CN118094607A - Customer service information service classified storage method and system based on multi-mode large model - Google Patents
Customer service information service classified storage method and system based on multi-mode large model Download PDFInfo
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Abstract
The application provides a customer service information service classified storage method and a customer service information service classified storage system based on a multi-mode large model, which relate to the technical field of data processing, and comprise the following steps: acquiring customer service information; obtaining a plurality of customer service data; acquiring a plurality of value degrees of a plurality of customer service data; obtaining a plurality of encrypted storage results; constructing a plurality of verification standards for performing access verification on the plurality of customer service data; and when the plurality of customer service data are accessed, verifying in the encrypted storage result. The application can solve the technical problems of poor data storage efficiency and insufficient data security caused by the lack of multi-mode feature analysis on data and the lack of pertinence of an encryption storage scheme in the prior art because customer service information is not classified, and the technical effects of improving the data storage management efficiency and the data storage and access security are achieved by classifying the customer service information, optimizing the encryption storage scheme based on value analysis and performing access verification.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a customer service information service classified storage method and system based on a multi-mode large model.
Background
In the power business, communication between customer service and users generates a large amount of customer service information, wherein a large amount of sensitive information is related to the customer service information, including personal information of the users, payment records, service requests and the like. The security of these data is critical to protecting user privacy, maintaining corporate reputation, and adhering to relevant laws and regulations. With rapid development of information technology and increasing network security threat, data security problems in power business systems are becoming a focus of industry attention.
At present, management of customer service information is not reasonably classified, so that storage and management efficiency of the customer service information is low. In addition, the existing data encryption method is often used for encryption processing by designing an encryption algorithm, so that multimode characteristic analysis on data is lacked, and an encryption storage scheme is lacked in pertinence, so that the data security is insufficient.
Disclosure of Invention
The application aims to provide a customer service information service classified storage method and system based on a multi-mode large model, which are used for solving the technical problems that in the prior art, customer service information is not classified, multi-mode feature analysis on data is lacking, and an encryption storage scheme lacks pertinence, so that the data storage efficiency is poor and the data security is insufficient.
In view of the above problems, the application provides a customer service information service classification storage method and system based on a multi-mode large model.
In a first aspect, the present application provides a method for classifying and storing customer service information services based on a multi-mode large model, the method is implemented by a customer service information service classifying and storing system based on the multi-mode large model, wherein the method includes: acquiring customer service information to be stored in an encrypted manner; based on service feature identification, performing service classification processing on the customer service information to obtain a plurality of customer service data; constructing a multi-mode large model, and performing multi-mode feature analysis on the plurality of customer service data to obtain a plurality of values of the plurality of customer service data, wherein the multi-mode feature analysis comprises power business feature analysis, user feature analysis, data privacy feature analysis and data type feature analysis; optimizing the encryption storage scheme of the customer service data according to the plurality of values to obtain an optimal encryption storage scheme, and carrying out encryption storage to obtain a plurality of encryption storage results, wherein encryption storage processing and merck tree processing of a secret key are carried out on the customer service data by adopting different encryption backup times in different encryption storage schemes; constructing a plurality of verification standards for performing access verification on the plurality of customer service data according to the plurality of value degrees; when the customer service data are accessed, verification is carried out in the encrypted storage result according to verification data provided by a user, verification parameters are obtained, and the verification result is obtained and access is allowed or denied by combining the verification standards.
In a second aspect, the present application further provides a service information service classification storage system based on a multi-mode large model, for executing the service information service classification storage method based on the multi-mode large model according to the first aspect, where the system includes: the system comprises a data acquisition module to be encrypted, a data storage module and a data storage module, wherein the data acquisition module to be encrypted is used for acquiring customer service information to be stored in an encrypted mode; the customer service data classification module is used for carrying out service classification processing on the customer service information based on service feature identification to obtain a plurality of customer service data; the multi-modal feature analysis module is used for constructing a multi-modal large model, carrying out multi-modal feature analysis on the plurality of customer service data, and obtaining a plurality of values of the plurality of customer service data, wherein the multi-modal feature analysis comprises electric power business feature analysis, user feature analysis, data privacy feature analysis and data type feature analysis; the encryption storage module is used for optimizing the encryption storage scheme of the customer service data according to the plurality of values to obtain an optimal encryption storage scheme, and carrying out encryption storage to obtain a plurality of encryption storage results, wherein in different encryption storage schemes, encryption storage processing and merck tree processing of keys are carried out on the customer service data by adopting different encryption backup times; the verification standard construction module is used for constructing a plurality of verification standards for performing access verification on the plurality of customer service data according to the plurality of value degrees; and the access verification module is used for verifying in the encrypted storage result according to the verification data provided by the user when the plurality of customer service data are accessed, obtaining verification parameters, combining the plurality of verification standards, obtaining the verification result and allowing access or refusing access.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
Acquiring customer service information to be stored in an encrypted manner; based on service feature identification, performing service classification processing on the customer service information to obtain a plurality of customer service data; constructing a multi-mode large model, and performing multi-mode feature analysis on the plurality of customer service data to obtain a plurality of values of the plurality of customer service data, wherein the multi-mode feature analysis comprises power business feature analysis, user feature analysis, data privacy feature analysis and data type feature analysis; optimizing the encryption storage scheme of the customer service data according to the plurality of values to obtain an optimal encryption storage scheme, and carrying out encryption storage to obtain a plurality of encryption storage results, wherein encryption storage processing and merck tree processing of a secret key are carried out on the customer service data by adopting different encryption backup times in different encryption storage schemes; constructing a plurality of verification standards for performing access verification on the plurality of customer service data according to the plurality of value degrees; when the customer service data are accessed, verification is carried out in the encrypted storage result according to verification data provided by a user, verification parameters are obtained, and the verification result is obtained and access is allowed or denied by combining the verification standards. The customer service information is classified, then encryption storage scheme optimization is performed based on value analysis, and access verification is performed, so that the technical effects of improving the data storage management efficiency and improving the data storage and access safety are achieved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent. It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
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In order to more clearly illustrate the application or the technical solutions of the prior art, the following brief description will be given of the drawings used in the description of the embodiments or the prior art, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained from the drawings provided without the inventive effort for a person skilled in the art.
