A smart park energy-saving management method and system based on the Internet of Things

Abstract

This technology proposes an energy-saving management method and system for smart parks based on the Internet of Things, involving the fields of Internet of Things technology and energy management technology. The system integrates sensor node modules, central server modules, digital twin modules, energy-saving algorithm modules, knowledge graph modules and digital map modeling modules to achieve real-time monitoring, secure transmission, efficient processing and intelligent optimization of energy consumption data. The system uses quantum random number generators and quantum key distribution protocols to ensure data transmission security; digital twin technology is combined with quantum computing to simulate and optimize device states; quantum machine learning algorithms improve the accuracy of energy-saving strategies; knowledge graphs realize intelligent reasoning between devices; and three-dimensional visualization displays enhance the intuitiveness of energy consumption management. The system aims to improve the energy utilization efficiency of smart parks, reduce energy consumption, and promote the sustainable development of smart parks.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things technology and energy management technology, and more specifically, to an energy-saving management method and system for a smart park based on the Internet of Things. Background Art

[0002] With the rapid development of information technology, smart parks, as an important part of smart cities, are increasingly receiving attention for their energy consumption management issues. The application of Internet of Things (IoT) technology provides a new solution for energy consumption management in smart parks. By deploying sensors throughout the park, IoT realizes the interconnection between devices and devices, and devices and users, improves data collection efficiency, and enhances the real-time nature of information. Big data analysis, as the core of the energy consumption management system, processes and analyzes large amounts of energy consumption data to discover potential energy-saving opportunities and optimize resource allocation and utilization efficiency. The introduction of cloud computing technology has effectively solved the bottleneck problems of traditional information systems in data storage and processing. The elasticity and scalability of the cloud platform enable the energy consumption management system to respond quickly to changes, while supporting multi-level and multi-device data management.

[0003] The advancement of artificial intelligence technology enables the system to continuously optimize scheduling strategies and improve operational efficiency through machine learning algorithms. Quantum computing, as an emerging technology, has broad application prospects in the energy field. It can help manage energy resources more effectively, improve energy utilization efficiency, and reduce energy consumption, thereby achieving energy conservation and emission reduction. Digital twin technology ensures that the system always maintains optimal performance through simulation optimization of virtual space and reduces the difficulty of maintenance and debugging. The application of knowledge graphs in the energy field, such as energy management and intelligent scheduling, represents the energy system by constructing knowledge graphs, realizes intelligent scheduling of equipment, and minimizes the total energy consumption of the system.

[0004] Problems with existing technologies include: low security of energy consumption data, low efficiency of energy-saving strategies, weak intelligent reasoning between devices, and insufficient visualization. Summary of the Invention

[0005] In order to overcome the problems of low energy consumption data security, low energy-saving strategy efficiency, weak intelligent reasoning between devices and insufficient visualization display in the existing technology, the present invention designs an energy-saving management method and system for a smart park based on the Internet of Things, which can effectively solve the above technical problems.

[0006] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0007] An IoT-based smart park energy-saving management system, including:

[0008] Sensor node modules are deployed on various energy-consuming devices in the smart park to monitor and collect energy consumption data in real time. They also have quantum random number generator functions to generate random numbers for encryption.

[0009] The central server module is used to process and store the energy consumption data of each energy-consuming device in the smart park, and establish a secure communication channel with the sensor node module through the quantum key distribution (QKD) protocol;

[0010] The digital twin module is used to create a digital map of each energy-consuming device in the smart park, and to achieve encrypted transmission of energy consumption data through quantum communication technology, while using quantum computing technology to simulate and optimize the state;

[0011] An energy-saving algorithm module, which utilizes the collected energy consumption data and the mapping information of the digital twin module to optimize the energy-saving strategy through quantum computing technology and adopts a quantum machine learning algorithm to improve the accuracy and efficiency of the strategy;

[0012] The knowledge graph module is used to build a relationship network between energy-consuming devices and systems in the smart park, realizing knowledge association and intelligent reasoning between energy-consuming devices;

[0013] The digital map modeling module is used to realize the three-dimensional visualization of the smart park and the spatial analysis of energy consumption data, thereby improving the intuitiveness and accuracy of energy consumption management.

[0014] Preferably, the digital twin module includes:

[0015] A mapping creation unit, configured to create a digital mapping according to the status of actual energy-consuming devices;

[0016] A model building unit, configured to build a digital twin model of the energy-consuming device based on the collected energy consumption data, and to use quantum computing to quickly iterate and optimize the digital twin model;

[0017] The simulation analysis unit is used to simulate the impact of different energy-saving strategies on energy consumption and provide optimization suggestions. The simulation analysis uses quantum simulation technology to improve simulation speed and accuracy.

