Hydraulic engineering construction monitoring data management method

Abstract

The invention relates to the technical field of water conservancy project construction monitoring and management, and discloses a water conservancy project construction monitoring data management method, which comprises the following steps: collecting underground, earth surface and air multi-source engineering data through a quantum sensing network; millisecond-level data transmission and edge-cloud cooperative computing are realized based on a 6G-THz communication network; fusing BIM, GIS and Internet of Things data to carry out space-time reference unification and block chain credible evidence storage; constructing an eleven-dimensional digital twinborn model; and carrying out risk prediction and multi-objective optimization decision making by adopting a neuromorphic calculation engine. Through the strain detection system, extremely small strain change of the dam foundation can be accurately captured, even if the dam foundation generates extremely weak strain due to factors such as underground water level change or uneven local stress in the construction process, the system can also make a response quickly, and strain information is presented in a high-precision data form. The problem of incomplete multi-source data acquisition of a traditional monitoring means is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy project construction monitoring and management, and in particular to a water conservancy project construction monitoring data management method. Background Art

[0002] In the field of water conservancy projects, as projects grow larger and the construction environment becomes more complex, construction monitoring faces numerous challenges. Traditional data collection methods are often limited to a single or a few dimensions, making it difficult to fully capture engineering data from multiple sources, including underground, surface, and aerial sources. This leads to an inaccurate understanding of the overall construction situation and the omission of potential safety hazards and key factors affecting construction progress.

[0003] In the past, data collection relied heavily on conventional sensors and manual measurement methods. These methods often only capture limited, specific data types, such as construction parameters in localized areas of the surface or environmental data from a few fixed locations. This makes it difficult to effectively collect comprehensive, multi-source engineering data from underground, surface, and aerial sources. For example, it is difficult to fully grasp data on changes in deep underground geological structures and the impact of meteorological dynamics at different altitudes on construction. This results in construction teams lacking a comprehensive and accurate understanding of the overall project status, and they are prone to missing key factors that could potentially affect construction safety and progress. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a water conservancy project construction monitoring data management method, which solves the problem of incomplete multi-source data collection in water conservancy project construction monitoring.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A water conservancy project construction monitoring data management method, comprising the following steps: S1. Collect multi-source engineering data from underground, surface, and air through quantum sensing networks; S2, based on the 6G-THz communication network, realizes millisecond-level data transmission and edge-cloud collaborative computing; S3, integrating BIM, GIS and IoT data to unify spatiotemporal benchmarks and implement blockchain-based trusted evidence storage; S4. Build an 11-dimensional digital twin model to dynamically simulate the construction process and multi-physics field coupling effects; S5. Use neuromorphic computing engines for risk prediction and multi-objective optimization decision-making; S6. Realize virtual-reality fusion operation and early warning feedback through a cross-dimensional human-computer interaction interface; S7. Maintain system security and structural health based on self-repair and anti-fragility mechanisms.

[0006] Preferably, the quantum sensing network described in S1 includes: a single-mode optical fiber laid along the dam foundation, with an attenuation coefficient of ≤0.17dB / km, and a Hong-Ou-Mandel interferometer to detect phase changes with a strain resolution of 10⁻¹ 5 ε / √Hz.

[0007] Preferably, the 6G-THz communication network described in S2 includes: a reconfigurable smart surface array of 128×128 units, a beamforming gain ≥30dB, and an air interface delay ≤0.1ms.

[0008] Preferably, the blockchain trusted evidence described in S3 includes: based on the HyperledgerBesu alliance chain, the key data hash value generates lightweight evidence through MerkleTree, and the evidence cost is reduced by 70%.

[0009] Preferably, the eleven-dimensional digital twin model in S4 includes the following models: Physical dimensions: spatial coordinates and time; Material dimensions: crystal orientation θ, 0-360°, dislocation density ρ, ≤10¹²m⁻²; Quantum dimension: entangled state parameters α, β, Bell basis measurement accuracy ≥ 99%; Social dimension: immigration cost C, cultural value weight W.

[0010] Preferably, the neuromorphic computing engine in S5 includes: a pulse neural network chip, the number of neurons is ≥1M, and the percolation warning inference energy consumption is <10mJ / time.

