein,
if the non-hidden part of the real-time data segment does not have any matching part of the encrypted characteristic data in the second characteristic set, the characteristic encryption module does not encrypt the real-time data segment;
and if the unhidden part in the real-time data segment has a matching part of any encrypted characteristic data in the second characteristic set, the characteristic encryption module encrypts the matching part of the real-time data segment and the encrypted characteristic data.
And carrying out feature matching of a second feature set on the real-time data segment which is hidden by the feature encryption module, encrypting part of the real-time data segment according to the matching result of the feature data, carrying out step-by-step hiding and encryption processing on any real-time data segment by the feature encryption module through the first feature set and the second feature set respectively, and carrying out multi-level protection on the user data, thereby further improving the use safety of the user data.
Specifically, the edge calculation module is internally provided with standard selection similarity Nb, the edge calculation module can receive the edge calculation information G and calculate data segment similarity Ni according to the edge calculation information G and any real-time data segment stored in the edge database, the edge calculation module compares the data segment similarity Ni with the standard selection similarity Nb,
when Ni is less than Nb, the edge calculation module judges that the similarity of the data segments does not reach the standard selection similarity, and the edge calculation module does not extract the corresponding real-time data segments;
when Ni is more than or equal to Nb, the edge calculation module judges that the similarity of the data segments reaches the standard selection similarity, and the edge calculation module judges the mark of the corresponding real-time data segment to determine whether to extract the real-time data segment.
In particular, when the edge calculation module determines that the similarity of the data segments has reached the standard selection similarity, the edge calculation module determines the label of the corresponding real-time data segment,
if the corresponding real-time data segment does not have the mark, the edge calculation module extracts the real-time data segment;
if the corresponding real-time data segment has a mark, the edge calculation module reserves the real-time data segment and judges according to the number of the reserved real-time data segments to determine whether extraction is performed or not.
The edge computing module is arranged to perform similarity matching in the edge database according to the edge computing information input from the outside, and the direct extraction and use or the retention judgment processing is performed according to the marking condition of the real-time data segment of the matching result, so that the safety of the user data participating in the edge computing is further ensured through the judgment processing of extraction in the use of the user data.
Specifically, a first preset reserved quantity Q1 and a second preset reserved quantity Q2 are arranged in the edge calculation module, wherein Q1 is smaller than Q2, when the edge calculation module finishes the comparison of the similarity of all real-time data segments stored in the edge database to the similarity of standard selection, the edge calculation module obtains the quantity Qc of reserved real-time data segments and compares the quantity Qc of reserved real-time data segments with the first preset reserved quantity Q1 and the second preset reserved quantity Q2,
when Qc is smaller than Q1, the edge calculation module judges that the number of reserved real-time data segments is lower than a first preset reserved number, and the edge calculation module extracts the reserved real-time data segments;
when Q1 is less than or equal to Qc is less than or equal to Q2, the edge calculation module judges that the number of reserved real-time data segments is between a first preset reserved number and a second preset reserved number, and the edge calculation module judges the total encryption character length in the reserved real-time data segments to determine whether to extract the reserved real-time data segments;
when Qc is more than Q2, the edge calculation module judges that the number of the reserved real-time data segments is higher than a second preset reserved number, does not extract the reserved real-time data segments, and marks the edge calculation information G as invalid calculation information.
Specifically, the edge calculation module is further provided with a marked encryption character length Lx, when the edge calculation module determines that the number of reserved real-time data segments is between the first preset reserved number and the second preset reserved number, the edge calculation module obtains the total encryption character length Lm in all reserved real-time data segments and compares the total encryption character length Lm with the marked encryption character length Lx,
when Lm is less than or equal to Lx, the edge calculation module judges that the total encryption character length of the reserved real-time data segment does not exceed the marked encryption character length, and the edge calculation module extracts the reserved real-time data segment;
when Lm is larger than Lx, the edge calculation module judges that the total encryption character length of the reserved real-time data segment exceeds the marked encryption character length, and the edge calculation module does not extract the reserved real-time data segment.
