An interactive intelligent system based on real-time data

Abstract

The present invention relates to the field of intelligent interaction technology, and discloses an interactive intelligent system based on real-time data, including a multidimensional perception module, an intelligent analysis module and an interactive service module. The system collects data sets through the multidimensional perception module, and the intelligent analysis module calculates and generates the real-time data group Jssj, and only analyzes and processes the variable data and emotional state data collected at the latest time point, which effectively reduces the amount of calculation in the data processing link, and the subsequent judgment and decision-making efficiency is higher. The intelligent analysis module calculates and generates the fluctuation index Bdzs and the abnormality index Ybzs, deeply analyzes the correlation between the fluctuation changes of the real-time data set and the fluctuation changes of the emotional data set, has strong emotional perception ability and more humanized interactive experience, and the interactive service module judges the interactive service content corresponding to the decision, and executes the interactive service content through the network connection human-computer interaction device, and has strong load balancing and flexible applicability.

Description

Technical Field

[0001] The present invention relates to the field of intelligent interaction technology, and in particular to an interactive intelligent system based on real-time data. Background Art

[0002] An interactive intelligent system based on real-time data is an intelligent system that uses real-time data streams to enhance user experience and system decision-making capabilities. It usually combines data collection, processing, analysis, and feedback mechanisms to achieve dynamic interaction with users or other systems. The workflow of an interactive intelligent system includes data collection, data processing and analysis, intelligent decision-making, interactive feedback, and learning optimization. An interactive intelligent system collects data in real time through various sensors, software agents, or APIs. This data can be user behavior data, environmental data, financial data, etc., depending on the application scope of the system. The collected data is sent to a central processing unit or distributed system for processing, and the data is cleaned, integrated, and analyzed to extract valuable information. The analysis may include machine learning algorithms, statistical models, or rule engines. Based on the results of processing and analysis, the system is able to make intelligent decisions. These decisions may be automatically executed or may be recommendations or warnings provided to users. The system interacts with users through graphical user interfaces (GUIs), voice responses, text messages, etc. to provide decision support, notifications, or other services. Interactive intelligent systems usually have the ability to learn and can optimize themselves through continuous interaction. Interactive intelligent systems focus on factors such as hardware performance, running time, system optimization, user interaction frequency, and environmental control, and more effectively control decision-making quality and interactive service levels.

[0003] At present, traditional interactive intelligent systems based on real-time data lack the ability to perceive emotions and are unable to understand and respond to users' emotional states, which affects the level of humanization and personalization of user experience. They also have weak load balancing capabilities when processing dynamic real-time data with a large time span and lack sufficient flexibility and applicability. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides an interactive intelligent system based on real-time data, which has the advantages of strong emotion perception ability, more humanized interactive experience, balanced load, flexible applicability and strong adaptability. It solves the problem that traditional interactive intelligent systems based on real-time data lack emotion perception ability, weak load balancing ability and lack of flexibility and applicability.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] According to a first aspect of the present invention, an interactive intelligent system based on real-time data is provided.

[0009] The system includes:

[0010] Multi-dimensional perception module, intelligent analysis module and interactive service module;

[0011] The multi-dimensional perception module includes a real-time data unit, an emotion perception unit and a logic data unit; the real-time data unit collects real-time data sets through a network connection with multiple sensors and transmits them to the intelligent analysis module through the network; the real-time data sets include variable data collected in real time by multiple sensors, and the real-time data set expression is t represents the time point of variable collection, z represents the type of variable collection, to The variable data content, time point and type are sequentially corresponding to the variable data content, time point and type collected by multiple sensors in real time; the emotion perception unit collects the emotion data set through the network connection human-computer interaction device, and transmits it to the intelligent analysis module through the network; the emotion data set includes the user's emotional state collected by the human-computer interaction device, and the emotion data set expression is s represents the time point when the user expresses his / her emotional state through voice, y represents the voice content when the user expresses his / her emotional state through voice, to The logical data unit collects the logical data set through the network connection database, and transmits it to the intelligent analysis module through the network; the logical data set includes the logical architecture used in the database for decision-making interactive service content, and the logical data set expression is d represents the priority of the logic level. to Corresponding to the logical content and priority level of each level of the logical architecture in turn; the multi-dimensional perception module is connected to the intelligent analysis module through a network;

