A hybrid bit information fuzzy system and method

Abstract

The present invention relates to the field of information processing technology, and in particular to a mixed-bit information fuzzy system and method, comprising: an information collection module for collecting mixed-bit information data from different information sources; an information preprocessing module for cleaning, integrating, and converting the mixed-bit information data collected by the information collection module, removing noise in the mixed-bit information data, and making the format, quality, and structure of the mixed-bit information data consistent; and a hierarchical fuzzy processing module for converting the standardized mixed-bit information data output by the information preprocessing module into a fuzzy form. The present 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 the exact data into fuzzy information through fuzzy logic, fuzzy sets, and fuzzy reasoning, thereby eliminating the uncertainty and ambiguity of the mixed-bit information.

Description

Technical Field

[0001] The present invention relates to the field of fuzzy systems, and in particular to a mixed bit information fuzzy system and method. Background Art

[0002] Fuzzy system JM Mendel, Uncertain rule-based fuzzy systems: Introduction and new directions, 2nd ed. Cham, Switzerland land: Springer, 2017 uses fuzzy rule base defined on fuzzy sets for reasoning, which is a generalization of deterministic systems; however, this system can only perform fuzzy set analysis on information, and its information contains a lot of noise and errors, and the information processed by this system still has great uncertainty and ambiguity.

[0003] Therefore, how to eliminate the uncertainty and ambiguity of mixed bit information has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0004] The purpose of the present invention is to provide a mixed-bit information fuzzy system and method. The present invention collects mixed-bit information from different information sources through an information collection module. The information preprocessing module preprocesses the mixed-bit information through data cleaning, integration, and conversion to ensure the consistency and reliability of the data. The hierarchical fuzzy processing module converts exact data into fuzzy information through fuzzy logic, fuzzy sets, and fuzzy reasoning, thereby eliminating the uncertainty and ambiguity of the mixed-bit information.

[0005] In order to achieve the above object, the present invention provides a mixed bit information fuzzy system, comprising:

[0006] An information collection module, used for collecting mixed bit information data from different information sources;

[0007] An information preprocessing module, used for cleaning, integrating and converting the mixed bit information data collected by the information collection module, removing noise in the mixed bit information data, making the format, quality and structure of the mixed bit information data consistent, and finally outputting it as standardized mixed bit information data;

[0008] A hierarchical fuzzy processing module, used for converting the standardized mixed bit information data output by the information preprocessing module into a fuzzy form;

[0009] The hierarchical fuzzy processing module includes multiple layers of fuzzy units, which are connected in series. Each layer of the fuzzy units has two standardized mixed bit information data input variables. Except that the two standardized mixed bit information data input variables of the fuzzy units in the first layer are actual standardized mixed bit information data input variables, the fuzzy units in other layers use the output of the fuzzy units in the previous layer as one of the standardized mixed bit information data input variables of the unit, and the other standardized mixed bit information data input variable is the actual standardized mixed bit information data input variable.

[0010] Furthermore, the hierarchical fuzzy processing module also includes:

[0011] A membership function selection unit, the membership function selection unit is responsible for selecting a function that best suits the characteristics of the standardized mixed bit information data from the available membership functions;

[0012] The fuzzy rule selection unit is used to select appropriate fuzzy rules to process the input standardized mixed bit information data, and place the selected fuzzy rules in a fuzzy rule base.

[0013] Furthermore, 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:

[0014] When the contribution of the newly obtained fuzzy rule based on each mixed bit information learning data to the fuzzy system output is greater than the preset threshold value A, the obtained fuzzy rule is added to the fuzzy rule base;

[0015] If it is less than or equal to the preset threshold value A, the number of fuzzy rules in the fuzzy rule base will not increase;

[0016] Then, the extended Kalman filter algorithm and the particle filter algorithm are used to update the parameters in the fuzzy rule that has the closest distance to the standardized mixed bit information data at the current moment. After the parameters are updated, the contribution of the fuzzy rule that has the closest distance to the standardized mixed bit information data at the current moment to the output of the fuzzy system is calculated. If the contribution of the fuzzy rule that has the closest distance to the standardized mixed bit information data at the current moment to the output of the fuzzy system is less than the set threshold value B, it is deleted from the fuzzy rule library.

