Smart park energy-saving management method and system based on Internet of Things

Abstract

The invention provides a smart park energy-saving management method and system based on the Internet of Things, and relates to the technical field of Internet of Things and energy management. The system integrates a sensor node module, a central server module, a digital twin module, an energy-saving algorithm module, a knowledge graph module and a digital map modeling module; real-time monitoring, secure transmission, efficient processing and intelligent optimization of energy consumption data are realized, and the system ensures data transmission security by using a quantum random number generator and a quantum key distribution protocol; performing equipment state simulation and optimization by combining a digital twin technology with quantum computing; the accuracy of an energy-saving strategy is improved by a quantum machine learning algorithm; the knowledge graph realizes intelligent reasoning between devices; three-dimensional visual display enhances energy consumption management intuition, and the system aims at improving the energy utilization efficiency of the smart park, reducing energy consumption and promoting sustainable development of the smart park.

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 valued for their energy management issues. The application of Internet of Things (IoT) technology provides a new solution for energy management in smart parks. IoT deploys sensors throughout the park to achieve interconnection between devices and devices, and between devices and users, improves data collection efficiency, and enhances the real-time nature of information. Big data analysis, as the core of the energy management system, processes and analyzes a large amount of energy consumption data to explore potential energy-saving opportunities and optimize resource allocation and utilization efficiency. The introduction of cloud computing technology effectively solves the bottleneck problem of traditional information systems in data storage and processing. The elasticity and scalability of the cloud platform enable the energy management system to respond quickly to changes, while supporting multi-level and multi-device data management.

[0003] The improvement of artificial intelligence technology enables the system to continuously optimize scheduling strategies and improve operational efficiency through machine learning algorithms. As an emerging technology, quantum computing 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 and reduces the difficulty of maintenance and debugging through simulation optimization of virtual space. 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 prior art, 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 solution of the present invention is as follows:

[0007] A smart park energy-saving management system based on the Internet of Things, comprising:

[0008] 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 the function of a quantum random number generator 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 for state simulation and optimization;

[0011] An energy-saving algorithm module, which uses 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, and realize knowledge association and intelligent reasoning between energy-consuming devices;

[0013] The digital map modeling module is used to realize the three-dimensional visualization display 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, used for creating a digital mapping according to the status of the actual energy consuming device;

[0016] A model building unit, used to build a digital twin model of the energy-consuming equipment according to the collected energy consumption data, and 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, used to process the energy consumption data collected by the sensor node module, and use quantum error correction coding to improve the security and accuracy of energy consumption data processing;

[0020] A quantum computing unit, used 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 according to 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] A relationship building unit, used to build a 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 according to 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, used to establish a secure communication channel between the central server module and the sensor node module, and 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] An 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 manner.

[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 node, and 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 node using a quantum key distribution (QKD) protocol;

[0037] Create digital maps of energy-consuming devices in the smart park, and use quantum communication technology to encrypt and transmit energy consumption data, and use quantum computing technology to simulate and optimize the state of digital maps;

[0038] Using the collected energy consumption data and digital mapping information, we 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 three-dimensional visualization of smart parks and spatial analysis of energy consumption data to improve 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 prior art, 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 the quantum machine learning algorithm further improves the accuracy and efficiency of the energy-saving strategy, 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 to keep 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, so that managers can intuitively understand the 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 drawings required for use in the implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived based on the provided drawings without paying 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 drawings are for illustrative purposes only and should not be construed as limiting the present patent;

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

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

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

[0054] Example 1

[0055] There is a comprehensive smart park, which 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 the energy consumption cost was 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 As shown, including:

[0057] 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 the function of a quantum random number generator to generate random numbers for encryption.

[0058] Sensor node modules are installed on every energy-consuming device in the smart park. For example, small sensors are 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, sensors can monitor key parameters such as cooling capacity, compressor power, air 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 each time data is collected and combined with encryption 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 a high-speed solid-state hard drive. The server's quantum key distribution unit and each sensor node module have established a quantum key distribution protocol communication link, using the BB84 protocol. It will also dynamically adjust parameters such as the quantum key generation rate and error correction coding method based on the network conditions in different areas. For example, the network signal in the storage area that is remote 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 the 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 for state simulation and optimization;

[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-scale production equipment in a production plant as an example, the digital map not only reflects the external dimensions and spatial location of the equipment, but also shows in detail the real-time operating status parameters of each key component inside the energy-consuming equipment, 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 the digital twin model. For example, for the air-conditioning system in the smart park, quantum computing is used to perform a large number of parameter adjustments and simulation calculations on the model in a short period of time, which is about 6 times faster than the traditional construction method. 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., are accurately simulated, so that the model can better reflect the operation and energy consumption characteristics of the actual energy-consuming equipment.

