Rapid data transmission method and system based on Internet of Things

Abstract

The invention discloses a rapid data transmission method and system based on the Internet of Things, and relates to the technical field of data transmission. The method comprises the following steps: 1, building a data acquisition model to acquire campus full-dimensional data, building a data feature extraction model after acquisition is completed, extracting features in the acquired data, and building a data analysis model to analyze the data after extraction; 2, an open platform model is built according to the analyzed data, and after the open platform model is built, the open platform model is built; 3, after collection is completed, a data backup model is made, the collected data are backed up, after backup is completed, a data optimization compression model is made, and the backed-up data are compressed. According to the invention, in the process of transmitting the data, the multi-dimensional data of the campus is collected and analyzed, so that the requirement of constructing the Internet of Things campus can be met, and the sufficiency of realizing the Internet of Things campus in the later period is improved through multi-dimensional full-amplification collection.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, specifically to a method and system for rapid data transmission based on the Internet of Things (IoT). Background Technology

[0002] Currently, achieving rapid data transmission in the Internet of Things requires combining key methods such as low-latency communication technology, efficient network architecture, intelligent transmission protocols, and edge computing. Especially when transmitting data within the campus, the inability to collect data from multiple dimensions and in all aspects will lead to insufficient data in the later processing process. Furthermore, the lack of a social communication platform during data transmission will prevent the data from being processed in a humane way. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for rapid data transmission based on the Internet of Things (IoT) to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for rapid data transmission based on the Internet of Things, comprising the following steps; Step 1: Build a data collection model to collect data from all dimensions of the campus. After the collection is completed, build a data feature extraction model to extract features from the collected data. After extraction, build a data analysis model to analyze the data. Step 2: Build an open platform model based on the analyzed data. Once the open platform model is built, develop an account management model to register and manage accounts that are equidistant from the open platform. After registration and management, build an experience data collection model to collect data generated during use. Step 3: After the data collection is complete, develop a data backup model to back up the collected data. After the backup is complete, develop a data optimization and compression model to compress the backed-up data.

[0005] Preferably, the specific steps for constructing the data acquisition model in step one are as follows: (1) Personnel behavior data: time students enter and leave dormitories and classrooms, canteen consumption, library borrowing and movement trajectory; collection methods: access control system, campus card consumption records, WiFi positioning, Bluetooth beacon and camera AI recognition; (2) Equipment operation data: classroom lighting, air conditioning status, laboratory instrument parameters and elevator operating frequency; acquisition methods: IoT sensors, smart meters and equipment API interfaces; (3) Environmental perception data: campus air quality, noise level, light intensity and rainfall / wind speed; collection methods: meteorological station, environmental monitoring micro-station and noise sensor network; (4) Teaching resource data: number of views on course videos, online test scores and number of textbook downloads; collection methods: LMS learning management system, academic affairs system API and web crawler; (5) Social interaction data: posts on campus forums, WeChat group chats and club activity registrations; collection methods: social media API, NLP sentiment analysis and relationship graph construction.

[0006] Preferably, the specific construction steps of the data feature extraction model in step one are as follows: (1) Temporal feature extraction: ① Statistical features: mean, variance, peak value and slope; ② Frequency domain features: periodicity is extracted by FFT; ③ Temporal pattern: 1000+ temporal features are automatically generated using the TSFresh library; (2) Spatial feature extraction: ① Heat map analysis: generating a heat map of people gathering based on Kernel Density Estimation; ② Path analysis: constructing student movement trajectories through WiFi positioning data and calculating the transfer probability of the "classroom-canteen-dormitory" path. (3) Text feature extraction: ① NLP processing: use the BERT model to classify the sentiment of posts on the campus forum; ② Keyword extraction: identify high-frequency words through the TF-IDF algorithm.