[0011] Preferably, the cross-dimensional human-computer interaction in S6 includes: a holographic quantum display device with a refresh rate of 120 Hz, a resolution of 4096×2160, and projection of eleven-dimensional data through a Wigner function.

[0012] Preferably, the self-repair anti-fragile mechanism in S7 includes: implanting Bacillus subtilis engineered bacteria, the spore concentration of which is ≥10 6 CFU / g concrete, CaCO3 generation rate ≥0.1g / (cm³・day).

[0013] Preferably, the method also includes data asset management: building a unified memory data format based on Apache Arrow, supporting BIM, GIS, and IoT data conversion efficiency increased by 70%.

[0014] Preferably, when the method is applied to a water conservancy project with a storage capacity of ≥ 100 million m³, the following conditions are met: Crack warning sensitivity ≤ 0.1μm, advance time ≥ 72 hours; The error between dam-break simulation and physical test results is ≤8%; The full life cycle operation and maintenance costs are reduced by ≥47%.

[0015] The present invention provides a method for managing water conservancy project construction monitoring data. It has the following beneficial effects: 1. The strain detection system in this invention accurately captures extremely small strain changes in the dam foundation. Even if the dam foundation experiences extremely weak strain due to factors such as groundwater level fluctuations or localized uneven stress during construction, the system can quickly respond and present the strain information in the form of high-precision data. This solves the problem of incomplete multi-source data collection in traditional monitoring methods.

[0016] 2. By constructing an 11-dimensional digital twin model, this invention can simulate the entire construction process of a water conservancy project, from its inception to completion and subsequent operation. It can also simulate the changes in dam strength caused by variations in internal material dislocation density at different construction stages, as well as the specific impact of resettlement issues on the construction progress. This overcomes the inaccuracy of traditional water conservancy project construction simulations.

[0017] 3. The present invention utilizes a pulse neural network chip to rapidly and deeply analyze and process large amounts of real-time monitoring data. Even at the earliest stages of seepage, when subtle changes are just beginning, the chip can keenly capture relevant features, accurately identify abnormal seepage trends, and issue timely seepage warnings. This addresses the inaccuracy of traditional water conservancy project seepage warnings.

[0018] 4. This invention uses the Wigner function to achieve intuitive visualization of complex data. Construction workers can clearly see at a glance the structural status of different parts of the dam, the changing trends of material properties, the influence of microscopic factors at the quantum level, and the constraints of social factors, greatly improving the efficiency of understanding complex data. This solves the problem of misunderstanding that is prone to traditional human-computer interaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a water conservancy project construction monitoring data management method proposed by the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0021] Please see the attached Figure 1The embodiment of the present invention provides a method for managing water conservancy project construction monitoring data, comprising the following steps: S1. Collect multi-source engineering data from underground, surface, and air through quantum sensing networks; S2, based on the 6G-THz communication network, realizes millisecond-level data transmission and edge-cloud collaborative computing; S3, integrating BIM, GIS and IoT data to unify spatiotemporal benchmarks and implement blockchain-based trusted evidence storage; S4. Build an 11-dimensional digital twin model to dynamically simulate the construction process and multi-physics field coupling effects; S5. Use neuromorphic computing engines for risk prediction and multi-objective optimization decision-making; S6. Realize virtual-reality fusion operation and early warning feedback through a cross-dimensional human-computer interaction interface; S7. Maintain system security and structural health based on self-repair and anti-fragility mechanisms.

[0022] The quantum sensing network in S1 includes: single-mode optical fiber laid along the dam foundation, with an attenuation coefficient of ≤0.17dB / km, and phase change detection through Hong-Ou-Mandel interferometry, with a strain resolution of 10⁻¹ 5 ε / √Hz.

[0023] Specifically, in the actual scenario of water conservancy project construction monitoring, strain is inevitably generated in the dam foundation as it bears various complex loads. To accurately capture this strain information, single-mode optical fiber is deployed along the dam foundation. This single-mode fiber has unique optical properties, with an attenuation coefficient of ≤0.17dB / km. This means that when optical signals are transmitted through the fiber, energy loss is minimal, ensuring that the optical signal maintains high intensity and quality during long-distance transmission.

