CN111008401A - Text saving method and device - Google Patents
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- CN111008401A CN111008401A CN201911259923.8A CN201911259923A CN111008401A CN 111008401 A CN111008401 A CN 111008401A CN 201911259923 A CN201911259923 A CN 201911259923A CN 111008401 A CN111008401 A CN 111008401A
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- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6209—Protecting access to data via a platform, e.g. using keys or access control rules to a single file or object, e.g. in a secure envelope, encrypted and accessed using a key, or with access control rules appended to the object itself
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Abstract
The application discloses a text saving method and a device, wherein the method comprises the following steps: and carrying out preset keyword retrieval on the text, and recording the occurrence frequency of each keyword in the text. And determining the risk degree corresponding to each keyword based on a preset corresponding relation, wherein the corresponding relation comprises the corresponding keyword and the risk degree. And determining the risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword. And under the condition that the risk value is smaller than a preset threshold value, storing the text to the local. Therefore, based on the risk degree of the keywords, the risk value of the text is evaluated, if the risk value is not smaller than the preset threshold value, the text is refused to be stored, the important information is prevented from being stored locally and leaking, and the safety risk of text storage is improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a text saving method and apparatus.
Background
When a bank teller uses a counter front-end system to execute transaction business, due to the fact that business operation steps are complex, in order to help the bank teller to quickly and accurately execute the operation steps, the bank teller can record and store relevant business information into a bank system so as to conveniently look up the business at any time.
However, important information (for example, information such as a transaction password and an account number of a customer) may exist in a text recorded by a teller, and if the important information is saved in a system by the teller, a great safety hazard may exist, for example, the important information is easily leaked or stolen. Therefore, a method for saving text is needed to identify the information that can be saved, so as to reduce the security risk of text saving.
Disclosure of Invention
The application provides a text preservation method and a text preservation device, and aims to solve the problem that the security risk of text preservation is high.
In order to achieve the above object, the present application provides the following technical solutions:
a text preservation method, comprising:
the method comprises the steps of carrying out preset keyword retrieval on a text, and recording the occurrence frequency of each keyword in the text;
determining a risk degree corresponding to each keyword based on a preset corresponding relation, wherein the corresponding relation comprises the corresponding keyword and the risk degree;
determining a risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword;
and under the condition that the risk value is smaller than a preset threshold value, storing the text to the local.
Optionally, the corresponding relationship includes:
corresponding key phrases and the risk degree, wherein the key phrases at least comprise the key words.
Optionally, the method further includes:
and sending an unsaveable prompt to the user when the risk value is not less than the preset threshold value.
Optionally, the determining the risk value of the text based on the occurrence number and the risk degree corresponding to each keyword includes:
calculating the product of the occurrence times and the risk degrees corresponding to the keywords to obtain the risk degree sum corresponding to the keywords;
and accumulating the risk degree sum corresponding to each keyword to obtain the risk value of the text.
A text saving device comprising:
the retrieval unit is used for carrying out preset keyword retrieval on the text and recording the occurrence frequency of each keyword in the text;
the first determining unit is used for determining the risk degree corresponding to each keyword based on a preset corresponding relation, wherein the corresponding relation comprises the corresponding keyword and the risk degree;
the second determining unit is used for determining the risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword;
and the storage unit is used for storing the text to the local part under the condition that the risk value is smaller than the preset threshold value.
Optionally, the corresponding relationship in the first determining unit includes:
corresponding key phrases and the risk degree, wherein the key phrases at least comprise the key words.
Optionally, the method further includes:
and the prompting unit is used for sending a prompt which cannot be stored to a user under the condition that the risk value is not less than the preset threshold value.
Optionally, the determining, by the second determining unit, a risk value of the text based on the occurrence number and the risk degree corresponding to each keyword includes:
the second determining unit is specifically configured to calculate a product of the occurrence frequency and the risk degree corresponding to each keyword to obtain a risk degree sum corresponding to each keyword;
and accumulating the risk degree sum corresponding to each keyword to obtain the risk value of the text.
A storage medium comprising a stored program, wherein the program executes the text saving method described above.
An apparatus, comprising: a processor, a memory, and a bus; the processor and the memory are connected through the bus;
the memory is used for storing programs, and the processor is used for running the programs, wherein the programs execute the text saving method when running.
