IP digital personalized theme man-machine interaction system based on edge computing gateway
By integrating user preference, behavior, and physiological information evaluation units on an edge computing gateway and combining them with deep belief network algorithms, the shortcomings of existing systems in user preference and behavior learning are addressed, achieving high-precision personalized recommendations and system versatility.
Patent Information
- Application Number
- CN202411696462.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-25
- Publication Date
- 2025-03-11
AI Technical Summary
Existing edge computing-based IP digital personalized theme human-computer interaction systems lack in-depth analysis of user preferences and behavioral learning, and fail to integrate physiological information, resulting in low accuracy of personalized recommendations, high system complexity, and difficulty in achieving platformization and universality.
It employs a user preference and user behavior information collection human-computer interaction unit, an IP digital personalized theme system human-computer interaction unit, a user physiological information evaluation unit, a usage scenario switching and adjustment unit, a control command generation interaction unit, and a deep belief network learning and optimization unit. Through the deep belief network algorithm, it performs real-time user physiological evaluation and data processing to achieve dynamic scenario switching and logic optimization.
It improves the accuracy and intelligence of personalized recommendations, enhances the system's adaptability and versatility in complex environments, simplifies logic processing, and improves the system's stability and reliability.
Smart Images
Figure CN119597155A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of IP digital personalized themes, and in particular to an IP digital personalized themes human-computer interaction system based on an edge computing gateway. Background Art
[0002] With the rapid development of Internet of Things technology and artificial intelligence, edge computing has become an important means to solve the real-time processing needs of massive data. The IP digital personalized theme human-computer interaction system based on edge computing gateway came into being, aiming to use IP elements (such as cultural symbols, cartoon characters, etc.) combined with personalized algorithms to provide immersive and diverse user experience. This system reduces the pressure on the cloud and improves the response speed by moving data processing to edge devices. At the same time, it combines user behavior data and preference analysis to realize customized interaction scenarios. This interactive system is widely used in smart home, education, retail and other fields, bringing users an interactive experience that is more in line with personalized needs. However, in practical applications, there are still many problems with existing methods that need to be solved.
[0003] Although the IP digital personalized theme system based on edge computing has advantages in real-time and distributed processing, the existing methods still have obvious defects. Traditional systems mostly use shallow algorithms or predefined rules to learn user preferences and behaviors, lacking in-depth analysis of dynamic data and complex user scenarios, resulting in low accuracy of personalized recommendations and low intelligence level of responses. In addition, existing user data processing methods usually fail to integrate multidimensional data such as physiological information evaluation, ignore the interactive relationship between user behavior and physiological state, and limit the system's potential in combining deep learning with human-computer interaction. These deficiencies not only affect the system's adaptability to complex environments, but also limit the actual promotion effect of IP digital personalized themes. In post-development use, the IP digital personalized theme system needs to receive the status reported by each human-computer interactor of the power system for relevant protection logic processing. The logic is complex, prone to errors, difficult to achieve platformization, and not universal. Summary of the invention
[0004] In order to overcome the shortcomings and deficiencies of the prior art, the present invention provides an IP digital personalized theme human-computer interaction system based on an edge computing gateway.
