Drainage port dynamic management system based on quantum parameter optimization and air-ground collaborative awareness

Abstract

The invention discloses a drainage port dynamic management system based on quantum parameter optimization and air-ground collaborative awareness, and relates to the technical field of smart city drainage systems, and the system comprises a data sensing layer which is used for collecting multi-source heterogeneous data of a drainage port and a surrounding environment through air-based remote sensing, a ground sensor and video monitoring equipment; the calculation processing layer is used for performing quantum state coding and cross-modal fusion on the multi-source heterogeneous data; and the decision support layer is used for carrying out anomaly identification based on the data after cross-modal fusion, carrying out flood prediction and deduction based on a quantum optimization algorithm, and finally supporting multi-user immersive interactive operation through a collaborative decision platform of an augmented reality platform and a quantum dot matrix rendering engine. The system not only can be widely applied to daily operation and maintenance of urban drainage pipe networks and flood control and drainage facilities, but also has actual combat ability to cope with extreme weather and sudden disasters, and can effectively support deep development of an intelligent water affair system.

Description

Technical Field

[0001] This invention relates to the field of smart city drainage system technology, and more specifically to a dynamic management system for drainage outlets based on quantum parameter optimization and air-ground collaborative sensing. Background Technology

[0002] Existing drainage outlet monitoring and flood early warning systems still have significant technical shortcomings in data perception, model prediction, and collaborative decision-making, making them unable to cope with complex and ever-changing urban hydrological environments.

[0003] ① In terms of anomaly detection at drainage outlets, traditional computer vision-based monitoring methods mainly rely on fixed rules or convolutional neural networks, which have limited ability to identify anomalies such as drainage outlet blockage, illegal discharge, and equipment damage. These methods experience a significant performance drop under adverse weather conditions (such as heavy rain or at night) and struggle to distinguish between different anomaly types, such as natural siltation and human-caused damage. Furthermore, for sensor data such as water level and flow velocity, existing time-series analysis methods lack the ability to deeply mine potential anomaly patterns, resulting in a persistently high false alarm rate.

[0004] ② In flood prediction modeling, existing systems employ numerical simulation methods (such as SWMM) with high computational complexity, while deep learning-based prediction models (such as LSTM) face problems of large parameter counts and high training energy consumption. These methods are inefficient in utilizing historical disaster data and struggle to capture the unique nonlinear characteristics of urban drainage systems, especially in data-scarce areas, where prediction accuracy is significantly insufficient. Furthermore, traditional models lack the ability to quantify uncertainty, failing to provide reliable risk assessments for decision-making.

[0005] ③ Regarding collaborative sensing, existing systems process data from satellite remote sensing, ground sensors, and weather forecasts independently, lacking an effective feature alignment mechanism. This fragmented approach prevents the system from establishing a correlation model between drainage outlet status and the surrounding environment (such as surface runoff and soil moisture), severely impacting the accuracy of flood risk assessment. Existing data fusion technologies also struggle to adapt to data differences across different spatiotemporal scales, resulting in low efficiency in information collaboration.

[0006] ④ In terms of emergency decision support, the current system mainly relies on two-dimensional GIS displays and static plans, resulting in low visualization and poor interactivity. There is a lack of efficient collaboration mechanisms between departments, making it difficult for decision-makers to obtain timely and intuitive flood evolution simulation results, and also hindering the assessment of the expected effects of different drainage outlet control schemes. Traditional simulation methods (such as Monte Carlo simulations) have high computational costs and cannot meet the real-time requirements of emergency response.

[0007] ⑤ The existing system is severely inadequate in adapting to extreme weather events, and the model lacks a flexible adjustment mechanism. When encountering rainfall events exceeding design standards, the system is prone to failure, lacking adaptive adjustment capabilities. Furthermore, the absence of cross-regional coordination mechanisms also limits the system's ability to cope with large-scale urban flooding events.

[0008] In summary, existing drainage outlet monitoring and flood early warning systems have significant shortcomings in anomaly detection robustness, predictive model efficiency, collaborative sensing, and collaborative decision support, which hinder the improvement of urban flood control and drainage capabilities. Therefore, there is an urgent need for a new system architecture that integrates advanced sensing technologies, efficient computing methods, and intelligent decision support to achieve accurate monitoring of drainage outlet status, reliable early warning of flood risks, and efficient collaboration in emergency response. Summary of the Invention

[0009] In view of this, this invention provides a dynamic management system for drainage outlets based on quantum parameter optimization and air-ground collaborative sensing, which can solve key problems in current urban drainage management such as insufficient sensing accuracy, low prediction efficiency, and lagging collaborative response. A hierarchical system architecture encompassing "full-domain sensing—intelligent reasoning—efficient response" is constructed. Its core design concept uses quantum optimization technology as the algorithm engine, air-based remote sensing (based on synthetic aperture radar, multispectral satellites, or sensing devices carried by UAVs) and ground sensing as the data foundation, and a visual collaborative platform as the decision-making hub to achieve dynamic monitoring of drainage outlet operation status, intelligent prediction of flood evolution, and rapid control in emergency scenarios.

[0010] To achieve the above objectives, the present invention adopts the following technical solution:

[0011] This invention provides a dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing, comprising:

[0012] The data sensing layer is used to collect multi-source heterogeneous data on the drainage outlet and its surrounding environment through airborne remote sensing, ground sensors and video surveillance equipment.

[0013] A computational processing layer is used for quantum state encoding and cross-modal fusion of the multi-source heterogeneous data;

[0014] The decision support layer identifies anomalies based on cross-modal fusion data and performs flood prediction and simulation based on quantum optimization algorithms. Finally, through a collaborative decision-making platform combining extended reality and quantum dot matrix rendering engines, it supports multi-user immersive interactive operation.

[0015] In one embodiment, the data sensing layer includes:

[0016] The airborne remote sensing acquisition module acquires dynamic monitoring remote sensing data of surface water bodies around the drainage outlet based on synthetic aperture radar, multispectral satellites, or sensing devices carried by UAVs, and transmits the data encrypted through a secure channel of quantum key distribution protocol.

[0017] The ground acquisition module collects real-time data on water level and flow velocity at the drainage outlet by deploying ultrasonic water level sensors and electromagnetic flow meters.

[0018] The video surveillance acquisition module acquires monitoring video data of the drainage outlet area through a 4K camera encoded with H.265, and uses a background difference algorithm to identify dynamic changes;

[0019] The collaborative preprocessing module is used to standardize the acquired heterogeneous data through a spatiotemporal registration mechanism and a quantum state encoding compression mechanism.

[0020] In one embodiment, the data sensing layer further includes:

[0021] The credibility assessment module is used to dynamically adjust the data fusion strategy based on data timeliness and source weight; the formula is as follows:

[0022]

[0023] in, This represents the fusion weight of the m-th channel; Spaceborne remote sensing acquisition module: 0.4, Ground acquisition module: 0.4, Video surveillance acquisition module: 0.2; M represents 3 channels; This represents the confidence level value of the corresponding information source, with a value range of [value missing]. ; This represents the data time lag; k represents the time decay adjustment parameter.

