Internet of Things signal adjusting system

Abstract

The invention discloses an Internet of Things signal adjusting system, belongs to the technical field of Internet of Things, and aims to solve the problems that data reliability and accuracy are reduced due to loss of logistics data transmission and overlarge errors, and bottleneck and blockage are caused due to the fact that logistics transportation paths and modes are often influenced by signals. By testing and optimizing the signal adjusting system, the reliability and stability of the system can be improved, the system can be popularized and applied, the application range of the Internet of Things in logistics and supply chain management is expanded, and the operation efficiency and speed of the whole supply chain are improved; all the modules of the system are comprehensively tested, so that the stability and reliability of the system are ensured, and the system is optimized and improved according to the test result.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an Internet of Things signal adjustment system. Background Art

[0002] IoT technology can collect and transmit data in real time through sensors, so that supply chain managers can accurately and in real time understand the status of logistics. Through IoT technology, supply chain managers can always grasp the location, temperature and humidity, transportation speed and other information of products, so as to find problems in time and take measures to improve logistics efficiency and service quality. The application of IoT technology can realize intelligent warehouse management. By installing sensors, RFID tags and other devices in the warehouse, IoT technology can monitor the inventory status, temperature and humidity, goods flow and other information in the warehouse in real time. Supply chain managers can check the status of the warehouse at any time through the remote monitoring system, and use intelligent algorithms to manage and dispatch goods. This not only improves the efficiency and accuracy of warehouse management, but also reduces errors and inefficiencies caused by manual operations.

[0003] As can be seen from the authorization publication number CN116669018B, a data processing method and device based on Internet of Things communication is disclosed. By applying the technical solution of this application, when private data is detected in a mobile terminal, it is no longer stored in the mobile terminal's own device, but transferred to an Internet of Things device with higher working stability in the area where the terminal frequently stays. Furthermore, in order to avoid the problem that malicious users obtain the interaction records with the Internet of Things device through the mobile terminal and then track the private data.

[0004] Logistics and supply chain management is a complex system involving multiple links and nodes. Different devices and sensors are needed to collect and share data. IoT technology can greatly improve the efficiency and quality of logistics and supply chain operations. However, in actual applications, IoT signals have some problems and drawbacks due to various factors. First, IoT devices and sensors are affected by different environments, and sometimes there will be problems such as loss of logistics data transmission and excessive errors, resulting in reduced data reliability and accuracy. Secondly, the path and mode of logistics transportation are often affected by signals, and bottlenecks and blockages will occur, affecting the efficiency and speed of logistics operations. Therefore, it is necessary to adjust and optimize IoT signals to better manage logistics and supply chain links intelligently.

[0005] In view of the above problems, the existing device was improved and an Internet of Things signal adjustment system was proposed. Summary of the invention

[0006] The purpose of the present invention is to provide an Internet of Things signal adjustment system, which solves the problems in the background technology of logistics data transmission loss and excessive errors leading to reduced data reliability and accuracy, and the paths and modes of logistics transportation are often affected by signals, resulting in bottlenecks and blockages.

[0007] To achieve the above object, the present invention provides the following technical solution: an Internet of Things signal adjustment system, comprising the following specific adjustment steps: S1: Problem and goal determination module, which needs to identify the problems existing in IoT signals in the logistics and supply chain management process, including data transmission loss and excessive errors, and formulate corresponding solution goals based on the problems; S2: Signal adjustment system architecture design module. After clarifying the problem and goal, a signal adjustment system architecture for the Internet of Things based on logistics and supply chain management is developed. The architecture includes data collection, data processing, data transmission, and data analysis modules. S3: sensor selection module, which selects appropriate sensors according to the items and environmental parameters to be monitored, including temperature sensors, humidity sensors, and pressure sensors; S4: Sensor and equipment deployment module, which deploys the selected sensors and other necessary equipment to specific locations in the warehouse and transportation vehicles; S5: Data collection and processing module: Based on the selected sensors and devices, a data collection and processing module is developed. This module collects object or environmental parameters in real time and processes the collected data, including data cleaning operations and format conversion operations. S6: Data transmission module, which transmits the processed data to the data management terminal and IoT cloud platform through the network for storage and analysis. During the development process, confirmation mechanism and compression algorithm are used to reduce transmission time and traffic; S7: Data analysis module: Based on the data transmitted to the data center or cloud platform, a data analysis module is developed. This module analyzes and mines the data and displays the location, status and other information of the items through data visualization technology; S8: Signal testing and optimization module. After completing the above steps, the developed signal adjustment system is tested and optimized. During the testing process, each module of the system is comprehensively tested to ensure the stability and reliability of the system. The system is optimized and improved according to the test results, including optimizing the data processing algorithm and improving the data transmission speed. S9: Signal deployment and promotion module, after completing testing and optimization, the signal adjustment system is deployed to the actual logistics and supply chain management process.