[0005] The present invention is an IP digital personalized theme human-computer interaction system based on an edge computing gateway, and the IP digital personalized theme human-computer interaction system includes a user preference, a user behavior information collection human-computer interaction unit, an IP digital personalized theme system human-computer interaction unit, a user physiological information evaluation unit, a usage scenario switching and adjustment unit, a control instruction generation interaction unit, and a deep belief network learning and optimization unit;
[0006] The user preference and user behavior information collection human-computer interaction unit is used for the edge computing gateway to analyze user preferences and user behavior demand information and capture instruction user preferences and user behavior human-computer interaction terminal information;
[0007] The IP digital personalized subject system human-computer interaction unit is used to acquire the capture instruction data in real time through analysis and interaction, and forward the data to the user physiological information evaluation unit;
[0008] The user physiological information evaluation unit is used for the edge computing gateway to perform real-time user physiological evaluation and instruction timing correction using a deep belief network algorithm, and is composed of different user information collection sensors and an edge computing gateway;
[0009] The usage scenario switching and adjustment unit stores the parameters used by the user and the intervals for parameter adjustment; the usage scenario switching and adjustment unit is used to select the dynamic change parameters and change time of the deep belief network algorithm for user usage control per unit time from the IP digital personalized theme human-computer interaction system control data repository, and send them to the edge computing gateway working cluster for unit time capture instructions;
[0010] The control instruction generation interaction unit is used to receive the capture instruction signal and generate and update the deep belief network control instruction according to the control instruction, and feed back the capture instruction result;
[0011] The deep belief network learning and optimization unit is used to obtain access to the deep belief network algorithm input parameters corresponding to the deep belief network algorithm data training request according to the deep belief network algorithm input parameters and deep belief network algorithm output parameters corresponding to the deep belief network algorithm data training request, access pre-stored data information through the deep belief network algorithm input parameters, and perform corresponding capture according to the human-computer interaction command.
[0012] As a preferred technical solution, the capture instruction user preferences and user behavior human-computer interaction information are stored in a deep belief network algorithm data controller; the deep belief network algorithm data controller is used to receive deep belief network algorithm control requests sent by user preferences and user behaviors; the deep belief network algorithm control request includes a deep belief network algorithm data training request including a deep belief network algorithm human-computer interaction program, deep belief network algorithm input parameters, deep belief network algorithm output parameters, deep belief network algorithm data, and human-computer interaction commands, and the deep belief network algorithm data training request is sent to the corresponding usage scenario switching and adjustment unit according to the deep belief network algorithm input parameters and deep belief network algorithm output parameters.
[0013] As a preferred technical solution, the user preferences and user behaviors sent by the capture instruction user preferences and user behavior human-computer interaction terminal information are converted into intelligent analysis results and user usage conditions and written into the user preferences and user behavior information collection human-computer interaction unit, and the capture instruction user preferences and user behavior human-computer interaction terminal information are sent to the terminal controller; the terminal controller receives the user preferences and user behavior human-computer interaction terminal information, writes them into the user physiological information evaluation unit, and sends the capture instruction user preferences and user behavior human-computer interaction terminal information to the edge computing gateway remote human-computer interaction platform when receiving the execution intelligent analysis result command of the control instruction generation interaction unit, and returns the capture instruction information inconsistent alarm signal when the capture instruction of the control instruction generation interaction unit is abnormal; the edge computing gateway remote human-computer interaction platform receives the user preferences and user behavior human-computer interaction terminal information, writes them into the user physiological information evaluation unit, and sends the capture instruction user preferences and user behavior human-computer interaction terminal information to the edge computing gateway when receiving the execution intelligent analysis result command of the control instruction generation interaction unit, and returns the capture instruction information inconsistent alarm signal when the capture instruction of the control instruction generation interaction unit is abnormal.
[0014] As a preferred technical solution, the control instruction generation interaction unit receives the capture instruction user preference, user behavior human-computer interaction end information, writes it into the user physiological information evaluation unit, and transfers the deep belief network algorithm modification and update rights according to the required content when receiving the execution intelligent analysis result command of the deep belief network learning and optimization unit, and returns a capture instruction information inconsistency alarm signal when receiving the capture instruction abnormality of the deep belief network learning and optimization unit; the IP digital personalized theme human-computer interaction system receives the capture instruction details, writes it into the user physiological information evaluation unit, and modifies and rewrites the human-computer interaction program according to the required content when receiving the execution intelligent analysis result command of the capture instruction decision unit, and returns a capture instruction information inconsistency alarm signal when receiving the capture instruction abnormality of the deep belief network learning and optimization unit; the IP digital personalized theme human-computer interaction system receives the information of transferring the deep belief network algorithm modification and update rights and rewriting the human-computer interaction program, and writes it into the user physiological information evaluation unit.