[0012] The intelligent analysis module includes a real-time analysis unit, a fluctuation analysis unit and an anomaly analysis unit; the real-time analysis unit calculates and generates an instant data set Jssj based on the real-time data set, the emotion data set and the logic data set, and transmits it to the anomaly analysis unit through the network; wherein the calculation formula of the instant data set Jssj is as follows:

[0013]

[0014] In the formula, Jssj represents the real-time data group. It generates a complete logic tree L by integrating the logic contents of all levels according to the priorities of different levels in the logic data set. It means that the variable data collected at the latest time in the real-time data set and the user emotional state data collected at the latest time in the emotional data set are substituted into the logic tree L, and after multi-level logical judgment, the real-time data group is calculated and generated; the fluctuation analysis unit calculates and generates the fluctuation index Bdzs according to the real-time data set and the emotional data set, and transmits it to the abnormality analysis unit through the network; the abnormality analysis unit calculates and generates the abnormality index Ybzs according to the real-time data group Jssj and the fluctuation index Bdzs; the intelligent analysis module is connected to the interactive service module through the network;

[0015] The interactive service module determines and decides the corresponding interactive service content according to the real-time data group Jssj, the fluctuation index Bdzs and the abnormality index Ybzs, and executes the interactive service content through the network connection human-computer interaction device.

[0016] Further: the fluctuation analysis unit calculates and generates the fluctuation index Bdzs according to the real-time data set and the sentiment data set, and the calculation process is as follows:

[0017] First calculate the real-time data set The variable fluctuation difference at each different time point, the first and last variable fluctuation difference and the average variable fluctuation difference;

[0018]

[0019] In the formula, It represents the difference in the variable fluctuation at each different time point from 1 to n in the real-time data set. It represents the difference in the fluctuation of the first and last variables obtained by subtracting the variable data at the latest time point from the variable data at the initial time point in the real-time data set. It represents the average variable fluctuation change difference obtained by subtracting the average variable fluctuation change at the previous time point from the average variable fluctuation change at the latest time point in the real-time data set;

[0020] Computing sentiment datasets The difference in mood fluctuation at each different time point, the difference in mood fluctuation at the beginning and end, and the average difference in mood fluctuation;

[0021]

[0022] In the formula, It represents the difference in emotional fluctuations at each different time point from 1 to n in the emotional data set. It represents the difference between the emotional state data at the latest time point and the emotional state data at the initial time point in the emotional data set, It represents the average emotion fluctuation change difference obtained by subtracting the average emotion fluctuation change at the previous time point from the average emotion fluctuation change at the latest time point in the emotion dataset;

[0023] Calculate the volatility index Bdzs based on the volatility change ΔS of the real-time data set and the volatility change ΔQ of the sentiment data set;

[0024]

[0025] In the formula, Bdzs represents the volatility index, Indicates the ratio of the fluctuation of the real-time dataset to the fluctuation of the sentiment dataset.

[0026] Further: the mutation analysis unit calculates and generates the mutation index Ybzs according to the real-time data group Jssj and the fluctuation index Bdzs, and the calculation formula is as follows:

[0027] Jsj - =Jssj t -Jssj t-1

[0028]

[0029] In the formula, Ybzs represents the mutation index, Jssj t -Jssj t-1 It represents the fluctuation change of the instant data group obtained by subtracting the instant data group at the previous time point from the instant data group at the latest time point. α represents the weight index for the fluctuation change of the instant data group in the mutation index calculation formula. β represents the weight index for the fluctuation index in the mutation index calculation formula. BZ represents the fixed parameter that defines the mutation standard in the mutation index calculation formula. It means the mutation index calculated by comparing the volatility change and volatility index of the real-time data group with fixed parameters under different weights.