[0008] Furthermore, in S5, the development of the data collection and processing module includes the following specific adjustments: Data cleaning module, which removes noise data and processes missing values ​​and outliers; Data format conversion module, which converts sensor data from raw format to standardized format. Sensors send data in different formats, which are converted into a unified format; The outlier processing module detects and processes abnormal data according to the set threshold or algorithm, marks it as abnormal data and processes it; The data storage module stores the processed data in the database.

[0009] Furthermore, in S6, developing the data transmission module includes the following specific adjustments: Network transmission protocol selection module, select TCP / IP and UDP for data transmission; Data transmission format design module, design a data transmission format, including data identification, type, content and other information, custom binary format and JSON format, select according to actual needs; The data compression module uses LZMA and Zlib algorithms to compress data. According to actual needs and network environment, a suitable compression algorithm is selected for data compression; Mechanism confirmation module, a confirmation mechanism is designed. After sending data, the sender waits for the receiver to confirm the information to determine whether the data is successfully transmitted. If no confirmation information is received or the confirmation information indicates that the transmission failed, the data is resent to ensure the integrity of the data; The data storage module needs to store and analyze the data after it is transmitted to the data management terminal. It can use relational databases or non-relational databases to store the data, and use data analysis tools and algorithms to further analyze and process the data.

[0010] Furthermore, in S7, the development data analysis module includes the following specific adjustments: Data preprocessing module: Before data analysis, data transmitted to the data management terminal or cloud platform needs to be preprocessed, including cleaning invalid data, processing missing values, and eliminating noise. Python programming language can be used to perform data preprocessing using the corresponding data processing library; The data analysis and conversion module analyzes and converts the transmitted data. It uses the Python programming language and the corresponding data processing library to analyze and convert the data to obtain the location and status information of the object. Data visualization display module uses data visualization technology to display the location, status and other information of items. It uses the front-end visualization library to develop the data visualization display interface. The Web-based display method supports multi-terminal access, including PCs, mobile phones and tablets. The data analysis and mining module uses data analysis and mining techniques to extract deep information from the data, including the movement trajectory, residence time, and flow of items. It uses the Python programming language and the corresponding data analysis library to perform data analysis and mining.

[0011] Furthermore, in S8, the signal testing and optimization module includes the following specific adjustment contents: Test modules: conduct separate tests on each module of the system to check whether the modules function normally and meet the design requirements, and find out the existing errors and potential problems; Integration test module combines various modules together for testing to verify whether the interfaces and interactions between the modules of the system are correct, test the overall performance and stability of the system, and the coordination ability between the modules; Load testing module, which simulates a large amount of data and signal adjustment requests to test the system to verify the system's load capacity, test the system's performance under high load, and how to deal with sudden traffic growth; The stress testing module conducts multiple rounds of extensive testing on the system to verify the stability and reliability of the system, test the performance of the system under long-term operation and heavy load, as well as the system's fault tolerance and recovery capabilities; The security testing module tests the security of the system, including data encryption transmission, permission control, etc., tests the security performance of the system, and how to prevent potential security threats.

[0012] Furthermore, in S9, the signal deployment and promotion module includes the following specific adjustments: Deployment environment selection module, select appropriate hardware equipment and network environment according to the actual needs of the system, select high-performance servers and stable network environment to ensure the normal operation of the system; The system parameter configuration module configures important system parameters according to the actual application scenario, sets the frequency of data transmission, the priority of data processing, etc., to ensure the efficient operation of the system.

