Geological disaster monitoring method and system based on quantum coupling effect

Abstract

The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster monitoring method and system based on a quantum coupling effect. The method comprises the following steps: acquiring geological environment parameters of a target monitoring area; laying a quantum sensor network; initializing a quantum bit; the tunneling effect of quantum bits is excited by using microwave signals; obtaining quantum state data by using a quantum measurement technology; analyzing a coupling relationship among the geological environment parameters to realize geological disaster risk early warning; the causes and development tracks of geological disasters are traced, and monitoring and early warning strategies are optimized. Through a quantum sensor network and a multi-field coupling analysis model, ultrahigh-precision monitoring of stress, displacement, temperature and water level parameters is realized by using a quantum entanglement state, topology protection and time reversal technology, a disaster chain reaction path is revealed, cause and risk evolution are dynamically traced, the early warning reliability is remarkably improved, and the early warning effect is improved. And the monitoring mode is promoted to transition from passive response to active intervention.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and in particular to a geological disaster monitoring method and system based on quantum coupling effect. Background Art

[0002] In recent years, with the frequent occurrence of geological disasters, the existing technology is insufficient in monitoring nonlinear geological processes, multi-parameter co-evolution and disaster chain transmission mechanisms, resulting in the prominent problems of delayed warning and high false alarm rate. In particular, in capturing hidden disaster precursors such as sudden changes in groundwater levels and redistribution of ground stress, traditional methods can no longer meet the needs of high-precision and real-time monitoring.

[0003] In the existing technology, the early warning system is usually built based on single physical quantity monitoring or empirical mechanical models, which has the following significant defects: First, traditional sensors are limited by the principle of classical physical measurement and are easily affected by electromagnetic interference and temperature drift factors in complex geological environments, resulting in data distortion; second, the existing models rely on linear assumptions or simplified coupling relationships, and cannot characterize the nonlinear dynamic interaction effects between multiple parameters of stress field, displacement field, groundwater level and temperature field, resulting in distortion in the analysis of disaster evolution mechanism; third, although the existing quantum sensing technology has high sensitivity, it is mostly limited to single-point application in geological monitoring, lacks multi-parameter collaborative coupling analysis methods based on quantum networks, and has not solved the decoherence problem of quantum states in non-ideal environments, making it difficult to achieve large-scale deployment. The above defects seriously restrict the accuracy and timeliness of geological disaster early warning.

[0004] At present, there are not enough studies on geological disaster monitoring with multi-physical quantities, and there is no specific geological disaster monitoring method that integrates quantum sensing, multi-field coupling analysis and quantum information processing. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides a geological disaster monitoring method and system based on quantum coupling effect.

[0006] In a first aspect, the present invention provides a method for monitoring geological disasters based on quantum coupling effect, comprising the following steps: obtaining geological environmental parameters of a target monitoring area, wherein the geological environmental parameters include stress field, temperature field, displacement field and groundwater level parameters; deploying a quantum sensor network in the target monitoring area based on the geological environmental parameters and in combination with performance indicators of quantum sensors, wherein the performance indicators include sensitivity indicators, coverage indicators and stability indicators; initializing quantum bits based on the quantum sensor network and obtaining initialization results; according to the initialization results, using microwave signals to excite the tunneling effect of quantum bits so that the quantum bits are coupled with the geological environmental parameters to obtain an entangled state; based on the entangled state, using quantum measurement technology, obtaining quantum state data, wherein the quantum state data includes quantum information of the geological environmental parameters; analyzing the coupling relationship between the geological environmental parameters through the quantum state data and obtaining analysis results; realizing geological disaster risk warning based on the analysis results; based on the geological disaster risk warning, tracing the causes and development trajectories of geological disasters, and optimizing monitoring and warning strategies. The invention realizes real-time and accurate monitoring of geological environment parameters through the high sensitivity and coverage of quantum sensor networks, breaks through the limitations of traditional monitoring methods in accuracy and response speed, and significantly improves the timeliness and accuracy of geological disaster warning. Through the coupling effect of quantum bits and geological environment parameters, quantum entangled states are applied to geological disaster monitoring, subverting the direct measurement of physical quantities by traditional monitoring methods, and realizing deep-level and multi-dimensional information acquisition of complex geological systems. Through the acquisition and analysis of quantum state data, the quantum coupling relationship between stress field, temperature field, displacement field and groundwater level is revealed, breaking through the limitations of traditional geomechanical models, and providing a new perspective for the quantum mechanical explanation of the causes of geological disasters. Through the high stability of quantum measurement technology, the long-term reliability and consistency of monitoring data are ensured, the defects of traditional sensors being easily disturbed in complex geological environments are overcome, and the robustness and adaptability of the monitoring system are significantly improved. Through the combination of geological disaster risk warning and cause tracing, closed-loop management from monitoring to warning to cause analysis is realized, the monitoring strategy is optimized, and a scientific basis is provided for the active prevention and control of geological disasters, which promotes the transformation of geological disaster monitoring from passive response to active prevention.

[0007] Optionally, deploying a quantum sensor network in the target monitoring area according to the geological environment parameters and in combination with performance indicators of quantum sensors includes: constructing a quantum sensor deployment model according to the geological environment parameters, and the quantum sensor deployment model satisfies the following expression: , in, Indicates The quantum sensor is in the stress field The temperature field is The displacement field is , the groundwater level is The sensitivity index is Indicates The coverage index of a quantum sensor at a location, Indicates Quantum sensors are located at The stability index at , , represents the weight coefficient, represents the number of quantum sensors; the sensitivity index satisfies the following relationship: , in, Indicates The sensitivity index of a quantum sensor is Indicates The coupling strength coefficient between the quantum sensor and the geological environment parameter; the coverage index satisfies the following relationship: , in, Indicates The coverage index of quantum sensors, Indicates The volume of geological environment that can be monitored by a quantum sensor, represents the total volume of the target monitoring area, Indicates The distance from a quantum sensor to the center of the target monitoring area, represents the distance tolerance threshold; the stability index satisfies the following relationship: , in, Indicates The stability index of a quantum sensor, represents the stress field, represents the temperature field, represents the displacement field, Represents the groundwater level, , , , They represent the average values ​​of stress field, temperature field, displacement field and groundwater level in the target monitoring area, respectively. , , , Respectively represent the standard deviation of stress field, temperature field, displacement field and groundwater level in the target monitoring area; use the improved genetic algorithm to solve the quantum sensor deployment model and obtain the optimal sensor deployment plan; according to the optimal sensor deployment plan, deploy the quantum sensor network. The present invention deeply integrates geological environment parameters with quantum sensor performance indicators by constructing a quantum sensor deployment model, realizes the optimal design of sensor networks based on geological characteristics and quantum technology characteristics, breaks through the limitation of traditional deployment methods relying on empirical rules, and significantly improves the scientificity and adaptability of the monitoring network; solves the quantum sensor deployment model by an improved genetic algorithm, realizes the multi-objective optimization of sensor location, quantity and performance indicators under complex geological environments, subverts the single-objective optimization mode of traditional deployment plans, and provides innovative solutions for the efficient deployment of large-scale quantum sensor networks; through the implementation of the optimal sensor deployment plan, the full coverage and high-precision monitoring of the target monitoring area by the quantum sensor network is realized, overcoming the limitations of traditional deployment methods under complex terrain and geological conditions, and providing extremely high spatial resolution and data reliability for geological disaster monitoring.