[0064] Quantum simulation technology is used to simulate and analyze 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 combined with personnel activity sensing are simulated, and the impact of each strategy on the overall indoor lighting effect and personnel 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 uses 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 and external electromagnetic interference that may occur during the transmission of energy consumption data, thereby making the energy consumption data processing more accurate.

[0067] Quantum approximate optimization algorithm and quantum heuristic algorithm are used to find the optimal energy-saving strategy. Taking the overall power distribution of the smart park as an example, the quantum computing unit quickly traverses and analyzes many possible power distribution schemes, involving power usage priorities and power distribution of different areas, different time periods and different equipment, and finds the optimal solution in a short 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, thereby reducing the number of invalid operations of the elevator and reducing the energy consumption of the elevator.

[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 and fan speed adjustment parameters for different time periods, dynamic adjustment according to the indoor and outdoor temperature difference 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 and without error. 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, and realize knowledge association and intelligent reasoning between energy-consuming devices;

[0070] The information on the energy-consuming equipment in the smart park, such as the manuals, past operation and maintenance records, physical connections between equipment, and energy consumption data, is collected to build a relationship network. 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 a number of nodes and a number of 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 increases abnormally, the intelligent reasoning unit will quickly correlate and analyze the relevant energy-consuming equipment involved and find that it is because 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 promptly issue an early warning to the park management, and give targeted solutions such as checking the air-conditioning refrigeration system and reasonably planning the power consumption time of new energy-consuming equipment.

[0072] According to the daily operation changes of the energy system of the smart park, 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 regularly (once a week) and the structure is optimized. For example, the smart park has introduced a new intelligent 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 adjust some of the original associations to ensure that the knowledge graph always fits the actual energy management status of the park, and provides 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 display 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, and a high-precision three-dimensional model of the park is created by combining satellite remote sensing image data and architectural design drawings and other information. 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 present 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 refrigeration energy consumption.

[0076] The results of the energy consumption data analysis are displayed in an intuitive and easy-to-understand graphical form on the large screen of the park's energy management center. A bar chart is used to compare the monthly energy consumption of different buildings, a line chart is used to show the seasonal changes in the park's overall energy consumption, and a pie chart is used to show the proportion of different types of energy-consuming equipment, such as lighting, air conditioning, and production equipment, in the park's total energy consumption. 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, used for creating a digital mapping according to the status of the actual energy consuming device;

[0079] A model building unit, used to build a digital twin model of the energy-consuming equipment according to the collected energy consumption data, and 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, used to process the energy consumption data collected by the sensor node module, and use quantum error correction coding to improve the security and accuracy of energy consumption data processing;

[0083] A quantum computing unit, used 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 according to 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] A relationship building unit, used to build a 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 according to 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, used to establish a secure communication channel between the central server module and the sensor node module, and 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] An 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 manner.

[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 node, and 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 node using a quantum key distribution (QKD) protocol;

[0101] Create digital maps of energy-consuming devices in the smart park, and use quantum communication technology to encrypt and transmit energy consumption data, and use quantum computing technology to simulate and optimize the state of digital maps;

[0102] Using the collected energy consumption data and digital mapping information, we 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 three-dimensional visualization of smart parks and spatial analysis of energy consumption data to improve 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 the computer's usage time, standby time and other data, so as to comprehensively evaluate the computer's energy consumption; on large-scale 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 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, each 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 disk 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, which will dynamically adjust parameters such as the key generation rate and error correction mechanism according to the real-time status of the network. For example, when it is detected that the network communication quality in a certain area is 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. It establishes a database table structure based on 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 stores the data for 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, rated power, etc. Taking a key production machine in the production workshop as an example, the digital mapping not only accurately presents the appearance shape, size, physical layout of each component, etc. of the equipment, but also can reflect 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, etc. 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, different workshop personnel density, etc., 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, and 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 summarizing the massive energy consumption data collected by the sensor nodes and the detailed equipment status information reflected by the digital mapping, the energy-saving strategy is optimized using quantum computing technology. The quantum deep learning neural network algorithm in the quantum machine learning algorithm is used to deeply mine and learn the historical energy consumption data and the corresponding multi-dimensional data such as the equipment operation status and environmental factors. For example, for the air-conditioning system of the office building, by analyzing the past different seasons, different time periods, working hours, lunch breaks, after get off work on weekdays, and the flow of personnel on different floors and the corresponding energy consumption data, it is found that a large number of air conditioners on some floors are still in high-power operation during lunch breaks and after get off work, resulting in energy waste. Based on this analysis result, an intelligent energy-saving strategy is formulated, that is, by linking with the smart park personnel clocking system, the office area intelligent monitoring system, etc., the air-conditioning operation mode is automatically adjusted according to the actual personnel distribution, such as lowering the temperature setting value, switching to low wind speed operation or directly shutting down some air-conditioning units, etc. At the same time, real-time fine-tuning is performed in combination with the changes in outdoor temperature. Compared with the conventional strategies formulated based on experience in the past, the energy-saving effect of the energy-saving strategy optimized by quantum computing technology is much higher, and the energy consumption cost of the air-conditioning system is significantly reduced.

