A joint learning system based on the Internet of Things

Abstract

The present invention discloses a joint learning system based on the Internet of Things, comprising an intelligent ecosystem, a joint learning planning system, a joint learning engine system, a platform support system, a local server, and an Internet of Things access learning terminal. The joint learning planning system is interconnected with the joint learning engine system, which is interconnected with the platform support system. The intelligent ecosystem is interconnected with the joint learning planning system, the joint learning engine system, and the platform support system. The proposed Internet of Things-based joint learning system utilizes a touch screen to input learning object information, making it easy to operate. The Internet of Things system is used to pre-process the transmitted learning object information, significantly reducing the pressure on the intelligent learning terminal. The intelligent learning terminal is used to cooperate with the ML / DL algorithm system for learning, improving accuracy and enabling real-time learning, thereby further improving the content of the intelligent ecosystem.

Description

Technical Field

[0001] The present invention relates to the field of joint learning technology, and in particular to a joint learning system based on the Internet of Things. Background Art

[0002] The existing smart ecosystem is used to store various types of data, including data, models, and resource information. However, the existing smart ecosystem cannot achieve real-time learning and update the database content. As a result, the retrieved information is not the latest, the content is improved slowly, and the timeliness of the updated data, models, and resources is not high. To solve the above problems, a joint learning system based on the Internet of Things is proposed. Summary of the Invention

[0003] The purpose of the present invention is to provide a joint learning system based on the Internet of Things, which facilitates the input of learning object information and uses the Internet of Things system to preprocess the transmitted learning object information, thereby greatly reducing the pressure on the intelligent learning terminal. The intelligent learning terminal is used in conjunction with the ML / DL algorithm system for learning, which improves accuracy, realizes real-time learning, and makes the content of the intelligent ecosystem more and more complete, so as to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a joint learning system based on the Internet of Things, including an intelligent ecosystem, a joint learning planning system, a joint learning engine system, a platform support system, a local server and an Internet of Things access learning terminal, the joint learning planning system is interconnected with the joint learning engine system, the joint learning engine system is interconnected with the platform support system, the intelligent ecosystem is interconnected with the joint learning planning system, the joint learning engine system and the platform support system, the local server is interconnected with the joint learning planning system, the joint learning engine system and the platform support system, and the Internet of Things access learning terminal is interconnected with the local server.

[0005] Preferably, the intelligent ecosystem includes a data information database, a resource information database, a model database, a model repository, a contribution value measurement and tracking mechanism and a fair incentive mechanism. The data information database is used to store data information, the resource information database is used to store learning resources, the model database is used to store data information of various models required for learning, and the model repository is used to store information of various models. The contribution value measurement and tracking mechanism is used to record the value and origin of various learning resources, information, and models. The fair incentive mechanism is used to encourage academic personnel to upload learning materials from the Internet of Things access learning terminal to update the data information database, resource information database, model database and model repository in real time.

[0006] Preferably, the joint learning planning system combines the data, models, tasks, etc. after local training configuration. The joint learning planning system includes a participant selection terminal, a model aggregation strategy system, a model integration deployment system, and an accuracy value feedback system. The participant selection terminal is used to select a learning mechanism; the model aggregation strategy system is used to summarize various models and aggregate various models to formulate learning strategies; the model integration deployment system is deployed according to the formulated learning strategy; the accuracy value feedback system is used to feedback the accuracy of the model, and evaluate and feedback the model value based on the accuracy and function.

[0007] Preferably, the joint learning engine system incorporates the deployment, training data, local model, exception sorting strategy and monitoring feedback information of the local training agent into the engine system for subsequent retrieval. The joint learning engine system includes an ML / DL algorithm system, an aggregation strategy system, a distributed exception handling module, a privacy and security protocol system and an adaptive mechanism. The ML / DL algorithm system is used to realize artificial intelligence learning. The aggregation strategy system formulates a new learning strategy based on the updated data and model after learning. The distributed exception handling module processes the abnormal data and provides feedback. The privacy and security protocol system is used to ensure the security of the data.