[0015] As a preferred technical solution, the deep belief network learning and optimization unit calls the information in the user physiological information evaluation unit and feeds back the current capture instruction user preference and user behavior human-computer interaction information during the capture instruction process to determine whether the capture instruction user preference and user behavior human-computer interaction information meet the user usage conditions of the intelligent analysis results. If the number of usage scenario switches stored in the usage scenario switching and adjustment unit is equal to the number of usage scenario switches agreed in the intelligent analysis results; the update time stored in the usage scenario switching and adjustment unit is equal to the update time agreed in the intelligent analysis results; the update memory regulation type of the usage scenario switching and adjustment unit is equal to the update memory regulation type agreed in the intelligent analysis results; the capture instruction time stored in the usage scenario switching and adjustment unit is equal to the capture instruction time agreed in the intelligent analysis results, then a command to execute the intelligent analysis results is sent, otherwise the capture instruction is sent abnormally.
[0016] As an optimal technical solution, the user physiological information evaluation unit regulates and manages the capture process of each capture instruction working end, and also regulates and manages the capture instruction information set of the data filter network; in the initial state, the edge computing gateway logs in to the regulation human-computer interaction unit, and according to policy regulations and capture instruction regulation and maintenance standards, the regulation and maintenance standards of the deep belief network algorithm and the capture instruction process are initially set, including trigger event standard setting, limit interval setting and initial value setting of the limit interval; the capture instruction data parsing controller also includes a regulation and maintenance parsing unit; the data acquisition device regularly sends the regulation and maintenance information related data to the capture instruction data parsing controller; the usage scenario switching and adjustment unit optimizes the real-time regulation and maintenance standard limit interval according to the actual user usage control data statistics and the historical capture instruction data statistics, generates a new regulation and maintenance standard, and sends the new regulation and maintenance standard to the regulation human-computer interaction unit to achieve regulation and maintenance optimization of the deep belief network algorithm and the capture instruction process.
[0017] The present invention is an IP digital personalized theme human-computer interaction system based on edge computing gateway, comprising the following steps:
[0018] Step S1, collecting user preference and user behavior information; the deep belief network algorithm data controller receives the deep belief network algorithm control request sent by the user preference and user behavior;
[0019] Step S2, the deep belief network algorithm data controller writes the user preference and user behavior information into the human-computer interaction unit according to the conversion into intelligent analysis results and user usage conditions; when the control instruction generates the interactive unit to execute the intelligent analysis result command, it sends the capture instruction user preference and user behavior human-computer interaction terminal information to the edge computing gateway remote human-computer interaction platform;
[0020] Step S3: When the control instruction generates the interactive unit to execute the intelligent analysis result command, it sends the capture instruction user preference and user behavior human-computer interaction terminal information to the edge computing gateway; when the deep belief network learning and optimization unit executes the intelligent analysis result command, it transfers the deep belief network algorithm modification and update rights according to the demand content;
[0021] Step S4, the IP digital personalized theme human-computer interaction system receives the capture instruction details, writes them into the user physiological information evaluation unit, and modifies and rewrites the human-computer interaction program according to the required content when receiving the execution intelligent analysis result command of the deep belief network learning and optimization unit;
[0022] Step S5, the deep belief network learning and optimization unit calls the information in the user physiological information evaluation unit during the capture instruction process and feeds back the current capture instruction user preference and user behavior human-computer interaction end information. If the negative feedback is successful, the deep belief network algorithm calculation is completed; the user physiological information evaluation unit regulates and manages the capture process of each capture instruction working end.
[0023] As a preferred technical solution, in step S1, the user preference and user behavior information collection human-computer interaction unit includes a user preference and user behavior real-time management unit for managing user preferences and user behavior real-time information and a capture instruction order management unit for managing capture instruction user preferences and user behavior human-computer interaction terminal information.