[0007] Preferably, the specific steps for constructing the data analysis model in step one are as follows: (1) Descriptive analysis: ① Visual dashboard: Use Tableau / Power BI to build a campus operation dashboard; ② Anomaly alarm: Set threshold rules to trigger DingTalk / email notifications; (2) Predictive analysis: ① Time series prediction: LSTM model predicts the number of diners in the cafeteria next week; ② Classification prediction: XGBoost predicts the risk of students failing courses; ③ Simulation: A campus pedestrian flow simulation model is built based on AnyLogic to optimize the queuing flow in the cafeteria. (3) Prescription analysis: ① Resource scheduling: Genetic algorithm optimizes the sharing of laboratory equipment, improving the utilization rate by 30%; ② Path planning: Dijkstra algorithm plans the shortest delivery path for campus logistics robots; ③ A / B testing: Compare the popularity of two canteen menus and select the optimal solution.

[0008] Preferably, the specific construction steps of the open platform model in step two are as follows: (1) Data processing layer: Cleaning, transforming, integrating and standardizing the collected data to ensure the accuracy and consistency of the data. It adopts stream processing technology and batch processing technology to support real-time data processing and historical data processing. (2) Needs survey and analysis: By surveying the business needs and current technology status of the campus, we will clarify the goals and scope of the data platform, formulate a detailed implementation plan, establish a dedicated data platform construction team, clarify the responsibilities and division of labor of each member, and ensure the smooth implementation of the project; (3) Data integration and governance: Establish a data governance system, formulate data standards and specifications, and manage and govern data in a unified manner. Through ETL tools or data integration platforms, integrate and manage heterogeneous data sources in a unified manner. (4) Platform construction and development: Based on the results of the requirements analysis, design the overall architecture of the platform, select the technical architecture and tools, and realize the functions of data collection, processing, storage, service and security; (5) System testing and optimization: Conduct system testing and optimization to ensure the platform's efficiency and stability, resolve problems and bottlenecks that arise during implementation, and continuously improve and optimize platform performance; (6) Establish data security strategies and compliance mechanisms to ensure the security and legality of data, strengthen security measures such as data encryption, access control and audit trail, and prevent data leakage and abuse.

[0009] Preferably, the specific steps for constructing the account management model in step two are as follows: (1) Unified identity authentication integration includes integration with campus systems, multi-factor authentication, and student ID / employee ID binding: ① Integration with campus systems: Integration with the school's academic affairs system, student affairs system, or unified identity authentication platform to achieve single sign-on and reduce repeated registration processes; ② Multi-factor authentication: During critical operations, secondary verification is required via SMS verification code, email verification, or identity authentication APP; ③ Student ID / employee ID binding: Users are required to bind their real student ID or employee ID to ensure that the account is associated with the campus identity, which facilitates subsequent permission management and problem tracing. (2) Role permissions include students, teachers, administrators and visitors, and each role has preset default permissions; (3) Data encryption and transmission security include end-to-end encryption and cryptographic strategies. ① End-to-end encryption: SSL / TLS encryption is used for sensitive operations such as private messages and file transfers to prevent man-in-the-middle attacks. ② Cryptographic strategies: Enforce password complexity, prompt for password changes regularly, and prohibit the use of common weak passwords. (4) Anti-fraud and risk control mechanisms: ① IP blacklist: intercepts high-frequency abnormal login IPs; ② Device fingerprint: records information of users' frequently used devices, triggering secondary verification when abnormal devices log in; ③ Content filtering: identifies sensitive words, images or links through AI models, automatically intercepts illegal content and notifies the administrator to review.

[0010] Preferably, the specific steps for constructing the experience data collection model in step two are as follows: (1) Build an experience feedback collection library. During the user's login process, collect the user's experience, classify the collected data into positive data and negative data, and support multiple forms of submission such as text, pictures, and ratings. (2) Establish a survey database. When a user completes the use of the platform, a questionnaire will pop up for the user to rate. After the rating is completed, the rating data will be saved and fed back to the platform.