[0024] When strain occurs in the dam foundation, it physically affects the single-mode optical fiber installed on it, causing it to undergo slight deformation. As light propagates through the fiber, its phase changes with the deformation. The Hong-Ou-Mandel interferometer is a key device that uses the principle of optical interference to detect this phase change. The interferometer introduces two beams of light into the interferometer system: one beam propagates through the strained fiber, and the other serves as a reference beam. When these two beams meet in the interferometer, interference occurs, forming interference fringes. The phase change of the optical fiber caused by strain causes the interference fringes to shift or change accordingly. By using a high-precision optical detector to monitor and record the changes in the interference fringes in real time and using advanced signal processing algorithms to analyze and process the collected interference fringe data, the magnitude of the strain on the fiber can be accurately calculated. Leveraging the low attenuation characteristics of single-mode fiber and the high sensitivity of the Hong-Ou-Mandel interferometer, the system achieves a strain resolution of up to 10⁻¹. 5 ε / √Hz.

[0025] The high-precision strain detection system can accurately capture extremely minute strain changes in the dam foundation in real time. In actual projects, even if the dam foundation experiences extremely slight strain due to factors such as groundwater level fluctuations, geological tectonic movements, or localized uneven stress during construction, the system can quickly respond and present the strain information in the form of highly accurate data. This high-resolution detection provides extremely reliable data support for construction monitoring and safety assessments of water conservancy projects. Based on this precise strain data, construction personnel and management personnel can promptly understand the stress state and deformation of the dam foundation, providing a strong basis for subsequent construction decisions and safety warnings. This solves the problem of incomplete multi-source data collection encountered by traditional monitoring methods.

[0026] The 6G-THz communication network in S2 includes: a reconfigurable smart surface array with 128×128 units, beamforming gain ≥30dB, and air interface delay ≤0.1ms.

[0027] Specifically, in the context of water conservancy project construction monitoring, the reconfigurable smart surface (RIS) array in the 6G-THz communication network plays a key role. The RIS array adopts a 128×128 unit structure and its operating principle is based on the intelligent control of the wireless signal propagation environment.

[0028] The RIS array is composed of numerous independently controllable units, each capable of modifying the phase, amplitude, and other characteristics of the incident electromagnetic wave. During communication, when a signal from the transmitter reaches the RIS array, each unit in the array adjusts the phase and amplitude of the incoming signal accordingly, based on a preset algorithm or real-time control instructions. Through this refined adjustment, multiple units work together to refocus and redirect signals that were originally dispersed or did not conform to the desired propagation direction, thus achieving beamforming.

[0029] After monitoring equipment installed in different areas of the construction site collects data and transmits it to the processing center, the data signals propagate toward the receiving end in the form of electromagnetic waves. En route, they encounter the RIS array. The array units adjust the signal's phase and amplitude based on the communication link's requirements, converging the signal along a predetermined direction. This enhances the signal's strength in that specific direction, ultimately achieving beamforming gain of ≥30dB. Because the RIS array's ability to rapidly adjust signals and its relatively efficient signal processing and control mechanisms minimize latency during signal processing and forwarding, ensuring that the signal's transmission latency at the air interface from transmitter to receiver is ≤0.1ms.

[0030] The beamforming gain of ≥30dB significantly enhances signal strength during transmission. Even in complex water conservancy project construction environments, where adverse factors such as building obstruction, undulating terrain, and electromagnetic interference from various construction equipment exist, data signals can still be transmitted with high strength and stability. This effectively reduces signal attenuation and bit error rates during transmission, ensuring long-distance, high-quality data transmission. The advantage of air interface latency of ≤0.1ms enables near-real-time data transmission from the acquisition source to the receiving end. For example, during construction, real-time data collected on groundwater level changes and dam structural stress can be quickly and accurately transmitted to the monitoring center, allowing construction managers to obtain the latest on-site conditions. Based on this accurate and timely data, they can make timely construction decisions and achieve precise control of the construction process. This solves the problem of signal attenuation and poor monitoring data transmission in traditional communication networks used in water conservancy project construction monitoring applications.

