CN115204158B - Data isolation application method and device, electronic equipment and storage medium - Google Patents
Data isolation application method and device, electronic equipment and storage medium Download PDFInfo
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- CN115204158B CN115204158B CN202210855691.8A CN202210855691A CN115204158B CN 115204158 B CN115204158 B CN 115204158B CN 202210855691 A CN202210855691 A CN 202210855691A CN 115204158 B CN115204158 B CN 115204158B
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Abstract
The invention relates to a data processing technology, and discloses a data isolation application method, which comprises the following steps: dividing service data areas corresponding to each service category according to the user identification and the service category; classifying and identifying the acquired corpus data to obtain corresponding corpus categories; screening business categories matched with corpus categories; storing the corpus data in the corresponding business data area according to the business category matched with the corpus category; when data application is needed, acquiring a service class of data to be applied, and obtaining a dialogue model by utilizing all data training models in a service data area corresponding to the acquired service class; and sending the dialogue model to preset terminal equipment. The invention also relates to a blockchain technique, the traffic class may be stored in a blockchain node. The invention also provides a data isolation application device, equipment and medium. The invention can improve the efficiency of data isolation application.
Description
Technical Field
The present invention relates to data processing technologies, and in particular, to a data isolation application method, a data isolation application device, an electronic device, and a storage medium.
Background
Natural language understanding is an important field in the field of artificial intelligence, along with the development of artificial intelligence, business corpus data of users can be subjected to data isolation to obtain corpus data of different business categories, model training is performed by using the corpus data of each business category, and therefore models in specific fields are obtained and are used for helping users communicate with clients on businesses of related business categories, and the working efficiency of the users is improved.
However, in the current data isolation application, when the corpus data of the service type is required to be applied, all the service corpus data of the user are screened, the corpus data training model of the service type is isolated, the screening speed is low, and the data isolation application efficiency is low.
Disclosure of Invention
The invention provides a data isolation application method, a data isolation application device, electronic equipment and a storage medium, and mainly aims to improve the efficiency of data isolation application.
Acquiring a user identifier of a user, and dividing a storage area in a preset storage space according to the user identifier to acquire a user data area;
acquiring all service categories of the user in real time, and segmenting the user data area according to the service categories to obtain service data areas corresponding to each service category;
acquiring the corpus data of the user in real time, and classifying and identifying the acquired corpus data to obtain corresponding corpus categories;
screening the business categories based on the corpus categories to obtain business categories matched with the corpus categories;
storing the corpus data corresponding to the corpus category in a business data area corresponding to the business category matched with the corpus category;
when a service data application request of the user is obtained, extracting a service category in the service data application request, and training a preset model by utilizing all data in a service data area corresponding to the extracted service category to obtain a target service dialogue model;
and sending the target service dialogue model to terminal equipment and/or preset terminal equipment which send out the service data application request.
Optionally, the dividing the storage area in the preset storage space according to the user identifier to obtain the user data area includes:
dividing a blank storage area with a preset size in the storage space;
and constructing a storage path for the divided blank storage area according to the user identifier so as to access the divided blank storage area by using the storage path to obtain the user data area.
Optionally, the classifying and identifying the obtained corpus data to obtain a corresponding corpus category includes:
converting the corpus data into a text format to obtain text corpus;
and carrying out intention recognition on the text corpus, and taking the recognized intention as the corpus category of the corpus data.
Optionally, the filtering the business category based on the corpus category to obtain a business category matched with the corpus category includes:
calculating the relevance between the corpus class and each business class;
and screening the business categories according to the correlation degree to obtain business categories matched with the corpus categories.
Optionally, the calculating the relevance between the corpus category and each business category includes:
converting the corpus category into a vector to obtain a corpus category vector;
converting the business category into a vector to obtain a business category vector;
and calculating the vector distance between the corpus class vector and the business class vector, and determining the vector distance as the relevance.
