CN112887408A - System and method for solving data state sharing of multi-kernel browser - Google Patents
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Abstract
The invention discloses a system and a method for solving the problem of data state sharing of a multi-kernel browser, relates to the technical field of computers, and solves the technical problem that data needs to be updated after kernel switching is not considered in the prior art; the invention is provided with the registration analysis module, the registration of the user is completed through the verification information, the browser history record of the user is analyzed, and the basis is laid for the switching prediction of the browser kernel while the data security is ensured; the model prediction module is arranged, and the main kernel of the browser is predicted through the prediction model, so that the manual switching of the kernels by a user is avoided, and the access efficiency of the user is improved; the invention is provided with the sharing analysis module, the main kernel and the alternative kernels are obtained through the prediction result, the browser data in the main kernel state is preloaded through the alternative kernels, the time waste caused by switching kernels and reloading the webpage is avoided, and the access efficiency is improved.
Description
Technical Field
The invention belongs to the field of computers, and particularly relates to a system and a method for solving data state sharing of a multi-kernel browser.
Background
With the development of browser technology, more and more browser kernels exist in the market, but different browser kernels support incompatible web page formats, for example, a webpage that a Chrome kernel can parse and possibly an IE kernel cannot parse, in order to solve the situation, a browser capable of switching kernels is proposed in the prior art, a user can select one kernel to open a webpage first according to the setting of a matching library in the process of accessing the webpage, but the preselection mode has high-probability errors, the user often needs to switch manually, and when the browser kernels are switched, the data state is refreshed, so that the range efficiency is lowered.
The invention patent with the authorization number of CN103631955B provides a method and a device for updating data of a browser kernel switching matching library.
According to the scheme, the screening result is obtained by analyzing the kernel switching record of the browser, and the matching library is updated, so that the effects of reducing the kernel matching error rate and the background manual analysis cost are achieved; however, the above scheme only analyzes the kernel switching angle, and does not consider the problem that data needs to be updated after kernel switching; therefore, the above solution still needs further improvement.
Disclosure of Invention
To solve the problems of the above solutions, the present invention provides a system and method for solving the data state sharing of a multi-kernel browser.
The purpose of the invention can be realized by the following technical scheme: a system for solving data state sharing of a multi-kernel browser comprises a control system, wherein the control system comprises a processor, a sharing analysis module, an attack early warning module, a data storage module, a registration analysis module and a model prediction module;
the registration analysis module comprises a user registration unit and a user analysis unit, wherein the user registration unit completes the registration of a user according to registration information and sends the registration information and a user account to the data storage module for storage; the user analysis unit is used for analyzing the browser history of the user and comprises the following steps:
acquiring a browser history record by switching a kernel matching library through the browser; the browser history record at least comprises a group of browsing data; the browsing data comprises a user account, browsing time, browser appearance, URL, Cookie, kernel identification, language and website permission;
deleting any one group of two browsing data which have the same user account, the same kernel identification, the same URL and the difference of the browsing time which is less than or equal to the browsing time threshold value, and marking the remaining browsing data as first merging data; the browsing time threshold is a real number greater than 0;
for first merging data with the same URL, merging the first merging data into the same group of data by taking the kernel identification and the user account as standards and marking the first merging data as second merging data;
extracting a user account, browsing time, RUL and website permission in the second merged data as first data; extracting the kernel identification in the second merged data as second data; and normalizing the first data and the second data and respectively sending the normalized first data and the normalized second data to the data storage module and the model prediction module through the processor.
