Business management method and system based on Internet of Things

Abstract

The invention discloses a business administration method and system based on the Internet of Things, and belongs to the field of integration of the Internet of Things technology and business administration. According to the system, through cooperative work of the Internet of Things sensing layer, the network layer, the platform layer and the application layer, intelligent management of the whole life cycle of a market subject is realized. The method comprises four stages of data acquisition and perception, data transmission and processing, intelligent analysis and decision making and management execution and feedback, and solves the problems of information lag, low supervision efficiency, passive service and the like in traditional business administration. Real-time acquisition, dynamic updating and intelligent analysis of market subject information are realized through the Internet of Things technology, the accuracy, high efficiency and service quality of business administration are improved, and the system has significant application value.

Description

Technical Field

[0001] This invention relates to the fields of Internet of Things (IoT) technology and business management technology, and more specifically, to an IoT-based business management method and system. Background Technology

[0002] Business administration is an important means of maintaining market order and promoting economic development. Traditional business administration methods mainly rely on manual inspections, paper records, and post-event supervision, which have many problems:

[0003] Information collection is delayed and incomplete.

[0004] In traditional business administration, information about market entities is mainly obtained through self-declaration by enterprises and periodic inspections. However, this information is often outdated and fails to fully reflect the actual operating conditions of enterprises. For example, information such as changes in business address or adjustments to business scope may not be promptly obtained by regulatory authorities, leading to regulatory ineffectiveness.

[0005] Inefficient regulation

[0006] Manual inspections are costly and inefficient, making it difficult to achieve comprehensive supervision of a large number of market entities. Supervisory personnel can often only conduct spot checks on key enterprises and key areas, resulting in regulatory blind spots and situations where illegal business practices are not investigated and dealt with in a timely manner.

[0007] Passive service approach

[0008] Traditional business administration services primarily involve businesses visiting the office to complete transactions. This passive approach fails to provide personalized and precise services. Businesses need to spend a significant amount of time and energy obtaining policy information and handling approval procedures.

[0009] Inadequate credit management

[0010] Credit information of market entities is scattered across different departments, making it difficult to share and integrate. The credit evaluation system is incomplete, and the mechanism for punishing dishonesty is not perfect, resulting in a weak sense of credit among enterprises and an imperfect market credit environment. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of the prior art and provide an Internet of Things-based business management method and system to solve problems such as insufficient data association and display capabilities, dynamic response performance bottlenecks, weak association analysis functions, and poor industry adaptability in the prior art.

[0012] To achieve the above objectives, the present invention adopts the following technical solution:

[0013] An Internet of Things (IoT)-based business management system includes a perception layer, a network layer, a platform layer, and an application layer. The perception layer collects various information from market entities, including RFID tags, sensors, cameras, and smart terminals. The network layer transmits the data collected by the perception layer to the platform layer, including IoT gateways, mobile communication networks, and wireless networks. The platform layer processes, stores, and analyzes the data, including a data processing module, a database, an intelligent analysis engine, and a blockchain module. The application layer provides various services to business management departments and market entities, including a regulatory subsystem, a service subsystem, a credit subsystem, and a decision support subsystem.

[0014] Furthermore, the RFID tags in the perception layer are attached to the business licenses, permits, and other documents of the market entity to identify the market entity's identity and store basic information; the sensors include environmental sensors, equipment sensors, and location sensors, which are used to collect environmental data of the business premises, operating data of production equipment, and location information of the market entity, respectively; the camera is used to monitor the real-time situation of the business premises; and the smart terminal is used for market entities to fill in information and receive notifications.

[0015] Furthermore, the IoT gateway in the network layer is used to realize protocol conversion between the sensing layer and the network layer, and to connect different types of sensors and devices to the network; the mobile communication network includes 4G and 5G networks, which are used to realize wide-area data transmission; the wireless network includes Wi-Fi, Bluetooth, etc., which are used to realize short-range data transmission.