[0018] Preferably, the energy-saving algorithm module includes:

[0019] A data processing unit, configured to process the energy consumption data collected by the sensor node module and employ quantum error correction coding to improve the security and accuracy of energy consumption data processing;

[0020] A quantum computing unit, configured to execute quantum algorithms to find optimal energy-saving strategies, wherein the quantum algorithms include quantum approximate optimization algorithms and quantum heuristic algorithms;

[0021] The control instruction generation unit is used to generate control instructions based on the quantum computing results and send them to the energy-consuming devices. The control instructions are securely transmitted using quantum teleportation (QT) technology.

[0022] Preferably, the knowledge graph module includes:

[0023] Relationship building unit, used to build the relationship network between various energy-consuming devices and systems in the park;

[0024] Intelligent reasoning unit, used to realize knowledge association and intelligent reasoning between energy-consuming devices;

[0025] The knowledge update unit is used to automatically update the knowledge graph content and optimize the knowledge graph structure based on the operational changes of the smart park energy system and the accumulation of new data.

[0026] Preferably, the quantum communication technology includes:

[0027] A quantum key distribution (QKD) unit, configured to establish a secure communication channel between the central server module and the sensor node module, wherein the quantum key distribution protocol supports dynamic adjustment to adapt to different network conditions;

[0028] A quantum random number generator unit is used to generate random numbers for encryption, and the random number generator adopts a quantum computing-resistant encryption (PQC) algorithm to improve security.

[0029] Preferably, the digital map modeling module includes:

[0030] 3D modeling unit, used to create a 3D model of the smart park;

[0031] Energy consumption data analysis unit, used to analyze the spatial distribution of energy consumption data;

[0032] The visualization display unit is used to display the energy consumption data analysis results in a graphical form.

[0033] A smart park energy-saving management method based on the Internet of Things includes the following steps:

[0034] Deploy multiple sensor nodes on various energy-consuming devices in the smart park to monitor and collect energy consumption data in real time;

[0035] A quantum random number generator is used to generate random numbers for encryption on the sensor nodes, and the energy consumption data of each energy-consuming device in the park is processed and stored through a central server;

[0036] establishing a secure communication channel between the central server and the sensor nodes using a quantum key distribution (QKD) protocol;

[0037] Create a digital map of each energy-consuming device in the smart park, use quantum communication technology to achieve encrypted transmission of energy consumption data, and use quantum computing technology to simulate and optimize the state of the digital map;

[0038] Utilize the collected energy consumption data and digital mapping information to optimize energy-saving strategies through quantum computing technology, and use quantum machine learning algorithms to improve the accuracy and efficiency of energy-saving strategies;

[0039] Build a relationship network between energy-consuming devices and systems in the smart park to achieve knowledge association and intelligent reasoning between energy-consuming devices;

[0040] Realize 3D visualization of smart parks and spatial analysis of energy consumption data, improving the intuitiveness and accuracy of energy consumption management.

[0041] An electronic device, comprising:

[0042] a memory storing executable program code;

[0043] a processor coupled to the memory;

[0044] The processor calls the executable program code stored in the memory to execute the energy-saving management method for a smart park based on the Internet of Things as described above.

[0045] A computer storage medium stores computer instructions, which, when called, are used to execute the energy-saving management method for a smart park based on the Internet of Things as described above.

[0046] Compared with the existing technology, the beneficial effects of the present invention are as follows: by integrating the quantum random number generator and the quantum key distribution protocol in the sensor node module, the system can establish a secure communication channel between the sensor node and the central server, effectively preventing the energy consumption data from being intercepted or tampered with during the transmission process. The non-cloning and randomness of the quantum key provide an unprecedented level of security for data transmission. The digital twin module, combined with quantum computing technology, can quickly iterate and optimize the digital twin model of energy-consuming equipment, simulate the impact of different energy-saving strategies on energy consumption, and provide optimization suggestions. The introduction of quantum machine learning algorithms further improves the accuracy and efficiency of energy-saving strategies, enabling the system to quickly respond to changes in energy consumption. The knowledge graph module builds a relationship network between energy-consuming equipment and systems, and realizes knowledge association and intelligent reasoning between equipment through the intelligent reasoning unit, which improves the system's autonomous decision-making ability. The knowledge update unit can automatically update the knowledge graph according to new data and system operation changes, keeping the system's reasoning ability up to date. The digital map modeling module provides a three-dimensional visualization of the smart park and spatial analysis of energy consumption data, allowing managers to intuitively understand energy consumption distribution and energy-saving effects, improving the intuitiveness and accuracy of energy consumption management. The combination of three-dimensional modeling and energy consumption data analysis units makes energy consumption data no longer limited to numbers, but presented in a graphical way to facilitate analysis and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the implementation methods or the description of the prior art. Obviously, the drawings described below are merely exemplary. For ordinary technicians in this field, other implementation drawings can be derived based on the provided drawings without any creative work.

