CN117669744A - Mixed bit information blurring system and method - Google Patents
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Abstract
The invention relates to the technical field of information processing, in particular to a mixed bit information blurring system and method, comprising the following steps: the information collection module is used for collecting mixed bit information data from different information sources; the information preprocessing module is used for cleaning, integrating and converting the mixed bit information data acquired by the information collecting module, removing noise in the mixed bit information data, and enabling the format, quality and structure of the mixed bit information data to be consistent; and the hierarchical fuzzy processing module is used for converting the standardized mixed bit information data output by the information preprocessing module into a fuzzy form. The invention collects mixed bit information from different information sources through the information collection module, the information preprocessing module cleans, integrates and converts the data, and the hierarchical fuzzy processing module converts exact data into fuzzy information through fuzzy logic, fuzzy aggregation, fuzzy reasoning and other modes to eliminate uncertainty and ambiguity of the mixed bit information.
Description
Technical Field
The invention relates to the field of fuzzy systems, in particular to a mixed bit information fuzzy system and a mixed bit information fuzzy method.
Background
The fuzzy system J.M. Mendel, uncertin rule-based fuzzy systems: introduction and new directions,2nd ed.Cham,Switzer land:Springer,2017 uses fuzzy rule base defined on fuzzy set to make reasoning, which is a popularization of deterministic system; but the system can only carry out fuzzy aggregation on the information, the information has more noise and errors, and the information processed by the system still has larger uncertainty and ambiguity.
How to eliminate the uncertainty and ambiguity of the mixed bit information becomes a technical problem to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide a mixed bit information fuzzy system and a mixed bit information fuzzy method, wherein mixed bit information from different information sources is collected through an information collection module, the mixed bit information is preprocessed through data cleaning, integration and conversion by an information preprocessing module, the consistency and reliability of data are ensured, and the precise data are converted into fuzzy information through a fuzzy logic, fuzzy aggregation, fuzzy reasoning and other modes by a layered fuzzy processing module, so that the uncertainty and the ambiguity of the mixed bit information are eliminated.
In order to achieve the above object, the present invention provides a mixed bit information blurring system comprising:
the information collection module is used for collecting mixed bit information data from different information sources;
the information preprocessing module is used for cleaning, integrating and converting the mixed bit information data acquired by the information collecting module, removing noise in the mixed bit information data, enabling the format, quality and structure of the mixed bit information data to be consistent, and finally outputting the mixed bit information data as standardized mixed bit information data;
the hierarchical fuzzy processing module is used for converting the standardized mixed bit information data output by the information preprocessing module into a fuzzy form;
the hierarchical fuzzy processing module comprises a plurality of layers of fuzzy units, wherein the layers of fuzzy units are connected in series, each layer of fuzzy unit is provided with two standardized mixed bit information data input variables, except that the two standardized mixed bit information data input variables of the first layer of fuzzy unit are the actual standardized mixed bit information data input variables, the output of the fuzzy unit of the previous layer of fuzzy unit of each layer of fuzzy unit is taken as one standardized mixed bit information data input variable of the unit, and the other standardized mixed bit information data input variable of the fuzzy unit of the previous layer of fuzzy unit is the actual standardized mixed bit information data input variable.
Further, the hierarchical fuzzy processing module further includes:
the membership function selection unit is responsible for selecting a function which is most suitable for standardized mixed bit information data characteristics from available membership functions;
and the fuzzy rule selection unit is used for selecting an appropriate fuzzy rule to process the input standardized mixed bit information data and placing the selected fuzzy rule in a fuzzy rule base.