[0031] The blockchain trusted evidence in S3 includes: based on the HyperledgerBesu alliance chain, the hash value of key data is used to generate lightweight evidence through MerkleTree, reducing the evidence cost by 70%.

[0032] Specifically, in the context of water conservancy project construction monitoring data management, blockchain-based trusted evidence storage utilizes a technical architecture based on the Hyperledger Besu consortium chain. First, various key data generated during water conservancy project construction are processed using a hash algorithm. A hash algorithm converts data of any length into a fixed-length hash value. This hash value is unique; even the slightest change in the original data will result in a completely different hash value. The hash values of these key data are further processed using a Merkle Tree structure. A Merkle Tree is a tree-like data structure built on hash values. It groups multiple hash values according to specific rules, performing layer-by-layer hash calculations to ultimately generate a root hash value. In this process, each leaf node corresponds to a unique key data hash value, and through continuous hashing upwards, the data from each node is linked.

[0033] By storing only the hash value and MerkleTree structure of key data, the amount of data stored is significantly reduced compared to traditional full data storage methods, thereby reducing the cost of evidence storage by 70%. Furthermore, the distributed ledger nature of the consortium chain ensures the immutability and traceability of data, allowing all participants to view and verify this evidence within their authorized scope.

[0034] The above working principle ensures the authenticity and integrity of the data. Due to the uniqueness of the hash value and the structural characteristics of the Merkle Tree, any tampering with key data is easily detected. Whenever the data changes, its corresponding hash value and the root hash value of the entire Merkle Tree will also change. Subsequent verification steps will detect anomalies, ensuring that the stored data always reflects the actual construction situation. This solves the problem of traditional data storage methods being prone to malicious data modification.

[0035] The eleven-dimensional digital twin model in S4 includes the following models: Physical dimensions: spatial coordinates and time; Material dimensions: crystal orientation θ, 0-360°, dislocation density ρ, ≤10¹²m⁻²; Quantum dimension: entangled state parameters α, β, Bell basis measurement accuracy ≥ 99%; Social dimension: immigration cost C, cultural value weight W.

[0036] Specifically, based on spatial coordinates (x, y, z) and time (t), various high-precision positioning sensors and time synchronization devices are deployed at the water conservancy project construction site to accurately obtain the spatial location information and corresponding time nodes of different construction sites at each moment. For example, the specific coordinate positions of different pouring layers of the dam body during construction, as well as the corresponding construction start and end times, are accurately recorded. This data provides the basic framework for subsequent simulations of the spatiotemporal evolution of the construction process.

[0037] Specialized material analysis instruments (such as electron backscatter diffractometers) are used to perform microscopic testing and analysis of various building materials used in water conservancy projects, such as concrete and steel, in terms of the material dimension, including crystal orientation θ (ranging from 0-360°) and dislocation density ρ (≤10¹²m⁻²). These instruments detect the alignment of the material's internal crystal structure, acquiring crystal orientation data and accurately measuring the density of microscopic defects such as dislocations. During the construction process, continuous monitoring and data collection are conducted on materials used in different batches and construction locations, providing detailed material microscopic property data for the digital twin model to reflect how the material changes under different construction conditions and stress states.

[0038] Using equipment with high-precision quantum measurement capabilities (such as quantum interferometers), we measure the entangled state parameters α and β involved in the quantum dimension, targeting materials or microstructures with quantum properties. (Although these represent a relatively small proportion of the macroscopic world in water conservancy projects, they can have a significant impact in key areas or in specific material applications.) Based on the principles of quantum mechanics, through specific experimental setups and measurement methods, we can obtain the entangled state parameters with a Bell basis measurement accuracy of ≥99%. These high-precision quantum dimension data reveal unique physical connections and effects at the microscopic level, providing a foundation for a comprehensive understanding of the characteristics of water conservancy projects at the intersection of the micro and macroscopic levels.