[0013] Furthermore, the collected data is cleaned and filtered to remove invalid, duplicate or erroneous data, using the following data cleaning formula: New data = (original data - mean) / standard deviation * constant + mean Among them, new data is the cleaned data, original data is the original data collected, mean is the average value of the data set, standard deviation is the standard deviation of the data set, and constant is the constant value of the adjusted data.

[0014] Furthermore, the collected raw data is converted into a unified format, and the data from different sensors are converted into the same unit and dimension, using the following data conversion formula: New data = (original data - minimum value) / (maximum value - minimum value) * (maximum value of target range - minimum value of target range) + minimum value of target range Among them, the new data is the converted data, the original data is the collected original data, the minimum value is the minimum value of the data set, the maximum value is the maximum value of the data set, the maximum value of the target range is the maximum value of the converted data, and the minimum value of the target range is the minimum value of the converted data.

[0015] Furthermore, data visualization technology is used to display the data, and the following data analysis formula is used: F(data)=parse(data)+mine(data)+visualize(data) Among them, parse(data) is the data parsing function, mine(data) is the data mining function, and visualize(data) is the data visualization function.

[0016] Furthermore, based on the results of the system test, the system is optimized and improved, using the following system test and optimization formula: T(system)=test(system)+optimize(system) Among them, test(system) is the system test function, and optimize(system) is the system optimization function.

[0017] Compared with the prior art, the beneficial effects of the present invention are: it solves the problems of data transmission loss and excessive errors caused by environmental influences on Internet of Things devices and sensors, while optimizing the paths and modes of logistics transportation, avoiding bottlenecks and blockages, and improving the efficiency and speed of logistics operations. In addition, real-time path planning and optimization of logistics transportation modes can be achieved, costs can be reduced, and profits can be increased. By testing and optimizing the signal adjustment system, the reliability and stability of the system can be improved, and then its application can be promoted, expanding the scope of application of the Internet of Things in logistics and supply chain management, and improving the operational efficiency and speed of the entire supply chain, so as to help enterprises better utilize Internet of Things technology to intelligently manage logistics and supply chain links and improve operational efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the overall structure of the present invention; Figure 2 It is the overall flow chart of the present invention; Figure 3 Develop a data acquisition and processing module system block diagram for the present invention; Figure 4 Develop a data transmission module system block diagram for the present invention; Figure 5 Develop a data analysis module system block diagram for the present invention; Figure 6 This is a system block diagram of the signal testing and optimization module of the present invention; Figure 7 This is a system block diagram of the signal deployment and promotion module of the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] See also Figure 1-Figure 7 In order to solve the problems of logistics data transmission loss and large errors that lead to reduced data reliability and accuracy, the paths and modes of logistics transportation are often affected by signals, resulting in bottlenecks and blockages. The following preferred technical solutions are provided: The IoT signal adjustment system includes the following specific adjustment steps: S1: Problem and goal determination module, which needs to identify the problems existing in IoT signals in the logistics and supply chain management process, including data transmission loss and excessive errors, and formulate corresponding solution goals based on the problems; S2: Signal adjustment system architecture design module. After clarifying the problem and goal, a signal adjustment system architecture for the Internet of Things based on logistics and supply chain management is developed. The architecture includes data collection, data processing, data transmission, and data analysis modules. S3: sensor selection module, which selects appropriate sensors according to the items and environmental parameters to be monitored, including temperature sensors, humidity sensors, and pressure sensors; S4: Sensor and equipment deployment module, which deploys the selected sensors and other necessary equipment to specific locations in the warehouse and transportation vehicles; S5: Data collection and processing module: Based on the selected sensors and devices, a data collection and processing module is developed. This module collects object or environmental parameters in real time and processes the collected data, including data cleaning operations and format conversion operations. The development framework is: 1. Install Django: First, you need to install Django in the Python environment, which can be installed through the pip command.

[0021] 2. Create a Django project: Create a new Django project through the Django command.

[0022] 3. Create a Django application: Create a new Django application based on the project.