[0024] In one embodiment, the quantum state encoding compression mechanism in the collaborative preprocessing module introduces parameterized quantum circuits to construct a joint encoded representation of the input data, with the following embedding mapping:

[0025]

[0026] in: These represent the quantum encoded states of the orbital images and ground measurements, respectively; This represents a parameterized quantum gate sequence used to construct jointly entangled states.

[0027] In one embodiment, the computation processing layer includes:

[0028] The quantum-classical hybrid computing module generates compressed neural network weights through variable quantum circuits and uses a dynamic gradient modulator for training and optimization.

[0029] The cross-modal spatiotemporal attention mechanism module is configured to achieve feature fusion of remote sensing data and ground data through quantum state similarity calculation and spatiotemporal constrained attention mask.

[0030] In one embodiment, the quantum state similarity calculation is performed in the cross-modal spatiotemporal attention mechanism module:

[0031]

[0032] This represents the construction of a quantum feature map, which maps features of different modes. After mapping to quantum states, similarity is measured using quantum inner product. ;

[0033] In the cross-modal spatiotemporal attention mechanism module, the spatiotemporal constrained attention mask is:

[0034] Introduce a differentiable masking mechanism based on spatial adjacency and temporal dependency:

[0035]

[0036] in, The spatiotemporal coordinates of features p and q are represented, including location and timestamp; p and q represent feature vector indices from different sources or different time slices. Indicates the locality adjustment coefficient; This indicates element-wise multiplication in Hadamard.

[0037] In one embodiment, the decision support layer includes:

[0038] An open environment perception anomaly detection engine uses quantum domain adaptive mapping and anti-artifact detection mechanisms to identify abnormal events at drainage outlets;

[0039] The quantum-driven hydrological prediction module transforms the hydrological evolution modeling of urban drainage systems into a programmable quantum Hamiltonian evolution model, and realizes the prediction of sudden water accumulation by solving quantum differential equations.

[0040] The extended reality collaborative decision-making platform is configured to generate 3D visualization scenes through a quantum dot matrix rendering engine and achieve low-latency interaction across multiple terminals using a quantum entanglement synchronization mechanism.

[0041] In one embodiment, the open environment sensing anomaly detection engine employs quantum domain adaptive mapping: a Hilbert spatial projection mechanism is used to construct a quantum mapping transformation between the source and target domains, achieving consistent reconstruction of observation data characteristics under different environments.

[0042]

[0043] in: The quantum state density matrix representing the source domain data; Representation domain adaptive unitary transform operator; Representation domain adaptive unitary transform operator The conjugate transpose of;

[0044] In the open environment perception anomaly discrimination engine, the adversarial artifact detection mechanism introduces a quantum generative adversarial structure to construct anomaly state discrimination boundaries in the quantum state space, enabling high-precision identification of irregular occlusions and water disturbance artifacts in drainage outlet monitoring images.

[0045]

[0046] in, The quantum state representation of the sample to be tested; This represents the abnormal state observation operator obtained from training; This indicates a trainable abnormal sensitivity factor; Represents trace operation; This represents the quantum state basis vector.

[0047] In one embodiment, the quantum-driven hydrological prediction module is specifically used for:

[0048] (1) Quantum modeling of water system structure:

[0049] The drainage relationship between nodes can be expressed as a quantum interaction Hamiltonian:

[0050]

[0051] in, Indicates the strength of the linkage between nodes; This indicates the external rainfall disturbance experienced by the node; Weight values ​​representing the connection relationships of the drainage pipe network; : Represents the Pauli-X operator acting on the i-th node of the drainage network; : Represents the Pauli-Z operator acting on the i-th and j-th nodes;

[0052] (2) Dynamic evolution and dissipation modeling: The Lindblad term is introduced to realize non-conservative evolution, which is used to simulate energy loss such as rainwater retention and leakage.

[0053]

[0054] in, The dissipation coefficient represents the degree of openness of the control system; L represents the Lindblad operator, used to model uncertain disturbance processes. This represents the quantum state vector of the system at time t; the prediction of sudden water accumulation is achieved by solving the quantum differential equation.

[0055] In one embodiment, the extended reality collaborative decision-making platform is specifically used for:

[0056] (1) Extend the reality collaborative decision-making platform and configure it to generate a 3D visualization scene through the quantum dot matrix rendering engine:

[0057]

[0058] in, A rendering Hamiltonian that encodes spatial location, color, and transparency information;

[0059] (2) Utilize quantum entanglement synchronization mechanism to achieve low-latency interaction among multiple terminals, enabling cross-platform decision sharing with extremely low latency:

[0060]

[0061] in, This represents a typical quantum entangled state.

[0062] As can be seen from the above technical solution, compared with the prior art, the present invention has the following technical advantages:

[0063] This invention provides a dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing. By integrating quantum parameter optimization, air-to-ground data collaboration, dynamic reasoning mechanisms, and an immersive collaborative platform, it comprehensively improves the sensing accuracy, reasoning efficiency, and response capabilities of the drainage outlet management system, demonstrating significant practicality and promotional value. This system can not only be widely applied to the daily operation and maintenance of urban drainage networks and flood control and drainage facilities, but also possesses the practical capability to cope with extreme weather and sudden disasters, effectively supporting the in-depth development of smart water management systems. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0065] Figure 1 This is a schematic diagram of a dynamic management system for drainage outlets based on quantum parameter optimization and air-ground collaborative sensing, provided by the present invention.

[0066] Figure 2 A schematic diagram of the data sensing layer provided by the present invention.

[0067] Figure 3 This is a schematic diagram of the computing processing layer provided by the present invention.

[0068] Figure 4 This is a schematic diagram of the decision support layer provided by the present invention.

[0069] Figure 5 This is a schematic diagram of a system structure according to an embodiment of the present invention.

[0070] Figure 6 This is a schematic diagram of the video processing module structure provided by the present invention.

[0071] Figure 7 A schematic diagram of the flood quantum prediction model provided by this invention. Detailed Implementation

[0072] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] This invention proposes a dynamic management system for drainage outlets based on quantum parameter optimization and air-ground collaborative sensing, which can be widely applied to the intelligent management of water infrastructure such as urban drainage systems, flood control and drainage facilities, and rainwater and sewage separation pipe networks.

[0074] Reference Figure 1 As shown, this invention discloses a dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing, comprising:

[0075] The data sensing layer is used to collect multi-source heterogeneous data on the drainage outlet and its surrounding environment through airborne remote sensing, ground sensors and video surveillance equipment.

[0076] A computational processing layer is used for quantum state encoding and cross-modal fusion of the multi-source heterogeneous data;

[0077] The decision support layer identifies anomalies based on cross-modal fusion data and performs flood prediction and simulation based on quantum optimization algorithms. Finally, through a collaborative decision-making platform combining extended reality and quantum dot matrix rendering engines, it supports multi-user immersive interactive operation.

