Intelligent leasing method, system and equipment based on Internet of Things and medium thereof

Abstract

The invention discloses an intelligent leasing method and system based on the Internet of Things, equipment and a medium thereof, and the method comprises the following steps: supporting an Internet of Things communication protocol based on an intelligent adaptation layer, achieving the communication between the equipment and an Internet of Things platform according to an MQTT protocol, and converting the data of different communication protocols into a unified format through employing a middleware layer; intelligent data processing based on edge computing: installing an edge computing node beside each rental item for processing the collected data, and performing data preprocessing on the collected data through the edge computing node; through the intelligent adaptation layer and the protocol selection algorithm, seamless connection and efficient cooperative work of different brands and standard Internet of Things devices are realized, and the flexibility and expandability of the system are improved; according to the method, data processing and decision making are carried out at the edge nodes, so that the network bandwidth consumption is reduced, the delay is reduced, and the real-time performance and the response speed of the system are improved.

Description

Technical Field

[0001] This invention relates to the fields of Internet of Things (IoT) technology and leasing service technology, specifically to IoT-based intelligent leasing methods, systems, equipment, and media. Background Technology

[0002] By leveraging IoT technology, the status, location, and usage of rented items can be monitored and managed in real time, automating and intelligentizing the rental process, ensuring item safety, and optimizing rental management. This approach not only improves efficiency but also reduces human error and operating costs.

[0003] There are many types of IoT devices, and different brands and models of devices often use different protocols and communication standards (such as MQTT, CoAP, Zigbee, Wi-Fi, etc.), resulting in poor interoperability between devices.

[0004] The Internet of Things (IoT) system involves a large amount of data collection, especially sensitive personal information and data on the use of items. How to ensure data security and user privacy is an urgent problem to be solved.

[0005] IoT devices typically rely on wireless networks for communication; however, network instability, insufficient bandwidth, and interference can affect device reliability and real-time response capabilities. Especially in scenarios with high-density device access, network congestion and latency can severely impact system performance.

[0006] IoT devices are typically widely distributed and may operate in harsh environments, making device failure and maintenance management highly complex. A key challenge is how to achieve intelligent monitoring and fault prediction of these devices, reducing manual intervention and improving operational efficiency.

[0007] How to intelligently schedule rental items based on real-time data to ensure their availability and efficiency is one of the core challenges in IoT-based smart rental systems.

[0008] In IoT-based smart leasing systems, user authentication and authorization management are crucial. Ensuring user legitimacy, access control, and payment security during the leasing process presents a significant challenge.

[0009] IoT-based smart leasing systems typically involve real-time billing, payment, and refund operations. Designing an efficient, accurate, and secure payment and settlement system is a technical challenge. To address this, we propose IoT-based smart leasing methods, systems, equipment, and media. Summary of the Invention

[0010] The purpose of this invention is to provide an IoT-based intelligent leasing method, system, device, and medium. The IoT-based intelligent leasing method and system utilize edge computing to optimize system performance, leverage machine learning technology for equipment fault prediction and intelligent scheduling, and combine blockchain technology to enhance data security, thus solving one of the problems existing in the prior art.

[0011] Firstly, to solve the aforementioned technical problems, this application adopts a technical solution: an intelligent leasing method based on the Internet of Things, comprising the following steps:

[0012] Based on the intelligent adaptation layer, it supports IoT communication protocols, realizes communication between devices and IoT platforms according to the MQTT protocol, and uses a middleware layer to convert data from different communication protocols into a unified format.

[0013] Intelligent data processing based on edge computing involves installing edge computing nodes next to each rental item to process the collected data and perform data preprocessing on the collected data through the edge computing nodes.

[0014] Based on intelligent fault prediction and maintenance, the sensor group in the Internet of Things device is used to monitor the health status of the device in real time, and the device operation status data is analyzed in time series and pattern recognition to form a device health record. Based on the device health record, a machine learning-based fault prediction model is constructed, and the machine learning model is trained based on historical operation data to predict the faults that will occur in the device.

[0015] Based on data security and privacy protection, the AES encryption standard is used to encrypt data, and an authentication mechanism based on public key infrastructure is established between users and devices to store key information such as device status and rental transaction records in the blockchain.