Optionally, the calculating the relevance between the corpus category and each business category includes:
judging whether each character in the corpus category from the first character is the same as the corresponding sequence character in the business category or not;
determining word order correlation coefficients corresponding to each character in the corpus category according to a judging result of whether the characters are the same;
calculating by using word order correlation coefficients of all characters in the corpus category to obtain a first character correlation degree;
calculating by using a preset standard word sequence coefficient corresponding to each character in the corpus category to obtain a second character relevance;
and calculating the ratio of the first character relevance to the second character relevance to obtain the relevance.
Optionally, the sending the target service dialogue model to the terminal device sending the service data application request and/or the preset terminal device includes:
extracting model parameters of the target business dialogue model;
obtaining model information of the preset model;
combining the model parameters with the model information to obtain model data;
and sending the model data to the terminal equipment so that the terminal equipment can deploy the target service dialogue model by using the model data.
In order to solve the above problems, the present invention also provides a data isolation application apparatus, the apparatus comprising:
the data identification module is used for acquiring a user identifier of a user, dividing a storage area in a preset storage space according to the user identifier, and obtaining a user data area; acquiring all service categories of the user in real time, and segmenting the user data area according to the service categories to obtain service data areas corresponding to each service category; acquiring the corpus data of the user in real time, and classifying and identifying the acquired corpus data to obtain corresponding corpus categories; screening the business categories based on the corpus categories to obtain business categories matched with the corpus categories;
the isolation storage module is used for storing the corpus data corresponding to the corpus category in a business data area corresponding to the business category matched with the corpus category;
the data application module is used for extracting service types in the service data application request when the service data application request of the user is acquired, and training a preset model by utilizing all data in a service data area corresponding to the extracted service types to obtain a target service dialogue model; and sending the target service dialogue model to terminal equipment and/or preset terminal equipment which send out the service data application request.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one computer program; a kind of electronic device with high-pressure air-conditioning system
And the processor executes the computer program stored in the memory to realize the data isolation application method.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned data isolation application method.
The embodiment of the invention segments the user data area according to the service category to obtain the service data area corresponding to each service category; acquiring the corpus data of the user in real time, and classifying and identifying the acquired corpus data to obtain corresponding corpus categories; screening the business categories based on the corpus categories to obtain business categories matched with the corpus categories; storing the corpus data corresponding to the corpus category in a business data area corresponding to the business category matched with the corpus category; the corpus data is identified, classified and isolated and stored, and when the data application is required, the data training model is directly extracted from the corresponding storage area to perform the data application, and all the corpus data is not required to be screened, so that the acquisition speed of the data requiring the isolated application is improved, and the data isolation application efficiency is further improved. Therefore, the data isolation application method, the data isolation application device, the electronic equipment and the readable storage medium provided by the embodiment of the invention improve the efficiency of data isolation application.
Drawings
FIG. 1 is a flow chart of a data isolation application method according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a data isolation application device according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device for implementing a data isolation application method according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a data isolation application method. The execution body of the data isolation application method includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the data isolation application method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: the server can be an independent server, or can be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDNs), basic cloud computing services such as big data and artificial intelligent platforms, and the like.
Referring to fig. 1, a flow chart of a data isolation application method according to an embodiment of the present invention is shown, where in the embodiment of the present invention, the data isolation application method includes:
s1, acquiring a user identifier of a user, and dividing a storage area in a preset storage space according to the user identifier to obtain a user data area;
the user identifier in the embodiment of the invention is a mark for identifying a user, has uniqueness, and can be a user name, a user ID, a tag text and the like.
Further, in order to better manage the service data generated by the user, the embodiment of the invention divides a storage area in a preset storage space according to the user identifier to obtain a user data area, so that the service data of the user is specially stored by using the user data area, and the special management of the service data of the user is realized.
Specifically, in the embodiment of the present invention, the dividing the storage area in the preset storage space according to the user identifier to obtain the user data area includes:
dividing a blank storage area with a preset size in the storage space;
constructing a storage path for the divided blank storage area according to the user identification so as to access the divided blank storage area by using the storage path;
and determining the blank storage area with the built storage path as the user data area.