[0078] This system aims to build a comprehensive, end-to-end, and fully intelligent drainage outlet operation and management platform. It seeks to achieve integrated closed-loop management from data collection, anomaly detection, and flood forecasting to emergency decision-making and risk assessment, significantly improving the operational efficiency and disaster prevention and mitigation capabilities of urban drainage systems. It fundamentally enhances the intelligence level and resilience of urban flood control and drainage systems.

[0079] The three-layer architecture described above will be explained in detail below:

[0080] I. Data Awareness Layer:

[0081] The data perception layer is designed based on a three-channel heterogeneous perception structure, integrating airborne imaging, ground detection, and dynamic visual channels. Through unified data standardization, structural reorganization, and reliable filtering mechanisms, it provides continuous data support with high precision, high timeliness, and high robustness for upper-layer intelligent computing. For example... Figure 2 The data awareness layer includes the following:

[0082] The airborne remote sensing acquisition module 21 acquires dynamic monitoring remote sensing data of surface water bodies around the drainage outlet based on synthetic aperture radar, multispectral satellites or sensing devices carried by UAVs, and transmits the data in encrypted form through a secure channel of quantum key distribution protocol.

[0083] The ground acquisition module 22 collects real-time data on water level and flow velocity at the drainage outlet by deploying an ultrasonic water level sensor and an electromagnetic flow meter.

[0084] The video surveillance acquisition module 23 acquires monitoring video data of the drainage outlet area through a 4K camera encoded with H.265, and uses a background difference algorithm to identify dynamic changes;

[0085] The collaborative preprocessing module 24 is used to standardize the acquired heterogeneous data through a spatiotemporal registration mechanism and a quantum state encoding compression mechanism.

[0086] Credibility assessment module 25 is used to dynamically adjust the data fusion strategy based on data timeliness and source weight; the formula is as follows:

[0087]

[0088] in, This represents the fusion weight of the m-th channel; Spaceborne remote sensing acquisition module: 0.4, Ground acquisition module: 0.4, Video surveillance acquisition module: 0.2; M represents 3 channels; This represents the confidence level value of the corresponding information source, with a value range of [value missing]. ; This represents the data time lag; k represents the time decay adjustment parameter.

[0089] The aforementioned modules balance accuracy, density, and channel complementarity, demonstrating key innovations in the system architecture at the data acquisition and management level.

[0090] 1. Construction of a 3D perception network (air-ground-line collaborative perception):

[0091] (1) Space-based remote sensing acquisition module:

[0092] For example, Sentinel-1 synthetic aperture radar imaging data (C-band, spatial resolution 5m) and Sentinel-2 multispectral satellite imagery (covering 10 spectral dimensions, resolution 10–60m) can be used as the primary spaceborne input sources to achieve dynamic monitoring of surface water bodies around drainage outlets. Both types of remote sensing satellites can be revisited at least twice daily, effectively increasing the monitoring frequency. Alternatively, remote sensing data can be acquired using sensing equipment mounted on unmanned aerial vehicles (UAVs).

[0093] Remote sensing data is encrypted and transmitted to edge nodes through a secure channel based on the quantum key distribution (QKD) protocol, which greatly enhances the security of the data link and prevents network threats such as man-in-the-middle attacks.

[0094] In addition, a quality assessment index can be formed by weighting its physical characteristic strength and spatial geometric accuracy:

[0095]

[0096] in, This indicates the weight of band b (SAR: 0.6, visible light: 0.3, infrared: 0.1). This indicates the signal-to-noise ratio for the corresponding band; The value represents the geometric error of the image (unit: meters); B represents the number of all bands, which is 3 here.

[0097] (2) Ground acquisition module:

[0098] For example, high-frequency ultrasonic water level sensors with an accuracy of ±1mm and an RS485 interface are deployed at key drainage points. Electromagnetic flow meters with an error controlled within 0.5% and a standard 4–20mA output are also deployed to ensure near real-time response. These sensors collect data every 10 seconds, and the data is wirelessly transmitted to the edge computing gateway via the LoRa WAN protocol to adapt to the complex communication environment of urban underground pipe networks and achieve low-power, long-distance transmission.

[0099] Its sensing coverage capability can be modeled as follows:

[0100]

[0101] in, Indicates the total number of active sensors; Indicates the duration of a single measurement; Indicates the ground coverage area (unit: km²). Indicates the time sampling period (unit: min).

[0102] (3) Video surveillance acquisition module:

[0103] For example, deploying 4K cameras based on H.265 codec at 30fps with a low-light perception capability of 0.001 Lux allows for continuous operation at night or in adverse weather conditions. An enhanced background subtraction algorithm is used to capture changes in the water at the discharge outlet, performing real-time analysis, automatically identifying moving targets within the outlet area, and accurately capturing and recording suspected abnormal events, thus providing crucial visual evidence for subsequent intelligent analysis. Dynamic feature extraction calculations are as follows:

[0104]

[0105] in, Represents the current frame image matrix; Indicates a background model reference; Represents the spatiotemporal gradient of an image; This represents the channel adjustment coefficient.

[0106] Finally, through the construction of the aforementioned three-dimensional perception network, the system can comprehensively and stably collect multimodal data covering the air-ground-video dimensions, providing solid data support for subsequent efficient data fusion and intelligent scheduling decisions.

[0107] 2. Collaborative Preprocessing Module:

[0108] This scheme constructs a data synchronization and compression preprocessing mechanism based on standard spatiotemporal relocation mechanism and quantum state encoding mapping, which solves the problem of consistency of different data sources in coordinate system, time reference and feature dimension.

[0109] (1) Unified spatiotemporal registration mechanism:

[0110] By constructing a rigid body transformation matrix, different spatial coordinates (such as SAR images and ground sensor locations) are mapped to a unified coordinate system (UTM). The processing flow is as follows:

[0111]

[0112] in, Represents the rotation matrix; Represents the translation vector; Represents the original coordinate point. This indicates the coordinates after registration.

[0113] (2) Quantum state encoding compression mechanism:

[0114] A parameterized quantum circuit (PQC) is introduced to construct a joint encoded representation of the input data, with the embedding mapping as follows:

[0115]

[0116] in: These represent the quantum encoded states of the orbital images and ground measurements, respectively; This represents a parameterized quantum gate sequence used to construct jointly entangled states.

[0117] Experimental results show that this compression mechanism can further reduce the overall data compression rate while maintaining the characterization capability, significantly reducing the subsequent computational burden.

[0118] 3. Credibility Assessment Module:

[0119] To ensure the stability and reliability of the system's input data, a fuzzy logic-driven source evaluation model is constructed. Data from different channels are dynamically weighted based on weight and timeliness, as shown in the following formula:

[0120]

[0121] in, The fusion weight of the m-th channel is represented by 0.4 (orbital imagery: 0.4, ground sensing: 0.4, visual imagery: 0.2); M represents 3 channels. This represents the confidence level (0–1) of the corresponding information source. This represents the data time lag; k represents the time decay adjustment parameter.