Further, in the fuzzy rule selection unit, the selection of the fuzzy rule includes adding or deleting the fuzzy rule, and the adding or deleting of the fuzzy rule is determined according to the contribution degree of the fuzzy rule to the output of the mixed bit information fuzzy system:
when the contribution degree of the fuzzy rule newly obtained according to each mixed bit information learning data to the fuzzy system output is greater than a preset threshold A, adding the obtained fuzzy rule into a fuzzy rule library;
if the number of the fuzzy rules is smaller than or equal to a preset threshold A, the number of the fuzzy rules in the fuzzy rule base is not increased;
and updating parameters in the fuzzy rule with the nearest distance to the standardized mixed bit information data at the present moment by using an extended Kalman filtering algorithm and a particle filtering algorithm, calculating the contribution degree of the fuzzy rule with the nearest distance to the standardized mixed bit information data at the present moment to the output of the fuzzy system after updating the parameters, and deleting the fuzzy rule from a fuzzy rule library if the contribution degree of the fuzzy rule with the nearest distance to the standardized mixed bit information data at the present moment to the output of the fuzzy system is smaller than a set threshold B.
Further, the fuzzy reasoning is carried out by adopting an integrated model, the integrated model mainly comprises the same mixed bit information bit value range section, unified fuzzy rules, missing information processing and mapping adjustment, namely, the fuzzy reasoning process of each fuzzy unit adopts the same mode, each input variable adopts the unified fuzzy mode, the fuzzy and the defuzzification are carried out on the same mixed bit information value range section, and the same fuzzy analysis method is adopted.
Further, the information collection module includes:
the data acquisition unit is used for executing mixed bit information grabbing and data acquisition tasks and collecting mixed bit information;
the data analysis unit is used for analyzing the mixed bit information data acquired by the data acquisition unit from the different source heads and converting the mixed bit information data into a structured data format for subsequent processing and analysis.
Further, the information preprocessing module includes:
the data cleaning unit is used for cleaning and processing the mixed bit information data collected by the information collecting module, so as to ensure the quality and usability of the mixed bit information data;
the data integration unit is used for converting and integrating the mixed bit information data which is washed and processed by the data washing unit, and creating a unified data set;
and the data storage unit is used for storing the mixed bit information data integrated and converted by the data integration unit in a data storage medium and is used for effectively managing, protecting and utilizing the mixed bit information data.
Further, the hierarchical fuzzy processing module further includes:
an inference unit that assists the system and applications in making decisions, inferences, and operations based on known information and rules;
and the visualization unit is used for displaying the output or the intermediate result of the blurring system in a graphical or visual mode, so that the output of the blurring system is more interpretable, and the blurring process and the output result can be displayed in a more visual mode.
Further, the hierarchical fuzzy processing module has the following steps when in operation:
(1) Determining an input variable and an output variable which need to be processed;
(2) A suitable membership function needs to be selected at each of the input variables and the output variables;
(3) Defining a fuzzy set for each of the input variables and the output variables;
(4) Mapping specific values of input variables to each fuzzy set, and calculating membership value of each fuzzy set;
(5) And establishing a fuzzy rule base.
Further, the mixed bit information blurring method includes the steps of;
s1, cleaning, integrating and converting collected mixed bit information data from different sources;
s2, selecting a proper membership function according to specific mixed bit information data characteristics, mapping a standardized mixed bit information value into a fuzzy set, and determining the mixed bit information value and the membership value of each fuzzy set;
s3, establishing a fuzzy rule base, and performing fuzzy reasoning by using the defined fuzzy rule.
Compared with the prior art, the invention has the beneficial effects that:
(1) The mixed bit information from different information sources can be preprocessed through the information collecting module and the information preprocessing module, the mixed bit information from different sources can be converted into a uniform format through collecting and preprocessing the mixed bit information, and the consistency of data is ensured, so that the mixed bit information is more suitable for fuzzy logic processing, contradiction and inconsistency in the data are avoided, and convenience is provided for subsequent data processing and analysis.
(2) The adoption of the hierarchical fuzzy processing module can convert exact data into a fuzzy form so as to better process the uncertainty and the ambiguity of mixed bit information; through the fuzzy processing of a plurality of layers, the system can better process the uncertainty and the ambiguity of the mixed bit information and process and analyze more effectively; the layering of the module structure is beneficial to decomposing and simplifying the complexity of a fuzzy system, reduces the complexity of calculation and enables the information processing to be more efficient.