[0016] Furthermore, the data processing module in the platform layer is used to clean, transform, and fuse the collected data, remove noise and redundant data, and convert data of different formats into a unified format; the database includes relational databases and non-relational databases, which are used to store structured data and unstructured data respectively; the intelligent analysis engine uses machine learning algorithms to analyze the data and uncover the operating patterns and potential risks of market entities; the blockchain module is used to store the credit information and transaction records of market entities to ensure that the data is tamper-proof and traceable.

[0017] Furthermore, the regulatory subsystem in the application layer is used to monitor the business activities of market entities in real time, including abnormal behavior monitoring, violation warnings, and law enforcement dispatch; the service subsystem is used to provide market entities with online services, information inquiries, and policy pushes; the credit subsystem is used to evaluate and manage the credit status of market entities, including credit scoring, credit disclosure, and penalties for dishonesty; and the decision support subsystem is used to provide data analysis and decision-making suggestions to the industrial and commercial administration departments to assist in the formulation of regulatory policies and development plans.

[0018] This invention also provides a business management method based on the Internet of Things, comprising the following steps:

[0019] Step 1: Data collection and sensing. The identification information, business premises environmental data, production equipment operation data, location information, real-time monitoring data and self-reported information of market entities are collected through RFID tags, sensors, cameras and smart terminals in the sensing layer.

[0020] Step 2: Data transmission and processing. The collected data is transmitted to the platform layer through the network layer. The data processing module of the platform layer cleans, transforms and merges the data, removes noise and redundant data, and converts data of different formats into a unified format before storing it in the database.

[0021] Step 3: Intelligent Analysis and Decision-Making. The intelligent analysis engine at the platform layer uses machine learning algorithms to analyze the data in the database, uncover the operating patterns and potential risks of market entities, and combine the credit information and transaction records stored in the blockchain module to generate regulatory suggestions and service plans, providing data support for the decision support subsystem.

[0022] Step 4: Management Execution and Feedback. Based on the results of intelligent analysis and decision-making, the application layer conducts real-time supervision and law enforcement scheduling through the regulatory subsystem, provides services to market entities through the service subsystem, and manages credit through the credit subsystem. At the same time, it collects feedback information from market entities to optimize management methods and system functions.

[0023] Furthermore, in step 1, data collection adopts a combination of real-time and periodic collection. Real-time collection is used for environmental data, equipment operation data, and real-time monitoring data, while periodic collection is used for basic information and self-reported information of market entities.

[0024] Furthermore, in step 2, data cleaning includes removing missing values, outliers, and duplicate values; data transformation includes format conversion, unit conversion, and encoding conversion; and data fusion includes the association and integration of multi-source data.

[0025] Furthermore, in step 3, the machine learning algorithms include classification algorithms, clustering algorithms, and regression algorithms. Classification algorithms are used to identify the business types and violations of market entities, clustering algorithms are used to mine the group characteristics and business models of market entities, and regression algorithms are used to predict the business status and development trends of market entities.

[0026] Furthermore, in step 4, management execution and feedback adopt a closed-loop management model. By collecting feedback information from market entities, the algorithm model of the intelligent analysis engine and the service functions of the application layer are continuously optimized to improve the efficiency and quality of business administration. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the management system process of the present invention;

[0028] Figure 2 This is a schematic diagram of the management method of the present invention.

[0029] The beneficial effects of this invention are:

[0030] 1. Real-time and comprehensive data acquisition

[0031] Traditional business management relies primarily on manual data collection and self-reported data from enterprises, resulting in information lag and incompleteness. This invention achieves real-time data collection through IoT sensing layer devices, covering various aspects such as the enterprise's operating environment, production equipment operation, and real-time monitoring, thus comprehensively reflecting the enterprise's actual operating status.

[0032] 2. The precision and efficiency of supervision

[0033] Traditional supervision employs a "blanket" inspection approach, resulting in low efficiency. This invention, through intelligent analysis engine data analysis, can accurately identify enterprises' violation risks, enabling targeted supervision, improving the accuracy and efficiency of supervision, while reducing supervision costs.

