A coal mining subsidence area photovoltaic power station monitoring method, device, equipment and medium

Abstract

The present application relates to photovoltaic power plant operation and maintenance technical field, disclose a kind of coal mining subsidence area's photovoltaic power plant monitoring method, device, equipment and medium, the method provided in the present application, utilize the real-time settlement monitoring data of the synthetic aperture radar interferometric monitoring data fusion of target photovoltaic power station acquired by multiple satellites, break through single satellite monitoring limitation, carry out accurate settlement monitoring to the area where photovoltaic power station is located;Real-time settlement data and historical geological exploration data are combined to construct geological constitutive model, and simulate multi-scenario settlement field, establish the internal correlation of surface subsidence and regional geological conditions in coal mining subsidence area, realize the forward-looking prediction of settlement trend within preset time;Finally, the settlement field prediction result is used as boundary, sensor data is used to correct photovoltaic support finite element model and is coupled to solve, realize the quantitative mechanical coupling of geological subsidence and photovoltaic support structure response, facilitate to accurately predict the structure safety of photovoltaic support of photovoltaic power station in coal mining subsidence area.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power plant monitoring technology, specifically to a method, device, equipment, and medium for monitoring photovoltaic power plants in coal mining subsidence areas. Background Technology

[0002] Large-scale coal mining subsidence areas are common in major coal-producing regions. These areas exhibit long-term, slow, and non-uniform surface subsidence, with complex geological conditions and poor stability. Simultaneously, these areas often possess abundant solar resources, making them important sites for large-scale photovoltaic (PV) power plant construction. However, the geological characteristics of coal mining subsidence areas pose severe challenges to the long-term safe operation of PV power plants. Non-uniform surface subsidence directly impacts the PV support foundation, leading to abnormal stress and strain in the support structure, and even causing tilting, loosening of connections, and structural damage. This seriously threatens the structural stability and normal power generation of the PV power plant. Therefore, accurate monitoring and early warning of the structural safety of PV support systems in PV power plants within coal mining subsidence areas has become a critical issue that the industry urgently needs to address.

[0003] The safety monitoring methods for photovoltaic power plants disclosed in related technologies include: manual measurement, sensor monitoring, or single synthetic aperture radar interferometry (InSAR) remote sensing monitoring. The manual measurement method involves conducting fixed-point and periodic manual inspections and data collection at photovoltaic power plants. The sensor monitoring method obtains structural data of photovoltaic supports in photovoltaic power plants by deploying a small number of local sensors of various types, such as tilt angle and strain sensors. The InSAR remote sensing monitoring method relies solely on synthetic aperture radar interferometry technology of a single satellite to collect surface subsidence data.

[0004] Because the geological evolution of coal mining subsidence areas is affected by multiple factors such as soil characteristics, groundwater level changes, and the spatial and temporal distribution of goaf, the surface subsidence pattern is complex. The monitoring methods disclosed in related technologies can only obtain surface subsidence or local deformation data of the support, making it difficult to provide early warning of the role of photovoltaic supports based on the subsidence characteristics of coal mining subsidence areas. Summary of the Invention

[0005] This invention provides a method, device, equipment, and medium for monitoring photovoltaic power stations in coal mining subsidence areas, in order to solve the problem that the monitoring methods disclosed in related technologies are difficult to use to provide early warning of the effects of photovoltaic supports based on the subsidence characteristics of coal mining subsidence areas.

[0006] In a first aspect, the present invention provides a method for monitoring a photovoltaic power station in a coal mining subsidence area, the method comprising:

[0007] Based on the synthetic aperture radar interferometric monitoring data of the target photovoltaic power station collected in real time by multiple satellites, the real-time settlement monitoring data of the corresponding target photovoltaic power station is obtained by using a multi-source data fusion algorithm.

[0008] Based on the real-time settlement monitoring data of the target photovoltaic power station, combined with the historical geological exploration data of the area where the target photovoltaic power station is located, a geological constitutive model is constructed and simulated to obtain the prediction results of various surface settlement fields in the area where the photovoltaic power station is located within a preset time period; the historical geological exploration data includes geological exploration reports, groundwater level change data, and spatiotemporal distribution information of mining subsidence areas;

[0009] Each predicted land subsidence field in the area where the photovoltaic power station is located is used as a boundary. The parameters of each preset finite element analysis model of the photovoltaic support are corrected using real-time monitoring data from sensors in the target photovoltaic power station. The corrected finite element analysis model of the photovoltaic support is then coupled and solved to obtain the monitoring results of the photovoltaic support under the corresponding predicted land subsidence field. The monitoring results include theoretical strain distribution cloud map, key node displacement, structural stress ratio and natural frequency change.

[0010] Through the above implementation method, real-time settlement monitoring data is obtained by fusing synthetic aperture radar interferometry monitoring data of the target photovoltaic power station collected in real time by multiple satellites, breaking through the limitations of single satellite monitoring and enabling precise settlement monitoring of the area where the photovoltaic power station is located. A geological constitutive model is constructed by combining real-time settlement data with historical geological exploration data, and multiple settlement fields are simulated to establish the intrinsic relationship between surface settlement and regional geological conditions in the coal mining subsidence area, achieving forward-looking prediction of settlement trends within a preset time. Finally, the settlement field prediction results are used as the boundary, and the finite element model of the photovoltaic support is corrected by sensor data and coupled and solved to achieve quantitative mechanical coupling between geological settlement and the structural response of the photovoltaic support, which facilitates accurate prediction of the structural safety of the photovoltaic support of the photovoltaic power station in the coal mining subsidence area.