[0011] Preferably, the specific steps for constructing the data backup model in step three are as follows; (1) Clean the data to be backed up: ① Deduplication and completion: delete duplicate records and fill in missing values; ② Outlier handling: identify and correct unreasonable data; ③ Standardization conversion: unify data format; (2) Retention analysis: Analyze the user retention rate on the next day, 7 days and 30 days to evaluate the platform stickiness; Text mining: Perform sentiment analysis and keyword extraction on user feedback text; Outlier handling: Identify and correct unreasonable data.

[0012] Preferably, the specific construction steps of the data optimization and compression model in step three are as follows; (1) Lightweight compression algorithm: The LZ4, Zstandard and other algorithms are used, the compression ratio can reach 3-5 times, and the compression / decompression speed is extremely fast. After compression, the data volume is reduced by 70% and the transmission time is shortened to one-third of the original. (2) Feature extraction and dimensionality reduction: Key features of the data are extracted by PCA or autoencoder to reduce the amount of data transmitted. During the data transmission process, the amount of data transmitted is reduced by 90%, and the latency is reduced to the millisecond level.

[0013] A data transmission system based on the Internet of Things includes a data acquisition and analysis unit, a platform construction unit, and a post-processing unit; The data acquisition and analysis unit builds a data acquisition model to collect data from all dimensions of the campus. After the acquisition is completed, it builds a data feature extraction model to extract features from the acquired data. After extraction, it builds a data analysis model to analyze the data. The platform building unit builds an open platform model based on the analyzed data. After the open platform model is built, an account management model is developed to register and manage accounts that are equidistant from the open platform. After registration and management, an experience data collection model is built to collect the data generated during use. The post-processing unit formulates a data backup model after the data collection is completed, backs up the collected data, and formulates a data optimization and compression model after the backup is completed, compressing the backed-up data.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention, through the collection and analysis of multi-dimensional campus data during data transmission, can meet the needs of building an IoT campus. By collecting data from multiple dimensions and in an open manner, it improves the sufficiency of realizing an IoT campus in the later stages and avoids data blind spots during later use. At the same time, it builds an open platform to increase social attributes, making the data management process more human-centered. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method flow provided in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1 Please see Figure 1 The present invention provides a technical solution: a method for fast data transmission based on the Internet of Things, comprising the following steps; Step 1: Build a data collection model to collect data from all dimensions of the campus. After the collection is completed, build a data feature extraction model to extract features from the collected data. After extraction, build a data analysis model to analyze the data. Step 2: Build an open platform model based on the analyzed data. Once the open platform model is built, develop an account management model to register and manage accounts that are equidistant from the open platform. After registration and management, build an experience data collection model to collect data generated during use. Step 3: After the data collection is complete, develop a data backup model to back up the collected data. After the backup is complete, develop a data optimization and compression model to compress the backed-up data.

[0018] The specific steps for constructing the data acquisition model in step one are as follows: (1) Personnel behavior data: time students enter and exit dormitories and classrooms, canteen consumption, library borrowing and movement trajectory; collection methods: access control system (RFID / face recognition), campus card consumption records, WiFi positioning, Bluetooth beacon and camera AI recognition; (2) Equipment operation data: classroom lighting, air conditioning status, laboratory instrument parameters and elevator operating frequency; acquisition methods: IoT sensors (temperature, humidity, current, vibration), smart meters and equipment API interfaces; (3) Environmental perception data: campus air quality, noise level, light intensity and rainfall / wind speed; collection methods: meteorological station, environmental monitoring micro-station and noise sensor network; (4) Teaching resource data: number of views on course videos, online test scores and number of textbook downloads; collection methods: LMS learning management system (such as Moodle), academic affairs system API and web crawler; (5) Social interaction data: posts on campus forums, WeChat group chats and club activity registrations; collection methods: social media API, NLP sentiment analysis and relationship graph construction.

