Smart park facility predictive maintenance system based on digital twinborn technology

Abstract

The invention discloses a smart park facility predictive maintenance system based on a digital twinborn technology, and relates to the field of smart park facility maintenance. Comprising a data acquisition module, a preprocessing module, a digital twin model construction module, a fault prediction module, a maintenance decision module, a maintenance resource management module, a user interaction module, a system management module, a spatio-temporal data analysis and prediction module and a social-technical system fusion module. The method comprises the following steps: collecting and fusing a risk-dependent frequency modulation rate of quantum sensing, improving speed and precision by means of quantum calculation, fusing a digital twin model into a meta-universe concept and an intelligent agent, predicting a fault by combining quantum machine learning and causal inference, optimizing a maintenance decision based on a game theory and reinforcement learning, and managing resources by using a block chain-Internet of Things fusion technology. The invention discloses a brain-computer interface and holographic projection interaction and quantum encryption dual-protection management system. The system is accurate in data acquisition, vivid in model construction, accurate in fault prediction, scientific in maintenance decision, efficient in resource management, immersive in interactive experience and safe and stable in system, and ensures stable operation of park facilities.

Description

Technical Field

[0001] The present invention relates to the technical field of smart park facility maintenance, and in particular to a smart park facility predictive maintenance system based on digital twin technology. Background Art

[0002] With the rapid development of science and technology, smart parks, as a key vehicle for intelligent urban development, require the efficient and stable operation of various facilities. Smart parks incorporate a wide range of complex facilities, including power, water supply and drainage, lighting, and communications. Traditional maintenance relies on manual inspections and post-fault repairs, which is not only inefficient but also difficult to effectively predict facility failures before they occur. Manual inspections cannot monitor facility status in real time, making it easy to miss potential issues and delaying repairs until after a failure occurs. This not only impacts the park's normal operations but can also result in significant economic losses.

[0003] While some existing facility maintenance systems incorporate information technology, such as sensors that collect partial operational data, these systems suffer from incomplete and inaccurate data collection. Most systems focus only on a few key parameters of a facility and fail to capture comprehensive operational information. Furthermore, the limited accuracy of traditional sensors makes it difficult to detect subtle changes in facility operation, which can often be early signs of a malfunction. Furthermore, data transmission is susceptible to interference, leading to data loss or errors, compromising accurate assessment of facility status.

[0004] Furthermore, existing facility maintenance decisions lack scientific and comprehensive rationale. The timing and method of maintenance are often determined based on simple threshold judgments or empirical evidence, without fully considering the facility's actual operating conditions, historical failure data, and future usage needs. This results in either excessively frequent maintenance, resulting in a waste of resources, or untimely maintenance, increasing the risk of facility failure. Furthermore, maintenance resource management lacks effective integration and optimization, leading to irrational allocation of resources such as maintenance personnel and spare parts, further reducing maintenance efficiency. In terms of user interaction, the existing system interface is unfriendly and complex to operate, making it difficult for park managers and maintenance personnel to quickly obtain the information they need and perform efficient operations. Therefore, the development of a predictive maintenance system for smart park facilities based on advanced technologies is urgent. Summary of the Invention

[0005] The present invention proposes a predictive maintenance system for smart park facilities based on digital twin technology to solve the problems mentioned in the above-mentioned prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a predictive maintenance system for smart park facilities based on digital twin technology, comprising:

[0007] Multi-source data acquisition module: Integrates quantum sensing with traditional sensors, uses quantum sensors to sense physical changes, utilizes drone inspections, calculates risk levels through a fuzzy comprehensive evaluation model, and dynamically adjusts data acquisition frequency;

[0008] Data preprocessing module: Introduces quantum computing technology to clean and extract features from data, detects abnormal data, combines chaos theory analysis, and uses adaptive dynamic normalization methods to dynamically adjust normalization parameters based on data distribution;

[0009] Digital twin model construction module: This module proposes a metaverse-based digital twin model to construct a virtual campus environment, introduces intelligent agent technology, uses quantum neural network to optimize the model, and updates parameters through an adaptive quantum filtering algorithm.

[0010] Fault prediction module: This module combines quantum machine learning and causal inference technology to predict faults, builds a causal graph model, introduces a fault propagation model, uses quantum entanglement to establish a correlation model, and calculates the probability of fault occurrence through a formula.