[0122] This mechanism ensures that under extreme conditions such as sudden weather events or equipment packet loss, the system can automatically eliminate low-reliability data, thereby improving the overall input quality.

[0123] This invention constructs a multi-source data joint sensing mechanism in the data perception layer, forming a three-dimensional sensing network through space-based remote sensing, ground sensors, and video surveillance to ensure accurate, all-weather, multi-scale monitoring of drainage outlet status. It also standardizes the input of heterogeneous data through a unified spatiotemporal coordinate mapping and resolution resampling method. To overcome the bottlenecks of traditional "fusion" technologies, quantum state mapping is used to compress and encode observation data, enabling efficient modeling of high-dimensional data under low-energy conditions, providing lightweight, structured input support for the computing layer. The data perception layer significantly improves the system's adaptability to open and dynamic environments, maintaining high sensing accuracy, especially in scenarios with degraded perception such as nighttime and heavy rain.

[0124] II. Computation Processing Layer:

[0125] The computing layer employs a cutting-edge quantum-classical hybrid computing architecture and a cross-modal dynamic sensing mechanism to construct the core engine of a novel intelligent sensing platform for smart discharge outlets, which combines high performance, strong generalization, and cross-modal understanding capabilities. For example... Figure 3 As shown, the entire architecture is divided into three core sub-modules: quantum-classical hybrid computing module 31, cross-modal spatiotemporal attention mechanism module 32, and lightweight inference engine 33.

[0126] 1. Quantum-Classical Hybrid Computing Module:

[0127] To overcome the limitations of traditional neural networks in terms of parameter scale, training efficiency, and feature representation capabilities, this invention introduces a quantum parameter adaptive optimization mechanism (QPA). The core idea is to dynamically generate model parameters using variable quantum circuits (VQC) and achieve quantum-classical hybrid optimization through time-varying coefficient adjustments. Unlike traditional gradient descent methods, QPA optimization can perform global exploration in the early stages of training and achieve fine-grained parameter convergence in the later stages, effectively avoiding getting trapped in local optima. Therefore, a hybrid modeling approach integrating variable quantum circuits (VQC) and classical neural networks is proposed.

[0128] (1) Quantum parameter generator (QPG):

[0129] Generative networks are constructed using variable quantum circuits to compress and generate the weight parameters in classical neural networks. Their quantum gate sequence structure is defined as follows:

[0130]

[0131] in, The depth of the quantum circuit is represented by N, which is the total number of parameters in the classical network. C represents the trainable rotation angle (variational parameter); CZ represents the controlled Z-gate used to construct quantum entangled states.

[0132] : Represents a quantum rotation gate around the Z-axis. Its function is to modulate the phase of a qubit. Parameters The rotation angle is determined. In this embodiment, the RZ gate is used to encode the temporal correlation in the drainage outlet monitoring data, and the differential expression of different time series patterns is achieved through phase rotation.

[0133] : Represents a quantum rotation gate around the X-axis. Its function is to change the superposition magnitude of qubits, and its parameters... Controlling the rotation angle. In this embodiment, the RX gate is used to express the amplitude variation characteristics of multi-source inputs (such as water level, flow rate, and remote sensing) of the drainage system, so that the quantum state can simultaneously characterize the dynamic characteristics of multimodal data.

[0134] CZ stands for Controlled-Z gate. Its function is to achieve entanglement between qubits, i.e., to establish correlation between qubits. In this embodiment, the CZ gate is used to simulate the interconnected relationships between pipeline nodes, ensuring that the topological dependency characteristics of the hydraulic network are maintained after compression mapping.

[0135] To address the challenges of complex spatiotemporal dynamics and strong nonlinearity in drainage outlet monitoring, this invention designs a ConvGRU-QNN hybrid structure. The ConvGRU component extracts spatiotemporal sequence features from drainage outlet video monitoring and sensor data through convolutional gated recurrent units, effectively modeling the nonlinear relationship between rainfall processes and drainage system responses.

[0136] A compressed representation of the classical weight matrix is ​​achieved using a quantum magnitude encoding method:

[0137]

[0138] in, The variable quantum state is represented by M; the observation operator matrix is ​​represented by M; the theoretical compression ratio of this compression mechanism is 1. Actual testing showed that the ConvGRU network structure can further save GPU memory.

[0139] (2) Hybrid Gradient Modulator:

[0140] An innovative quantum-classical joint training strategy is proposed to dynamically adjust the dominant weights of the two types of networks during training. The hybrid gradient update rule is as follows:

[0141]

[0142] in, : The time-varying mixing coefficient that varies with the number of training rounds t, where τ is the decay constant.

[0143] : Indicates the gradient update direction of the quantum network. This gradient comes from the parameter optimization results of the quantum circuit (such as the variable quantum circuit VQC or the quantum domain adaptation module) during training. It is mainly responsible for global search, helping the model quickly capture nonlinear features and complex correlations in the drainage outlet monitoring data in the early stages of training.

[0144] : This represents the gradient update direction of the classical neural network (ConvGRU). This gradient is obtained based on traditional backpropagation and is adept at fine-tuning local patterns and detailed features. In the later stages of training, the classical gradient gradually dominates and is used to refine the results of drainage system anomaly detection and flood prediction.

[0145] : Represents the combined gradient update direction of the quantum-classical joint gradient. It dynamically weights the quantum and classical gradients using a time-varying mixing coefficient α. In the early stages of training, α≈1, the quantum gradient dominates, achieving rapid global exploration; in the later stages of training, α→0, the classical gradient gradually gains dominance, leading to local convergence.

[0146] This strategy allows the quantum network to dominate large-scale exploration in the early stages of training, while the classical network is finely adjusted in the later stages to achieve better generalization results.

[0147] 2. Cross-modal spatiotemporal attention mechanism module:

[0148] To address the issues of inconsistent spatiotemporal distribution and different semantic levels between remote sensing imagery and ground-based sensor data, a "quantum key-value query cross-modal attention mechanism" is proposed.

[0149] (1) Calculation of quantum state similarity:

[0150] Constructing quantum feature maps Different modal features After mapping to quantum states, similarity is measured using quantum inner product:

[0151]

[0152] Compared to classical cosine similarity, this method can more effectively capture nonlinear coherence between modes.

[0153] (2) Spatiotemporal Constraint Attention Mask:

[0154] Introduce a differentiable masking mechanism based on spatial adjacency and temporal dependency:

[0155]

[0156] in, The spatiotemporal coordinates of features p and q are represented, including location and timestamp; p and q represent feature vector indices from different sources or different time slices. Indicates the locality adjustment coefficient; This represents Hadamard (elemental) multiplication.

[0157] This mechanism enables the model to focus on spatially proximate and temporally relevant cross-modal associations, improving the contextual consistency of feature fusion.

[0158] 3. Lightweight Inference Engine:

[0159] To achieve rapid response and efficient deployment on edge devices, a lightweight inference engine based on importance sampling and dynamic parameter freezing mechanisms is built.