[0016] Based on automated payment and settlement, an intelligent billing model is constructed. According to the usage time, location and environmental data of the device, the intelligent billing model automatically calculates the rental fee.

[0017] More preferably, the different communication protocols include Zigbee, Wi-Fi, and LoRa protocols.

[0018] More preferably, the data preprocessing includes denoising, compression, and filtering of the data; the intelligent scheduling algorithm for edge computing is a machine learning and deep learning algorithm; based on the machine learning and deep learning algorithm, data processing and decision-making are performed at the edge nodes; and rental items are intelligently scheduled by predicting device usage patterns and demand; wherein the formula for the intelligent scheduling algorithm is:

[0019] ;

[0020] in, For the decision-making of item allocation, For the cost of using the item, As scheduling constraints, For rental demand, To represent in vector As decision variables, is the scheduling amount for the i-th type of item; m: the upper limit of the item index participating in the scheduling.

[0021] More preferably, the sensor group includes a temperature sensor, an acceleration sensor, and a pressure sensor, and the fault prediction model formula is:

[0022] ;

[0023] in, (Fault) represents the probability of a device malfunction occurring. This provides real-time monitoring data for the equipment.

[0024] More preferably, the step of training a machine learning model based on historical operating data to predict equipment failures specifically includes:

[0025] Build an automated maintenance and repair model;

[0026] When a failure is predicted, the automated maintenance and repair model automatically initiates remote repair operations or on-site repairs.

[0027] Repair records are uploaded to the blockchain using blockchain technology.

[0028] More preferably, the AES encryption standard is end-to-end encryption, and the AES encryption standard is embedded in IoT devices to protect sensitive information.

[0029] Furthermore, the intelligent billing model automatically calculates the rental fee using the following formula:

[0030] ;

[0031] in, The total rental cost, For equipment usage time, Cost per unit of time Other additional fees.

[0032] Secondly, to solve the aforementioned technical problems, another technical solution adopted in this application is: an intelligent leasing method based on the Internet of Things, including the following steps:

[0033] Step 1: Employ an AI adaptive communication protocol selection algorithm to dynamically select the optimal communication protocol and automatically adjust network settings to optimize communication efficiency between devices, thereby building a multimodal data fusion and sensor self-organizing network for communication.

[0034] Step Two: Deploy federated learning algorithms on edge nodes, enabling each edge node to train locally and optimize device operation decisions through a decentralized learning model. Device scheduling and resource allocation are performed based on reinforcement learning. The federated learning algorithm specifically includes:

[0035] At each device's edge node, assuming there are For each device node, a model is trained:

[0036] ;

[0037] in, For device nodes The training model, Update weights for the global model;

[0038] Step 3: Perform time-series analysis on equipment operation data based on the Long Short-Term Memory network model, predict faults based on the historical state of the equipment, and realize automatic repair and remote adjustment of equipment faults based on IoT gateway and remote robot technology;

[0039] Step 4: Protect data privacy using zero-knowledge proof technology;

[0040] Step 5: Based on the dynamic pricing algorithm and blockchain payment model, dynamically adjust the rental fee according to user demand, equipment usage, and market changes. The billing algorithm formula is as follows:

[0041] ;

[0042] in, The rental fee is the cost of the rental. For the rental period, Based on the frequency of equipment use, This is market demand data for the equipment.

[0043] Thirdly, to solve the aforementioned technical problems, another technical solution adopted in this application is: an intelligent rental system based on the Internet of Things, comprising:

[0044] The communication module is configured based on the intelligent adaptation layer, supports IoT communication protocols, realizes communication between the device and the IoT platform according to the MQTT protocol, and uses the middleware layer to convert data of different communication protocols into a unified format.

[0045] An edge computing module is configured for intelligent data processing based on edge computing. An edge computing node is installed next to each rental item to process the collected data and perform data preprocessing on the collected data through the edge computing node.

[0046] The fault prediction module is configured to be based on intelligent fault prediction and maintenance. It uses the sensor group in the Internet of Things device to monitor the health status of the device in real time, performs time series analysis and pattern recognition on the device operation status data to form a device health record, and builds a machine learning-based fault prediction model based on the device health record. The machine learning model is trained based on historical operation data to predict the faults that will occur in the device.