The similarity matching in the edge database is completed according to the edge calculation information through the edge calculation module, the number of reserved real-time data segments is controlled through setting a first preset reserved number and a second preset reserved number, the situation that the user data are subjected to a large amount of splicing reduction after extraction, so that the user data are leaked is avoided, and the safety of the user data in the edge calculation is further improved through judging the total encryption character length in all reserved real-time data segments.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A user data management method based on edge calculation is characterized by comprising the following steps of,
step S1, a data acquisition module is arranged at a client side of edge calculation, user data of the client side are acquired in real time, the acquired user data are segmented in real time through a data processing module to form real-time data segments, and each real-time data segment is stored in an edge database;
step S2, performing feature matching on the real-time data segment stored in the edge database through a first feature set arranged in the feature encryption module, hiding a matching part in the real-time data segment, performing feature matching on the real-time data segment through a second feature set arranged in the feature encryption module, and encrypting the matching part in the real-time data segment;
s3, judging the hidden part duty ratio of the real-time data segment through a characteristic encryption module, and determining whether to mark the real-time data segment or delete the real-time data segment in the edge database according to the hidden part duty ratio of the real-time data segment;
s4, receiving edge calculation information through an edge calculation module, and selecting real-time data segments from the edge database according to the edge calculation information for extraction;
the characteristic encryption module is provided with a first characteristic set, the first characteristic set comprises a plurality of hidden characteristic data, when the data processing module stores the real-time data segment into the edge database, the characteristic encryption module performs characteristic matching on the real-time data segment according to the first characteristic set, wherein,
if no matching part of any hidden characteristic data in the first characteristic set exists in the real-time data segment, the characteristic encryption module does not hide the real-time data segment, and performs characteristic matching on the real-time data segment according to the second characteristic set so as to determine whether to encrypt the real-time data segment;
if any matching part of the hidden characteristic data in the first characteristic set exists in the real-time data segment, the characteristic encryption module is used for hiding the matching part of the real-time data segment and the hidden characteristic data, and judging the occupation ratio of the hidden part so as to determine whether to delete the real-time data segment in the edge database;
the characteristic encryption module is internally provided with a first preset duty ratio Y1 and a second preset duty ratio Y2, wherein Y1 is smaller than Y2, when the characteristic encryption module conceals the part of the real-time data segment, which is matched with the concealed characteristic data, the characteristic encryption module acquires the total character length Lz and the concealed character length Lc of the real-time data segment, calculates the concealed part duty ratio Ye of the real-time data segment, ye=lc/Lz, compares the concealed part duty ratio Ye with the first preset duty ratio Y1 and the second preset duty ratio Y2,
when Ye is smaller than Y1, the characteristic encryption module judges that the hidden part duty ratio of the real-time data segment is lower than a first preset duty ratio, and the characteristic encryption module performs characteristic matching on the real-time data segment according to a second characteristic set so as to determine whether to encrypt the real-time data segment;
when Y1 is more than or equal to Ye and less than or equal to Y2, the characteristic encryption module judges that the hidden part duty ratio of the real-time data segment is between a first preset duty ratio and a second preset duty ratio, the characteristic encryption module marks the real-time data segment, and performs characteristic matching on the real-time data segment according to a second characteristic set so as to determine whether to encrypt the real-time data segment;
when Ye is more than Y2, the characteristic encryption module judges that the hidden part ratio of the real-time data segment is higher than a second preset ratio, and the characteristic encryption module deletes the real-time data segment from the edge database;
the characteristic encryption module is provided with a second characteristic set, the second characteristic set comprises a plurality of encrypted characteristic data, when the characteristic encryption module performs characteristic matching on the real-time data segment according to the second characteristic set, the characteristic matching is performed on the non-hidden part of the real-time data segment according to the second characteristic set, wherein,
if the non-hidden part of the real-time data segment does not have any matching part of the encrypted characteristic data in the second characteristic set, the characteristic encryption module does not encrypt the real-time data segment;
and if the unhidden part in the real-time data segment has a matching part of any encrypted characteristic data in the second characteristic set, the characteristic encryption module encrypts the matching part of the real-time data segment and the encrypted characteristic data.
2. The edge computing-based user data management method as claimed in claim 1, wherein the data processing module is internally provided with a standard segment character length Lb, and when the data acquisition module acquires the user data a of the client in real time, the data processing module acquires a real-time acquisition character length Ls of the user data a and compares the real-time acquisition character length Ls with the standard segment character length Lb,
when Ls is smaller than Lb, the data processing module judges that the real-time acquisition character length of the user data A is lower than the standard segmentation character length, and the data processing module judges the real-time acquisition time length of the user data A so as to determine whether to segment and store the user data A;
when Ls is more than or equal to Lb, the data processing module judges that the real-time acquisition character length of the user data A reaches the standard segmentation character length, the data processing module intercepts the user data A acquired in the data acquisition module to form a real-time data segment Au of the user data A, the real-time data segment Au is stored in the edge database, the data acquisition module does not interrupt the real-time acquisition of the user data A of the client, and the data processing module judges, intercepts and stores the next real-time data segment.