[0017] Furthermore, an integrated model is used for fuzzy reasoning, and the integrated model is mainly composed of the same mixed-bit information bit value range interval, unified fuzzy rules, missing information processing and mapping adjustment, that is, the fuzzy reasoning process of each fuzzy unit adopts the same method, each input variable adopts a unified fuzzification method, and is fuzzified and defuzzified on the same mixed-bit information value range interval, using the same fuzzy analysis method.

[0018] Furthermore, the information collection module includes:

[0019] A data acquisition unit, the data acquisition unit is used to perform mixed bit information capture and data acquisition tasks, and collect the mixed bit information;

[0020] The data parsing unit is used to parse the mixed bit information data acquired by the data acquisition unit from different sources and convert it into a structured data format for subsequent processing and analysis.

[0021] Furthermore, the information preprocessing module includes:

[0022] A data cleaning unit, the data cleaning unit is used to clean and process the mixed bit information data collected by the information collection module to ensure the quality and availability of the mixed bit information data;

[0023] The data integration unit converts and integrates the mixed bit information data cleaned and processed by the data cleaning unit to create a unified data set;

[0024] The data storage unit stores the mixed bit information data integrated and converted by the data integration unit in a data storage medium, so as to effectively manage, protect and utilize the mixed bit information data.

[0025] Furthermore, the hierarchical fuzzy processing module also includes:

[0026] A reasoning unit that helps systems and applications make decisions, inferences, and actions based on known information and rules;

[0027] The visualization unit is used to display the fuzzy system output or intermediate results in a graphical or visual way, so that the output of the fuzzy system is more interpretable and the fuzzification process and output results can be displayed in a more intuitive way.

[0028] Furthermore, when the layered fuzzy processing module is working, the following steps are performed:

[0029] (1) Determine the input variables and output variables that need to be processed;

[0030] (2) It is necessary to select appropriate membership functions for each input variable and output variable;

[0031] (3) Define fuzzy sets for each input variable and output variable;

[0032] (4) Map the specific values ​​of the input variables to each fuzzy set and calculate the membership value of each fuzzy set;

[0033] (5) Establish a fuzzy rule base.

[0034] Furthermore, the mixed bit information blurring method comprises the following steps:

[0035] S1. Clean, integrate and convert the collected initial mixed bit information data from different sources;

[0036] S2. For specific mixed bit information data characteristics, select an appropriate membership function, map the standardized mixed bit information value to the fuzzy set and determine the membership value of the mixed bit information value and each fuzzy set;

[0037] S3. Establish a fuzzy rule base and use the defined fuzzy rules to perform fuzzy reasoning.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] (1) Mixed bit information from different information sources can be preprocessed through the information collection module and the information preprocessing module. By collecting and preprocessing the mixed bit information, the mixed bit information from different sources can be converted into a unified format to ensure data consistency, making it more suitable for fuzzy logic processing, avoiding contradictions and inconsistencies in the data, and facilitating subsequent data processing and analysis.

[0040] (2) The hierarchical fuzzy processing module can be used to convert exact data into fuzzy form to better handle the uncertainty and ambiguity of mixed-bit information. Through multiple levels of fuzzy processing, the system can better handle the uncertainty and ambiguity of mixed-bit information and process and analyze it more effectively. The hierarchical module structure helps to decompose and simplify the complexity of the fuzzy system, reduce the complexity of calculation, and make information processing more efficient.

[0041] (3) The present invention adopts an integrated model, and the fuzzy reasoning process of each fuzzy unit is in the same way, which means using the same bit information value range, unified fuzzy rules, missing information processing and mapping adjustment to maintain the consistency of the fuzzy reasoning process; different input variables adopt a unified fuzzification method, which ensures that fuzzification and defuzzification are performed on the same bit information value range, which helps to make the processing methods of different input variables comparable and consistent; and the present invention adopts the same fuzzy analysis method, which ensures that similar logic and methods are used in different fuzzy units to process information, thereby improving the consistency and predictability of the system, so that the mixed bit information fuzzy system in the present invention can perform fuzzy reasoning of mixed bit information more stably and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the following detailed description of the preferred embodiment.The drawings are only for the purpose of illustrating the preferred embodiments and are not to be construed as limiting the invention.

[0043] In the attached picture:

[0044] Figure 1 A schematic diagram of a mixed bit information fuzzy system of the present invention;

[0045] Figure 2 This is a step diagram of the mixed bit information blurring method of the present invention. DETAILED DESCRIPTION

[0046] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0047] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0048] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0049] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0050] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings.

[0051] like Figure 1 As shown, an embodiment of the present invention discloses a mixed bit information fuzzy system and method, including:

[0052] The information collection module is used to collect mixed bit information data from different information sources.

