CN116800547B - Big data-based information processing method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses an information processing method, a device, equipment and a storage medium based on big data, which relate to the technical field of information security processing and solve the technical problems that an encryption mode is single and cannot verify the transmitted data and timely find whether the data is tampered.
Description
Technical Field
The invention relates to the technical field of information security processing, in particular to an information processing method, device, equipment and storage medium based on big data.
Background
As the application of big data systems is more and more widespread, the security is very important, and the data is exploded, so that the information becomes a strategic asset; big data technology affects national governance, enterprise decision-making, people's life, etc.; however, providing new challenges to information security for big data applications; how to ensure the safety of big data system application is the problem to be solved in the present day.
According to the patent application CN201910251673.7, the method comprises: acquiring input information of a user in the current conversation process and feedback information output by an intelligent customer service conversation system; based on the input information of the user and the feedback information corresponding to the input information, determining preset parameter information of the current dialogue process; if the current time meets the preset conditions, pushing information generated based on the preset parameter information to the user, wherein the pushing information is used for enabling the user to evaluate the current dialogue process, and the user is not required to be queried for the evaluation of the current dialogue process by adopting a fixed questionnaire only when the user finishes the interaction with the intelligent customer service dialogue system, so that the method is rich in form, the probability of obtaining the evaluation of the current dialogue process by the user is improved, and further optimization of the intelligent customer service dialogue system is facilitated.
When part of the existing information data is processed, the security of the information data needs to be ensured because the information data stores different personal data and important information, but the encryption effect of part of the existing encryption storage mode is single and is easy to cause information data leakage, and secondly, the situation that the information data is tampered exists in the process of information data transmission, the situation that the data is tampered cannot be found timely by operators, and the follow-up incorrect data use can cause a certain influence.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an information processing method, device, equipment and storage medium based on big data, which solve the problems that the encryption mode is single, verification can not be carried out on the transmitted data, and whether the data is tampered or not can not be found in time.
In order to achieve the above purpose, the invention is realized by the following technical scheme: an information processing method based on big data specifically comprises the following steps:
step one: basic information of a target object is acquired, wherein the target object is transmission information data, and the basic information comprises: calculating a risk value of a target object according to basic information, and comparing the risk value with a preset value YS to obtain a comparison result, wherein the comparison result comprises: optimization processing is needed and is not needed;
step two: then, the target object is obtained, meanwhile, the target object is combined with basic information to carry out segmentation processing to generate an information data packet, then, different modes of recombination are carried out on the information data packet according to the segmentation number to obtain recombined data, wherein the recombined data packet comprises: an odd reassembly data packet and an even reassembly data packet;
step three: acquiring a reorganized data packet, identifying characteristic data of the reorganized data packet, determining polygon nodes according to the characteristic data, virtually polygonal according to the characteristic data, sequencing the reorganized data packet through the virtual polygons, and obtaining optimization information;
step four: acquiring all the optimized information, acquiring characteristic data corresponding to the optimized information, judging the parity of the characteristic data, and storing the characteristic data in different modes according to the parity of the characteristic data to obtain storage information;
step five: and then obtaining the target object, carrying out verification processing on the target object, verifying the characteristic data of the target object to obtain verification data, and judging whether the verification data are identical to obtain a corresponding verification result.
As a further aspect of the invention: the specific way of generating the comparison result in the first step is as follows:
s1: substituting the capacity value RL, the transmission speed SD and the transmission time CT into the formulaCalculating to obtain a risk value FX of the target object, wherein a1 and a2 are preset proportionality coefficients, a1 is a first weight coefficient, and a2 is a second weight coefficient;
s2: comparing the calculated risk value FX with a preset value YS, when FX > YS, indicating that the safety of the target object is not required to be optimized, otherwise, when FX is less than or equal to YS, indicating that the safety hidden danger of the target object is required to be optimized. The specific YS is a preset proportionality coefficient, and the specific numerical value is set by the user according to the experience of the operator.