[0024] As a preferred technical solution, regulation management is handled through the restriction interval of regulation and maintenance standards. The optimization of the restriction interval of regulation and maintenance standards is carried out based on the artificial intelligence analysis method that captures the instruction history. The appropriate feature extraction algorithm is selected, and the restriction interval of specific compliance regulation and maintenance standards and the corresponding deep belief network algorithm profit data are used as training sample inputs for calculation. The feature extraction algorithm is continuously improved through deep learning, so as to achieve the best effect of the restriction interval of compliance regulation and maintenance standards.
[0025] The present invention has the following beneficial effects:
[0026] The present invention receives the deep belief network algorithm control request sent by the user preference and user behavior through the deep belief network algorithm data controller, and writes the user preference and user behavior information into the human-computer interaction unit according to the conversion into intelligent analysis results and user usage conditions. The deep belief network learning and optimization unit calls the information in the user physiological information evaluation unit during the capture instruction process and feeds back the current capture instruction user preference and user behavior human-computer interaction terminal information to complete the deep belief network algorithm development, improve the utilization rate of the deep belief network algorithm information and the deep belief network algorithm development data management strength, and enhance the deep belief network control quality. The invention uses the deep belief network algorithm to learn user parameters and human-computer interaction. The IP digital personalized theme system needs to receive the status reported by each human-computer interaction device for related protection logic processing, and uses artificial intelligence to perform logical processing on various data, which has good versatility. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.
[0028] Figure 1 This is a diagram showing the composition of the IP digital personalized theme human-computer interaction system unit of the present invention;
[0029] Figure 2 This is a flow chart of the use of the IP digital personalized theme human-computer interaction system of the present invention. DETAILED DESCRIPTION
[0030] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other. The present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0031] like Figure 1 As shown, the present invention is an IP digital personalized theme human-computer interaction system based on edge computing gateway, including a user preference, user behavior information collection human-computer interaction unit, an IP digital personalized theme system human-computer interaction unit, a user physiological information evaluation unit, a deep belief network algorithm management controller, a control instruction generation interaction unit, and a deep belief network learning and optimization unit;
[0032] User preference and user behavior information collection human-computer interaction unit, used for edge computing gateway to analyze user preference and user behavior demand information and capture instruction user preference and user behavior human-computer interaction terminal information;
[0033] The human-computer interaction unit of the IP digital personalized subject system is used to obtain the capture instruction data in real time through analysis and interaction, and forward the data to the user physiological information evaluation unit;
[0034] The user physiological information evaluation unit is used by the edge computing gateway to perform real-time user physiological evaluation and instruction timing correction using the deep belief network algorithm. It consists of different user information collection sensors and an edge computing gateway.
[0035] The usage scenario switching and adjustment unit stores the parameters used by the user and the intervals for parameter adjustment; the usage scenario switching and adjustment unit is used to select the dynamic change parameters and change time of the deep belief network algorithm for user usage control per unit time from the IP digital personalized theme human-computer interaction system control data storage library, and send it to the trader work cluster for unit time capture instructions;
[0036] A control instruction generation interaction unit is used to receive a capture instruction signal and generate and update a deep belief network control instruction according to the control instruction, and feed back the capture instruction result;
[0037] The deep belief network learning and optimization unit is used to obtain the deep belief network algorithm input parameters corresponding to the deep belief network algorithm data training request according to the deep belief network algorithm input parameters and deep belief network algorithm output parameters corresponding to the deep belief network algorithm data training request, access the pre-stored data information through the deep belief network algorithm input parameters and perform corresponding capture according to the human-computer interaction commands.
[0038] The captured instruction user preferences and user behavior human-computer interaction information are stored in the deep belief network algorithm data controller; the deep belief network algorithm data controller is used to receive deep belief network algorithm control requests sent by user preferences and user behaviors; the deep belief network algorithm control requests include deep belief network algorithm data training requests including deep belief network algorithm human-computer interaction program, deep belief network algorithm input parameters, deep belief network algorithm output parameters, deep belief network algorithm data, and human-computer interaction commands, and the deep belief network algorithm data training requests are sent to the corresponding usage scenario switching and adjustment units according to the deep belief network algorithm input parameters and deep belief network algorithm output parameters.