[0011] Maintenance Decision Module: Optimizes maintenance decisions based on game theory and reinforcement learning, applies game theory to establish an interest model to analyze strategy selection and interest conflicts, introduces sustainable development indicators, and predicts the remaining life of facilities through quantum aging models;

[0012] Maintenance resource management module: Using blockchain-IoT fusion technology, it automates resource allocation and procurement processes through smart contracts, introduces the concept of virtual inventory management, and uses quantum optimization algorithms to optimize transportation routes and scheduling plans;

[0013] User interaction module: Create an immersive interactive interface based on brain-computer interface and holographic projection, support brain signal interaction, holographic display of facility status, introduce emotional intelligent interaction system, and use quantum speech recognition technology to achieve voice interaction;

[0014] System management module: Establish a dual security protection system based on quantum encryption and blockchain, introduce quantum immune algorithm for self-repair optimization, and use quantum sensors to monitor performance, analyze bottlenecks, and make optimization adjustments.

[0015] Furthermore, it also includes:

[0016] Spatiotemporal data analysis and prediction module: Utilize spatiotemporal big data technology to analyze the operation rules and change trends of park facilities, integrate facility location information, operation data and time information by building a spatiotemporal geographic information system (GIS-T), and visualize the spatiotemporal distribution and dynamic changes of facilities; use spatiotemporal sequence analysis methods to predict facility operation status and failure probability, combine meteorological data and geographic environment information to analyze the impact of external factors on facility spatiotemporal distribution and operation status, and calculate the spatiotemporal correlation S using the spatiotemporal mutual information algorithm, the formula is S = I (Xt ,X s ), where X t is time series data, X s is the spatial sequence data, and I is the mutual information function.

[0017] Furthermore, it also includes:

[0018] Social-technical system integration module: Considering the social factors of personnel skill level, work attitude, and team collaboration during the maintenance of park facilities, the relationship and collaboration mode between maintenance personnel are analyzed by building a social network analysis model, and the team collaboration efficiency and knowledge dissemination ability are evaluated. The social factors and technical systems are integrated to establish a social-technical collaborative optimization model. The calculation of the social-technical synergy degree C adopts the fuzzy comprehensive evaluation method, and the formula is: Among them, wsi and wtj are the weights of social factors and technical factors respectively, and si and tj are the quantitative values of social factors and technical factors respectively.

[0019] Furthermore, the data collection frequency in the multi-source data collection module is dynamically adjusted according to the real-time risk level of the facility. The risk level R is calculated through the fuzzy comprehensive evaluation model, and the formula is: where w i is the weight of each risk factor, r i is the quantitative value of the corresponding risk factor;

[0020] Biosensors are used to collect biological environmental data around the facility, detect microbial growth and enzyme activity information, infer the facility's operating status and potential failure risks by analyzing biological indicators, and use satellite remote sensing technology to obtain the park's macro-geographic information and meteorological data.

[0021] Furthermore, the data normalization in the data preprocessing module adopts an adaptive dynamic normalization method, which dynamically adjusts the normalization parameters according to the real-time distribution of the data. The formula is: Where μ(t) and σ(t) are the dynamic mean and standard deviation of the data at time t respectively;

[0022] Quantum noise processing technology is introduced, and quantum error correction codes and quantum denoising algorithms are used to correct and denoise the data after quantum computing processing, and the data is encoded and transmitted in combination with channel coding principles.

[0023] Furthermore, the model parameter update in the digital twin model construction module adopts the adaptive quantum filtering algorithm, and the formula is in is the estimated value of the state at time k, K k is the adaptive quantum filter gain, zk is the measurement value, and H is the observation matrix;

[0024] Build a cross-park facility digital twin model based on quantum entanglement, remotely link and collaboratively manage facilities between different parks, use the characteristics of quantum entanglement to synchronize the status information of facilities in different parks in real time, and share resources and optimize configuration through the cross-park digital twin model.

[0025] Furthermore, the probability of failure in the fault prediction module P f The formula is P f =ω1×P f-QML +ω2×P f-CI +ω3×P f-FP , where P f-QML is the failure probability predicted by quantum machine learning, P f-CI is the failure probability obtained by causal inference analysis, P f-FP is the failure probability calculated by the fault propagation model, ω1, ω2, ω3 are the corresponding weights;

[0026] Combining quantum genetic algorithm and deep learning to select fault features, a dynamic adjustment mechanism of the fault warning threshold is introduced to adaptively adjust the fault warning threshold according to the real-time operating status of the facility and historical fault data.

[0027] Furthermore, the maintenance decision module uses the quantum aging model to predict the remaining service life of the facility L remaining , the formula is Where L0 is the initial service life of the facility, d i It is the aging loss of the facility during each operation;

[0028] By adopting ecological optimization algorithm, integrating ant colony algorithm and ecological footprint model, an ecological compensation strategy is formulated, and ecological compensation is carried out through planting green plants and purifying water quality.

[0029] Furthermore, the user interaction module has developed a user community based on a virtual reality social platform, where users share facility maintenance experiences, exchange troubleshooting methods, and put forward improvement suggestions in the virtual community. This promotes knowledge dissemination and sharing through social interaction, and uses virtual avatar technology to communicate and engage in activities in the virtual community through personalized images.