As a further aspect of the invention: the specific mode of obtaining the recombined data packet in the second step is as follows:
s3: obtaining a capacity value RL of a target object, calculating the sum of the values of the capacity values RL and recording the sum as n, equally dividing the target object into n equal parts according to the capacity to generate n information data packets with the same capacity, wherein n=1, 2, … and m, and the capacity of a single information data packet isThe method comprises the steps of carrying out a first treatment on the surface of the The specific n is expressed as the sum of the capacity value numbers, if the capacity value is 143, n is expressed as 1+4+3=8, and then the target object is equally divided by 8 equal parts according to the capacity.
S4: then judging the parity of n, when n is odd, recombining n information data packets with the same capacity into an odd number recombination data packet as k according to a group of three, wherein k=1, 2, …,When n is even, recombining n parts of information data packets with the same capacity into a group according to two parts to generate even recombined data packet marks as j, wherein j=1, 2, … and =>The method comprises the steps of carrying out a first treatment on the surface of the Specifically, when n is determined to be odd, default +.>Is an integer, and when n is determined to be even, the same default +.>Is an integer.
As a further aspect of the invention: the specific mode of obtaining the optimization information in the third step is as follows:
s5: acquiring and marking a reorganization data packet as i, wherein i=1, 2, … and c, acquiring digital data, text data and picture data in the reorganization data packet i, marking the number of the reorganization data packet as ai, bi and ci respectively, and simultaneously taking ai, bi and ci as characteristic data of the reorganization data packet i; specifically, the feature data of the reorganized data packet denoted by ai, bi and ci is denoted by i, and so on, feature data of all reorganized data packets are obtained and recorded.
S6: then calculating the sum of the values of ai, bi and ci to be recorded as B, generating a virtual polygon, wherein the number of sides of the virtual polygon is B, equally dividing the reorganized data packet i into B parts according to the capacity to generate a B-part single-part reorganized data packet, recording the capacity of the reorganized data packet i as Li, and determining the node of the virtual polygon, wherein the specific mode of determining the node of the virtual polygon is as follows:
s61: obtaining a virtual polygon and judging the shape of the virtual polygon, if the virtual polygon is a vertex symmetric polygon, generating node data according to the sequence of digital information and character information by a node generating mode, and if the virtual polygon is non-vertex symmetric multi-deformation, generating node data according to the sequence of character information and digital information by a node generating mode; in particular, vertex symmetric polygon representation means that vertices can be divided symmetrically according to their vertices, such as quadrilaterals and hexagons, and the resulting polygons are all the same in side length.
S62: the capacity of the single-component reorganized data packet is then obtained asThen>Generating node data with the same capacity and the same size, and generating corresponding virtual node data according to the generation mode in S61;
s7: and (3) sequencing and optimizing the B-component reorganization data packets according to the shape of the virtual polygon judged in the step (S61), when the virtual polygon is a vertex symmetric polygon, combining the B-component reorganization data packets into the virtual polygon according to the sequence, and when the virtual polygon is an non-vertex symmetric polygon, combining the B-component reorganization data packets into the virtual polygon according to the reverse sequence. Specifically, the sequence and the reverse sequence represent that the reorganized data packets segmented according to the sequence are respectively combined according to different situations of the sequence and the reverse sequence, and the combination mode is that a single data packet is one side of a virtual polygon.
As a further aspect of the invention: the specific way of obtaining the stored information in the fourth step is as follows:
when the feature data corresponding to the optimization information is judged to be odd, the numerical value of the feature data is taken as the single storage space capacity, then the optimization information is stored to generate the storage information, and when the feature data corresponding to the optimization information is judged to be even, the numerical value of the feature data is taken as the single storage space capacity, then the optimization information is stored and the storage information is generated.