[0160] Importance-driven parameter activation function:

[0161] Based on the weight magnitudes of the quantum generation parameters, the activation probabilities of the parameters are obtained through a sigmoid mapping:

[0162]

[0163] in, This represents the activation threshold adjustment coefficient; This represents the Sigmoid function, used to normalize the activation strength of parameters; This represents the weight matrix of the compressed neural network generated by the variational quantum circuit (VQC). These weights are not directly derived from traditional gradient descent optimization, but are calculated through parameterized rotation gates (such as RZ, RX, CZ, etc.) of the quantum circuit, thus possessing high-dimensional compression capabilities.

[0164] Unlike traditional weights, By leveraging the properties of quantum superposition and entanglement, more complex dynamic monitoring feature patterns of drainage outlets can be represented with lower energy consumption. : Indicates the magnitude of the quantum weight, used to measure the importance of a parameter to the final model prediction result.

[0165] Only the reasoning phase is activated The core parameters are measured to accelerate inference speed by 3.7 times while maintaining prediction accuracy.

[0166] The computational processing layer introduces a quantum parameter generation mechanism and a quantum enhanced feature extraction network. It generates compressed neural network weights through variable quantum circuits (VQC) and introduces a dynamic gradient control strategy. In the early stage of training, quantum computing dominates the search of the structure space, while in the later stage of training, classical models finely adjust the parameters, achieving a dual improvement in modeling accuracy and computational efficiency.

[0167] III. Decision Support Layer:

[0168] Combination Figure 1 The system architecture of the core functional layer and the decision support layer are designed around three key sub-modules: Figure 4The system comprises an open environment perception anomaly detection engine 41 (OWG-DS module), a quantum-driven hydrological prediction module 42 (QPA optimized), and an extended reality collaborative decision-making platform 43 (RoutScape-XR). In this embodiment, the quantum computing graph structure is combined with urban water environment modeling, achieving significant technological breakthroughs in uncertainty expression, dynamic evolution prediction, and interactive collaboration.

[0169] 1. Open Environment Awareness and Anomaly Detection Engine:

[0170] By introducing quantum domain adaptive mapping and adversarial artifact learning mechanisms, an intelligent recognition model that can adapt to complex interference factors (such as occlusion, abnormal water injection, and illegal discharge) in open environments is constructed.

[0171] (1) Constructor of invariant features in quantum fields:

[0172] The quantum mapping transformation between the source and target domains is constructed using the Hilbert spatial projection mechanism:

[0173]

[0174] in, The quantum state density matrix representing the source domain data; This represents the domain-adaptive unitary transform operator (composed of 15 sets of tunable parameter quantum gates). In quantum mechanics, the symbol... This represents the Hermitian conjugate of the operator. Therefore, Representation domain adaptive unitary transform operator The conjugate transpose of . Its function is to ensure the reversibility and physical realizability of quantum state transformations, so that the mapping process satisfies the canonical requirements of unitary operations. In other words, It is used to ensure that the uniform mapping between the source domain quantum state and the target domain quantum state does not violate the normalization and positive definiteness of the state.

[0175] This mechanism enables consistent reconstruction of observation data characteristics under different environments, effectively suppressing distribution shifts caused by external disturbances.

[0176] (2) Adversarial artifact recognition mechanism:

[0177] By introducing a quantum generative adversarial structure, a discriminative boundary for anomalous states is constructed in the quantum state space:

[0178]

[0179] in, The quantum state representation of the sample to be tested; This represents the abnormal state observation operator obtained from training; This indicates a trainable abnormal sensitivity factor.

[0180] in, This represents the trace operation, which is the summation of the elements along the main diagonal of a matrix. Here, This represents the calculation of the quantum state density matrix of the input sample. In anomaly observation operators The expected value is calculated. The result can be understood as the probability score of an input sample belonging to an abnormal state, used to construct the discriminant boundary for adversarial artifact detection. (Symbol) Represents the quantum state basis vector, used to construct the anomalous observation operator. .

[0181] In the formula In, each They are all projection operators, and This corresponds to the abnormal sensitivity factor. By superimposing multiple weighted projection operators, the system can form a detection boundary for abnormal features in the quantum state space, thereby effectively distinguishing between normal and abnormal drainage outlet states.

[0182] This mechanism can accurately identify artifacts such as irregular obstructions and water disturbances in drainage outlet monitoring images. For anomaly identification in open environments, this system constructs an anomaly detection engine that combines quantum domain projection with adversarial artifact detection. This engine maps observation data from different scenarios to a unified quantum state space by constructing a domain-adaptive unitary transformation matrix, achieving consistent feature alignment under varying natural conditions. Simultaneously, a quantum generative adversarial network (QGAN) is introduced to construct anomaly sample boundaries, demonstrating strong identification capabilities for complex interferences such as drainage outlet obstruction, illegal discharge, and equipment malfunctions.

[0183] 2. Quantum-driven hydrological prediction module:

[0184] The hydrological evolution modeling of urban drainage systems is transformed into a programmable quantum Hamiltonian evolution problem, breaking through the limitations of traditional methods in modeling complex interactions.

[0185] (1) Quantum modeling of water system structure: The drainage relationship between nodes is represented as a quantum interaction Hamiltonian:

[0186]

[0187] Where i represents the number of a node in the drainage system (such as a drain outlet, manhole, or pipe junction). j represents the number of another node that is directly connected to node i. N represents the total number of nodes in the drainage network.

[0188] Indicates the strength of the linkage between nodes; This indicates the external rainfall disturbance experienced by the node; The weight value represents the connection relationship of the drainage pipe network; this Hamiltonian describes the energy transfer process between "point-line-surface" in the urban flood system.

[0189] : Represents the Pauli-X operator acting on the i-th node of the drainage network, also known as the "flip operator". It corresponds to the lateral perturbation of the node state in the quantum state space, which can be intuitively compared to the dynamic response of the node under external rainfall input.

[0190] : Represents the Pauli-Z operator acting on the i-th and j-th nodes, also known as the "phase operator". It characterizes the longitudinal stability of the nodes in quantum state space and is used to describe the connectivity and hydraulic coupling of interactions between nodes. When there is a pipe connection between two nodes, The term reflects this structural interaction.

[0191] : These are the vertical operators corresponding to nodes i and j, used to model the interactions between nodes in the pipe network structure; these operators together constitute the Hamiltonian for the quantum modeling of the drainage system, enabling the topology and dynamic driving factors of the pipe network to be characterized within a unified quantum framework.

[0192] The quantum-driven hydrological prediction module constructs a quantum dynamic prediction model based on Hamiltonian evolution, which can quantize the nonlinear hydrodynamic processes in urban drainage systems and perform real-time deduction through a differential equation solver, thereby further reducing the modeling response time while maintaining prediction accuracy.

[0193] (2) Dynamic evolution and dissipation modeling: The Lindblad term is introduced to realize non-conservative evolution, which is used to simulate energy loss such as rainwater retention and leakage.