The text storage method and the text storage device conduct preset keyword retrieval on the text and record the occurrence frequency of each keyword in the text. And determining the risk degree corresponding to each keyword based on a preset corresponding relation, wherein the corresponding relation comprises the corresponding keyword and the risk degree. And determining the risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword. And under the condition that the risk value is smaller than a preset threshold value, storing the text to the local. Therefore, based on the risk degree of the keywords, the risk value of the text is evaluated, if the risk value is not smaller than the preset threshold value, the text is refused to be stored, the important information is prevented from being stored locally and leaking, and the safety risk of text storage is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application 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 application, 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 a text saving method according to an embodiment of the present application;
fig. 2 is a schematic diagram of another text saving method provided in an embodiment of the present application;
fig. 3 is a schematic diagram of a specific implementation process for calculating a text risk value according to an embodiment of the present application;
fig. 4 is a schematic diagram of another specific implementation process for calculating a text risk value according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a text saving device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.
As known from the background technology, a bank teller stores texts of service information, and if important information is recorded in the texts, potential safety hazards of leakage may exist. Therefore, the embodiment of the application provides a text saving method for identifying a text which can be saved, that is, a text which does not contain important information, so that convenience can be brought to the work of a teller, the leakage of the important information can be avoided, and the safety risk of text saving is reduced.
As shown in fig. 1, a schematic diagram of a text saving method provided in an embodiment of the present application includes the following steps:
s101: and carrying out preset keyword retrieval on the text, and recording the occurrence frequency of each keyword in the text.
The specific content and number of the keywords can be set by a technician according to actual conditions, such as numeric strings, character strings, word vocabularies and the like. Of course, the specific implementation process of keyword search for text is common knowledge familiar to those skilled in the art, and will not be described herein again.
S102: and determining the risk degree corresponding to each keyword based on the preset corresponding relation.
Wherein, the corresponding relation comprises corresponding keywords and risk degree.
Specifically, it is assumed that the keywords include "account", "anti-money laundering", and "111111", and the preset threshold is 1, where the risk degree corresponding to "account" is 60%, the risk degree corresponding to "anti-money laundering" is 20%, and the risk degree corresponding to the numeric string (6-19 digits) is 100%.
It should be noted that the above specific implementation process is only for illustration.
Optionally, the corresponding relationship includes a corresponding keyword group and a risk degree. The keyword group at least comprises keywords.
The specific number of keyword groups and the keywords included in the keywords
Specifically, it is assumed that the keyword phrases include a first keyword phrase, a second keyword phrase, and a third keyword phrase. The risk degree corresponding to the first keyword group is 100%, the risk degree corresponding to the second keyword group is 60%, and the risk degree corresponding to the third keyword group is 20%. The first key phrase includes a string of numbers (a string of 6-19 digits, such as "123456"), the second key phrase includes an "account number", "customer number", "certificate number" and "card number", and the third key phrase includes "anti-money laundering", "sender" and "recipient". Based on the corresponding relationship, it can be determined that the risk degree corresponding to "123456" is 100%, the risk degrees corresponding to "account number", "customer number", "certificate number" and "card number" are 60%, and the risk degrees corresponding to "anti-money laundering", "sender" and "receiver" are 20%.
It should be noted that the above specific implementation process is only for illustration.
S103: and determining the risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword.
For determining the risk value of the text based on the occurrence number and the risk degree corresponding to each keyword, refer to the steps shown in fig. 3 and fig. 4 and the corresponding explanations of the steps.
S104: and under the condition that the risk value is smaller than a preset threshold value, storing the text to the local.
The specific size of the preset threshold may be set by a technician according to actual situations, for example, set to 1.
In the embodiment of the application, the preset keyword retrieval is carried out on the text, and the occurrence frequency of each keyword in the text is recorded. And determining the risk degree corresponding to each keyword based on a preset corresponding relation, wherein the corresponding relation comprises the corresponding keyword and the risk degree. And determining the risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword. And under the condition that the risk value is smaller than a preset threshold value, storing the text to the local. Therefore, based on the risk degree of the keywords, the risk value of the text is evaluated, if the risk value is not smaller than the preset threshold value, the text is refused to be stored, the important information is prevented from being stored locally and leaking, and the safety risk of text storage is improved.