(3) The fuzzy reasoning process of each fuzzy unit adopts the same mode through the integrated model, which means that the same bit information value range interval, unified fuzzy rule, missing information processing and mapping adjustment are used to maintain the consistency of the fuzzy reasoning process; the unified blurring mode is adopted for different input variables, so that blurring and defuzzification are carried out on the same bit information value range interval, and the processing modes of the different input variables are comparable and consistent; the invention adopts the same fuzzy analysis method, which ensures that similar logic and methods are used for processing information in different fuzzy units, improves the consistency and predictability of the system, and ensures that the mixed bit information fuzzy system can perform fuzzy reasoning of mixed bit information more stably and accurately.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.
In the drawings:
FIG. 1 is a schematic diagram of a mixed bit information ambiguity system of the present invention;
FIG. 2 is a step diagram of a mixed bit information blurring method according to the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
In the description of the present application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate description of the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present application.
The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art in a specific context.
Preferred embodiments of the present invention will be described below with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention discloses a mixed bit information blurring system and method, including:
and the information collection module is used for collecting mixed bit information data from different information sources.
The information preprocessing module is used for cleaning, integrating and converting the mixed bit information data acquired by the information collecting module, removing noise in the mixed bit information data, enabling the format, quality and structure of the mixed bit information data to be consistent, and finally outputting the mixed bit information data as standardized mixed bit information data.
And the hierarchical fuzzy processing module is used for converting the standardized mixed bit information data output by the information preprocessing module into a fuzzy form.
The hierarchical fuzzy processing module comprises a plurality of layers of fuzzy units, the plurality of layers of fuzzy units are connected in series, each layer of fuzzy unit is provided with two standardized mixed bit information data input variables, except that the two standardized mixed bit information data input variables of the first layer of fuzzy unit are all actual standardized mixed bit information data input variables, the output of the previous layer of fuzzy unit is taken as one standardized mixed bit information data input variable of the unit, and the other standardized mixed bit information data input variable is the actual standardized mixed bit information data input variable.
It can be seen that when the mixed bit information fuzzy system operates, firstly, the information collecting module collects mixed bit information data from different information sources, the information preprocessing module carries out preprocessing such as cleaning, calibration, integration and the like on the information data collected by the information collecting module, and the mixed bit information of different sources is converted into a uniform format, so that the consistency of the data is ensured, the mixed bit information fuzzy system is more suitable for fuzzy logic processing, contradiction and inconsistency in the data are avoided, and convenience is provided for processing and analyzing subsequent data.
Wherein the blurring units comprise a plurality of levels of blurring units, each level of blurring units is to process normalized mixed bit information data and combine the outputs of previous levels to generate a higher level output, convert the normalized mixed bit information data into a blurred form, and process uncertainties and ambiguities in the mixed bit information.
Specifically, the hierarchical fuzzy processing module further includes:
the membership function selection unit is responsible for selecting a function which is most suitable for the standardized mixed bit information data characteristics from available membership functions.
And the fuzzy rule selection unit is used for selecting an appropriate fuzzy rule to process the input standardized mixed bit information data and placing the selected fuzzy rule in a fuzzy rule base.
It can be seen that in the membership function selection unit, according to the characteristics of the mixed bit information, the most suitable function is selected from available membership functions, and then the most suitable membership function is used for mapping the exact mixed bit information into the fuzzy set so as to determine the membership of the bit information value and different fuzzy sets; in the fuzzy rule selecting unit, selecting a proper fuzzy rule according to the input mixed bit information for fuzzy logic reasoning or decision; by selecting membership functions, fuzzy rules and performing layer-by-layer data processing on fuzzy units, mixed bit information can be processed more efficiently.