As a further aspect of the invention: the specific mode of obtaining the verification result in the fifth step is as follows:
s8: the input end obtains the characteristic data corresponding to the target object, simultaneously carries out MD5 operation on the characteristic data, generates a characteristic sequence and marks the characteristic sequence as a characteristic sequence I, and then the output end obtains the transmitted target object, and also carries out MD5 operation on the transmitted target object, generates the characteristic sequence and marks the characteristic sequence as a characteristic sequence II;
s9: comparing the obtained first characteristic sequence with the second characteristic sequence, when the first characteristic sequence and the second characteristic sequence are identical, the target object is not tampered in the transmission process, otherwise, when the first characteristic sequence and the second characteristic sequence are different, the target object is tampered in the transmission process, and meanwhile, the abnormal second characteristic sequence is obtained. Specifically, before the target object is transmitted, a sequence value is obtained by performing MD5 operation on the characteristic data, finally, the cloud obtains the transmitted target object, performs the same MD5 operation, and generates a sequence value, if the two sequence values are the same, the data is not tampered, if the two sequence values are different, the data is tampered, and meanwhile, different sequence values are marked.
The device and the equipment at least comprise at least one group of processors, at least one group of decoders and one group of verifiers, wherein the processors are used for dividing the information data according to the capacity of the information data, generating a plurality of groups of data packets after the division is finished, acquiring characteristic data of the data packets at the same time, and encrypting the plurality of groups of data packets by combining the characteristic data of the data packets;
the decoder is used for decrypting the information data stored in the encryption template when information is extracted according to the encryption logic originally set by the processor;
and the verifier is used for acquiring the decrypted information data and carrying out verification processing on the decrypted information data according to the characteristic data.
The storage medium comprises a plurality of storage partitions, wherein different storage partitions are used for storing information packages of different round-trip authentication rings, a plurality of storage spaces are arranged in the storage partitions, and different storage spaces store different information packages.
Advantageous effects
The invention provides an information processing method, device, equipment and storage medium based on big data. Compared with the prior art, the method has the following beneficial effects:
according to the invention, whether the information data needs to be optimized or not is calculated according to the capacity, the transmission speed and the transmission time of the information data, the information data needing to be optimized is subjected to segmentation processing, the characteristic data of the information data after the segmentation processing is obtained, the information data is encrypted and optimized according to the characteristic data, different encryption processing modes are carried out by judging the property of the characteristic data, so that the defect of low encryption safety caused by a single encryption mode is avoided, the safety of the whole information data is improved, verification operation is carried out on the transmitted information data, whether the data has tampered behaviors in the transmission process is judged, the tampered parts are marked, so that the rapid positioning can be conveniently carried out later, the transmission operation is carried out again, and the whole accuracy of the information data is ensured.
Drawings
FIG. 1 is a process diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides an information processing method based on big data, which specifically comprises the following steps:
step one: basic information of a target object is acquired, wherein the target object is transmission information data, and the basic information comprises: calculating a risk value of a target object according to basic information, and comparing the risk value with a preset value YS to generate a comparison result, wherein the comparison result comprises: the specific judging modes of the method need to be optimized and do not need to be optimized are as follows:
s1: substituting the capacity value RL, the transmission speed SD and the transmission time CT into the formulaCalculating to obtain a risk value FX of the target object, wherein a1 and a2 are preset proportionality coefficients, a1 is a first weight coefficient, and a2 is a second weight coefficient; specifically, the transmission time is represented by a current time node, the time node is represented by four numerical values, and the formulas in the transmission time are allThe capacity value, the transmission speed and the transmission time are calculated by removing the dimension, and the relation between the capacity value, the transmission speed and the transmission time is expressed by a formula, and in combination with the practical description, the transmission speed is slower when the capacity value is larger in some cases, so that the capacity value and the transmission speed are in inverse proportion, and the time node is specifically shown as 15:30 is represented by four values 1530, and the transmission time is subjected to dimensionalization in this way, and substituted into the above formula to calculate the risk value.
S2: comparing the calculated risk value FX with a preset value YS, when FX > YS, indicating that the safety of the target object is not required to be optimized, otherwise, when FX is less than or equal to YS, indicating that the safety hidden danger of the target object is required to be optimized. The specific YS is a preset proportionality coefficient, and the specific numerical value is set by the user according to the experience of the operator. If the risk value exceeds the preset value, the risk value is high, and the optimization management is not needed, otherwise, if the risk value is lower than the preset value, the risk value is low, and the optimization management is needed.