FIG. 1 is a flow diagram of a customer service information service classification storage method based on a multi-mode large model;
fig. 2 is a schematic structural diagram of a customer service information service classification storage system based on a multi-mode large model.
Reference numerals illustrate: the system comprises a data acquisition module to be encrypted, a customer service data classification module, a multi-mode feature analysis module, an encryption storage module, a verification standard construction module and an access verification module, wherein the data acquisition module to be encrypted, the customer service data classification module, the multi-mode feature analysis module, the encryption storage module and the verification standard construction module are respectively arranged in sequence.
Detailed Description
The customer service information business classified storage method and system based on the multi-mode large model solve the technical problems that in the prior art, customer service information is not classified and multi-mode feature analysis on data is lacked, and an encryption storage scheme is lacked in pertinence, so that data storage efficiency is poor and data safety is insufficient.
In the following, the technical solutions of the present application will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application, and that the present application is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present application are shown.
Example 1
Referring to fig. 1, the application provides a customer service information service classification storage method based on a multi-mode large model, wherein the method is applied to a customer service information service classification storage system based on the multi-mode large model, and the method specifically comprises the following steps:
Step one: acquiring customer service information to be encrypted and stored, wherein the customer service information comprises a plurality of customer service data generated by customer service communication of a plurality of users;
Specifically, the customer service information service classification storage method based on the multi-mode large model provided by the application can be applied to an electric power service scene to encrypt customer service data generated by communication between customer service and a user, so that the privacy of the user is ensured. The customer service information is data generated by communication between customer service and user, and can be text, voice, picture and other data.
Step two: based on service feature recognition, service classification processing is carried out on the customer service information, and a plurality of customer service data are obtained:
specifically, the customer service information refers to data generated by communication between customer service and a user, and may be, for example, information such as advice, complaint, etc. of the user on electric service, where the number of customer service information generated in real time is random, and if the data generated at a certain moment is too large, the system calculation power is limited, which results in untimely data processing, so that the customer service information can be classified according to service types to obtain multiple customer service data belonging to different service types.
Step three: constructing a multi-mode large model, and performing multi-mode feature analysis on the plurality of customer service data to obtain a plurality of values of the plurality of customer service data, wherein the multi-mode feature analysis comprises power business feature analysis, user feature analysis, data privacy feature analysis and data type feature analysis;
the multi-mode large model is a functional model for performing multi-mode feature analysis, wherein the multi-mode large model power feature analysis branch, the user feature analysis branch, the data privacy feature analysis branch and the data type feature analysis branch are constructed based on the training of the existing machine learning model, and the power feature analysis branch, the user feature analysis branch, the data privacy feature analysis branch and the data type feature analysis branch respectively perform power business feature analysis, user feature analysis, data privacy feature analysis and data type feature analysis on a plurality of customer service data to obtain a plurality of values of the plurality of customer service data in a plurality of dimensions. The power service features refer to service scale, and the larger the service scale is, the larger the value is; the user characteristics refer to personal characteristics of the user, such as the longer the user's age, the greater the value; the data privacy features refer to privacy contents such as addresses, contact ways and the like in customer service information, the more the privacy contents are, the value degree is, the data types refer to data such as pictures, voices and the like in the customer service information, the more valuable the data are than text data, and the more the data such as the pictures, the voices and the like are, the higher the value degree is.
The greater the value, the greater the security risk of revealing data or unauthorized access.
Step four: optimizing the encryption storage scheme of the customer service data according to the plurality of values to obtain an optimal encryption storage scheme, and carrying out encryption storage to obtain a plurality of encryption storage results, wherein encryption storage processing and merck tree processing of a secret key are carried out on the customer service data by adopting different encryption backup times in different encryption storage schemes;
Specifically, the encryption storage is to perform multiple encryption storage for each customer service data for different times, perform merck tree processing for the encrypted multiple keys, and perform merck tree processing for the encrypted multiple encryption storage for different times corresponding to each customer service information, wherein the merck tree processing for the key is an encryption storage scheme, the greater the value of the customer service data is, the greater the security requirement is, the greater the required encryption storage times are, each encryption storage needs corresponding calculation support, and an encryption storage function is constructed to perform fitness analysis in combination with multiple values to obtain an optimal encryption storage scheme.
Step five: constructing a plurality of verification standards for performing access verification on the plurality of customer service data according to the plurality of value degrees;
Specifically, the plurality of verification criteria refers to verification criteria based on the merck tree verification key, including a verification qualification rate threshold, the greater the value, the greater the verification qualification rate threshold.
Step six: when the customer service data are accessed, verification is carried out in the encrypted storage result according to verification data provided by a user, verification parameters are obtained, and the verification result is obtained and access is allowed or denied by combining the verification standards.
Specifically, when accessing the plurality of customer service data, the user provides verification data, the verification data comprises a plurality of verification keys, the merck tree processing is carried out on the plurality of verification keys, the merck tree obtained here is compared with the merck tree in the obtained encryption storage result, the same merck tree duty ratio is obtained as a verification parameter, if the verification parameter meets the verification standard, the verification result is verification passing, and the user is allowed to access; if the verification parameters do not meet the verification standard, the verification result is that the verification is not passed, and the user is refused to access. Therefore, customer service information in the power service scene is stored in an encrypted mode and is verified in an access mode, customer service data safety in the power service is improved, and customer privacy or industry important data are prevented from being leaked.