Preferably, the model prediction module is configured to obtain a detection model, and includes:
after the model prediction module receives the first data and the second data, a self-adaptive weighted multitask deep neural network model is constructed;
dividing the first data and the second data into a verification set and a test set according to a set proportion; the set ratio is greater than 1.5;
inputting the verification set and the test set into a self-adaptive weighted multi-task deep neural network for forward propagation to obtain a training set loss and a verification set loss; when the number of training iterations is larger than the iteration number threshold, updating the attribute weight, and marking the trained self-adaptive weighted multi-task deep neural network as a prediction model;
when a user searches through a browser of the intelligent terminal, acquiring a user account, a URL (uniform resource locator), website permission and system time in the browser; and normalizing the user account, the URL, the website authority and the system time in the browser, and then inputting the normalized user account, URL, website authority and system time into a prediction model to obtain a prediction result, wherein the prediction result is a predicted kernel identifier.
Preferably, the sharing analysis module is configured to share the browser data among the kernels, and includes:
marking the kernel corresponding to the kernel identification in the prediction result as a main kernel, automatically switching the kernel of the browser into the main kernel, and loading the URL; meanwhile, counting the total switching times of the kernel identifications corresponding to the user accounts, sequencing according to the total switching times from large to small to obtain a sequencing table, when the prediction result is consistent with the first corresponding kernel identification of the sequencing table, obtaining the kernel identification corresponding to the second kernel identification of the sequencing table as an alternative kernel, and preloading through the alternative kernel after loading the URL by the main kernel; when the prediction result is inconsistent with the kernel identification corresponding to the first sorting table, acquiring the kernel identification mark corresponding to the first sorting table as an alternative kernel;
preloading browser data in a main kernel state through an alternative kernel; the browser data includes user accounts and website permissions.
Preferably, the attack early warning module is used for monitoring hacker attack of the browser, and when the browser is attacked by hacker, sending a hacker attack signal to the attack early warning module; and the attack early warning module cuts off the access authority of the data storage module and the processor after receiving the hacking signal.
Preferably, the updating method of the attribute weight specifically includes: obtaining the average value of the loss of the verification sets in the previous set time period and the previous two set time periods, and respectively marking the average value as a first average value and a second average value; subtracting the first mean value and the second mean value to obtain an absolute value, obtaining the change rate of the verification set loss among different attributes, obtaining a new attribute weight after the change rate is normalized and marking the new attribute weight as a distribution weight, multiplying the distribution weight by the training set loss corresponding to the attributes to obtain a new loss value and marking the new loss value as a distribution loss, carrying out reverse propagation on the distribution loss to update the attribute weight, and continuously iterating to finish the training of the self-adaptive weighted multi-task deep neural network.
Preferably, the processor is configured to update the browser kernel switching matching library, and includes:
acquiring a switching record after switching the kernel of the intelligent terminal browser;
analyzing each switching record to obtain one piece of data to be screened; the data to be screened comprises a URL and a switched kernel identifier;
aiming at each piece of screening data with the same URL, merging the screening data into a piece of data to be screened by taking the kernel identifier as a standard, and recording the switching times of the corresponding kernel identifier in the piece of data to be screened;
screening the merged data to be screened based on the browser kernel switching matching library to obtain a screening result, and generating a verification table based on the screening result; and updating a browser kernel switching matching library based on the verification table.
Preferably, the step of obtaining the verification table includes:
aiming at the data to be screened with the same URL after being merged, judging whether a record corresponding to the URL exists in a kernel switching matching library; if the record corresponding to the URL does not exist, recording the data to be screened corresponding to the URL in a verification table; if the record corresponding to the URL exists, confirming the kernel identification recorded in the kernel switching matching library, deleting the corresponding confirmed kernel identification data, and recording the data to be screened, except the confirmed kernel identification, of which the switching times are greater than the switching time threshold, in a verification table; the switching number threshold is a real number greater than 0.
Preferably, the user registering unit is configured to obtain a user account, and includes:
a user creates a page in an account of a browser in an intelligent terminal and inputs registration information; the intelligent terminal comprises an intelligent mobile phone, a tablet computer and a notebook computer; the registration information comprises a name, an identity card image and a mobile phone number;
the consistency of the name and the identity card image and the consistency of the name and the mobile phone number are respectively verified through a third-party platform, when the name and the identity card image are consistent with the name and the mobile phone number, a verification code is sent to the mobile phone number through a user registration unit, a user inputs the verification code through an intelligent terminal, when the verification code is correct, the user is judged to complete registration, and a user account is sent to the mobile phone number of the user through the user registration unit; the account name of the user account is a mobile phone number, and the password of the user account is a random password; the third party platform comprises a police platform;
the user account is sent to a data storage module by the processor.