[0117] Build a relationship network among 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, resulting in the need for 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, and indirectly affect 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 the lighting, air conditioning, computers and other equipment on that floor, and combine it 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 have not set the usage time 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. In turn, 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 is 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 is controlled within ±0.3 meters, and the building's appearance and internal structure accuracy error is controlled within ±0.2 meters. The specific location and basic information of each energy-consuming equipment are 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 the air-conditioning outdoor unit, 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 is intuitively presented on the three-dimensional model. The energy consumption concentrated areas in the production workshop and the floors with higher energy consumption in the office building can be seen at a glance; by analyzing the spatial variation trends of energy consumption data in different time periods (days, weeks, months, etc.), it is found that some 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, which 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 only used for illustrative purposes 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 relevant field, other different forms of changes or modifications can be made on the basis of the above description. It is not necessary and impossible to list 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 protection scope of the claims of the present invention.

Claims

1. A smart park energy-saving management system based on the Internet of Things, characterized in that: 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 the function of a quantum random number generator to generate random numbers for encryption. 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 protocol; 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 for state simulation and optimization; An energy-saving algorithm module, which uses 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; The knowledge graph module is used to build a relationship network between energy-consuming devices and systems in the smart park, and realize knowledge association and intelligent reasoning between energy-consuming devices; The digital map modeling module is used to realize the three-dimensional visualization display of the smart park and the spatial analysis of energy consumption data, thereby improving the intuitiveness and accuracy of energy consumption management.

2. According to the Internet of Things-based smart park energy-saving management system of claim 1, it is characterized in that: The digital twin module includes: A mapping creation unit, used for creating a digital mapping according to the status of the actual energy consuming device; A model building unit, used to build a digital twin model of the energy-consuming equipment according to the collected energy consumption data, and 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 to improve simulation speed and accuracy.

3. According to the IoT-based smart park energy-saving management system of claim 1, it is characterized in that: The energy-saving algorithm module includes: A data processing unit, used to process the energy consumption data collected by the sensor node module, and use quantum error correction coding to improve the security and accuracy of energy consumption data processing; A quantum computing unit, used 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 according to the quantum computing results and send them to the energy-consuming devices. The control instructions are securely transmitted using quantum teleportation technology.

4. According to the IoT-based smart park energy-saving management system of claim 1, it is characterized in that: The knowledge graph module includes: A relationship building unit, used to build a 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 according to the operational changes of the smart park energy system and the accumulation of new data.

5. According to the IoT-based smart park energy-saving management system of claim 1, it is characterized in that: The quantum communication technology includes: A quantum key distribution unit, used to establish a secure communication channel between the central server module and the sensor node module, and the quantum key distribution protocol supports dynamic adjustment to adapt to different network conditions; 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 to improve security.

6. According to the IoT-based smart park energy-saving management system of claim 1, it is characterized in that: The digital map modeling module includes: 3D modeling unit, used to create a 3D model of the smart park; An 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 manner.

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 node, and energy consumption data of each energy-consuming device in the park is processed and stored through a central server; Using a quantum key distribution protocol to establish a secure communication channel between the central server and the sensor node; Create digital maps of energy-consuming devices in the smart park, and use quantum communication technology to encrypt and transmit energy consumption data, and use quantum computing technology to simulate and optimize the state of digital maps; Using the collected energy consumption data and digital mapping information, we optimize energy-saving strategies through quantum computing technology and use quantum machine learning algorithms to improve the accuracy and efficiency of energy-saving strategies; 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 to improve the intuitiveness and accuracy of energy consumption management.

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, and when the computer instructions are called, they are used to execute the energy-saving management method for a smart park based on the Internet of Things as described in claim 7.

Images