[0011] In one optional implementation, the synthetic aperture radar interferometry monitoring data of the target photovoltaic power station, acquired in real time by multiple satellites, is used to obtain real-time settlement monitoring data of the corresponding target photovoltaic power station using a multi-source data fusion algorithm, including:

[0012] Based on the weather forecast and current risk level of the area where the target photovoltaic power station is located, multiple pre-selected satellites are determined using satellite transit forecast and mission planning algorithms;

[0013] Based on the synthetic aperture radar interferometry monitoring data of each preselected satellite for the area where the target photovoltaic power station is located, the synthetic aperture radar interferometry monitoring data to be fused for the corresponding preselected satellite is obtained by using the data correction method.

[0014] By integrating synthetic aperture radar interferometry monitoring data from multiple pre-selected satellites, and using a multi-source data fusion algorithm, real-time settlement monitoring data of the target photovoltaic power station is obtained.

[0015] Through the above implementation methods, combined with the weather forecast and current risk level of the target photovoltaic power station area, satellite transit forecasts and mission planning algorithms are used to facilitate precise scheduling of satellite resources, ensuring the availability of monitoring data for the target photovoltaic power station area and avoiding the blindness of single satellite scheduling. At the same time, data correction methods are used to correct the monitoring data of each pre-selected satellite, improving the accuracy and effectiveness of the monitoring data of each pre-selected satellite. Then, a multi-source data fusion algorithm is used to integrate the corrected monitoring data of each pre-selected satellite, giving full play to the complementary advantages of different satellites in monitoring cycle, resolution, and wavelength, making up for the shortcomings of single satellite monitoring, and finally obtaining real-time settlement monitoring data covering the target photovoltaic power station, providing accurate basic data for subsequent geological modeling, settlement prediction, and support structure response analysis.

[0016] In one optional implementation, the step of obtaining the corresponding synthetic aperture radar interferometry monitoring data to be fused for the pre-selected satellites based on the acquired synthetic aperture radar interferometry monitoring data of each pre-selected satellite over the area where the target photovoltaic power station is located, using a data correction method, includes:

[0017] Based on the synthetic aperture radar interferometry monitoring data of each pre-selected satellite over the area where the target photovoltaic power station is located, and combined with the precise orbit data of the corresponding satellite, the first synthetic aperture radar interferometry monitoring data of the corresponding pre-selected satellite is obtained by using the orbital fine correction method; the first synthetic aperture radar interferometry monitoring data includes the synthetic aperture radar interferometry monitoring data of the area where the target photovoltaic power station is located after removing orbital errors.

[0018] Based on the first synthetic aperture radar interferometry monitoring data of each pre-selected satellite, combined with external digital elevation model data, the second synthetic aperture radar interferometry monitoring data of the corresponding pre-selected satellite is obtained using the terrain phase removal and correction method; the second synthetic aperture radar interferometry monitoring data includes the first synthetic aperture radar interferometry monitoring data of the target photovoltaic power station area after removing terrain errors.

[0019] Based on the second synthetic aperture radar interferometry monitoring data of each preselected satellite, combined with ERA5 atmospheric reanalysis data, the corresponding synthetic aperture radar interferometry monitoring data to be fused for the preselected satellites is obtained using the atmospheric phase screen estimation method; the synthetic aperture radar interferometry monitoring data to be fused includes the second synthetic aperture radar interferometry monitoring data of the area where the target photovoltaic power station is located after removing atmospheric delay errors.

[0020] Through the above implementation method, orbital errors are first eliminated by combining the precise orbital data of each pre-selected satellite with orbital fine correction. Then, terrain phase errors are eliminated by relying on external digital elevation model data and terrain phase removal correction. Finally, ERA5 atmospheric reanalysis data is fused, and atmospheric delay errors are further eliminated by using atmospheric phase screen estimation method. This process eliminates the interference of multiple systematic errors such as orbit, terrain, and atmosphere in the original monitoring data of each pre-selected satellite, so that the synthetic aperture radar interferometric monitoring data to be fused output by each pre-selected satellite has a unified accuracy benchmark and data quality. This lays a standardized and high-quality data foundation for the efficient fusion of subsequent multi-source satellite data.

[0021] In one alternative implementation, it further includes:

[0022] Based on the building information model of each photovoltaic support in the target photovoltaic power station, the key structural parameters of the building information model of each photovoltaic support are extracted and constructed into the corresponding finite element analysis model of the photovoltaic support.