Optionally, as shown in fig. 2, a schematic diagram of another text saving method provided in the embodiment of the present application includes the following steps:
s201: and carrying out preset keyword retrieval on the text, and recording the occurrence frequency of each keyword in the text.
The specific implementation process and implementation principle of S201 are consistent with the specific implementation process and implementation principle of S101 shown in fig. 1, and are not described herein again.
S202: and determining the risk degree corresponding to each keyword based on the preset corresponding relation.
The specific implementation process and implementation principle of S202 are consistent with the specific implementation process and implementation principle of S102 shown in fig. 1, and are not described herein again.
S203: and determining the risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword.
The specific implementation process and implementation principle of S203 are consistent with the specific implementation process and implementation principle of S103 shown in fig. 1, and are not described herein again
S204: and judging whether the risk value is smaller than a preset threshold value.
If the risk value is smaller than the preset threshold, S205 is executed, otherwise S206 is executed.
S205: the text is saved locally.
S206: and sending a non-savable prompt to the user.
In the embodiment of the application, the preset keyword retrieval is carried out on the text, and the occurrence frequency of each keyword in the text is recorded. And determining the risk degree corresponding to each keyword based on a preset corresponding relation, wherein the corresponding relation comprises the corresponding keyword and the risk degree. And determining the risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword. And judging whether the risk value is smaller than a preset threshold value, if so, storing the text to the local, otherwise, sending an unsaveable prompt to the user. And sending an unsaveable prompt to the user aiming at the text with the risk value not less than the preset threshold value, and reminding the user that the text is high in risk value and is not suitable for being stored locally.
Optionally, as shown in fig. 3, a schematic diagram of a specific implementation process for calculating a text risk value provided in the embodiment of the present application includes the following steps:
s301: and calculating the product of the occurrence times and the risk degree corresponding to each keyword to obtain the risk degree sum corresponding to each keyword.
The specific calculation process of the product of the occurrence frequency and the risk corresponding to each keyword is common knowledge familiar to those skilled in the art, and is not described herein again.
Specifically, it is assumed that the number of occurrences of the first keyword is 2 and the risk degree is 60%, the number of occurrences of the second keyword is 1 and the risk degree is 20%, and the number of occurrences of the third keyword is 0 and the risk degree is 100%. Therefore, the sum of the risk degrees corresponding to the first keyword is 1.2, the sum of the risk degrees corresponding to the second keyword is 0.2, and the sum of the risk degrees corresponding to the third keyword is 0.
It should be noted that the above specific implementation process is only for illustration.
S302: and accumulating the risk degree sum corresponding to each keyword to obtain the risk value of the text.
The specific calculation process for accumulating the sum of the risk degrees corresponding to each keyword is common general knowledge familiar to those skilled in the art, and is not described herein again.
Specifically, assume that the sum of the risk degrees corresponding to the first keyword is 1.2, the sum of the risk degrees corresponding to the second keyword is 0.2, and the sum of the risk degrees corresponding to the third keyword is 0. Accumulating the risk degree sum corresponding to the first key word, the second key word and the third key word respectively to obtain the risk value of the text of 1.4
It should be noted that the above specific implementation process is only for illustration.
In the embodiment of the application, the product of the occurrence times and the risk degrees corresponding to the keywords is calculated to obtain the risk degree sum corresponding to the keywords, and the risk degree sum corresponding to the keywords is accumulated to obtain the risk value of the text. Therefore, based on the occurrence times and the risk degree corresponding to each keyword, the risk value of the text can be calculated, and a judgment basis is provided for the storage of the text.
Optionally, as shown in fig. 4, a schematic diagram of another specific implementation process for calculating a text risk value provided in the embodiment of the present application includes the following steps:
s401: and determining the occurrence times of the keywords in each keyword group and the corresponding risk degree of each keyword group.
Wherein, the occurrence frequency corresponding to any one key phrase is as follows: the sum of the occurrence times corresponding to each keyword in the keyword group.
Specifically, it is assumed that the first keyword group includes a first keyword and a second keyword, the occurrence frequency corresponding to the first keyword is 2, and the occurrence frequency corresponding to the second keyword is 1. Therefore, the number of occurrences corresponding to the first keyword group is 3.
It should be noted that the above specific implementation process is only for illustration.
S402: and calculating the product of the occurrence times and the risk degree corresponding to each key phrase to obtain the risk degree sum corresponding to each key phrase.