[0039] Regarding the social dimension of resettlement costs C (measured in 10,000 yuan / km²) and cultural value weight W (ranging from 0 to 1), we collaborated with relevant government departments and social research institutions to collect data on resettlement plans and compensation standards for water conservancy project construction areas to quantify resettlement costs. Furthermore, integrating multidisciplinary research methods from history and sociology, we assessed the historical and cultural relics and folk customs of the project sites to determine their cultural value weights. This data will be dynamically updated as project construction progresses and the surrounding social environment changes, incorporating data on social influencing factors into the digital twin model, enabling it to comprehensively consider societal constraints and influences.

[0040] By constructing such an 11-dimensional digital twin model, the entire construction process of a water conservancy project, from commencement to completion and subsequent operational phases, can be recreated. This model not only captures construction progress and structural morphology changes, but also reveals the evolution of material microstructures at different stages, the impact of microscopic quantum effects in local areas, and the constraints imposed by social factors on project decision-making and progress. For example, it can simulate the changes in dam strength due to variations in internal dislocation density at different stages of construction, as well as the specific impact of resettlement issues on construction progress. This addresses the inaccuracy of traditional water conservancy project construction simulations.

[0041] The neuromorphic computing engine in S5 includes: a pulse neural network chip with ≥1M neurons and a percolation warning inference energy consumption of <10mJ / time.

[0042] Specifically, the spiking neural network chip is built based on the working principles of biological neurons, mimicking the way in which neurons in the biological nervous system transmit and process information through pulse signals. The chip integrates a large number of artificial neuron units (≥1M neurons), which are interconnected through complex relationships, forming a topological structure similar to that of a biological neural network.

[0043] When faced with water conservancy project construction monitoring data, such as real-time groundwater level data, humidity data at different parts of the dam body, and soil pore water pressure data, this data is first converted into corresponding pulse signals and input into the spiking neural network chip. Each neuron decides whether to generate a new pulse and transmit it to other connected neurons based on the received pulse signal and its own preset threshold, activation function, and other conditions.

[0044] Taking seepage warning as an example, the chip continuously receives data from various seepage monitoring sensors deployed on the dam and surrounding areas. After converting this data into pulse signals, it is continuously propagated, converged, integrated, and subjected to nonlinear calculations within the chip's internal neural network. As data continues to accumulate and calculations continue, the neural network will judge and infer the current seepage situation based on its learned patterns and existing seepage-related knowledge characteristics. Moreover, thanks to the chip's advantages in hardware design and algorithm optimization, the entire seepage warning reasoning process can be completed with extremely low energy consumption, with each inference consuming less than 10mJ / time, achieving efficient and energy-saving computing processing.

[0045] The pulse neural network chip can quickly and deeply analyze and process large amounts of real-time monitoring data. Even at the earliest stages of seepage, when changes are extremely subtle, it can keenly capture relevant characteristics, accurately determine the trend of abnormal seepage, and issue timely seepage warning information. This allows construction personnel to be aware of seepage risks as early as possible and take appropriate countermeasures such as reinforcing the dam and diverting water, effectively preventing further deterioration of seepage problems and the resulting serious consequences such as damage to the dam structure or even dam failure. This solves the problem of insufficient accuracy of seepage warnings in traditional water conservancy projects.

[0046] The cross-dimensional human-computer interaction in S6 includes: a holographic quantum display device with a refresh rate of 120Hz, a resolution of 4096×2160, and projection of eleven-dimensional data through the Wigner function.

[0047] Specifically, the holographic quantum display device features a high refresh rate (120Hz) and a high resolution (4096×2160). Its core lies in the use of the Wigner function to project and display eleven-dimensional data. First, the complex eleven-dimensional data collected during the construction of water conservancy projects, encompassing multiple dimensions such as physics, materials, quantum, and society, has different attributes and numerical ranges, and is intricately correlated and coupled with each other.

[0048] The Wigner function, a mathematical tool in quantum mechanics, uniquely characterizes and describes quantum states. It is used here to transform 11-dimensional data from abstract digital form into visual image information. Specifically, through a specific algorithm and mapping mechanism, the parameters of each dimensional data set are mapped to the variables of the Wigner function according to corresponding rules, so that each data set can find a corresponding mapping point in the Wigner function space.

