A quantum parameter optimization and air-ground collaborative perception dynamic management system for a drainage outlet

Abstract

The application discloses a kind of quantum parameter optimization and air-ground collaborative perception's drainage port dynamic management system, it is related to intelligent city drainage system technical field, the system includes: data perception layer, for through air-based remote sensing, ground sensor and video monitoring equipment acquisition drainage port and its surrounding environment Multisource Heterogeneous Data;Computational processing layer is used to the multisource Heterogeneous Data is quantum state encoding and cross-modal fusion;Decision support layer is based on the data after cross-modal fusion carries out abnormal identification, and based on quantum optimization algorithm carries out flood prediction and deduction, finally through the collaborative decision-making platform of extended reality platform and quantum dot array rendering engine, support multi-user immersive interactive operation.The system not only can be widely applied to urban drainage pipe network, flood control and drainage facilities daily operation and maintenance, more have the actual combat capability of coping with extreme weather and sudden disaster, can effectively support the depth development of intelligent water system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart city drainage systems, and more particularly to a quantum parameter optimization and air-ground collaborative perception-based dynamic management system for drainage outlets. BACKGROUND

[0002] The existing drainage outlet monitoring and flood warning systems still have significant technical defects in data perception, model prediction, collaborative decision-making, etc., and are difficult to cope with complex and variable urban hydrological environments:

[0003] ①In terms of abnormal detection of drainage outlets, traditional monitoring methods based on computer vision mainly rely on fixed rules or convolutional neural networks, and have limited ability to identify abnormal situations such as drainage outlet blockage, illegal discharge, and equipment damage. The detection performance of these methods under adverse weather conditions (such as heavy rain, night) decreases significantly, and it is difficult to distinguish different abnormal types such as natural accumulation and human damage. In addition, for water level, flow rate and other sensor data, existing time series analysis methods lack the ability to deeply mine potential abnormal patterns, resulting in a high false alarm rate.

[0004] ②In terms of flood prediction modeling, the numerical simulation methods (such as SWMM) used by existing systems have high computational complexity, while the prediction models based on deep learning (such as LSTM) face the problems of large parameter quantity and high energy consumption in training. These methods have low utilization efficiency of historical disaster data, and are difficult to capture the unique nonlinear characteristics of urban drainage systems, especially in data-scarce areas, the prediction accuracy is obviously insufficient. In addition, traditional models lack the ability to quantify uncertainty, and cannot provide reliable risk assessment for decision-making.

[0005] ③In terms of collaborative perception, existing systems handle satellite remote sensing, ground sensors, weather forecasts, and other data separately, lacking effective feature alignment mechanisms. This fragmented processing approach prevents the system from establishing a correlation model between drainage outlet status and surrounding environment (such as surface runoff, soil moisture), severely affecting the accuracy of flood risk assessment. Existing data fusion techniques also fail to adapt to data differences at different temporal and spatial scales, resulting in low information collaboration efficiency.

[0006] ④In terms of emergency decision support, current systems mainly rely on two-dimensional GIS display and static plans, with low visualization and poor interactivity. There is a lack of efficient collaboration mechanisms between departments, making it difficult for decision-makers to obtain intuitive flood evolution simulation results in a timely manner, and to assess the expected effects of different drainage outlet control schemes. Traditional simulation methods (such as Monte Carlo) have high computational overhead 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 air-based remote sensing acquisition module, based on a synthetic aperture radar, a multispectral satellite, or a sensing device carried by a drone, acquires dynamic monitoring remote sensing data of a surface water body around a drainage outlet and transmits the data through a secure channel of a quantum key distribution protocol for encryption;

[0017] The ground acquisition module acquires real-time drainage outlet water level and flow rate data by deploying ultrasonic water level sensors and electromagnetic flowmeters;

[0018] The video monitoring acquisition module acquires drainage outlet area monitoring video data through a 4K camera with H.265 encoding and identifies dynamic changes using a background difference algorithm;

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

[0020] In one embodiment, the data perception layer further comprises:

[0021] The credibility evaluation module is configured to dynamically adjust a data fusion strategy according to data timeliness and source weight. The formula is as follows:

[0022]

[0023] wherein, the fusion weight of the mth channel is 0.4 for the air-based remote sensing acquisition module, 0.4 for the ground acquisition module, and 0.2 for the video monitoring acquisition module; and M represents 3 channels. represents the confidence value of the corresponding source, and the value range is ; represents the data time lag; and k represents a time decay adjustment parameter.