Specifically, in the fuzzy rule selection unit, the selection of the fuzzy rule includes adding or deleting the fuzzy rule, and the adding or deleting of the fuzzy rule is determined according to the contribution degree of the fuzzy rule to the output of the mixed bit information fuzzy system:
and when the contribution degree of the fuzzy rule newly obtained according to each mixed bit information learning data to the fuzzy system output is greater than a preset threshold A, adding the obtained fuzzy rule into a fuzzy rule library.
If the number of the fuzzy rules is smaller than or equal to the preset threshold A, the number of the fuzzy rules is not increased.
And updating parameters in the fuzzy rule with the closest distance to the mixed bit information learning data at the present moment by using an extended Kalman filtering algorithm and a particle filtering algorithm, calculating the contribution degree of the fuzzy rule with the closest distance to the mixed bit information learning data at the present moment to the output of the fuzzy system after the parameters are updated, and deleting the fuzzy rule from a fuzzy rule base if the contribution degree of the fuzzy rule with the closest distance to the mixed bit information learning data at the present moment to the output of the fuzzy system is smaller than a set threshold value B.
It will be appreciated that by dynamically adding or deleting fuzzy rules, the system can continually optimize its own performance at run-time. When the contribution of the newly obtained rule to the fuzzy system is large, the newly obtained rule is added into a rule base, so that the accuracy and the robustness of the system decision are improved; and judging whether to join the rule base according to the contribution degree, the mechanism avoids storing redundant rules, saves storage resources, and simultaneously only reserves the rules actually contributing to the output of the system, thereby reducing the calculation of the rules with smaller contribution to the output by the system and saving calculation resources.
Furthermore, the invention is connected in series through the multi-layer fuzzy units, the mixed bit information system can more effectively process complex bit information, and the decision accuracy and the reasoning capacity of the system are improved; each layer of fuzzy units has two bit information input variables, and the variables comprise the output of the fuzzy unit of the previous layer and the actual bit information input variables on different layers; the multi-layer fuzzy units are connected in series, and information transmission and cascade processing are realized by taking the output of the fuzzy unit of the previous layer as one input of the current unit; and the fuzzy unit of each layer can integrate the output and the actual input of the previous layer so as to more comprehensively process the information, cope with the ambiguity and the uncertainty in the complex bit information and enhance the robustness and the accuracy of the decision process. Meanwhile, the system can more effectively transfer, process and synthesize the position information by connecting the fuzzy units of different layers in series, so as to improve the reasoning and decision making capability of the system.
Specifically, the fuzzy reasoning is carried out by adopting an integrated model, the integrated model mainly comprises the same bit information value range section, unified fuzzy rules, missing information processing and mapping adjustment, namely the fuzzy reasoning process of each fuzzy unit adopts the same mode, each input variable adopts the unified fuzzy mode, the fuzzy and the defuzzification are carried out on the same bit information value range section, and the same fuzzy analysis method is adopted.
It can be understood that the fuzzy reasoning process of each fuzzy unit adopts the same mode through the integrated model, which means that the same bit information value range interval, unified fuzzy rule, missing information processing and mapping adjustment are used to maintain the consistency of the fuzzy reasoning process; the unified blurring mode is adopted for different input variables, so that blurring and defuzzification are carried out on the same bit information value range interval, and the processing modes of the different input variables are comparable and consistent; the invention adopts the same fuzzy analysis method, which ensures that similar logic and methods are used for processing information in different fuzzy units, improves the consistency and predictability of the system, and ensures that the mixed bit information fuzzy system can perform fuzzy reasoning of mixed bit information more stably and accurately.
Specifically, the information collection module includes:
the data acquisition unit is used for executing mixed bit information grabbing and data acquisition tasks and collecting mixed bit information.
The data analysis unit is used for analyzing the mixed bit information data acquired by the data acquisition unit from different source heads and converting the mixed bit information data into a structured data format for subsequent processing and analysis.