Preferably, the processor is respectively in communication connection with the shared analysis module, the attack early warning module, the data storage module, the registration analysis module and the model prediction module, and the data storage module is in communication connection with the supply early warning module.
A method for addressing multi-kernel browser data state sharing, the method comprising the steps of:
the method comprises the following steps: a user creates a page in an account of a browser in an intelligent terminal and inputs registration information; the consistency of the name and the identity card image and the consistency of the name and the mobile phone number are respectively verified through a third-party platform, when the name and the identity card image are consistent with the name and the mobile phone number, a verification code is sent to the mobile phone number through a user registration unit, a user inputs the verification code through an intelligent terminal, when the verification code is correct, the user is judged to complete registration, and a user account is sent to the mobile phone number of the user through the user registration unit;
step two: acquiring a browser history record by switching a kernel matching library through the browser; the browser history record at least comprises a group of browsing data; deleting any one group of two browsing data which have the same user account, the same kernel identification, the same URL and the difference of the browsing time which is less than or equal to the browsing time threshold value, and marking the remaining browsing data as first merging data; for first merging data with the same URL, merging the first merging data into the same group of data by taking the kernel identification and the user account as standards and marking the first merging data as second merging data; extracting a user account, browsing time, RUL and website permission in the second merged data as first data; extracting the kernel identification in the second merged data as second data; the first data and the second data are subjected to normalization processing and are respectively sent to a data storage module and a model prediction module through a processor;
step three: after the model prediction module receives the first data and the second data, a self-adaptive weighted multitask deep neural network model is constructed; dividing the first data and the second data into a verification set and a test set according to a set proportion; inputting the verification set and the test set into a self-adaptive weighted multi-task deep neural network for forward propagation to obtain a training set loss and a verification set loss; when the number of training iterations is larger than the iteration number threshold, updating the attribute weight, and marking the trained self-adaptive weighted multi-task deep neural network as a prediction model; when a user searches through a browser of the intelligent terminal, acquiring a user account, a URL (uniform resource locator), website permission and system time in the browser; normalizing the user account, the URL, the website authority and the system time in the browser and then inputting the normalized user account, the URL, the website authority and the system time into a prediction model to obtain a prediction result;
step four: marking the kernel corresponding to the kernel identification in the prediction result as a main kernel, automatically switching the kernel of the browser into the main kernel, and loading the URL; meanwhile, counting the total switching times of the kernel identifications corresponding to the user accounts, sequencing according to the total switching times from large to small to obtain a sequencing table, when the prediction result is consistent with the first corresponding kernel identification of the sequencing table, obtaining the kernel identification corresponding to the second kernel identification of the sequencing table as an alternative kernel, and preloading through the alternative kernel after loading the URL by the main kernel; when the prediction result is inconsistent with the kernel identification corresponding to the first sorting table, acquiring the kernel identification mark corresponding to the first sorting table as an alternative kernel; and preloading browser data in the main kernel state through an alternative kernel.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention sets up the registration analysis module, the arrangement includes user's registration unit and user's analysis unit; the registration analysis module completes the registration of the user through the verification information and analyzes the browser history record of the user, so that the data security is ensured, and a foundation is laid for the switching prediction of the browser kernel;
2. the invention provides a model prediction module, which is used for obtaining a detection model; the prediction model is obtained through the historical record training of the browser of the user, when the user opens the browser again, the main kernel of the browser is predicted through the prediction model, the kernel is prevented from being manually switched by the user, and the access efficiency of the user is improved;