[0030] Furthermore, the system management module uses quantum cloud computing technology to expand elasticity and shared resources. Quantum cloud computing automatically adjusts computing resources according to system load and prevents data leakage and malicious attacks through quantum key distribution technology.

[0031] Compared with the existing technology, the beneficial effects of the present invention are:

[0032] In terms of data collection, the integration of quantum sensing technology and various advanced acquisition methods enables comprehensive, high-precision collection of facility operating data. This enables early detection of minor facility hazards, providing sufficient data support for subsequent accurate analysis. Data preprocessing incorporates cutting-edge technologies such as quantum computing, significantly improving data processing speed and accuracy, ensuring high data quality and laying a solid foundation for digital twin model construction and fault prediction.

[0033] Digital twin model construction innovatively incorporates the concept of the metaverse and intelligent agent technology. This not only accurately simulates the physical state of a facility but also reflects the impact of complex environmental factors on the facility, enhancing the model's realism and practicality. Fault prediction, combined with cutting-edge technologies such as quantum machine learning and causal inference, can more accurately predict the probability of failure, analyze the root cause of failures, and fully understand the fault propagation path, significantly improving the accuracy and comprehensiveness of fault prediction.

[0034] Maintenance decision-making, based on game theory and reinforcement learning, comprehensively considers multiple factors to optimize maintenance strategies. This reduces maintenance costs while improving facility reliability and user satisfaction, while also addressing sustainable development goals. Maintenance resource management utilizes blockchain-IoT fusion technology to ensure resource data security, enable automated scheduling, and enable virtual inventory sharing, improving resource utilization efficiency.

[0035] User interaction utilizes technologies such as brain-computer interfaces and holographic projection to create an immersive, personalized experience. System management utilizes dual security features of quantum encryption and blockchain, as well as quantum immune algorithm self-healing, ensuring stable and secure operation. Overall, this system comprehensively enhances the intelligent maintenance of smart park facilities, ensuring stable operation and promoting the efficient and sustainable development of smart parks. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic block diagram of a smart park facility predictive maintenance system based on digital twin technology proposed in the present invention;

[0037] Figure 2 This is a schematic diagram of the comparison of fault prediction accuracy;

[0038] Figure 3 This is a schematic diagram of the maintenance cost reduction;

[0039] Figure 4 This is a comparative diagram of resource utilization improvement. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0042] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the accompanying drawings.

[0043] Reference Figures 1 to 4 : A predictive maintenance system for smart park facilities based on digital twin technology, including:

[0044] Multi-source data acquisition module: innovatively integrates quantum sensing technology and traditional sensors. Quantum sensors can sense tiny physical changes in facilities with extremely high precision. For example, in power facilities, quantum magnetic sensors can detect weak magnetic field changes generated by current, with an accuracy n times higher than that of traditional sensors (n is a value greater than 1 determined based on actual conditions). For underground pipelines in the park, quantum gravity sensors are used to detect subtle changes in soil density around the pipelines and discover hidden dangers of pipeline leakage in advance. At the same time, drones equipped with multispectral imaging equipment are used to regularly inspect park facilities to obtain appearance images and spectral data of the facilities. The frequency of data collection is dynamically adjusted according to the real-time risk level of the facility. The risk level R is calculated through a fuzzy comprehensive evaluation model, and the formula is: where w i is the weight of each risk factor, r i This is a quantitative value for the corresponding risk factor. This ensures more intensive data collection as facility risk increases.

[0045] Data preprocessing module: Introduce quantum computing technology for data cleaning and feature extraction. The parallel computing capability of quantum computing greatly improves the data processing speed and can process massive multi-source data in a short time. For the detection of abnormal data, the quantum support vector machine algorithm is used, which is based on the superposition and entanglement characteristics of quantum states and can more accurately identify abnormal points in the data. In terms of feature extraction, the quantum principal component analysis (QPCA) algorithm is used to mine the most representative feature information in the data. At the same time, the data is analyzed in combination with chaos theory, and the complexity and stability of the data are judged by calculating the chaotic characteristic parameters of the data, such as the Lyapunov exponent, to provide more comprehensive information for subsequent analysis. Data normalization adopts an adaptive dynamic normalization method to dynamically adjust the normalization parameters according to the real-time distribution of the data. The formula is Where μ(t) and σ(t) are the dynamic mean and standard deviation of the data at time t, respectively.