Further, the second step of the present application further comprises:
Extracting a plurality of service types in the customer service information based on service feature identification; and classifying the customer service information according to the service types to obtain a plurality of customer service data.
Specifically, based on service feature recognition, service classification processing is performed on the customer service information, and the process of obtaining a plurality of customer service data is as follows: first, a plurality of service types, such as service consultation, complaints, advice, etc., are defined according to service scope and customer requirements. According to each service type, a large amount of customer service information is collected, including various forms of data such as words, voice, mails and the like. The service features related to a plurality of service types are extracted from customer service information through the existing natural language processing technology, such as keyword extraction, word frequency statistics, semantic analysis and the like. And automatically classifying and identifying the customer service information based on the service characteristics corresponding to the service types, and aggregating the customer service information of the same service type to form a plurality of customer service data. Therefore, customer service information classification based on service types is realized, storage encryption is convenient to carry out according to different service types later, and data storage efficiency is improved.
Further, the third step of the present application further comprises:
Acquiring a sample customer service data set according to the recorded customer service information; constructing an electric power characteristic analysis branch, a user characteristic analysis branch, a data privacy characteristic analysis branch and a data type characteristic analysis branch according to the sample customer service data set to obtain the multi-mode large model; extracting power service characteristic data, user characteristic data, data privacy characteristic data and data type characteristic data in the plurality of customer service data, and carrying out recognition analysis through the multi-mode large model to obtain a plurality of service value degrees, user value degrees, privacy value degrees and type value degrees; and weighting and calculating the service value degrees, the user value degrees, the privacy value degrees and the type value degrees to obtain the value degrees.
Further, the application also comprises the following steps:
According to the sample customer service data set, extracting and obtaining a sample power service characteristic data set and a sample user characteristic data set, and extracting and obtaining a sample data privacy characteristic data set and a sample data type characteristic data set; evaluating and acquiring a sample service value set, a sample user value set, a sample privacy value set and a sample type value set according to the sample power service characteristic data set, the sample user characteristic data set, the sample data privacy characteristic data set and the sample data type characteristic data set, wherein the sample type value and the number of image data in the sample data type characteristic data are positively correlated; and constructing the electric power characteristic analysis branch, the user characteristic analysis branch, the data privacy characteristic analysis branch and the data type characteristic analysis branch by adopting the sample electric power service characteristic data set, the sample user characteristic data set, the sample data privacy characteristic data set and the sample data type characteristic data set and combining the sample service value set, the sample user value set, the sample privacy value set and the sample type value set.
Specifically, the method for constructing a multi-mode large model and performing multi-mode feature analysis on the plurality of customer service data comprises the following steps:
The recorded customer service information is communication information between a historical user and power service customer service, the recorded customer service information is arranged to be used as a sample customer service data set, and a power characteristic analysis branch, a user characteristic analysis branch, a data privacy characteristic analysis branch and a data type characteristic analysis branch are constructed according to the sample customer service data set to obtain the multi-mode large model, and the specific method is as follows:
According to the sample customer service data set, a sample power service characteristic data set and a sample user characteristic data set are obtained based on extraction in the prior art, and a sample data privacy characteristic data set and a sample data type characteristic data set are obtained, for example, the sample power service characteristic data can be obtained by identifying the power service scale corresponding to the sample customer service data in the sample customer service data set; the opening time of the user account can be identified as a sample user characteristic data set by identifying the user account corresponding to the sample customer service data in the sample customer service data set; the data such as address, mobile phone number, identity card number and the like contained in the sample customer service data can be extracted to be used as a sample data privacy feature data set; the data extraction type, such as picture, voice, may be set, and all of the picture, voice data is extracted as a sample data type feature data set.
Further according to the sample power service feature data set, the sample user feature data set, the sample data privacy feature data set and the sample data type feature data set, evaluating and obtaining a sample service value set, a sample user value set, a sample privacy value set and a sample type value set, wherein the sample type value and the number of image data in the sample data type feature data are positively correlated; the sample service value set is positively correlated with the service scale in the sample power service characteristic data set; the sample user value set is positively correlated with data in the sample user feature dataset, e.g., the longer the user ages, the greater the sample user value; the more the content of the privacy data in the sample privacy value set and the sample data privacy feature data set, such as address, identity card number and the like, the greater the sample privacy value. Based on the evaluation rules, the person skilled in the art sets corresponding value degrees for the sample power business feature data set, the sample user feature data set, the sample data privacy feature data set and the sample data type feature data set to obtain a sample business value degree set, a sample user value degree set, a sample privacy value degree set and a sample type value degree set.
And finally, constructing the electric power characteristic analysis branch, the user characteristic analysis branch, the data privacy characteristic analysis branch and the data type characteristic analysis branch based on the existing machine learning model, such as a neural network model, training the electric power characteristic analysis branch to be converged by adopting the sample electric power service characteristic data set and the sample service value set, training the user characteristic analysis branch to be converged by adopting the sample user characteristic data set and the sample user value set, training the data privacy characteristic analysis branch to be converged by adopting the sample data privacy characteristic data set and the sample privacy value set, training the data type characteristic analysis branch to be converged by adopting the sample data type characteristic data set and the sample type value set, obtaining the electric power characteristic analysis branch, the user characteristic analysis branch, the data privacy characteristic analysis branch and the data type characteristic analysis branch trained to be converged, integrating the electric power characteristic analysis branch, the user characteristic analysis branch, the data privacy characteristic analysis branch and the data type characteristic analysis branch to be converged, and obtaining a multi-mode large model by adopting the sample data privacy characteristic analysis branch and the sample value set, and providing a model foundation for subsequent value analysis.