3. the invention is provided with a sharing analysis module, which is used for sharing the browser data among kernels; the shared analysis module obtains the main kernel and the alternative kernels according to the prediction result, browser data in the main kernel state are preloaded through the alternative kernels, when a user needs to switch to the alternative kernels, the user can directly access the browser data, time waste caused by the fact that the kernels are switched to reload the webpage is avoided, and access efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the control system of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a system for solving data state sharing of a multi-core browser includes a control system, where the control system includes a processor, a sharing analysis module, an attack early warning module, a data storage module, a registration analysis module, and a model prediction module;
the registration analysis module comprises a user registration unit and a user analysis unit, the user registration unit completes the registration of the user according to the registration information and sends the registration information and the user account to the data storage module for storage; the user analysis unit is used for analyzing the browser history of the user and comprises the following steps:
acquiring a browser history record by switching a kernel matching library through the browser; the browser history record at least comprises a group of browsing data; the browsing data comprises user account, browsing time, browser appearance, URL, Cookie, kernel identification, language and website permission;
deleting any one group of two browsing data which have the same user account, the same kernel identification, the same URL and the difference of the browsing time which is less than or equal to the browsing time threshold value, and marking the remaining browsing data as first merging data; the browsing time threshold is a real number greater than 0;
for first merging data with the same URL, merging the first merging data into the same group of data by using the kernel identification and the user account as standards and marking the merged data as second merging data;
extracting a user account, browsing time, RUL and website permission in the second merged data as first data; extracting the kernel identification in the second merged data as second data; and normalizing the first data and the second data and respectively sending the normalized first data and the normalized second data to the data storage module and the model prediction module through the processor.
Further, the model prediction module is configured to obtain a detection model, and includes:
after the model prediction module receives the first data and the second data, a self-adaptive weighted multitask deep neural network model is constructed;
dividing the first data and the second data into a verification set and a test set according to a set proportion; setting the proportion to be more than 1.5;
inputting the verification set and the test set into a self-adaptive weighted multi-task deep neural network for forward propagation to obtain a training set loss and a verification set loss; when the number of training iterations is larger than the iteration number threshold, updating the attribute weight, and marking the trained self-adaptive weighted multi-task deep neural network as a prediction model;
when a user searches through a browser of the intelligent terminal, acquiring a user account, a URL (uniform resource locator), website permission and system time in the browser; and normalizing the user account, the URL, the website authority and the system time in the browser, and then inputting the normalized user account, URL, website authority and system time into a prediction model to obtain a prediction result, wherein the prediction result is a predicted kernel identifier.
Further, the sharing analysis module is used for sharing the browser data among the kernels, and comprises:
marking the kernel corresponding to the kernel identification in the prediction result as a main kernel, automatically switching the kernel of the browser into the main kernel, and loading the URL; meanwhile, counting the total switching times of the kernel identifications corresponding to the user accounts, sequencing according to the total switching times from large to small to obtain a sequencing table, when the prediction result is consistent with the first corresponding kernel identification of the sequencing table, obtaining the kernel identification corresponding to the second kernel identification of the sequencing table as an alternative kernel, and preloading through the alternative kernel after loading the URL by the main kernel; when the prediction result is inconsistent with the kernel identification corresponding to the first sorting table, acquiring the kernel identification mark corresponding to the first sorting table as an alternative kernel;
preloading browser data in a main kernel state through an alternative kernel; the browser data includes user accounts and website permissions.
Further, the attack early warning module is used for monitoring the hacker attack of the browser, and when the browser suffers from the hacker attack, sending a hacker attack signal to the attack early warning module; and the attack early warning module cuts off the access authority of the data storage module and the processor after receiving the hacking signal.