[0019] The specific construction steps of the data feature extraction model in step one are as follows: (1) Temporal feature extraction: ① Statistical features: mean, variance, peak value and slope (e.g., daily change trend of classroom traffic); ② Frequency domain features: extract periodicity through FFT (Fast Fourier Transform) (e.g., peak dining period in the cafeteria is 3 hours); ③ Temporal pattern: automatically generate 1000+ temporal features (e.g., autocorrelation, quantiles) using the TSFresh library. (2) Spatial feature extraction: ① Heat map analysis: a case of generating a heat map of people gathering based on Kernel Density Estimation; ② Path analysis: a case of constructing student movement trajectories through WiFi positioning data and calculating the transfer probability of the "classroom-canteen-dormitory" path. (3) Text feature extraction: ① NLP processing: use the BERT model to classify the sentiment of campus forum posts (positive / negative); ② Keyword extraction: use the TF-IDF algorithm to identify high-frequency words (such as "exam", "club", "part-time job").

[0020] The specific steps for constructing the data analysis model in step one are as follows: (1) Descriptive analysis: ① Visual dashboard: Use Tableau / Power BI to build a campus operation dashboard; ② Anomaly alarm: Set threshold rules (such as daily water and electricity bills exceeding the average by 2 times) to trigger DingTalk / email notifications; (2) Predictive analysis: ① Time series prediction: LSTM model predicts the number of diners in the cafeteria next week (MAPE<5%); ② Classification prediction: XGBoost predicts the risk of students failing courses (accuracy 85%); ③ Simulation: Based on AnyLogic, a campus pedestrian flow simulation model is built to optimize the queuing flow in the cafeteria. (3) Prescription analysis: ① Resource scheduling: Genetic algorithm optimizes the sharing of laboratory equipment, improving the utilization rate by 30%; ② Path planning: Dijkstra algorithm plans the shortest delivery path for campus logistics robots; ③ A / B testing: Compare the popularity of two canteen menus and select the optimal solution.

[0021] The specific construction steps of the open platform model in step two are as follows: (1) Data processing layer: Cleaning, transforming, integrating and standardizing the collected data to ensure the accuracy and consistency of the data. It adopts stream processing technology and batch processing technology to support real-time data processing and historical data processing. (2) Needs survey and analysis: By surveying the business needs and current technology status of the campus, we will clarify the goals and scope of the data platform, formulate a detailed implementation plan, establish a dedicated data platform construction team, clarify the responsibilities and division of labor of each member, and ensure the smooth implementation of the project; (3) Data integration and governance: Establish a data governance system, formulate data standards and specifications, and manage and govern data in a unified manner. Through ETL tools or data integration platforms, integrate and manage heterogeneous data sources in a unified manner. (4) Platform construction and development: Based on the results of the requirements analysis, design the overall architecture of the platform, select the technical architecture and tools, and realize the functions of data collection, processing, storage, service and security; (5) System testing and optimization: Conduct system testing and optimization to ensure the platform's efficiency and stability, resolve problems and bottlenecks that arise during implementation, and continuously improve and optimize platform performance; (6) Establish data security strategies and compliance mechanisms to ensure the security and legality of data, strengthen security measures such as data encryption, access control and audit trail, and prevent data leakage and abuse.

[0022] The specific steps for constructing the account management model in step two are as follows: (1) Unified identity authentication integration includes integration with campus systems, multi-factor authentication, and student ID / employee ID binding: ① Integration with campus systems: Integration with the school's academic affairs system, student affairs system, or unified identity authentication platform (such as CAS, OAuth2.0) to achieve single sign-on (SSO) and reduce repeated registration processes; ② Multi-factor authentication: During critical operations (such as changing passwords, binding devices), secondary verification is required via SMS verification code, email verification, or identity authentication APP; ③ Student ID / employee ID binding: Users are required to bind their real student ID or employee ID to ensure that the account is associated with the campus identity, which facilitates subsequent permission management and problem tracing. (2) Role permissions include students, teachers, administrators and visitors. Each role has preset default permissions (e.g., students can post, teachers can post announcements). (3) Data encryption and transmission security include end-to-end encryption and password strategy. ① End-to-end encryption: SSL / TLS encryption is used for sensitive operations such as private messages and file transfers to prevent man-in-the-middle attacks. ② Password strategy: Force password complexity (such as including uppercase and lowercase letters, numbers, and special characters), prompt to change passwords regularly, and prohibit the use of common weak passwords. (4) Anti-fraud and risk control mechanisms: ① IP blacklist: intercepts high-frequency abnormal login IPs (such as multiple failed logins in a short period of time); ② Device fingerprint: records information of users' frequently used devices, and triggers secondary verification when abnormal devices log in; ③ Content filtering: identifies sensitive words, images or links through AI models, automatically intercepts illegal content and notifies the administrator to review.