[0030] Further: when only the instant data group Jssj exists among the instant data group Jssj, the fluctuation index Bdzs and the mutation index Ybzs, the interactive service module determines that the time span of the current dynamic real-time data is small, and the human-computer interaction device only executes the interactive service content according to the instant data group Jssj.

[0031] Further: when any value of the fluctuation index Bdzs and the mutation index Ybzs exceeds the preset value, the interactive service module determines that the current dynamic real-time data fluctuation is large, and the human-computer interaction device simultaneously executes the interactive service content according to the maximum and minimum values ​​of the real-time data group Jssj.

[0032] Furthermore: when the fluctuation index Bdzs is greater than 1, the interactive service module determines that there is a correlation between the fluctuation change of the real-time data set and the fluctuation change of the emotional data set, and the human-computer interaction device actively adds an emotional comfort voice service while executing the interactive service content.

[0033] According to a second aspect of the present invention, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method in the interactive intelligent system based on real-time data is executed.

[0034] According to a third aspect of the present invention, there is provided a storage medium, which stores a computer program that can be executed by one or more processors and can be used to implement the method in the interactive intelligent system based on real-time data.

[0035] Compared with the prior art, the present invention provides an interactive intelligent system based on real-time data, which has the following beneficial effects:

[0036] 1. The present invention sets a real-time data unit, an emotion perception unit and a logic data unit through a multi-dimensional perception module, and collects real-time data sets, emotion data sets and logic data sets respectively. The intelligent analysis module sets a real-time analysis unit, a fluctuation analysis unit and an abnormality analysis unit. The real-time analysis unit calculates and generates an instant data group Jssj, and only analyzes and processes the variable data and emotion state data collected at the latest time point, which effectively reduces the amount of calculation in the data processing link, and the subsequent judgment and decision-making efficiency is higher. The fluctuation analysis unit calculates and generates a fluctuation index Bdzs, and analyzes the fluctuation changes of the real-time data set and the fluctuation changes of the emotion data set from three dimensions, and deeply analyzes the correlation between the fluctuation changes of the real-time data set and the fluctuation changes of the emotion data set. When the fluctuation index Bdzs is greater than 1, the interactive service module determines that there is a correlation between the fluctuation changes of the real-time data set and the fluctuation changes of the emotion data set, indicating that the user's emotional state is fluctuating with the real-time data. While the human-computer interaction device executes the interactive service content, it actively adds a voice service for emotional comfort, and the emotional perception ability is strong and the interactive experience is more humane.

[0037] 2. The present invention calculates and generates the mutation index Ybzs according to the real-time data group Jssj and the fluctuation index Bdzs through the mutation analysis unit, analyzes the fluctuation changes of the real-time data group from a macro perspective, and judges the degree of mutation of real-time data and real-time decision-making in combination with the fluctuation index, which helps to improve the accuracy of subsequent judgments and decisions. When only the real-time data group Jssj exists in the real-time data group Jssj, the fluctuation index Bdzs and the mutation index Ybzs, the interactive service module determines that the time span of the current dynamic real-time data is small, and the human-computer interaction device only executes the interactive service content according to the real-time data group Jssj. When any value of the fluctuation index Bdzs and the mutation index Ybzs exceeds the preset value, the interactive service module determines that the current dynamic real-time data fluctuation changes greatly, and the human-computer interaction device simultaneously executes the interactive service content according to the maximum and minimum values ​​of the real-time data group Jssj, and the load balancing is flexible and highly applicable. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present invention and do not constitute a limitation of the present invention. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0039] Figure 1 A schematic diagram of the structure of an interactive intelligent system based on real-time data according to an embodiment of the present invention is shown;