[0048] Figure 1 This is a structural diagram of a smart park energy-saving management system based on the Internet of Things;

[0049] Figure 2 This is a step-by-step diagram of an energy-saving management method for a smart park based on the Internet of Things. DETAILED DESCRIPTION

[0050] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0051] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0052] It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0053] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0054] Example 1

[0055] There is a comprehensive smart park that covers multiple office buildings, production plants, storage facilities and supporting living service areas. There are a wide variety of energy-consuming equipment in the park, such as lighting fixtures, air-conditioning units, various production equipment, elevators, etc. Previously, energy management was relatively extensive and energy consumption costs were high. The park management decided to introduce this energy-saving management system to improve energy utilization efficiency.

[0056] A smart park energy-saving management system based on the Internet of Things, such as Figure 1 Shown, including:

[0057] Sensor node modules are deployed on various energy-consuming devices in the smart park to monitor and collect energy consumption data in real time. They also have quantum random number generator functions to generate random numbers for encryption.

[0058] A sensor node module is installed on every energy-consuming device in the smart park. For example, a small sensor is embedded in the lighting fixtures in the office building, which can accurately collect energy consumption data such as the real-time power and cumulative power consumption time of the lamps every 5 minutes; at the air-conditioning unit, the sensor can monitor key parameters such as cooling capacity, compressor power, outlet temperature and fan speed in real time to calculate energy consumption. At the same time, the quantum random number generator function of the sensor node module will generate random numbers in real time. These random numbers are used to encrypt the collected energy consumption data according to a specific encryption algorithm. For example, a 128-bit random number is immediately generated and combined with the encryption after each data is collected to ensure the security of energy consumption data transmission.

[0059] The central server module is used to process and store the energy consumption data of each energy-consuming device in the smart park, and establish a secure communication channel with the sensor node module through the quantum key distribution (QKD) protocol;

[0060] The smart park is equipped with a high-performance central server module, which is equipped with a multi-core processor, large-capacity memory and high-speed solid-state hard drive. The server's quantum key distribution unit and each sensor node module establish a quantum key distribution protocol communication link, using the BB84 protocol. It will dynamically adjust the quantum key generation rate, error correction coding method and other parameters based on the network conditions in different areas. For example, the network signal in the storage area far away from the server is slightly weaker. For example, in areas with weak signals, the key generation rate is appropriately slowed down but the error correction capability is enhanced to ensure that the encrypted energy consumption data transmitted from each sensor node can be accurately received and decrypted. The server then classifies and stores these decrypted data, and stores them in an orderly manner according to dimensions such as device type, location, and collection time, to facilitate subsequent calls and queries.

[0061] The digital twin module is used to create a digital map of each energy-consuming device in the smart park, and to achieve encrypted transmission of energy consumption data through quantum communication technology, while using quantum computing technology to simulate and optimize the state;

[0062] After obtaining the energy consumption data of each energy-consuming device from the central server, a detailed digital map is created for each energy-consuming device. Taking a large production equipment in a production plant as an example, the digital map not only reflects the equipment's external dimensions and spatial location, but also meticulously displays the real-time operating status parameters of each key component within the energy-consuming device, including motor speed, temperature, and load conditions of transmission components.

[0063] Based on the energy consumption data collected by sensors and the operating data of energy-consuming equipment, the parallel computing capabilities of quantum computing are used to build and iteratively optimize digital twin models. For example, for the air-conditioning system in a smart park, quantum computing can be used to perform a large number of parameter adjustments and simulation calculations on the model in a short period of time, about 6 times faster than traditional construction methods. It accurately simulates the energy consumption changes and overall performance of the air-conditioning system under different working conditions, such as different seasons, different indoor and outdoor temperature differences, different personnel densities, etc., so that the model can better reflect the operation and energy consumption characteristics of actual energy-consuming equipment.

[0064] Quantum simulation technology is used to conduct simulation analysis of different energy-saving strategies. For example, for the lighting system in the smart park, the energy consumption under various energy-saving strategies such as timed switching, zoning control, intelligent dimming, automatic brightness adjustment according to ambient light, and personnel activity sensing are simulated. The impact of each strategy on the overall indoor lighting effect and personnel usage experience is analyzed. With the help of quantum simulation technology, after simulation and comparison, optimization suggestions are provided for the smart park lighting system, such as using timed shutdown during non-working hours and using intelligent dimming combined with personnel sensing control in public areas to reduce lighting energy consumption.