Step two: then, the target object is obtained and is simultaneously combined with basic information to carry out segmentation processing to generate an information data packet, and then, different modes of recombination are carried out to the information data packet according to the segmentation number to generate recombined data which comprises the following steps: the specific way of obtaining the reorganized data packet is as follows:
s3: obtaining a capacity value RL of a target object, calculating the sum of the values of the capacity values RL and recording the sum as n, equally dividing the target object into n equal parts according to the capacity to generate n information data packets with the same capacity, wherein n=1, 2, … and m, and the capacity of a single information data packet isThe method comprises the steps of carrying out a first treatment on the surface of the The specific n is expressed as the sum of the capacity value numbers, if the capacity value is 143, n is expressed as 1+4+3=8, and then the target object is equally divided by 8 equal parts according to the capacity.
S4: then judging the parity of n, when n is odd, recombining n information data packets with the same capacity into an odd number recombined data packet as a group according to three, and taking the odd number recombined data packet ask, and k=1, 2, …,When n is even, recombining n parts of information data packets with the same capacity into a group according to two parts to generate even recombined data packet marks as j, wherein j=1, 2, … and =>The method comprises the steps of carrying out a first treatment on the surface of the Specifically, when n is determined to be odd, default +.>Is an integer, and when n is determined to be even, the same default +.>Is an integer.
Step three: the method comprises the steps of obtaining a reorganized data packet, identifying characteristic data of the reorganized data packet, determining polygon nodes according to the characteristic data, virtually polygon according to the characteristic data, sequencing the reorganized data packet through the virtual polygon, generating optimization information, and generating the optimization information in the following specific modes:
s5: acquiring and marking a reorganization data packet as i, wherein i=1, 2, … and c, acquiring digital data, text data and picture data in the reorganization data packet i, marking the number of the reorganization data packet as ai, bi and ci respectively, and simultaneously taking ai, bi and ci as characteristic data of the reorganization data packet i; specifically, the feature data of the reorganized data packet denoted by ai, bi and ci is denoted by i, and so on, feature data of all reorganized data packets are obtained and recorded.
S6: then calculating the sum of the values of ai, bi and ci to be recorded as B, generating a virtual polygon, wherein the number of sides of the virtual polygon is B, equally dividing the reorganized data packet i into B parts according to the capacity to generate a B-part single-part reorganized data packet, recording the capacity of the reorganized data packet i as Li, and determining the node of the virtual polygon, wherein the specific mode of determining the node of the virtual polygon is as follows:
s61: obtaining a virtual polygon and judging the shape of the virtual polygon, if the virtual polygon is a vertex symmetric polygon, generating node data according to the sequence of digital information and character information by a node generating mode, and if the virtual polygon is non-vertex symmetric multi-deformation, generating node data according to the sequence of character information and digital information by a node generating mode; in particular, vertex symmetric polygon representation means that vertices can be divided symmetrically according to their vertices, such as quadrilaterals and hexagons, and the resulting polygons are all the same in side length.
S62: the capacity of the single-component reorganized data packet is then obtained asThen>Generating node data with the same capacity and the same size, and generating corresponding virtual node data according to the generation mode in S61;
s7: and (3) sequencing and optimizing the B-component reorganization data packets according to the shape of the virtual polygon judged in the step (S61), when the virtual polygon is a vertex symmetric polygon, combining the B-component reorganization data packets into the virtual polygon according to the sequence, and when the virtual polygon is an non-vertex symmetric polygon, combining the B-component reorganization data packets into the virtual polygon according to the reverse sequence. Specifically, the sequence and the reverse sequence represent that the reorganized data packets segmented according to the sequence are respectively combined according to different situations of the sequence and the reverse sequence, and the combination mode is that a single data packet is one side of a virtual polygon.
Step four: all the optimization information is acquired, then the characteristic data corresponding to the optimization information is acquired, the parity of the characteristic data is judged, meanwhile, the characteristic data is stored in different modes according to the parity of the characteristic data, storage information is generated, and the specific mode of generating the storage information is as follows:
when the feature data corresponding to the optimization information is judged to be odd, the numerical value of the feature data is taken as the single storage space capacity, then the optimization information is stored to generate the storage information, and when the feature data corresponding to the optimization information is judged to be even, the numerical value of the feature data is taken as the single storage space capacity, then the optimization information is stored and the storage information is generated.