[0040] Figure 2 A block diagram of an exemplary electronic device capable of implementing embodiments of the present invention is shown. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Figure 1 A schematic diagram of the structure of an interactive intelligent system based on real-time data according to an embodiment of the present invention is shown, and the system includes:

[0043] Multi-dimensional perception module 110, intelligent analysis module 120 and interactive service module 130;

[0044] The multi-dimensional perception module 110 includes a real-time data unit 111, an emotion perception unit 112 and a logic data unit 113. The real-time data unit 111 collects real-time data sets through a network connection with multiple sensors and transmits the real-time data sets to the intelligent analysis module 120 through the network. The real-time data sets include variable data collected in real time by multiple sensors. The real-time data set expression is: t represents the time point of variable collection, z represents the type of variable collection, to The variable data content, time point and type collected by multiple sensors in real time are sequentially corresponded. For the continuously generated real-time data, the real-time data unit 111 uses dynamic data integration technology to ensure that new data can be quickly integrated into the input system processing flow, effectively speeding up the system processing speed. The emotion perception unit 112 collects the emotion data set through the network connection human-computer interaction device and transmits it to the intelligent analysis module 120 through the network. The emotion data set includes the user's emotional state collected by the human-computer interaction device. The emotion data set expression is s represents the time point when the user expresses his / her emotional state through voice, y represents the voice content when the user expresses his / her emotional state through voice, to The corresponding human-computer interaction device collects the user's emotional state, expression time point and voice content. The voice contains rich emotional information. The speaker's emotional state can be quickly inferred through intonation, speaking speed and volume. The emotional data set contains user emotional feature information with decision-making significance. The high perception ability ensures the accuracy of system decision-making and the quality of interaction. The logical data unit 113 collects the logical data set through the network connection database and transmits it to the intelligent analysis module 120 through the network. The logical data set includes the logical architecture in the database for decision-making interactive service content. The logical framework is a permanent and fixed logical architecture. The logical data set expression is d represents the priority of the logic level. to Corresponding to the logical content and priority level of each level of the logical architecture, each level of the constant logical architecture has specific responsibilities, and each level has low coupling. Changing the logical content of one level will not affect other levels, making the system easier to maintain and update, and has good scalability, which helps to improve subsequent development efficiency and system performance. The multi-dimensional perception module 110 is connected to the intelligent analysis module 120 through a network;

[0045] The intelligent analysis module 120 includes a real-time analysis unit 121, a fluctuation analysis unit 122 and an abnormality analysis unit 123. The real-time analysis unit 121 calculates and generates an instant data set Jssj based on the real-time data set, the emotion data set and the logic data set, and transmits it to the abnormality analysis unit 123 through the network. The calculation formula is as follows:

[0046]

[0047]

[0048] In the formula, Jssj represents the real-time data group. According to the priorities of different levels in the logical data set, the complete logic tree L is generated by integrating the logic contents of all levels. As the logic contents change, the system can easily adjust and expand the logic layer, which improves the response speed and reliability of the system. It means that the variable data collected at the latest time in the real-time data set and the user emotional state data collected at the latest time in the emotional data set are substituted into the logic tree L. After multi-level logical judgment, the instant data group generated is calculated. According to the instant data group Jssj, only the variable data and emotional state data collected at the latest time point are analyzed and processed, which effectively reduces the calculation amount of the data processing link, and the subsequent judgment and decision-making efficiency is higher;

[0049] The fluctuation analysis unit 122 calculates and generates the fluctuation index Bdzs based on the real-time data set and the sentiment data set, and transmits it to the abnormality analysis unit 123 through the network. The calculation process is as follows:

[0050] First calculate the real-time data set The variable fluctuation difference at each different time point, the first and last variable fluctuation difference and the average variable fluctuation difference;

[0051]