[0046] Digital twin model construction module: A digital twin model based on the concept of the metaverse is proposed. This model not only contains the physical information and operating status of the facility, but also constructs a virtual campus environment to simulate the impact of factors such as personnel activities and weather changes on the facility. In the virtual environment, intelligent agent technology is introduced. Each facility corresponds to an intelligent agent. The intelligent agent has the ability to learn and make decisions independently and can adjust its own behavior according to changes in the surrounding environment and the status of the facility. The digital twin model is optimized using quantum neural networks. The quantum bits of quantum neural networks can handle more complex nonlinear relationships and improve the accuracy and generalization ability of the model. The model parameters are updated using an adaptive quantum filtering algorithm, which combines the principles of quantum mechanics and traditional filtering algorithms to track changes in facility status more quickly and accurately. The formula is in is the estimated value of the state at time k, K k is the adaptive quantum filter gain, zk is the measurement value, and H is the observation matrix.

[0047] Fault prediction module: Combine quantum machine learning and causal inference technology to predict faults. Quantum machine learning algorithms can quickly find fault patterns in high-dimensional data space, while causal inference technology can analyze the root cause of the fault. Clarify the causal relationship between the various parameters of the facility, and use Pearl's causal inference theory to trace and predict the cause of the fault. Introduce a fault propagation model to simulate the propagation path and impact range of the fault in the facility system, and calculate the probability distribution of fault propagation through the Monte Carlo simulation method. At the same time, the use of quantum entanglement characteristics to establish a fault correlation model can discover fault correlation relationships that are difficult to detect with traditional methods, thereby improving the accuracy and comprehensiveness of fault prediction. Fault probability P f The calculation of P comprehensively considers the prediction results of quantum machine learning, the results of causal inference analysis and the output of the fault propagation model. The formula is P f =ω1×P f-QML +ω2×P f-CI +ω3×P f-FP , where P f-QML is the failure probability predicted by quantum machine learning, P f-CI is the failure probability obtained by causal inference analysis, P f-FP is the failure probability calculated by the fault propagation model, and ω1, ω2, and ω3 are the corresponding weights.

[0048] Maintenance decision module: Multi-objective optimization maintenance decision-making based on game theory and reinforcement learning. Taking into account the involvement of multiple stakeholders in the maintenance process, such as park management, maintenance personnel, facility users, etc., game theory is used to establish the interest model of each party, and analyze the strategic choices and interest conflicts of each party. Reinforcement learning algorithms are used to find the optimal maintenance strategy in a game environment, with the goal of minimizing maintenance costs, maximizing facility reliability and user satisfaction. At the same time, sustainable development indicators such as energy consumption and carbon emissions are introduced and incorporated into the objective function of maintenance decision-making. The formulation of maintenance strategies also takes into account the prediction of the remaining service life of the facility. The remaining service life L of the facility is predicted by the quantum aging model. remaining , the formula is Where L0 is the initial service life of the facility, d i It is the aging loss of the facility during each operation.

[0049] Maintenance resource management module: Blockchain-IoT fusion technology is used to achieve efficient management of maintenance resources. Blockchain technology ensures the security, non-tamperability and traceability of maintenance resource data, and IoT technology realizes real-time monitoring and automated scheduling of resources. Resource allocation and procurement processes are automatically executed through smart contracts. When the spare parts inventory falls below the set threshold, the smart contract automatically triggers a purchase order. At the same time, the concept of virtual inventory is introduced to incorporate idle resources of different facilities in the park into virtual inventory management. The ownership and usage of resources are recorded through blockchain to achieve resource sharing and optimal allocation. The transportation path and scheduling plan of maintenance resources are optimized using quantum optimization algorithms to reduce transportation costs and time. The formula is: where c ij is the cost of transporting resources from point i to point j, x ij is the transportation decision variable.

[0050] User Interaction Module: This module creates an immersive interactive interface based on brain-computer interface (BCI) and holographic projection technology. Users can interact with the system through neural signals emitted by the brain, eliminating the need for manual operation. These interactions can include querying facility information and issuing maintenance instructions. Holographic projection technology presents digital twin models of park facilities in three-dimensional form, allowing users to observe the operating status and fault information of facilities from different angles. Furthermore, an emotional intelligence interaction system is introduced to automatically adjust the style and content of the interactive interface by analyzing emotional signals such as facial expressions and voice intonation, providing more personalized and user-friendly services. The voice interaction function uses quantum speech recognition technology to improve speech recognition accuracy and anti-interference capabilities.

[0051] System Management Module: A dual security system based on quantum cryptography and blockchain has been established. Quantum cryptography utilizes quantum key distribution (QKD) to ensure absolute data security, preventing data theft and tampering. Blockchain technology is used to record system operation logs and user permission changes, ensuring traceability and non-repudiation of operations. Furthermore, a quantum immune algorithm is introduced to enable self-repair and optimization of the system. When the system detects an anomaly or failure, the quantum immune algorithm can quickly identify and clear abnormal code, restoring normal system operation. System performance monitoring uses quantum sensors to monitor the system's hardware resource usage, such as CPU utilization and memory usage, in real time. Quantum algorithms are used to analyze system performance bottlenecks and make timely optimization adjustments.