[0008] Preferably, the local server is used to collect data provided by the Internet of Things access learning terminal, including the Internet of Things system, the local training configuration system, the local training agent system, and the local resource management collaboration system. The Internet of Things system is connected to the Internet of Things access learning terminal, the output end of the Internet of Things system is connected to the input end of the local training configuration system and the local training agent system, the local training configuration system is interconnected with the joint learning planning system, the local training agent system is interconnected with the joint learning engine system, and the local resource management collaboration system is interconnected with the platform support system.

[0009] Preferably, the Internet of Things system includes a visualization and application system, a data analysis module, a local database, a data preprocessing module and a data acquisition module. The data acquisition module is connected to the output end of the Internet of Things access learning terminal to input data information imported by the Internet of Things access learning terminal. The output end of the data acquisition module is connected to the data preprocessing module to transmit the collected information to the data preprocessing module for processing. The output end of the data preprocessing module is connected to the local database to transmit the collected data to the local database for storage. The output end of the local database is connected to the data analysis module to obtain data from the local database for analysis and processing.

[0010] Preferably, the local training configuration system performs task configuration on the data in the local server, and the local training configuration system includes a data / model / task query system, a joint task configuration system and a local model deduction system; the local training agent system performs model training on the data in the local server, and the local training agent system includes an agent local deployment system, a training data acquisition system, a local model training system, an upload / download model system, an exception handling strategy and a monitoring information feedback module.

[0011] Preferably, the local server also includes an intelligent learning terminal, which is connected to the Internet of Things system, the local training configuration system, the local training agent system and the local resource management collaboration system. The intelligent learning terminal includes an information input module, a retrieval system, an information comparison and processing system, a quantitative solution output system, an innovation judgment system, and an information archiving system. The information input module uses a touch screen input, the output end of the information input module is connected to the input end of the retrieval system, the output end of the retrieval system is connected to the input end of the information comparison and processing system, the output end of the information comparison and processing system is connected to the input end of the quantitative solution output system, the output end of the quantitative solution output system is connected to the innovation judgment system, the output end of the innovation judgment system is connected to the input end of the information archiving system, and the feedback end of the innovation judgment system is connected to the input end of the retrieval system.

[0012] Preferably, the learning process of the intelligent learning terminal includes the following steps:

[0013] S1: Input analysis object;

[0014] S2: Retrieve a set of similar objects in the smart ecosystem;

[0015] S3: Compare the input analysis object with a set of similar objects to find similarities and differences;

[0016] S4: Output quantized solution;

[0017] S5: Determine whether to obtain new content. If yes, archive it. If no, return to the smart ecosystem and search again. If the searched content is the same, stop comparing.

[0018] Preferably, the Internet of Things access learning terminal includes a main control module, a battery, a power conversion module, a DDR working module, a dual power amplifier module, a speaker, a WIFI module, a storage module, a data adapter and a touch screen. The battery is interconnected with the power conversion module, the output end of the power conversion module is connected to the DDR working module, the main control module, the WIFI module and the input end of the dual power amplifier module, the DDR working module is interconnected with the main control module, the storage module is interconnected with the main control module, the output end of the main control module is connected to the input end of the dual power amplifier module and the input end of the WIFI module, the output end of the dual power amplifier module is connected to the input end of the speaker, the control end of the main control module is connected to the input end of the data adapter, and the output end of the data adapter is connected to the input end of the touch screen.

[0019] Compared with the prior art, the present invention has the following beneficial effects: the joint learning system based on the Internet of Things proposed by the present invention inputs learning object information through the Internet of Things access learning terminal, and the transmitted learning object information is preliminarily processed by the Internet of Things system and then transmitted to the intelligent learning terminal for learning. The intelligent learning terminal performs preliminary learning, obtains similarities and differences, analyzes the differences, inputs quantitative solutions, and determines whether new content is obtained. When new content is obtained, it is transmitted to the ML / DL algorithm system for further learning. The ML / DL algorithm system implements artificial intelligence learning, and the aggregation strategy system formulates new learning strategies in real time based on the learned content, and feeds back to the data information library, resource information library, model database or model storage library, updating data in real time, enriching the intelligent ecosystem, and making the content of the intelligent ecosystem more and more complete; the overall input of learning object information is convenient through the touch screen, and the Internet of Things system is used to preprocess the transmitted learning object information, which greatly reduces the pressure on the intelligent learning terminal. The intelligent learning terminal and the ML / DL algorithm system are used to cooperate in learning, which improves accuracy, realizes real-time learning, and makes the content of the intelligent ecosystem more and more complete. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0021] Figure 2 This is a functional block diagram of the IoT access learning terminal of the present invention;

