Park monitoring management method based on Internet of Things

Abstract

The invention discloses a park monitoring management method based on the Internet of Things, and relates to the technical field of monitoring management, and the method comprises the following steps: deploying an Internet of Things terminal for data collection, and obtaining collected data; performing event triggering type data analysis according to the collected data to obtain abnormal event data; performing multi-channel data association analysis according to the abnormal event data to obtain an event analysis result; performing power distribution scheduling according to the event analysis result to obtain a scheduling scheme; building energy consumption dynamic distribution is carried out according to the scheduling scheme, and building energy consumption data are obtained; performing energy storage and load adjustment according to the building energy consumption data to obtain an adjustment result; performing energy transaction according to the adjustment result to obtain transaction data; and performing park cost prediction and resource optimization according to the transaction data. According to the invention, the accuracy and response speed of anomaly identification are improved, the risk of power waste and local overload is reduced, the adaptive ability of energy scheduling is improved, and the operation and maintenance cost of the park is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring and management, and in particular to a park monitoring and management method based on the Internet of Things. Background Art

[0002] As science and technology parks continue to develop, their operations and management are becoming increasingly complex. Current park monitoring and management primarily relies on traditional video surveillance, access control systems, security sensors, and energy management systems, with energy management often employing pre-set scheduling strategies. The development of the Internet of Things (IoT) offers new technological solutions for park management.

[0003] Existing technologies for analyzing abnormal events have shortcomings: Traditional park monitoring data analysis often relies on fixed-interval sampling, periodically collecting sensor data and then performing batch calculations. While this approach can be effective for long-term trend analysis, it suffers from slow response times when unexpected abnormal events (such as abnormal energy consumption and security breaches) occur, and can lead to false alarms and missed alerts, preventing timely detection and resolution of issues. This makes it difficult to address nonlinear events in complex park environments.

[0004] Existing power distribution and scheduling technologies are inadequate: Existing campus energy management systems often rely on scheduled scheduling or load forecasting, but lack the ability to adapt to sudden load fluctuations. For example, when equipment in a certain area of the campus starts up in a concentrated manner or when there is a sudden peak in power consumption, traditional systems struggle to quickly balance the load, which can easily lead to local grid overloads and energy waste, increasing campus costs. Summary of the Invention

[0005] In view of the deficiencies of the existing technology, the present invention provides a campus monitoring and management method based on the Internet of Things to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a campus monitoring and management method based on the Internet of Things, comprising the following steps: S1. Deploy IoT terminals to collect data and obtain collected data; S2. Perform event-triggered data analysis based on the collected data to obtain abnormal event data; S3. Perform multi-channel data correlation analysis based on the abnormal event data to obtain event analysis results; S4. Perform power distribution scheduling based on the event analysis results and obtain a scheduling plan; S5. Dynamically allocate building energy consumption according to the scheduling plan to obtain building energy consumption data; S6. Perform energy storage and load adjustment based on building energy consumption data to obtain adjustment results; S7. Conduct energy transactions based on the adjustment results and obtain transaction data; S8. Conduct park cost forecasting and resource optimization based on transaction data.

[0007] To further optimize this technical solution, the event-triggered data analysis in S2 includes: The introduction of edge computing technology enables IoT terminal devices to have data pre-processing capabilities, and use a comprehensive trigger factor model to detect abnormal events based on the collected data.

[0008] To further optimize this technical solution, the comprehensive trigger factor model includes: Calculate the anomaly of each data source, and then combine these anomaly degrees by weight to obtain a comprehensive trigger factor to determine whether in-depth analysis needs to be triggered. ; in: : The comprehensive trigger factor at time t, which triggers in-depth analysis after exceeding the set threshold; : Total number of data sources; : The weight corresponding to the abnormality of each data source; : The abnormality of the i-th data source at time t.

[0009] To further optimize this technical solution, the abnormality calculation includes: Abnormality of environmental status: ; in: : The abnormality of the environmental state at time t. The higher the value, the stronger the abnormality. : Environmental monitoring sensor data at time t, including temperature and smoke concentration; , : are the mean and standard deviation of historical environmental data respectively; Target behavior abnormality: ; in: : The abnormality of the target behavior at time t. The higher the value, the more abnormal the behavior is. : The weights of different target behaviors; , : Behavioral characteristics of the i-th target at time t and t-1, including movement speed, direction, and residence time; : The total number of monitored target behaviors; Abnormality of equipment status: ; in: : The abnormality of the device status at time t. The higher the value, the stronger the abnormality. : The total number of devices; , : The state of the jth device at time t, including current, voltage, and vibration intensity; Historical anomaly correction items: ; in: , : The historical abnormal accumulation value at time t and t-1, used to avoid false triggering of single abnormal fluctuations; : Historical impact coefficient, which controls the influence of new and old data on the current abnormal judgment; : The comprehensive trigger factor at time t-1.