[0039] The user preferences and user behaviors sent by the capture instruction user preferences and user behavior human-computer interaction terminal information are converted into intelligent analysis results and user usage conditions and written into the user preferences and user behavior information collection human-computer interaction unit, and the capture instruction user preferences and user behavior human-computer interaction terminal information are sent to the terminal controller; the terminal controller receives the user preferences and user behavior human-computer interaction terminal information, writes it into the user physiological information evaluation unit, and sends the capture instruction user preferences and user behavior human-computer interaction terminal information to the edge computing gateway remote human-computer interaction platform when receiving the execution intelligent analysis result command of the control instruction generation interaction unit, and returns the capture instruction information inconsistent alarm signal when the capture instruction of the control instruction generation interaction unit is abnormal; the edge computing gateway remote human-computer interaction platform receives the user preferences and user behavior human-computer interaction terminal information, writes it into the user physiological information evaluation unit, and sends the capture instruction user preferences and user behavior human-computer interaction terminal information to the edge computing gateway when receiving the execution intelligent analysis result command of the control instruction generation interaction unit, and returns the capture instruction information inconsistent alarm signal when the capture instruction of the control instruction generation interaction unit is abnormal.
[0040] The control instruction generation interaction unit receives the capture instruction user preference, user behavior human-computer interaction end information, writes it into the user physiological information evaluation unit, and transfers the deep belief network algorithm modification and update rights according to the required content when receiving the execution intelligent analysis result command of the deep belief network learning and optimization unit, and returns the capture instruction information inconsistency alarm signal when receiving the capture instruction abnormality of the deep belief network learning and optimization unit; the IP digital personalized theme human-computer interaction system receives the capture instruction details, writes it into the user physiological information evaluation unit, and modifies and rewrites the human-computer interaction program according to the required content when receiving the execution intelligent analysis result command of the deep belief network learning and optimization unit, and returns the capture instruction information inconsistency alarm signal when receiving the capture instruction abnormality of the deep belief network learning and optimization unit; the IP digital personalized theme human-computer interaction system receives the information of transferring the deep belief network algorithm modification and update rights and rewriting the human-computer interaction program, and writes it into the user physiological information evaluation unit.
[0041] During the capture instruction process, the deep belief network learning and optimization unit calls the information in the user physiological information evaluation unit and feeds back the current capture instruction user preference and user behavior human-computer interaction information to determine whether the capture instruction user preference and user behavior human-computer interaction information meet the user usage conditions of the intelligent analysis results. If the number of usage scenario switching times stored in the usage scenario switching and adjustment unit is equal to the number of usage scenario switching times agreed in the intelligent analysis results; the update time stored in the usage scenario switching and adjustment unit is equal to the update time agreed in the intelligent analysis results; the update memory control type of the deep belief network algorithm management controller is equal to the update memory control type agreed in the intelligent analysis results; the capture instruction time stored in the usage scenario switching and adjustment unit is equal to the capture instruction time agreed in the intelligent analysis results, then send the execution intelligent analysis result command, otherwise send the capture instruction exception.
[0042] The user physiological information evaluation unit regulates and manages the capture process of each capture instruction working end, and also regulates and manages the capture instruction information set of the data filter network; in the initial state, the edge computing gateway logs in to the control human-computer interaction unit, and according to policy regulations and capture instruction control and maintenance standards, the control and maintenance standards of the deep belief network algorithm and the capture instruction process are initially set, including the trigger event standard setting, the interval setting of the limit interval and the initial value setting of the limit interval; the capture instruction data parsing controller also includes a control and maintenance parsing unit; the data acquisition device regularly sends the control and maintenance information related data to the capture instruction data parsing controller; the usage scenario switching and adjustment unit optimizes the real-time control and maintenance standard limit interval according to the actual user usage control data statistics and the historical capture instruction data statistics, generates a new control and maintenance standard, and sends the new control and maintenance standard to the control human-computer interaction unit to achieve the control and maintenance optimization of the deep belief network algorithm and the capture instruction process.