[0052] In the formula, It represents the difference in the variable fluctuation at each different time point from 1 to n in the real-time data set. It represents the difference in the fluctuation of the first and last variables obtained by subtracting the variable data at the latest time point from the variable data at the initial time point in the real-time data set. It represents the average variable fluctuation change difference obtained by subtracting the average variable fluctuation change at the previous time point from the average variable fluctuation change at the latest time point in the real-time data set;

[0053] Computing sentiment datasets The difference in mood fluctuation at each different time point, the difference in mood fluctuation at the beginning and end, and the average difference in mood fluctuation;

[0054]

[0055] In the formula, It represents the difference in emotional fluctuations at each different time point from 1 to n in the emotional data set. It represents the difference between the emotional state data at the latest time point and the emotional state data at the initial time point in the emotional data set, It represents the average emotion fluctuation change difference obtained by subtracting the average emotion fluctuation change at the previous time point from the average emotion fluctuation change at the latest time point in the emotion dataset;

[0056] Calculate the volatility index Bdzs based on the volatility change ΔS of the real-time data set and the volatility change ΔQ of the sentiment data set;

[0057]

[0058] In the formula, Bdzs represents the volatility index, It indicates the ratio of the fluctuation change of the real-time data set to the fluctuation change of the emotional data set. According to the fluctuation index Bdzs, the fluctuation change of the real-time data set and the fluctuation change of the emotional data set are analyzed from three dimensions. At the same time, the correlation between the fluctuation change of the real-time data set and the fluctuation change of the emotional data set is deeply analyzed. The emotional perception ability is strong and the interactive experience is more humanized.

[0059] The abnormality analysis unit 123 calculates and generates the abnormality index Ybzs according to the real-time data group Jssj and the fluctuation index Bdzs, and the calculation formula is as follows:

[0060] Jsj - =Jssj t -Jssj t-1

[0061]

[0062] In the formula, Ybzs represents the mutation index, Jssj t -Jssj t-1 It represents the fluctuation change of the instant data group obtained by subtracting the instant data group at the previous time point from the instant data group at the latest time point. α represents the weight index for the fluctuation change of the instant data group in the mutation index calculation formula. β represents the weight index for the fluctuation index in the mutation index calculation formula. BZ represents the fixed parameter that defines the mutation standard in the mutation index calculation formula. It means that under different weights, the fluctuation change of the real-time data group and the fluctuation index are compared with fixed parameters, and the calculated mutation index is used to analyze the fluctuation change of the real-time data group from a macro perspective according to the mutation index Ybzs. Then, the degree of mutation of real-time data and real-time decision-making is judged in combination with the fluctuation index, which helps to improve the accuracy of subsequent judgments.

[0063] The intelligent analysis module 120 is connected to the interactive service module 130 via a network;

[0064] The interactive service module 130 determines the interactive service content corresponding to the decision according to the real-time data group Jssj, the fluctuation index Bdzs and the abnormality index Ybzs, and executes the interactive service content through the network connection human-computer interaction device, and the load balancing is flexible and applicable;

[0065] When only the real-time data group Jssj exists among the real-time data group Jssj, the fluctuation index Bdzs and the abnormality index Ybzs, the interactive service module 130 determines that the time span of the current dynamic real-time data is small, and the human-computer interaction device only performs the interactive service content according to the real-time data group Jssj;

[0066] When any value of the fluctuation index Bdzs and the abnormality index Ybzs exceeds the preset value, the interactive service module 130 determines that the current dynamic real-time data fluctuation is large, and the human-computer interaction device simultaneously executes the interactive service content according to the maximum and minimum values ​​of the real-time data group Jssj, which effectively improves the applicability of the interactive service;

[0067] When the fluctuation index Bdzs is greater than 1, the interactive service module 130 determines that there is a correlation between the fluctuation changes of the real-time data set and the fluctuation changes of the emotional data set, indicating that the user's emotional state is fluctuating with the real-time data. While the human-computer interaction device executes the interactive service content, it actively adds emotional comfort voice services, and the emotional perception ability is strong, and the interactive experience is more humane.