[0034] 3. Personalized and proactive service

[0035] Traditional business administration services are passive, requiring businesses to proactively visit the company to handle their affairs. The service subsystem of this invention can proactively push personalized policies and services based on the characteristics and needs of each enterprise, achieving more precise and proactive services and improving enterprise satisfaction.

[0036] 4. The reliability and authority of credit management

[0037] In traditional credit management, credit information is scattered, easily tampered with, and lacks credibility. This invention uses blockchain technology to store credit information and transaction records, ensuring the immutability and traceability of data, improving the reliability and authority of credit management, and contributing to a sound market credit environment.

[0038] 5. The scientific and forward-looking nature of decision-making

[0039] Traditional decision-making relies primarily on experience and limited data, lacking scientific rigor and foresight. This invention, through the analysis and mining of massive amounts of data, accurately grasps market dynamics and industry development trends, providing a scientific basis for decision-making and making decisions more rational and forward-looking. Detailed Implementation

[0040] The present invention will be further described in detail below with reference to specific embodiments.

[0041] An Internet of Things (IoT)-based business management system includes a perception layer, a network layer, a platform layer, and an application layer.

[0042] Perception Layer: RFID tags utilize UHF RFID technology (860-960MHz), with a recognition distance of 3-5 meters and a storage capacity of 512 bytes, capable of storing basic enterprise information. Environmental sensors include temperature and humidity sensors, and air quality sensors. The temperature and humidity sensors measure temperatures from -40℃ to 85℃, and humidity ranges from 0% to 100%RH, with accuracies of ±0.5℃ and ±2%RH respectively. The air quality sensor detects PM2.5, formaldehyde, and other pollutant concentrations. Equipment sensors include current, voltage, and vibration sensors to collect parameters such as current, voltage, and vibration from production equipment. Position sensors employ GPS / BeiDou dual-mode positioning, achieving a positioning accuracy of up to 1 meter. The camera is a high-definition network camera with a resolution of 1920×1080, a frame rate of 25fps, and supports night vision. Smart terminals utilize smartphones and tablets, equipped with a dedicated business management app.

[0043] Network Layer: The IoT gateway adopts an industrial-grade gateway, supporting multiple communication protocols such as ZigBee, LoRa, Bluetooth, and Wi-Fi, with data transmission rates up to 100Mbps. The mobile communication network uses a 5G network, with download speeds up to 1Gbps and upload speeds up to 100Mbps, ensuring fast data transmission. The wireless network uses Wi-Fi 6 technology, with transmission rates up to 1.2Gbps and a coverage range of up to 100 meters.

[0044] Platform Layer: The data processing module employs distributed computing frameworks such as Hadoop and Spark, capable of handling massive amounts of data with a processing latency of less than 1 second. The database uses MySQL as a relational database to store basic enterprise information and registration details; and MongoDB as a non-relational database to store unstructured data such as images, videos, and logs. The intelligent analysis engine utilizes deep learning frameworks such as TensorFlow and PyTorch to implement machine learning algorithms such as classification, clustering, and regression. The blockchain module adopts a consortium blockchain architecture, with nodes from the industrial and commercial administration department, banks, and tax authorities, ensuring data security and reliability.

[0045] Application Layer: The regulatory subsystem adopts a B / S architecture. Regulatory personnel log in to the system via a browser to view real-time monitoring footage and operational data of market entities, receive violation alerts, and conduct enforcement dispatch. The service subsystem also uses a B / S architecture, allowing market entities to log in via the internet to conduct business and query information. The credit subsystem has established a comprehensive credit evaluation indicator system, including business status, contract performance, and violation records. Credit scores range from 0 to 100, with 80 points or above considered good credit, 60-80 points considered average credit, and below 60 points considered untrustworthy. The decision support subsystem uses data visualization technology to display analysis results in the form of charts, maps, etc., intuitively reflecting market dynamics and industry development trends.