And extracting the power service feature data, the user feature data, the data privacy feature data and the data type feature data in the plurality of customer service data based on the prior art, and respectively analyzing the power service feature data, the user feature data, the data privacy feature data and the data type feature data through a power feature analysis branch, a user feature analysis branch, a data privacy feature analysis branch and a data type feature analysis branch in the multi-mode large model to output a plurality of service value degrees, user value degrees, privacy value degrees and type value degrees. And finally, carrying out weighted calculation on the service value values, the user value values, the privacy value values and the type value values, wherein the weight used by the weighted calculation is set by a person skilled in the art in combination with the actual demand, and the weighted calculation result is used as the value values of the customer service data, so that the value identification of the customer service data is realized, the subsequent encryption storage scheme optimization based on the value values is facilitated, and the data security is improved.
Further, the fourth step of the present application further comprises:
Constructing an encryption storage function for optimizing encryption storage schemes of the plurality of customer service data, wherein the encryption storage function comprises the following formula:
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；
Wherein, Encryption storage fitness for ith customer service data,/>And/>Sum of 1 is calculated force weight and safety weight respectively,/>For the number of times of encrypted backup storage of the ith customer service data in the encrypted storage scheme,/>Adopting the calculation force required by the j-th encryption backup storage for the i-th customer service data in the encryption storage scheme,/>Value of ith customer service data,/>Adopting j-th encryption backup storage complexity level for ith customer service data,/>For the total fitness, N is the number of a plurality of customer service data,/>The weight of the ith customer service data is distributed according to the value degree of the plurality of customer service data;
And optimizing the encryption storage scheme of the plurality of customer service data according to the encryption storage function.
Further, the application also comprises the following steps:
Randomly generating a first encryption storage scheme, wherein the first encryption storage scheme comprises a plurality of times of encryption storage of the plurality of customer service data, a plurality of encryption algorithms and a hash algorithm of merck tree processing; according to the encryption storage function, calculating to obtain a first total fitness of the first encryption storage scheme; and adopting an intelligent optimization algorithm, continuing to randomly generate an encryption storage scheme to optimize until convergence, and outputting the encryption storage scheme with the maximum total adaptability to obtain the optimal encryption storage scheme.
Further, the application also comprises the following steps:
Acquiring a plurality of encryption algorithm sets and a plurality of hash algorithm sets for the plurality of customer service data according to the optimal encryption storage scheme; encrypting the customer service data by adopting the plurality of encryption algorithm sets to obtain a plurality of encrypted data sets and a plurality of key sets; carrying out hash processing on a plurality of keys in the plurality of key sets by adopting the plurality of hash algorithm sets to obtain a plurality of bottom hash nodes; based on the merck tree, continuing to hash the plurality of bottom hash nodes, constructing and obtaining a plurality of key merck tree sets, and storing the plurality of key merck tree sets in combination with the plurality of encrypted data sets to obtain a plurality of encrypted storage results.
Specifically, according to the plurality of values, the encryption storage scheme of the plurality of customer service data is optimized, and the method for obtaining the optimal encryption storage scheme is as follows:
Constructing an encryption storage function for optimizing encryption storage schemes of the plurality of customer service data, wherein the encryption storage function comprises the following formula:
；
；
Wherein, Encryption storage fitness for ith customer service data,/>And/>The sum of (1) is calculated as the force weight and the safety weight, which are set by the person skilled in the art according to the actual requirementFor the number of times of encrypted backup storage of the ith customer service data in the encrypted storage scheme,/>The calculation force required by the j-th encryption backup storage is adopted for the i-th customer service data in the encryption storage scheme, and specifically, a calculation force mapping database can be built based on the historical encryption backup storage, the historical encryption data quantity and the corresponding historical calculation force, and the corresponding calculation force can be obtained by traversing and comparing the i-th customer service data and the j-th encryption backup storage in the calculation force mapping database. /(I)The value degree of the ith customer service data is extracted based on a plurality of value degrees,The j-th encryption backup storage complexity level is adopted for the ith customer service data, the higher the complexity level is, the safer is, the specific requirement is determined according to the used algorithm, the corresponding complexity level can be set for different algorithms by combining actual experience by a person skilled in the art, and the matching is carried out subsequently. /(I)For the total fitness, N is the number of a plurality of customer service data,/>For the weight of the ith customer service data allocated according to the value degree of the plurality of customer service data, the higher the value degree is, the larger the corresponding weight is, for example, the ratio of the value degree corresponding to each customer service data to the sum of the plurality of value degrees can be calculated as the weight, that is, the sum of the encryption storage fitness of each customer service data needs to be calculated respectively, and then the encryption storage fitness corresponding to each customer service data is weighted according to the weight, so as to obtain the total fitness of the encryption storage scheme.
Further, according to the encryption storage function, the encryption storage scheme of the plurality of customer service data is optimized, and the specific implementation steps are as follows:
A first encryption storage scheme is randomly generated, where the first encryption storage scheme includes a number of times the plurality of customer service data is encrypted and stored for a plurality of times, a plurality of encryption algorithms, such as AES, RSA, SHA-256, and a hash algorithm, such as SHA-256, of a plurality of merck tree processes. The method comprises the steps of further analyzing the calculation forces required by a plurality of encryption algorithms and hash algorithms processed by the merck tree, specifically, constructing a calculation force mapping database based on the history algorithms, the history encryption data amount and the corresponding history calculation forces used by the history encryption backup storage, performing traversal comparison in the calculation force mapping database based on the plurality of encryption algorithms and the hash algorithms processed by the merck tree, obtaining the corresponding calculation forces, matching auxiliary levels of the encryption algorithms and the hash algorithms, substituting the calculation forces required by the hash algorithms processed by the merck tree in combination with the times of the encryption storage, the plurality of encryption algorithms and the merck tree into the encryption storage function, and calculating to obtain the first total fitness of the first encryption storage scheme. And adopting an intelligent optimization algorithm, such as an existing genetic algorithm, particle swarm optimization or simulated annealing, continuing to randomly generate an encryption storage scheme, optimizing by calculating the total fitness until convergence, namely reaching the preset iteration times, such as 100 times, and outputting the encryption storage scheme with the maximum total fitness as the optimal encryption storage scheme. Therefore, the encryption storage scheme is optimized, and the data storage safety is improved.