[0194]

[0195] in, The dissipation coefficient represents the degree of openness of the control system; L represents the Lindblad operator, which models uncertain disturbance processes. This prediction mechanism can achieve rapid response to sudden flooding situations. Indicates the system at time... The quantum state vector is the overall state description of the drainage system within the quantum modeling framework.

[0196] Here, the quantum state vector is not simply a physical quantum state, but a mathematical mapping of the operating state of the urban drainage system after quantum encoding.

[0197] Specifically This comprehensively characterizes the joint distribution of physical quantities such as water level, flow rate, and pressure at drainage nodes, as well as external environmental factors such as rainfall intensity, surface runoff, and groundwater infiltration, in quantum state space. In the evolution equation, The changes reflect the dynamic evolution of the system over time:

[0198] : The evolution of Hamiltonian in a closed system is used to simulate hydraulic transmission and node interaction within a drainage network;

[0199] : Corresponds to the dissipation process of open systems, used to simulate the loss effects such as rainwater retention, leakage, and evaporation that occur during drainage.

[0200] 3. Extend the reality-based collaborative decision-making platform:

[0201] To address the visualization needs of collaborative decision-making across multiple departments and terminals, a novel intelligent human-machine interface integrating quantum dot matrix rendering and entangled state synchronization is proposed.

[0202] (1) Holographic quantum dot matrix renderer: Optimizes the energy structure of 3D scene rendering through VQE (variable quantum eigenvalue solver):

[0203]

[0204] in, A rendering Hamiltonian that encodes spatial location, color, and transparency information;

[0205] In the quantum parameter optimization module: This represents a variational quantum state. This quantum state is generated by a variational quantum circuit (VQC). This represents the trainable rotation angle parameter. Its function is to map classical network weights to quantum state space, used for weight compression and parameter generation. Essentially, it is a quantum state representation for machine learning optimization problems.

[0206] In the holographic quantum dot renderer (XR-CDM module):

[0207] Here It is also a variable quantum state, but its specific meaning is different: it is the optimal quantum state generated by VQE (variable quantum eigenvalue solver) and is used to approximate the Hamiltonian. The lowest energy state. It still involves adjustable angles in parametric quantum circuits, but the optimization objective is not weight compression, but rather minimizing the energy required for 3D scene rendering. Essentially, it's a quantum state representation addressing the XR rendering problem.

[0208] This mechanism maintains physical accuracy while supporting real-time rendering of 2 million point clouds per second, and is compatible with AR / XR wearable terminals.

[0209] (2) Entanglement-driven multi-terminal interaction mechanism:

[0210] Control commands from each user terminal are synchronized via a quantum teleportation protocol, enabling cross-platform decision sharing with extremely low latency.

[0211]

[0212] This represents a typical quantum entangled state (Bell state), which is one of the most entangled states consisting of two qubits.

[0213] In the technical solution of this embodiment, It was used to model quantum synchronization mechanisms between multiple terminals. When different users or devices hold two qubits of this entangled state, regardless of their distance, the measurement result of one qubit will immediately be consistent with that of the other. This property ensures the synchronization of decision information across platforms and regions, thereby enabling low-latency interaction among multiple users in an XR collaborative environment.

[0214] This mechanism achieves millisecond-level collaborative response, with a measured collaborative latency of less than 8ms, meeting the emergency response requirements for flood control.

[0215] At the decision support level, the system constructs a collaborative decision-making platform based on extended reality (XR) and a quantum dot matrix rendering engine, supporting immersive interactive operations for multiple users. A high-precision point cloud model of the realistic drainage environment is built using a variable quantum quantum solver (VQE), and a quantum entanglement synchronization mechanism is introduced to ensure that control operations by multiple terminal users can complete state sharing within milliseconds, effectively supporting cross-departmental collaborative scheduling and risk assessment. The platform has a built-in strategy simulator that can evolve the flood situation in real time under different scenarios, providing quantitative support and visualization assistance for emergency response.

[0216] Furthermore, considering the vulnerability of urban drainage systems to extreme weather conditions, this system also includes a multi-layered adaptive mechanism, encompassing strategies such as model credibility decay control, anomaly-triggered reconstruction, lightweight module switching, and edge autonomous operation, ensuring the system possesses resilient recovery and local decision-making capabilities in the event of sudden disasters. The system adopts a modular architecture, allowing for flexible configuration based on the scale and complexity of different urban drainage systems. It also supports integration with existing smart water management platforms to achieve data sharing and functional expansion.

[0217] In terms of real-time monitoring, this system integrates collaborative sensing data from space-based remote sensing data acquisition modules, ground sensors, and monitoring videos to construct a cross-modal feature alignment model, enabling accurate perception and dynamic modeling of the drainage outlet's status. Through efficient quantum parameter learning and self-supervised feature extraction technology, the system can reliably identify various anomalies, including drainage outlet blockage, equipment failure, and human sabotage, significantly improving the robustness and adaptability of the monitoring system.

[0218] In flood prediction, this invention employs a quantum-optimized lightweight prediction model, reducing computational energy consumption while maintaining prediction accuracy. The system utilizes spatiotemporal feature fusion technology to establish a correlation model between drainage outlet status and the surrounding hydrological environment, enabling accurate prediction of urban flooding risks. Notably, the system possesses small-sample learning capabilities, effectively addressing prediction needs in areas with insufficient historical data.

[0219] In terms of emergency decision support, the system introduces an extended reality (XR) collaborative platform to provide flood control command departments with an immersive and visualized decision-making environment. Through quantum-accelerated simulation, the system can evaluate the effectiveness of different dispatch strategies in real time, quantify potential risks, and provide a scientific basis for emergency response. The system is particularly enhanced to adapt to extreme weather events and supports cross-regional and multi-departmental collaborative operations.

[0220] In terms of system scalability, this invention adopts a modular design, which can be flexibly configured according to the characteristics of drainage systems in different cities. The system supports integration with existing smart water management platforms, enabling data sharing and functional complementarity.

[0221] This invention integrates cutting-edge technologies such as quantum computing optimization, collaborative sensing, open environment anomaly detection, and XR collaborative decision-making to construct a highly accurate, efficient, and reliable intelligent drainage management system. This system is not only suitable for the operation and maintenance of daily drainage facilities but also provides strong technical support for urban flood control and disaster reduction, possessing broad market application prospects and significant socio-economic benefits.

[0222] For example:

[0223] Taking the combined sewer system of inland city A as an example, there are over 120 main outlets distributed throughout the city, covering both old urban areas and newly developed areas. The discharge of rainwater and sewage is affected by both extreme rainfall events and tidal backwater effects. Traditional monitoring and early warning systems are not timely in their response, posing risks of flooding and overflow pollution. For example... Figure 5 As shown, the implementation process of the system of the present invention in this region is as follows:

[0224] 1. Data acquisition layer (construction of air-ground collaborative sensing network);

[0225] Aerial sensing: The system calls upon high-resolution SAR remote sensing satellite data and UAV low-altitude patrol images to monitor the distribution of surface water accumulation and pipeline overflow points in urban areas in real time;

[0226] Ground-based sensing: Ultrasonic water level gauges, electromagnetic flow meters, and multi-parameter water quality monitoring probes are installed at the main discharge outlets to collect key indicators such as flow rate, liquid level, ammonia nitrogen, COD, and pH.