[0024] In one embodiment, the quantum state encoding compression mechanism in the collaborative preprocessing module introduces a parameterized quantum circuit to construct a joint encoding representation of the input data, and the embedding mapping is as follows:

[0025]

[0026] wherein, respectively represent the quantum encoding state of the orbital image and the ground measurement; represents a parameterized quantum gate sequence used to construct a joint feature entangled state.

[0027] In one embodiment, the computing processing layer comprises:

[0028] The quantum-classical hybrid computing module generates compressed neural network weights through a variational quantum circuit and performs training optimization using a dynamic gradient modulator.

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

[0030] In one embodiment, in the cross-modal spatio-temporal attention mechanism module, the quantum state similarity calculation comprises:

[0031]

[0032] The quantum state similarity calculation represents constructing a quantum feature mapping, mapping different modal features into quantum states, and measuring similarity through quantum inner product .

[0033] In the cross-modal spatio-temporal attention mechanism module, the spatio-temporal constraint attention mask is:

[0034] A differentiable mask mechanism based on spatial adjacency and temporal dependence is introduced:

[0035]

[0036] wherein, the spatio-temporal coordinates of the features p and q include position and timestamp; p and q represent feature vector indexes of different sources or different time slices; represents a locality adjustment coefficient; represents Hadamard element-wise multiplication.

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

[0038] The open environment perception anomaly discrimination engine adopts a quantum domain adaptive mapping and an anti-artifact detection mechanism to identify drainage outlet abnormal events;

[0039] The quantum-driven hydrological prediction module converts the hydrological evolution modeling of the urban drainage system into a programmable quantum Hamiltonian evolution model, and realizes sudden waterlogging situation prediction by solving quantum differential equations;

[0040] The extended reality collaborative decision-making platform is configured to generate a three-dimensional visual scene through a quantum dot array rendering engine, and realize multi-terminal low-latency interaction using a quantum entanglement synchronization mechanism.

[0041] In one embodiment, in the open environment perception anomaly discrimination engine, the quantum domain adaptive mapping comprises: adopting a Hilbert space projection mechanism to construct a quantum mapping transformation between the source and target domains, to realize consistent reconstruction of the features of observation data under different environments:

[0042]

[0043] wherein: a quantum state density matrix representing source domain data; a domain adaptive unitary transformation operator; a domain adaptive unitary transformation operator a conjugate transpose of the domain adaptive unitary transformation operator;

[0044] In the open environment anomaly discrimination engine, the anti-artifact detection mechanism constructs a discrimination boundary of an abnormal state in a quantum state space by introducing a quantum generative adversarial structure, and can perform high-precision identification on irregular occlusion and water disturbance artifacts in a drainage outlet monitoring picture:

[0045]

[0046] wherein, a quantum state representation of a sample to be detected; an abnormal state observation operator trained; a trainable abnormality sensitive factor; a trace operation; a quantum state basis vector.

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

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

[0049] The drainage relationship between nodes is represented as a quantum interaction Hamiltonian:

[0050]

[0051] wherein, an inter-node linkage strength; an external rainfall disturbance suffered by the node; a weight value of the drainage network connection relationship; : a Pauli-X operator acting on the i-th node of the drainage network; : a Pauli-Z operator acting on the i-th and j-th nodes;

[0052] (2) Dynamic evolution and dissipation modeling: introducing Lindblad terms to realize non-conservative evolution, for simulating energy loss such as rainwater retention and leakage:

[0053]

[0054] wherein, a dissipation coefficient, controlling the openness of the system; L represents a Lindblad operator, modeling an uncertain interference process; represents the quantum state vector of the system at time t; the bursty waterlogging state prediction is realized by solving quantum differential equations.

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

[0056] (1) The extended reality collaborative decision-making platform is configured to generate a three-dimensional visual scene through a quantum dot array rendering engine:

[0057]

[0058] wherein, represents a rendering Hamiltonian coded with spatial position, color, and transparency information;

[0059] (2) A quantum entanglement synchronization mechanism is used to realize multi-terminal low-latency interaction, and extremely low-latency cross-platform decision sharing is realized:

[0060]

[0061] wherein, represents a typical quantum entangled state.

[0062] According to the technical solutions described above, compared with the prior art, the present application has the following technical advantages:

[0063] The quantum parameter optimization and air-ground collaborative perception drainage outlet dynamic management system provided by the present application comprehensively improves the perception accuracy, inference efficiency, and response capability of the drainage outlet management system by fusing quantum parameter optimization, air-ground data collaboration, dynamic inference mechanism, and an immersive collaborative platform, and has significant practicality and promotional value. The system not only can be widely applied to the daily operation and maintenance of urban drainage networks and flood control and drainage facilities, but also has the ability to deal with extreme weather and sudden disasters, and can effectively support the deep development of the intelligent water management system. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.