Further, the updating method of the attribute weight specifically includes: obtaining the average value of the loss of the verification sets in the previous set time period and the previous two set time periods, and respectively marking the average value as a first average value and a second average value; subtracting the first mean value and the second mean value to obtain an absolute value, obtaining the change rate of the verification set loss among different attributes, obtaining a new attribute weight after the change rate is normalized and marking the new attribute weight as a distribution weight, multiplying the distribution weight by the training set loss corresponding to the attributes to obtain a new loss value and marking the new loss value as a distribution loss, carrying out reverse propagation on the distribution loss to update the attribute weight, and continuously iterating to finish the training of the self-adaptive weighted multi-task deep neural network.
Further, the processor is configured to update a browser kernel switching matching library, and includes:
acquiring a switching record after switching the kernel of the intelligent terminal browser;
analyzing each switching record to obtain one piece of data to be screened; the data to be screened comprises a URL and a switched kernel identifier;
aiming at each piece of screening data with the same URL, merging the screening data into a piece of data to be screened by taking the kernel identifier as a standard, and recording the switching times of the corresponding kernel identifier in the piece of data to be screened;
screening the merged data to be screened based on the browser kernel switching matching library to obtain a screening result, and generating a verification table based on the screening result; and updating the browser kernel switching matching library based on the verification table.
Further, the step of obtaining the verification table comprises:
aiming at the data to be screened with the same URL after being merged, judging whether a record corresponding to the URL exists in a kernel switching matching library; if the record corresponding to the URL does not exist, recording the data to be screened corresponding to the URL in a verification table; if the record corresponding to the URL exists, confirming the kernel identification recorded in the kernel switching matching library, deleting the corresponding confirmed kernel identification data, and recording the data to be screened, except the confirmed kernel identification, of which the switching times are greater than the switching time threshold, in a verification table; the switching number threshold is a real number greater than 0.
Further, the user registering unit is used for acquiring a user account, and includes:
a user creates a page in an account of a browser in an intelligent terminal and inputs registration information; the intelligent terminal comprises an intelligent mobile phone, a tablet computer and a notebook computer; the registration information comprises a name, an identity card image and a mobile phone number;
the consistency of the name and the identity card image and the consistency of the name and the mobile phone number are respectively verified through a third-party platform, when the name and the identity card image are consistent with the name and the mobile phone number, a verification code is sent to the mobile phone number through a user registration unit, a user inputs the verification code through an intelligent terminal, when the verification code is correct, the user is judged to complete registration, and a user account is sent to the mobile phone number of the user through the user registration unit; the account name of the user account is a mobile phone number, and the password of the user account is a random password; the third party platform comprises a police platform;
the user account is sent to a data storage module by the processor.
Further, the processor is in communication connection with the sharing analysis module, the attack early warning module, the data storage module, the registration analysis module and the model prediction module respectively, and the data storage module is in communication connection with the supply early warning module.
A method for addressing multi-kernel browser data state sharing, the method comprising the steps of:
the method comprises the following steps: a user creates a page in an account of a browser in an intelligent terminal and inputs registration information; the consistency of the name and the identity card image and the consistency of the name and the mobile phone number are respectively verified through a third-party platform, when the name and the identity card image are consistent with the name and the mobile phone number, a verification code is sent to the mobile phone number through a user registration unit, a user inputs the verification code through an intelligent terminal, when the verification code is correct, the user is judged to complete registration, and a user account is sent to the mobile phone number of the user through the user registration unit;
step two: acquiring a browser history record by switching a kernel matching library through the browser; the browser history record at least comprises a group of browsing data; deleting any one group of two browsing data which have the same user account, the same kernel identification, the same URL and the difference of the browsing time which is less than or equal to the browsing time threshold value, and marking the remaining browsing data as first merging data; for first merging data with the same URL, merging the first merging data into the same group of data by using the kernel identification and the user account as standards and marking the merged data as second merging data; extracting a user account, browsing time, RUL and website permission in the second merged data as first data; extracting the kernel identification in the second merged data as second data; the first data and the second data are subjected to normalization processing and are respectively sent to a data storage module and a model prediction module through a processor;