[0010] To further optimize this technical solution, the power distribution scheduling in S4 includes: Based on the results of the event analysis, the park's power distribution strategy is adjusted, including load demand adjustment and power supply adjustment in the abnormal area, and coordinated energy storage scheduling.

[0011] Further optimizing this technical solution, the load demand adjustment includes: Event-triggered load adjustment model: ; in: : target load demand of region j at time t; : Baseline load demand of region j under normal conditions; : The sensitivity coefficient of the area to abnormal events. Different abnormal events have different corresponding values. : Abnormality of region j at time t.

[0012] Further optimizing this technical solution, the power supply adjustment includes: ; in: : the actual power supply of region j at time t; , : The power supply of area j and area k under normal conditions; : The power supply adjustment weight of region j for abnormal events; : The total number of regions; : The power supply weight factor between area j and other areas k is used to ensure that the dynamic adjustment of power supply meets the overall optimization goals of the park.

[0013] To further optimize this technical solution, the energy storage coordinated scheduling includes: ; in: , : The electrical energy storage at time t and t-1; : The timing adjustment coefficient of the energy storage system is used to control the smoothness of the change of electric energy reserves; : The energy storage compensation coefficient of region j, which is used to determine the impact of the abnormality of the region on the energy storage system.

[0014] To further optimize this technical solution, the energy storage and load regulation in S6 include: Based on the dynamic allocation data of building energy consumption and combined with smart microgrid energy storage technology, the energy storage system within the park is used for peak shaving and valley filling and load balancing management.

[0015] To further optimize this technical solution, the energy transaction in S7 includes: Based on the results of energy storage and load regulation, the energy storage situation in the park is obtained, and a blockchain-based energy trading model is used to realize energy trading between buildings in different areas of the park.

[0016] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a campus monitoring and management method based on the Internet of Things as described in the first aspect of the present invention are implemented.

[0017] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, wherein: when the computer program instructions are executed by a processor, the steps of a campus monitoring and management method based on the Internet of Things as described in the first aspect of the present invention are implemented.

[0018] Compared with the existing technology, the present invention provides a campus monitoring and management method based on the Internet of Things, which has the following beneficial effects: This IoT-based campus monitoring and management method uses a comprehensive trigger factor model to calculate the abnormality of events based on historical data, environmental changes, device status, and target behavior, and dynamically adjusts the abnormality threshold, thereby improving the accuracy and response speed of abnormality identification, reducing false alarms and missed alarms, and improving the ability to respond to nonlinear events.

[0019] Through a distribution strategy that combines load demand adjustment, power supply adjustment, and energy storage collaborative scheduling, dynamic energy adjustment is achieved, ensuring efficient energy utilization, reducing the risk of power waste and local overload, improving the adaptability of energy scheduling, and reducing the park's operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 This is a flow chart of a campus monitoring and management method based on the Internet of Things proposed by the present invention; Figure 2 This is a flow chart of a comprehensive trigger factor model of a campus monitoring and management method based on the Internet of Things proposed by the present invention; Figure 3 This is a flow chart of power distribution scheduling in a campus monitoring and management method based on the Internet of Things proposed by the present invention; Figure 4 This is a flow chart of a blockchain-based energy trading model for an IoT-based park monitoring and management method proposed in the present invention. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0025] Example 1: Reference Figures 1 to 4 , which is the first embodiment of the present invention, provides a campus monitoring and management method based on the Internet of Things, comprising the following steps: S1. Deploy IoT terminals to collect data and obtain collected data.

[0026] In this embodiment, data collection includes: IoT devices, including smart cameras, smart electricity and water meters, environmental monitoring sensors, and access control systems, were deployed in key areas of the campus. These devices collected various data and transmitted it to the management system in real time using LoRa wireless communication technology (existing technology). LoRa technology's low power consumption and wide coverage ensured long-term, stable operation of multiple devices within the campus. This step provided the data foundation for subsequent steps.

[0027] S2. Perform event-triggered data analysis based on the collected data to obtain abnormal event data.