[0047] The data protection module is configured to encrypt data based on the AES encryption standard, based on data security and privacy protection, and establish an authentication mechanism based on public key infrastructure between users and devices, storing key information such as device status and rental transaction records in the blockchain;

[0048] The billing module is configured for automated payment and settlement, and builds an intelligent billing model. Based on the device's usage time, location, and environmental data, the intelligent billing model automatically calculates the rental fee.

[0049] Fourthly, to solve the above-mentioned technical problems, another technical solution adopted in this application is: an electronic device, including a processor, a memory and a communication interface, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of any of the above-mentioned IoT-based smart leasing methods.

[0050] Fifth aspect: To solve the above-mentioned technical problems, another technical solution adopted in this application is: a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the smart leasing method based on the Internet of Things as described above.

[0051] Advantages of this invention:

[0052] This invention achieves seamless connection and efficient collaborative work of IoT devices of different brands and standards through an intelligent adaptation layer and protocol selection algorithm, thereby improving the system's flexibility and scalability.

[0053] This invention employs data processing and decision-making at edge nodes, which not only reduces network bandwidth consumption but also decreases latency and improves the system's real-time performance and response speed, especially in high-frequency, high-concurrency scenarios where it has significant advantages.

[0054] This invention uses machine learning algorithms to predict and analyze equipment operating data, enabling early warning and automated repair of equipment failures, which greatly reduces operation and maintenance costs and equipment failure rate.

[0055] This invention stores key information such as device status and transaction records in the blockchain, ensuring the immutability and transparency of the data, and enhancing the trustworthiness of the system and users' confidence in data privacy protection.

[0056] This invention, through an intelligent billing system based on Internet of Things data, not only ensures the accuracy of cost calculation but also significantly simplifies the payment and settlement process.

[0057] This invention, based on a machine learning-based fault prediction model, improves the accuracy of fault detection and the ability to provide early warnings. Before equipment failure occurs, the system can proactively take measures, significantly reducing equipment downtime and maintenance costs.

[0058] This invention ensures the security of user data and device information from multiple levels through end-to-end encryption, blockchain technology, and differential privacy protection technology. In particular, it effectively prevents data leakage and tampering in the context of multi-party data exchange and cross-platform data sharing.

[0059] This invention, through edge computing, protocol adaptation layer, and intelligent data processing and decision-making, can flexibly respond to rental needs of different scales and scenarios. Whether it is small-scale single device rental or large-scale item sharing and rental, the system can operate efficiently and expand dynamically. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a flowchart illustrating an intelligent leasing method based on the Internet of Things according to the present invention.

[0062] Figure 2 This is a flowchart illustrating Embodiment 2 of the present invention;

[0063] Figure 3 This is a block diagram of an IoT-based intelligent rental system according to Embodiment 3 of the present invention;

[0064] Figure 4 This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation

[0065] 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.

[0066] Figure 1 This is a flowchart illustrating an IoT-based smart leasing method according to an embodiment of the present invention. It should be noted that if substantially the same result is achieved, the method of this application is not necessarily identical. Figure 1 The sequence of processes shown is limited.

[0067] Example 1

[0068] Currently, IoT-based smart leasing methods and systems face the following prominent problems:

[0069] Poor device interoperability: IoT devices often use different communication protocols and standards, which makes coordination and data sharing between different devices in the system difficult.

[0070] Poor network reliability and latency issues: IoT devices and systems are highly dependent on the network environment. Network instability or high latency may affect the real-time monitoring and scheduling of rented items.

[0071] Data security and privacy protection: Sensitive data involving users' personal information and rented items poses a significant technical challenge in ensuring data security and privacy.

[0072] Equipment Maintenance and Fault Prediction: IoT devices are widely distributed across different regions, and their health management and fault prediction still rely heavily on manual intervention, making automated maintenance and troubleshooting difficult. This solution aims to address these issues by introducing cutting-edge edge computing and intelligent prediction technologies, combined with IoT sensors, to design an intelligent leasing method and system.