[0008] Optionally, the initializing the quantum bits and obtaining the initialization results based on the quantum sensor network includes: constructing an initialization model of geological environment parameters and a quantum bit coupled tensor network based on the quantum sensor, wherein the initialization model satisfies the following expression: , in, Initialize the quantum bit state. is the geological environment coupling term, is the multi-body entanglement term, is the topology protection term, is the environmental normalization term, is a decoherence suppression term; through the initialization model, the quantum bit is initialized and the initialization result is obtained. The present invention constructs an initialization model of geological environment parameters and quantum bit coupled tensor network, accurately associates the multidimensional parameters of the geological environment with the initialization state of the quantum bit, breaks through the limitation of the traditional quantum initialization method that only relies on a single physical quantity, and realizes the high-precision and high-stability initialization of the quantum bit in geological monitoring; by introducing multi-body entanglement terms and topological protection terms, the anti-interference ability and information storage stability of the quantum bit in a complex geological environment are significantly enhanced, effectively overcoming the defect that the traditional quantum system is easy to decoherent in a non-ideal environment, and providing a reliable quantum information processing basis for geological disaster monitoring; through the optimization design of the environmental normalization term and the decoherence suppression term, the influence of geological environment noise on the initialization of the quantum bit is effectively reduced, and the high-fidelity initialization of the quantum state is realized, and the difficulty of maintaining quantum coherence under complex geological conditions in traditional methods is overcome, providing important technical support for the quantization of geological disaster monitoring.

[0009] Optionally, analyzing the coupling relationship between the geological environment parameters through the quantum state data and obtaining the analysis results includes: constructing a multi-field coupling analysis model through the quantum state data, and the multi-field coupling analysis model satisfies the following expression: , in, Represents the spatial position of the geological stress field and time The distribution function under Represents the geological displacement field in space and time The distribution function under Indicates the spatial location of groundwater and time The distribution function under Represents the temperature field in space and time The distribution function under Indicates the pore water pressure of the geological medium in space and time The distribution function under represents the diffusion coefficient of the stress field, represents the coupling coefficient of the displacement field to the stress field, represents the coupling coefficient of the temperature field to the stress field, represents the nonlinear influence coefficient of groundwater level on displacement field, represents the correction coefficient of pore water pressure to temperature field, represents the weight coefficient of the time integral term, represents the nonlinear modulation coefficient of the time integral of groundwater level, represents the coupling coefficient of pore water pressure gradient to stress field, represents the correction coefficient of the temperature field to the pore water pressure gradient, is a time variable; according to the multi-field coupling analysis model, the coupling relationship between the geological environment parameters is analyzed and the analysis results are obtained. The present invention, by constructing a multi-field coupling analysis model, incorporates geological stress field, displacement field, groundwater level, temperature field and pore water pressure parameters into a unified framework, realizes the accurate quantitative analysis of multi-field coupling relationship under complex geological environment, breaks through the limitations of traditional single field analysis methods, and provides a scientific basis for comprehensive monitoring and early warning of geological disasters; by introducing nonlinear influence coefficients and correction coefficients, the nonlinear effect of groundwater level on displacement field and the dynamic correction of temperature field on pore water pressure are accurately described, subverting the simplified assumptions of traditional linear coupling models, and significantly improving the analysis accuracy and prediction ability of multi-field coupling relationship; through the optimization design of time integral terms and nonlinear modulation coefficients, the dynamic coupling analysis of geological environment parameters evolving over time is realized, overcoming the defect that traditional static models cannot reflect the dynamic development process of geological disasters, and providing important technical means for real-time monitoring and risk early warning of geological disasters.

[0010] Optionally, analyzing the coupling relationship between the geological environment parameters according to the multi-field coupling analysis model and obtaining analysis results includes: analyzing the coupling relationship between the stress field and the displacement field according to the multi-field coupling analysis model and obtaining analysis results; analyzing the coupling relationship between the temperature field and the stress field according to the multi-field coupling analysis model and obtaining analysis results; analyzing the coupling relationship between the pore water pressure gradient and the stress field according to the multi-field coupling analysis model and obtaining analysis results; analyzing the coupling relationship between the displacement field and the groundwater level according to the multi-field coupling analysis model and obtaining analysis results. The present invention realizes the dynamic correlation modeling between geological stress and deformation by analyzing the coupling relationship between stress field and displacement field, breaks through the dependence of traditional geomechanical models on static stress analysis, provides high-precision dynamic data support for deformation prediction of geological disasters, and significantly improves the early warning capability of landslide and collapse disasters; by analyzing the coupling relationship between temperature field and stress field, it reveals the deep-seated influence of temperature change on geological stress distribution, overcomes the limitation of ignoring temperature effect in traditional geological monitoring, and provides a new scientific perspective and technical means for geological disaster monitoring in geothermal activity areas or extreme climate conditions; by analyzing the coupling relationship between pore water pressure gradient and stress field, it accurately depicts the nonlinear effect of groundwater dynamic change on geological stress field, overcomes the deficiency of simplified processing of pore water pressure in traditional models, and provides a breakthrough solution for cause analysis and early warning of groundwater-induced geological disasters; by analyzing the coupling relationship between displacement field and groundwater level, it realizes the coordinated monitoring of geological deformation and groundwater level change, provides comprehensive and dynamic data support for geological stability assessment, and promotes the leapfrog development of geological disaster monitoring from single parameter to multi-parameter coordinated analysis.

[0011] Optionally, implementing geological disaster risk warning according to the analysis result includes: constructing a geological disaster risk warning model according to the analysis result, and the geological disaster risk warning model satisfies the following expression: , in, Indicates the position in space and time The geological disaster risk warning index under Represents the spatial position of the geological stress field and time The distribution function under Represents the geological displacement field in space and time The distribution function under Indicates the spatial location of groundwater and time The distribution function under Represents the temperature field in space and time The distribution function under Indicates the pore water pressure of the geological medium in space and time The distribution function under Represents the spatial position of the geological stress field The maximum value at Represents the geological displacement field in space The maximum value at Represents the temperature field in space The maximum value at Indicates the pore water pressure of the geological medium in space The maximum value at represents the diffusion coefficient of the stress field, represents the coupling coefficient of the displacement field to the stress field, represents the coupling coefficient of the temperature field to the stress field, represents the nonlinear influence coefficient of groundwater level on displacement field, represents the correction coefficient of pore water pressure to temperature field, represents the weight coefficient of the time integral term, represents the nonlinear modulation coefficient of the time integral of groundwater level, represents the coupling coefficient of pore water pressure gradient to stress field, represents the correction coefficient of the temperature field to the pore water pressure gradient, is a time variable; the geological disaster risk warning model is used to realize the geological disaster risk warning. The present invention realizes the comprehensive quantitative assessment of geological disaster risk by constructing a geological disaster risk warning model, dynamically integrating geological stress field, displacement field, groundwater level, temperature field and pore water pressure parameters, breaking through the limitation of traditional warning models relying on a single parameter, and significantly improving the warning accuracy and reliability; by introducing nonlinear influence coefficients and correction coefficients, the nonlinear effect of groundwater level on displacement field and the dynamic correction of pore water pressure by temperature field are accurately described, subverting the simplified assumptions of traditional linear warning models, and providing high-precision dynamic prediction capabilities for disaster warning under complex geological conditions; through the optimization design of time integral terms and nonlinear modulation coefficients, the dynamic evolution analysis of geological disaster risks is realized, overcoming the defect that traditional static models cannot reflect the dynamic changes of disaster risks, providing important technical means for real-time monitoring and accurate warning of geological disasters, and promoting the transformation of geological disaster prevention and control from passive response to active prevention.