It will be appreciated that the data acquisition unit may obtain mixed bit information from data sources of different origins. These sources may include sensors, databases, web services, etc. The data acquisition unit can perform mixed bit information grabbing and data acquisition tasks. The data analysis unit can analyze the original data acquired from different source heads and convert the original data into a structured data format, which lays a foundation for subsequent processing, analysis and application. That is, the information collection module in the invention communicates with data sources of different sources through the data collection unit and collects mixed bit information, and finally the data analysis unit converts and analyzes the original data, so that the data has a uniform structure and format in the system, and convenience is provided for subsequent processing and analysis.
Specifically, the information preprocessing module includes:
and the data cleaning unit is used for cleaning and processing the mixed bit information data collected by the information collecting module, so as to ensure the quality and usability of the mixed bit information data.
And the data integration unit is used for converting and integrating the mixed bit information data which is washed and processed by the data washing unit, and creating a unified data set.
And the data storage unit is used for storing the mixed bit information data integrated and converted by the data integration unit in a data storage medium and is used for effectively managing, protecting and utilizing the mixed bit information data.
It will be appreciated that data cleansing and processing techniques are implemented by the data cleansing unit, including identifying and correcting errors, removing duplicate or redundant data, processing missing and outliers, etc. Techniques such as data cleansing algorithms, outlier detection, data verification, etc. are used to improve the quality of the information. The data integration unit in the invention integrates data from different sources and different formats into a unified data set by adopting proper data conversion and integration technology, and the data set comprises data structure standardization, field mapping, data merging and the like. The data storage unit of the present invention stores and manages information using a data storage medium (e.g., a database system). Appropriate security measures (encryption, access control, etc.) are employed to ensure the security of the data, and backup and restore mechanisms are employed to ensure the integrity and availability of the data.
Specifically, the invention also provides a mixed bit information blurring method applying the mixed bit information blurring system, and the layered blurring processing module has the following steps when in operation:
(1) The input variable and the output variable which need to be processed are determined; (2) A suitable membership function needs to be selected at each of the input variables and the output variables; (3) Defining a fuzzy set for each of the input variables and the output variables; (4) Mapping specific values of input variables to each fuzzy set, and calculating membership value of each fuzzy set; (5) establishing a fuzzy rule base.
It will be appreciated that by determining input and output variables, the information content that the system needs to be concerned with is clarified by defining the input and output variables that need to be processed. And selecting appropriate membership functions for each input variable and output variable, wherein the functions can be fuzzy set functions such as triangle, trapezoid, gaussian and the like so as to better describe the fuzzy properties of the variables; the present invention also defines fuzzy sets for each input variable and output variable, which sets may be defined based on specific ranges, rules or experiences to map specific values to membership values of fuzzy sets; the invention maps specific input variable values onto each fuzzy set and calculates the membership value of each fuzzy set. For example, membership functions and fuzzification algorithms, such as fuzzy maximum operations, weighted averages, etc., are used to calculate membership values for variables. When the fuzzy rule is defined, the fuzzy set of the input variable and the fuzzy set of the output variable are associated based on the existing knowledge and experience. These rules are based on expert knowledge or data driven methods for fuzzy reasoning and decision making.
The steps are that specific bit information is converted into fuzzy sets, and a rule base is established, so that the system can perform fuzzy reasoning and decision. The key of the method is to accurately model the ambiguity of the information and process the ambiguity through a proper membership function and rule base, so that the system can carry out fuzzy reasoning to obtain meaningful output.
The layered fuzzy processing module in the invention further comprises:
and an inference unit that assists the system and applications in making decisions, inferences, and operations based on known information and rules.
And the visualization unit is used for displaying the output or the intermediate result of the blurring system in a graphical or visual mode, so that the output of the blurring system is more interpretable, and the blurring process and the output result can be displayed in a more visual mode.