[0023] The specific steps for constructing the experience data collection model in step two are as follows: (1) Build an experience feedback collection library. During the user's login process, collect the user's experience, classify the collected data into positive data and negative data, and support multiple forms of submission such as text, pictures, and ratings. (2) Establish a survey database. When a user completes the use of the platform, a questionnaire will pop up for the user to rate. After the rating is completed, the rating data will be saved and fed back to the platform.

[0024] The specific steps for constructing the data backup model in step three are as follows: (1) Clean the data to be backed up: ① Deduplication and completion: delete duplicate records and fill in missing values ​​(such as replacing them with the mean or median); ② Outlier handling: identify and correct unreasonable data (such as user stay time exceeding 24 hours); ③ Standardization conversion: unify data format (such as timestamp and format); (2) Retention analysis: Analyze the user retention rate on the next day, 7 days and 30 days to evaluate platform stickiness; Text mining: Perform sentiment analysis (positive / negative / neutral) and keyword extraction on user feedback text; Outlier handling: Identify and correct unreasonable data (such as user stay time exceeding 24 hours), where unreasonable data is incomplete data, data inconsistent with other data sources, data that is older, and data that is more than half a year old; The specific construction steps of the data optimization and compression model in step three are as follows: (1) Lightweight compression algorithm: The LZ4, Zstandard and other algorithms are adopted, and the compression rate can reach 3-5 times. The compression / decompression speed is extremely fast (GB / s level). After compression, the data volume is reduced by 70% and the transmission time is shortened to one-third of the original. (2) Feature extraction and dimensionality reduction: Key features of the data are extracted by PCA (principal component analysis) or autoencoder to reduce the amount of data transmitted. During the data transmission process, the amount of data transmitted is reduced by 90%, and the latency is reduced to the millisecond level.

[0025] Example 2 A data transmission system based on the Internet of Things includes a data acquisition and analysis unit, a platform construction unit, and a post-processing unit; The data acquisition and analysis unit builds a data acquisition model to collect data from all dimensions of the campus. After the acquisition is completed, it builds a data feature extraction model to extract features from the acquired data. After extraction, it builds a data analysis model to analyze the data. The platform building unit builds an open platform model based on the analyzed data. After the open platform model is built, an account management model is developed to register and manage accounts that are equidistant from the open platform. After registration and management, an experience data collection model is built to collect the data generated during use. The post-processing unit formulates a data backup model after the data collection is completed, backs up the collected data, and formulates a data optimization and compression model after the backup is completed, compressing the backed-up data.