[0049] The Wigner function enables intuitive visualization of complex data, allowing construction workers to clearly understand various aspects of the dam, such as the structural status of different parts of the dam, the changing trends of material properties, the influence of microscopic factors at the quantum level, and the constraints of social factors. This greatly improves the efficiency of understanding complex data and solves the problem of misunderstanding that is prone to traditional human-computer interaction.

[0050] The self-repair anti-fragile mechanism in S7 includes: implanting Bacillus subtilis engineered bacteria, with a spore concentration of ≥10 6 CFU / g concrete, CaCO3 generation rate ≥0.1g / (cm³・day).

[0051] Specifically, the engineered bacteria Bacillus subtilis is implanted into the concrete structure of a water conservancy project. Its spores remain in a relatively stable dormant state within the concrete, awaiting the right triggering conditions. When the concrete structure is affected by external factors, such as microcracks caused by thermal expansion and contraction due to temperature fluctuations, or internal damage caused by long-term load bearing, these dormant spores begin to germinate and grow. When the local humidity, pH, and other conditions reach the appropriate range for the engineered bacteria to activate, these spores begin to germinate and grow.

[0052] During its growth, the engineered bacteria Bacillus subtilis utilizes substances in the surrounding environment to carry out metabolic activities. Its metabolic process can promote chemical reactions, causing some components inside the concrete to react with substances entering from the outside to generate CaCO3. This process relies on the specific enzyme system and metabolic pathways possessed by the engineered bacteria itself. Through these biochemical mechanisms, it realizes the transformation of surrounding substances. In addition, due to the activity and quantity of the engineered bacteria, its spore concentration is ≥10 6 CFU / g concrete ensures sufficient engineered bacteria participate in the process, enabling a CaCO3 production rate of ≥0.1g / (cm³・day). The generated CaCO3 gradually deposits and crystallizes in cracks or damaged areas of the concrete, filling these gaps and repairing the structure, strengthening its integrity and thereby enhancing its resistance to subsequent damage, achieving self-healing and anti-fragility properties.

[0053] The engineered bacteria's self-repair mechanism actively generates CaCO₃ to fill and repair damaged areas, gradually reducing crack width until they close and restoring the integrity of the concrete structure. For example, in concrete structures such as small water channels and small dams in water conservancy projects, cracks caused by water erosion or minor geological changes during daily operation can be prevented from further expansion by the self-repairing action of the engineered bacteria, maintaining the structure's normal function and extending its service life. This addresses the traditional lack of self-repair capabilities in concrete structures in water conservancy projects.

[0054] The method also includes data asset management: building a unified in-memory data format based on Apache Arrow to support BIM, GIS, and IoT data conversion efficiency increased by 70%.

[0055] Specifically, Apache Arrow is a cross-language in-memory data structure that defines a standardized, efficient memory layout format. BIM (Building Information Modeling) data, GIS (Geographic Information System) data, and IoT (Internet of Things) data involved in water conservancy projects originally had different data formats and storage structures.

[0056] BIM data usually contains structured information such as detailed three-dimensional geometric information of water conservancy project buildings, component properties, and construction progress. Its format is often designed to meet specific needs in architectural design, construction management, etc.; GIS data focuses on the expression of geographic-related information such as geographic spatial location, topography, and landforms, and has its own unique spatial data format; IoT data is a large amount of multi-source heterogeneous data about the on-site environment of water conservancy projects, equipment operating status, etc. collected in real time through various sensors. It has diverse formats and huge data volumes.

[0057] When data integration and management is required, Apache Arrow's mechanisms convert data from various sources and formats according to its prescribed unified in-memory data format standard. Specifically, it first analyzes the logical structure and semantics of each type of data, identifying key elements such as component numbers and geometric dimensions in BIM data, coordinate information in GIS data, and sensor identification and acquisition time in IoT data. These parsed elements are then reorganized and arranged according to unified memory layout rules, allocating appropriate memory space. This allows these different types of data to be stored and represented in memory in a unified, standardized format.

[0058] Because different types of data come in varying formats, integrating them for comprehensive analysis or shared use often requires significant time and effort, including programming specialized format conversion programs and data cleaning. This can easily lead to data loss and conversion errors. The unified in-memory data format built on Apache Arrow facilitates the aggregation of data from diverse sources while ensuring data integrity and accuracy during conversion. This provides a reliable data foundation for comprehensive water conservancy project construction monitoring, analysis, and decision-making, resolving the challenges of traditional data integration.