[0023] Through the above implementation method, based on the building information model of each photovoltaic support, the key structural parameters of the building information model of each photovoltaic support are extracted and constructed into the corresponding photovoltaic support finite element analysis model. This ensures that the constructed photovoltaic support finite element analysis model is highly matched with the actual structural mechanical properties of the photovoltaic support. At the same time, it can quickly generate corresponding and suitable finite element analysis models for different types of photovoltaic supports in the power station, avoiding the errors and inefficiencies of manual modeling. It is convenient to use the constructed photovoltaic support finite element analysis model for subsequent parameter correction and coupling solution in combination with the settlement field prediction results.

[0024] In one optional implementation, the step of extracting key structural parameters from the building information model of each photovoltaic support within the target photovoltaic power station to construct a corresponding finite element analysis model for the photovoltaic support includes:

[0025] Based on the 3D geometric data, material property data, and node connection data of the building information model of each photovoltaic support in the target photovoltaic power station, key structural parameters of the building information model of each photovoltaic support are extracted; the key structural parameters include: the cross-sectional dimensions of the support column, the angle of the diagonal bracing arrangement, the foundation constraint form, and the elastic modulus of the steel.

[0026] Based on the key structural parameters of the building information model of each photovoltaic support, a corresponding finite element analysis model of the photovoltaic support is constructed using parametric modeling and finite element mesh generation methods.

[0027] Through the above implementation method, the cross-sectional dimensions of the support column, the angle of the diagonal bracing, the form of the foundation constraint, and the elastic modulus of the steel are extracted from the building information model of each photovoltaic support. Redundant information is eliminated while the core features of the support structure are accurately anchored, ensuring the relevance and effectiveness of the basic data for modeling. Then, relying on parametric modeling and finite element mesh generation methods, the extracted key parameters are transformed into a finite element analysis model that fits the actual structural topology of the photovoltaic support, so as to achieve a high degree of matching between the constructed finite element analysis model of the photovoltaic support and the real structure of the photovoltaic support.

[0028] In one optional implementation, the geological constitutive model is constructed based on the real-time settlement monitoring data of the target photovoltaic power station and combined with historical geological exploration data of the area where the target photovoltaic power station is located. This model is then simulated to obtain multiple predicted surface subsidence fields for the area where the photovoltaic power station is located within a preset time period, including:

[0029] Based on real-time settlement monitoring data of the target photovoltaic power station and historical geological exploration data of the area where the target photovoltaic power station is located, an initial geological constitutive model is constructed using the rheological-elastoplastic coupling modeling method.

[0030] Based on the initial geological constitutive model, the corrected geological constitutive model is obtained by calibrating it using inversion analysis.

[0031] Based on the modified geological constitutive model, the simulation is carried out in combination with various working scenarios of geological deformation process under different environmental disturbances to obtain the prediction results of various surface subsidence fields in the area where the photovoltaic power station is located within a preset time. The prediction results of the surface subsidence fields include: the magnitude of surface displacement, the direction of displacement, and the subsidence rate.

[0032] Through the above implementation method, firstly, by combining real-time settlement monitoring data of the target photovoltaic power station with historical geological exploration data of the area where the target photovoltaic power station is located, a rheological-elastoplastic coupling modeling method is used to construct an initial geological constitutive model, so that the initial geological constitutive model fits the geological characteristics and actual settlement state of the coal mining subsidence area from the source. Then, the initial model is precisely calibrated by inversion analysis method to correct the model parameter deviation and improve the model's fit to the regional geological evolution law, resulting in a more realistic geological constitutive model. Finally, based on the corrected model, various environmental disturbance conditions such as different rainfall and groundwater level changes are simulated, and the prediction results of various surface subsidence fields, including the magnitude, direction and rate of surface displacement, are accurately output, so as to realize the refined prediction of the geological subsidence trend of the area where the target photovoltaic power station is located within a preset time.

[0033] In one alternative implementation, it further includes:

[0034] Based on the monitoring results of the photovoltaic support under the prediction results of each land subsidence field, and combined with the support type, service life, current health status and seasonal environmental factors of each photovoltaic support, the remaining service life of each photovoltaic support under the prediction results of the corresponding land subsidence field is obtained by using the remaining service life assessment method.

[0035] Through the above implementation methods, the monitoring results of photovoltaic support structures under different subsidence field prediction results are deeply integrated with the support type, service life, current health status and seasonal environmental factors. The remaining life assessment method is used to conduct targeted life analysis for each photovoltaic support, so as to achieve accurate quantitative assessment of the remaining service life of each photovoltaic support under different geological subsidence evolution trends.

[0036] Secondly, the present invention provides a monitoring device for a photovoltaic power station in a coal mining subsidence area, the device comprising:

[0037] The settlement monitoring module is used to obtain real-time settlement monitoring data of the target photovoltaic power station based on the synthetic aperture radar interferometry monitoring data of the target photovoltaic power station collected in real time by multiple satellites and using a multi-source data fusion algorithm.

[0038] The subsidence field prediction module is used to construct a geological constitutive model and simulate it based on the real-time subsidence monitoring data of the target photovoltaic power station and the historical geological exploration data of the area where the target photovoltaic power station is located, so as to obtain various surface subsidence field prediction results for the area where the photovoltaic power station is located within a preset time. The historical geological exploration data includes geological exploration reports, groundwater level change data and spatiotemporal distribution information of mining subsidence areas.