[0073] like Figure 1 The present invention relates to an IoT-based smart leasing method, comprising the following steps:

[0074] S10: Based on the intelligent adaptation layer, it supports IoT communication protocols, realizes communication between devices and IoT platforms according to the MQTT protocol, and uses a middleware layer to convert data from different communication protocols into a unified format.

[0075] Specifically, it improves the interoperability of various IoT devices in the system and solves the problem of devices not being able to communicate smoothly.

[0076] Design an intelligent adaptation layer that supports multiple IoT communication protocols (such as Zigbee, Wi-Fi, LoRa, etc.) and manages devices uniformly through standardized interfaces.

[0077] This system enables efficient communication between devices and IoT platforms based on the MQTT protocol, and uses a middleware layer to convert data from different protocols into a unified format.

[0078] The model can dynamically identify and connect to different IoT devices, supporting automatic registration and initial configuration of devices, reducing manual intervention.

[0079] An adaptive protocol selection algorithm is used to dynamically select the optimal communication protocol based on device performance, network environment, and real-time requirements.

[0080] For example, you can configure the device to prioritize low-power protocols such as LoRa when it is in a low-power state, and to prioritize Wi-Fi when there is a high bandwidth requirement.

[0081] S20. Intelligent data processing based on edge computing: An edge computing node is installed next to each rental item to process the collected data. The collected data is preprocessed through the edge computing node.

[0082] Specifically, it addresses network latency issues in IoT systems, reduces bandwidth consumption for data transmission, and improves real-time performance and reliability.

[0083] Edge computing nodes are installed next to each rental item to process the collected data, such as the device's usage status, location, and environmental parameters.

[0084] By performing noise reduction, compression, and filtering operations on data through edge nodes, only meaningful data is uploaded to the cloud, significantly reducing bandwidth consumption.

[0085] Using machine learning and deep learning algorithms, data processing and decision-making are performed at edge nodes.

[0086] By predicting equipment usage patterns and demand, rental items can be intelligently scheduled.

[0087] Intelligent scheduling algorithm:

[0088] ;

[0089] in, For the decision-making of item allocation, For the cost of using the item, As scheduling constraints, For rental demand, To represent in vector As decision variables, is the scheduling amount for the i-th type of item; m: the upper limit of the item index participating in the scheduling.

[0090] Through this optimization model, the system achieves optimal scheduling of leased resources.

[0091] S30. Based on intelligent fault prediction and maintenance, the sensor group in the Internet of Things device is used to monitor the health status of the device in real time, and the device operation status data is analyzed in time series and pattern recognition is performed to form a device health record. Based on the device health record, a fault prediction model based on machine learning is constructed, and the machine learning model is trained based on historical operation data to predict the faults that will occur in the device.

[0092] Specifically, intelligent methods can be used to predict equipment failures, reduce maintenance costs, and improve system reliability.

[0093] Equipment health monitoring and fault data acquisition: Real-time monitoring of equipment health status using sensor arrays (such as temperature sensors, acceleration sensors, pressure sensors, etc.) in IoT devices.

[0094] Perform time-series analysis and pattern recognition on equipment operating status data to create equipment health records.

[0095] Fault prediction model based on machine learning:

[0096] Use historical operating data to train machine learning models (such as random forests, support vector machines, neural networks, etc.) to predict potential equipment failures.

[0097] The formula for the fault prediction model is as follows:

[0098] ;

[0099] in, (Fault) represents the probability of a device malfunction occurring. This provides real-time monitoring data for the equipment (such as temperature, vibration, etc.).

[0100] This fault prediction model can diagnose equipment faults in advance and trigger early warnings, reducing manual intervention and downtime.

[0101] Automated maintenance and repair model:

[0102] When a fault is predicted to occur, the model automatically initiates remote repair operations or notifies relevant maintenance personnel to carry out on-site repairs.

[0103] By combining blockchain technology to record maintenance information on the blockchain, the transparency and traceability of equipment maintenance can be ensured.

[0104] S40. Based on data security and privacy protection, the AES encryption standard is used to encrypt data, and an authentication mechanism based on public key infrastructure is established between users and devices to store key information such as device status and rental transaction records in the blockchain.

[0105] Specifically, ensure the security of user data and device data, and prevent data leakage and tampering.