[0012] Optionally, the implementation of geological disaster risk warning through the geological disaster risk warning model includes: obtaining a geological disaster risk warning index of the spatial position of any quantum sensor in the target monitoring area under any time condition through the geological disaster warning model; dividing the threshold interval of the geological disaster risk warning index into multiple threshold intervals; setting different warning levels according to the multiple threshold intervals, and the warning levels include extremely high risk, high risk, medium risk, low risk and extremely low risk levels; comparing the geological disaster risk warning index of the spatial position of each quantum sensor deployed in the target monitoring area under any time condition with the multiple threshold intervals to obtain a local geological disaster risk determination result; based on the local geological disaster risk determination result, obtaining an overall geological disaster risk determination result; based on the local geological disaster risk determination result and the overall geological disaster risk determination result, implementing early warning of local and overall risks of geological disasters. The present invention realizes the refined hierarchical warning of geological disaster risks by dividing the geological disaster risk warning index into multiple threshold intervals and setting different warning levels, breaking through the extensive limitations of traditional warning methods on risk level division, significantly improving the pertinence and operability of warnings, and providing a more scientific decision-making basis for disaster prevention and control; by comparing the geological disaster risk warning index and threshold interval of each quantum sensor in the target monitoring area, the accurate determination of local geological disaster risks is realized, subverting the general assessment of regional overall risks by traditional warning methods, and providing technical support for accurate monitoring and rapid response of local high-risk areas; by integrating the results of local geological disaster risk determination, a dynamic comprehensive assessment of overall geological disaster risks is realized, overcoming the defect that traditional methods cannot take into account both local and overall risks, and providing innovative solutions for all-round and multi-level early warning of geological disasters, promoting the leapfrog development of geological disaster warning from single scale to multi-scale collaborative analysis.

[0013] Optionally, obtaining the overall geological hazard risk determination result based on the local geological hazard risk determination result includes: constructing an overall geological hazard risk determination model based on the local geological hazard risk determination result, and the overall geological hazard risk determination model satisfies the following expression: , in, Represents the overall geological disaster risk determination index, Indicates The weight of the warning level score corresponding to the quantum sensor position, Indicates The warning risk level score corresponding to each quantum sensor position, represents the number of quantum sensors; according to the overall geological disaster risk determination model, the threshold interval of the overall geological disaster risk determination index is set; the overall geological disaster risk determination index is compared with the threshold interval of the overall geological disaster risk determination index to obtain the overall geological disaster risk determination result. The present invention realizes the quantitative assessment of the overall risk of geological disasters by constructing an overall geological disaster risk determination model and dynamically integrating the warning level scores and weight coefficients of local quantum sensors, significantly improving the scientificity and accuracy of the overall risk determination; by setting the threshold interval of the overall geological disaster risk determination index, the refined hierarchical determination of the overall risk is realized, overcoming the defects of the traditional method of fuzzy assessment of the overall risk, and providing accurate data support for the global prevention and control of geological disasters; by comparing the overall geological disaster risk determination index with the threshold interval, the dynamic real-time determination of the overall risk is realized, overcoming the deficiency that the traditional method cannot reflect the dynamic changes of the risk, and providing an innovative solution for the full-region and full-process early warning of geological disasters, and promoting the leapfrog development of geological disaster prevention and control from local management to global coordination.

[0014] Optionally, based on the geological disaster risk warning, tracing the causes and development trajectories of geological disasters, and optimizing monitoring and early warning strategies include: based on the geological disaster risk warning, using quantum state time inversion technology to obtain spatiotemporal evolution data; using principal component analysis and random forest algorithm to extract sensitive parameters that dominate disaster evolution, and determine the importance ranking of the sensitive parameters; calibrating the disaster threshold of the sensitive parameters; constructing a spatiotemporal correlation map through the spatiotemporal evolution data, the sensitive parameters and the disaster threshold, and the spatiotemporal correlation map is used to identify the key conduction channels of disaster chain reactions; according to the spatiotemporal correlation map, optimizing monitoring and early warning strategies. The present invention obtains spatiotemporal evolution data through quantum state time inversion technology, realizes high-precision tracing of the causes and development trajectories of geological disasters, breaks through the limitation of traditional methods that it is difficult to restore the evolution process of disasters, and provides a new technical means for the cause analysis and prediction of geological disasters; extracts sensitive parameters and determines their importance ranking through principal component analysis and random forest algorithm, realizes accurate identification of the dominant factors of disaster evolution, overcomes the defect of subjective judgment of disaster factor weights in traditional methods, and significantly improves the objectivity and scientificity of disaster risk analysis; identifies the key transmission channels of disaster chain reactions by constructing spatiotemporal correlation maps, realizes in-depth analysis of disaster evolution mechanisms, overcomes the defect of insufficient identification of disaster chain reaction transmission paths by traditional methods, provides innovative solutions for optimizing monitoring and early warning strategies, and promotes the transformation of geological disaster prevention and control from passive response to active intervention.

[0015] In the second aspect, the present invention provides a geological disaster monitoring system based on quantum coupling effect, including an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the system uses the method for monitoring geological disasters based on quantum coupling effect. The system provided by the present invention has high integration, smooth information transmission between components, and breaks through the sensitivity limit of classical sensors by utilizing the coupling effect of quantum bits and geological environmental parameters, and realizes ultra-high precision synchronous monitoring of stress, displacement and water level parameters; through nonlinear multi-field coupling models and spatiotemporal correlation maps, the key conduction paths of geological disaster chain reactions are accurately revealed, and the simplified distortion problem of traditional models for complex interactions is solved; by combining quantum state time inversion and disaster threshold calibration technology, dynamic tracing of disaster causes and advance prediction of risk evolution are realized, and the monitoring mode is promoted from passive response to active intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a method for monitoring geological disasters based on quantum coupling effect according to an embodiment of the present invention; Figure 2 A flowchart of an improved genetic algorithm according to an embodiment of the present invention; Figure 3 Schematic diagram of the structure of a geological disaster monitoring system based on quantum coupling effect according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0018] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0019] See also Figure 1 The embodiment of the present invention provides a method for monitoring geological disasters based on quantum coupling effect, the method comprising the following steps: S1. Acquire geological environment parameters of the target monitoring area, wherein the geological environment parameters include stress field, temperature field, displacement field and groundwater level parameters.