[0046] The specific implementation process of an Internet of Things-based business management method is as follows:

[0047] Step 1: Specific Operations of Data Acquisition and Sensing

[0048] When market entities register, the industry and commerce administration department affixes RFID tags to their business licenses and related permits. These tags contain the enterprise's unified social credit code, name, registered address, and other basic information. Simultaneously, based on the enterprise's business type and scale, appropriate sensors and cameras are installed at their business premises. For example, for manufacturing enterprises, environmental sensors (monitoring temperature, humidity, and dust concentration), equipment sensors (monitoring production line speed, energy consumption, and fault information), and high-definition cameras (monitoring the production process) are installed in the production workshop; for catering enterprises, temperature and humidity sensors, gas leak sensors, and cameras are installed in the kitchen, and customer flow sensors are installed in the lobby.

[0049] Businesses can download and install the business administration app on their smart devices, complete real-name authentication, and then use the app to submit their annual reports, financial statements, personnel changes, and other information. The system has an automatic reminder function that sends reminders to businesses before the report submission deadline to ensure they complete the submissions on time.

[0050] The devices in the perception layer collect data at a set frequency. Environmental sensors and device sensors collect data every 10 seconds, cameras capture a frame every 30 seconds and upload it, and location sensors update the company's location information every 5 minutes (mainly for market entities that operate on a mobile basis, such as street vendors).

[0051] Step 2: Detailed Process of Data Transmission and Processing

[0052] IoT gateways are deployed near business premises to aggregate and convert data collected by sensors and cameras. For example, they convert ZigBee sensor data to TCP / IP and then transmit it to the platform layer via 5G networks or Wi-Fi. For large amounts of video data, edge computing technology is used to compress it locally, reducing the amount of data transmitted.

[0053] After receiving the data, the platform-level data processing module first performs a data cleaning operation. For missing values, if the missing percentage is less than 5%, the mean imputation method is used; if the missing percentage is large, the data is removed. For outliers, box plots are used to identify them; values ​​exceeding the upper and lower limits are considered outliers and removed. For duplicate data, the most recent data is retained based on the data collection time.

[0054] During the data conversion process, temperature units were uniformly converted to degrees Celsius, humidity units to percentages, and energy consumption units to kilowatt-hours. Textual information, such as a company's business scope, was encoded using One-Hot encoding to convert it into numerical data.

[0055] During data fusion, an enterprise's RFID tag information, sensor data, camera data, and data submitted via the mobile app are linked together, using the unified social credit code as a unique identifier to establish a complete data profile for the enterprise. For example, the equipment operation data of a manufacturing enterprise can be linked with production footage captured by cameras to analyze the relationship between equipment operating status and production efficiency.

[0056] The processed data is stored in the corresponding databases according to whether it is structured or unstructured. Structured data such as basic enterprise information, registration information, and financial statements are stored in a MySQL database, while unstructured data such as videos, images, and logs are stored in a MongoDB database.

[0057] Step 3: Practical Applications of Intelligent Analysis and Decision Making

[0058] The intelligent analytics engine performs multi-dimensional analysis of data in the database. It employs a Support Vector Machine (SVM) classification algorithm to categorize business operations and identify any violations. For example, by analyzing kitchen camera data and gas leak sensor data from catering businesses, it can determine if the business is illegally using gas; similarly, by analyzing equipment operation data and product testing data from manufacturing companies, it can identify whether substandard products are being produced.

[0059] The K-means clustering algorithm is used to perform cluster analysis on market entities, uncovering the business models and characteristics of enterprises of different industries and sizes. For example, clustering retail enterprises can reveal differences in operating hours, customer traffic, and sales volume among different types of enterprises, such as large supermarkets, chain convenience stores, and small grocery stores, providing a basis for formulating targeted regulatory and support policies.

[0060] The system uses linear regression algorithms to predict a company's operating performance. Based on historical sales, costs, and profits, a predictive model is built to forecast future operating trends. For companies whose forecasts indicate poor performance, the system automatically marks them as high-priority targets, alerting regulatory authorities to strengthen oversight.

[0061] The blockchain module stores a company's credit information (such as whether taxes are paid on time, whether there are any violations, and contract performance) and important transaction records (such as large contract signings and equity transfers) on the blockchain. Each node has a complete blockchain ledger, ensuring data consistency and immutability. When it is necessary to query a company's credit information, the relevant records can be quickly retrieved and their authenticity verified through a blockchain explorer.