[0026] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0027] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for fast data transmission based on Internet of Things, characterized in that Comprise the following steps; Step one: build a data collection model to collect the full-dimensional data of the campus, when the collection is completed, build a data feature extraction model to extract the features in the collected data, and build a data analysis model to analyze the data after extraction; Step two: build an open platform model according to the analyzed data, when the open platform model is built, formulate an account management model to register and manage the accounts to the open platform, and build an experience data collection model after registration management to collect the data generated during use; Step three: when the collection is completed, formulate a data backup model to backup the collected data, and formulate a data optimization compression model to compress the backup data, and build a data transmission model to transmit the processed data. 2.The data fast transmission method based on the Internet of Things according to claim 1, characterized in that: The specific construction steps of the data collection model in step one are as follows: (1) Personnel behavior data: student access time to dormitory and classroom, canteen consumption, library borrowing and movement trajectory; Collection method: access control system, one-card consumption record, WiFi positioning, Bluetooth beacon and camera AI recognition; (2) Equipment operation data: classroom lighting, air conditioning state, laboratory instrument parameters and elevator operation frequency; Collection method: IoT sensor, smart meter and device API interface; (3) Environmental perception data: campus air quality, noise level, light intensity and rainfall / wind speed; Collection method: weather station, environmental monitoring micro station and noise sensor network; (4) Teaching resource data: course video click volume, online test score and teaching material download volume; Collection method: LMS learning management system, school affairs system API and web crawler; (5) Social interaction data: campus forum post, WeChat group chat and community activity registration; Collection method: social media API, NLP sentiment analysis and relationship graph construction. 3.The data fast transmission method based on the Internet of Things according to claim 2, characterized in that: The specific construction steps of the data feature extraction model in step one are as follows: (1) Time series feature extraction: ①Statistical features: mean, variance, peak and slope; ②Frequency domain features: extract periodicity through FFT; ③Time series pattern: use TSFresh library to automatically generate 1000+ time series features; (2) Spatial feature extraction: ①Heat map analysis: generate personnel gathering heat map based on Kernel Density Estimation; ②Path analysis: construct student movement trajectory through WiFi positioning data, and calculate the transfer probability of classroom-dining room-dormitory path; (3) Text feature extraction: ①NLP processing: use BERT model to classify the sentiment of campus forum posts; ②Keyword extraction: identify high-frequency words through TF-IDF algorithm.

4. The data fast transmission method based on the Internet of Things according to claim 3, characterized in that: The specific construction steps of the data analysis model in step one are as follows: (1) Descriptive analysis: ①Visualization dashboard: use Tableau / Power BI to build a campus operation dashboard; ②Abnormal alarm: set threshold rules to trigger Dingding / email notification; (2) Predictive analysis: ① Time series forecasting: LSTM model predicts the number of canteen diners next week; ② Classification prediction: XGBoost predicts the risk of students failing; ③ Simulation: build a campus flow simulation model based on AnyLogic to optimize canteen queuing lines; (3) Prescriptive analysis: ① Resource scheduling: genetic algorithm optimizes laboratory equipment sharing, improving utilization by 30%; ② Path planning: Dijkstra algorithm plans the shortest delivery path for campus logistics robots; ③ A / B testing: compare the popularity of two canteen menus to choose the optimal solution.

5. The data fast transmission method based on the Internet of Things according to claim 1, characterized in that: The specific construction steps of the open platform model in step two are as follows: (1) Data processing layer: clean, convert, integrate and standardize the collected data to ensure data accuracy and consistency, use stream processing technology and batch processing technology to support real-time data processing and historical data processing; (2) Demand research and analysis: through research on the business needs and technical status of the campus, clarify the goals and scope of the data platform, develop a detailed implementation plan, establish a dedicated data platform construction team, and clarify the responsibilities and division of labor of each member to ensure smooth implementation of the project; (3) Data integration and governance: establish a data governance system, develop data standards and specifications, and manage and govern data uniformly, integrate and manage heterogeneous data sources through ETL tools or data integration platforms; (4) Platform building and development: based on the results of demand analysis, design the overall architecture of the platform, select the technical architecture and tools, and implement data collection, processing, storage, service and security functions; (5) System testing and optimization: test and optimize the system to ensure its efficiency and stability, solve problems and bottlenecks that arise during implementation, and continuously improve and optimize platform performance; (6) Establish data security policies and compliance mechanisms to ensure data security and legality, strengthen data encryption, access control and audit tracking to prevent data leakage and misuse, and divide accounts into different levels based on login time and the amount of data published on the platform. The longer the login time and the more data published, the higher the level. 6.The data fast transmission method based on the Internet of Things according to claim 1, characterized in that: The specific construction steps of the account management model in step two are as follows: (1) Unified identity authentication integration includes interfacing with campus systems, multi-factor authentication and binding with student ID / employee ID: ① Interface with campus systems: interface with the school's administrative system, student and worker system or unified identity authentication platform to achieve single sign-on and reduce the registration process; ② Multi-factor authentication: require secondary verification through SMS verification code, email verification or identity authentication APP during critical operations; ③ Student ID / employee ID binding: require users to bind their real student ID or employee ID to ensure that the account is associated with the campus identity, making it easier to manage permissions and trace problems in the future; (2) Role permissions include students, teachers, administrators and visitors, with default permissions for each role; (3) Data encryption and transmission security includes end-to-end encryption and password policy, ① End-to-end encryption: use SSL / TLS encryption for sensitive operations such as private messages and file transfers to prevent man-in-the-middle attacks; ② Password policy: enforce password complexity, periodically prompt for password change, and prohibit the use of common weak passwords; (4) Anti-fraud and risk control mechanism: ① IP black list: block high-frequency abnormal login IP; ② Device fingerprint: record user's commonly used device information, trigger secondary verification when abnormal device login; ③ Content filtering: identify sensitive words, pictures or links through AI model, automatically block illegal content and notify administrator for review.