[0065] Figure 1 The quantum parameter optimization and air-ground collaborative perception drainage outlet dynamic management system provided by the present application is shown in the structure diagram.

[0066] Figure 2 The structure diagram of the data perception layer provided by the present application is shown in the structure diagram.

[0067] Figure 3 A structural schematic diagram of a computing processing layer provided by the present application.

[0068] Figure 4 A structural schematic diagram of a decision support layer provided by the present application.

[0069] Figure 5 A structural schematic diagram of an embodiment system provided by the present application.

[0070] Figure 6 A structural schematic diagram of a video processing module provided by the present application.

[0071] Figure 7 A schematic diagram of a flood quantum prediction model provided by the present application. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0073] The present application provides a quantum parameter optimization and air-ground collaborative perception dynamic management system for drainage outlets, which is widely applied to intelligent management of water infrastructure such as urban drainage systems, flood control and drainage facilities, and rain and sewage diversion pipe networks.

[0074] Referring to Figure 1 The embodiment of the present application discloses a quantum parameter optimization and air-ground collaborative perception dynamic management system for drainage outlets, which comprises:

[0075] A data perception layer is configured to collect multi-source heterogeneous data of drainage outlets and their surrounding environment through air-based remote sensing, ground sensors and video monitoring equipment.

[0076] A computing processing layer is configured to perform quantum state encoding and cross-modal fusion on the multi-source heterogeneous data.

[0077] A decision support layer is configured to perform anomaly recognition based on the cross-modal fused data, and perform flood prediction and deduction based on a quantum optimization algorithm. Finally, through a collaborative decision-making platform of an extended reality platform and a quantum dot array rendering engine, the decision support layer 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 above-mentioned various modules balance precision, density and channel complementarity, and reflect the key innovation of the system architecture in the data acquisition and management level.

[0090] 1. Three-dimensional perception network construction (air-ground-vision collaborative perception):

[0091] (1) Airborne remote sensing acquisition module:

[0092] For example, Sentinel-1 synthetic aperture radar imaging data (C band, spatial resolution 5 m) and Sentinel-2 multispectral satellite images (covering 10 spectral dimensions, resolution 10-60 m) are selected as the main airborne input source to realize dynamic monitoring of the surrounding surface water body of the drainage outlet. Two types of remote sensing satellites can realize at least twice a day revisit, effectively improving the monitoring frequency. In addition, unmanned aerial vehicles carrying sensing devices can also be used to obtain remote sensing data.

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

[0094] In addition, the quality evaluation index can be formed by weighting the physical feature intensity and spatial geometric accuracy:

[0095]

[0096] wherein, represents the weight of the bth band (SAR: 0.6, visible light: 0.3, infrared: 0.1); represents the signal-to-noise ratio of the corresponding band; represents the image geometric error (unit: meters); B represents the number of all bands, which is 3 in this case.

[0097] (2) Ground acquisition module:

[0098] For example, high-frequency ultrasonic water level sensors are placed at key drainage nodes, with an accuracy of ±1 mm, using RS485 interface, electromagnetic flowmeters with an error controlled within 0.5%, standard 4-20mA output, ensuring near real-time response. These sensors perform data acquisition every 10 seconds, and the data is transmitted wirelessly to the edge computing gateway through the LoRa WAN protocol to adapt to the complex communication environment of urban underground pipe networks and achieve low power consumption and long distance transmission.

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

[0100]

[0101] wherein, represents the total number of effective working sensors; represents the single measurement duration; represents the ground coverage area (unit: km²); represents the time sampling period (unit: min).

[0102] (3) Video monitoring acquisition module:

[0103] For example, a 4K camera based on H.265 coding is deployed, with a frame rate of 30 fps and a low-illumination sensing capability of 0.001 Lux, which can work continuously at night or in bad weather. Enhanced background difference algorithm is used to capture the change of the water body of the outlet, and real-time analysis is performed to automatically identify the moving targets in the outlet area and accurately capture and record suspected abnormal events, thereby providing important visual basis for subsequent intelligent analysis. The dynamic feature extraction calculation is as follows:

[0104]

[0105] wherein, represents the current frame image matrix; represents the background model reference; represents the spatiotemporal gradient of the image; represents the channel adjustment coefficient.

[0106] Finally, through the construction of the above three-dimensional perception network, the system can comprehensively and stably collect multi-modal data covering the space-ground-video dimension, providing solid data support for subsequent efficient data fusion and intelligent scheduling decision-making.