[0059] When this method is applied to water conservancy projects with a storage capacity of ≥ 100 million m³, the following conditions must be met: Crack warning sensitivity ≤ 0.1μm, advance time ≥ 72 hours; The error between dam-break simulation and physical test results is ≤8%; The full life cycle operation and maintenance costs are reduced by ≥47%.

[0060] Specifically, in the application scenario of large-scale water conservancy projects with a reservoir capacity of ≥100 million m³, the multi-dimensional data collection and analysis system constructed by this method can achieve highly sensitive crack early warning. First, through a quantum sensing network (such as single-mode optical fiber laid along the dam foundation combined with high-precision detection methods such as Hong-Ou-Mandel interferometers), the tiny strains of the dam structure are monitored in real time and with high precision. It can accurately capture extremely subtle deformations of the dam body caused by factors such as internal stress changes and external environmental influences, with a strain resolution of up to 10⁻¹. 5 ε / √Hz, capable of detecting microscopic trends in the dam structure. An 11-dimensional digital twin model comprehensively simulates the construction process and subsequent operational phases, incorporating spatial coordinate and temporal information from the physical dimension, as well as crystal orientation and dislocation density from the material dimension. This model analyzes the changes in mechanical properties of different parts of the dam structure under varying operating conditions, and identifies areas of vulnerability to cracks based on the evolution of the material's microscopic properties. A neuromorphic computing engine (such as a spiking neural network chip) rapidly analyzes and infers the massive amounts of real-time data collected. Based on learned patterns of crack formation and data variation, a warning signal is issued if the monitored data indicates a minor anomaly in the dam structure developing toward crack formation, even if the potential change in crack width is less than or equal to 0.1μm. Thanks to the efficient collaboration of all components, potential cracks can be detected at least 72 hours in advance, allowing ample time for preventive and repair measures.

[0061] A high-precision digital twin model, combined with advanced computational methods, achieves a highly accurate match between dam-break simulations and physical test results. The 11-dimensional digital twin model takes into account multiple factors, including quantum entangled state parameters, social migration costs, and cultural values. This allows for a more realistic representation of the complexities of actual water conservancy project operations and the interplay between these factors. Based on this model, combined with real-time data collected (such as water level fluctuations, seepage, and dam stresses), and employing specialized computational algorithms coupled with multidisciplinary disciplines such as hydraulics and structural mechanics, the stability of the dam is simulated and analyzed under various operating conditions (such as extreme events like flood impacts and earthquakes), predicting the likelihood and progression of a dam break. By continuously optimizing model parameters and algorithm accuracy, and comparing and verifying the model with actual physical test results, the error between the simulation and test results is kept within 8%, ensuring the accuracy and reliability of the dam-break simulation and providing a robust basis for assessing the safety of water conservancy projects.

[0062] From a data asset management perspective, a unified in-memory data format based on Apache Arrow efficiently integrates data from multiple sources, including BIM, GIS, and IoT. This facilitates precise control of all aspects of the project during the construction phase, optimizes construction plans, and reduces subsequent operational costs caused by construction errors and inappropriate designs. For example, by accurately analyzing discrepancies between BIM data and actual on-site construction data (IoT data), timely adjustments to construction schedules and processes are made, avoiding unnecessary rework and waste of resources. During construction monitoring, self-healing anti-fragile mechanisms (such as the implantation of engineered Bacillus subtilis bacteria) are utilized to proactively repair minor damage to concrete structures, reducing the large-scale repair costs caused by accumulated structural damage. Real-time risk prediction (using neuromorphic computing engines, etc.) enables proactive preventative measures to avoid costly repairs and losses caused by safety incidents. Furthermore, efficient data transmission and collaborative computing enabled by a 6G-THz communication network ensure the efficient operation of the entire monitoring system, optimize resource allocation, and, through these combined efforts, reduce the operational costs of water conservancy projects throughout their lifecycle by 47% or more compared to traditional methods.