[0023] 4. Write views and templates: Write views and templates in Django applications.

[0024] 5. Write the model: define the model class in the Django application.

[0025] 6. Implement URL routing: Define URL routing in the Django project and map URLs to corresponding views or templates.

[0026] 7. Integrate sensors and devices: Use Python's serial communication library (PySerial) or other network communication libraries (requests).

[0027] 8. Data processing algorithms: Write data processing algorithms in Django views.

[0028] 9. Data display: Display the processed data in the Django template.

[0029] S6: Data transmission module, which transmits the processed data to the data management terminal and IoT cloud platform through the network for storage and analysis. During the development process, confirmation mechanism and compression algorithm are used to reduce transmission time and traffic; S7: Data analysis module: Based on the data transmitted to the data center or cloud platform, a data analysis module is developed. This module analyzes and mines the data and displays the location, status and other information of the items through data visualization technology; The development framework is: 1. Install Django: First, you need to install Django in the Python environment, which can be installed through the pip command.

[0030] 2. Create a Django project: Use the Django command to create a new Django project.

[0031] 3. Create a Django application: Create a new Django application based on the project to implement the functions of the data analysis module.

[0032] 4. Write views and templates: Write views and templates in Django applications.

[0033] 5. Write the model: define the model class in the Django application.

[0034] 6. Implement URL routing: Define URL routing in the Django project and map URLs to corresponding views or templates.

[0035] 7. Data analysis and mining algorithms: Use Python code to implement data analysis and mining algorithms.

[0036] S8: Signal testing and optimization module. After completing the above steps, the developed signal adjustment system is tested and optimized. During the testing process, each module of the system is comprehensively tested to ensure the stability and reliability of the system. The system is optimized and improved according to the test results, including optimizing the data processing algorithm and improving the data transmission speed. S9: Signal deployment and promotion module, after completing testing and optimization, the signal adjustment system is deployed to the actual logistics and supply chain management process.

[0037] In S5, the development of data collection and processing modules includes the following specific adjustments: Data cleaning module, which removes noise data and processes missing values ​​and outliers; Data format conversion module, which converts sensor data from raw format to standardized format. Sensors send data in different formats, which are converted into a unified format; The outlier processing module detects and processes abnormal data according to the set threshold or algorithm, marks it as abnormal data and processes it; The data storage module stores the processed data in the database.

[0038] In S6, the development of the data transmission module includes the following specific adjustments: Network transmission protocol selection module, select TCP / IP and UDP for data transmission; Data transmission format design module, design a data transmission format, including data identification, type, content and other information, custom binary format and JSON format, select according to actual needs; The data compression module uses LZMA and Zlib algorithms to compress data. According to actual needs and network environment, a suitable compression algorithm is selected for data compression; Mechanism confirmation module, a confirmation mechanism is designed. After sending data, the sender waits for the receiver to confirm the information to determine whether the data is successfully transmitted. If no confirmation information is received or the confirmation information indicates that the transmission failed, the data is resent to ensure the integrity of the data; The data storage module needs to store and analyze the data after it is transmitted to the data management terminal. It can use relational databases or non-relational databases to store the data, and use data analysis tools and algorithms to further analyze and process the data.

[0039] In S7, the development of the data analysis module includes the following specific adjustments: Data preprocessing module: Before data analysis, data transmitted to the data management terminal or cloud platform needs to be preprocessed, including cleaning invalid data, processing missing values, and eliminating noise. Python programming language can be used to perform data preprocessing using the corresponding data processing library; The data analysis and conversion module analyzes and converts the transmitted data. It uses the Python programming language and the corresponding data processing library to analyze and convert the data to obtain the location and status information of the object. Data visualization display module uses data visualization technology to display the location, status and other information of items. It uses the front-end visualization library to develop the data visualization display interface. The Web-based display method supports multi-terminal access, including PCs, mobile phones and tablets. The data analysis and mining module uses data analysis and mining techniques to extract deep information from the data, including the movement trajectory, residence time, and flow of items. It uses the Python programming language and the corresponding data analysis library to perform data analysis and mining.