[0028] In this embodiment, event-triggered data analysis includes: The introduction of edge computing technology enables IoT terminal devices to have data pre-processing capabilities, detect abnormal events based on collected data, and use a comprehensive trigger factor model so that data is only deeply analyzed and uploaded when an abnormal event is detected, thereby reducing system resource usage and improving the efficiency of abnormal event detection.

[0029] Furthermore, the comprehensive trigger factor model includes: Calculate the anomaly of each data source, and then combine these anomaly degrees by weight to obtain a comprehensive trigger factor to determine whether in-depth analysis needs to be triggered. ; in: : The comprehensive trigger factor at time t, which triggers in-depth analysis after exceeding the set threshold; : Total number of data sources; : The weight corresponding to the abnormality of each data source; : The abnormality of the i-th data source at time t.

[0030] Specific uses of this model include: Data collection: Data from multiple data sources in step S1 are collected from each area, including environmental status, equipment status, target behavior, etc., for comprehensive triggering factors Calculation of Event-triggered data analysis: analyze the abnormality of each area based on the collected data Calculate and reduce false triggering by calculating historical anomaly correction items; Abnormal event judgment: according to the degree of abnormality and the calculation results of the comprehensive trigger factor , and compare it with the pre-set threshold For comparison, if the comprehensive trigger factor Greater than threshold , then enter deep analysis to obtain abnormal event data for data analysis in step S3.

[0031] Furthermore, the abnormality calculation includes: Abnormality of environmental status: ; in: : The abnormality of the environmental state at time t. The higher the value, the stronger the abnormality. : Environmental monitoring sensor data at time t, including temperature and smoke concentration; , : are the mean and standard deviation of historical environmental data respectively; Target behavior abnormality: ; in: : The abnormality of the target behavior at time t. The higher the value, the more abnormal the behavior is. : The weights of different target behaviors; , : Behavioral characteristics of the i-th target at time t and t-1, including movement speed, direction, and residence time; : The total number of monitored target behaviors; Abnormality of equipment status: ; in: : The abnormality of the device status at time t. The higher the value, the stronger the abnormality. : The total number of devices; , : The state of the jth device at time t, including current, voltage, and vibration intensity; Historical anomaly correction items: ; in: , : The historical abnormal accumulation value at time t and t-1, used to avoid false triggering of single abnormal fluctuations; : Historical impact coefficient, which controls the influence of new and old data on the current abnormal judgment; : The comprehensive trigger factor at time t-1.

[0032] S3. Perform multi-channel data correlation analysis based on the abnormal event data to obtain event analysis results.

[0033] In this embodiment, multi-channel data association analysis includes: Based on abnormal event data, association rule algorithms (existing technology) are used to jointly analyze data from different sources. For example, when the monitoring system detects a fire warning in a certain area, it automatically uses data from environmental sensors (such as temperature and humidity) for secondary confirmation and combines it with access control data to analyze whether anyone is staying there. The goal of this step is to reduce false alarms, increase the credibility of abnormal events, and provide more dimensional data support for subsequent decision-making.

[0034] S4. Perform power distribution scheduling based on the event analysis results to obtain a scheduling plan.

[0035] In this embodiment, power distribution scheduling includes: Based on the results of event analysis, the campus' power distribution strategy is adjusted. This includes adjusting load demand in areas where anomalies occur to avoid local overloads and optimize energy utilization. Power supply is then adjusted based on the adjusted load demand, prioritizing power to critical areas (such as server rooms and monitoring centers) while reducing power consumption in non-critical areas (such as lighting and advertising screens). Energy storage is coordinated and dispatched based on the adjusted power supply data, reducing reliance on utility power and improving power supply stability. For example, if a fire alarm sounds in a specific area, power to that area is reduced or completely shut off to prevent the further spread of the electrical fire. If server room equipment is overloaded, power to that area is increased to ensure normal operation. This technology not only improves safety but also reduces energy waste and lowers campus operation and maintenance costs.

[0036] Furthermore, the load demand adjustment includes: Event-triggered load adjustment model: When the abnormality level in a certain area is high, its load demand will be dynamically adjusted. For example, when the monitoring center detects a major security incident, its load demand will increase to ensure the normal operation of the equipment; ; in: : target load demand of region j at time t; : Baseline load demand of region j under normal conditions; : The sensitivity coefficient of the area to abnormal events. Different abnormal events have different corresponding values. : Abnormality of region j at time t.