Specifically, when the number is determined to be odd, a single storage space is established with the feature data, then the optimization information is stored in the storage space, the capacity capable of storing a plurality of pieces of optimization information is calculated, the capacity of the single storage space cannot be exceeded at maximum, when the number is determined to be even, the single storage space is established with the double value of the feature data, and meanwhile, the optimization information is stored in the same way.
Step five: and then acquiring a target object, performing verification processing on the target object, generating verification data by verifying the characteristic data of the target object, judging whether the verification data are identical to generate a corresponding verification result, and generating the verification result in the following specific mode:
s8: the input end obtains the characteristic data corresponding to the target object, simultaneously carries out MD5 operation on the characteristic data, generates a characteristic sequence and marks the characteristic sequence as a characteristic sequence I, and then the output end obtains the transmitted target object, and also carries out MD5 operation on the transmitted target object, generates the characteristic sequence and marks the characteristic sequence as a characteristic sequence II;
s9: comparing the obtained first characteristic sequence with the second characteristic sequence, when the first characteristic sequence and the second characteristic sequence are identical, the target object is not tampered in the transmission process, otherwise, when the first characteristic sequence and the second characteristic sequence are different, the target object is tampered in the transmission process, and meanwhile, the abnormal second characteristic sequence is obtained. Specifically, before the target object is transmitted, a sequence value is obtained by performing MD5 operation on the characteristic data, finally, the cloud end obtains the transmitted target object again, performs the same MD5 operation, and generates a sequence value, if the two sequence values are the same, the data is not tampered, if the two sequence values are different, the data is tampered, and meanwhile, different sequence values are marked.
The device and the equipment at least comprise at least one group of processor, at least one group of decoder and one group of verifier, wherein the processor is used for dividing the information data according to the capacity of the information data, generating a plurality of groups of data packets after the division is finished, acquiring the characteristic data of the data packets at the same time, and encrypting the plurality of groups of data packets by combining the characteristic data of the data packets;
the decoder is used for decrypting the information data stored in the encryption template when information is extracted according to the encryption logic originally set by the processor;
and the verifier is used for acquiring the decrypted information data and carrying out verification processing on the decrypted information data according to the characteristic data.
The storage medium comprises a plurality of storage partitions, wherein different storage partitions are used for storing information packages of different round-trip authentication rings, a plurality of storage spaces are arranged in the storage partitions, and different storage spaces store different information packages.
Some of the data in the above formulas are numerical calculated by removing their dimensionality, and the contents not described in detail in the present specification are all well known in the prior art.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (4)
1. The information processing method based on big data is characterized by comprising the following steps:
step one: basic information of a target object is acquired, wherein the target object is transmission information data, and the basic information comprises: calculating a risk value of a target object according to basic information, and comparing the risk value with a preset value YS to obtain a comparison result, wherein the comparison result comprises: optimization processing is needed and is not needed;
step two: then, the target object is obtained, meanwhile, the target object is combined with basic information to carry out segmentation processing to generate an information data packet, then, different modes of recombination are carried out on the information data packet according to the segmentation number to obtain a recombined data packet, and the recombined data packet comprises: an odd reassembly data packet and an even reassembly data packet;
step three: acquiring a reorganized data packet, identifying characteristic data of the reorganized data packet, determining polygon nodes according to the characteristic data, virtually polygonal according to the characteristic data, sequencing the reorganized data packet through the virtual polygons, and obtaining optimization information;
step four: acquiring all the optimized information, acquiring characteristic data corresponding to the optimized information, judging the parity of the characteristic data, and storing the characteristic data in different modes according to the parity of the characteristic data to obtain storage information;
step five: then obtaining a target object, performing verification processing on the target object, verifying the characteristic data of the target object to obtain verification data, and judging whether the verification data are identical to obtain a corresponding verification result;
the specific way of obtaining the recombined data packet in the second step is as follows:
s3: obtaining a capacity value RL of a target object, calculating the sum of each numerical value of the capacity value RL and marking the sum as n, equally dividing the target object into n equal parts according to the capacity to generate n parts of information data packets with the same capacity, wherein n=1, 2, … and m, and the capacity of a single part of information data packet is；
S4: then judging the parity of n, when n is odd, recombining n information data packets with the same capacity into an odd number recombination data packet as k according to a group of three, wherein k=1, 2, …,And->When n is an even number, recombining n information data packets with the same capacity into an even number recombined data packet according to two groups to be recorded asJ, and j=1, 2, …, +.>；
The specific way of obtaining the optimization information in the third step is as follows:
s5: acquiring and marking the reorganized data packet as i, wherein i=1, 2, … and c, then acquiring digital data, text data and picture data in the reorganized data packet i, recording the number of the reorganized data packet i as ai, bi and ci respectively, and simultaneously taking the ai, bi and ci as characteristic data of the reorganized data packet i;
s6: then calculating the sum of the values of ai, bi and ci to be recorded as B, generating a virtual polygon, wherein the number of sides of the virtual polygon is B, equally dividing the reorganized data packet i into B parts according to the capacity to generate a B-part single-part reorganized data packet, recording the capacity of the reorganized data packet i as Li, and determining the node of the virtual polygon, wherein the specific mode of determining the node of the virtual polygon is as follows:
s61: obtaining a virtual polygon and judging the shape of the virtual polygon, if the virtual polygon is a vertex symmetric polygon, generating node data according to the sequence of digital information and character information by a node generating mode, and if the virtual polygon is non-vertex symmetric multi-deformation, generating node data according to the sequence of character information and digital information by a node generating mode;
s62: the capacity of the single-component reorganized data packet is then obtained asThen>Generating node data with the same capacity and the same size, and generating corresponding virtual node data according to the generation mode in S61;
s7: and (3) sequencing and optimizing the B-component reorganization data packets according to the shape of the virtual polygon judged in the step (S61), when the virtual polygon is a vertex symmetric polygon, combining the B-component reorganization data packets into the virtual polygon according to the sequence, and when the virtual polygon is an non-vertex symmetric polygon, combining the B-component reorganization data packets into the virtual polygon according to the reverse sequence.
2. The information processing method based on big data according to claim 1, wherein the specific way of generating the comparison result in the first step is as follows:
s1: substituting the capacity value RL, the transmission speed SD and the transmission time CT into the formulaCalculating to obtain a risk value FX of the target object, wherein a1 and a2 are preset proportionality coefficients, a1 is a first weight coefficient, and a2 is a second weight coefficient;
s2: comparing the calculated risk value FX with a preset value YS, when FX is larger than YS, indicating that the safety of the target object is not required to be optimized, otherwise, when FX is smaller than or equal to YS, indicating that the potential safety hazard of the target object is required to be optimized, wherein the specific YS is a preset proportionality coefficient, and the specific numerical value is set by the operator according to the experience of the operator.
3. The information processing method based on big data according to claim 1, wherein the specific way of obtaining the stored information in the fourth step is as follows:
when the feature data corresponding to the optimization information is judged to be odd, the numerical value of the feature data is taken as the single storage space capacity, then the optimization information is stored to generate the storage information, and when the feature data corresponding to the optimization information is judged to be even, the numerical value of the feature data is taken as the single storage space capacity, then the optimization information is stored and the storage information is generated.
4. The information processing method based on big data according to claim 1, wherein the specific way of obtaining the verification result in the fifth step is as follows:
s8: the input end obtains the characteristic data corresponding to the target object, simultaneously carries out MD5 operation on the characteristic data, generates a characteristic sequence and marks the characteristic sequence as a characteristic sequence I, and then the output end obtains the transmitted target object, and also carries out MD5 operation on the transmitted target object, generates the characteristic sequence and marks the characteristic sequence as a characteristic sequence II;
s9: comparing the obtained first characteristic sequence with the second characteristic sequence, when the first characteristic sequence and the second characteristic sequence are identical, the target object is not tampered in the transmission process, otherwise, when the first characteristic sequence and the second characteristic sequence are different, the target object is tampered in the transmission process, and meanwhile, the abnormal second characteristic sequence is obtained.
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