[0020] Among them, S1 includes the following steps: S11. Obtain stress field parameters of the target monitoring area.

[0021] In one embodiment, a geological model of the target monitoring area is first established based on geological exploration data.

[0022] Furthermore, according to the principles of geomechanics, reasonable boundary conditions and loads are imposed on the model.

[0023] Furthermore, a finite element analysis method is used to perform stress field analysis on the geological model to obtain stress field parameters.

[0024] S12. Obtain temperature field parameters of the target monitoring area.

[0025] In one embodiment, a plurality of ground temperature sensors are deployed in the target monitoring area to form a sensor network for continuous temperature monitoring.

[0026] Furthermore, soil temperature data is collected in real time through a sensor network, and the collected data is summarized and analyzed using a computer system to obtain temperature field parameters.

[0027] S13. Obtain the displacement field parameters of the target monitoring area.

[0028] In one embodiment, GPS receivers are deployed within the target monitoring area.

[0029] Furthermore, a GPS receiver is used to collect three-dimensional coordinate data of the monitoring points.

[0030] Furthermore, the collected data is processed and analyzed to obtain displacement field parameters.

[0031] S14. Obtain groundwater level parameters in the target monitoring area.

[0032] In one embodiment, monitoring wells are deployed above the aquifer in the target monitoring area.

[0033] Furthermore, a water level meter is installed in the monitoring well to measure the groundwater level.

[0034] Furthermore, the water level meter is read and the groundwater level data is recorded.

[0035] It should be noted that the reason for preliminarily obtaining data on stress field, temperature field, displacement field and groundwater level parameters is to enable subsequent steps to better achieve the optimal deployment of quantum sensors based on the performance indicator requirements of quantum sensors.

[0036] S2. Based on the geological environment parameters and the performance indicators of quantum sensors, a quantum sensor network is deployed in the target monitoring area, wherein the performance indicators include sensitivity indicators, coverage indicators and stability indicators.

[0037] Among them, S2 includes the following steps: S21. Construct a quantum sensor deployment model based on the geological environment parameters.

[0038] In one embodiment, the optimal deployment of the quantum sensor network is achieved based on the stress field, temperature field, displacement field and groundwater level parameters of the target monitoring area.

[0039] Specifically, the stress field, temperature field, displacement field and groundwater level parameters are first quantified; Furthermore, the deployment indicators of quantum sensors are defined to evaluate the deployment effects of quantum sensors at different locations. The deployment indicators include sensitivity index, coverage index and stability index. The sensitivity index reflects the sensitivity of the sensor to changes in geological environment parameters, the coverage index reflects the coverage of the sensor network to the target monitoring area, and the stability index reflects the stability and reliability of the sensor in the geological environment.

[0040] Furthermore, a quantum sensor deployment model is constructed, and the quantum sensor deployment model satisfies the following expression: , in, Indicates The quantum sensor is in the stress field The temperature field is The displacement field is , the groundwater level is The sensitivity index is Indicates The coverage index of a quantum sensor at a location, Indicates Quantum sensors are located at The stability index at , , represents the weight coefficient, represents the number of quantum sensors; the sensitivity index satisfies the following relationship: , in, Indicates The sensitivity index of a quantum sensor is Indicates The coupling strength coefficient between the quantum sensor and the geological environment parameter; the coverage index satisfies the following relationship: , in, Indicates The coverage index of quantum sensors, Indicates The volume of geological environment that can be monitored by a quantum sensor, represents the total volume of the target monitoring area, Indicates The distance from a quantum sensor to the center of the target monitoring area, represents the distance tolerance threshold; the stability index satisfies the following relationship: , in, Indicates The stability index of a quantum sensor, represents the stress field, represents the temperature field, represents the displacement field, Represents the groundwater level, , , , They represent the average values ​​of stress field, temperature field, displacement field and groundwater level in the target monitoring area, respectively. , , , They respectively represent the standard deviations of stress field, temperature field, displacement field and groundwater level in the target monitoring area.

[0041] S22. Using an improved genetic algorithm, solve the quantum sensor deployment model to obtain an optimal sensor deployment solution.

[0042] In one embodiment, see Figure 2 ,The quantum sensor deployment model in step S21 was solved using the improved genetic algorithm and the ,optimal sensor deployment scheme was obtained.

[0043] Specifically, binary coding is first used to represent the position of each quantum sensor.

[0044] Furthermore, a set of initial solutions is randomly generated as a population, each of which represents a possible sensor deployment scheme.

[0045] Furthermore, a fitness function, ie, an objective function value, is defined according to a given quantum sensor deployment model.

[0046] Furthermore, individuals with higher fitness are selected as parents through the roulette wheel selection method.

[0047] Furthermore, a crossover operation is performed on the selected parent individuals to generate new child individuals.

[0048] Furthermore, mutation operations are performed on offspring individuals to increase the diversity of the population.

[0049] Furthermore, the selection, crossover, and mutation steps are repeated until a preset number of iterations or fitness convergence condition is reached, and the optimal solution, i.e., the optimal sensor deployment scheme, is output. This step reflects the difference between the improved genetic algorithm and the basic genetic algorithm.

[0050] Specifically, the improved genetic algorithm proposed in the present invention has the following improvements compared to the basic genetic algorithm: adaptive weight adjustment, elite retention strategy, crossover and mutation probability adaptation, and local search strategy.

[0051] The adaptive weight adjustment refers to dynamically adjusting the weight coefficient according to the fitness distribution of the population during the iteration process to balance the importance of different indicators.

[0052] The elite retention strategy refers to retaining the individuals with the highest fitness in each generation and directly copying them to the next generation to avoid the loss of excellent solutions during the iteration process.

[0053] The crossover and mutation probability adaptation refers to dynamically adjusting the crossover and mutation probabilities according to the fitness of the individuals, so that the algorithm can maintain diversity and converge quickly during the search process.

[0054] The local search strategy refers to introducing a local search algorithm based on the basic genetic algorithm to further optimize the excellent solution and improve the accuracy of the solution. The local search algorithm includes gradient descent and simulated annealing.

[0055] This means that the improved genetic algorithm can more effectively solve the quantum sensor deployment model and obtain a better sensor deployment plan by introducing improved measures such as adaptive weight adjustment, elite retention strategy, crossover and mutation probability adaptation, and local search strategy.

[0056] S23. Deploy a quantum sensor network according to the optimal sensor deployment plan.

[0057] In one embodiment, quantum sensors are specifically deployed according to the optimal sensor deployment method obtained in step S22, thereby forming a quantum sensor network.