[0053] The information preprocessing module is used to clean, integrate and convert the mixed-bit information data collected by the information collection module, remove noise in the mixed-bit information data, make the format, quality and structure of the mixed-bit information data consistent, and finally output it as standardized mixed-bit information data.

[0054] The hierarchical fuzzy processing module is used to convert the standardized mixed bit information data output by the information preprocessing module into a fuzzy form.

[0055] The hierarchical fuzzy processing module includes multiple layers of fuzzy units, which are connected in series. Each layer of fuzzy units has 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 units are both actual standardized mixed bit information data input variables, the other layers of fuzzy units use the output of the previous layer of fuzzy units 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.

[0056] It can be seen that when the mixed-bit information fuzzy system is running, the mixed-bit information data from different information sources is first collected through the information collection module, and the information preprocessing module cleans, calibrates, integrates and other preprocesses the information data collected by the information collection module, and converts the mixed-bit information from different sources into a unified format to ensure data consistency, making it more suitable for fuzzy logic processing, avoiding contradictions and inconsistencies in the data, and also facilitating subsequent data processing and analysis.

[0057] Among them, the fuzzy unit includes multiple levels of fuzzy units, each layer of fuzzy units will process the standardized mixed bit information data, and combine the output of the previous layer to generate a higher-level output, convert the standardized mixed bit information data into a fuzzy form, and process the uncertainty and ambiguity in the mixed bit information.

[0058] Specifically, the hierarchical fuzzy processing module also includes:

[0059] The membership function selection unit is responsible for selecting the function that best suits the characteristics of the standardized mixed bit information data from the available membership functions.

[0060] The fuzzy rule selection unit is used to select appropriate fuzzy rules to process the input standardized mixed bit information data, and place the selected fuzzy rules in a fuzzy rule base.

[0061] It can be seen that in the membership function selection unit, the most suitable function is selected from the available membership functions according to the characteristics of the mixed bit information, and then the most suitable membership function is used to map the exact mixed bit information to the fuzzy set to determine the membership of the bit information value with different fuzzy sets; in the fuzzy rule selection unit, appropriate fuzzy rules are selected according to the input mixed bit information for fuzzy logic reasoning or decision-making; through the selection of membership functions, the selection of fuzzy rules and the layer-by-layer data processing in the fuzzy unit, the mixed bit information can be processed more efficiently.

[0062] Specifically, in the fuzzy rule selection unit, the selection of fuzzy rules includes the addition or deletion of fuzzy rules, and the addition or deletion of fuzzy rules is determined according to the contribution of the fuzzy rules to the output of the mixed bit information fuzzy system:

[0063] When the contribution of the newly obtained fuzzy rule based on each mixed bit information learning data to the fuzzy system output is greater than a preset threshold value A, the obtained fuzzy rule is added to the fuzzy rule base.

[0064] If it is less than or equal to the preset threshold A, the number of fuzzy rules in the fuzzy rule base will not increase.

[0065] Then, the extended Kalman filter algorithm and the particle filter algorithm are used to update the parameters of the fuzzy rule that is closest to the mixed bit information learning data at the current moment. After the parameters are updated, the contribution of the fuzzy rule that is closest to the mixed bit information learning data at the current moment to the output of the fuzzy system is calculated. If the contribution of the fuzzy rule that is closest to the mixed bit information learning data at the current moment to the output of the fuzzy system is less than the set threshold B, it will be deleted from the fuzzy rule base.

[0066] It is understandable that by dynamically adding or deleting fuzzy rules, the system can continuously optimize its performance during operation. When the newly obtained rules contribute more to the fuzzy system, they are added to the rule base, thereby improving the accuracy and robustness of the system's decision-making; whether to add them to the rule base is determined based on the contribution. This mechanism avoids storing redundant rules and saves storage resources. At the same time, only the rules that actually contribute to the system output are retained, which also reduces the system's calculation of those rules that contribute less to the output, saving computing resources.

[0067] Furthermore, the present invention connects multiple layers of fuzzy units in series, and the hybrid bit information system can more effectively process complex bit information, improve the accuracy of decision-making and the reasoning ability of the system; each layer of fuzzy units has two bit information input variables, and at different levels, these variables include the output of the fuzzy unit of the previous layer and the actual bit information input variable; these multiple layers of fuzzy units are connected in series, and the information transmission and cascade processing are realized by taking the output of the fuzzy unit of the previous layer as an input of the current unit; and the fuzzy unit of each layer can integrate the output and actual input of the previous layer to process information more comprehensively, deal with the fuzziness and uncertainty in complex bit information, and enhance the robustness and accuracy of the decision-making process. At the same time, the present invention connects different layers of fuzzy units in series, and the system can more effectively transmit, process and integrate position information to improve the reasoning and decision-making ability of the system.