[0227] Video surveillance: Install 4K video surveillance cameras at key drainage outlets and underpasses, and process them through a video processing module (see...). Figure 6 Automatically identifies whether there is blockage, abnormal overflow, or solid waste obstruction at the discharge outlet;

[0228] In the drainage outlet dynamic management system of this invention, the video processing module serves as the core sensing unit, primarily used for structured analysis and abnormal target detection of the monitoring video stream. Its innovation lies in combining quantum parameter optimization mechanisms with adaptive video feature modeling, overcoming the problem of insufficient robustness of traditional visual detection in complex environments such as low light and heavy rain.

[0229] like Figure 6 As shown, the video stream is first input into the ResNet backbone network, which outputs feature maps of different scales at different stages. The feature maps in stage C1 have high resolution but relatively little semantic information; the features extracted from C2 can provide more suitable input for subsequent modules, enabling the generated directional candidate boxes to accurately locate targets and distinguish different targets based on sufficient semantic information. Spatial features at different scales (C2, C3, C4, C5) are obtained through multi-layer convolutional feature extraction. Subsequently, these features are integrated into a spatial multi-scale feature representation to characterize the dynamic changes and subtle texture information of the water body around the drainage outlet.

[0230] In the feature modeling stage, the system introduces a memory module and a deformable encoder. The memory module stores temporal features of typical drainage outlet states (such as unobstructed, blocked, illegal discharge, etc.) for rapid comparison and retrieval; the deformable encoder achieves accurate modeling of occluded and non-rigid targets in complex scenes through dynamic convolutional kernel adjustment. Furthermore, an adaptive oriented candidate box optimization (OPR) mechanism progressively filters candidate regions to ensure that the bounding boxes of the detection results highly match the real drainage outlet targets.

[0231] In the decoding and prediction stages, this system employs a deformable decoder and prediction head structure. The prediction head not only outputs the conventional category and rotated bounding box, but also adds a "no-target-object" detection branch. This branch, combined with an anomaly discrimination threshold generated through quantum parameter optimization, can effectively identify invalid regions or artifact targets in extreme environments, avoiding false alarms and missed detections. Its discrimination function is:

[0232]

[0233] in, This represents the quantum state representation corresponding to a video frame. The abnormal sensitivity factors obtained during training provide reliable video perception input for subsequent dynamic modeling of drainage outlets and emergency decision support.

[0234] Mobile sensing: Municipal emergency response staff upload on-site photos and notes in real time via a mobile app, forming a complementary data collection mechanism of "human + AI".

[0235] 2. Data processing and feature modeling;

[0236] The system standardizes, synchronizes, and encodes quantum amplitude compression for multi-source data, constructs a heterogeneous modal correlation model, and realizes joint modeling of spatial hydrodynamic parameters, video image features, and water quality indicators.

[0237] During the data fusion process, a source-domain mutual guidance correlation enhancement mechanism is adopted to improve the anomaly detection capability in weak signal scenarios, ensuring that the status of the discharge outlet can still be accurately identified in the rainy night environment.

[0238] Flood Quantum Prediction Module (see Figure 7 This invention introduces a quantum parameter optimization mechanism and a variable quantum circuit (VQC) neural network into the flood prediction process, and constructs a flood quantum prediction model with efficient modeling and low energy consumption.

[0239] like Figure 7 As shown, the overall model structure includes a quantum neural network layer, a mapping modeling layer, a parameter generation layer, and a prediction and inference layer:

[0240] The quantum neural network layer constructs a trainable quantum state evolution circuit through multiple Ry rotation gates, encoding the input flood history data and environmental variables (such as rainfall, water level, and flow velocity) into quantum states.

[0241] Each qubit corresponds to a one-dimensional feature of the input data. The superposition and entanglement of quantum states enable the model to efficiently capture the nonlinear flood evolution law in high-dimensional space.

[0242] After multiple iterations (depth L), the output quantum measurement results are used as a potential context vector, containing the spatiotemporal dependency information of the drainage system.

[0243] The mapping modeling layer (MLP fusion) inputs the state vector measured by the quantum neural network into the multilayer perceptron (MLP) to complete the mapping between quantum features and classical parameters.

[0244] This process outputs a set of high-dimensional block parameters for subsequent flood dynamics prediction.

[0245] By leveraging the compressed representation of quantum states, the number of parameters can be significantly reduced, resulting in lower memory usage and computational overhead compared to traditional LSTM / ConvGRU.

[0246] (1) Parameter generation layer (LoRA efficient learning);

[0247] Learnable parameter blocks (parameter blocks 1, 2, ..., n) are dynamically generated using the low-rank adaptation (LoRA) mechanism.

[0248] This layer ensures efficient parameter updates, enabling the model to maintain robust generalization ability even with small sample sizes.

[0249] At the same time, quantum parameter optimization (QPA) is introduced to automatically adjust the contribution ratio of quantum and classical parameters during training, so as to achieve the best balance between prediction accuracy and energy consumption.

[0250] (2) Prediction and extrapolation layer (flood quantum prediction model);

[0251] The generated parameters are input into the flood quantum prediction model, and the flood evolution result at time i+1 is derived by combining the input state at time i.

[0252] The model supports multi-time step prediction and can output future water level curves, flood spread range, and drainage outlet overflow risk levels.

[0253] The collected water level, flow rate and rainfall forecast data are input into the flood quantum prediction model, and the prediction parameters are generated by the variable quantum circuit (VQC) that combines quantum neural network (QNN) and MLP.

[0254] The LoRA parameter efficient learning method compresses training results and improves model computation efficiency.

[0255] The system predicts that the water level at some discharge outlets may exceed the warning threshold within the next 1-3 hours and generates a dynamic risk distribution map.

[0256] 3. Decision support and visual interaction;

[0257] Data and forecast results are displayed on the discharge outlet supervision platform (see...). Figure 5 The data is displayed in the form of a visual dashboard, including real-time outlet status, rainwater inflow, weather conditions, and regional alarm distribution;

[0258] The system also pushes early warning information to a mobile app, allowing on-duty personnel to obtain information on abnormal events and overflow risks at the discharge outlet as soon as possible, and to issue work orders for handling.

[0259] With the help of the XR decision-making system, emergency management personnel can view a holographic model of the city's pipe network in an immersive virtual environment and dispatch emergency drainage pumping stations and traffic control measures in real time.

[0260] 4. Anomaly detection and early warning response;

[0261] When a discharge outlet experiences a rapid rise in water level for 15 consecutive minutes and video surveillance detects solid waste obstructing the view, the system triggers a red alert.