[0022] Figure 3 This is a flow chart of information input for the IoT access learning terminal of the present invention;

[0023] Figure 4 This is a functional block diagram of the intelligent learning terminal of the present invention;

[0024] Figure 5 This is a learning flow chart of the intelligent learning terminal of the present invention;

[0025] Figure 6This is a flow chart of information collection and processing for the Internet of Things system of the present invention;

[0026] Figure 7 This is a flow chart of uploading learning information to the intelligent learning terminal of the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0028] See also Figure 1-7 A joint learning system based on the Internet of Things includes an intelligent ecosystem, a joint learning planning system, a joint learning engine system, a platform support system, a local server and an Internet of Things access learning terminal. The joint learning planning system is interconnected with the joint learning engine system, the joint learning engine system is interconnected with the platform support system, the intelligent ecosystem is interconnected with the joint learning planning system, the joint learning engine system and the platform support system, the local server is interconnected with the joint learning planning system, the joint learning engine system and the platform support system, and the Internet of Things access learning terminal is interconnected with the local server.

[0029] As a further solution of the present invention: Figure 1 As shown in the figure, the intelligent ecosystem includes a data information database, a resource information database, a model database, a model repository, a contribution value measurement and tracking mechanism, and a fair incentive mechanism. The data information database is used to store data information, the resource information database is used to store learning resources, the model database is used to store data information of various models required for learning, and the model repository is used to store information of various models. The contribution value measurement and tracking mechanism is used to record the value and origin of various learning resources, information, and models. The fair incentive mechanism is used to encourage academic personnel to upload learning materials from the Internet of Things access learning terminal to update the data information database, resource information database, model database, and model repository in real time.

[0030] By adopting the above technical solution, the data information library, resource information library, model database, and model repository are used to store various learning resources for retrieval and query. The joint learning plan system retrieves corresponding data from the data information library, resource information library, model database, and model repository for learning. The joint learning engine system retrieves data from the data information library, resource information library, model database, and model repository, and obtains the learning plan in the joint learning plan system. It is calculated through the ML / DL algorithm system to determine whether there is any abnormality in the poem.

[0031] As a further solution of the present invention: Figure 1 As shown in the figure, the joint learning planning system combines the data, models, tasks, etc. after local training configuration. The joint learning planning system includes a participant selection terminal, a model aggregation strategy system, a model integration deployment system, and an accuracy value feedback system. The participant selection terminal is used to select the learning mechanism; the model aggregation strategy system is used to summarize various models and aggregate various models to formulate learning strategies; the model integration deployment system is deployed according to the formulated learning strategy; the accuracy value feedback system is used to feedback the accuracy of the model, and evaluate and feedback the model value based on the accuracy and function.

[0032] By adopting the above technical solution, the joint learning planning system calls out data from the data information library, resource information library, model database and model storage library, and transmits it to the intelligent learning terminal for learning and use by the intelligent learning terminal.

[0033] As a further solution of the present invention: Figure 1 As shown, the joint learning engine system incorporates the deployment, training data, local model, exception sorting strategy and monitoring feedback information of the local training agent into the engine system for subsequent retrieval. The joint learning engine system includes an ML / DL algorithm system, an aggregation strategy system, a distributed exception handling module, a privacy and security protocol system and an adaptive mechanism. The ML / DL algorithm system is used to realize artificial intelligence learning. The aggregation strategy system formulates a new learning strategy based on the updated data and model after learning. The distributed exception handling module processes the abnormal data and provides feedback. The privacy and security protocol system is used to ensure the security of the data.

[0034] By adopting the above technical solution, the required data can be retrieved from the data information library, resource information library, model database and model repository through the joint learning engine system, and used to realize artificial intelligence learning through the ML / DL algorithm system. The aggregation strategy system will formulate new learning strategies in real time based on the learned content and feed back to the data information library, resource information library, model database or model repository to update data in real time, enrich the intelligent ecosystem, and make the content of the intelligent ecosystem more and more complete.