step three: after the model prediction module receives the first data and the second data, a self-adaptive weighted multitask deep neural network model is constructed; dividing the first data and the second data into a verification set and a test set according to a set proportion; inputting the verification set and the test set into a self-adaptive weighted multi-task deep neural network for forward propagation to obtain a training set loss and a verification set loss; when the number of training iterations is larger than the iteration number threshold, updating the attribute weight, and marking the trained self-adaptive weighted multi-task deep neural network as a prediction model; when a user searches through a browser of the intelligent terminal, acquiring a user account, a URL (uniform resource locator), website permission and system time in the browser; normalizing the user account, the URL, the website authority and the system time in the browser and then inputting the normalized user account, the URL, the website authority and the system time into a prediction model to obtain a prediction result;
step four: marking the kernel corresponding to the kernel identification in the prediction result as a main kernel, automatically switching the kernel of the browser into the main kernel, and loading the URL; meanwhile, counting the total switching times of the kernel identifications corresponding to the user accounts, sequencing according to the total switching times from large to small to obtain a sequencing table, when the prediction result is consistent with the first corresponding kernel identification of the sequencing table, obtaining the kernel identification corresponding to the second kernel identification of the sequencing table as an alternative kernel, and preloading through the alternative kernel after loading the URL by the main kernel; when the prediction result is inconsistent with the kernel identification corresponding to the first sorting table, acquiring the kernel identification mark corresponding to the first sorting table as an alternative kernel; and preloading browser data in the main kernel state through an alternative kernel.
The above formulas are all calculated by removing dimensions and taking values thereof, the formula is one closest to the real situation obtained by collecting a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The working principle of the invention is as follows:
a user creates a page in an account of a browser in an intelligent terminal and inputs registration information; the consistency of the name and the identity card image and the consistency of the name and the mobile phone number are respectively verified through a third-party platform, when the name and the identity card image are consistent with the name and the mobile phone number, a verification code is sent to the mobile phone number through a user registration unit, a user inputs the verification code through an intelligent terminal, when the verification code is correct, the user is judged to complete registration, and a user account is sent to the mobile phone number of the user through the user registration unit;
acquiring a browser history record by switching a kernel matching library through the browser; the browser history record at least comprises a group of browsing data; deleting any one group of two browsing data which have the same user account, the same kernel identification, the same URL and the difference of the browsing time which is less than or equal to the browsing time threshold value, and marking the remaining browsing data as first merging data; for first merging data with the same URL, merging the first merging data into the same group of data by taking the kernel identification and the user account as standards and marking the first merging data as second merging data; extracting a user account, browsing time, RUL and website permission in the second merged data as first data; extracting the kernel identification in the second merged data as second data; the first data and the second data are subjected to normalization processing and are respectively sent to a data storage module and a model prediction module through a processor;
after the model prediction module receives the first data and the second data, a self-adaptive weighted multitask deep neural network model is constructed; dividing the first data and the second data into a verification set and a test set according to a set proportion; inputting the verification set and the test set into a self-adaptive weighted multi-task deep neural network for forward propagation to obtain a training set loss and a verification set loss; when the number of training iterations is larger than the iteration number threshold, updating the attribute weight, and marking the trained self-adaptive weighted multi-task deep neural network as a prediction model; when a user searches through a browser of the intelligent terminal, acquiring a user account, a URL (uniform resource locator), website permission and system time in the browser; normalizing the user account, the URL, the website authority and the system time in the browser and then inputting the normalized user account, the URL, the website authority and the system time into a prediction model to obtain a prediction result;