[0052] The present invention also includes the following modules:

[0053] Spatiotemporal data analysis and prediction module: This module uses spatiotemporal big data technology to analyze the operating rules and changing trends of park facilities in the time and space dimensions. By building a spatiotemporal geographic information system (GIS-T), the location information, operating data and time information of the facilities are integrated to visualize the spatiotemporal distribution and dynamic changes of the facilities. Spatiotemporal sequence analysis methods, such as the spatiotemporal autoregressive moving average model (STARMA), are used to predict the operating status and failure probability of facilities in the future within a certain period of time and spatial range. At the same time, combined with meteorological data and geographic environment information, the impact of external factors on the spatiotemporal distribution and operating status of facilities is analyzed, and response strategies are formulated in advance. The spatiotemporal correlation S is calculated using the spatiotemporal mutual information algorithm, and the formula is S=I(X t ,X s ), where X t is time series data, X s is the spatial sequence data, and I is the mutual information function.

[0054] The present invention also includes the following modules:

[0055] Social-technical system integration module: This module takes into account the social factors in the process of park facility maintenance, such as personnel skill level, work attitude, teamwork, etc. By constructing a social network analysis model, the relationship and collaboration mode between maintenance personnel are analyzed, and the team's collaboration efficiency and knowledge dissemination ability are evaluated. At the same time, social factors are integrated with the technical system to establish a social-technical collaborative optimization model. By adjusting personnel allocation, training plans and technical solutions, the performance and efficiency of the entire maintenance system are improved. The calculation of the social-technical collaboration degree C adopts the fuzzy comprehensive evaluation method, and the formula is where w si and w tj are the weights of social and technical factors, s i and t j are the quantitative values of social factors and technical factors respectively.

[0056] In this invention, the multi-source data acquisition module utilizes advanced micro-nanosensing technology to accurately detect information such as microbial growth rates, metabolite concentrations, and changes in enzyme activity. Leveraging highly sensitive electrochemical and optical detection principles, biological indicators are converted into electrical or optical signals. Through signal amplification and analog-to-digital conversion, the module accurately captures the subtle dynamics of the biological environment surrounding the facility. For example, in a sewage treatment facility, when biosensors detect an abnormal decrease in the number of key functional bacteria in the microbial community or a significant decrease in enzyme activity, combined with a machine learning-based data analysis model, it can quickly infer potential problems in the treatment process, such as substrate concentration imbalances, abnormal temperature fluctuations, or toxic intrusion, providing timely warnings of potential failure risks. Satellite remote sensing technology also plays a significant role in this module. Using multispectral and hyperspectral remote sensing satellites, it acquires image data of the park in multiple wavelengths, including visible light, infrared, and microwaves. Preprocessing techniques such as radiometric correction and geometric correction accurately extract macroscopic geographic information such as the park's topography, vegetation cover, and water distribution, as well as meteorological data such as temperature, humidity, and wind speed. This rich and comprehensive environmental information, deeply integrated with facility operation data, provides a more three-dimensional and accurate decision-making basis for facility operation and maintenance, helping the smart park facility predictive maintenance system to better ensure the stable operation of park facilities.

[0057] In the present invention, the data preprocessing module constructs a specific coding structure by adding redundant bits to the original quantum bits. Taking the surface code as an example, the logical bits are encoded in a two-dimensional lattice composed of multiple physical bits. When noise causes errors in individual physical bits, the errors can be accurately identified and corrected by measuring the bits in the lattice and using a specific error correction algorithm to restore the original information. The quantum denoising algorithm improves data quality from another dimension. By utilizing the coherence and entanglement characteristics of the quantum state and designing a specific unitary transformation operation, the quantum state contaminated by noise is processed. For example, a denoising method based on quantum tomography is used to remove the aliasing caused by noise by measuring and reconstructing the quantum state multiple times, making the quantum state closer to the real state. In the data transmission stage, combined with the channel coding principle in information theory, advanced coding methods such as low-density parity check code (LDPC) are adopted. This coding makes the encoded data have stronger anti-interference ability during the transmission process by carefully designing the check matrix. At the receiving end, the iterative decoding algorithm can efficiently correct bit errors caused by noise, fading and other factors during the transmission process, greatly reducing the data transmission error rate and providing an accurate and reliable data basis for subsequent facility predictive maintenance analysis.