[0035] As a further solution of the present invention: Figure 1 As shown, the local server is used to collect data provided by the Internet of Things access learning terminal, including the Internet of Things system, the local training configuration system, the local training agent system, and the local resource management collaboration system. The Internet of Things system is connected to the Internet of Things access learning terminal, the output end of the Internet of Things system is connected to the input end of the local training configuration system and the local training agent system, the local training configuration system is interconnected with the joint learning plan system, the local training agent system is interconnected with the joint learning engine system, and the local resource management collaboration system is interconnected with the platform support system.

[0036] By adopting the above technical solution, the content to be learned is stored on the local server for learning and use by the intelligent learning terminal and the ML / DL algorithm system.

[0037] As a further solution of the present invention: Figure 1 As shown, the Internet of Things system includes a visualization and application system, a data analysis module, a local database, a data preprocessing module and a data acquisition module. The data acquisition module is connected to the output end of the Internet of Things access learning terminal to input data information imported by the Internet of Things access learning terminal. The output end of the data acquisition module is connected to the data preprocessing module to transmit the collected information to the data preprocessing module for processing. The output end of the data preprocessing module is connected to the local database to transmit the collected data to the local database for storage. The output end of the local database is connected to the data analysis module to obtain data from the local database for analysis and processing.

[0038] By adopting the above technical solution, combined with Figure 6 It can be seen that the data acquisition module of the Internet of Things system receives the information transmitted by the Internet of Things access learning terminal, and transmits the transmitted learning object information to the data preprocessing module for preprocessing. It has determined the direction, usability and value of the transmitted learning object, and made corresponding classifications. After classification, it is saved in the data repository. The data analysis module calls out the learning object information in the data repository for processing, and after marking the called-out data, it transmits it to the intelligent learning terminal for learning.

[0039] As a further solution of the present invention: Figure 1 As shown, the local training configuration system performs task configuration on the data in the local server. The local training configuration system includes a data / model / task query system, a joint task configuration system, and a local model deduction system. The local training agent system performs model training on the data in the local server. The local training agent system includes an agent local deployment system, a training data acquisition system, a local model training system, an upload / download model system, an exception handling strategy, and a monitoring information feedback module.

[0040] By adopting the above technical solution, the local training configuration system can query similar data / model / task information based on the basic information of the learning object information transmitted to the intelligent learning terminal. The data / model / task query system can retrieve corresponding data from the data information library, resource information library, model database and model repository. After the data / model / task query system queries similar data, it will be transmitted to the intelligent learning terminal for learning and use by the intelligent learning terminal.

[0041] As a further solution of the present invention: Figure 4As shown, the local server also includes an intelligent learning terminal, which is connected to the Internet of Things system, the local training configuration system, the local training agent system and the local resource management collaboration system. The intelligent learning terminal includes an information input module, a retrieval system, an information comparison and processing system, a quantitative solution output system, an innovation judgment system, and an information archiving system. The information input module uses a touch screen input, and the output end of the information input module is connected to the input end of the retrieval system, the output end of the retrieval system is connected to the input end of the information comparison and processing system, the output end of the information comparison and processing system is connected to the input end of the quantitative solution output system, the output end of the quantitative solution output system is connected to the innovation judgment system, the output end of the innovation judgment system is connected to the input end of the information archiving system, and the feedback end of the innovation judgment system is connected to the input end of the retrieval system.

[0042] By adopting the above technical solution, combined with Figure 4-5 It can be seen that the information input module is used to input information transmitted by the Internet of Things system, retrieve a set of similar objects from the data / model / task query system through the retrieval system, compare the transmitted information with the retrieved similar objects, obtain similarities and differences, input quantitative solutions, determine whether new content is obtained, archive it if so, and transmit it to the ML / DL algorithm system for further learning.