marking the kernel corresponding to the kernel identification in the prediction result as a main kernel, automatically switching the kernel of the browser into the main kernel, and loading the URL; meanwhile, counting the total switching times of the kernel identifications corresponding to the user accounts, sequencing according to the total switching times from large to small to obtain a sequencing table, when the prediction result is consistent with the first corresponding kernel identification of the sequencing table, obtaining the kernel identification corresponding to the second kernel identification of the sequencing table as an alternative kernel, and preloading through the alternative kernel after loading the URL by the main kernel; when the prediction result is inconsistent with the kernel identification corresponding to the first sorting table, acquiring the kernel identification mark corresponding to the first sorting table as an alternative kernel; and preloading browser data in the main kernel state through an alternative kernel.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (7)
1. A system for solving the data state sharing of a multi-kernel browser is characterized by comprising a control system, wherein the control system comprises a processor, a sharing analysis module, an attack early warning module, a data storage module, a registration analysis module and a model prediction module;
the registration analysis module comprises a user registration unit and a user analysis unit, wherein the user registration unit completes the registration of a user according to registration information and sends the registration information and a user account to the data storage module for storage; the user analysis unit is used for analyzing the browser history of the user and comprises the following steps:
acquiring a browser history record by switching a kernel matching library through the browser; the browser history record at least comprises a group of browsing data; the browsing data comprises a user account, browsing time, browser appearance, URL, Cookie, kernel identification, language and website permission;
deleting any one group of two browsing data which have the same user account, the same kernel identification, the same URL and the difference of the browsing time which is less than or equal to the browsing time threshold value, and marking the remaining browsing data as first merging data; the browsing time threshold is a real number greater than 0;
for first merging data with the same URL, merging the first merging data into the same group of data by taking the kernel identification and the user account as standards and marking the first merging data as second merging data;
extracting a user account, browsing time, RUL and website permission in the second merged data as first data; extracting the kernel identification in the second merged data as second data; and normalizing the first data and the second data and respectively sending the normalized first data and the normalized second data to the data storage module and the model prediction module through the processor.
2. The system for solving the data state sharing of the multi-core browser according to claim 1, wherein the model prediction module is used for obtaining a detection model, and comprises:
after the model prediction module receives the first data and the second data, a self-adaptive weighted multitask deep neural network model is constructed;
dividing the first data and the second data into a verification set and a test set according to a set proportion; the set ratio is greater than 1.5;
inputting the verification set and the test set into a self-adaptive weighted multi-task deep neural network for forward propagation to obtain a training set loss and a verification set loss; when the number of training iterations is larger than the iteration number threshold, updating the attribute weight, and marking the trained self-adaptive weighted multi-task deep neural network as a prediction model;
when a user searches through a browser of the intelligent terminal, acquiring a user account, a URL (uniform resource locator), website permission and system time in the browser; and normalizing the user account, the URL, the website authority and the system time in the browser, and then inputting the normalized user account, URL, website authority and system time into a prediction model to obtain a prediction result, wherein the prediction result is a predicted kernel identifier.
3. The system for solving the multi-core browser data state sharing of claim 2, wherein the sharing analysis module is configured to share browser data among cores, and comprises:
marking the kernel corresponding to the kernel identification in the prediction result as a main kernel, automatically switching the kernel of the browser into the main kernel, and loading the URL; meanwhile, counting the total switching times of the kernel identifications corresponding to the user accounts, sequencing according to the total switching times from large to small to obtain a sequencing table, when the prediction result is consistent with the first corresponding kernel identification of the sequencing table, obtaining the kernel identification corresponding to the second kernel identification of the sequencing table as an alternative kernel, and preloading through the alternative kernel after loading the URL by the main kernel; when the prediction result is inconsistent with the kernel identification corresponding to the first sorting table, acquiring the kernel identification mark corresponding to the first sorting table as an alternative kernel;
preloading browser data in a main kernel state through an alternative kernel; the browser data includes user accounts and website permissions.
4. The system for solving the data state sharing of the multi-kernel browser according to claim 1, wherein the attack early warning module is used for monitoring the hacking attack of the browser, and when the browser is attacked by the hacking attack, sending a hacking attack signal to the attack early warning module; and the attack early warning module cuts off the access authority of the data storage module and the processor after receiving the hacking signal.