[0058] In this invention, the digital twin model construction module constructs a cross-park facility digital twin model based on quantum entanglement, bringing innovation to park facility management. Quantum entanglement, a fascinating quantum mechanical phenomenon, allows multiple entangled particles to maintain an instantaneous connection regardless of their distance. In this model, through the careful preparation and manipulation of quantum entangled states, key status information of different park facilities is encoded into entangled particle pairs. For example, the operating parameters of the power facilities in Park A are correlated with the parameters of similar facilities in Park B. When the power facilities in Park A experience a load anomaly, the entangled particles instantly transmit this change information to the corresponding particles in Park B, allowing Park B to perceive the change in the status of Park A's facilities in real time. Advanced quantum communication technology is used to ensure the security and accuracy of status information during cross-park transmission. Combined with a high-precision sensor network, various data from the facilities, including temperature, pressure, and vibration, are collected in real time. This data is continuously updated and input into the digital twin model, enabling the model to accurately reflect the real-time status of the facilities. This cross-park digital twin model enables each park to break down information barriers, enabling resource sharing and optimized allocation. For example, if Park C is running low on a particular spare part, the model can quickly identify excess inventory in Park D, enabling timely coordination and allocation, thus avoiding maintenance delays caused by spare part shortages. This significantly improves the efficiency and reliability of facility maintenance across the entire park cluster, providing a solid foundation for the efficient operation of the smart park.

[0059] In this invention, the fault prediction module integrates quantum genetic algorithms and deep learning to select fault signatures, significantly improving the accuracy and efficiency of fault prediction. Quantum genetic algorithms leverage the unique properties of quantum bits (qubits) to overcome the limitations of traditional algorithms. The superposition property of qubits enables them to represent multiple states simultaneously. For example, in the fault signature search space, a single qubit can simultaneously represent both "the fault signature is present" and "the fault signature is absent," significantly broadening the search range. Entanglement creates strong correlations between qubits, which accelerate information transfer and collaborative search. Within a vast set of fault signatures, the algorithm continuously evolves the states of qubits through operations such as quantum rotating gates, rapidly converging on an optimal subset of fault signatures and selecting the most representative fault signatures, such as abnormal device temperature changes and vibration frequency fluctuations. These carefully selected fault signatures are fed into a deep learning model, enabling the model to more efficiently learn fault patterns. Furthermore, a dynamic adjustment mechanism for fault warning thresholds is introduced. The system continuously monitors the real-time operating status of the facility, collecting data such as current, voltage, and pressure, and analyzing them in conjunction with historical fault data. For example, when equipment ages or environmental conditions change, the system adaptively adjusts fault warning thresholds based on new data distributions, using statistical analysis and machine learning algorithms. Fixed temperature warning thresholds are dynamically adjusted based on the equipment's recent temperature fluctuations and trends. This prevents false alarms and missed alarms caused by inappropriate thresholds, ensuring timely and accurate fault warnings and saving valuable time for facility maintenance.

[0060] The maintenance decision-making module in this invention fully considers the close connection between facility maintenance and the park's ecological environment. This module innovatively incorporates a series of ecological and environmental indicators, such as soil, water, and air pollution, into the objective function for maintenance decisions. A high-precision sensor network monitors key data such as heavy metal content in soil, chemical oxygen demand in water, and particulate matter concentration in air in real time. During the decision-making process, an ecological optimization algorithm is employed, organically combining an ant colony algorithm with an ecological footprint model. The ant colony algorithm simulates the pheromone release of ants while foraging. Numerous "virtual ants" explore the search space of maintenance plans, accumulating and updating pheromones to gradually identify the optimal maintenance path. The ecological footprint model quantitatively assesses the consumption of ecological resources and waste generation in the park under different maintenance plans. These two algorithms work together to continuously adjust maintenance strategies based on meeting facility maintenance needs, striving to minimize the negative impact of maintenance activities on the park's ecological environment. Furthermore, this module has carefully formulated an ecological compensation strategy. If maintenance activities inevitably cause damage to the ecological environment, the system will immediately activate a corresponding compensation mechanism. For example, if wastewater generated during maintenance pollutes the park's waterways, high-efficiency microbial agents are deployed to purify the water, and aquatic plants are planted in the surrounding waters to enhance the water's self-purification capacity. If construction damages park green spaces, appropriate plants are promptly replanted to restore the ecological landscape. This series of scientific and meticulous measures ensures a balance between facility maintenance and ecological environmental protection within the smart park, ultimately achieving sustainable development.