[0106] Data encryption and privacy protection:

[0107] End-to-end encryption: Data is encrypted using AES (Advanced Encryption Standard) to ensure data security during transmission.

[0108] Establish a public key infrastructure (PKI)-based authentication mechanism between users and devices to ensure the integrity and legitimacy of data transmission.

[0109] Blockchain technology applied to data storage:

[0110] Key information such as equipment status and rental transaction records are stored in the blockchain to ensure the immutability of the data.

[0111] The application of blockchain smart contracts can automatically execute lease contract terms, ensuring transparency and fairness in transactions.

[0112] Privacy protection algorithm:

[0113] Differential privacy technology is introduced to ensure that no personal privacy information is leaked even when processing user data.

[0114] S50. Based on automated payment and settlement, an intelligent billing model is constructed. According to the usage time, location, and environmental data of the equipment, the intelligent billing model automatically calculates the rental fee.

[0115] Specifically, it enables the automatic calculation, payment, and settlement of rental fees, improving user experience and reducing human intervention.

[0116] Intelligent billing model:

[0117] The system automatically calculates the rental fee based on data such as the equipment's usage time, location, and environment.

[0118] Billing formula:

[0119] ;

[0120] in, The total rental cost, For equipment usage time, Cost per unit of time For other additional costs (such as damage compensation, etc.).

[0121] Automated payment and settlement system:

[0122] The system supports multiple payment methods (such as credit cards and digital currencies) and automatically executes fee settlement and payment through smart contracts. At the end of the rental period, the system automatically generates a settlement statement based on the equipment usage records and rental contract, and users can pay with one click through the app.

[0123] By combining cutting-edge technologies such as edge computing, machine learning, blockchain, and intelligent adaptation protocols, the core problems existing in the current IoT smart leasing system, such as poor device interoperability, high network latency, difficulty in ensuring data security, and untimely equipment maintenance, have been solved.

[0124] The interoperability problem between different devices is solved by using an intelligent adaptation layer and protocol selection algorithm, ensuring the flexibility of the system.

[0125] It reduced network latency and improved the system's real-time performance and resource scheduling capabilities.

[0126] Machine learning-based fault prediction systems can detect problems early and reduce the occurrence of faults, thereby lowering maintenance costs.

[0127] Blockchain ensures the immutability of data and automates and makes the leasing process more transparent.

[0128] Automated payment and settlement systems improve transaction efficiency and user experience.

[0129] Example 2

[0130] like Figure 2 As shown, to solve the above-mentioned technical problems, based on Embodiment 1, another technical solution adopted in this application is:

[0131] A smart leasing method based on the Internet of Things includes the following steps:

[0132] Step 1: Employ an AI adaptive communication protocol selection algorithm to dynamically select the optimal communication protocol and automatically adjust network settings to optimize communication efficiency between devices, thereby building a multimodal data fusion and sensor self-organizing network for communication.

[0133] AI-adaptive communication protocol selection algorithm is used:

[0134] This algorithm can dynamically select the most suitable communication protocol based on network status, device requirements, and application scenarios, and automatically adjust network settings to optimize communication efficiency between devices.

[0135] By combining deep reinforcement learning with optimization and adaptation strategies, the algorithm can adaptively adjust the optimal configuration in different environments.

[0136] When faced with different manufacturers or equipment models, the algorithm can automatically identify and adjust the working mode without human intervention.

[0137] It is more adaptable and supports a wider range of device access.

[0138] For example, assuming the algorithm needs to connect to smart locks from different brands, the algorithm can select protocols such as Wi-Fi, Zigbee, or NB-IoT for communication in real time, based on the device's workload and network latency.

[0139] Multimodal data fusion and sensor self-organizing networks:

[0140] By employing physical layer self-organizing network technologies (such as SDN and network slicing), devices can communicate efficiently through a seamless self-organizing network mechanism, avoiding algorithm crashes caused by single points of failure.

[0141] Data fusion between different sensors (such as temperature, pressure, motion, etc.) can form a more comprehensive equipment status monitoring system, improve data quality, and support more accurate intelligent decision-making.