[0039] The coupling analysis module is used to take the predicted results of each surface subsidence field of the photovoltaic power station as the boundary, use the real-time monitoring data of the sensors in the target photovoltaic power station to correct the parameters of the preset finite element analysis model of each photovoltaic support, and perform coupled solution on the corrected finite element analysis model of the photovoltaic support to obtain the monitoring results of the photovoltaic support under the corresponding surface subsidence field prediction results; the monitoring results include theoretical strain distribution cloud map, key node displacement, structural stress ratio and natural frequency change.

[0040] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic power station monitoring method for coal mining subsidence areas described in the first aspect or any corresponding embodiment.

[0041] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the photovoltaic power station monitoring method for coal mining subsidence areas described in the first aspect or any corresponding embodiment. Attached Figure Description

[0042] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram of the first process of a photovoltaic power station monitoring method in a coal mining subsidence area according to an embodiment of the present invention;

[0044] Figure 2 This is a schematic diagram of the second process of a photovoltaic power station monitoring method in a coal mining subsidence area according to an embodiment of the present invention;

[0045] Figure 3 This is a schematic diagram of the third process of the photovoltaic power station monitoring method in the coal mining subsidence area according to an embodiment of the present invention;

[0046] Figure 4 This is a schematic diagram of the fourth process of the photovoltaic power station monitoring method in the coal mining subsidence area according to an embodiment of the present invention;

[0047] Figure 5 This is a schematic diagram of the fifth process of the photovoltaic power station monitoring method in a coal mining subsidence area according to an embodiment of the present invention;

[0048] Figure 6 This is a structural block diagram of a photovoltaic power station monitoring device in a coal mining subsidence area according to an embodiment of the present invention;

[0049] Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0052] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0053] The disclosed methods for photovoltaic power plant safety monitoring in related technologies include manual measurement, single-sensor monitoring, or single synthetic aperture radar interferometry (InSAR) remote sensing monitoring. Single synthetic aperture radar interferometry remote sensing monitoring is limited by satellite revisit period, resolution, wavelength, etc., making it difficult to obtain high-frequency and high-precision settlement data for the entire power plant area, resulting in insufficient data completeness and accuracy. Traditional monitoring methods can only obtain superficial data on surface settlement or local deformation of the support structure, and cannot establish a quantitative physical and mechanical relationship between the surface settlement process and the internal stress and strain response of the photovoltaic support structure, resulting in a serious disconnect between monitoring and structural safety assessment. At the same time, there is a lack of professional modeling capabilities that combine regional geological exploration data, making it impossible to predict the future surface settlement trend of the subsidence area in multiple scenarios, or to quantitatively analyze the specific structural impact of different settlement modes on the photovoltaic support. This results in monitoring only being able to achieve passive alarms, making it difficult to predict potential structural safety risks in advance, and failing to provide scientific and accurate quantitative basis for operation and maintenance decisions.

[0054] To overcome the aforementioned technical deficiencies, this invention provides a method for monitoring photovoltaic power stations in coal mining subsidence areas. It utilizes synthetic aperture radar interferometry monitoring data from multiple satellites to fuse real-time data of the target photovoltaic power station, thereby overcoming the limitations of single-satellite monitoring and enabling precise subsidence monitoring of the area where the photovoltaic power station is located. A geological constitutive model is constructed by combining real-time subsidence data with historical geological exploration data, and multiple subsidence fields are simulated to establish the intrinsic correlation between surface subsidence and regional geological conditions in the coal mining subsidence area, achieving forward-looking prediction of subsidence trends within a preset timeframe. Finally, using the subsidence field prediction results as boundaries, the finite element model of the photovoltaic support is corrected using sensor data and coupled for solution, achieving quantitative mechanical coupling between geological subsidence and the structural response of the photovoltaic support. This facilitates accurate prediction of the structural safety of photovoltaic support structures in coal mining subsidence areas.

[0055] According to an embodiment of the present invention, a method for monitoring a photovoltaic power station in a coal mining subsidence area is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0056] This embodiment provides a method for monitoring photovoltaic power stations in coal mining subsidence areas, which can be used in the server terminal of photovoltaic power stations. Figure 1 This is a schematic diagram of the first process of a photovoltaic power station monitoring method in a coal mining subsidence area according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps:

[0057] S101 uses synthetic aperture radar interferometry monitoring data of the target photovoltaic power station collected in real time by multiple satellites and a multi-source data fusion algorithm to obtain the real-time settlement monitoring data of the corresponding target photovoltaic power station.

[0058] Synthetic Aperture Radar Interferometric Monitoring Data is radar imagery of the same area acquired by synthetic aperture radar from multiple satellites. Through subsequent interferometric processing techniques, monitoring data on minute surface deformations is extracted, enabling large-scale, non-contact monitoring of surface subsidence. In this embodiment, it is used to acquire subsidence monitoring data for the area where the target photovoltaic power station is located.

[0059] Multi-source data fusion algorithms acquire synthetic aperture radar interferometric monitoring data from different satellites, with different resolutions and monitoring periods. By using algorithms that unify spatiotemporal references, correct errors, and integrate complementary data, the limitations of single-satellite monitoring data are overcome, thereby improving the accuracy, continuity, and full coverage of subsequent real-time settlement monitoring data.