[0062] Based on the above analysis results, the intelligent analysis engine generates regulatory recommendations and service plans. For enterprises with potential compliance risks, it generates regulatory recommendations such as "increasing the frequency of inspections and conducting special inspections"; for enterprises with sound business operations, it generates service plans such as "providing policy support and recommending them for participation in commendation activities." The decision support subsystem organizes and analyzes these recommendations and plans, presenting them in a visual format to decision-makers in the industrial and commercial administration departments.

[0063] Step 4: Establish a closed-loop mechanism for management execution and feedback.

[0064] The regulatory subsystem classifies and categorizes enterprises based on regulatory recommendations. For high-risk enterprises, the system monitors their operations in real time. Once violations are detected, an early warning is immediately issued and pushed to the mobile terminals of law enforcement officers in the jurisdiction. Upon receiving the warning, law enforcement officers use the system to query the enterprise's detailed information and evidence of violations, and promptly go to the scene to investigate and deal with the matter. The results of the investigation are entered into the system and stored on the blockchain as the enterprise's credit record.

[0065] The service subsystem provides personalized services to enterprises based on the service plan. For example, it pushes relevant policies such as R&D expense deduction and intellectual property protection to high-tech enterprises; and it pushes support policies such as tax reduction and exemption and financing guarantees to micro and small enterprises. Enterprises can receive policy information through the APP and submit policy applications online. The system automatically reviews and provides feedback on the results.

[0066] The credit subsystem assigns monthly credit scores to enterprises based on their credit information and publishes the results on the official website. Enterprises with a credit score of 80 or above can enjoy incentives such as "green channel" services (e.g., priority approval for business processing) and participation in government-organized commendation activities; enterprises with a credit score below 60 are included in the blacklist and subject to joint disciplinary action, including restrictions on participation in government procurement, restrictions on loan financing, and restrictions on high-end consumption.

[0067] The system establishes a feedback collection mechanism, allowing businesses to evaluate and provide suggestions on regulatory work and service quality via an app. Regulatory authorities regularly analyze the feedback and promptly rectify issues raised by businesses. For example, if multiple businesses report cumbersome procedures for a particular service, the service subsystem will optimize the process, reducing approval steps and required documentation.

[0068] Meanwhile, based on feedback and system operation data, technical personnel continuously optimize the algorithm model of the intelligent analysis engine. For example, if the classification algorithm has a low accuracy rate in identifying a certain type of violation, the accuracy rate is improved by increasing training samples and adjusting model parameters. The various subsystems at the application layer also undergo functional upgrades based on feedback, such as adding new service modules and optimizing interface design, continuously improving system performance and user experience.

[0069] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances. Obviously, the embodiments described above are only some embodiments of this invention, not all embodiments. The accompanying drawings show preferred embodiments of this invention, but do not limit the patent scope of this invention. This invention can be implemented in many different forms; on the contrary, the purpose of providing these embodiments is to make the disclosure of this invention more thorough and complete. Although the invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. An Internet of Things-based business management system, characterized in that, It includes a perception layer, a network layer, a platform layer, and an application layer; the perception layer is used to collect various information from market entities, including RFID tags, sensors, cameras, and smart terminals; the network layer is used to transmit the data collected by the perception layer to the platform layer, including IoT gateways, mobile communication networks, and wireless networks; the platform layer is used to process, store, and analyze the data, including a data processing module, a database, an intelligent analysis engine, and a blockchain module. The application layer is used to provide various services to industrial and commercial administration departments and market entities, including a regulatory subsystem, a service subsystem, a credit subsystem, and a decision support subsystem.

2. The industrial and commercial management system based on the Internet of Things according to claim 1, characterized in that, The RFID tags in the perception layer are attached to the business licenses, permits, and other documents of the market entity to identify the market entity's identity and store basic information; the sensors include environmental sensors, equipment sensors, and location sensors, which are used to collect environmental data of the business premises, operating data of production equipment, and location information of the market entity, respectively; the camera is used to monitor the real-time situation of the business premises; and the smart terminal is used for market entities to fill in information and receive notifications.