[0107] 2. Collaborative preprocessing module:

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

[0109] (1) Unified spatiotemporal registration mechanism:

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

[0111]

[0112] wherein, represents the rotation matrix; represents the translation vector; represents the original coordinate point, represents the registered coordinate.

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

[0114] The parameterized quantum circuit (PQC) is introduced to construct a joint encoding representation for the input data, and the embedding mapping is as follows:

[0115]

[0116] Wherein: Respectively represent the quantum encoding state of the orbital image and the ground measurement; Indicates a sequence of parameterized quantum gates, which is used to construct a joint feature entangled state.

[0117] The experimental results show that, by using the compression mechanism, the overall data compression rate can be further reduced while maintaining the representation ability, and the subsequent calculation burden is significantly reduced.

[0118] 3. Credibility evaluation module:

[0119] In order to ensure the stability and credibility of the system input data, a fuzzy logic driven source evaluation model is constructed, and the data from different channels is dynamically weighted according to the weight and timeliness, and the formula is as follows:

[0120]

[0121] Wherein, Indicates the fusion weight of the mth channel (orbital image: 0.4, ground sensor: 0.4, visual image: 0.2); M represents 3 channels; Indicates the confidence value (0-1) of the corresponding source; Indicates the data time lag; k represents the time decay adjustment parameter.

[0122] This mechanism ensures that in extreme conditions such as sudden weather and device packet loss, the system can automatically eliminate low-confidence data and improve the overall input quality.

[0123] The present application constructs a set of multi-source data joint perception mechanism in the data perception layer, forms a three-dimensional perception network through space-based remote sensing, ground sensors and video monitoring, and ensures to realize all-weather, multi-scale accurate monitoring of the drainage port state. And through the unified spatio-temporal coordinate mapping and resolution resampling method, the standardized input of heterogeneous data is realized. In order to break through the bottleneck of traditional "fusion" technology, the quantum state mapping method is used to compress and encode the observation data, so that high-dimensional data can complete efficient modeling under low energy consumption conditions, providing lightweight and structured input support for the calculation layer. The data perception layer greatly improves the adaptability of the system to open and dynamic environment, especially in the night, heavy rain and other perception degradation scenes, it can still maintain high perception accuracy.

[0124] II. Calculation processing layer:

[0125] The computing processing layer adopts a leading quantum-classical hybrid computing architecture and a cross-modal dynamic perception mechanism to construct a new intelligent outlet intelligent perception platform core engine with high efficiency, strong generalization and cross-modal understanding capability. Figure 3 As shown in the figure, the entire architecture is divided into three core submodules: quantum-classical hybrid computing module 31, cross-modal spatio-temporal attention mechanism module 32, and lightweight inference engine 33.

[0126] 1. Quantum-classical hybrid computing module:

[0127] To break through the limitations of traditional neural networks in parameter scale, training efficiency and feature representation ability, the present application introduces a quantum parameter adaptive optimization mechanism (QPA, Quantum Parameter Adaptive Optimization). The core idea is to use a variational quantum circuit (VQC) to dynamically generate model parameters, and to realize quantum-classical hybrid optimization through time-varying adjustment coefficients. Unlike traditional gradient descent methods, QPA optimization can perform global exploration in the early stage of training and achieve fine convergence of parameters in the later stage, effectively avoiding falling into local optimum; therefore, a hybrid modeling method combining variational quantum circuit (VQC) and classical neural network is proposed.

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

[0129] A generative network is constructed by a variational quantum circuit to compress and generate the weight parameters in the classical neural network. Its quantum gate sequence structure is defined as:

[0130]

[0131] wherein, represents the depth of the quantum circuit, and N is the total amount of classical network parameters; represents the trainable rotation angle (variational parameter); CZ represents a controlled Z gate used to construct a quantum entangled state.

[0132] : represents a quantum rotation gate around the Z-axis (Rotation around Z-axis). Its function is to modulate the phase of the quantum bit, and the parameter determines the rotation angle. In this embodiment, the RZ gate is used to encode the time correlation in the outlet monitoring data, and the difference expression of different time sequence patterns is realized through phase rotation.

[0133] : represents a quantum rotation gate around the X-axis (Rotation around X-axis). Its function is to change the superposition amplitude of the quantum bit, and the parameter 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] : denotes the gradient update direction of the classical neural network (ConvGRU). This gradient is based on traditional backpropagation and is good at fine modeling of local patterns and detailed features. In the later training stage, the classical part gradient gradually dominates and is used to fine-tune the results of drainage system anomaly detection and flood prediction.