[0065] An energy-saving algorithm module, which utilizes the collected energy consumption data and the mapping information of the digital twin module to optimize the energy-saving strategy through quantum computing technology and adopts a quantum machine learning algorithm to improve the accuracy and efficiency of the strategy;

[0066] After obtaining the mapping information of energy consumption data and digital twin modules from the central server, quantum error correction coding technology is used to deeply process the data. For example, when processing the energy consumption data of production energy-consuming equipment, quantum error correction coding can effectively correct energy consumption data deviations caused by quantum bit errors, external electromagnetic interference, etc. that may occur during the transmission of energy consumption data, thereby making the energy consumption data processing more accurate.

[0067] Quantum approximate optimization algorithms and quantum heuristic algorithms are used to find the optimal energy-saving strategy. Taking the overall power distribution of a smart park as an example, the quantum computing unit quickly traverses and analyzes many possible power distribution schemes, involving power priorities and power distribution in different areas, different time periods, and different equipment. The optimal solution is found in a short period of time, which reduces the peak-to-valley difference in power in the smart park and effectively improves the stability of the power system and energy utilization efficiency. At the same time, in order to save energy in the operation of the elevator system, the quantum heuristic algorithm is used to optimize the elevator scheduling strategy, and the elevator's stop and running direction are reasonably arranged according to factors such as the call frequency of personnel on each floor and the real-time flow of people, reducing the number of ineffective operations of the elevator and achieving reduced elevator energy consumption.

[0068] Based on the energy-saving strategy results obtained by the quantum computing unit, detailed control instructions are generated. For example, for the air-conditioning system, specific temperature setting values ​​are generated, such as setting different comfortable temperature ranges for different time periods, fan speed adjustment parameters, dynamic adjustment according to indoor and outdoor temperature differences and personnel density, compressor start and stop time and other control instructions; for the lighting system, instructions such as the switching time of different areas and the dimming level of different lamps are generated. These instructions are safely and quickly transmitted to the corresponding energy-consuming equipment through quantum teleportation technology to ensure that the energy-saving strategy can be executed accurately. The fidelity of the entire transmission process is high, and the transmission status can be monitored in real time. If an abnormality occurs, the instruction can be retransmitted in time.

[0069] The knowledge graph module is used to build a relationship network between energy-consuming devices and systems in the smart park, realizing knowledge association and intelligent reasoning between energy-consuming devices;

[0070] By collecting various information such as the manuals of energy-consuming equipment in the smart park, past operation and maintenance records, physical connections between equipment, and energy consumption data, a relationship network is constructed. For example, the relationship between the lighting system in the office building and the office hours of personnel, the functions of different areas, conference rooms, office areas, corridors, etc. is clarified; the dependency between production equipment and the power supply system and cooling system it serves, as well as the changes in their energy consumption at different stages of the production process, etc. The relationship network covers several nodes and several edges, comprehensively covering all aspects of the energy system of the smart park.

[0071] Intelligent reasoning is achieved based on the constructed knowledge graph. For example, when the overall energy consumption of an office building shows an abnormal increase, the intelligent reasoning unit will quickly correlate and analyze the relevant energy-consuming equipment involved. It is found that some high-power electronic equipment has been newly installed on some floors recently. At the same time, the air-conditioning system has poor cooling effect, resulting in long-term high-load operation. These factors combined lead to increased energy consumption, and then timely issue an early warning to the park management, and provide targeted solutions such as checking the air-conditioning and refrigeration system, and reasonably planning the power consumption time of new energy-consuming equipment.

[0072] Based on the daily operational changes of the smart park's energy system, such as the replacement of energy-consuming equipment, the implementation of new energy-saving transformation measures, and the continuous accumulation of energy consumption data, the knowledge graph content is automatically updated and the structure is optimized regularly (once a week). For example, the smart park has newly introduced a smart energy management system for energy consumption control in some areas. The knowledge update unit will integrate the relevant parameters of this system, the interaction relationship with other energy-consuming equipment, and other information into the knowledge graph, and at the same time adjust some of the original associations to ensure that the knowledge graph always fits the actual energy management status of the park, providing accurate and effective knowledge support for applications such as intelligent reasoning.

[0073] The digital map modeling module is used to realize the three-dimensional visualization of the smart park and the spatial analysis of energy consumption data, thereby improving the intuitiveness and accuracy of energy consumption management.