[0142] Step Two: Deploy federated learning algorithms on edge nodes, enabling each edge node to train locally and optimize device operation decisions through a decentralized learning model. Device scheduling and resource allocation are performed based on reinforcement learning. The federated learning algorithm specifically includes:

[0143] At each device's edge node, assuming there are For each of the 10 device nodes, the model is trained as follows:

[0144] ;

[0145] in, For device nodes The training model, Update weights for the global model;

[0146] Specifically, it addresses issues such as network latency and bandwidth consumption, further enhancing the system's real-time data processing capabilities and improving the response speed of IoT devices.

[0147] Deploying Federated Learning algorithms on edge nodes enables each edge node to train locally and optimize device operation decisions through decentralized learning models.

[0148] It reduces the pressure of cloud data transmission, protects user privacy (because the data never leaves the device), and improves processing efficiency.

[0149] Edge nodes can perform intelligent status analysis and prediction based on local data, reducing reliance on the cloud.

[0150] Mathematical model: At each edge node of the device, it is assumed that there are For each of the 10 device nodes, the model is trained as follows:

[0151] ;

[0152] in, For device nodes The training model, These are the updated weights for the global model.

[0153] Reinforcement learning is used for equipment scheduling and resource allocation. Through intelligent scheduling, the model can assess the usage status of leased equipment in real time, predict future demand, and dynamically adjust equipment allocation and usage strategies.

[0154] Deep Q-learning algorithm is used to optimize scheduling strategy and dynamically adjust device scheduling to minimize response time and maximize resource utilization.

[0155] Step 3: Perform time-series analysis on equipment operation data based on the Long Short-Term Memory network model, predict faults based on the historical state of the equipment, and realize automatic repair and remote adjustment of equipment faults based on IoT gateway and remote robot technology;

[0156] Improve the accuracy of fault prediction and achieve automated maintenance and repair, significantly reducing downtime and equipment maintenance costs. Use an LSTM (Long Short-Term Memory) model to perform time-series analysis on equipment operating data and perform fault prediction based on the equipment's historical state (such as temperature, vibration, etc.).

[0157] LSTM models can better capture the timing characteristics of equipment operation and improve the accuracy of fault prediction.

[0158] Prediction formula: Assuming the health status of the equipment depends on historical data. :

[0159] ;

[0160] in, The current health status of the device. Input time series data.

[0161] By combining IoT gateways with remote robot technology, automatic repair and remote adjustment of equipment faults can be achieved.

[0162] For example, the model can initiate a device self-healing process via a remote control module, or perform real-time maintenance via a robot installed in the device.

[0163] Step 4: Protect data privacy using zero-knowledge proof technology;

[0164] Specifically, it ensures the privacy and security of user and device data, and guarantees that the system complies with privacy protection regulations when processing sensitive data.

[0165] Blockchain technology and smart contracts: In addition to storing transaction records in traditional blockchains, zero-knowledge proof (ZKP) technology will be combined to protect data privacy.

[0166] Zero-knowledge proofs ensure data authenticity without exposing the data content. They guarantee transaction verification while protecting user privacy, preventing third-party theft of personal information. Homomorphic encryption is used during data storage, allowing for analysis and processing even when data is stored encrypted.

[0167] While ensuring data privacy, it supports efficient data processing and analysis.

[0168] Step 5: Based on the dynamic pricing algorithm and blockchain payment model, dynamically adjust the rental fee according to user demand, equipment usage, and market changes. The billing algorithm formula is as follows:

[0169] ;

[0170] in, The rental fee is the cost of the rental. For the rental period, Based on the frequency of equipment use, This is market demand data for the equipment.

[0171] Specifically, this involves enabling intelligent and dynamic billing of rental fees, while ensuring the security and convenience of the payment process.

[0172] By combining dynamic pricing algorithms and blockchain payment systems, rental fees are dynamically adjusted based on user needs, equipment usage, and market changes.

[0173] Through AI pricing models (such as pricing algorithms based on time series analysis), the system can automatically adjust fees based on data such as the rental duration and usage frequency of the equipment, ensuring that each user rents at a fair price.

[0174] Algorithm formula:

[0175] ;

[0176] in, The rental fee is the cost of the rental. For the rental period, Based on the frequency of equipment use, This is market demand data for the equipment.