S2, acquiring all service categories of the user in real time, and segmenting the user data area according to the service categories to obtain service data areas corresponding to each service category;
the service processed by the user in the embodiment of the invention may relate to a plurality of service lines, each service line has a corresponding service class, and further, because the service processed by the user may be dynamically expanded, the service classes are correspondingly increased, so that all the service classes of the user are acquired in real time.
Further, in order to independently manage the service data corresponding to each service class, the embodiment of the invention performs data isolation on the service data of different service classes to realize efficient management of data classification, so that the user data area is segmented according to the service classes to obtain the service data area corresponding to each service class.
In detail, in the embodiment of the present invention, the slicing the user data area according to the service class to obtain the service data area corresponding to each service class includes:
and dividing a blank storage area with preset storage size from the user data area, constructing a storage path for the divided blank storage area according to the service type, and accessing the divided blank storage area by using the storage path to obtain the service data area corresponding to the service type.
Further, in the embodiment of the present invention, when the size of the blank storage area in the user data area is smaller than the preset storage size, the user data area may be subjected to capacity expansion update before division, so that the updated blank storage area in the user data area is not smaller than the preset storage size.
In another embodiment of the present invention, the service class may be stored in a blockchain node, and the high throughput characteristic of the blockchain node is utilized to improve the data access efficiency.
S3, acquiring the corpus data of the user in real time, and classifying and identifying the acquired corpus data to obtain corresponding corpus categories;
the corpus data in the embodiment of the invention is related business corpus data generated when the user processes business, such as: the speech corpus that the user communicates with the customer or the text corpus that the user communicates with the customer.
Further, in the embodiment of the present invention, the step of classifying and identifying the obtained corpus data to obtain the corresponding corpus category includes:
converting the corpus data into a text format to obtain text corpus;
and carrying out intention recognition on the text corpus, and taking the recognized intention as the corpus category of the corpus data.
According to the embodiment of the invention, the intention recognition can be performed by using methods such as rule template analysis, deep learning and the like, and the specific method for the intention recognition is not limited in the embodiment of the invention.
S4, screening the business categories based on the corpus categories to obtain business categories matched with the corpus categories;
in order to store the corpus data in the business data area corresponding to the business category matched with the corpus category of the corpus data, the business category matched with the corpus category needs to be screened first.
Further, in the embodiment of the present invention, the filtering the business category based on the corpus category to obtain the business category matched with the corpus category includes:
calculating the relevance between the corpus class and each business class;
and screening the business categories according to the correlation degree to obtain business categories matched with the corpus categories.
In detail, in the embodiment of the present invention, calculating the relevance between the corpus category and each business category includes:
converting the corpus category into a vector to obtain a corpus category vector;
converting the business category into a vector to obtain a business category vector;
and calculating the vector distance between the corpus class vector and the business class vector, and determining the vector distance as the relevance.
In the embodiment of the invention, the corpus category and the business category are text, for example: the method for converting the text into the vector is not limited by the embodiment of the invention, and the method for converting the text into the vector can be used for converting the text into the vector by using the Bert model, converting the text into the vector by using a one-hot algorithm and the like.
Further, in the embodiment of the present invention, the filtering the business category by using the relevance to obtain the business category matched with the corpus category includes:
and determining the business category corresponding to the maximum relevance as the business category matched with the corpus category.
In another embodiment of the present invention, the calculating the relevance between the corpus category and each of the business categories includes:
judging whether each character in the corpus category from the first character is the same as the corresponding sequence character in the business category or not;
determining word order correlation coefficients corresponding to each character in the corpus category according to a judging result of whether the characters are the same;
in detail, in the embodiment of the present invention, when the judgment results are the same, the word order correlation coefficient of the corresponding character is 1, and when the judgment results are different, the word order correlation coefficient of the corresponding character is 0.