The specific steps for obtaining a plurality of encrypted storage results are as follows: and extracting a plurality of encryption algorithm sets and a plurality of hash algorithm sets for the plurality of customer service data according to the optimal encryption storage scheme, and encrypting the plurality of customer service data by adopting the plurality of encryption algorithm sets to obtain a plurality of encryption data sets and a plurality of key sets. Further adopting the hash algorithm sets to hash the keys in the key sets to obtain a plurality of bottom hash nodes, continuing to hash the bottom hash nodes based on the merck tree, namely processing the keys into a plurality of hash values and using the hash values as the bottom hash nodes, then adopting the hash algorithm to hash the hash values again according to two hash values in the hash values, processing the two hash values to obtain a hash value, thus obtaining a plurality of upper hash values, continuing to hash the two hash values in the upper hash values again, and so on until finally obtaining a hash value, and carrying out hierarchical connection arrangement on the hash values obtained in each layer, wherein the obtained result is a key merck tree, and the key sets correspond to the merck tree sets. And storing the plurality of key merck tree sets and the plurality of encrypted data sets, wherein the obtained results are a plurality of encrypted storage results, so that the encrypted storage of customer service data is realized, the data security is improved, and the access verification is carried out through the plurality of encrypted storage results to ensure the data access security.
Further, the fifth step of the present application further comprises:
Acquiring a basic verification standard for performing access verification on the plurality of customer service data, wherein the basic verification standard comprises a basic qualification rate threshold of input verification data; and correcting the basic verification standard according to the plurality of values to obtain a plurality of corrected qualification rate thresholds serving as the plurality of verification standards.
Specifically, according to the plurality of value degrees, the method for constructing a plurality of verification standards for performing access verification on the plurality of customer service data is as follows:
and obtaining a basic verification standard for performing access verification on the plurality of customer service data, wherein the basic verification standard comprises a basic qualification rate threshold of input verification data, the access verification is performed through a plurality of key merck tree sets in a plurality of encryption storage results, that is, final hash values generated by the same plurality of keys are identical, and final hash values generated by different data are completely different, so that hash values generated by the plurality of verification keys provided by a user during access are consistent with the plurality of key merck tree sets in the plurality of encryption storage results, that is, the plurality of verification merck trees generated by the plurality of verification keys provided by the user are identical with the corresponding key merck tree sets in the plurality of encryption storage results, and the basic qualification rate threshold is a proportion threshold occupied by the same merck tree in the plurality of verification key generated by the user and the corresponding key merck tree set in the encryption storage results, that is, the proportion threshold passing verification is considered to be set by a person skilled in the art in combination with practical experience.
Further, the basic verification standard is adjusted according to the values, for example, a ratio of each value to a sum of the values is used as an adjustment coefficient, and the basic qualification rate threshold corresponding to the customer service data of the basic verification standard is increased according to the adjustment coefficient, so that a plurality of corrected qualification rate thresholds are obtained as the verification standards. Therefore, the fine adjustment of the verification standard is realized, and the data access security is improved.
Further, the sixth step of the present application further comprises:
Acquiring verification data provided when a user accesses any target customer service data in the plurality of customer service data, wherein the verification data comprises a plurality of verification keys; performing Merck tree processing on the verification keys by using a hash algorithm set used for target customer service data encryption storage to obtain verification Merck trees; and combining the merck tree sets of the plurality of verification merck trees and the key merck tree set of the target customer service data, performing verification calculation to obtain verification qualification rate, and judging whether the corresponding verification standard is met or not to obtain a verification result.
When the customer service data are accessed, verification is carried out in the encrypted storage result according to verification data provided by a user, verification parameters are obtained, and a plurality of verification standards are combined, so that a method for obtaining the verification result is as follows:
When a user needs to access a plurality of customer service data, the user inputs verification data to perform access verification, and the verification data provided when the user accesses any target customer service data in the plurality of customer service data is extracted, wherein the verification data comprises a plurality of verification keys. And carrying out Merck tree processing on the verification keys by adopting a hash algorithm set used for target customer service data encryption storage, namely carrying out hash processing on the verification keys to obtain a plurality of hash values, carrying out hash processing on two hash values in the hash values, and the like to obtain a plurality of layers of hash nodes, and connecting to obtain a plurality of verification Merck trees. And combining the merck tree sets of the verification merck trees and the key merck tree set of the target customer service data, calculating the proportion of the merck tree sets of the verification merck tree and the key merck tree set of the target customer service data as verification qualification rate, and further judging whether the verification qualification rate meets the corresponding verification standard, namely judging whether the verification qualification rate is greater than or equal to the corresponding correction qualification rate threshold value, if so, the verification result is verification passing, otherwise, the verification is not passed. Only if the verification is passed, access to the target customer service data is allowed.