[0043] like Figure 2 As shown, the present invention is an IP digital personalized theme human-computer interaction system based on edge computing gateway, comprising the following steps:
[0044] Step S1, collecting user preference and user behavior information; the deep belief network algorithm data controller receives the deep belief network algorithm control request sent by the user preference and user behavior;
[0045] Step S2, the deep belief network algorithm data controller writes the user preference and user behavior information into the human-computer interaction unit according to the conversion into intelligent analysis results and user usage conditions; when the control instruction generates the interactive unit to execute the intelligent analysis result command, it sends the capture instruction user preference and user behavior human-computer interaction terminal information to the edge computing gateway remote human-computer interaction platform;
[0046] Step S3: When the control instruction generates the interactive unit to execute the intelligent analysis result command, it sends the capture instruction user preference and user behavior human-computer interaction terminal information to the edge computing gateway; when the deep belief network learning and optimization unit executes the intelligent analysis result command, it transfers the deep belief network algorithm modification and update rights according to the demand content;
[0047] Step S4, the IP digital personalized theme human-computer interaction system receives the capture instruction details, writes them into the user physiological information evaluation unit, and modifies and rewrites the human-computer interaction program according to the required content when receiving the execution intelligent analysis result command of the deep belief network learning and optimization unit;
[0048] Step S5, the deep belief network learning and optimization unit calls the information in the user physiological information evaluation unit during the capture instruction process and feeds back the current capture instruction user preference and user behavior human-computer interaction end information. If the negative feedback is successful, the deep belief network algorithm calculation is completed; the user physiological information evaluation unit regulates and manages the capture process of each capture instruction working end.
[0049] The user preference and user behavior information collection human-computer interaction unit includes a user preference and user behavior real-time management unit and a capture instruction user preference and user behavior terminal management unit.
[0050] Regulation management is handled through the restriction interval of regulation and maintenance standards. The optimization of the restriction interval of regulation and maintenance standards is carried out based on the artificial intelligence analysis method that captures the instruction history. The appropriate feature extraction algorithm is selected, and the restriction interval of specific compliance regulation and maintenance standards and the corresponding deep belief network algorithm profit data are used as training sample inputs for calculation. The feature extraction algorithm is continuously improved through deep learning, so as to achieve the best effect of the restriction interval of compliance regulation and maintenance standards.
[0051] It is worth noting that in the above-mentioned IP digital personalized theme human-computer interaction system embodiment, the various units included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.
[0052] In addition, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiments can be completed by commanding related hardware through a program, and the corresponding program can be stored in a computer-readable storage medium.
[0053] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", "connect", and "fix" 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 elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances.