Calculating by using word order correlation coefficients of all characters in the corpus category to obtain a first character correlation degree;
calculating by using a preset standard word sequence coefficient corresponding to each character in the corpus category to obtain a second character relevance;
and calculating the ratio of the first character relevance to the second character relevance to obtain the relevance.
Specifically, in the embodiment of the present invention, the first character correlation is calculated using the following formula;
wherein i is the order of the characters in the corpus class, c i For word order correlation coefficient of the characters with the order of i in the corpus category, p is the number of the characters in the corpus category, and x p A degree of correlation for the first character;
in detail, the embodiment of the present invention calculates the second character correlation using the following formula;
wherein y is the preset standard word order coefficient, x b And the second character relativity.
S5, storing the corpus data corresponding to the corpus category in a business data area corresponding to the business category matched with the corpus category;
the embodiment of the invention obtains and stores the corpus data corresponding to the corpus category in the business data area corresponding to the business category matched with the corpus category.
Specifically, in the embodiment of the invention, a storage path of the service data area is obtained, and the corpus data is written into the service data area according to the storage path.
S6, when a service data application request of the user is obtained, extracting service types in the service data application request, and training a preset model by utilizing all data in a service data area corresponding to the extracted service types to obtain a target service dialogue model;
the service data application request in the embodiment of the invention is a request for applying service data of a certain service class, and the service data application request contains the service class of the service data needing to be applied.
Further, in the embodiment of the invention, the data in the service data area are all acquired corpus data, and all the data in the service data area corresponding to the extracted service category are utilized to train the preset model so as to obtain a target service dialogue model, so that the target service dialogue model can be utilized to replace the communication between a user and a client of corresponding service, the service workload of the user is reduced, and the service work efficiency is improved.
Further, the target service dialogue model in the embodiment of the invention can give a response answer to the questions presented by the clients, and the embodiment of the invention does not limit the type of the model and the specific training method.
And S7, the target service dialogue model is sent to terminal equipment and/or preset terminal equipment which send out the service data application request.
The terminal device in the embodiment of the invention is a terminal device capable of receiving and deploying a model, for example: and a server.
Further, the embodiment of the invention sends the target service dialogue model to the terminal equipment which sends the service data application request and/or the preset terminal equipment.
In an embodiment of the present invention, since the direct transmission model results in a larger data transmission amount, the sending the target service session model to the terminal device and/or the preset terminal device that sent the service data application request includes:
extracting model parameters of the target business dialogue model;
the model parameters in the embodiment of the invention are all parameters changed in the target business dialogue model compared with the preset model.
Obtaining model information of the preset model;
in the embodiment of the invention, the model information is specific information for constructing the preset model, and the preset model can be constructed through the model information.
Combining the model parameters with the model information to obtain model data;
and sending the model data to the terminal equipment so that the terminal equipment can deploy the target service dialogue model by using the model data.
Further, in order to ensure that transmission data in the transmission process does not have transmission errors, the sending the model data to the terminal device in the embodiment of the present invention includes:
calculating an MD5 value of the model data;
marking model data with the MD5 values;
and sending the MD5 value mark model data to the terminal equipment.
As shown in fig. 2, a functional block diagram of the data isolation application device of the present invention is shown.
The data isolation application 100 of the present invention may be installed in an electronic device. Depending on the functions implemented, the data isolation application may comprise a data identification module 101, an isolation storage module 102, and a data application module 103, which may also be referred to as a unit, refers to a series of computer program segments capable of being executed by a processor of an electronic device and of performing a fixed function, which are stored in a memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the data identification module 101 is configured to obtain a user identifier of a user, and divide a storage area in a preset storage space according to the user identifier to obtain a user data area; acquiring all service categories of the user in real time, and segmenting the user data area according to the service categories to obtain service data areas corresponding to each service category; acquiring the corpus data of the user in real time, and classifying and identifying the acquired corpus data to obtain corresponding corpus categories; screening the business categories based on the corpus categories to obtain business categories matched with the corpus categories;
the isolation storage module 102 is configured to store corpus data corresponding to the corpus category in a business data area corresponding to a business category matched with the corpus category;
the data application module 103 is configured to extract a service class in the service data application request when the service data application request of the user is acquired, and train a preset model by using all data in a service data area corresponding to the extracted service class to obtain a target service dialogue model; and sending the target service dialogue model to terminal equipment and/or preset terminal equipment which send out the service data application request.