[0060] Real-time settlement monitoring data is precise data reflecting the current settlement status of the entire surface of the target photovoltaic power station after being processed by a multi-source data fusion algorithm. It includes the settlement amount and settlement rate at each point on the surface, which facilitates the subsequent construction of a geological constitutive model.

[0061] For example, the above S101 can be implemented as follows:

[0062] By accessing SAR satellite data from satellites A, B, and C, and employing a multi-source data fusion algorithm, the data gaps or insufficient accuracy caused by the limitations of revisit period, wavelength, and resolution of a single satellite are overcome, ultimately obtaining real-time settlement monitoring data that covers the entire target photovoltaic power station area, is spatiotemporally continuous, and has high precision.

[0063] S102, Based on the real-time settlement monitoring data of the target photovoltaic power station and combined with the historical geological exploration data of the area where the target photovoltaic power station is located, a geological constitutive model is constructed and simulated to obtain the prediction results of various surface settlement fields in the area where the photovoltaic power station is located within a preset time period; the historical geological exploration data includes geological exploration reports, groundwater level change data and spatiotemporal distribution information of mining subsidence areas.

[0064] Historical geological exploration data is a collection of historical data obtained from geological surveys conducted in the area where the photovoltaic power station is located. The core data includes geological survey reports (soil layer distribution, soil mechanical parameters, stratigraphic structure, etc.), groundwater level change data (regional groundwater depth, water level rise and fall patterns, etc.), and spatiotemporal distribution information of mining goaf areas (location, range, formation time, and evolution trend of mining goaf areas, etc.), which facilitates the subsequent construction of geological constitutive models.

[0065] A geological constitutive model is a mathematical-mechanical model that describes the mechanical properties and deformation patterns of geological bodies (soil and rock strata) in the area where a photovoltaic power station is located, as well as the interaction between the geological bodies and factors such as mining subsidence areas and groundwater. It is used to accurately characterize the deformation response of regional geological bodies under the influence of external factors.

[0066] The surface subsidence field prediction results are obtained by simulating the global distribution of surface subsidence in the area where the photovoltaic power station is located within a preset time using a geological constitutive model. The results include the magnitude, direction, and rate of surface displacement at each point in the area. The prediction results can also be output under different operating conditions (rainfall, groundwater level changes, etc.) to provide geological boundary conditions for subsequent analysis of the photovoltaic support structure response.

[0067] By deeply integrating real-time settlement monitoring data with historical geological exploration data and relying on the constructed geological constitutive model, the model is ensured to not only conform to the basic mechanical properties of the regional soil and rock mass, but also accurately match the current actual settlement state of the power station. Based on the calibrated geological constitutive model, various surface settlement field prediction results are obtained within a preset time. Combining the key influencing factors of settlement in coal mining subsidence areas, the model outputs full-area settlement distribution prediction results under different working conditions, including the magnitude, direction, and settlement rate of surface displacement. This allows for accurate prediction of various possibilities in future geological evolution and facilitates the analysis of the differentiated structural impact of different settlement modes on photovoltaic supports.

[0068] S103, using the predicted results of each type of surface subsidence field in the area where the photovoltaic power station is located as boundaries, the parameters of each preset finite element analysis model of the photovoltaic support are corrected using real-time monitoring data from sensors in the target photovoltaic power station, and the corrected finite element analysis model of the photovoltaic support is coupled and solved to obtain the monitoring results of the photovoltaic support under the corresponding surface subsidence field prediction results; the monitoring results include theoretical strain distribution cloud map, key node displacement, structural stress ratio and natural frequency change.

[0069] The real-time monitoring data of the sensors in the target photovoltaic power station utilizes data from micro-vibration sensors, temperature and humidity sensors, high-precision biaxial MEMS tilt sensors, fiber optic grating (FBG) strain sensor arrays, and triaxial micro-accelerometers. Specifically, the micro-vibration sensors monitor for loose connections in the photovoltaic support structure; the temperature and humidity sensors correct for measurement drift; the high-precision biaxial MEMS tilt sensors, with a range of ±15° and a resolution of 0.001°, and built-in temperature drift compensation, monitor the static tilt angle and dynamic sway frequency of the support columns and beams; the fiber optic grating strain sensor array is deployed at 5-8 measuring points at the bottom, middle, and top of the columns of individual key support structures, and at the mid-span and connection points of the beams, for monitoring strain field distribution; and the triaxial micro-accelerometers, with a range of ±2g, capture environmental vibrations (wind-induced, micro-vibration) and abnormal structural vibrations (such as characteristic frequency changes caused by loose bolts).

[0070] The finite element analysis model of photovoltaic support is a digital mechanical model of photovoltaic support built based on the finite element analysis method. It can simulate the structural response of the support under different loads and boundary conditions. It includes key information such as the geometric features, material properties, and node connection forms of the support. It is the core model used to analyze the stress, strain, and displacement of the support structure.