[0068] Specifically, an integrated model is used for fuzzy reasoning. The integrated model is mainly composed of the same bit information value range, unified fuzzy rules, missing information processing and mapping adjustment. That is, the fuzzy reasoning process of each fuzzy unit adopts the same method, and each input variable adopts a unified fuzzification method. Fuzzification and defuzzification are performed on the same bit information value range, and the same fuzzy analysis method is used.

[0069] It can be understood that the present invention adopts the same fuzzy reasoning process of each fuzzy unit through an integrated model, which means using the same bit information value range, unified fuzzy rules, missing information processing and mapping adjustment to maintain the consistency of the fuzzy reasoning process; different input variables adopt a unified fuzzification method, which ensures that fuzzification and defuzzification are performed on the same bit information value range, which helps to make the processing methods of different input variables comparable and consistent; and the present invention adopts the same fuzzy analysis method, which ensures that similar logic and methods are used in different fuzzy units to process information, improves the consistency and predictability of the system, and enables the mixed bit information fuzzy system in the present invention to perform fuzzy reasoning of mixed bit information more stably and accurately.

[0070] Specifically, the information collection module includes:

[0071] The data acquisition unit is used to perform mixed-bit information capture and data acquisition tasks, and collect mixed-bit information.

[0072] The data parsing unit is used to parse the mixed bit information data obtained by the data acquisition unit from different sources and convert it into a structured data format for subsequent processing and analysis.

[0073] It is understandable that the data acquisition unit can obtain mixed bit information from data sources of different sources. These sources may include sensors, databases, network services, etc. The data acquisition unit can perform mixed bit information capture and data acquisition tasks. The data parsing unit can parse the raw data obtained from different sources and convert the raw data into a structured data format, which lays the foundation for subsequent processing, analysis and application. In other words, the information collection module in the present invention communicates with data sources from different sources through the data acquisition unit and collects mixed bit information, and finally converts and parses the raw data through the data parsing unit, so that the data has a unified structure and format within the system, which provides convenience for subsequent processing and analysis.

[0074] Specifically, the information preprocessing module includes:

[0075] The data cleaning unit is used to clean and process the mixed-bit information data collected by the information collection module to ensure the quality and availability of the mixed-bit information data.

[0076] The data integration unit converts and integrates the mixed bit information data cleaned and processed by the data cleaning unit to create a unified data set.

[0077] The data storage unit stores the mixed bit information data integrated and converted by the data integration unit in a data storage medium, so as to effectively manage, protect and utilize the mixed bit information data.

[0078] It is understandable that, through the data cleaning unit, data cleaning and processing techniques are implemented, including identifying and correcting errors, removing duplicate or redundant data, processing missing values ​​and outliers, etc. Use techniques such as data cleaning algorithms, outlier detection, data verification, etc. to improve information quality. The data integration unit in the present invention uses appropriate data conversion and integration techniques to integrate data from different sources and different formats into a unified data set, and the data set includes data structure standardization, field mapping, data merging, etc. The data storage unit in the present invention uses a data storage medium (such as a database system) to store and manage information. Appropriate security measures (encryption, access control, etc.) are adopted to ensure the security of data, and backup and recovery mechanisms are used to ensure the integrity and availability of data.

[0079] Specifically, the present invention also provides a mixed bit information fuzzy method using the mixed bit information fuzzy system. When the layered fuzzy processing module is working, it has the following steps:

[0080] (1) It is necessary to determine the input variables and output variables that need to be processed; (2) It is necessary to select an appropriate membership function for each input variable and output variable; (3) Define a fuzzy set for each input variable and output variable; (4) Map the specific values ​​of the input variables to each fuzzy set and calculate the membership value of each fuzzy set; (5) Establish a fuzzy rule base.