[0058] S3. Based on the quantum sensor network, initialize quantum bits and obtain initialization results.

[0059] In one embodiment, a new qubit initialization model is constructed, which is called the initialization model of geological environment parameters and qubit coupled tensor network. The initialization model directly couples the geological environment parameters with the qubit initialization process and utilizes the multi-body entanglement structure and fractional differential dynamics of the tensor network to achieve environment-driven qubit initialization. The initialization model satisfies the following expression: , in, Initialize the quantum bit state. is the geological environment coupling term, is the multi-body entanglement term, is the topology protection term, is the environmental normalization term, is the decoherence suppression term. The geological environment coupling term Satisfies the following expression: , in, For the A geological environment parameter tensor, is the stress field, is the temperature field, is the temperature field, is the groundwater level, is the Pauli operator, which is used to map classical geological parameters to quantum space. The target monitoring area space, is a fractional exponent that satisfies the following relationship: , It should be noted that this expression represents the base of the natural logarithm, reflecting nonlinear coupling. Satisfies the following expression: , in, and All belong to the Pauli operator, For the and The coupling strength driven by the geological parameters satisfies the following relationship: , It should be noted that this term constructs entanglement between quantum bits through the gradient field of geological parameters. Satisfies the following relationship: , in, For the topological invariants, determined by the spatial distribution of geological parameters, The space representing the geological parameters. Satisfies the following expression: , in, For the A geological environment parameter tensor, represents the environmental sensitivity index, is the reference geological parameter value. Satisfies the following expression: , in, is the Boltzmann constant, is the effective temperature, which is determined by the temperature field and groundwater. is the decoherence length, which is related to the gradient of geological parameters, For the A geological environment parameter tensor.

[0060] It should be noted that the present invention directly embeds stress field, temperature field, displacement field and groundwater level into the quantum initialization process, replacing the traditional electromagnetic field initialization method; nonlinear coupling is achieved through fractional-order microdynamics; topological invariants are constructed using the spatial distribution of geological parameters to ensure that the initialization process is robust to local disturbances; the initialization process is dynamically adjusted to adapt to complex geological environments through environmental normalization terms and decoherence suppression terms; and entanglement between quantum bits is constructed using the gradient field of geological parameters to achieve efficient initialization. This method has high environmental adaptability and robustness, and is suitable for quantum sensor networks in complex geological environments.

[0061] Furthermore, through the initialization model, the quantum bits are initialized and the initialization results are obtained.

[0062] S4. Based on the initialization result, microwave signals are used to stimulate the tunneling effect of quantum bits, so that the quantum bits are coupled with the geological environment parameters to obtain an entangled state.

[0063] In one embodiment, for the quantum bit initialization state obtained in step S3, a microwave signal source is used to emit a microwave signal with a specific frequency and power, and the microwave signal can cause the quantum bit to undergo a tunneling effect, causing the quantum bit to transition between energy levels. Under the excitation of the microwave signal, the quantum bit undergoes a tunneling effect, and its state begins to be affected by geological environment parameters.

[0064] Furthermore, by precisely controlling the frequency and power of the microwave signal and the parameters of the quantum bit system, the quantum bits can be tightly coupled with the geological environment parameters to obtain an entangled state.

[0065] It should be noted that the tunneling effect of quantum bits is an important phenomenon in quantum mechanics. It refers to the phenomenon that quantum bits tunnel to the other side through some non-classical path when they do not have enough energy to overcome the potential barrier.

[0066] S5. Based on the entangled state, quantum measurement technology is used to obtain quantum state data, where the quantum state data includes quantum information of the geological environment parameters.

[0067] In one embodiment, the entangled state is measured using a selected quantum measurement technique, which includes projection measurement, weak measurement, and quantum non-destructive measurement techniques.

[0068] Furthermore, the data obtained during the measurement process are accurately recorded, and the data reflects the entangled relationship between the quantum bit state and the geological environment parameters, thereby containing quantum information of the geological environment parameters.

[0069] S6. Analyze the coupling relationship between the geological environment parameters through the quantum state data and obtain analysis results.

[0070] Wherein, S6 further comprises the following steps: S61. Construct a multi-field coupling analysis model based on the quantum state data.

[0071] In one embodiment, based on step S5, a multi-field coupling analysis model is constructed. The model is used to describe the coupling relationship between the multi-field parameters of geological stress field, displacement field, temperature field and groundwater level, and realize high-precision analysis through quantum computing. The multi-field coupling analysis model satisfies the following expression:

[0072] in, Represents the spatial position of the geological stress field and time The distribution function under Represents the geological displacement field in space and time The distribution function under Indicates the spatial location of groundwater and time The distribution function under Represents the temperature field in space and time The distribution function under Indicates the pore water pressure of the geological medium in space and time The distribution function under Represents the diffusion coefficient of the stress field, describing the propagation characteristics of stress in the medium. It represents the coupling coefficient of displacement field to stress field, reflecting the influence of displacement change on stress distribution. It represents the coupling coefficient of temperature field to stress field, reflecting the influence of temperature change on stress distribution. It represents the nonlinear influence coefficient of groundwater level on displacement field, and describes the modulation effect of water level change on displacement field. represents the correction coefficient of pore water pressure to temperature field, represents the weight coefficient of the time integral term, It represents the nonlinear modulation coefficient of the time integral of the groundwater level, describing the correction of the historical coupling effect by the water level change. represents the coupling coefficient of pore water pressure gradient to stress field, represents the correction coefficient of the temperature field to the pore water pressure gradient, is the time variable.

[0073] S62. According to the multi-field coupling analysis model, the coupling relationship between the geological environment parameters is analyzed and the analysis results are obtained.

[0074] In one embodiment, firstly, relevant data of the stress field are inputted, and the change of the displacement field is calculated, wherein the relevant data include ground stress and boundary condition data.

[0075] Furthermore, the influence of stress field changes on displacement field and the adjustment of stress field by displacement field feedback are analyzed to obtain the coupling analysis results of stress field and displacement field.

[0076] Furthermore, relevant data of the temperature field are input, and the change of the temperature field is calculated, wherein the relevant data includes initial temperature distribution and heat source distribution data.

[0077] Furthermore, the influence of temperature field changes on stress field and the influence of stress field changes on temperature field distribution are analyzed to obtain the coupling analysis results of temperature field and stress field.

[0078] Furthermore, relevant data of pore water pressure gradient are input, and the change of pore water pressure is calculated, wherein the relevant data includes groundwater level change and seepage pressure data.

[0079] Furthermore, the influence of the change of pore water pressure gradient on the stress field and the influence of the change of stress field on the pore water pressure distribution are analyzed to obtain the coupling analysis results of pore water pressure gradient and stress field.

[0080] Furthermore, relevant data of the groundwater level are input, and the change of the groundwater level is calculated, wherein the relevant data includes water level change and infiltration rate data.

[0081] Furthermore, the influence of displacement field changes on groundwater level and the feedback effect of groundwater level changes on displacement field are analyzed to obtain the coupling analysis results of displacement field and groundwater level.