[0071] The theoretical strain distribution cloud map is a result of coupled solution and is presented in a cloud map form to intuitively show the strain distribution inside the photovoltaic support. Different colors represent different strain magnitudes, which are used to quickly identify strain concentration areas of the support and determine structural weak points.

[0072] The critical node displacement refers to the amount and direction of displacement of key stress-bearing locations on the photovoltaic support (such as the bottom of the column, the connection of the beam, the foundation of the support, etc.) under the action of settlement.

[0073] The structural stress ratio is the ratio of the actual stress borne by the photovoltaic support structure to the allowable stress of the material. It is a key indicator for judging whether the support structure is in a safe state. The closer the ratio is to 1, the closer the support structure is to its limit state and the higher the safety risk.

[0074] The change in natural frequency is the change in the natural vibration frequency of a photovoltaic support structure under the action of settlement. When the structure of the photovoltaic support becomes loose, deformed, or damaged, the natural frequency will change significantly.

[0075] By applying the predicted results of the surface subsidence field as the boundary condition of the displacement load to the finite element analysis model of the photovoltaic support, and combining the structural characteristics of each photovoltaic support, the analysis process of the structural response of the corresponding photovoltaic support under the action of subsidence is solved, thereby realizing the mechanical correlation analysis between the geological subsidence field and the photovoltaic support structure, and accurately quantifying the impact of subsidence on the structure of the support.

[0076] For example, S103 above can be implemented as follows:

[0077] Specifically, the predicted future settlement field is used as the boundary condition for displacement load and applied to the foundation nodes of the corresponding photovoltaic support in the finite element analysis model. By using the real-time coupled solver of "settlement field-structural response", the photovoltaic support finite element analysis model is driven to perform nonlinear static or transient dynamic analysis, and the theoretical strain distribution cloud map, key node displacement, structural stress ratio and natural frequency change of each photovoltaic support under the predicted settlement are obtained.

[0078] Furthermore, by comparing the strain, tilt angle, and vibration data measured by sensors with the theoretical values ​​calculated by the coupled solver, a sensitivity-based model update method is adopted to inversely correct the uncertain parameters (such as connection stiffness and material degradation coefficient) in the finite element analysis model of the photovoltaic support, making the model increasingly closer to the actual structural state.

[0079] This invention provides a method for monitoring photovoltaic power stations in coal mining subsidence areas. It utilizes synthetic aperture radar interferometry monitoring data from multiple satellites to acquire real-time settlement monitoring data, overcoming the limitations of single-satellite monitoring and enabling precise settlement monitoring of the area where the photovoltaic power station is located. A geological constitutive model is constructed by combining real-time settlement data with historical geological exploration data, and multiple settlement fields are simulated to establish the intrinsic correlation between surface subsidence and regional geological conditions in the coal mining subsidence area, achieving forward-looking prediction of settlement trends within a preset timeframe. Finally, using the settlement field prediction results as boundaries, the finite element model of the photovoltaic support is corrected using sensor data and coupled for solution, realizing quantitative mechanical coupling between geological subsidence and the structural response of the photovoltaic support. This facilitates precise monitoring of the structural safety of photovoltaic supports in coal mining subsidence areas.

[0080] This embodiment provides a method for monitoring photovoltaic power stations in coal mining subsidence areas, which can be used in the server terminal of photovoltaic power stations. Figure 2 This is a schematic diagram of the second process of the photovoltaic power station monitoring method in a coal mining subsidence area according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps:

[0081] S201 uses synthetic aperture radar interferometry monitoring data of the target photovoltaic power station collected in real time by multiple satellites and a multi-source data fusion algorithm to obtain the real-time settlement monitoring data of the corresponding target photovoltaic power station.

[0082] Specifically, S201 above includes:

[0083] S2011, based on the weather forecast and current risk level of the area where the target photovoltaic power station is located, uses satellite transit forecast and mission planning algorithms to determine multiple pre-selected satellites.

[0084] S2012: Based on the synthetic aperture radar interferometry monitoring data of each pre-selected satellite on the area where the target photovoltaic power station is located, the corresponding synthetic aperture radar interferometry monitoring data to be fused is obtained by using the data correction method.

[0085] For example, S2012 above includes:

[0086] a1. Based on the synthetic aperture radar interferometry monitoring data of each pre-selected satellite for the area where the target photovoltaic power station is located, and combined with the precise orbit data of the corresponding satellite, the first synthetic aperture radar interferometry monitoring data of the corresponding pre-selected satellite is obtained by using the orbital fine correction method; the first synthetic aperture radar interferometry monitoring data includes the synthetic aperture radar interferometry monitoring data of the area where the target photovoltaic power station is located after removing orbital errors.

[0087] a2, based on the first synthetic aperture radar interferometry monitoring data of each pre-selected satellite, combined with external digital elevation model data, the second synthetic aperture radar interferometry monitoring data of the corresponding pre-selected satellite is obtained by using the terrain phase removal and correction method; the second synthetic aperture radar interferometry monitoring data includes the first synthetic aperture radar interferometry monitoring data of the area where the target photovoltaic power station is located after removing terrain errors;

[0088] a3. Based on the second synthetic aperture radar interferometry monitoring data of each pre-selected satellite, combined with ERA5 atmospheric reanalysis data, the corresponding synthetic aperture radar interferometry monitoring data to be fused for the pre-selected satellites is obtained using the atmospheric phase screen estimation method; the synthetic aperture radar interferometry monitoring data to be fused includes the second synthetic aperture radar interferometry monitoring data of the area where the target photovoltaic power station is located after removing atmospheric delay errors.