[0081] It can be understood that by determining the input and output variables, the input variables and output variables to be processed are defined to clarify the information content that the system needs to pay attention to. And for each input variable and output variable, an appropriate membership function is selected. These functions can be fuzzy set functions such as triangle, trapezoid, Gaussian, etc., to better describe the fuzzy nature of the variable; the present invention also defines fuzzy sets for each input variable and output variable. These sets can be defined based on specific ranges, rules or experience, so as to map specific values ​​to membership values ​​of fuzzy sets; the present invention maps specific input variable values ​​to 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 the membership values ​​of variables. When defining fuzzy rules, the present invention establishes an association between the fuzzy set of input variables and the fuzzy set of output variables based on existing knowledge and experience. These rules are based on expert knowledge or data-driven methods for fuzzy reasoning and decision-making.

[0082] The above steps transform the specific bit information into fuzzy sets and establish a rule base so that the system can perform fuzzy reasoning and decision-making. The key to this method is to accurately model the fuzziness of information and handle this fuzziness through appropriate membership functions and rule bases, so that the system can perform fuzzy reasoning to obtain meaningful output.

[0083] The hierarchical fuzzy processing module in the present invention also includes:

[0084] Reasoning Unit,The reasoning unit helps systems and applications make decisions, inferences, and actions based on known information and rules.

[0085] The visualization unit is used to display the fuzzy system output or intermediate results in a graphical or visual way, so that the output of the fuzzy system is more interpretable and the fuzzification process and output results can be displayed in a more intuitive way.

[0086] It is understandable that the reasoning unit of the present invention uses the rule base to perform fuzzy reasoning and decision-making on the input information. This includes reasoning based on expert knowledge or data-driven, thereby obtaining the decision results of the system. When the decision output is made through the reasoning unit, the system will provide explainable results, such as which rules are triggered, how the input information affects the results, etc., so that the user can understand the basis and logic of the decision.

[0087] The visualization unit in the present invention can display the output results of the fuzzy system, such as fuzzy sets, membership functions, rule triggering in the reasoning process, etc., through charts, graphs or other visualization means, so as to more intuitively present the fuzzification process and output results. At the same time, the visualization unit in the present invention also displays the fuzzification process in a visual manner, such as the intersection of fuzzy sets, changes in membership functions, and the final decision output, making the whole process more intuitive and easy to understand. In summary, the integration of the reasoning unit and the visualization unit in the present invention makes the output of the system more explanatory and visually displayed, so that users or other systems can more easily understand the reasoning process and decision results of the fuzzy system. This method improves the acceptability and application scope of mixed bit information fuzzy systems.

[0088] Specifically, the mixed bit information fuzzification method includes the following steps: S1. Cleaning, integrating and converting the collected initial mixed bit information data from different sources; S2. Selecting an appropriate membership function for specific mixed bit information data features, mapping the standardized mixed bit information value to the fuzzy set and determining the membership value of the mixed bit information value to each fuzzy set; S3. Establishing a fuzzy rule base and using the defined fuzzy rules for fuzzy reasoning.

[0089] It is understandable that the present invention uses data cleaning technology, including but not limited to identifying and processing noise data, correcting erroneous data, filling missing values, and removing duplicate or redundant data to ensure data quality and consistency. And the present invention selects appropriate membership functions, such as triangular, trapezoidal, Gaussian and other functions, according to the characteristics and scope of the mixed bit information, to map the exact bit information value to the corresponding fuzzy set. And for each bit information value, use membership functions and fuzzification algorithms, such as fuzzy maximum operation, weighted average, etc., to calculate its degree of membership to each fuzzy set.

[0090] The present invention establishes a rule base, including fuzzy rules for processing input fuzzy information. These rules are based on expert knowledge or data-driven methods. Once the preprocessing and fuzzification work is completed and the rule base is established, the system can use fuzzy logic for reasoning and decision-making, perform fuzzy logic operations based on the input mixed bit information, and thus produce decision results.

[0091] That is, the present invention processes the mixed bit information into fuzzy data through the above method, and establishes a rule base for fuzzy logic reasoning. These steps ensure the accuracy of the mixed bit information, the effectiveness of the fuzzification process and the decision-making ability of the fuzzy system.