[0054] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various equivalent changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. IP digital personalized theme human-computer interaction system based on edge computing gateway, characterized in that: It includes user preference, user behavior information collection human-computer interaction unit, IP digital personalized theme system human-computer interaction unit, user physiological information evaluation unit, usage scenario switching and adjustment unit, control instruction generation interaction unit, and deep belief network learning and optimization unit; The user preference and user behavior information collection human-computer interaction unit is used for the edge computing gateway to analyze user preferences and user behavior demand information and capture instruction user preferences and user behavior human-computer interaction terminal information; The IP digital personalized subject system human-computer interaction unit is used to acquire the capture instruction data in real time through analysis and interaction, and forward the data to the user physiological information evaluation unit; The user physiological information evaluation unit is used for the edge computing gateway to perform real-time user physiological evaluation and instruction timing correction using a deep belief network algorithm, and is composed of different user information collection sensors and an edge computing gateway; The usage scenario switching and adjustment unit stores the parameters used by the user and the intervals for parameter adjustment; The usage scenario switching and adjustment unit is used to select the dynamic change parameters and change time of the deep belief network algorithm for user use control per unit time from the IP digital personalized theme human-computer interaction system control data repository, and send it to the edge computing gateway working cluster for unit time capture instructions; The control instruction generation interaction unit is used to receive the capture instruction signal and generate and update the deep belief network control instruction according to the control instruction, and feed back the capture instruction result; The deep belief network learning and optimization unit is used to obtain access to the deep belief network algorithm input parameters corresponding to the deep belief network algorithm data training request according to the deep belief network algorithm input parameters and deep belief network algorithm output parameters corresponding to the deep belief network algorithm data training request, access pre-stored data information through the deep belief network algorithm input parameters, and perform corresponding capture according to the human-computer interaction command.
2. According to claim 1, the IP digital personalized theme human-computer interaction system based on edge computing gateway is characterized in that: The captured instruction user preferences and user behavior human-computer interaction information are stored in the deep belief network algorithm data controller; the deep belief network algorithm data controller is used to receive deep belief network algorithm control requests sent by user preferences and user behaviors; the deep belief network algorithm control request includes a deep belief network algorithm data training request including a deep belief network algorithm human-computer interaction program, deep belief network algorithm input parameters, deep belief network algorithm output parameters, deep belief network algorithm data, and human-computer interaction commands, and the deep belief network algorithm data training request is sent to the corresponding usage scenario switching and adjustment unit according to the deep belief network algorithm input parameters and deep belief network algorithm output parameters.
3. The IP digital personalized theme human-computer interaction system based on edge computing gateway according to claim 1 is characterized in that: The user preferences and user behaviors sent by the user are converted into intelligent analysis results and user usage conditions and written into the user preferences and user behavior information collection human-computer interaction unit, and the user preferences and user behavior human-computer interaction information are sent to the terminal controller; The terminal controller receives user preferences and user behavior human-computer interaction information, writes it into the user physiological information evaluation unit, and sends the capture instruction user preferences and user behavior human-computer interaction information to the edge computing gateway remote human-computer interaction platform when receiving the execution intelligent analysis result command of the control instruction generation interaction unit, and returns the capture instruction information inconsistent alarm signal when receiving the capture instruction of the control instruction generation interaction unit abnormally; The edge computing gateway remote human-computer interaction platform receives user preferences and user behavior human-computer interaction end information, writes it into the user physiological information evaluation unit, and sends the capture instruction user preferences and user behavior human-computer interaction end information to the edge computing gateway when receiving the control instruction to generate the interactive unit's execution intelligent analysis result command. When receiving the control instruction to generate the interactive unit's capture instruction and the capture instruction information is abnormal, it returns an alarm signal of inconsistent capture instruction information.
4. The IP digital personalized theme human-computer interaction system based on edge computing gateway according to claim 1 is characterized in that: The control instruction generation interaction unit receives the capture instruction user preference and user behavior human-computer interaction terminal information, writes it into the user physiological information evaluation unit, transfers the deep belief network algorithm modification and update rights according to the demand content when receiving the execution intelligent analysis result command of the deep belief network learning and optimization unit, and returns the capture instruction information inconsistent alarm signal when receiving the capture instruction abnormality of the deep belief network learning and optimization unit; The IP digital personalized theme human-computer interaction system receives the capture instruction details, writes them into the user physiological information evaluation unit, modifies and rewrites the human-computer interaction program according to the required content when receiving the execution intelligent analysis result command of the deep belief network learning and optimization unit, and returns the capture instruction information inconsistent alarm signal when receiving the capture instruction abnormality of the deep belief network learning and optimization unit; The IP digital personalized subject human-computer interaction system receives the transfer of deep belief network algorithm modification and update rights, rewrites the information of the human-computer interaction program, and writes it into the user physiological information evaluation unit.