[0089] First, precise orbital data from each pre-selected satellite is used to eliminate orbital errors through orbital calibration. Then, terrain phase errors are eliminated through terrain phase removal correction based on external digital elevation model data. Finally, ERA5 atmospheric reanalysis data is fused, and atmospheric delay errors are further eliminated using atmospheric phase screen estimation methods. This process removes interference from various systematic errors, such as orbital, terrain, and atmospheric errors, from the original monitoring data of each pre-selected satellite. This ensures that the synthetic aperture radar interferometric monitoring data to be fused from each pre-selected satellite has a unified accuracy benchmark and data quality, laying a standardized and high-quality data foundation for the efficient fusion of subsequent multi-source satellite data.

[0090] S2013 integrates synthetic aperture radar interferometry monitoring data from multiple pre-selected satellites and uses a multi-source data fusion algorithm to obtain real-time settlement monitoring data of the target photovoltaic power station.

[0091] By combining weather forecasts and current risk levels for the target photovoltaic power station area, satellite transit forecasts and mission planning algorithms are used to facilitate precise scheduling of satellite resources, ensuring the availability of monitoring data for the target photovoltaic power station area and avoiding the blindness of single-satellite scheduling. Simultaneously, data correction methods are used to correct the monitoring data of each pre-selected satellite, improving the accuracy and effectiveness of the monitoring data from each pre-selected satellite. Then, a multi-source data fusion algorithm is used to integrate the corrected monitoring data from each pre-selected satellite, fully leveraging the complementary advantages of different satellites in monitoring cycle, resolution, and wavelength, compensating for the shortcomings of single-satellite monitoring, and ultimately obtaining real-time settlement monitoring data covering the target photovoltaic power station. This provides accurate basic data for subsequent geological modeling, settlement prediction, and support structure response analysis.

[0092] S202, based on the real-time settlement monitoring data of the target photovoltaic power station and combined with the historical geological exploration data of the area where the target photovoltaic power station is located, a geological constitutive model is constructed and simulated to obtain prediction results of various surface subsidence fields in the area where the photovoltaic power station is located within a preset time period; the historical geological exploration data includes geological survey reports, groundwater level change data, and spatiotemporal distribution information of mining subsidence areas. For details, please refer to... Figure 1 S102 of the illustrated embodiment will not be described again here.

[0093] S203: Using the predicted results of each type of surface subsidence field in the area where the photovoltaic power station is located as boundaries, the parameters of each preset finite element analysis model of the photovoltaic support are corrected using real-time monitoring data from sensors in the target photovoltaic power station. The corrected finite element analysis models of the photovoltaic support are then coupled and solved to obtain the monitoring results of the photovoltaic support under the corresponding predicted surface subsidence field. The monitoring results include theoretical strain distribution cloud maps, key node displacements, structural stress ratios, and natural frequency changes. For details, please refer to... Figure 1 S103 of the illustrated embodiment will not be described again here.

[0094] This invention provides a method for monitoring photovoltaic power stations in coal mining subsidence areas. It utilizes synthetic aperture radar interferometry monitoring data from multiple satellites to acquire real-time settlement monitoring data, overcoming the limitations of single-satellite monitoring and enabling precise settlement monitoring of the area where the photovoltaic power station is located. A geological constitutive model is constructed by combining real-time settlement data with historical geological exploration data, and multiple settlement fields are simulated to establish the intrinsic correlation between surface subsidence and regional geological conditions in the coal mining subsidence area, achieving forward-looking prediction of settlement trends within a preset timeframe. Finally, using the settlement field prediction results as boundaries, the finite element model of the photovoltaic support is corrected using sensor data and coupled for solution, realizing quantitative mechanical coupling between geological subsidence and the structural response of the photovoltaic support. This facilitates precise monitoring of the structural safety of photovoltaic supports in coal mining subsidence areas.

[0095] This embodiment provides a method for monitoring photovoltaic power stations in coal mining subsidence areas, which can be used in the server terminal of photovoltaic power stations. Figure 3 This is a schematic diagram of the third process of the photovoltaic power station monitoring method in a coal mining subsidence area according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps:

[0096] S301 uses synthetic aperture radar interferometry monitoring data of the target photovoltaic power station, acquired in real time by multiple satellites, and employs a multi-source data fusion algorithm to obtain real-time settlement monitoring data for the corresponding target photovoltaic power station. For details, please refer to [link to relevant documentation]. Figure 1 S101 of the illustrated embodiment will not be described again here.

[0097] S302, Based on the real-time settlement monitoring data of the target photovoltaic power station and combined with the historical geological exploration data of the area where the target photovoltaic power station is located, a geological constitutive model is constructed and simulated to obtain the prediction results of various surface settlement fields in the area where the photovoltaic power station is located within a preset time period; the historical geological exploration data includes geological exploration reports, groundwater level change data and spatiotemporal distribution information of mining subsidence areas.