[0092] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A hybrid bit information fuzzy system, It is characterized in that include: An information collection module, used for collecting mixed bit information data from different information sources; An information preprocessing module, used for cleaning, integrating and converting the mixed bit information data collected by the information collection module, removing noise in the mixed bit information data, making the format, quality and structure of the mixed bit information data consistent, and finally outputting it as standardized mixed bit information data; A hierarchical fuzzy processing module, used for converting the standardized mixed bit information data output by the information preprocessing module into a fuzzy form; The hierarchical fuzzy processing module includes multiple layers of fuzzy units, the multiple layers of fuzzy units are connected in series, and each layer of the fuzzy units has two standardized mixed bit information data input variables. Except that the two standardized mixed bit information data input variables of the fuzzy units in the first layer are both actual standardized mixed bit information data input variables, the fuzzy units in other layers use the output of the fuzzy units in the previous layer as one of the standardized mixed bit information data input variables of the unit, and the other standardized mixed bit information data input variable is the actual standardized mixed bit information data input variable; The hierarchical fuzzy processing module also includes: A membership function selection unit, the membership function selection unit is responsible for selecting a function that best suits the characteristics of the standardized mixed bit information data from the available membership functions; A fuzzy rule selection unit, used for selecting appropriate fuzzy rules to process the input standardized mixed bit information data, and placing the selected fuzzy rules in a fuzzy rule base; 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 of the newly obtained fuzzy rule based on each mixed bit information learning data to the fuzzy system output is greater than the preset threshold value A, the obtained fuzzy rule is added to the fuzzy rule base; If it is less than or equal to the preset threshold value A, the number of fuzzy rules in the fuzzy rule base will not increase; Then, the extended Kalman filter algorithm and the particle filter algorithm are used to update the parameters in the fuzzy rule that has the closest distance to the standardized mixed bit information data at the current moment. After the parameters are updated, the contribution of the fuzzy rule that has the closest distance to the standardized mixed bit information data at the current moment to the output of the fuzzy system is calculated. If the contribution of the fuzzy rule that has the closest distance to the standardized mixed bit information data at the current moment to the output of the fuzzy system is less than the set threshold value B, it is deleted from the fuzzy rule library.

2. A mixed bit information fuzzy system according to claim 1, It is characterized in that An integrated model is used for fuzzy reasoning. The integrated model is mainly composed of the same mixed-bit information value range, unified fuzzy rules, missing information processing and mapping adjustment. That is, the fuzzy reasoning process of each fuzzy unit adopts the same method, each input variable adopts a unified fuzzification method, and is fuzzified and defuzzified on the same mixed-bit information value range, using the same fuzzy analysis method.

3. A mixed bit information fuzzy system according to claim 1, It is characterized in that The information collection module includes: A data acquisition unit, the data acquisition unit is used to perform mixed bit information capture and data acquisition tasks, and collect the mixed bit information; The data parsing unit is used to parse the mixed bit information data acquired by the data acquisition unit from different sources and convert it into a structured data format for subsequent processing and analysis.

4. A mixed bit information fuzzy system according to claim 1, It is characterized in that The information preprocessing module comprises: A data cleaning unit, the data cleaning unit is used to clean and process the mixed bit information data collected by the information collection module to ensure the quality and availability of the mixed bit information data; The data integration unit converts and integrates the mixed bit information data cleaned and processed by the data cleaning unit to create a unified data set; The data storage unit stores the mixed bit information data integrated and converted by the data integration unit in a data storage medium, so as to effectively manage, protect and utilize the mixed bit information data.

5. A mixed bit information fuzzy system according to claim 1, It is characterized in that The hierarchical fuzzy processing module also includes: A reasoning unit that helps systems and applications make decisions, inferences, and actions based on known information and rules; The visualization unit is used to display the fuzzy system output or intermediate results in a graphical or visual way, so that the output of the fuzzy system is more interpretable and the fuzzification process and output results can be displayed in a more intuitive way.

6. A mixed bit information fuzzification method, applied in a mixed bit information fuzzification system as claimed in any one of claims 1 to 5, It is characterized in that When the layered fuzzy processing module is working, the following steps are performed: (1) Determine the input variables and output variables that need to be processed; (2) It is necessary to select appropriate membership functions for each input variable and output variable; (3) Define fuzzy sets for each input variable and output variable; (4) Map the specific values ​​of the input variables to each fuzzy set and calculate the membership value of each fuzzy set; (5) Establish a fuzzy rule base.

7. The mixed bit information blurring method according to claim 6, It is characterized in that The mixed bit information fuzzy method comprises the following steps: S1. Clean, integrate and convert the collected initial mixed bit information data from different sources; S2. For specific mixed bit information data characteristics, select an appropriate membership function, map the standardized mixed bit information value to the fuzzy set and determine the membership value of the mixed bit information value and each fuzzy set; S3. Establish a fuzzy rule base and use the defined fuzzy rules to perform fuzzy reasoning.

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