It can be understood that the inference unit of the present invention uses a rule base to make fuzzy inference and decision on the input information. Including expert knowledge or data driven reasoning to arrive at a decision result for the system. Upon decision output by the inference unit, the system will provide interpretable results, such as which rules are triggered, how the input information affects the results, etc., so that the user understands the basis and logic of the decision.
The visualization unit can display the output result of the fuzzy system, such as fuzzy set, membership function, rule trigger in the reasoning process and the like, through charts, graphs or other visualization means, so that the fuzzy process and the output result can be displayed more intuitively. Meanwhile, the visualization unit in the invention also displays the blurring process in a visual mode, such as intersection of the blurring set, change of membership function and final decision output, so that the whole process is more visual and easy to understand. In summary, the integrated reasoning unit and the visualization unit in the invention enable the output of the system to have more explanatory and visual display, so that a user or other systems can more easily understand the reasoning process and decision result of the fuzzy system. The method improves the acceptability and the application range of the mixed bit information fuzzy system.
Specifically, the mixed bit information blurring method includes the steps of: s1, cleaning, integrating and converting collected mixed bit information data from different sources; s2, selecting a proper membership function according to specific mixed bit information data characteristics, mapping a standardized mixed bit information value into a fuzzy set, and determining the mixed bit information value and the membership value of each fuzzy set; s3, establishing a fuzzy rule base, and performing fuzzy reasoning by using the defined fuzzy rule.
It is to be appreciated that the present invention employs data cleansing techniques including, but not limited to, identifying and processing noisy data, correcting erroneous data, filling in missing values, removing duplicate or redundant data to ensure data quality and consistency. And according to the characteristics and the range of the mixed bit information, the invention selects proper membership functions, such as functions of triangle, trapezoid, gaussian and the like, so as to map the exact bit information value to the corresponding fuzzy set. And for each bit information value, using membership functions and fuzzification algorithms, such as fuzzy maximum operations, weighted averages, etc., the degree of membership to each fuzzy set is calculated.
The invention establishes a rule base comprising fuzzy rules for processing input fuzzy information. These rules are based on expert knowledge or data driven methods. Once the preprocessing and the blurring work are completed and the rule base is established, the system can utilize the fuzzy logic to perform reasoning and decision, and perform fuzzy logic operation according to the input mixed bit information so as to generate a decision result.
That is, the present invention processes mixed bit information into fuzzy data by the above-described method and establishes a rule base for fuzzy logic reasoning. These steps ensure the accuracy of the mixed bit information, the effectiveness of the blurring process, and the decision making capability of the blurring system.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A mixed bit information blurring system, comprising:
the information collection module is used for collecting mixed bit information data from different information sources;
the information preprocessing module is used for cleaning, integrating and converting the mixed bit information data acquired by the information collecting module, removing noise in the mixed bit information data, enabling the format, quality and structure of the mixed bit information data to be consistent, and finally outputting the mixed bit information data as standardized mixed bit information data;
the hierarchical fuzzy processing module is used for converting the standardized mixed bit information data output by the information preprocessing module into a fuzzy form;
the hierarchical fuzzy processing module comprises a plurality of layers of fuzzy units, wherein the layers of fuzzy units are connected in series, each layer of fuzzy unit is provided with two standardized mixed bit information data input variables, except that the two standardized mixed bit information data input variables of the first layer of fuzzy unit are the actual standardized mixed bit information data input variables, the output of the fuzzy unit of the previous layer of fuzzy unit of each layer of fuzzy unit is taken as one standardized mixed bit information data input variable of the unit, and the other standardized mixed bit information data input variable of the fuzzy unit of the previous layer of fuzzy unit is the actual standardized mixed bit information data input variable.
2. The mixed bit information obfuscation system of claim 1, wherein the hierarchical obfuscation processing module further includes:
the membership function selection unit is responsible for selecting a function which is most suitable for standardized mixed bit information data characteristics from available membership functions;
and the fuzzy rule selection unit is used for selecting an appropriate fuzzy rule to process the input standardized mixed bit information data and placing the selected fuzzy rule in a fuzzy rule base.