5. The system for resolving data state sharing in a multi-core browser according to claim 1, wherein said processor is configured to update a browser core switch matching library, comprising:
acquiring a switching record after switching the kernel of the intelligent terminal browser;
analyzing each switching record to obtain one piece of data to be screened; the data to be screened comprises a URL and a switched kernel identifier;
aiming at each piece of screening data with the same URL, merging the screening data into a piece of data to be screened by taking the kernel identifier as a standard, and recording the switching times of the corresponding kernel identifier in the piece of data to be screened;
screening the merged data to be screened based on the browser kernel switching matching library to obtain a screening result, and generating a verification table based on the screening result; and updating a browser kernel switching matching library based on the verification table.
6. The system for resolving data state sharing in a multi-kernel browser according to claim 5, wherein said step of obtaining a validation table comprises:
aiming at the data to be screened with the same URL after being merged, judging whether a record corresponding to the URL exists in a kernel switching matching library; if the record corresponding to the URL does not exist, recording the data to be screened corresponding to the URL in a verification table; if the record corresponding to the URL exists, confirming the kernel identification recorded in the kernel switching matching library, deleting the corresponding confirmed kernel identification data, and recording the data to be screened, except the confirmed kernel identification, of which the switching times are greater than the switching time threshold, in a verification table; the switching number threshold is a real number greater than 0.
7. A method for addressing multi-kernel browser data state sharing, the method comprising the steps of:
the method comprises the following steps: a user creates a page in an account of a browser in an intelligent terminal and inputs registration information; the consistency of the name and the identity card image and the consistency of the name and the mobile phone number are respectively verified through a third-party platform, when the name and the identity card image are consistent with the name and the mobile phone number, a verification code is sent to the mobile phone number through a user registration unit, a user inputs the verification code through an intelligent terminal, when the verification code is correct, the user is judged to complete registration, and a user account is sent to the mobile phone number of the user through the user registration unit;
step two: acquiring a browser history record by switching a kernel matching library through the browser; the browser history record at least comprises a group of browsing data; deleting any one group of two browsing data which have the same user account, the same kernel identification, the same URL and the difference of the browsing time which is less than or equal to the browsing time threshold value, and marking the remaining browsing data as first merging data; for first merging data with the same URL, merging the first merging data into the same group of data by taking the kernel identification and the user account as standards and marking the first merging data as second merging data; extracting a user account, browsing time, RUL and website permission in the second merged data as first data; extracting the kernel identification in the second merged data as second data; the first data and the second data are subjected to normalization processing and are respectively sent to a data storage module and a model prediction module through a processor;
step three: after the model prediction module receives the first data and the second data, a self-adaptive weighted multitask deep neural network model is constructed; dividing the first data and the second data into a verification set and a test set according to a set proportion; inputting the verification set and the test set into a self-adaptive weighted multi-task deep neural network for forward propagation to obtain a training set loss and a verification set loss; when the number of training iterations is larger than the iteration number threshold, updating the attribute weight, and marking the trained self-adaptive weighted multi-task deep neural network as a prediction model; when a user searches through a browser of the intelligent terminal, acquiring a user account, a URL (uniform resource locator), website permission and system time in the browser; normalizing the user account, the URL, the website authority and the system time in the browser and then inputting the normalized user account, the URL, the website authority and the system time into a prediction model to obtain a prediction result;
step four: marking the kernel corresponding to the kernel identification in the prediction result as a main kernel, automatically switching the kernel of the browser into the main kernel, and loading the URL; meanwhile, counting the total switching times of the kernel identifications corresponding to the user accounts, sequencing according to the total switching times from large to small to obtain a sequencing table, when the prediction result is consistent with the first corresponding kernel identification of the sequencing table, obtaining the kernel identification corresponding to the second kernel identification of the sequencing table as an alternative kernel, and preloading through the alternative kernel after loading the URL by the main kernel; when the prediction result is inconsistent with the kernel identification corresponding to the first sorting table, acquiring the kernel identification mark corresponding to the first sorting table as an alternative kernel; and preloading browser data in the main kernel state through an alternative kernel.
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