[0061] In this invention, the user interaction module develops a user community based on a virtual reality social platform. This community provides users with a highly immersive and interactive communication space. Entering the virtual community, users experience themselves as if they were immersed in a virtual environment that closely resembles the real-world smart campus. Here, users can freely share their facility maintenance experiences. For example, Zhang, an electrician at the campus, can use a 3D demonstration to demonstrate in detail how he uses thermal imaging technology to precisely locate potential overheating fault points in electrical equipment, accompanied by voice narration and text instructions. Other users can not only visually observe the entire process but also pause and replay it at any time to delve into every detail. The virtual community provides powerful simulation capabilities for exchanging troubleshooting methods. When user Xiao Li encounters an abnormal cooling problem with his air conditioning unit, he can initiate a call for help in the community. Other users can join the virtual air conditioning room scene in real time and, through gestures and voice communication, jointly analyze the cause of the problem and discuss solutions such as checking refrigerant pressure and troubleshooting fan failures. They can also simulate operations in the virtual environment to verify the feasibility of their solutions. Users can also provide suggestions for system improvements, which are presented visually for collaborative discussion and improvement. Avatar technology plays a crucial role in enhancing user engagement and a sense of belonging. Users can customize their own avatars based on their preferences, creating unique features from appearance to clothing. During community interactions and activities, these avatars vividly reflect the user's movements and expressions, making them as natural as face-to-face interactions in real life. This social interaction rapidly disseminates and shares knowledge among users, significantly increasing user engagement and satisfaction and enabling them to become active participants in the maintenance of smart campus facilities.

[0062] In this invention, the system management module uses quantum cloud computing technology to achieve flexible system expansion and resource sharing. Quantum cloud computing combines the advantages of quantum computing and cloud computing. Leveraging the superposition and entanglement properties of qubits, quantum computing possesses powerful parallel computing capabilities. Processing massive amounts of operational data from smart campus facilities, such as multidimensional data like temperature, humidity, and pressure collected in real time by sensors, traditional computing methods can be time-consuming. Quantum computing, however, can complete complex data analysis and simulations in a fraction of the time. Combined with the flexibility of cloud computing, the system can dynamically allocate computing resources based on load. When large-scale inspections of campus facilities generate a surge in data volume, the quantum cloud computing platform automatically senses the load change and rapidly allocates idle computing resources to data processing tasks, ensuring rapid system response and efficient operation. Quantum key distribution technology plays an irreplaceable role in data security. Based on the fundamental principles of quantum mechanics, it utilizes the quantum state of single photons to transmit cryptographic keys. Because quantum states are unclonable, any attempt to eavesdrop on the key inevitably perturbs the quantum state, making it detectable by both communicating parties. In a cloud computing environment, critical data such as facility maintenance data and user information in smart campuses is encrypted during transmission and storage using high-strength keys generated by quantum key distribution technology. Even if hackers attempt to intercept the data, they cannot decrypt the information encrypted with quantum keys, effectively preventing data leaks and malicious attacks. This provides a strong defense for the data security of the smart campus facility predictive maintenance system and ensures stable and reliable system operation.

[0063] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A predictive maintenance system for smart park facilities based on digital twin technology, characterized by: Includes the following modules: Multi-source data acquisition module: Integrates quantum sensing with traditional sensors, uses quantum sensors to sense physical changes, utilizes drone inspections, calculates risk levels through a fuzzy comprehensive evaluation model, and dynamically adjusts data acquisition frequency; Data preprocessing module: Introduces quantum computing technology to clean and extract features from data, detects abnormal data, combines chaos theory analysis, and uses adaptive dynamic normalization methods to dynamically adjust normalization parameters based on data distribution; Digital twin model construction module: This module proposes a metaverse-based digital twin model to construct a virtual campus environment, introduces intelligent agent technology, uses quantum neural network to optimize the model, and updates parameters through an adaptive quantum filtering algorithm. Fault prediction module: This module combines quantum machine learning and causal inference technology to predict faults, builds a causal graph model, introduces a fault propagation model, uses quantum entanglement to establish a correlation model, and calculates the probability of fault occurrence through a formula. Maintenance Decision Module: Optimizes maintenance decisions based on game theory and reinforcement learning, applies game theory to establish an interest model to analyze strategy selection and interest conflicts, introduces sustainable development indicators, and predicts the remaining life of facilities through quantum aging models; Maintenance resource management module: Using blockchain-IoT fusion technology, it automates resource allocation and procurement processes through smart contracts, introduces the concept of virtual inventory management, and uses quantum optimization algorithms to optimize transportation routes and scheduling plans; User interaction module: Create an immersive interactive interface based on brain-computer interface and holographic projection, support brain signal interaction, holographic display of facility status, introduce emotional intelligent interaction system, and use quantum speech recognition technology to achieve voice interaction; System management module: Establish a dual security protection system based on quantum encryption and blockchain, introduce quantum immune algorithm for self-repair optimization, and use quantum sensors to monitor performance, analyze bottlenecks, and make optimization adjustments.