[0145] : denotes the comprehensive gradient update direction of quantum-classical combination. It dynamically weights between quantum and classical gradients through a time-varying mixing coefficient α. In the early training stage, α≈1, the quantum gradient dominates, achieving fast global exploration; in the later training stage, α→0, the classical gradient gradually dominates, achieving local convergence.

[0146] This strategy enables the quantum network to dominate large-scale exploration in the early training stage and the classical network to fine-tune in the later stage, achieving better generalization.

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

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

[0149] (1) Quantum state similarity calculation:

[0150] Construct quantum feature mapping After mapping different modal features into quantum states, the similarity is measured by quantum inner product:

[0151]

[0152] Compared with the classical cosine similarity, this method can more effectively capture the non-linear coherence between cross-modalities.

[0153] (2) Spatio-temporal constraint attention mask:

[0154] A differentiable mask mechanism based on spatial adjacency and temporal dependence is introduced:

[0155]

[0156] where, denotes the spatio-temporal coordinates of features p, q, including position and timestamp; p, q represent feature vector indices of different sources or different time slices; denotes the locality adjustment coefficient; denotes Hadamard (element-level) multiplication.

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

[0158] 3. Lightweight inference engine:

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

[0160] Importance-driven parameter activation function:

[0161] According to the weight amplitude of quantum-generated parameters, the parameter activation probability is obtained through Sigmoid mapping:

[0162]

[0163] where, represents the activation threshold adjustment coefficient; represents the Sigmoid function, which is used to normalize the parameter activation intensity; represents the compressed neural network weight matrix 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 quantum circuits, which have high-dimensional compression expression ability.

[0164] Unlike traditional weights, through the superposition and entanglement characteristics of quantum, more complex drainage outlet dynamic monitoring feature patterns can be represented at lower energy consumption. : represents the amplitude of the quantum weight, which is used to measure the importance of a certain parameter to the final model prediction result.

[0165] In the inference stage, only the core parameters of are activated, and the actual acceleration inference speed is 3.7 times, while the prediction accuracy remains unchanged.

[0166] The calculation processing layer introduces quantum parameter generation mechanism and quantum-enhanced feature extraction network, generates compressed neural network weights through variational quantum circuit (VQC), and introduces dynamic gradient control strategy. In the early stage of training, quantum computation dominates the search of structure space, and in the later stage of training, the parameters are fine-tuned by classical model, realizing the dual improvement of modeling accuracy and computational efficiency.

[0167] III. Decision support layer:

[0168] Combined with the system structure of Figure 1 the core function layer, the decision support layer is designed around three key submodules: respectively Figure 4The open environment perception anomaly discrimination engine 41 (OWG-DS module), the quantum driven hydrological prediction module 42 (QPA optimization), and the extended reality collaborative decision platform 43 (RoutScape-XR) in the figure. In this embodiment, the quantum computing graph structure is combined with urban water environment modeling, and major technological breakthroughs are achieved in uncertainty expression, dynamic evolution prediction, and interactive collaboration.

[0169] 1. Open environment perception anomaly discrimination engine:

[0170] Introduce quantum domain adaptive mapping and adversarial artifact learning mechanism, and construct an intelligent identification model that can adapt to complex interference factors (such as occlusion, abnormal water injection, illegal discharge) in an open environment.

[0171] (1) Quantum domain invariant feature constructor:

[0172] Adopt Hilbert space projection mechanism to construct quantum mapping transformation between source and target domains:

[0173]

[0174] Wherein, represents the quantum state density matrix of the source domain data; represents the domain adaptive unitary transformation operator (composed of 15 groups of adjustable parameter quantum gates). In quantum mechanics, the symbol represents the conjugate transpose (Hermitian conjugate) of the operator. Therefore, represents the conjugate transpose of the domain adaptive unitary transformation operator . Its role is to ensure the reversibility and physical realizability of quantum state transformation, so that the mapping process meets the specification requirements of unitary operation. In other words, is used to ensure that the consistent mapping between the quantum state of the source domain and the quantum state of the target domain will not destroy the normalization and positive definiteness of the state.

[0175] Through this mechanism, the consistency reconstruction of the features of observation data under different environments is realized, and the distribution deviation caused by external condition disturbance is effectively suppressed.

[0176] (2) Adversarial artifact identification mechanism:

[0177] Introduce quantum generative adversarial structure to construct the discrimination boundary of abnormal state in the quantum state space:

[0178]

[0179] Wherein, represents the quantum state representation of the sample to be detected; represents the observation operator of the abnormal state trained; represents the trainable abnormal sensitivity factor.