[0074] Laser scanning technology is used to conduct an all-round scan of the smart park. Combined with satellite remote sensing image data and architectural design drawings, a high-precision three-dimensional model of the park is created. The shape and position accuracy of each building in the model is controlled within ±0.2 meters, and the specific location of each energy-consuming equipment in the park is clearly marked, allowing people to intuitively see situations such as the distribution of air-conditioning outdoor units in the building and the layout of lighting fixtures on each floor.

[0075] The collected energy consumption data is integrated with the three-dimensional model of the park to analyze the spatial distribution of energy consumption. For example, by analyzing the energy consumption density of different areas, office buildings, production plants, storage areas, etc., and different floors, an energy consumption heat map is generated to intuitively show the distribution of high and low energy consumption in the park. It was found that the energy consumption on the top floors of some production plants was significantly higher. Further analysis showed that this was due to poor roof insulation, which led to increased air-conditioning and cooling energy consumption.

[0076] The results of the energy consumption data analysis are displayed in an intuitive and easy-to-understand graphical format on the large screen of the park's energy management center. The monthly energy consumption of different buildings is compared through bar charts, and the seasonal trend of the park's overall energy consumption is displayed with a line chart. The proportion of different types of energy-consuming equipment, such as lighting, air conditioning, and production equipment, in the park's total energy consumption is presented with a pie chart. This makes it easier for smart park managers to understand the current status of the park's energy consumption management and provides strong data visualization support for making more accurate energy-saving management decisions.

[0077] The digital twin module includes:

[0078] A mapping creation unit, configured to create a digital mapping according to the status of actual energy-consuming devices;

[0079] A model building unit, configured to build a digital twin model of the energy-consuming device based on the collected energy consumption data, and to use quantum computing to quickly iterate and optimize the digital twin model;

[0080] The simulation analysis unit is used to simulate the impact of different energy-saving strategies on energy consumption and provide optimization suggestions. The simulation analysis uses quantum simulation technology to improve simulation speed and accuracy.

[0081] The energy-saving algorithm module includes:

[0082] A data processing unit, configured to process the energy consumption data collected by the sensor node module and employ quantum error correction coding to improve the security and accuracy of energy consumption data processing;

[0083] A quantum computing unit, configured to execute quantum algorithms to find optimal energy-saving strategies, wherein the quantum algorithms include quantum approximate optimization algorithms and quantum heuristic algorithms;

[0084] The control instruction generation unit is used to generate control instructions based on the quantum computing results and send them to the energy-consuming devices. The control instructions are securely transmitted using quantum teleportation (QT) technology.

[0085] The knowledge graph module includes:

[0086] Relationship building unit, used to build the relationship network between various energy-consuming devices and systems in the park;

[0087] Intelligent reasoning unit, used to realize knowledge association and intelligent reasoning between energy-consuming devices;

[0088] The knowledge update unit is used to automatically update the knowledge graph content and optimize the knowledge graph structure based on the operational changes of the smart park energy system and the accumulation of new data.

[0089] The quantum communication technology includes:

[0090] A quantum key distribution (QKD) unit, configured to establish a secure communication channel between the central server module and the sensor node module, wherein the quantum key distribution protocol supports dynamic adjustment to adapt to different network conditions;

[0091] A quantum random number generator unit is used to generate random numbers for encryption, and the random number generator adopts a quantum computing-resistant encryption (PQC) algorithm to improve security.

[0092] The digital map modeling module includes:

[0093] 3D modeling unit, used to create a 3D model of the smart park;

[0094] Energy consumption data analysis unit, used to analyze the spatial distribution of energy consumption data;

[0095] The visualization display unit is used to display the energy consumption data analysis results in a graphical form.

[0096] Example 2

[0097] A smart park energy-saving management method based on the Internet of Things, such as Figure 2 As shown, the following steps are included:

[0098] Deploy multiple sensor nodes on various energy-consuming devices in the smart park to monitor and collect energy consumption data in real time;

[0099] A quantum random number generator is used to generate random numbers for encryption on the sensor nodes, and the energy consumption data of each energy-consuming device in the park is processed and stored through a central server;

[0100] establishing a secure communication channel between the central server and the sensor nodes using a quantum key distribution (QKD) protocol;

[0101] Create a digital map of each energy-consuming device in the smart park, use quantum communication technology to achieve encrypted transmission of energy consumption data, and use quantum computing technology to simulate and optimize the state of the digital map;

[0102] Utilize the collected energy consumption data and digital mapping information to optimize energy-saving strategies through quantum computing technology, and use quantum machine learning algorithms to improve the accuracy and efficiency of energy-saving strategies;

[0103] Build a relationship network between energy-consuming devices and systems in the smart park to achieve knowledge association and intelligent reasoning between energy-consuming devices;

[0104] Realize 3D visualization of smart parks and spatial analysis of energy consumption data, improving the intuitiveness and accuracy of energy consumption management.