2. The predictive maintenance system for smart park facilities based on digital twin technology according to claim 1 is characterized in that: Also includes: Spatiotemporal data analysis and prediction module: Utilize spatiotemporal big data technology to analyze the operation rules and change trends of park facilities, integrate facility location information, operation data and time information by building a spatiotemporal geographic information system (GIS-T), and visualize the spatiotemporal distribution and dynamic changes of facilities; use spatiotemporal sequence analysis methods to predict facility operation status and failure probability, combine meteorological data and geographic environment information to analyze the impact of external factors on facility spatiotemporal distribution and operation status, and calculate the spatiotemporal correlation S using the spatiotemporal mutual information algorithm, the formula is S = I (X t ,X s ), where X t is time series data, X s is the spatial sequence data, and I is the mutual information function.

3. The predictive maintenance system for smart park facilities based on digital twin technology according to claim 1 is characterized in that: Also includes: Social-technical system integration module: Considering the social factors of personnel skill level, work attitude, and team collaboration during the maintenance of park facilities, the relationship and collaboration mode between maintenance personnel are analyzed by building a social network analysis model, and the team collaboration efficiency and knowledge dissemination ability are evaluated. The social factors and technical systems are integrated to establish a social-technical collaborative optimization model. The calculation of the social-technical synergy degree C adopts the fuzzy comprehensive evaluation method, and the formula is: Among them, wsi and wtj are the weights of social factors and technical factors respectively, and si and tj are the quantitative values of social factors and technical factors respectively.

4. The predictive maintenance system for smart park facilities based on digital twin technology according to claim 1 is characterized in that: The data collection frequency in the multi-source data collection module is dynamically adjusted according to the real-time risk level of the facility. The risk level R is calculated through the fuzzy comprehensive evaluation model. The formula is: where w i is the weight of each risk factor, r i is the quantitative value of the corresponding risk factor; Biosensors are used to collect biological environmental data around the facility, detect microbial growth and enzyme activity information, infer the facility's operating status and potential failure risks by analyzing biological indicators, and use satellite remote sensing technology to obtain the park's macro-geographic information and meteorological data.

5. The predictive maintenance system for smart park facilities based on digital twin technology according to claim 1 is characterized in that: The data normalization in the data preprocessing module adopts the adaptive dynamic normalization method, which dynamically adjusts the normalization parameters according to the real-time distribution of the data. The formula is: Where μ(t) and σ(t) are the dynamic mean and standard deviation of the data at time t respectively; Quantum noise processing technology is introduced, and quantum error correction codes and quantum denoising algorithms are used to correct and denoise the data after quantum computing processing, and the data is encoded and transmitted in combination with channel coding principles.

6. The predictive maintenance system for smart park facilities based on digital twin technology according to claim 1 is characterized in that: The model parameter update in the digital twin model construction module adopts the adaptive quantum filtering algorithm, and the formula is in is the estimated value of the state at time k, K k is the adaptive quantum filter gain, zk is the measurement value, and H is the observation matrix; Build a cross-park facility digital twin model based on quantum entanglement, remotely link and collaboratively manage facilities between different parks, use the characteristics of quantum entanglement to synchronize the status information of facilities in different parks in real time, and share resources and optimize configuration through the cross-park digital twin model.

7. The predictive maintenance system for smart park facilities based on digital twin technology according to claim 1 is characterized in that: Fault occurrence probability P in the fault prediction module f The formula is P f =ω1×P f-QML +ω2×P f-CI +ω3×P f-FP Among them, P f-QML is the failure probability predicted by quantum machine learning, P f-CI is the failure probability obtained by causal inference analysis, P f-FP is the failure probability calculated by the fault propagation model, ω1, ω2, ω3 are the corresponding weights; Combining quantum genetic algorithm and deep learning to select fault features, a dynamic adjustment mechanism of the fault warning threshold is introduced to adaptively adjust the fault warning threshold according to the real-time operating status of the facility and historical fault data.

8. The predictive maintenance system for smart park facilities based on digital twin technology according to claim 1 is characterized in that: In the maintenance decision module, the remaining service life L of the facility is predicted by the quantum aging model remaining , the formula is Where L0 is the initial service life of the facility, d i It is the aging loss of the facility during each operation; By adopting ecological optimization algorithm, integrating ant colony algorithm and ecological footprint model, an ecological compensation strategy is formulated, and ecological compensation is carried out through planting green plants and purifying water quality.

9. The predictive maintenance system for smart park facilities based on digital twin technology according to claim 1 is characterized in that: The user interaction module has developed a user community based on a virtual reality social platform. Users share facility maintenance experiences, exchange troubleshooting methods, and put forward improvement suggestions in the virtual community. It promotes knowledge dissemination and sharing through social interaction and uses virtual avatar technology to communicate and engage in activities in the virtual community through personalized images.

10. The predictive maintenance system for smart park facilities based on digital twin technology according to claim 1 is characterized in that: The system management module uses quantum cloud computing technology to expand elasticity and shared resources. Quantum cloud computing automatically adjusts computing resources according to system load and prevents data leakage and malicious attacks through quantum key distribution technology.

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