[0180] where, denotes the trace operation, i.e., the summation of the main diagonal elements of a matrix. Here, denotes the quantum state density matrix of the input sample under the abnormal observation operator . Its result can be understood as the probability score of the input sample belonging to the abnormal state, which is used to construct the discriminant boundary for the artifact detection. The symbol denotes the quantum state basis vector (basis state) used to construct the abnormal observation operator .

[0181] In the formula , each is a projection operator, and is the corresponding abnormal sensitivity factor. By superimposing multiple weighted projection operators, the system can form a detection boundary of abnormal features in the quantum state space, effectively distinguishing between normal and abnormal drainage outlet states.

[0182] This mechanism can accurately identify irregular occlusions, water disturbances, and other artifacts in the drainage outlet monitoring image. For abnormal state recognition in open environments, the system constructs an abnormal detection engine that combines quantum domain projection and 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 feature consistency alignment under natural conditions. Meanwhile, it introduces a quantum generative adversarial network (QGAN) to construct an abnormal sample boundary, which has strong recognition ability for complex interference such as drainage outlet occlusion, illegal discharge, and equipment failure.

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

[0184] The hydrological evolution of the urban drainage system is modeled as 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 (such as a drainage outlet, inspection well, or pipe segment intersection) in the drainage system. j represents the number of another node that has a direct connection with node i. N represents the total number of nodes in the drainage network.

[0188] represents the linkage strength between nodes; represents the external rainfall disturbance on the node; represents the weight value of 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 horizontal disturbance 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 describes the longitudinal stability of the nodes in the quantum state space and is used to describe the connectivity and hydraulic coupling between the nodes. When there is a pipe connection between two nodes, the term reflects this structural interaction.

[0191] : respectively corresponds to the longitudinal operator of i, j nodes, used to model the interaction between nodes under the pipe network structure; these operators together constitute the Hamiltonian of the quantum modeling of the drainage system, so that the topological structure and dynamic driving factors of the pipe network can be represented in a unified quantum framework.

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

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

[0194]

[0195] where, represents the dissipation coefficient, which controls the openness of the system; L represents the Lindblad operator, which models the uncertainty disturbance process. This prediction mechanism can quickly respond to sudden waterlogging situations. represents the quantum state vector of the system at time , which is the overall state description of the drainage system under the quantum modeling framework.

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

[0197] Specifically, The joint distribution of water level, flow rate, pressure, and other physical quantities of drainage nodes, as well as rainfall intensity, surface runoff, and underground leakage, is represented in the quantum state space. The changes in the evolution equation reflect the dynamic evolution of the system over time:

[0198] : Hamiltonian evolution corresponding to a closed system, simulating hydraulic transmission and node interaction within the drainage network;

[0199] : Dissipative process corresponding to an open system, used to simulate the loss effects of rainwater retention, leakage, and evaporation during drainage.

[0200] 3. Extended reality collaborative decision-making platform:

[0201] To meet the visualization needs of multi-department and multi-terminal collaborative decision-making, a new intelligent human-machine interface is proposed, which combines quantum dot array rendering and entangled state synchronization.

[0202] (1) Holographic quantum dot array renderer: optimize the three-dimensional scene rendering energy structure through VQE (Variational Quantum Eigen-Solver):

[0203]

[0204] where represents the rendering Hamiltonian encoded with spatial position, color, and transparency information;

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

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

[0207] Here, is also a variational quantum state, but its specific meaning is different: it is the optimal quantum state generated by VQE (Variational Quantum Eigen-Solver) to approximate the lowest energy state of the rendering Hamiltonian . Still a parameterized quantum circuit with adjustable angles, but the optimization goal is not weight compression, but energy minimization of three-dimensional scene rendering. Essentially, it is a quantum state representation for XR rendering problems. ​

[0208] The mechanism supports real-time rendering of 2 million points per second while maintaining physical precision, suitable for AR / XR wearable terminals.

[0209] (2) Multi-terminal interaction mechanism driven by entanglement:

[0210] User terminal control instructions are synchronized through quantum teleportation protocols, enabling extremely low-latency cross-platform decision sharing:

[0211]

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

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

[0214] The mechanism achieves millisecond-level collaborative response, with actual measured collaborative delay less than 8ms, meeting the needs of flood dispatching emergency response.

[0215] At the decision support level, the system builds a collaborative decision-making platform based on extended reality (XR) and quantum dot array rendering engine, supporting multi-user immersive interaction. Through the variational quantum solver (VQE), a high-precision point cloud model of the real drainage environment is constructed, and the quantum entanglement synchronization mechanism is introduced to ensure that the control operations of multiple terminal users can share states 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 schemes, providing quantitative support and visual assistance for emergency response.