[0037] Furthermore, the power supply adjustment includes: When the abnormality level in a certain area increases, such as when equipment in a server room is overloaded, the power supply will be increased accordingly, while the power supply to low-priority areas, such as landscape lighting, will be reduced to ensure the stability of the power grid; ; in: : the actual power supply of region j at time t; , : The power supply of area j and area k under normal conditions; : The power supply adjustment weight of region j for abnormal events; : The total number of regions; : The power supply weight factor between area j and other areas k is used to ensure that the dynamic adjustment of power supply meets the overall optimization goals of the park.

[0038] Furthermore, the energy storage coordinated scheduling includes: When abnormalities in certain areas are high, such as a sudden security incident causing a surge in power demand, the system dynamically adjusts energy storage power supply to reduce the utility load and improve the stability of the campus power supply. ; in: , : The electrical energy storage at time t and t-1; : The timing adjustment coefficient of the energy storage system is used to control the smoothness of the change of electric energy reserves; : The energy storage compensation coefficient of region j, which is used to determine the impact of the abnormality of the region on the energy storage system.

[0039] S5. Dynamically allocate building energy consumption according to the scheduling plan to obtain building energy consumption data.

[0040] In this embodiment, the dynamic allocation of building energy consumption includes: Based on the resulting scheduling plan, the building automation system (BAS) dynamically allocates energy to different buildings in each area, generating dynamic energy consumption data for each building. This step further refines intelligent power distribution down to the building level, improving energy efficiency.

[0041] S6. Perform energy storage and load adjustment based on building energy consumption data to obtain adjustment results.

[0042] In this embodiment, energy storage and load regulation include: Based on dynamic building energy consumption data and combined with existing smart microgrid energy storage technology, the park's energy storage system is used for peak load shifting and load balancing. For example, when electricity prices are low, the microgrid energy storage system stores energy and releases it during peak periods, reducing the park's electricity procurement costs. Furthermore, when abnormal events occur, energy storage coordination and dispatch improve the reliability of the park's power supply.

[0043] S7. Conduct energy transactions based on the adjustment results and obtain transaction data.

[0044] In this embodiment, energy trading includes: Based on the results of energy storage and load regulation, the energy storage status of the park is determined. A blockchain-based energy trading model is then used to enable energy trading between buildings in different areas of the park, thereby improving energy efficiency and reducing park costs. For example, when a building reduces its energy consumption due to energy-saving measures, its remaining photovoltaic power generation can be sold to other high-energy-consuming buildings in the park through smart contracts, thereby increasing the utilization rate of renewable energy and reducing the entire park's dependence on the external power grid.

[0045] Furthermore, the blockchain-based energy trading model includes: Energy supply and demand matching model: ; in: : The settlement amount of the transaction from supplier m to demander n; : The amount of electricity available from supplier m at time t; : electricity demand of demand side n at time t; : Energy transaction willingness coefficient, which indicates the willingness of both parties to conduct energy transactions; : basic electricity price; , : price adjustment coefficient; : the abnormality of region n at time t; : A small positive number to prevent the denominator from being zero.

[0046] The steps for using the above energy supply and demand matching model include: Obtain supply and demand information: obtain the supply side's available power and demand from the demand side , combined with the abnormality information obtained in step S2 , the willingness coefficient for energy trading and price adjustment coefficient , Make adjustments; Calculate transaction volume and price: Calculate transaction volume and electricity price based on supply and demand information to get the total transaction amount ; Execute smart contracts: Complete transactions through smart contracts, achieve transaction transparency and traceability, and obtain transaction data for park cost forecasting and resource optimization in S8.

[0047] S8. Conduct park cost forecasting and resource optimization based on transaction data.

[0048] In this embodiment, park cost forecasting and resource optimization include: Based on transaction data, a machine learning-based time series forecasting algorithm (existing technology) is used to predict the park's overall energy costs and optimize future resource allocation. For example, by analyzing historical energy transaction data, changes in the park's energy demand for the next month can be predicted, allowing procurement plans to be developed in advance to reduce costs. Furthermore, energy usage strategies can be adjusted based on real-time grid electricity prices to further reduce operating expenses.

[0049] Example 2: This embodiment also provides a computer device, which is suitable for a campus monitoring and management method based on the Internet of Things, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a campus monitoring and management method based on the Internet of Things proposed in the above embodiment.

[0050] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, an Internet of Things-based campus monitoring and management method proposed in the above embodiment is implemented.