[0105] An electronic device, comprising:

[0106] a memory storing executable program code;

[0107] a processor coupled to the memory;

[0108] The processor calls the executable program code stored in the memory to execute the energy-saving management method for a smart park based on the Internet of Things as described above.

[0109] A computer storage medium stores computer instructions, which, when called, are used to execute the energy-saving management method for a smart park based on the Internet of Things as described above.

[0110] In specific implementation, sensor nodes are carefully installed on each energy-consuming device in the smart park. For example, for office building lighting fixtures, small power sensor nodes are installed at the lamp holders, which can monitor the power changes of the lamps in real time, record the instantaneous power every 5 minutes, and accumulate the energy consumption data of each lamp; for computer equipment, smart sensor nodes are installed at the power strips, which can not only obtain the real-time power of the computer, but also record data such as the computer's usage time and standby time, so as to comprehensively evaluate the computer's energy consumption; on large production machinery in the production workshop, multi-parameter sensor nodes are installed around key energy-consuming parts such as motors and transmission devices to monitor the equipment's current, voltage, speed, torque and other parameters, and convert the real-time energy consumption of the energy-consuming equipment through built-in algorithms.

[0111] At the same time, the quantum random number generator equipped in each sensor node will generate random numbers for encryption according to certain rules. For example, every time a set of energy consumption data is collected, a 128-bit random number is generated. The energy consumption data is encrypted using the Advanced Encryption Standard (AES) combined with the random number to ensure the security of the data during transmission and prevent data leakage and malicious tampering.

[0112] The smart park has built a powerful central server equipped with a multi-core processor, large-capacity memory and high-speed solid-state hard drive storage. The server establishes a secure communication link with each sensor node through the quantum key distribution (QKD) protocol. The optimized BB84 protocol is used here, and parameters such as the key generation rate and error correction mechanism are dynamically adjusted according to the real-time status of the network. For example, when the network communication quality in a certain area is detected to be slightly poor, such as the location of some sensor nodes in the corner of the workshop, the key generation rate is appropriately reduced and the error correction coding redundancy is increased to ensure the accuracy of energy consumption data transmission.

[0113] After the sensor node sends the encrypted energy consumption data to the central server, the server uses the pre-negotiated quantum key to decrypt it, and then classifies, organizes and stores the decrypted data. A database table structure is established according to the area to which the energy-consuming equipment belongs, such as office building A, production workshop, etc., equipment type, lighting, power equipment, etc., and collection time, and the data is stored to facilitate subsequent quick query and call analysis.

[0114] With the help of the digital twin modeling platform, a digital mapping is created for each energy-consuming device in the smart park based on the detailed energy consumption data of each energy-consuming device obtained from the central server and the basic parameters of the device itself, such as device model and rated power. Taking a key production machine in the production workshop as an example, the digital mapping not only accurately presents the appearance, size, and physical layout of the equipment, but also reflects the operating parameters of key parts inside the equipment in real time, such as the real-time temperature of the motor, the energy consumption corresponding to the current speed, the load pressure of the transmission chain, and the resulting energy consumption changes. The digital mapping has a high degree of similarity with the actual equipment status, laying the foundation for subsequent precise simulation optimization.

[0115] The quantum Monte Carlo simulation method in quantum computing technology is used to simulate and optimize the state of the created digital mapping. For example, for the ventilation equipment in the production workshop, different production conditions, such as different product production batches and different workshop personnel density, are simulated. By adjusting parameters such as the fan speed, air outlet angle, and ventilation time interval, the energy consumption of the equipment and changes in environmental indicators such as air quality and temperature in the workshop are observed. The comprehensive benefits under different parameter combinations are compared. Through the parallel processing capabilities of quantum computing, a reliable reference basis can be provided for the formulation of energy-saving strategies more efficiently.

[0116] After collecting and aggregating massive amounts of energy consumption data collected by sensor nodes and detailed device status information reflected by digital mapping, quantum computing technology is used to optimize energy-saving strategies. Using the quantum deep learning neural network algorithm within quantum machine learning, the system conducts in-depth mining and learning of historical energy consumption data, as well as corresponding multi-dimensional data such as device operating status and environmental factors. For example, by analyzing past energy consumption data for office building air conditioning systems across different seasons and time periods, including weekday working hours, lunch breaks, and after-hours, as well as personnel flow on different floors, the system discovered that a large number of air conditioners on some floors were still operating at high power during lunch breaks and after-hours, resulting in energy waste. Based on this analysis, an intelligent energy-saving strategy was developed. This strategy, linked to the smart campus personnel clock-in system and the office area intelligent monitoring system, automatically adjusts the air conditioning operating mode based on actual occupancy distribution, such as lowering the temperature setpoint, switching to low fan speed operation, or directly shutting down some air conditioning units. Furthermore, real-time fine-tuning is performed based on changes in outdoor temperature. Compared to conventional strategies developed based on previous experience, the energy-saving strategy optimized using quantum computing technology achieves significantly improved energy savings and significantly reduces the energy consumption costs of the air conditioning system.