The specific calculation process of the product of the occurrence frequency and the risk degree corresponding to each keyword group is common knowledge familiar to those skilled in the art, and is not described herein again.
Specifically, assume that the occurrence number of the first keyword group is 2 and the risk degree is 60%, the occurrence number of the second keyword group is 1 and the risk degree is 20%, and the occurrence number of the third keyword group is 0 and the risk degree is 100%. Therefore, the sum of the risk degrees corresponding to the first keyword group is 1.2, the sum of the risk degrees corresponding to the second keyword group is 0.2, and the sum of the risk degrees corresponding to the third keyword group is 0.
It should be noted that the above specific implementation process is only for illustration.
S403: and accumulating the risk degree sum corresponding to each key phrase to obtain the risk value of the text.
The specific calculation process for accumulating the sum of the risk degrees corresponding to each keyword group is common knowledge familiar to those skilled in the art, and is not described herein again.
Specifically, assume that the sum of the risk degrees corresponding to the first keyword group is 1.2, the sum of the risk degrees corresponding to the second keyword group is 0.2, and the sum of the risk degrees corresponding to the third keyword group is 0. Accumulating the risk degree sum corresponding to the first key word group, the second key word group and the third key word group respectively to obtain the risk value of the text of 1.4
It should be noted that the above specific implementation process is only for illustration.
In the embodiment of the application, the occurrence times of the keywords in each keyword group and the risk degree corresponding to each keyword group are determined. And calculating the product of the occurrence times and the risk degree corresponding to each key phrase to obtain the risk degree sum corresponding to each key phrase. And accumulating the risk degree sum corresponding to each key phrase to obtain the risk value of the text. Therefore, based on the occurrence times and the risk degree corresponding to each keyword group, the risk value of the text can be calculated, and a judgment basis is provided for the storage of the text.
Corresponding to the text saving method provided in the embodiment of the present application, an embodiment of the present application further provides a text saving apparatus, as shown in fig. 5, including:
the search unit 100 is configured to perform a preset keyword search on the text, and record the occurrence number of each keyword in the text.
The first determining unit 200 determines a risk level corresponding to each keyword based on a preset corresponding relationship, where the corresponding relationship includes the corresponding keyword and the risk level.
The corresponding relationship in the first determining unit 200 includes: corresponding keyword groups and risk degrees, wherein the keyword groups at least comprise keywords.
And a second determining unit 300, configured to determine a risk value of the text based on the occurrence number and the risk degree corresponding to each keyword.
The second determining unit 300 determines a specific implementation process of the risk value of the text based on the occurrence number and the risk degree corresponding to each keyword, including: and calculating the product of the occurrence times and the risk degree corresponding to each keyword to obtain the risk degree sum corresponding to each keyword. And accumulating the risk degree sum corresponding to each keyword to obtain the risk value of the text.
And the saving unit 400 is used for saving the text to the local area under the condition that the risk value is smaller than the preset threshold value.
And the prompting unit 500 is used for feeding back an unsaveable prompt to the user under the condition that the risk value is not less than the preset threshold value.
The text storage method and the text storage device conduct preset keyword retrieval on the text and record the occurrence frequency of each keyword in the text. And determining the risk degree corresponding to each keyword based on a preset corresponding relation, wherein the corresponding relation comprises the corresponding keyword and the risk degree. And determining the risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword. And under the condition that the risk value is smaller than a preset threshold value, storing the text to the local. Therefore, based on the risk degree of the keywords, the risk value of the text is evaluated, if the risk value is not smaller than the preset threshold value, the text is refused to be stored, the important information is prevented from being stored locally and leaking, and the safety risk of text storage is improved.
Further, an embodiment of the present application further provides a storage medium, where the storage medium includes a stored program, and the program executes the text saving method. Specifically, a preset keyword retrieval is carried out on a text, and the occurrence frequency of each keyword in the text is recorded; determining a risk degree corresponding to each keyword based on a preset corresponding relation, wherein the corresponding relation comprises the corresponding keyword and the risk degree; determining a risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword; and under the condition that the risk value is smaller than a preset threshold value, storing the text to the local. And sending an unsaveable prompt to the user when the risk value is not less than the preset threshold value.
The corresponding relation comprises: corresponding key phrases and the risk degree, wherein the key phrases at least comprise the key words.