In summary, the customer service information service classification storage method based on the multi-mode large model provided by the application has the following technical effects:
Acquiring customer service information to be stored in an encrypted manner; based on service feature identification, performing service classification processing on the customer service information to obtain a plurality of customer service data; constructing a multi-mode large model, and performing multi-mode feature analysis on the plurality of customer service data to obtain a plurality of values of the plurality of customer service data, wherein the multi-mode feature analysis comprises power business feature analysis, user feature analysis, data privacy feature analysis and data type feature analysis; optimizing the encryption storage scheme of the customer service data according to the plurality of values to obtain an optimal encryption storage scheme, and carrying out encryption storage to obtain a plurality of encryption storage results, wherein encryption storage processing and merck tree processing of a secret key are carried out on the customer service data by adopting different encryption backup times in different encryption storage schemes; constructing a plurality of verification standards for performing access verification on the plurality of customer service data according to the plurality of value degrees; when the customer service data are accessed, verification is carried out in the encrypted storage result according to verification data provided by a user, verification parameters are obtained, and the verification result is obtained and access is allowed or denied by combining the verification standards. The customer service information is classified, then encryption storage scheme optimization is performed based on value analysis, and access verification is performed, so that the technical effects of improving the data storage management efficiency and improving the data storage and access safety are achieved.
Example two
Based on the same inventive concept as the customer service information service classification storage method based on the multi-mode large model in the foregoing embodiment, the present application further provides a customer service information service classification storage system based on the multi-mode large model, referring to fig. 2, where the system includes:
The system comprises a data acquisition module 11 to be encrypted, wherein the data acquisition module 11 to be encrypted is used for acquiring customer service information to be encrypted and stored, and the customer service information comprises a plurality of customer service data generated by customer service communication of a plurality of users;
the customer service data classification module 12 is used for carrying out service classification processing on the customer service information based on service feature identification to obtain a plurality of customer service data;
the multi-mode feature analysis module 13 is configured to construct a multi-mode large model, perform multi-mode feature analysis on the plurality of customer service data, and obtain a plurality of values of the plurality of customer service data, where the multi-mode feature analysis includes power service feature analysis, user feature analysis, data privacy feature analysis, and data type feature analysis;
The encryption storage module 14 is configured to optimize an encryption storage scheme of the customer service data according to the multiple values, obtain an optimal encryption storage scheme, and perform encryption storage to obtain multiple encryption storage results, where in different encryption storage schemes, different encryption backup times are used to perform encryption storage processing and merck tree processing of a key on the customer service data;
The verification standard construction module 15 is configured to construct a plurality of verification standards for performing access verification on the plurality of customer service data according to the plurality of values;
And the access verification module 16 is configured to, when accessing the plurality of customer service data, perform verification in the encrypted storage result according to the verification data provided by the user, obtain a verification parameter, obtain a verification result in combination with the plurality of verification standards, and allow access or deny access.
Further, the customer service data classification module 12 in the system is further configured to:
Extracting a plurality of service types in the customer service information based on service feature identification;
And classifying the customer service information according to the service types to obtain a plurality of customer service data.
Further, the multi-modal feature analysis module 13 in the system is further configured to:
acquiring a sample customer service data set according to the recorded customer service information;
Constructing an electric power characteristic analysis branch, a user characteristic analysis branch, a data privacy characteristic analysis branch and a data type characteristic analysis branch according to the sample customer service data set to obtain the multi-mode large model;
extracting power service characteristic data, user characteristic data, data privacy characteristic data and data type characteristic data in the plurality of customer service data, and carrying out recognition analysis through the multi-mode large model to obtain a plurality of service value degrees, user value degrees, privacy value degrees and type value degrees;
And weighting and calculating the service value degrees, the user value degrees, the privacy value degrees and the type value degrees to obtain the value degrees.
Further, the multi-modal feature analysis module 13 in the system is further configured to:
According to the sample customer service data set, extracting and obtaining a sample power service characteristic data set and a sample user characteristic data set, and extracting and obtaining a sample data privacy characteristic data set and a sample data type characteristic data set;
Evaluating and acquiring a sample service value set, a sample user value set, a sample privacy value set and a sample type value set according to the sample power service characteristic data set, the sample user characteristic data set, the sample data privacy characteristic data set and the sample data type characteristic data set, wherein the sample type value and the number of image data in the sample data type characteristic data are positively correlated;
And constructing the electric power characteristic analysis branch, the user characteristic analysis branch, the data privacy characteristic analysis branch and the data type characteristic analysis branch by adopting the sample electric power service characteristic data set, the sample user characteristic data set, the sample data privacy characteristic data set and the sample data type characteristic data set and combining the sample service value set, the sample user value set, the sample privacy value set and the sample type value set.
Further, the encryption storage module 14 in the system is further configured to:
Constructing an encryption storage function for optimizing encryption storage schemes of the plurality of customer service data, wherein the encryption storage function comprises the following formula:
；
；
Wherein, Encryption storage fitness for ith customer service data,/>And/>Sum of 1 is calculated force weight and safety weight respectively,/>For the number of times of encrypted backup storage of the ith customer service data in the encrypted storage scheme,/>Adopting the calculation force required by the j-th encryption backup storage for the i-th customer service data in the encryption storage scheme,/>Value of ith customer service data,/>Adopting j-th encryption backup storage complexity level for ith customer service data,/>For the total fitness, N is the number of a plurality of customer service data,/>The weight of the ith customer service data is distributed according to the value degree of the plurality of customer service data;
And optimizing the encryption storage scheme of the plurality of customer service data according to the encryption storage function.
Further, the encryption storage module 14 in the system is further configured to:
Randomly generating a first encryption storage scheme, wherein the first encryption storage scheme comprises a plurality of times of encryption storage of the plurality of customer service data, a plurality of encryption algorithms and a hash algorithm of merck tree processing;
According to the encryption storage function, calculating to obtain a first total fitness of the first encryption storage scheme;
and adopting an intelligent optimization algorithm, continuing to randomly generate an encryption storage scheme to optimize until convergence, and outputting the encryption storage scheme with the maximum total adaptability to obtain the optimal encryption storage scheme.