[0117] Build a relationship network between various energy-consuming equipment and systems in the smart park, and integrate various information resources, including the manuals of energy-consuming equipment, operation and maintenance records, physical connection relationships between equipment, energy supply and consumption flow relationships, and the mutual influence relationship between the energy consumption of various equipment in different production or office scenarios. For example, in the knowledge graph, clearly sort out the relationship between the operating power of production machinery in the production workshop and the energy consumption of ventilation equipment and cooling equipment. That is, long-term high-load operation of production machinery will generate a lot of heat, causing ventilation and cooling equipment to increase power operation to maintain a suitable workshop environment temperature, thereby increasing overall energy consumption; for example, the opening time and brightness setting of the lighting system in the office building will affect the indoor temperature, indirectly affecting the energy consumption of the air-conditioning system.

[0118] When there is an abnormal fluctuation in energy consumption in a certain area, for example, it is found that the overall energy consumption of a certain floor of an office building has increased significantly over a period of time, the system will use the constructed knowledge graph for intelligent reasoning analysis. The system will quickly associate the changes in the operating status of lighting, air conditioning, computers and other equipment on that floor. Combined with information such as personnel entry and exit records, it is inferred that this may be because some offices have newly added high-power electrical equipment and the usage time has not been set reasonably. At the same time, the air-conditioning system continues to operate at high power due to the increase in indoor heat load, resulting in increased energy consumption. Then, early warning information is promptly issued to the smart park managers, and targeted solutions are given, such as checking the power consumption of new equipment and reasonably adjusting the air-conditioning temperature setting value, to assist managers in quickly locating the root cause of the problem and taking effective countermeasures.

[0119] A 3D model of the entire smart park was created using high-precision 3D modeling software combined with the smart park's architectural design drawings, geographic information data, and field laser scanning measurement data. The model's terrain accuracy error was controlled within ±0.3 meters, and the building's exterior and internal structure accuracy error was controlled within ±0.2 meters. The specific location and basic information of each energy-consuming equipment were accurately marked on the model, allowing people to intuitively see situations such as the distribution of lighting fixtures on each floor of the office building, the installation location of air-conditioning outdoor units, and the layout of various large equipment in the production workshop.

[0120] The collected energy consumption data is deeply integrated with the three-dimensional model to carry out spatial analysis and visualization of the energy consumption data. For example, by generating energy consumption heat maps with different color labels, the energy consumption distribution of each area, each floor and even each equipment in the park can be intuitively presented on the three-dimensional model. At a glance, the energy consumption concentrated areas in the production workshop and the floors with higher energy consumption in the office building can be seen; by analyzing the spatial variation trends of energy consumption data in different time periods (day, week, month, etc.), it is found that certain areas in the park, such as the lighting part of the parking lot, have a high proportion of energy consumption at night, and there is potential for energy-saving optimization. This provides strong data support and intuitive decision-making basis for the subsequent formulation of more refined and targeted energy-saving management measures.

[0121] The same or similar reference numerals correspond to the same or similar components;

[0122] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0123] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the field, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A smart park energy-saving management system based on the Internet of Things, characterized by: include: The sensor node module is deployed on various energy-consuming devices in the smart park to monitor and collect energy consumption data in real time. It also has a quantum random number generator function to generate random numbers for the energy consumption data, which are used to encrypt the energy consumption data. The central server module is used to receive energy consumption data encrypted by the sensor node module, decrypt it using the quantum key established with the sensor node module through the BB84 protocol, and store it in a classified manner. It can also dynamically adjust the quantum key generation rate and error correction coding method according to the network conditions in different regions. The digital twin module is used to create a digital map for each energy-consuming device based on the energy consumption data and basic device parameters stored in the central server. It receives encrypted energy consumption data through quantum communication technology to update the digital map and uses the quantum Monte Carlo simulation method in quantum computing technology to simulate and optimize the state of the digital map. The energy-saving algorithm module is used to obtain energy consumption data and mapping information of the digital twin module from the central server module. The mapping information reflects the device status. It uses quantum algorithms to find the optimal energy-saving strategy and combines quantum deep learning neural network algorithms to mine historical energy consumption data, device operating status, and environmental factors for in-depth learning. It generates control instructions based on quantum computing results and uses quantum teleportation technology to securely transmit control instructions to energy-consuming devices. The knowledge graph module is used to build a relationship network between energy-consuming devices and systems, realize knowledge association and intelligent reasoning between devices, and automatically update the knowledge graph content and structure according to system operation changes and new data; The digital map modeling module is used to combine laser scanning data, remote sensing images and architectural drawings to create a three-dimensional model of the smart park. The three-dimensional model marks the location of each energy-consuming equipment, integrates energy consumption data with the three-dimensional model for spatial analysis, generates energy consumption heat map results and displays them visually.