Determining a risk value of the text based on the occurrence number and the risk degree corresponding to each keyword, including:
calculating the product of the occurrence times and the risk degrees corresponding to the keywords to obtain the risk degree sum corresponding to the keywords;
and accumulating the risk degree sum corresponding to each keyword to obtain the risk value of the text.
Further, an embodiment of the present application further provides an apparatus, including: a processor, a memory, and a bus. The processor and the memory are connected by a bus. The memory is used for storing programs, and the processor is used for running the programs, wherein the programs execute the text saving method when running. Specifically, a preset keyword retrieval is carried out on a text, and the occurrence frequency of each keyword in the text is recorded; determining a risk degree corresponding to each keyword based on a preset corresponding relation, wherein the corresponding relation comprises the corresponding keyword and the risk degree; determining a risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword; and under the condition that the risk value is smaller than a preset threshold value, storing the text to the local. And sending an unsaveable prompt to the user when the risk value is not less than the preset threshold value.
The corresponding relation comprises: corresponding key phrases and the risk degree, wherein the key phrases at least comprise the key words.
Determining a risk value of the text based on the occurrence number and the risk degree corresponding to each keyword, including:
calculating the product of the occurrence times and the risk degrees corresponding to the keywords to obtain the risk degree sum corresponding to the keywords;
and accumulating the risk degree sum corresponding to each keyword to obtain the risk value of the text.
The functions described in the method of the embodiment of the present application, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution to the prior art of the embodiments of the present application or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including several instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for saving text, comprising:
the method comprises the steps of carrying out preset keyword retrieval on a text, and recording the occurrence frequency of each keyword in the text;
determining a risk degree corresponding to each keyword based on a preset corresponding relation, wherein the corresponding relation comprises the corresponding keyword and the risk degree;
determining a risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword;
and under the condition that the risk value is smaller than a preset threshold value, storing the text to the local.
2. The method of claim 1, wherein the correspondence comprises:
corresponding key phrases and the risk degree, wherein the key phrases at least comprise the key words.
3. The method of claim 1, further comprising:
and sending an unsaveable prompt to the user when the risk value is not less than the preset threshold value.
4. The method according to claim 1, wherein determining the risk value of the text based on the occurrence number and the risk degree corresponding to each keyword comprises:
calculating the product of the occurrence times and the risk degrees corresponding to the keywords to obtain the risk degree sum corresponding to the keywords;
and accumulating the risk degree sum corresponding to each keyword to obtain the risk value of the text.
5. A text saving apparatus, comprising:
the retrieval unit is used for carrying out preset keyword retrieval on the text and recording the occurrence frequency of each keyword in the text;
the first determining unit is used for determining the risk degree corresponding to each keyword based on a preset corresponding relation, wherein the corresponding relation comprises the corresponding keyword and the risk degree;
the second determining unit is used for determining the risk value of the text based on the occurrence frequency and the risk degree corresponding to each keyword;
and the storage unit is used for storing the text to the local part under the condition that the risk value is smaller than the preset threshold value.
6. The apparatus according to claim 5, wherein the correspondence relationship in the first determination unit includes:
corresponding key phrases and the risk degree, wherein the key phrases at least comprise the key words.
7. The apparatus of claim 5, further comprising:
and the prompting unit is used for sending a prompt which cannot be stored to a user under the condition that the risk value is not less than the preset threshold value.
8. The apparatus according to claim 5, wherein the second determining unit determines the risk value of the text based on the number of occurrences and the risk degree corresponding to each of the keywords, and includes:
the second determining unit is specifically configured to calculate a product of the occurrence frequency and the risk degree corresponding to each keyword to obtain a risk degree sum corresponding to each keyword;
and accumulating the risk degree sum corresponding to each keyword to obtain the risk value of the text.
9. A storage medium comprising a stored program, wherein the program executes the text saving method of any one of claims 1 to 4.
10. An apparatus, comprising: a processor, a memory, and a bus; the processor and the memory are connected through the bus;
the memory is used for storing a program, and the processor is used for running the program, wherein the program executes the text saving method of any one of claims 1 to 4 when running.
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| CN113689148A (en) * | 2021-09-26 | 2021-11-23 | 支付宝(杭州)信息技术有限公司 | Text risk identification method, device and equipment |
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