Further, the encryption storage module 14 in the system is further configured to:
acquiring a plurality of encryption algorithm sets and a plurality of hash algorithm sets for the plurality of customer service data according to the optimal encryption storage scheme;
encrypting the customer service data by adopting the plurality of encryption algorithm sets to obtain a plurality of encrypted data sets and a plurality of key sets;
Carrying out hash processing on a plurality of keys in the plurality of key sets by adopting the plurality of hash algorithm sets to obtain a plurality of bottom hash nodes;
based on the merck tree, continuing to hash the plurality of bottom hash nodes, constructing and obtaining a plurality of key merck tree sets, and storing the plurality of key merck tree sets in combination with the plurality of encrypted data sets to obtain a plurality of encrypted storage results.
Further, the verification criteria construction module 15 in the system is further configured to:
Acquiring a basic verification standard for performing access verification on the plurality of customer service data, wherein the basic verification standard comprises a basic qualification rate threshold of input verification data;
And correcting the basic verification standard according to the plurality of values to obtain a plurality of corrected qualification rate thresholds serving as the plurality of verification standards.
Further, the access authentication module 16 in the system is also configured to:
Acquiring verification data provided when a user accesses any target customer service data in the plurality of customer service data, wherein the verification data comprises a plurality of verification keys;
performing Merck tree processing on the verification keys by using a hash algorithm set used for target customer service data encryption storage to obtain verification Merck trees;
And combining the merck tree sets of the plurality of verification merck trees and the key merck tree set of the target customer service data, performing verification calculation to obtain verification qualification rate, and judging whether the corresponding verification standard is met or not to obtain a verification result.
In this description, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the foregoing method and specific example for storing the customer service classification based on the multi-mode large model in the first embodiment of fig. 1 are also applicable to the customer service classification storage system based on the multi-mode large model in this embodiment, and by the foregoing detailed description of the method for storing the customer service classification based on the multi-mode large model, those skilled in the art can clearly know the customer service classification storage system based on the multi-mode large model in this embodiment, so that for brevity of description, they will not be described in detail herein. For the system disclosed in the embodiment, since the system corresponds to the method disclosed in the embodiment, the description is simpler, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalent techniques thereof, the present application is also intended to include such modifications and variations.
Claims (8)
1. The customer service information service classified storage method based on the multi-mode large model is characterized by comprising the following steps of:
acquiring customer service information to be stored in an encrypted manner;
Based on service feature identification, performing service classification processing on the customer service information to obtain a plurality of customer service data;
Constructing a multi-mode large model, and performing multi-mode feature analysis on the plurality of customer service data to obtain a plurality of values of the plurality of customer service data, wherein the multi-mode feature analysis comprises power business feature analysis, user feature analysis, data privacy feature analysis and data type feature analysis;
Optimizing the encryption storage scheme of the customer service data according to the plurality of values to obtain an optimal encryption storage scheme, and carrying out encryption storage to obtain a plurality of encryption storage results, wherein encryption storage processing and merck tree processing of a secret key are carried out on the customer service data by adopting different encryption backup times in different encryption storage schemes;
Constructing a plurality of verification standards for performing access verification on the plurality of customer service data according to the plurality of value degrees;
When the customer service data are accessed, verifying in the encrypted storage result according to verification data provided by a user to obtain verification parameters, and combining the verification standards to obtain a verification result and allow access or deny access;
Optimizing the encryption storage scheme of the customer service data according to the plurality of values to obtain an optimal encryption storage scheme, wherein the method comprises the following steps:
Constructing an encryption storage function for optimizing encryption storage schemes of the plurality of customer service data, wherein the encryption storage function comprises the following formula:
；
；
Wherein, Encryption storage fitness for ith customer service data,/>And/>Sum of 1 is calculated force weight and safety weight respectively,/>For the number of times of encrypted backup storage of the ith customer service data in the encrypted storage scheme,/>Adopting the calculation force required by the j-th encryption backup storage for the i-th customer service data in the encryption storage scheme,/>The value of the ith customer service data,Adopting j-th encryption backup storage complexity level for ith customer service data,/>For the total fitness, N is the number of a plurality of customer service data,/>The weight of the ith customer service data is distributed according to the value degree of the plurality of customer service data;
optimizing the encryption storage scheme of the plurality of customer service data according to the encryption storage function;
Optimizing the encryption storage scheme of the plurality of customer service data according to the encryption storage function, wherein the method comprises the following steps:
Randomly generating a first encryption storage scheme, wherein the first encryption storage scheme comprises a plurality of times of encryption storage of the plurality of customer service data, a plurality of encryption algorithms and a hash algorithm of merck tree processing;
According to the encryption storage function, calculating to obtain a first total fitness of the first encryption storage scheme;
and adopting an intelligent optimization algorithm, continuing to randomly generate an encryption storage scheme to optimize until convergence, and outputting the encryption storage scheme with the maximum total adaptability to obtain the optimal encryption storage scheme.
2. The method of claim 1, wherein performing a service classification process on the customer service information based on service feature identification to obtain a plurality of customer service data comprises:
Extracting a plurality of service types in the customer service information based on service feature identification;
And classifying the customer service information according to the service types to obtain a plurality of customer service data.
3. The method of claim 1, wherein constructing a multi-modal large model for multi-modal feature analysis of the plurality of customer service data comprises:
acquiring a sample customer service data set according to the recorded customer service information;
Constructing an electric power characteristic analysis branch, a user characteristic analysis branch, a data privacy characteristic analysis branch and a data type characteristic analysis branch according to the sample customer service data set to obtain the multi-mode large model;
extracting power service characteristic data, user characteristic data, data privacy characteristic data and data type characteristic data in the plurality of customer service data, and carrying out recognition analysis through the multi-mode large model to obtain a plurality of service value degrees, user value degrees, privacy value degrees and type value degrees;
And weighting and calculating the service value degrees, the user value degrees, the privacy value degrees and the type value degrees to obtain the value degrees.