[0051] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0052] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0053] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0054] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0055] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0056] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A campus monitoring and management method based on the Internet of Things, characterized in that: The following steps are involved: S1. Deploy IoT terminals to collect data and obtain collected data; S2. Perform event-triggered data analysis based on the collected data to obtain abnormal event data; S3. Perform multi-channel data correlation analysis based on the abnormal event data to obtain event analysis results; S4. Perform power distribution scheduling based on the event analysis results and obtain a scheduling plan; S5. Dynamically allocate building energy consumption according to the scheduling plan to obtain building energy consumption data; S6. Perform energy storage and load adjustment based on building energy consumption data to obtain adjustment results; S7. Conduct energy transactions based on the adjustment results and obtain transaction data; S8. Conduct park cost forecasting and resource optimization based on transaction data.

2. The method for monitoring and managing a park based on the Internet of Things according to claim 1, characterized in that: The event-triggered data analysis in S2 includes: The introduction of edge computing technology enables IoT terminal devices to have data pre-processing capabilities, and use a comprehensive trigger factor model to detect abnormal events based on the collected data.

3. The method for monitoring and managing a park based on the Internet of Things according to claim 2, characterized in that: The comprehensive trigger factor model includes: Calculate the abnormality of each data source obtained from multiple data sources, and then combine the abnormalities by weight to obtain a comprehensive trigger factor to determine whether in-depth analysis needs to be triggered. ； in: : The comprehensive trigger factor at time t, which triggers in-depth analysis after exceeding the set threshold; : Total number of data sources; : The weight corresponding to the abnormality of each data source; : The abnormality of the i-th data source at time t.

4. The method for monitoring and managing a park based on the Internet of Things according to claim 3, characterized in that: The abnormality calculation includes: Abnormality of environmental status: ； in: : The abnormality of the environmental state at time t. The higher the value, the stronger the abnormality. : Environmental monitoring sensor data at time t, including temperature and smoke concentration; , : are the mean and standard deviation of historical environmental data respectively; Target behavior abnormality: ； in: : The abnormality of the target behavior at time t. The higher the value, the more abnormal the behavior is. : The weights of different target behaviors; , : Behavioral characteristics of the i-th target at time t and t-1, including movement speed, direction, and residence time; : The total number of monitored target behaviors; Abnormality of equipment status: ； in: : The abnormality of the device status at time t. The higher the value, the stronger the abnormality. : The total number of devices; , : The state of the jth device at time t, including current, voltage, and vibration intensity; Historical anomaly correction items: ； in: , : The historical abnormal accumulation value at time t and t-1, used to avoid false triggering of single abnormal fluctuations; : Historical impact coefficient, which controls the influence of new and old data on the current abnormal judgment; : The comprehensive trigger factor at time t-1.

5. The method for monitoring and managing a park based on the Internet of Things according to claim 1, wherein: The power distribution scheduling in S4 includes: Based on the results of the event analysis, the park's power distribution strategy is adjusted, including load demand adjustment and power supply adjustment in the abnormal area, and coordinated energy storage scheduling.

6. The method for monitoring and managing a park based on the Internet of Things according to claim 5, characterized in that: The load demand adjustment includes: Event-triggered load adjustment model: ； in: : target load demand of region j at time t; : Baseline load demand of region j under normal conditions; : The sensitivity coefficient of the area to abnormal events. Different abnormal events have different corresponding values. : Abnormality of region j at time t.

7. The method for monitoring and managing a park based on the Internet of Things according to claim 5, characterized in that: The power supply adjustment includes: ； in: : the actual power supply of region j at time t; , : The power supply of area j and area k under normal conditions; : The power supply adjustment weight of region j for abnormal events; : The total number of regions; : The power supply weight factor between area j and other areas k is used to ensure that the dynamic adjustment of power supply meets the overall optimization goals of the park.

8. The method for monitoring and managing a park based on the Internet of Things according to claim 5, characterized in that: The energy storage coordinated scheduling includes: ； in: , : The electrical energy storage at time t and t-1; : The timing adjustment coefficient of the energy storage system is used to control the smoothness of the change of electric energy reserves; : The energy storage compensation coefficient of region j, which is used to determine the impact of the abnormality of the region on the energy storage system.

9. The method for monitoring and managing a park based on the Internet of Things according to claim 1, characterized in that: The energy storage and load regulation in S6 includes: Based on the dynamic allocation data of building energy consumption and combined with smart microgrid energy storage technology, the energy storage system within the park is used for peak shaving and valley filling and load balancing management.

10. The method for monitoring and managing a park based on the Internet of Things according to claim 1, characterized in that: The energy transactions in S7 include: Based on the results of energy storage and load regulation, the energy storage situation in the park is obtained, and a blockchain-based energy trading model is used to realize energy trading between buildings in different areas of the park.

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