[0068] According to an embodiment of the present invention, the present invention also provides an electronic device and a readable storage medium.

[0069] Figure 2 A schematic block diagram of an electronic device that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0070] The electronic device includes a computing unit 201, which can perform various appropriate actions and processes according to a computer program stored in ROM 202 or a computer program loaded from a storage unit 208 into RAM 203. In RAM 203, various programs and data required for the operation of the electronic device can also be stored. The computing unit 201, ROM 202, and RAM 203 are connected to each other via a bus 204. An I / O interface 205 is also connected to the bus 204.

[0071] A number of components in the electronic device are connected to the I / O interface 205, including: an input unit 206, such as a keyboard, a mouse, etc.; an output unit 207, such as various types of displays, speakers, etc.; a storage unit 208, such as a disk, an optical disk, etc.; and a communication unit 209, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 209 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0072] The computing unit 201 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 201 performs the various methods and processes described above. For example, in some embodiments, the method in the interactive intelligent system based on real-time data may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 208. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 202 and / or a communication unit 209. When the computer program is loaded into RAM 203 and executed by the computing unit 201, one or more steps of the method in the interactive intelligent system based on real-time data described above may be performed. Alternatively, in other embodiments, the computing unit 201 may be configured to perform the method in the interactive intelligent system based on real-time data in any other appropriate manner (e.g., by means of firmware).

[0073] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0074] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0075] In the context of the present invention, a readable storage medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A readable storage medium may be a machine-readable signal medium or a machine-readable storage medium. A readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. More specific examples of readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0076] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0077] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communications network). Examples of communications networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0078] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0079] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various 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. An interactive intelligent system based on real-time data, characterized in that: Including multi-dimensional perception module, intelligent analysis module and interactive service module; The multi-dimensional perception module includes a real-time data unit, an emotion perception unit and a logic data unit; The real-time data unit collects real-time data sets through network connection with multiple sensors and transmits them to the intelligent analysis module through the network; the real-time data sets include variable data collected by multiple sensors in real time, and the real-time data set expression is: t represents the time point of variable collection, z represents the type of variable collection, to The variable data content, time point and type are sequentially corresponding to the variable data content, time point and type collected by multiple sensors in real time; the emotion perception unit collects the emotion data set through the network connection human-computer interaction device, and transmits it to the intelligent analysis module through the network; the emotion data set includes the user's emotional state collected by the human-computer interaction device, and the emotion data set expression is s represents the time point when the user expresses his / her emotional state through voice, y represents the voice content when the user expresses his / her emotional state through voice, to The logical data unit collects the logical data set through the network connection database, and transmits it to the intelligent analysis module through the network; the logical data set includes the logical architecture used in the database for decision-making interactive service content, and the logical data set expression is d represents the priority of the logic level. to Corresponding to the logical content and priority level of each level of the logical architecture in turn; the multi-dimensional perception module is connected to the intelligent analysis module through a network; The intelligent analysis module includes a real-time analysis unit, a fluctuation analysis unit and an abnormality analysis unit; The real-time analysis unit calculates and generates the instant data set Jssj according to the real-time data set, the emotion data set and the logic data set, and transmits it to the abnormality analysis unit through the network; wherein the calculation formula of the instant data set Jssj is as follows: In the formula, Jssj represents the real-time data group. It generates a complete logic tree L by integrating the logic contents of all levels according to the priorities of different levels in the logic data set. It means that the variable data collected at the latest time in the real-time data set and the user emotional state data collected at the latest time in the emotional data set are substituted into the logic tree L, and after multi-level logical judgment, the real-time data group is calculated and generated; the fluctuation analysis unit calculates and generates the fluctuation index Bdzs according to the real-time data set and the emotional data set, and transmits it to the abnormality analysis unit through the network; the abnormality analysis unit calculates and generates the abnormality index Ybzs according to the real-time data group Jssj and the fluctuation index Bdzs; the intelligent analysis module is connected to the interactive service module through the network; The interactive service module determines and decides the corresponding interactive service content according to the real-time data group Jssj, the fluctuation index Bdzs and the abnormality index Ybzs, and executes the interactive service content through the network connection human-computer interaction device.