[0082] S7. Implement geological disaster risk warning based on the analysis results.

[0083] In one embodiment, based on the results of multi-field coupling analysis, a geological disaster risk warning model is constructed, and the geological disaster risk warning model satisfies the following expression:

[0084] in, Indicates the position in space and time The geological disaster risk warning index under Represents the spatial position of the geological stress field and time The distribution function under Represents the geological displacement field in space and time The distribution function under Indicates the spatial location of groundwater and time The distribution function under Represents the temperature field in space and time The distribution function under Indicates the pore water pressure of the geological medium in space and time The distribution function under Represents the spatial position of the geological stress field The maximum value at Represents the geological displacement field in space The maximum value at Represents the temperature field in space The maximum value at Indicates the pore water pressure of the geological medium in space The maximum value at represents the diffusion coefficient of the stress field, represents the coupling coefficient of the displacement field to the stress field, represents the coupling coefficient of the temperature field to the stress field, represents the nonlinear influence coefficient of groundwater level on displacement field, represents the correction coefficient of pore water pressure to temperature field, represents the weight coefficient of the time integral term, represents the nonlinear modulation coefficient of the time integral of groundwater level, represents the coupling coefficient of pore water pressure gradient to stress field, represents the correction coefficient of the temperature field to the pore water pressure gradient, is the time variable.

[0085] It should be noted that the model can accurately assess the risk of geological disasters with only one geological disaster risk warning index by comprehensively considering the interaction between geological stress field, geological displacement field, groundwater, temperature field and pore water pressure of geological media.

[0086] Specifically, through the geological disaster warning model, the geological disaster risk warning index of the spatial position of any quantum sensor in the target monitoring area under any time conditions is obtained; the geological disaster risk warning index is also called the local geological disaster risk determination index.

[0087] Furthermore, the threshold interval of the geological disaster risk warning index is divided into multiple threshold intervals, and the multiple threshold intervals include an extremely high threshold interval, a high threshold interval, a medium threshold interval, a low threshold interval and an extremely low threshold interval.

[0088] Furthermore, different warning levels are set according to the multiple threshold intervals, and the warning levels include extremely high risk, high risk, medium risk, low risk and extremely low risk levels.

[0089] Furthermore, the geological disaster risk warning index of each quantum sensor deployed in the target monitoring area under any time condition is compared with the multiple threshold intervals to obtain a local geological disaster risk determination result.

[0090] Furthermore, based on the local geological disaster risk determination results, an overall geological disaster risk determination model is constructed, and the overall geological disaster risk determination model satisfies the following expression:

[0091] in, Represents the overall geological disaster risk determination index, Indicates The weight of the warning level score corresponding to the quantum sensor position, Indicates The warning risk level score corresponding to each quantum sensor position, Represents the number of quantum sensors.

[0092] It should be noted that the warning level score refers to the score corresponding to the warning level reached by the geological disaster risk warning index at the local spatial location under any time conditions, as follows: When the local geological disaster risk warning index falls into the extremely high threshold range, the warning level is the extremely high risk level, and the warning level score is 5 points; When the local geological disaster risk warning index falls into the high threshold range, the warning level is a high risk level, and the warning level score is 4 points; When the local geological disaster risk warning index falls into the medium threshold range, the warning level is the medium risk level, and the warning level score is 3 points; When the local geological disaster risk warning index falls into the low threshold range, the warning level is a low risk level, and the warning level score is 2 points; When the local geological disaster risk warning index falls into the extremely low threshold range, the warning level is the extremely low risk level, and the warning level score is 1 point; Furthermore, according to the overall geological disaster risk determination model, a threshold range of the overall geological disaster risk determination index is set; It should be noted that this threshold interval is also divided into extremely high threshold interval, high threshold interval, medium threshold interval, low threshold interval and extremely low threshold interval, but the size of the threshold interval is different from that of the local geological hazard risk determination index.

[0093] Furthermore, the overall geological hazard risk determination index is compared with a threshold range of the overall geological hazard risk determination index to obtain an overall geological hazard risk determination result.

[0094] Furthermore, based on the local geological disaster risk assessment results and the overall geological disaster risk assessment results, early warning of local and overall geological disaster risks is achieved.

[0095] It should be noted that the present invention has successfully solved the problem of coordinated early warning of local risks and overall risks in the field of geological disaster early warning, and has effectively overcome the defects of traditional methods such as inconsistent monitoring scales, neglected coupling effects, and obvious early warning blind spots. By introducing an innovative technical system of quantum sensor networks and multi-physical field coupling modeling, a fundamental innovation in the geological disaster early warning method has been achieved.

[0096] S8. Based on the above geological disaster risk warning, trace the causes and development trajectory of geological disasters and optimize monitoring and early warning strategies.

[0097] In one embodiment, advanced quantum sensing equipment is deployed in areas with high risk of geological disasters. The quantum sensing equipment can capture tiny signals of geological micro-movements and groundwater level changes with high precision.

[0098] Furthermore, the quantum state time reversal technology is introduced to reconstruct the spatiotemporal evolution process before the geological disaster by reversely analyzing the historical evolution information of the quantum state. The quantum state time reversal technology utilizes the non-classical characteristics of the quantum state to achieve the retrospection of the historical state of the complex geological system, breaking through the limitations of traditional monitoring methods in time resolution and accuracy.

[0099] Furthermore, the data obtained by quantum state time inversion technology is combined with traditional geological monitoring data, including GPS displacement monitoring data and groundwater level monitoring data, to construct a spatiotemporal evolution data set of geological disasters. The data set contains not only spatial distribution information, but also dynamic changes in the time dimension, providing rich materials for subsequent data analysis.

[0100] Furthermore, principal component analysis is used to reduce the dimensionality of spatiotemporal evolution data and identify the principal components that have the greatest impact on the evolution of geological hazards. This step aims to screen out key variables from numerous monitoring parameters, reduce data redundancy, and improve the efficiency of subsequent analysis.

[0101] Furthermore, based on the preliminary screening by principal component analysis, the remaining parameters are further analyzed using the random forest algorithm. The random forest can accurately evaluate the contribution of each parameter to the evolution of geological hazards by constructing multiple decision trees and integrating their prediction results. Through this step, the sensitive parameters that dominate the evolution of disasters can be accurately extracted and their importance ranking can be determined.

[0102] Furthermore, we collect sensitive parameter data before and after geological disasters in history and build a historical disaster database. Through in-depth analysis of historical data, we can understand the changing trends and characteristics of sensitive parameters before and after disasters.

[0103] Furthermore, based on the historical disaster database, statistical methods, including extreme value analysis and Bayesian network methods, are used to calibrate the disaster threshold of each sensitive parameter. The disaster threshold represents the critical value that the sensitive parameter may reach before a disaster occurs, which is of vital importance to the early warning system.

[0104] Furthermore, the spatiotemporal correlation map is constructed by combining spatiotemporal evolution data, sensitive parameters and their disaster thresholds using complex network theory. The map uses sensitive parameters as nodes and the spatiotemporal correlations between them as edges, which intuitively shows the interactions and dependencies between various parameters in the process of geological disaster evolution.