3. The industrial and commercial management system based on the Internet of Things according to claim 1, characterized in that, The IoT gateway in the network layer is used to realize protocol conversion between the sensing layer and the network layer, and to connect different types of sensors and devices to the network; the mobile communication network includes 4G and 5G networks, which are used to realize wide-area data transmission; the wireless network includes Wi-Fi, Bluetooth, etc., which are used to realize short-range data transmission.

4. The industrial and commercial management system based on the Internet of Things according to claim 1, characterized in that, The data processing module in the platform layer is used to clean, transform, and fuse the collected data, remove noise and redundant data, and convert data of different formats into a unified format; the database includes relational databases and non-relational databases, which are used to store structured data and unstructured data respectively; the intelligent analysis engine uses machine learning algorithms to analyze the data and uncover the operating patterns and potential risks of market entities; the blockchain module is used to store the credit information and transaction records of market entities to ensure that the data is tamper-proof and traceable.

5. The industrial and commercial management system based on the Internet of Things according to claim 1, characterized in that, The regulatory subsystem in the application layer is used to monitor the business activities of market entities in real time, including abnormal behavior monitoring, violation warnings, and law enforcement dispatch; the service subsystem is used to provide market entities with online services, information inquiries, and policy pushes; the credit subsystem is used to evaluate and manage the credit status of market entities, including credit scoring, credit disclosure, and penalties for dishonesty; and the decision support subsystem is used to provide data analysis and decision-making suggestions to the industrial and commercial administration departments to assist in the formulation of regulatory policies and development plans.

6. A business management method based on the Internet of Things, characterized in that, Includes the following steps: Step 1: Data collection and sensing. The identification information, business premises environmental data, production equipment operation data, location information, real-time monitoring data and self-reported information of market entities are collected through RFID tags, sensors, cameras and smart terminals in the sensing layer. Step 2: Data transmission and processing. The collected data is transmitted to the platform layer through the network layer. The data processing module of the platform layer cleans, transforms and merges the data, removes noise and redundant data, and converts data of different formats into a unified format before storing it in the database. Step 3: Intelligent Analysis and Decision-Making. The intelligent analysis engine at the platform layer uses machine learning algorithms to analyze the data in the database, uncover the operating patterns and potential risks of market entities, and combine the credit information and transaction records stored in the blockchain module to generate regulatory suggestions and service plans, providing data support for the decision support subsystem. Step 4: Management Execution and Feedback. Based on the results of intelligent analysis and decision-making, the application layer conducts real-time supervision and law enforcement scheduling through the regulatory subsystem, provides services to market entities through the service subsystem, and manages credit through the credit subsystem. At the same time, it collects feedback information from market entities to optimize management methods and system functions.

7. A business management method based on the Internet of Things according to claim 6, characterized in that, In step 1, data collection adopts a combination of real-time and periodic collection. Real-time collection is used for environmental data, equipment operation data, and real-time monitoring data, while periodic collection is used for basic information and self-reported information of market entities.

8. A business management method based on the Internet of Things according to claim 6, characterized in that, Step 2 includes data cleaning (removing missing, outlier, and duplicate values), data transformation (format conversion, unit conversion, and encoding conversion), and data fusion (association and integration of multi-source data).

9. A business management method based on the Internet of Things according to claim 6, characterized in that, In step 3, the machine learning algorithms include classification algorithms, clustering algorithms, and regression algorithms. Classification algorithms are used to identify the business types and violations of market entities, clustering algorithms are used to mine the group characteristics and business models of market entities, and regression algorithms are used to predict the business status and development trends of market entities.

10. A business management method based on the Internet of Things according to claim 6, characterized in that, In step 4, the management execution and feedback adopt a closed-loop management model. By collecting feedback information from market entities, the algorithm model of the intelligent analysis engine and the service functions of the application layer are continuously optimized to improve the efficiency and quality of business administration.

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