In detail, each module in the data isolation application apparatus 100 in the embodiment of the present invention adopts the same technical means as the data isolation application method described in fig. 1 and can produce the same technical effects when in use, and will not be described herein.
Fig. 3 is a schematic structural diagram of an electronic device implementing the data isolation application method according to the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a data isolation application, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of data isolation applications, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., data isolation application, etc.) stored in the memory 11, and calling data stored in the memory 11.
The communication bus 12 may be a peripheral component interconnect standard (perIPheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure classification circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
Optionally, the communication interface 13 may comprise a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The data isolation application stored by the memory 11 in the electronic device is a combination of a plurality of computer programs that, when run in the processor 10, implement:
acquiring a user identifier of a user, and dividing a storage area in a preset storage space according to the user identifier to acquire a user data area;
acquiring all service categories of the user in real time, and segmenting the user data area according to the service categories to obtain service data areas corresponding to each service category;
acquiring the corpus data of the user in real time, and classifying and identifying the acquired corpus data to obtain corresponding corpus categories;
screening the business categories based on the corpus categories to obtain business categories matched with the corpus categories;
storing the corpus data corresponding to the corpus category in a business data area corresponding to the business category matched with the corpus category;
when a service data application request of the user is obtained, extracting a service category in the service data application request, and training a preset model by utilizing all data in a service data area corresponding to the extracted service category to obtain a target service dialogue model;
and sending the target service dialogue model to terminal equipment and/or preset terminal equipment which send out the service data application request.
In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring a user identifier of a user, and dividing a storage area in a preset storage space according to the user identifier to acquire a user data area;
acquiring all service categories of the user in real time, and segmenting the user data area according to the service categories to obtain service data areas corresponding to each service category;
acquiring the corpus data of the user in real time, and classifying and identifying the acquired corpus data to obtain corresponding corpus categories;
screening the business categories based on the corpus categories to obtain business categories matched with the corpus categories;
storing the corpus data corresponding to the corpus category in a business data area corresponding to the business category matched with the corpus category;
when a service data application request of the user is obtained, extracting a service category in the service data application request, and training a preset model by utilizing all data in a service data area corresponding to the extracted service category to obtain a target service dialogue model;
and sending the target service dialogue model to terminal equipment and/or preset terminal equipment which send out the service data application request.
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (6)
1. A method of data isolation application, the method comprising:
acquiring a user identifier of a user, and dividing a storage area in a preset storage space according to the user identifier to acquire a user data area;
acquiring all service categories of the user in real time, and segmenting the user data area according to the service categories to obtain service data areas corresponding to each service category;
acquiring the corpus data of the user in real time, and classifying and identifying the acquired corpus data to obtain corresponding corpus categories;
screening the business categories based on the corpus categories to obtain business categories matched with the corpus categories;
storing the corpus data corresponding to the corpus category in a business data area corresponding to the business category matched with the corpus category;
when a service data application request of the user is obtained, extracting a service category in the service data application request, and training a preset model by utilizing all data in a service data area corresponding to the extracted service category to obtain a target service dialogue model;
the target service dialogue model is sent to terminal equipment sending the service data application request and/or preset terminal equipment;
the step of dividing the storage area in the preset storage space according to the user identification to obtain a user data area comprises the following steps: dividing a blank storage area with a preset size in the storage space; constructing a storage path for the divided blank storage area according to the user identification so as to access the divided blank storage area by using the storage path; determining the blank storage area with the built storage path as the user data area;
the step of classifying and identifying the acquired corpus data to obtain corresponding corpus categories comprises the following steps: converting the corpus data into a text format to obtain text corpus; carrying out intention recognition on the text corpus, and taking the recognized intention as the corpus category of the corpus data;
the filtering the business category based on the corpus category to obtain the business category matched with the corpus category comprises the following steps: calculating the relevance between the corpus class and each business class; screening the business categories according to the correlation degree to obtain business categories matched with the corpus categories;
the sending the target service dialogue model to the terminal equipment sending the service data application request and/or the preset terminal equipment comprises the following steps: extracting model parameters of the target business dialogue model; obtaining model information of the preset model; combining the model parameters with the model information to obtain model data; and sending the model data to the terminal equipment so that the terminal equipment can deploy the target service dialogue model by using the model data.