[0177] Using smart contracts, the leasing agreement is fully automated in the payment and equipment handover processes, ensuring transparency and security throughout the leasing process.

[0178] It supports multiple cryptocurrencies and traditional payment methods, making the payment process more global and convenient.

[0179] Specifically, the combination of edge computing and federated learning solves the latency problem between devices and cloud platforms, improving the real-time performance of data processing.

[0180] The combination of deep learning and time series analysis improves the accuracy of equipment failure prediction and reduces equipment downtime.

[0181] The application of dynamic pricing and smart contracts has enabled automated adjustment and payment of rental fees, improving the user experience.

[0182] The introduction of zero-knowledge proofs and homomorphic encryption technologies has greatly enhanced data security and user privacy protection.

[0183] Example 3

[0184] like Figure 3 As shown, to solve the above-mentioned technical problems, based on Embodiment 1, another technical solution adopted in this application is: an intelligent rental system based on the Internet of Things, comprising:

[0185] The communication module is configured based on the intelligent adaptation layer, supports IoT communication protocols, realizes communication between the device and the IoT platform according to the MQTT protocol, and uses the middleware layer to convert data of different communication protocols into a unified format.

[0186] An edge computing module is configured for intelligent data processing based on edge computing. An edge computing node is installed next to each rental item to process the collected data and perform data preprocessing on the collected data through the edge computing node.

[0187] The fault prediction module is configured to be based on intelligent fault prediction and maintenance. It uses the sensor group in the Internet of Things device to monitor the health status of the device in real time, performs time series analysis and pattern recognition on the device operation status data to form a device health record, and builds a machine learning-based fault prediction model based on the device health record. The machine learning model is trained based on historical operation data to predict the faults that will occur in the device.

[0188] The data protection module is configured to encrypt data based on the AES encryption standard, based on data security and privacy protection, and establish an authentication mechanism based on public key infrastructure between users and devices, storing key information such as device status and rental transaction records in the blockchain;

[0189] The billing module is configured for automated payment and settlement, and builds an intelligent billing model. Based on the device's usage time, location, and environmental data, the intelligent billing model automatically calculates the rental fee.

[0190] For other details regarding the implementation techniques of each module in the above embodiments, please refer to the description of an IoT-based smart leasing method in the above embodiments, which will not be repeated here.

[0191] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system-type embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0192] Example 4

[0193] like Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. It illustrates a structural schematic diagram suitable for implementing the electronic device in the embodiment of the present disclosure. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0194] like Figure 4 As shown, an electronic device includes a processor, a memory, and a communication interface. The memory stores a computer program, and when the processor executes the computer program, it implements the IoT-based smart leasing method described in the aforementioned embodiments of this disclosure. The electronic device can exchange data with other devices or systems through the communication interface, enabling real-time updates and sharing of drug information.

[0195] The processor in the aforementioned electronic device serves as its core, responsible for executing the computer program stored in the memory to implement various functions of the paperless conference terminal's intelligent interaction method. The processor can employ a high-performance multi-core CPU or a dedicated chip to meet the demands of complex calculations and real-time processing. The memory stores the operating system, applications, data, and computer programs. In this embodiment, the memory stores the computer program implementing the paperless conference terminal's intelligent interaction method. The memory can be RAM, ROM, Flash memory, or other types of non-volatile memory. The communication interface connects the electronic device to other devices or networks, enabling data transmission and exchange. In this embodiment, the communication interface supports multiple communication protocols and interface standards, such as Wi-Fi, Bluetooth, USB, and Ethernet, to meet communication needs in different scenarios.

[0196] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0197] Example 5

[0198] According to an embodiment of the present disclosure, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the functions of the aforementioned smart leasing method based on the Internet of Things according to various embodiments of the present disclosure.