2. The interactive intelligent system based on real-time data according to claim 1, characterized in that: The fluctuation analysis unit calculates and generates the fluctuation index Bdzs based on the real-time data set and the sentiment data set. The calculation process is as follows: First calculate the real-time data set The variable fluctuation difference at each different time point, the first and last variable fluctuation difference and the average variable fluctuation difference; In the formula, It represents the difference in the variable fluctuation at each different time point from 1 to n in the real-time data set. It represents the difference in the fluctuation of the first and last variables obtained by subtracting the variable data at the latest time point from the variable data at the initial time point in the real-time data set. It represents the average variable fluctuation change difference obtained by subtracting the average variable fluctuation change at the previous time point from the average variable fluctuation change at the latest time point in the real-time data set; Computing sentiment datasets The difference in mood fluctuation at each different time point, the difference in mood fluctuation at the beginning and end, and the average difference in mood fluctuation; In the formula, It represents the difference in emotional fluctuations at each different time point from 1 to n in the emotional data set. It represents the difference between the emotional state data at the latest time point and the emotional state data at the initial time point in the emotional data set, It represents the average emotion fluctuation change difference obtained by subtracting the average emotion fluctuation change at the previous time point from the average emotion fluctuation change at the latest time point in the emotion dataset; Calculate the volatility index Bdzs based on the volatility change ΔS of the real-time data set and the volatility change ΔQ of the sentiment data set; In the formula, Bdzs represents the volatility index, Indicates the ratio of the fluctuation of the real-time dataset to the fluctuation of the sentiment dataset.

3. The interactive intelligent system based on real-time data according to claim 2, characterized in that: The variation analysis unit calculates and generates the variation index Ybzs according to the real-time data group Jssj and the fluctuation index Bdzs, and the calculation formula is as follows: Yessss - =Jssj t -Jssj t-1 In the formula, Ybzs represents the mutation index, Jssj t -Jssj t-1 It represents the fluctuation change of the instant data group obtained by subtracting the instant data group at the previous time point from the instant data group at the latest time point. α represents the weight index for the fluctuation change of the instant data group in the mutation index calculation formula. β represents the weight index for the fluctuation index in the mutation index calculation formula. BZ represents the fixed parameter that defines the mutation standard in the mutation index calculation formula. It means the mutation index calculated by comparing the volatility change and volatility index of the real-time data group with fixed parameters under different weights.

4. The interactive intelligent system based on real-time data according to claim 3, characterized in that: When only the real-time data group Jssj exists among the real-time data group Jssj, the fluctuation index Bdzs and the mutation index Ybzs, the interactive service module determines that the time span of the current dynamic real-time data is small, and the human-computer interaction device only executes the interactive service content according to the real-time data group Jssj.

5. The interactive intelligent system based on real-time data according to claim 4, characterized in that: When any value of the fluctuation index Bdzs and the mutation index Ybzs exceeds the preset value, the interactive service module determines that the current dynamic real-time data fluctuation is large, and the human-computer interaction device simultaneously executes the interactive service content according to the maximum and minimum values ​​of the real-time data group Jssj.

6. The interactive intelligent system based on real-time data according to claim 5, characterized in that: When the fluctuation index Bdzs is greater than 1, the interactive service module determines that there is a correlation between the fluctuation change of the real-time data set and the fluctuation change of the emotional data set, and the human-computer interaction device actively adds an emotional comfort voice service while executing the interactive service content.

7. An electronic device, characterized in that: The system comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method in the interactive intelligent system based on real-time data as claimed in any one of claims 1 to 6 is executed.

8. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the method in the interactive intelligent system based on real-time data as described in any one of claims 1 to 6.

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