[0043] As a further solution of the present invention: Figure 5 As shown, the learning process of the intelligent learning terminal includes the following steps:

[0044] Step 1: Input the analysis object;

[0045] Step 2: Retrieve a set of similar objects in the smart ecosystem;

[0046] Step 3: Compare the input analysis object with a set of similar objects to find out the similarities and differences;

[0047] Step 4: Output quantitative solution;

[0048] Step 5: Determine whether to obtain new content. If yes, archive it. If no, return to the smart ecosystem and search again. If the searched content is the same, stop comparing.

[0049] As a further solution of the present invention: Figure 2-3As shown, the IoT access learning terminal includes a main control module, a battery, a power conversion module, a DDR working module, a dual-power amplifier module, a speaker, a WIFI module, a storage module, a data adapter and a touch screen. The battery is interconnected with the power conversion module, the output end of the power conversion module is connected to the DDR working module, the main control module, the WIFI module and the input end of the dual-power amplifier module, the DDR working module is interconnected with the main control module, the storage module is interconnected with the main control module, the output end of the main control module is connected to the input end of the dual-power amplifier module and the input end of the WIFI module, the output end of the dual-power amplifier module is connected to the input end of the speaker, the control end of the main control module is connected to the input end of the data adapter, and the output end of the data adapter is connected to the input end of the touch screen.

[0050] By adopting the above technical solution, combined with Figure 2-3 It can be seen that when the touch screen is turned on and the learning object information is uploaded through the touch screen, the data adapter uploads the input information to the main control module, the main control module establishes a connection with the Internet of Things system through the WIFI circuit, and transmits the object information to the information acquisition module. At the same time, the main control module stores the transmitted information in the storage module for backup for retrieval and use. The main control module transmits the transmitted information to the dual-power amplifier module, and the dual-power amplifier module transmits it to the speaker for voice broadcast to ensure the accuracy of the uploaded information and avoid errors.

[0051] In summary, the IoT-based joint learning system proposed in the present invention uses the IoT access learning terminal to input learning object information and upload the learning object information to the information collection module. The information collection module transmits the transmitted learning object information to the data preprocessing module for preprocessing, determines the direction, usability and value of the transmitted learning object, and performs corresponding classification. After classification, it is stored in the data repository. The data analysis module retrieves the learning object information from the data repository for processing, and after marking the retrieved data, transmits it to the intelligent learning terminal for learning. The intelligent learning terminal performs preliminary learning, obtains similarities and differences, analyzes the differences, inputs quantitative solutions, and determines whether new content is obtained. When new content is obtained, it is transmitted to the ML / DL algorithm system for further learning. The ML / DL algorithm system implements artificial intelligence learning, and the aggregation strategy system formulates new learning strategies in real time based on the learned content, and feeds back to the data information library, resource information library, model database or model repository, updating data in real time, enriching the intelligent ecosystem, and making the content of the intelligent ecosystem more and more complete.

[0052] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A joint learning system based on the Internet of Things, characterized by: Including the intelligent ecosystem, joint learning planning system, joint learning engine system, platform support system, local server and Internet of Things access learning terminal, the joint learning planning system is interconnected with the joint learning engine system, the joint learning engine system is interconnected with the platform support system, the intelligent ecosystem is interconnected with the joint learning planning system, the joint learning engine system and the platform support system, the local server is interconnected with the joint learning planning system, the joint learning engine system and the platform support system, and the Internet of Things access learning terminal is interconnected with the local server; The smart ecosystem includes a data information database, a resource information database, a model database, a model repository, a contribution value measurement and tracking mechanism, and a fair incentive mechanism. The data information database is used to store data information, the resource information database is used to store learning resources, the model database is used to store data information of various models required for learning, and the model repository is used to store information of various models. The contribution value measurement and tracking mechanism is used to record the value and source of various learning resources, information, and models. The fair incentive mechanism is used to encourage academic personnel to upload learning materials from the Internet of Things access learning terminal to update the data information database, resource information database, model database, and model repository in real time. The joint learning planning system combines the data, models, and tasks configured after local training. The joint learning planning system includes a participant selection terminal, a model aggregation strategy system, a model integration deployment system, and an accuracy value feedback system. The participant selection terminal is used to select a learning mechanism; The model aggregation strategy system is used to summarize various models and aggregate them to formulate learning strategies; The model integration deployment system is deployed according to the established learning strategy; the accuracy value feedback system is used to feedback the accuracy of the model, and evaluate and provide feedback on the model value based on accuracy and effectiveness; The joint learning engine system incorporates the deployment, training data, local model, anomaly sorting strategy and monitoring feedback information of the local training agent into the engine system for subsequent retrieval. The joint learning engine system includes an ML / DL algorithm system, an aggregation strategy system, a distributed anomaly handling module, a privacy and security protocol system and an adaptive mechanism. The ML / DL algorithm system is used to implement artificial intelligence learning. The aggregation strategy system formulates new learning strategies based on the updated data and model after learning. The distributed anomaly handling module processes the abnormal data and provides feedback. The privacy and security protocol system is used to ensure data security. The local server is used to collect data provided by the Internet of Things access learning terminal, including the Internet of Things system, the local training configuration system, the local training agent system, and the local resource management collaboration system. The Internet of Things system is connected to the Internet of Things access learning terminal, the output end of the Internet of Things system is connected to the input end of the local training configuration system and the local training agent system, the local training configuration system is interconnected with the joint learning plan system, the local training agent system is interconnected with the joint learning engine system, and the local resource management collaboration system is interconnected with the platform support system.