[0105] Furthermore, in the spatiotemporal correlation map, the key transmission channels of the disaster chain reaction are identified through algorithms. These channels represent the most vulnerable and critical links in the evolution of disasters, which are of guiding significance for the formulation of effective monitoring and early warning strategies.

[0106] Furthermore, according to the key transmission channels of disaster chain reactions revealed by the spatiotemporal correlation map, the monitoring and early warning strategies for geological disasters can be optimized. For example, more accurate monitoring equipment can be deployed on key transmission channels, or the triggering conditions of the early warning system can be adjusted to more accurately reflect disaster risks.

[0107] Furthermore, the optimized monitoring and early warning strategies are put into practice, and feedback data is continuously collected during the actual operation process. Through continuous data analysis and strategy adjustment, the effectiveness and accuracy of the early warning system are ensured.

[0108] It should be noted that the present invention has achieved a fundamental innovation in the geological disaster early warning method by introducing an innovative technical system of quantum sensor networks and multi-physical field coupling modeling, combined with advanced data analysis algorithms and disaster threshold calibration methods, demonstrating its creativity, breakthrough and subversiveness.

[0109] See also Figure 3 , Figure 3The schematic diagram of the structure of the geological disaster monitoring system based on quantum coupling effect in the embodiment of the present invention. The system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the system uses the geological disaster monitoring method based on quantum coupling effect.

[0110] In this embodiment, the input device includes a sensor network and a quantum coupling interface, which is used to collect geological environmental parameter data of geological disaster risk points in real time and realize efficient and real-time transmission of data.

[0111] Specifically, the sensor network includes stress sensors, temperature sensors, displacement sensors and groundwater level sensors, which are used to monitor various geological parameters of geological disaster risk points in real time. The sensor network can capture the tiny signals of geological micro-movements and groundwater level changes with high precision through advanced sensing technology; the quantum coupling interface serves as a bridge between the sensor network and the processor. The quantum coupling interface uses quantum effects such as quantum entanglement to achieve efficient and real-time transmission between sensor data and the processor. The design of the quantum coupling interface fully considers the advantages of quantum communication, such as high confidentiality and long-distance transmission, to ensure the accuracy and security of the data.

[0112] The processor is the core part of the geological disaster monitoring system, including a quantum computing unit and a data analysis module, which is used for data processing, analysis and generation of early warning information.

[0113] Specifically, the quantum computing unit uses the high efficiency and parallel processing capabilities of quantum computing to quickly process and analyze data transmitted from the input device. The design of the quantum computing unit fully considers the real-time and accuracy requirements of geological disaster monitoring, and can efficiently process large amounts of complex data; the data analysis module has built-in advanced data analysis algorithms and models, such as machine learning algorithms and big data analysis technology, which are used to conduct in-depth mining and analysis of the collected data and identify potential risks and trends of geological disasters.

[0114] The output devices include display screens, alarms and communication devices, which are used to display and convey monitoring and warning information.

[0115] Specifically, the display screen is used to display various data and analysis results of geological disaster monitoring in real time, such as changing trends of geological parameters and early warning information. The design of the display screen fully considers user-friendliness and readability, and can intuitively display monitoring data and analysis results; when the processor triggers the early warning mechanism, the alarm will emit an audible and visual alarm signal to promptly notify relevant personnel to take countermeasures. The design of the alarm fully considers reliability and response speed, and can ensure the timely communication of early warning information; the communication equipment is used to remotely transmit early warning information and other important data to relevant departments and personnel. The communication equipment supports multiple communication methods, and can ensure remote transmission and reception of data.

[0116] In summary, the present invention has achieved four major breakthroughs by constructing a quantum sensor network and a multi-field coupling analysis model, and introducing quantum entangled states, topological protection terms and time reversal technology into the field of geological disaster monitoring: First, by utilizing the coupling effect of quantum bits and geological environmental parameters, the sensitivity limit of classical sensors has been broken through, and ultra-high precision synchronous monitoring of stress, displacement and water level parameters has been achieved; second, through nonlinear multi-field coupling models and spatiotemporal correlation maps, the key conduction paths of geological disaster chain reactions are accurately revealed, solving the problem of simplified distortion of complex interactions by traditional models; third, by combining quantum state time reversal with disaster threshold calibration technology, dynamic tracing of disaster causes and advance prediction of risk evolution are achieved, promoting the transition of monitoring mode from passive response to active intervention. The present invention significantly improves the reliability of geological disaster warnings, and provides innovative technical support for deep resource development and major engineering safety scenarios.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A geological disaster monitoring method based on quantum coupling effect, characterized in that: The method comprises the following steps: Acquire geological environment parameters of the target monitoring area, wherein the geological environment parameters include stress field, temperature field, displacement field and groundwater level parameters; According to the geological environment parameters and in combination with the performance indicators of quantum sensors, a quantum sensor network is deployed in the target monitoring area, wherein the performance indicators include sensitivity indicators, coverage indicators and stability indicators; Based on the quantum sensor network, initializing quantum bits and obtaining initialization results; According to the initialization result, the tunneling effect of the quantum bit is stimulated by the microwave signal, so that the quantum bit is coupled with the geological environment parameter to obtain an entangled state; Based on the entangled state, using quantum measurement technology, obtaining quantum state data, wherein the quantum state data includes quantum information of the geological environment parameters; Analyzing the coupling relationship between the geological environment parameters and obtaining analysis results through the quantum state data; Based on the analysis results, early warning of geological disaster risks is achieved; Based on the above geological disaster risk warning, trace the causes and development trajectory of geological disasters and optimize monitoring and early warning strategies.

2. A method for monitoring geological disasters based on quantum coupling effect according to claim 1, characterized in that: Deploying a quantum sensor network in the target monitoring area based on the geological environment parameters and in combination with the performance indicators of the quantum sensor includes: According to the geological environment parameters, a quantum sensor deployment model is constructed, and the quantum sensor deployment model satisfies the following expression: ， in, Indicates The quantum sensor is in the stress field The temperature field is The displacement field is , the groundwater level is The sensitivity index is Indicates The coverage index of a quantum sensor at a location, Indicates Quantum sensors are located at The stability index at , , represents the weight coefficient, represents the number of quantum sensors; the sensitivity index satisfies the following relationship: ， in, Indicates The sensitivity index of a quantum sensor is Indicates The coupling strength coefficient between the quantum sensor and the geological environment parameter; the coverage index satisfies the following relationship: ， in, Indicates The coverage index of quantum sensors, Indicates The volume of geological environment that can be monitored by a quantum sensor, represents the total volume of the target monitoring area, Indicates The distance from a quantum sensor to the center of the target monitoring area, represents the distance tolerance threshold; the stability index satisfies the following relationship: ， in, Indicates The stability index of a quantum sensor, represents the stress field, represents the temperature field, represents the displacement field, Represents the groundwater level, , , , They represent the average values ​​of stress field, temperature field, displacement field and groundwater level in the target monitoring area, respectively. , , , They represent the standard deviations of stress field, temperature field, displacement field and groundwater level in the target monitoring area respectively; Using an improved genetic algorithm to solve the quantum sensor deployment model and obtain an optimal sensor deployment solution; According to the optimal sensor deployment plan, a quantum sensor network is deployed.