2. The smart park energy-saving management system based on the Internet of Things according to claim 1 is characterized in that: The digital twin module includes: A mapping creation unit, configured to create a digital mapping according to the status of actual energy-consuming devices; A model building unit, configured to build a digital twin model of the energy-consuming device based on the collected energy consumption data, and to use quantum computing to quickly iterate and optimize the digital twin model; The simulation analysis unit is used to simulate the impact of different energy-saving strategies on energy consumption and provide optimization suggestions. The simulation analysis uses quantum simulation technology.

3. The smart park energy-saving management system based on the Internet of Things according to claim 1 is characterized in that: The energy-saving algorithm module includes: a data processing unit, configured to process the energy consumption data collected by the sensor node module, and to process the energy consumption data using quantum error correction coding technology to correct transmission deviations, and to use the mapping information to simulate and analyze the impact of different energy-saving strategies on energy consumption; A quantum computing unit, configured to execute quantum algorithms to find optimal energy-saving strategies, wherein the quantum algorithms include quantum approximate optimization algorithms and quantum heuristic algorithms; The control instruction generation unit is used to generate control instructions based on the deep mining results and quantum computing results, and send them to energy-consuming devices. The control instructions are securely transmitted using quantum teleportation technology.

4. The smart park energy-saving management system based on the Internet of Things according to claim 1 is characterized in that: The knowledge graph module includes: Relationship building unit, used to build the relationship network between various energy-consuming devices and systems in the park; Intelligent reasoning unit, used to realize knowledge association and intelligent reasoning between energy-consuming devices; The knowledge update unit is used to automatically update the knowledge graph content and optimize the knowledge graph structure based on the operational changes of the smart park energy system and the accumulation of new data.

5. The smart park energy-saving management system based on the Internet of Things according to claim 1 is characterized in that: The quantum communication technology includes: A quantum key distribution unit, configured to establish a secure communication channel between the central server module and the sensor node module, wherein the quantum key distribution protocol supports dynamic adjustment; A quantum random number generator unit is used to generate random numbers for encryption, and the random number generator adopts an encryption algorithm that is resistant to quantum computing cracking.

6. The smart park energy-saving management system based on the Internet of Things according to claim 1 is characterized in that: The digital map modeling module includes: 3D modeling unit, used to create a 3D model of the smart park; Energy consumption data analysis unit, used to analyze the spatial distribution of energy consumption data; The visualization display unit is used to display the energy consumption data analysis results in a graphical form.

7. A smart park energy-saving management method based on the smart park energy-saving management system based on the Internet of Things according to any one of claims 1 to 6, characterized in that: The following steps are involved: Deploy multiple sensor nodes on various energy-consuming devices in the smart park to monitor and collect energy consumption data in real time; A quantum random number generator is used to generate random numbers for encryption on the sensor nodes, and the energy consumption data of each energy-consuming device in the park is processed and stored through a central server; Establishing a secure communication channel between the central server and the sensor node using a quantum key distribution protocol; Create a digital map of each energy-consuming device in the smart park, use quantum communication technology to achieve encrypted transmission of energy consumption data, and use quantum computing technology to simulate and optimize the state of the digital map; Utilizing the collected energy consumption data and digital mapping information, we optimize energy-saving strategies through quantum computing technology and employ quantum machine learning algorithms for deep mining and learning to generate control instructions. Build a relationship network between energy-consuming devices and systems in the smart park to achieve knowledge association and intelligent reasoning between energy-consuming devices; Realize three-dimensional visualization of smart parks and spatial analysis of energy consumption data.

8. An electronic device, characterized in that: The electronic device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the energy-saving management method for a smart park based on the Internet of Things as described in claim 7.

9. A computer storage medium, characterized in that The computer storage medium stores computer instructions, which, when called, are used to execute the energy-saving management method for a smart park based on the Internet of Things as described in claim 7.

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