3. The edge calculation-based user data management method according to claim 2, wherein the data processing module is internally provided with a maximum acquisition segmentation duration Tz, and when the data processing module determines that the real-time acquisition character length of the user data a is lower than the standard segmentation character length, the data processing module acquires the real-time acquisition duration Ts of the data acquisition module for the user data a and compares the real-time acquisition duration Ts with the maximum acquisition segmentation duration Tz,
when Ts is smaller than Tz, the data processing module judges that the real-time acquisition time length does not reach the maximum acquisition segmentation time length, and the data processing module does not intercept the user data A acquired in the data acquisition module;
when Ts is more than or equal to Tz, the data processing module judges that the real-time acquisition time length reaches the maximum acquisition segmentation time length, the data processing module intercepts the acquired user data A in the data acquisition module to form a real-time data segment Au of the user data A, the real-time data segment Au is stored in the edge database, the data acquisition module does not interrupt the real-time acquisition of the user data A of the client, and the data processing module clears the real-time acquisition time length of the user data A and judges, intercepts and stores the next real-time data segment.
4. The method of claim 1, wherein the edge computing module is provided with a standard selection similarity Nb, the edge computing module is capable of receiving the edge computing information G and computing a data segment similarity Ni according to the edge computing information G and any real-time data segment stored in the edge database, the edge computing module compares the data segment similarity Ni with the standard selection similarity Nb,
when Ni is less than Nb, the edge calculation module judges that the similarity of the data segments does not reach the standard selection similarity, and the edge calculation module does not extract the corresponding real-time data segments;
when Ni is more than or equal to Nb, the edge calculation module judges that the similarity of the data segments reaches the standard selection similarity, and the edge calculation module judges the mark of the corresponding real-time data segment to determine whether to extract the real-time data segment.
5. The method of claim 4, wherein when the edge computing module determines that the similarity of the data segments has reached a standard chosen similarity, the edge computing module determines a label of the corresponding real-time data segment,
if the corresponding real-time data segment does not have the mark, the edge calculation module extracts the real-time data segment;
if the corresponding real-time data segment has a mark, the edge calculation module reserves the real-time data segment and judges according to the number of the reserved real-time data segments to determine whether extraction is performed or not.
6. The method of claim 5, wherein a first preset reserved number Q1 and a second preset reserved number Q2 are set in the edge calculation module, wherein Q1 is smaller than Q2, when the edge calculation module compares all real-time data segments stored in the edge database with a standard selection similarity, the edge calculation module obtains the reserved real-time data segment number Qc, and compares the reserved real-time data segment number Qc with the first preset reserved number Q1 and the second preset reserved number Q2,
when Qc is smaller than Q1, the edge calculation module judges that the number of reserved real-time data segments is lower than a first preset reserved number, and the edge calculation module extracts the reserved real-time data segments;
when Q1 is less than or equal to Qc is less than or equal to Q2, the edge calculation module judges that the number of reserved real-time data segments is between a first preset reserved number and a second preset reserved number, and the edge calculation module judges the total encryption character length in the reserved real-time data segments to determine whether to extract the reserved real-time data segments;
when Qc is more than Q2, the edge calculation module judges that the number of the reserved real-time data segments is higher than a second preset reserved number, does not extract the reserved real-time data segments, and marks the edge calculation information G as invalid calculation information.
7. The method of claim 6, wherein the edge calculation module further has a mark encryption character length Lx, and when the edge calculation module determines that the number of reserved real-time data segments is between the first preset reserved number and the second preset reserved number, the edge calculation module obtains a total encryption character length Lm in all reserved real-time data segments and compares the total encryption character length Lm with the mark encryption character length Lx,
when Lm is less than or equal to Lx, the edge calculation module judges that the total encryption character length of the reserved real-time data segment does not exceed the marked encryption character length, and the edge calculation module extracts the reserved real-time data segment;
when Lm is larger than Lx, the edge calculation module judges that the total encryption character length of the reserved real-time data segment exceeds the marked encryption character length, and the edge calculation module does not extract the reserved real-time data segment.
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