[0063] Through ultra-early, high-precision early warning of cracks in dam bodies of water conservancy projects, crack width changes of 0.1 μm or less can be detected promptly, and relevant personnel can be notified at least 72 hours in advance. This buys sufficient time for construction and operation and maintenance teams to take targeted preventive and repair measures such as dam reinforcement and adjustment of operating conditions, effectively preventing further development and expansion of cracks, and preventing more serious safety hazards such as seepage and dam structural damage caused by cracks, thereby ensuring the long-term stable operation of water conservancy projects. This solves the problem that traditional crack monitoring methods rely on manual inspections.

[0064] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for managing water conservancy project construction monitoring data, characterized in that: The following steps are involved: S1. Collect multi-source engineering data from underground, surface, and air through quantum sensing networks; S2, based on the 6G-THz communication network, realizes millisecond-level data transmission and edge-cloud collaborative computing; S3, integrating BIM, GIS and IoT data to unify spatiotemporal benchmarks and implement blockchain-based trusted evidence storage; S4. Build an 11-dimensional digital twin model to dynamically simulate the construction process and multi-physics field coupling effects; S5. Use neuromorphic computing engines for risk prediction and multi-objective optimization decision-making; S6. Realize virtual-reality fusion operation and early warning feedback through a cross-dimensional human-computer interaction interface; S7. Maintain system security and structural health based on self-repair and anti-fragility mechanisms.

2. A water conservancy project construction monitoring data management method according to claim 1, characterized in that: The quantum sensing network described in S1 includes: single-mode optical fiber laid along the dam foundation, with an attenuation coefficient of ≤0.17dB / km, and phase change detection through Hong-Ou-Mandel interferometry with a strain resolution of 10⁻¹ 5 ε / √Hz.

3. A water conservancy project construction monitoring data management method according to claim 1, characterized in that: The 6G-THz communication network described in S2 includes: a reconfigurable smart surface array with 128×128 units, a beamforming gain ≥30dB, and an air interface delay ≤0.1ms.

4. A water conservancy project construction monitoring data management method according to claim 1, characterized in that: The blockchain trusted evidence described in S3 includes: based on the HyperledgerBesu alliance chain, the hash value of key data is used to generate lightweight evidence through MerkleTree, reducing the evidence cost by 70%.

5. A water conservancy project construction monitoring data management method according to claim 1, characterized in that: The eleven-dimensional digital twin model in S4 includes the following models: Physical dimensions: spatial coordinates and time; Material dimensions: crystal orientation θ, 0-360°, dislocation density ρ, ≤10¹²m⁻²; Quantum dimension: entangled state parameters α, β, Bell basis measurement accuracy ≥ 99%; Social dimension: immigration cost C, cultural value weight W.

6. A water conservancy project construction monitoring data management method according to claim 1, characterized in that: The neuromorphic computing engine in S5 includes: a pulse neural network chip with ≥1M neurons and a percolation warning inference energy consumption of <10mJ / time.

7. A water conservancy project construction monitoring data management method according to claim 1, characterized in that: The cross-dimensional human-computer interaction described in S6 includes: a holographic quantum display device with a refresh rate of 120 Hz, a resolution of 4096×2160, and projection of eleven-dimensional data through a Wigner function.

8. A water conservancy project construction monitoring data management method according to claim 1, characterized in that: The self-repair anti-fragile mechanism described in S7 includes: implanting Bacillus subtilis engineered bacteria, whose spore concentration is ≥10 6 CFU / g concrete, CaCO3 generation rate ≥0.1g / (cm³・day).

9. A water conservancy project construction monitoring data management method according to claim 1, characterized in that: The method also includes data asset management: building a unified in-memory data format based on Apache Arrow, supporting BIM, GIS, and IoT data conversion efficiency increased by 70%.

10. A water conservancy project construction monitoring data management method according to claim 1, characterized in that: When the method is applied to a water conservancy project with a storage capacity of ≥100 million m³, the following conditions are met: Crack warning sensitivity ≤ 0.1μm, advance time ≥ 72 hours; The error between dam-break simulation and physical test results is ≤8%; The full life cycle operation and maintenance costs are reduced by ≥47%.

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