3. The system according to claim 2, wherein the selection of the fuzzy rule in the fuzzy rule selection unit includes addition or deletion of the fuzzy rule, the addition or deletion of the fuzzy rule being determined according to a degree of contribution of the fuzzy rule to the output of the system:
when the contribution degree of the fuzzy rule newly obtained according to each mixed bit information learning data to the fuzzy system output is greater than a preset threshold A, adding the obtained fuzzy rule into a fuzzy rule library;
if the number of the fuzzy rules is smaller than or equal to a preset threshold A, the number of the fuzzy rules in the fuzzy rule base is not increased;
and updating parameters in the fuzzy rule with the nearest distance to the standardized mixed bit information data at the present moment by using an extended Kalman filtering algorithm and a particle filtering algorithm, calculating the contribution degree of the fuzzy rule with the nearest distance to the standardized mixed bit information data at the present moment to the output of the fuzzy system after updating the parameters, and deleting the fuzzy rule from a fuzzy rule library if the contribution degree of the fuzzy rule with the nearest distance to the standardized mixed bit information data at the present moment to the output of the fuzzy system is smaller than a set threshold B.
4. The mixed bit information fuzzy system according to claim 2, wherein an integrated model is adopted for fuzzy reasoning, the integrated model mainly comprises the same mixed bit information bit value range section, unified fuzzy rules, missing information processing and mapping adjustment, namely the fuzzy reasoning process of each fuzzy unit adopts the same mode, each input variable adopts the unified fuzzy mode, the fuzzy and the defuzzification are carried out on the same mixed bit information value range section, and the same fuzzy analysis method is adopted.
5. The mixed bit information obfuscation system of claim 1, wherein the information collection module includes:
the data acquisition unit is used for executing mixed bit information grabbing and data acquisition tasks and collecting mixed bit information;
the data analysis unit is used for analyzing the mixed bit information data acquired by the data acquisition unit from the different source heads and converting the mixed bit information data into a structured data format for subsequent processing and analysis.
6. The mixed bit information obfuscation system of claim 1, wherein the information preprocessing module includes:
the data cleaning unit is used for cleaning and processing the mixed bit information data collected by the information collecting module, so as to ensure the quality and usability of the mixed bit information data;
the data integration unit is used for converting and integrating the mixed bit information data which is washed and processed by the data washing unit, and creating a unified data set;
and the data storage unit is used for storing the mixed bit information data integrated and converted by the data integration unit in a data storage medium and is used for effectively managing, protecting and utilizing the mixed bit information data.
7. The mixed bit information obfuscation system of claim 1, wherein the hierarchical obfuscation processing module further includes:
an inference unit that assists the system and applications in making decisions, inferences, and operations based on known information and rules;
and the visualization unit is used for displaying the output or the intermediate result of the blurring system in a graphical or visual mode, so that the output of the blurring system is more interpretable, and the blurring process and the output result can be displayed in a more visual mode.
8. A mixed bit information blurring method applied to the mixed bit information system of any of claims 1-7, wherein the hierarchical blurring processing module has the following steps when in operation:
(1) Determining an input variable and an output variable which need to be processed;
(2) A suitable membership function needs to be selected at each of the input variables and the output variables;
(3) Defining a fuzzy set for each of the input variables and the output variables;
(4) Mapping specific values of input variables to each fuzzy set, and calculating membership value of each fuzzy set;
(5) And establishing a fuzzy rule base.
9. The mixed bit information blurring method according to claim 8, wherein the mixed bit information blurring method includes the steps of;
s1, cleaning, integrating and converting collected mixed bit information data from different sources;
s2, selecting a proper membership function according to specific mixed bit information data characteristics, mapping a standardized mixed bit information value into a fuzzy set, and determining the mixed bit information value and the membership value of each fuzzy set;
s3, establishing a fuzzy rule base, and performing fuzzy reasoning by using the defined fuzzy rule.
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