[0040] In S8, the signal testing and optimization module includes the following specific adjustments: Test modules: conduct separate tests on each module of the system to check whether the modules function normally and meet the design requirements, and find out the existing errors and potential problems; Integration test module combines various modules together for testing to verify whether the interfaces and interactions between the modules of the system are correct, test the overall performance and stability of the system, and the coordination ability between the modules; Load testing module, which simulates a large amount of data and signal adjustment requests to test the system to verify the system's load capacity, test the system's performance under high load, and how to deal with sudden traffic growth; The stress testing module conducts multiple rounds of extensive testing on the system to verify the stability and reliability of the system, test the performance of the system under long-term operation and heavy load, as well as the system's fault tolerance and recovery capabilities; The security testing module tests the security of the system, including data encryption transmission, permission control, etc., tests the security performance of the system, and how to prevent potential security threats.

[0041] In S9, the signal deployment and promotion module includes the following specific adjustments: Deployment environment selection module, select appropriate hardware equipment and network environment according to the actual needs of the system, select high-performance servers and stable network environment to ensure the normal operation of the system; The system parameter configuration module configures important system parameters according to the actual application scenario, sets the frequency of data transmission, the priority of data processing, etc., to ensure the efficient operation of the system.

[0042] Clean and filter the collected data to remove invalid, duplicate or erroneous data. Use the following data cleaning formula: New data = (original data - mean) / standard deviation * constant + mean Among them, new data is the cleaned data, original data is the original data collected, mean is the average value of the data set, standard deviation is the standard deviation of the data set, and constant is the constant value of the adjusted data.

[0043] Convert the collected raw data into a unified format, and convert the data from different sensors into the same unit and dimension, using the following data conversion formula: New data = (original data - minimum value) / (maximum value - minimum value) * (maximum value of target range - minimum value of target range) + minimum value of target range Among them, the new data is the converted data, the original data is the collected original data, the minimum value is the minimum value of the data set, the maximum value is the maximum value of the data set, the maximum value of the target range is the maximum value of the converted data, and the minimum value of the target range is the minimum value of the converted data.

[0044] Data visualization technology is used to display the data, and the following data analysis formula is used: F(data)=parse(data)+mine(data)+visualize(data) Among them, parse(data) is the data parsing function, mine(data) is the data mining function, and visualize(data) is the data visualization function.

[0045] According to the results of the system test, the system is optimized and improved, using the following system test and optimization formula: T(system)=test(system)+optimize(system) Among them, test(system) is the system test function, and optimize(system) is the system optimization function.

[0046] Case 1 In this embodiment, in order to solve the problem that IoT devices and sensors are affected by different environments, and sometimes there will be loss of logistics data transmission and large errors, the following preferred technical solutions are provided: S1': Identify problems and goals, clarify the manifestations and impact scope of data transmission loss and excessive errors, and determine the goals to be achieved, such as reducing transmission loss rate and error rate; S2': Design the signal adjustment system architecture, including four modules: data acquisition, data processing, data transmission and data analysis; S3': Select appropriate sensors, such as temperature, humidity, pressure, location, etc., according to logistics and supply chain management requirements; S4': Deploy sensors and devices, select appropriate devices according to actual application scenarios and ensure stable data collection; S5': Develop data acquisition and processing modules, develop modules to collect raw data from IoT devices and sensors, and perform cleaning, filtering and integration; S6': Develop a data transmission module, develop a data transmission module, and adopt a reliable data transmission protocol, such as MQTT, to achieve real-time data transmission; S7': Develop a data analysis module to analyze and mine the transmitted data to discover the patterns and trends hidden in it; S8': Test and optimize signal adjustment, conduct comprehensive testing on the entire system, and optimize the system based on the test results, including improving data processing algorithms, increasing data transmission speed, etc.; S9': Deploy and promote signal adjustment, deploy and promote application while ensuring system security and stability.