[0216] In addition, considering the vulnerability of urban drainage systems under extreme weather, the system also includes a multi-level adaptive mechanism, including model credibility decay control, abnormal trigger reconstruction, lightweight module switching, and edge autonomous operation strategies, to ensure the system's resilience and local decision-making capabilities in the face of sudden disasters. The system uses a modular architecture that can be flexibly configured according to the scale and complexity of different urban drainage systems, supports integration with existing smart water platforms, and realizes data sharing and function extension.

[0217] In real-time monitoring, the system integrates air-based remote sensing data acquisition module, ground sensors, and monitoring videos for collaborative perception data, constructs a cross-modal feature alignment model, and realizes accurate perception and dynamic modeling of the drainage outlet state. Through quantum parameter efficient learning and self-supervised feature extraction technology, the system can reliably identify various abnormal situations, including drainage outlet blockage, equipment failure, and human damage, greatly improving the robustness and adaptability of the monitoring system.

[0218] In flood prediction, the invention uses a lightweight prediction model optimized by quantum, reducing the energy consumption while maintaining the prediction accuracy. The system establishes a correlation model between the drainage outlet state and the surrounding hydrological environment through spatio-temporal feature fusion technology, realizing accurate prediction of urban waterlogging risk. In particular, the system has small sample learning ability, which can effectively meet the prediction needs of areas with insufficient historical data.

[0219] In emergency decision support, the system introduces an extended reality (XR) collaborative platform to provide an immersive visual decision-making environment for flood control command departments. Through quantum-accelerated simulation and deduction, the system can evaluate the effects of different scheduling strategies in real time, quantify potential risks, and provide scientific basis for emergency response. The system has particularly enhanced adaptability to extreme weather events, supporting cross-regional and multi-department collaboration.

[0220] In system scalability, the invention adopts modular design, which can be flexibly configured according to the characteristics of drainage systems in different cities. The system supports connection with existing smart water platforms, realizing data sharing and function complementation.

[0221] The invention integrates quantum computing optimization, collaborative perception, open environment anomaly detection, and XR collaborative decision-making technologies to build an intelligent drainage management system with high precision, high efficiency, and high reliability. The system not only applies to daily operation and maintenance of drainage facilities, but also provides strong technical support for urban flood control and disaster reduction, with broad market application prospects and significant social and economic benefits.

[0222] For example:

[0223] Taking the rain and sewage combined drainage outlet group in the main urban area of inland city A as an example, there are more than 120 main drainage outlets in the city area, covering old urban areas and newly developed areas. The rain and sewage discharge is affected by extreme rainfall events and tidal jacking effect. The traditional monitoring and early warning system responds not timely, and there is a risk of waterlogging and overflow pollution. Figure 5 As shown in the figure, the implementation process of the system in this area is as follows:

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

[0225] Airborne perception: System calls high-resolution SAR remote sensing satellite data and low-altitude patrol images of unmanned aerial vehicles to monitor urban surface water and pipe network overflow point distribution in real time;

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

[0227] Video monitoring: 4K video monitoring cameras are installed at key discharge outlets and sunken interchanges. The 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 the present application, the video processing module serves as the core perception unit, mainly for structured analysis and abnormal target detection of the monitoring video stream. Its innovation lies in combining quantum parameter optimization mechanism and adaptive video feature modeling, breaking through the problem of insufficient robustness of traditional visual detection in complex environments such as low light and heavy rain.

[0229] As shown in Figure 6 , the video stream is first input into the ResNet backbone network, which outputs feature maps of different scales at different stages. The feature map at stage C1 has high resolution but relatively less semantic information; the features extracted from C2 can provide more suitable input for subsequent modules, enabling the generated directional candidate box to accurately locate the target and distinguish different targets based on sufficient semantic information. Through multi-layer convolution feature extraction, spatial features of different scales are obtained at C2, C3, C4, and C5. Subsequently, these features are integrated into a spatial multi-scale feature representation to depict the dynamic changes and fine 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 is used to store the time series features of typical drainage outlet states (such as unobstructed, blocked, illegal discharge, etc.) for quick comparison and retrieval; the deformable encoder adjusts the dynamic convolution kernel to achieve accurate modeling of occlusion and non-rigid targets in complex scenes. Further, the adaptive directional candidate box optimization (OPR) mechanism gradually filters the candidate regions to ensure that the bounding box of the detection result is highly matched with the real drainage outlet target.