2. According to a joint learning system based on the Internet of Things as described in claim 1, the Internet of Things system includes a visualization application system, a data analysis module, a local database, a data preprocessing module and a data acquisition module. The data acquisition module is connected to the output end of the Internet of Things access learning terminal to input data information imported by the Internet of Things access learning terminal. The output end of the data acquisition module is connected to the data preprocessing module to transmit the collected information to the data preprocessing module for processing. The output end of the data preprocessing module is connected to the local database to transmit the collected data to the local database for storage. The output end of the local database is connected to the data analysis module to obtain data from the local database for analysis and processing.

3. According to the IoT-based joint learning system as described in claim 1, the local training configuration system performs task configuration on the data in the local server, and the local training configuration system includes data, models, task query systems, joint task configuration systems and local model deduction systems; the local training agent system performs model training on the data in the local server, and the local training agent system includes an agent local deployment system, a training data acquisition system, a local model training system, a model upload and download system, an exception handling strategy and a monitoring information feedback module.

4. According to the IoT-based joint learning system of claim 1, the local server further comprises an intelligent learning terminal, the intelligent learning terminal being connected to the IoT system, the local training configuration system, the local training agent system, and the local resource management collaboration system, the intelligent learning terminal comprising an information input module, a retrieval system, an information comparison and processing system, a quantitative solution output system, an innovation determination system, and an information archiving system, the information input module using a touch screen for input, an output end of the information input module being connected to an input end of the retrieval system, an output end of the retrieval system being connected to an input end of the information comparison and processing system, an output end of the information comparison and processing system being connected to an input end of the quantitative solution output system, an output end of the quantitative solution output system being connected to an innovation determination system, an output end of the innovation determination system being connected to an input end of the information archiving system, and a feedback end of the innovation determination system being connected to an input end of the retrieval system; The intelligent learning terminal learning process includes the following steps: S1: Input analysis object; S2: Retrieve a set of similar objects in the smart ecosystem; S3: Compare the input analysis object with a set of similar objects to find similarities and differences; S4: Analyze the differences and output quantitative solutions; S5: Determine whether to obtain new content. If yes, archive; if no, return to the smart ecosystem and search again. If the searched content is the same, stop comparing.

5. According to the IoT-based joint learning system as described in claim 1, the IoT access learning terminal includes a main control module, a battery, a power conversion module, a DDR working module, a dual-power amplifier module, a speaker, a WIFI module, a storage module, a data adapter and a touch screen. The battery is interconnected with the power conversion module, the output end of the power conversion module is connected to the DDR working module, the main control module, the WIFI module and the input end of the dual-power amplifier module, the DDR working module is interconnected with the main control module, the storage module is interconnected with the main control module, the output end of the main control module is connected to the input end of the dual-power amplifier module and the input end of the WIFI module, the output end of the dual-power amplifier module is connected to the input end of the speaker, the control end of the main control module is connected to the input end of the data adapter, and the output end of the data adapter is connected to the input end of the touch screen.

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