[0047] Case 2 In this embodiment, in order to solve the problem that the path and mode of logistics transportation are often affected by signals, bottlenecks and blockages will occur, affecting the efficiency and speed of logistics operations, the following preferred technical solutions are provided: S1”: Identify problems and goals, clarify the problems encountered in the logistics and transportation process (inaccurate route planning, transportation delays caused by signal interference, etc.), and determine the goals that need to be solved (improve logistics and transportation efficiency, reduce transportation time and costs, etc.); S2”: Design a signal adjustment system architecture to adjust logistics transportation routes and modes based on real-time signals to avoid bottlenecks and congestion; S3”: Select appropriate sensors, such as GPS sensors, speed sensors, temperature sensors, etc., that can monitor and collect data related to the logistics transportation process; S4”: Deploy sensors and equipment (deploy selected sensors to logistics transportation vehicles, goods and other equipment to ensure real-time monitoring and collection of relevant data); S5”: Develop data collection and processing modules to process various types of data and ensure the accuracy and real-time nature of the data; S6”: Develop a data transmission module and use a reliable data transmission protocol (MQTT) to achieve real-time data transmission; S7”: Develop data analysis modules to analyze and mine the transmitted data; S8”: Test and optimize signal adjustment, conduct comprehensive tests on the entire signal adjustment system, and optimize the system based on the test results (improve data processing algorithms, increase data transmission speed, etc.); S9”: Deploy and promote signal adjustment. While ensuring the safety and stability of the system, deploy the signal adjustment system in the actual logistics and transportation process and promote its application.

[0048] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. The Internet of Things signal adjustment system is characterized by: The specific adjustment steps include the following: S1: Problem and goal determination module, which needs to identify the problems existing in IoT signals in the logistics and supply chain management process, including data transmission loss and excessive errors, and formulate corresponding solution goals based on the problems; S2: Signal adjustment system architecture design module. After clarifying the problem and goal, a signal adjustment system architecture for the Internet of Things based on logistics and supply chain management is developed. The architecture includes data collection, data processing, data transmission, and data analysis modules. S3: sensor selection module, which selects appropriate sensors according to the objects and environmental parameters to be monitored, including temperature sensors, humidity sensors, and pressure sensors; S4: Sensor and equipment deployment module, which deploys the selected sensors and other necessary equipment to specific locations in the warehouse and transportation vehicles; S5: Data collection and processing module: Based on the selected sensors and devices, a data collection and processing module is developed. This module collects object or environmental parameters in real time and processes the collected data, including data cleaning operations and format conversion operations. S6: Data transmission module, which transmits the processed data to the data management terminal and IoT cloud platform through the network for storage and analysis. During the development process, confirmation mechanism and compression algorithm are used to reduce transmission time and traffic; S7: Data analysis module: Based on the data transmitted to the data center or cloud platform, a data analysis module is developed. This module analyzes and mines the data and displays the location, status and other information of the items through data visualization technology; S8: Signal testing and optimization module. After completing the above steps, the developed signal adjustment system is tested and optimized. During the testing process, each module of the system is comprehensively tested to ensure the stability and reliability of the system. The system is optimized and improved according to the test results, including optimizing the data processing algorithm and improving the data transmission speed. S9: Signal deployment and promotion module, after completing testing and optimization, the signal adjustment system is deployed to the actual logistics and supply chain management process.

2. The IoT signal adjustment system according to claim 1, wherein: In S5, developing a data acquisition and processing module includes the following specific adjustments: Data cleaning module, which removes noise data and processes missing values ​​and outliers; Data format conversion module, which converts sensor data from raw format to standardized format. Sensors send data in different formats, which are converted into a unified format; The outlier processing module detects and processes abnormal data according to the set threshold or algorithm, marks it as abnormal data and processes it; The data storage module stores the processed data in the database.

3. The IoT signal adjustment system according to claim 1, wherein: In S6, developing the data transmission module includes the following specific adjustments: Network transmission protocol selection module, select TCP / IP and UDP for data transmission; Data transmission format design module, design a data transmission format, including data identification, type, content and other information, custom binary format and JSON format, select according to actual needs; The data compression module uses LZMA and Zlib algorithms to compress data. According to actual needs and network environment, a suitable compression algorithm is selected for data compression; Mechanism confirmation module, a confirmation mechanism is designed. After sending data, the sender waits for the receiver to confirm the information to determine whether the data is successfully transmitted. If no confirmation information is received or the confirmation information indicates that the transmission failed, the data is resent to ensure the integrity of the data; The data storage module needs to store and analyze the data after it is transmitted to the data management terminal. It can use relational databases or non-relational databases to store the data, and use data analysis tools and algorithms to further analyze and process the data.