5. The IP digital personalized theme human-computer interaction system based on edge computing gateway according to claim 1 is characterized in that: The deep belief network learning and optimization unit calls the information in the user physiological information evaluation unit and the current capture instruction user preference and user behavior human-computer interaction terminal information for feedback during the capture instruction process, and determines whether the capture instruction user preference and user behavior human-computer interaction terminal information meet the user use conditions of the intelligent analysis results, if the number of use scene switching stored in the use scene switching and adjustment unit is equal to the number of use scene switching agreed in the intelligent analysis results; The update time stored in the scene switching and adjustment unit is equal to the update time agreed in the intelligent analysis result; The update memory control type of the usage scenario switching and adjustment unit is equal to the update memory control type agreed in the intelligent analysis result; The capture instruction time stored in the scene switching and adjustment unit is equal to the capture instruction time agreed in the intelligent analysis result. If it is, the intelligent analysis result execution command is sent, otherwise the capture instruction is sent abnormally.
6. The IP digital personalized theme human-computer interaction system based on edge computing gateway according to claim 1 is characterized in that: The user physiological information evaluation unit regulates and manages the capture process of each capture instruction working end, and also regulates and manages the capture instruction information set of the data filter network; in the initial state, the edge computing gateway logs in to the regulation human-computer interaction unit, and according to policy regulations and capture instruction regulation and maintenance standards, initially sets the regulation and maintenance standards of the deep belief network algorithm and the capture instruction process, including trigger event standard setting, limit interval setting and initial value setting of the limit interval; the capture instruction data parsing controller also includes a regulation and maintenance parsing unit; the data acquisition device regularly sends the regulation and maintenance information related data to the capture instruction data parsing controller; the usage scenario switching and adjustment unit optimizes the real-time regulation and maintenance standard limit interval according to the actual user usage control data statistics and the historical capture instruction data statistics, generates a new regulation and maintenance standard, and sends the new regulation and maintenance standard to the regulation human-computer interaction unit to achieve regulation and maintenance optimization of the deep belief network algorithm and the capture instruction process.
7. According to the IP digital personalized theme human-computer interaction system based on edge computing gateway according to claims 1-6, the operation of the system comprises the following steps: Step S1, collecting user preference and user behavior information; the deep belief network algorithm data controller receives the deep belief network algorithm control request sent by the user preference and user behavior; Step S2, the deep belief network algorithm data controller writes the user preference and user behavior information into the human-computer interaction unit according to the conversion into intelligent analysis results and user usage conditions; when the control instruction generates the interactive unit to execute the intelligent analysis result command, it sends the capture instruction user preference and user behavior human-computer interaction terminal information to the edge computing gateway remote human-computer interaction platform; Step S3: When the control instruction generates the interactive unit to execute the intelligent analysis result command, it sends the capture instruction user preference and user behavior human-computer interaction terminal information to the edge computing gateway; when the deep belief network learning and optimization unit executes the intelligent analysis result command, it transfers the deep belief network algorithm modification and update rights according to the demand content; Step S4, the IP digital personalized theme human-computer interaction system receives the capture instruction details, writes them into the user physiological information evaluation unit, and modifies and rewrites the human-computer interaction program according to the required content when receiving the execution intelligent analysis result command of the deep belief network learning and optimization unit; Step S5, the deep belief network learning and optimization unit calls the information in the user physiological information evaluation unit during the capture instruction process and feeds back the current capture instruction user preference and user behavior human-computer interaction terminal information. If the negative feedback is successful, the deep belief network algorithm calculation is completed; The user physiological information evaluation unit regulates and manages the capture process of each capture instruction working end.
8. The IP digital personalized theme human-computer interaction system based on edge computing gateway according to claim 7 is characterized in that: The user preference and user behavior information collection human-computer interaction unit includes a user preference and user behavior real-time management unit and a capture instruction user preference and user behavior terminal management unit.