7. The data fast transmission method based on the Internet of Things according to claim 1, characterized in that: The specific construction steps of the experience data collection model in step two are as follows: (1) Build an experience feedback collection library. During user login and platform use, collect experience data, classify collected data into positive and negative data, and support multiple forms of text, picture, and rating submission; (2) Set up a questionnaire database. When the user completes the use of the platform, a questionnaire is popped up for the user to rate. After the rating is completed, the rating data is saved directly and fed back to the platform. 8.The data fast transmission method based on the Internet of Things according to claim 1, characterized in that: The specific construction steps of the data backup model in step three are as follows; (1) Clean the data to be backed up, ① De-duplication and completion: delete duplicate records and fill in missing values; ② Abnormal value processing: identify and correct unreasonable data; ③ Standardization conversion: unify data format; (2) Retention analysis: analyze user retention rates for the next day, 7 days, and 30 days to evaluate platform stickiness; Text mining: sentiment analysis and keyword extraction on user feedback text; Abnormal value processing: identify and correct unreasonable data. 9.The data fast transmission method based on the Internet of Things according to claim 1, characterized in that: The specific construction steps of the data optimization and compression model in step three are as follows; (1) Lightweight compression algorithm: use LZ4 and Zstandard algorithms, compression ratio is 3-5 times, after compression, data volume is reduced by 70%, transmission time is shortened to one third of original; (2) Feature extraction and dimension reduction: extract key features of data through PCA or self-encoder, reduce transmission volume, transmission volume is reduced by 90% during data transmission, delay is reduced to milliseconds; The data transmission model integrates transmission methods, including short-range wireless transmission and long-range wireless transmission, and sets up a custom module. During data transmission, the custom module is used to select transmission methods based on file size, network speed, and data type. After transmission is completed, the data file can be shared on an open platform by setting up an account in advance. If the file can only be accessed by accounts of a certain level, the level of access is limited. If local access is required, communicate with the data platform construction team to limit access. When multiple people need to be transmitted, they can be transmitted directly to the open platform after limiting.

10. An Internet of Things based data fast transmission system characterized by: The data rapid transmission system is suitable for the data rapid transmission method based on the Internet of Things in any one of claims 1-9, and comprises a data acquisition and analysis unit, a platform building unit and a post-processing unit. The data acquisition and analysis unit builds a data acquisition model to acquire full-dimensional data of the campus, builds a data feature extraction model to extract features in the acquired data after the acquisition is completed, and builds a data analysis model to analyze the data. The platform building unit builds an open platform model according to the analyzed data, formulates an account management model to register and manage accounts to the open platform when the open platform model is built, and builds an experience data collection model to collect data generated during use after the registration and management. The post-processing unit formulates a data backup model to backup the collected data after the collection is completed, formulates a data optimization and compression model to compress the backup data after the backup is completed.

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