[0199] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0200] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0201] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An Internet of Things-based intelligent leasing method, characterized in that, The method comprises the following steps: Based on the intelligent adaptation layer, support the Internet of Things communication protocol, according to the MQTT protocol to realize the communication between the device and the Internet of Things platform, adopt the middleware layer to convert the data of different communication protocols into a unified format; Based on the intelligent data processing of edge computing, install an edge computing node beside each rental item for processing the collected data, and perform data preprocessing on the collected data through the edge computing node; Based on intelligent fault prediction and maintenance, use the sensor group in the Internet of Things device to monitor the health status of the device in real time, perform time series analysis and pattern recognition on the device operation state data, form a device health record, and construct a fault prediction model based on machine learning according to the device health record, and train the machine learning model according to the historical operation data to predict the faults of the device; Based on data security and privacy protection, the data is encrypted by using the AES encryption standard, an identity verification mechanism based on public key infrastructure is established between the user and the device, and key information such as device status and rental transaction records is stored in the blockchain; Based on automatic payment and settlement, an intelligent charging model is constructed, which automatically calculates the rental fee according to the usage time, location and environmental data of the device. 2.The IoT-based smart leasing method according to claim 1, wherein, The different communication protocols include Zigbee protocol, Wi-Fi protocol and LoRa protocol. 3.The IoT-based smart leasing method according to claim 1, wherein, The data preprocessing includes denoising, compression and screening of data, the intelligent scheduling algorithm of edge computing is machine learning and deep learning algorithm, according to the machine learning and deep learning algorithm, data processing and decision are performed on the edge node, and the rental items are intelligently scheduled through the use mode and demand prediction of the device, wherein the formula of the intelligent scheduling algorithm is: ； wherein, a dispatch decision for the item, a usage cost for the item, a dispatch constraint condition, a rental demand quantity, is a vector representing is a decision variable, is a dispatch quantity of the i-th item; m: upper limit of item index participating in dispatch. 4.The smart leasing method based on Internet of Things according to claim 1, characterized in that, The sensor group includes temperature sensor, acceleration sensor and pressure sensor, and the formula of the fault prediction model is: ； wherein, (failure) is the probability of a device failure occurring, is real-time monitoring data of the device. 5.The smart leasing method based on Internet of Things according to claim 1, wherein, The machine learning model is trained according to the historical operation data to predict the faults of the device, specifically including: An automatic maintenance and repair model is constructed; When predicting the occurrence of faults, the automatic maintenance and repair model automatically starts remote repair operation or on-site repair; Based on the blockchain technology, the maintenance records are chained. 6.The smart leasing method based on Internet of Things according to claim 1, wherein, The AES encryption standard is end-to-end encryption, and the AES encryption standard is embedded in the Internet of Things device to protect sensitive information. 7.The IoT-based smart leasing method according to claim 1, wherein, The intelligent charging model automatically calculates the rental fee, and the charging formula is: ； wherein, is the total cost of the lease, is the length of time the equipment is used, is the cost per unit of time, is other additional costs.

8. An intelligent leasing system based on Internet of Things, applied to the intelligent leasing method based on Internet of Things in any one of claims 1-7, characterized in that, It includes: The communication module is configured based on the intelligent adaptation layer, supports the Internet of Things communication protocol, realizes the communication between the device and the Internet of Things platform according to the MQTT protocol, and adopts the middleware layer to convert the data of different communication protocols into a unified format; The edge computing module is configured based on the intelligent data processing of edge computing, installs an edge computing node beside each rental item for processing the collected data, and performs data preprocessing on the collected data through the edge computing node; A fault prediction module is configured to predict faults based on intelligent fault prediction and maintenance, utilize sensors in Internet of Things devices to monitor device health status in real time, perform time series analysis and pattern recognition on device operation status data, form a device health record, construct a fault prediction model based on machine learning based on the device health record, and train the machine learning model based on historical operation data to predict faults in the device; A data protection module is configured to protect data security and privacy, encrypt data using the AES encryption standard, establish an identity verification mechanism based on a public key infrastructure between users and devices, and store key information such as device status and lease transaction records in a blockchain. A billing module is configured to automatically process payments and settlements, construct an intelligent billing model, and automatically calculate lease fees based on device usage duration, location, and environmental data.

9. An electronic device, characterized by A computer readable storage medium stores a computer program, and a processor executes the computer program to implement the intelligent leasing method based on Internet of Things as claimed in any one of claims 1 to 7.

10. A computer readable storage medium, characterized in that, A computer readable storage medium stores a computer program, and a processor executes the computer program to implement the intelligent leasing method based on Internet of Things as claimed in any one of claims 1 to 7.

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