2. The data isolation application method of claim 1, wherein said calculating a relevance of said corpus category to each of said business categories comprises:
converting the corpus category into a vector to obtain a corpus category vector;
converting the business category into a vector to obtain a business category vector;
and calculating the vector distance between the corpus class vector and the business class vector, and determining the vector distance as the relevance.
3. The data isolation application method of claim 1, wherein said calculating a relevance of said corpus category to each of said business categories comprises:
judging whether each character in the corpus category from the first character is the same as the corresponding sequence character in the business category or not;
determining word order correlation coefficients corresponding to each character in the corpus category according to a judging result of whether the characters are the same;
calculating by using word order correlation coefficients of all characters in the corpus category to obtain a first character correlation degree;
calculating by using a preset standard word sequence coefficient corresponding to each character in the corpus category to obtain a second character relevance;
and calculating the ratio of the first character relevance to the second character relevance to obtain the relevance.
4. A data isolation application apparatus, comprising:
the data identification module is used for acquiring a user identifier of a user, dividing a storage area in a preset storage space according to the user identifier, and obtaining a user data area; acquiring all service categories of the user in real time, and segmenting the user data area according to the service categories to obtain service data areas corresponding to each service category; acquiring the corpus data of the user in real time, and classifying and identifying the acquired corpus data to obtain corresponding corpus categories; screening the business categories based on the corpus categories to obtain business categories matched with the corpus categories;
the isolation storage module is used for storing the corpus data corresponding to the corpus category in a business data area corresponding to the business category matched with the corpus category;
the data application module is used for extracting service types in the service data application request when the service data application request of the user is acquired, and training a preset model by utilizing all data in a service data area corresponding to the extracted service types to obtain a target service dialogue model; the target service dialogue model is sent to terminal equipment sending the service data application request and/or preset terminal equipment;
the step of dividing the storage area in the preset storage space according to the user identification to obtain a user data area comprises the following steps: dividing a blank storage area with a preset size in the storage space; constructing a storage path for the divided blank storage area according to the user identification so as to access the divided blank storage area by using the storage path; determining the blank storage area with the built storage path as the user data area;
the step of classifying and identifying the acquired corpus data to obtain corresponding corpus categories comprises the following steps: converting the corpus data into a text format to obtain text corpus; carrying out intention recognition on the text corpus, and taking the recognized intention as the corpus category of the corpus data;
the filtering the business category based on the corpus category to obtain the business category matched with the corpus category comprises the following steps: calculating the relevance between the corpus class and each business class; screening the business categories according to the correlation degree to obtain business categories matched with the corpus categories;
the sending the target service dialogue model to the terminal equipment sending the service data application request and/or the preset terminal equipment comprises the following steps: extracting model parameters of the target business dialogue model; obtaining model information of the preset model; combining the model parameters with the model information to obtain model data; and sending the model data to the terminal equipment so that the terminal equipment can deploy the target service dialogue model by using the model data.
5. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data isolation application method of any one of claims 1 to 3.
6. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements a data isolation application method according to any one of claims 1 to 3.
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