4. A method according to claim 3, wherein constructing a power profile branch, a user profile branch, a data privacy profile branch, and a data type profile branch comprises:
According to the sample customer service data set, extracting and obtaining a sample power service characteristic data set and a sample user characteristic data set, and extracting and obtaining a sample data privacy characteristic data set and a sample data type characteristic data set;
Evaluating and acquiring a sample service value set, a sample user value set, a sample privacy value set and a sample type value set according to the sample power service characteristic data set, the sample user characteristic data set, the sample data privacy characteristic data set and the sample data type characteristic data set, wherein the sample type value and the number of image data in the sample data type characteristic data are positively correlated;
And constructing the electric power characteristic analysis branch, the user characteristic analysis branch, the data privacy characteristic analysis branch and the data type characteristic analysis branch by adopting the sample electric power service characteristic data set, the sample user characteristic data set, the sample data privacy characteristic data set and the sample data type characteristic data set and combining the sample service value set, the sample user value set, the sample privacy value set and the sample type value set.
5. The method of claim 1, wherein performing the encrypted storage to obtain a plurality of encrypted storage results comprises:
acquiring a plurality of encryption algorithm sets and a plurality of hash algorithm sets for the plurality of customer service data according to the optimal encryption storage scheme;
encrypting the customer service data by adopting the plurality of encryption algorithm sets to obtain a plurality of encrypted data sets and a plurality of key sets;
Carrying out hash processing on a plurality of keys in the plurality of key sets by adopting the plurality of hash algorithm sets to obtain a plurality of bottom hash nodes;
based on the merck tree, continuing to hash the plurality of bottom hash nodes, constructing and obtaining a plurality of key merck tree sets, and storing the plurality of key merck tree sets in combination with the plurality of encrypted data sets to obtain a plurality of encrypted storage results.
6. The method of claim 1, wherein constructing a plurality of authentication criteria for access authentication of the plurality of customer service data based on the plurality of valuations comprises:
Acquiring a basic verification standard for performing access verification on the plurality of customer service data, wherein the basic verification standard comprises a basic qualification rate threshold of input verification data;
And correcting the basic verification standard according to the plurality of values to obtain a plurality of corrected qualification rate thresholds serving as the plurality of verification standards.
7. The method of claim 5, wherein upon accessing the plurality of customer service data, validating within the encrypted storage result based on the user-provided validation data to obtain validation parameters, and wherein combining the plurality of validation criteria to obtain the validation result comprises:
Acquiring verification data provided when a user accesses any target customer service data in the plurality of customer service data, wherein the verification data comprises a plurality of verification keys;
performing Merck tree processing on the verification keys by using a hash algorithm set used for target customer service data encryption storage to obtain verification Merck trees;
And combining the merck tree sets of the plurality of verification merck trees and the key merck tree set of the target customer service data, performing verification calculation to obtain verification qualification rate, and judging whether the corresponding verification standard is met or not to obtain a verification result.
8. Customer service information service classification storage system based on a multimodal mass model, characterized by the steps for implementing the method according to any of claims 1 to 7, said system comprising:
the system comprises a data acquisition module to be encrypted, a data storage module and a data storage module, wherein the data acquisition module to be encrypted is used for acquiring customer service information to be stored in an encrypted mode;
The customer service data classification module is used for carrying out service classification processing on the customer service information based on service feature identification to obtain a plurality of customer service data;
The multi-modal feature analysis module is used for constructing a multi-modal large model, carrying out multi-modal feature analysis on the plurality of customer service data, and obtaining a plurality of values of the plurality of customer service data, wherein the multi-modal feature analysis comprises electric power business feature analysis, user feature analysis, data privacy feature analysis and data type feature analysis;
The encryption storage module is used for optimizing the encryption storage scheme of the customer service data according to the plurality of values to obtain an optimal encryption storage scheme, and carrying out encryption storage to obtain a plurality of encryption storage results, wherein in different encryption storage schemes, encryption storage processing and merck tree processing of keys are carried out on the customer service data by adopting different encryption backup times;
the verification standard construction module is used for constructing a plurality of verification standards for performing access verification on the plurality of customer service data according to the plurality of value degrees;
The access verification module is used for verifying in the encrypted storage result according to verification data provided by a user when accessing the plurality of customer service data to obtain verification parameters, and combining the plurality of verification standards to obtain a verification result and allow access or deny access;
The encryption storage module is further configured to:
Constructing an encryption storage function for optimizing encryption storage schemes of the plurality of customer service data, wherein the encryption storage function comprises the following formula:
；
；
Wherein, Encryption storage fitness for ith customer service data,/>And/>Sum of 1 is calculated force weight and safety weight respectively,/>For the number of times of encrypted backup storage of the ith customer service data in the encrypted storage scheme,/>Adopting the calculation force required by the j-th encryption backup storage for the i-th customer service data in the encryption storage scheme,/>The value of the ith customer service data,Adopting j-th encryption backup storage complexity level for ith customer service data,/>For the total fitness, N is the number of a plurality of customer service data,/>The weight of the ith customer service data is distributed according to the value degree of the plurality of customer service data;
optimizing the encryption storage scheme of the plurality of customer service data according to the encryption storage function;
The encryption storage module is further configured to:
Randomly generating a first encryption storage scheme, wherein the first encryption storage scheme comprises a plurality of times of encryption storage of the plurality of customer service data, a plurality of encryption algorithms and a hash algorithm of merck tree processing;
According to the encryption storage function, calculating to obtain a first total fitness of the first encryption storage scheme;
and adopting an intelligent optimization algorithm, continuing to randomly generate an encryption storage scheme to optimize until convergence, and outputting the encryption storage scheme with the maximum total adaptability to obtain the optimal encryption storage scheme.
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