[0231] In the decoding and prediction stage, the system designs a deformable decoder and a prediction head structure. The prediction head not only outputs the conventional class, rotation bounding box, but also adds a "no target object" detection branch. This branch combines the abnormality discrimination threshold generated by the quantum parameter optimization to effectively identify invalid regions or false targets in extreme environments, avoiding false positives and missed detections. Its discrimination function is:

[0232]

[0233] wherein, represents the quantum state representation corresponding to the video frame, is the abnormal sensitivity factor obtained by training. It provides reliable video perception input for subsequent drainage outlet dynamic modeling and emergency decision support.

[0234] Mobile perception: municipal emergency department staff upload real-time on-site pictures and note information through a mobile app, forming a "manual + intelligent" complementary collection mechanism.

[0235] 2. Data processing and feature modeling;

[0236] The system standardizes, time synchronizes and quantum amplitude compresses and encodes 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] In the data fusion process, a source-domain mutual induction correlation enhancement mechanism is used to improve the abnormal detection capability in weak signal scenarios, ensuring that the drainage outlet state can still be accurately identified in a stormy night environment.

[0238] The flood quantum prediction module (see Figure 7 The present application introduces a quantum parameter optimization mechanism and a variational quantum circuit (VQC) neural network in the flood prediction link, and constructs a flood quantum prediction model with efficient modeling and low energy consumption characteristics.

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

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

[0241] Each quantum bit corresponds to a one-dimensional feature of the input data, and the superposition and entanglement of the quantum state 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 result is used as a potential context vector, containing the spatiotemporal dependence information of the drainage system.

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

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

[0245] With the compression representation of quantum states, the number of parameters can be significantly reduced, and the memory usage and computational overhead can be lower than traditional LSTM / ConvGRU implementations.

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

[0247] Through the low-rank adaptation (LoRA) mechanism, dynamically generate learnable parameter blocks (parameter blocks 1, 2, …, n).

[0248] This layer ensures the efficiency of parameter updating, so that the model still has robust generalization ability under small sample conditions.

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

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

[0251] Input the generated parameters into the flood quantum prediction model, combine the input state at the i-th time, and deduce the flood evolution result at the i+1-th time.

[0252] This model supports multi-time step prediction and can output future water level curve, flood diffusion range, and drainage outlet overflow risk level.

[0253] Input the collected water level, flow, and rainfall forecast data into the flood quantum prediction model, and use the variational quantum circuit (VQC) combined with quantum neural network (QNN) and MLP to generate prediction parameters;

[0254] Compress the training results through the LoRA parameter efficient learning method to improve the computational efficiency of the model;

[0255] The system predicts that some drainage outlets may exceed the warning threshold within 1-3 hours in the future, and generates a dynamic risk distribution map.

[0256] 3. Decision support and visual interaction;

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

[0258] The system also pushes warning information to the mobile app, so that on-duty personnel can obtain drainage outlet abnormal events and overflow risk points in the first time and issue disposal work orders;

[0259] With the XR decision system, emergency department managers can view the city pipe network holographic model in an immersive virtual environment, and real-time dispatch emergency drainage pump stations and traffic diversion measures.

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

[0261] When the water level of a certain outlet rises rapidly for 15 minutes in succession and the video monitoring identifies solid waste obstruction, the system triggers a red alert;

[0262] Abnormal information is synchronized and pushed to the platform, mobile phone and XR system, and the municipal department immediately dispatches emergency personnel to the scene for disposal;

[0263] At the same time, the system records the event and feeds it back to the quantum parameter optimization engine for updating the model weight and realizing adaptive learning.

[0264] 5. System superiority;

[0265] Through quantum parameter optimization, the system can still maintain sub-second response under the condition of monitoring 200+ outlets; air-ground collaborative sensing effectively avoids the blind area 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, and the actual measurement accuracy is significantly improved compared with traditional LSTM.

[0267] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0268] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to 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 performs anomaly identification based on cross-modal fusion data and flood prediction and simulation based on quantum optimization algorithms. Ultimately, it supports multi-user immersive interactive operation through a collaborative decision-making platform combining an extended reality platform and a quantum dot matrix rendering engine. 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 between multiple terminals using a quantum entanglement synchronization mechanism. The quantum-driven hydrological prediction module is specifically used for: (1) Quantum modeling of water system structure: The drainage relationship between nodes can be expressed 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 rainwater retention and infiltration energy loss: ； 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.

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 in encrypted form 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 a spatiotemporal registration mechanism and a quantum state encoding compression mechanism.

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, 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 a discrimination boundary for anomalies in the quantum state space, enabling high-precision identification of irregular occlusions and water disturbance artifacts in the drainage outlet monitoring image. ； 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.

8. The dynamic management system for drainage outlets based on quantum parameter optimization and air-to-ground collaborative sensing according to claim 1, 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, Represents a variable quantum state; 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.

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