[0262] The abnormal information was simultaneously pushed to the platform, mobile phone and XR system, and the municipal department immediately dispatched emergency personnel to the scene for handling;

[0263] At the same time, the system records the event and feeds it back to the quantum parameter optimization engine to update the model weights and achieve adaptive learning.

[0264] 5. The system's superiority is evident;

[0265] Through quantum parameter optimization, the system can still maintain sub-second response even when monitoring 200+ outlets simultaneously; air-ground collaborative sensing effectively avoids blind spots caused by the failure of a single data source.

[0266] Heterogeneous modal correlation modeling and ConvGRU-QNN time series prediction improve the prediction accuracy in extreme rainstorm scenarios, with a significant improvement in measured accuracy compared to traditional LSTM.

[0267] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0268] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing, characterized in that, include: The data sensing layer is used to collect multi-source heterogeneous data on the drainage outlet and its surrounding environment through airborne remote sensing, ground sensors and video surveillance equipment. A computational processing layer is used for quantum state encoding and cross-modal fusion of the multi-source heterogeneous data; The decision support layer identifies anomalies based on cross-modal fusion data and performs flood prediction and simulation based on quantum optimization algorithms. Finally, through a collaborative decision-making platform combining extended reality and quantum dot matrix rendering engines, it supports multi-user immersive interactive operation.

2. The dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing as described in claim 1, characterized in that, The data perception layer includes: The airborne remote sensing acquisition module acquires dynamic monitoring remote sensing data of surface water bodies around the drainage outlet based on synthetic aperture radar, multispectral satellites, or sensing devices carried by UAVs, and transmits the data encrypted through a secure channel of quantum key distribution protocol. The ground acquisition module collects real-time data on water level and flow velocity at the drainage outlet by deploying ultrasonic water level sensors and electromagnetic flow meters. The video surveillance acquisition module acquires monitoring video data of the drainage outlet area through a 4K camera encoded with H.265, and uses a background difference algorithm to identify dynamic changes; The collaborative preprocessing module is used to standardize the acquired heterogeneous data through spatiotemporal registration and quantum state encoding compression mechanisms.

3. The dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing according to claim 2, characterized in that, The data perception layer also includes: The credibility assessment module is used to dynamically adjust the data fusion strategy based on data timeliness and source weight; the formula is as follows: ； in, This represents the fusion weight of the m-th channel; Spaceborne remote sensing acquisition module: 0.4, Ground acquisition module: 0.4, Video surveillance acquisition module: 0.2; M represents 3 channels; This represents the confidence level value of the corresponding information source, with a value range of [value missing]. ; This represents the data time lag; k represents the time decay adjustment parameter.

4. The dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing according to claim 2, characterized in that, The quantum state encoding compression mechanism in the collaborative preprocessing module introduces parameterized quantum circuits to construct a joint encoded representation of the input data, with the following embedding mapping: ； in: These represent the quantum encoded states of the orbital images and ground measurements, respectively; This represents a parameterized quantum gate sequence used to construct jointly entangled states.

5. A dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing as described in claim 2, characterized in that, The computing processing layer includes: The quantum-classical hybrid computing module generates compressed neural network weights through variable quantum circuits and uses a dynamic gradient modulator for training and optimization. The cross-modal spatiotemporal attention mechanism module is configured to achieve feature fusion of remote sensing data and ground data through quantum state similarity calculation and spatiotemporal constrained attention mask.

6. The dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing according to claim 5, characterized in that, In the cross-modal spatiotemporal attention mechanism module, quantum state similarity calculation is performed as follows: ； This represents the construction of a quantum feature map, which maps features of different modes. After mapping to quantum states, similarity is measured using quantum inner product. ; In the cross-modal spatiotemporal attention mechanism module, the spatiotemporal constrained attention mask is: Introduce a differentiable masking mechanism based on spatial adjacency and temporal dependency: ； in, The spatiotemporal coordinates of features p and q are represented, including location and timestamp; p and q represent feature vector indices from different sources or different time slices. Indicates the locality adjustment coefficient; This indicates element-wise multiplication in Hadamard.

7. A dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing as described in claim 1, characterized in that, The decision support layer includes: An open environment perception anomaly detection engine uses quantum domain adaptive mapping and anti-artifact detection mechanisms to identify abnormal events at drainage outlets; The quantum-driven hydrological prediction module transforms the hydrological evolution modeling of urban drainage systems into a programmable quantum Hamiltonian evolution model, and realizes the prediction of sudden water accumulation by solving quantum differential equations. The extended reality collaborative decision-making platform is configured to generate 3D visualization scenes through a quantum dot matrix rendering engine and achieve low-latency interaction across multiple terminals using a quantum entanglement synchronization mechanism.

8. A dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing as described in claim 7, characterized in that, In the open environment sensing anomaly detection engine, quantum domain adaptive mapping is employed: a Hilbert spatial projection mechanism is used to construct a quantum mapping transformation between the source and target domains, achieving consistent reconstruction of observation data characteristics under different environments. ； in: The quantum state density matrix representing the source domain data; Representation domain adaptive unitary transform operator; Representation domain adaptive unitary transform operator The conjugate transpose of; In the open environment perception anomaly discrimination engine, the adversarial artifact detection mechanism introduces a quantum generative adversarial structure to construct anomaly state discrimination boundaries in the quantum state space, enabling high-precision identification of irregular occlusions and water disturbance artifacts in drainage outlet monitoring images. ； in, The quantum state representation of the sample to be tested; This represents the abnormal state observation operator obtained from training; This indicates a trainable abnormal sensitivity factor; Represents trace operation; This represents the quantum state basis vector.

9. A dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing as described in claim 7, characterized in that, The quantum-driven hydrological prediction module is specifically used for: (1) Quantum modeling of water system structure: The drainage relationship between nodes can be represented as a quantum interaction Hamiltonian: ； in, Indicates the strength of the linkage between nodes; This indicates the external rainfall disturbance experienced by the node; Weight values ​​representing the connection relationships of the drainage pipe network; : Represents the Pauli-X operator acting on the i-th node of the drainage network; : Represents the Pauli-Z operator acting on the i-th and j-th nodes; (2) Dynamic evolution and dissipation modeling: Introducing the Lindblad term to achieve non-conservative evolution, used to simulate energy losses such as rainwater retention and seepage: ； in, The dissipation coefficient represents the degree of openness of the control system; L represents the Lindblad operator, used to model uncertain disturbance processes. This represents the quantum state vector of the system at time t; the prediction of sudden water accumulation is achieved by solving the quantum differential equation.

10. A dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing according to claim 7, characterized in that, The extended reality collaborative decision-making platform is specifically used for: (1) Extend the reality collaborative decision-making platform and configure it to generate a 3D visualization scene through the quantum dot matrix rendering engine: ； in, A rendering Hamiltonian that encodes spatial location, color, and transparency information; (2) Utilize quantum entanglement synchronization mechanism to achieve low-latency interaction among multiple terminals, enabling cross-platform decision sharing with extremely low latency: ； in, This represents a typical quantum entangled state.

Images