4. The IoT signal adjustment system according to claim 1, wherein: In S7, developing the data analysis module includes the following specific adjustments: Data preprocessing module: Before data analysis, data transmitted to the data management terminal or cloud platform needs to be preprocessed, including cleaning invalid data, processing missing values, and eliminating noise. Python programming language can be used to perform data preprocessing using the corresponding data processing library; The data parsing and conversion module parses and converts the transmitted data. It uses the Python programming language and the corresponding data processing library to parse and convert the data to obtain the location and status information of the object. Data visualization display module uses data visualization technology to display the location, status and other information of items. It uses the front-end visualization library to develop the data visualization display interface. The Web-based display method supports multi-terminal access, including PCs, mobile phones and tablets. The data analysis and mining module uses data analysis and mining techniques to extract deep information from the data, including the movement trajectory, residence time, and flow of items. It uses the Python programming language and the corresponding data analysis library to perform data analysis and mining.

5. The IoT signal adjustment system according to claim 1, wherein: In S8, the signal testing and optimization module includes the following specific adjustment contents: Test modules: conduct separate tests on each module of the system to check whether the modules function normally and meet the design requirements, and find out the existing errors and potential problems; Integration test module combines various modules together for testing to verify whether the interfaces and interactions between the modules of the system are correct, test the overall performance and stability of the system, and the coordination ability between the modules; Load testing module, which simulates a large amount of data and signal adjustment requests to test the system to verify the system's load capacity, test the system's performance under high load, and how to deal with sudden traffic growth; The stress testing module conducts multiple rounds of extensive testing on the system to verify the stability and reliability of the system, test the performance of the system under long-term operation and heavy load, as well as the system's fault tolerance and recovery capabilities; The security testing module tests the security of the system, including data encryption transmission, permission control, etc., tests the security performance of the system, and how to prevent potential security threats.

6. The IoT signal adjustment system according to claim 1, wherein: In S9, the signal deployment and promotion module includes the following specific adjustments: Deployment environment selection module, select appropriate hardware equipment and network environment according to the actual needs of the system, select high-performance servers and stable network environment to ensure the normal operation of the system; The system parameter configuration module configures important system parameters according to the actual application scenario, sets the frequency of data transmission, the priority of data processing, etc., to ensure the efficient operation of the system.

7. The IoT signal adjustment system according to claim 2, wherein: Clean and filter the collected data to remove invalid, duplicate or erroneous data. Use the following data cleaning formula: New data = (original data - mean) / standard deviation * constant + mean Among them, new data is the cleaned data, original data is the original data collected, mean is the average value of the data set, standard deviation is the standard deviation of the data set, and constant is the constant value of the adjusted data.

8. The Internet of Things signal adjustment system as claimed in claim 2, characterized in that: Convert the collected raw data into a unified format, and convert the data from different sensors into the same unit and dimension using the following data conversion formula: New data = (original data - minimum value) / (maximum value - minimum value) * (maximum value of target range - minimum value of target range) + minimum value of target range Among them, the new data is the converted data, the original data is the collected original data, the minimum value is the minimum value of the data set, the maximum value is the maximum value of the data set, the maximum value of the target range is the maximum value of the converted data, and the minimum value of the target range is the minimum value of the converted data.

9. The Internet of Things signal adjustment system as claimed in claim 4, characterized in that: Data visualization technology is used to display the data, and the following data analysis formula is used: F(data)=parse(data)+mine(data)+visualize(data) Among them, parse(data) is the data parsing function, mine(data) is the data mining function, and visualize(data) is the data visualization function.

10. The Internet of Things signal adjustment system according to claim 5, characterized in that: According to the results of the system test, the system is optimized and improved, using the following system test and optimization formula: T(system)=test(system)+optimize(system) Among them, test(system) is the system test function, and optimize(system) is the system optimization function.

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