3. A geological disaster monitoring method based on quantum coupling effect according to claim 1, characterized in that: Initializing the quantum bits and obtaining the initialization results based on the quantum sensor network includes: Based on the quantum sensor, an initialization model of geological environment parameters and quantum bit coupling tensor network is constructed, and the initialization model satisfies the following expression: ， in, Initialize the quantum bit state. is the geological environment coupling term, is the multi-body entanglement term, is the topology protection term, is the environmental normalization term, is the decoherence suppression term; Through the initialization model, the quantum bits are initialized and the initialization results are obtained.

4. A geological disaster monitoring method based on quantum coupling effect according to claim 1, characterized in that: Analyzing the coupling relationship between the geological environment parameters and obtaining analysis results through the quantum state data includes: A multi-field coupling analysis model is constructed through the quantum state data, and the multi-field coupling analysis model satisfies the following expression: ， in, Indicates the spatial position of the geological stress field and time The distribution function under Represents the geological displacement field in space and time The distribution function under Indicates the spatial location of groundwater and time The distribution function under Represents the temperature field in space and time The distribution function under Indicates the pore water pressure of the geological medium in space and time The distribution function under represents the diffusion coefficient of the stress field, represents the coupling coefficient of the displacement field to the stress field, represents the coupling coefficient of the temperature field to the stress field, represents the nonlinear influence coefficient of groundwater level on displacement field, represents the correction coefficient of pore water pressure to temperature field, represents the weight coefficient of the time integral term, represents the nonlinear modulation coefficient of the time integral of groundwater level, represents the coupling coefficient of pore water pressure gradient to stress field, represents the correction coefficient of the temperature field to the pore water pressure gradient, is the time variable; According to the multi-field coupling analysis model, the coupling relationship between the geological environment parameters is analyzed and the analysis results are obtained.

5. A method for monitoring geological disasters based on quantum coupling effect according to claim 4, characterized in that: Analyzing the coupling relationship between the geological environment parameters and obtaining analysis results according to the multi-field coupling analysis model includes: According to the multi-field coupling analysis model, analyzing the coupling relationship between the stress field and the displacement field and obtaining analysis results; According to the multi-field coupling analysis model, analyzing the coupling relationship between the temperature field and the stress field and obtaining analysis results; According to the multi-field coupling analysis model, the coupling relationship between the pore water pressure gradient and the stress field is analyzed and the analysis results are obtained; According to the multi-field coupling analysis model, the coupling relationship between the displacement field and the groundwater level is analyzed and the analysis results are obtained.

6. A geological disaster monitoring method based on quantum coupling effect according to claim 1, characterized in that: According to the analysis results, the geological disaster risk warning is realized including: According to the analysis results, a geological disaster risk warning model is constructed, and the geological disaster risk warning model satisfies the following expression: ， in, Indicates the position in space and time The geological disaster risk warning index under Indicates the spatial position of the geological stress field and time The distribution function under Represents the geological displacement field in space and time The distribution function under Indicates the spatial location of groundwater and time The distribution function under Represents the temperature field in space and time The distribution function under Indicates the pore water pressure of the geological medium in space and time The distribution function under Indicates the spatial position of the geological stress field The maximum value at Represents the geological displacement field in space The maximum value at Represents the temperature field in space The maximum value at Indicates the pore water pressure of the geological medium in space The maximum value at represents the diffusion coefficient of the stress field, represents the coupling coefficient of the displacement field to the stress field, represents the coupling coefficient of the temperature field to the stress field, represents the nonlinear influence coefficient of groundwater level on displacement field, represents the correction coefficient of pore water pressure to temperature field, represents the weight coefficient of the time integral term, represents the nonlinear modulation coefficient of the time integral of groundwater level, represents the coupling coefficient of pore water pressure gradient to stress field, represents the correction coefficient of the temperature field to the pore water pressure gradient, is the time variable; Geological disaster risk warning is achieved through the geological disaster risk warning model.

7. A method for monitoring geological disasters based on quantum coupling effect according to claim 6, characterized in that: The method of implementing geological disaster risk warning through the geological disaster risk warning model includes: Through the geological disaster early warning model, the geological disaster risk early warning index of the spatial position of any quantum sensor in the target monitoring area under any time condition is obtained; Dividing the threshold interval of the geological disaster risk warning index into multiple threshold intervals; According to the multiple threshold intervals, different warning levels are set, the warning levels including extremely high risk, high risk, medium risk, low risk and extremely low risk levels; Compare the geological disaster risk warning index of each quantum sensor deployed in the target monitoring area under any time condition with the multiple threshold intervals to obtain a local geological disaster risk determination result; Based on the local geological disaster risk determination result, obtaining the overall geological disaster risk determination result; According to the local geological disaster risk assessment results and the overall geological disaster risk assessment results, early warning of local and overall geological disaster risks is achieved.

8. A method for monitoring geological disasters based on quantum coupling effect according to claim 7, characterized in that: The obtaining of the overall geological disaster risk determination result based on the local geological disaster risk determination result comprises: Based on the local geological disaster risk determination results, an overall geological disaster risk determination model is constructed, and the overall geological disaster risk determination model satisfies the following expression: ， in, Represents the overall geological disaster risk determination index, Indicates The weight of the warning level score corresponding to the quantum sensor position, Indicates The warning risk level score corresponding to each quantum sensor position, represents the number of quantum sensors; According to the overall geological disaster risk determination model, a threshold range of the overall geological disaster risk determination index is set; The overall geological disaster risk determination index is compared with the threshold range of the overall geological disaster risk determination index to obtain the overall geological disaster risk determination result.

9. The method for monitoring geological disasters based on quantum coupling effect according to claim 1, characterized in that: Based on the geological disaster risk warning, the causes and development trajectories of geological disasters are traced, and the monitoring and early warning strategies are optimized, including: Based on the geological disaster risk warning, the time-space evolution data is obtained by using quantum state time inversion technology; Using principal component analysis and random forest algorithm, extract sensitive parameters that dominate disaster evolution and determine the importance ranking of the sensitive parameters; Calibrate the catastrophic threshold of the sensitive parameter; Constructing a spatiotemporal correlation map through the spatiotemporal evolution data, the sensitive parameters and the disaster threshold, wherein the spatiotemporal correlation map is used to identify key transmission channels of disaster chain reactions; Based on the spatiotemporal correlation map, the monitoring and early warning strategies are optimized.

10. A geological disaster monitoring system based on quantum coupling effect, the system using a geological disaster monitoring method based on quantum coupling effect according to any one of claims 1 to 9, characterized in that: The system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions.

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