[0098] Specifically, S302 above includes:

[0099] S3021. Based on real-time settlement monitoring data of the target photovoltaic power station and combined with historical geological exploration data of the area where the target photovoltaic power station is located, an initial geological constitutive model is constructed using the rheological-elastoplastic coupling modeling method.

[0100] The initial geological constitutive model includes an improved Segall slip model that considers the rheological properties of the soil or a numerical model based on FLAC3D. The model parameters are calibrated using subsequent inversion analysis methods to predict the surface subsidence field (displacement magnitude, direction, and rate) under different rainfall and groundwater level change scenarios in the next 1-3 months.

[0101] S3022, Based on the initial geological constitutive model, the corrected geological constitutive model is obtained by calibrating it using inversion analysis.

[0102] S3023, based on the modified geological constitutive model, simulates the geological deformation process under various environmental disturbances to obtain multiple surface subsidence field prediction results for the photovoltaic power station area within a preset time; the surface subsidence field prediction results include: surface displacement magnitude, displacement direction and subsidence rate.

[0103] First, by combining real-time settlement monitoring data of the target photovoltaic power station with historical geological exploration data of the area where the target photovoltaic power station is located, a rheological-elastoplastic coupling modeling method is used to construct an initial geological constitutive model. This ensures that the initial geological constitutive model closely matches the geological characteristics and actual settlement state of the coal mining subsidence area from the source. Then, the initial model is precisely calibrated through inversion analysis to correct model parameter deviations and improve the model's fit to the regional geological evolution law, resulting in a more realistic geological constitutive model. Finally, based on the corrected model, various environmental disturbance conditions such as different rainfall and groundwater level changes are simulated to accurately output multiple surface subsidence field prediction results, including the magnitude, direction, and rate of surface displacement. This enables refined prediction of the geological subsidence trend of the area where the target photovoltaic power station is located within a preset time.

[0104] S303: Using the predicted surface subsidence field for each area of ​​the photovoltaic power station as boundaries, real-time monitoring data from sensors in the target photovoltaic power station are used to correct the parameters of each preset finite element analysis model for the photovoltaic support. The corrected finite element analysis models are then coupled and solved to obtain the monitoring results of the photovoltaic support under the corresponding predicted surface subsidence field. These monitoring results include theoretical strain distribution cloud maps, key node displacements, structural stress ratios, and natural frequency variations. For details, please refer to [link to relevant documentation]. Figure 1 S103 of the illustrated embodiment will not be described again here.

[0105] This invention provides a method for monitoring photovoltaic power stations in coal mining subsidence areas. It utilizes synthetic aperture radar interferometry monitoring data from multiple satellites to acquire real-time settlement monitoring data, overcoming the limitations of single-satellite monitoring and enabling precise settlement monitoring of the area where the photovoltaic power station is located. A geological constitutive model is constructed by combining real-time settlement data with historical geological exploration data, and multiple settlement fields are simulated to establish the intrinsic correlation between surface subsidence and regional geological conditions in the coal mining subsidence area, achieving forward-looking prediction of settlement trends within a preset timeframe. Finally, using the settlement field prediction results as boundaries, the finite element model of the photovoltaic support is corrected using sensor data and coupled for solution, realizing quantitative mechanical coupling between geological subsidence and the structural response of the photovoltaic support. This facilitates precise monitoring of the structural safety of photovoltaic supports in coal mining subsidence areas.

[0106] This embodiment provides a method for monitoring photovoltaic power stations in coal mining subsidence areas, which can be used in the server terminal of photovoltaic power stations. Figure 4 This is a schematic diagram of the fourth process of the photovoltaic power station monitoring method in coal mining subsidence areas according to an embodiment of the present invention, as shown below. Figure 4 As shown, the process includes the following steps:

[0107] S401 uses synthetic aperture radar interferometry monitoring data of the target photovoltaic power station, acquired in real time by multiple satellites, and employs a multi-source data fusion algorithm to obtain real-time settlement monitoring data for the corresponding target photovoltaic power station. For details, please refer to [link to relevant documentation]. Figure 1 S101 of the illustrated embodiment will not be described again here.

[0108] S402, based on the real-time settlement monitoring data of the target photovoltaic power station and combined with the historical geological exploration data of the area where the target photovoltaic power station is located, a geological constitutive model is constructed and simulated to obtain prediction results of various surface settlement fields in the area where the photovoltaic power station is located within a preset time period; the historical geological exploration data includes geological survey reports, groundwater level change data, and spatiotemporal distribution information of mining subsidence areas. For details, please refer to... Figure 1 S102 of the illustrated embodiment will not be described again here.

[0109] S403, based on the building information model of each photovoltaic support in the target photovoltaic power station, extracts the key structural parameters of the building information model of each photovoltaic support and constructs the corresponding photovoltaic support finite element analysis model.

[0110] Specifically, S403 includes:

[0111] S4031, Based on the three-dimensional geometric data, material property data, and node connection form data of the building information model of each photovoltaic support in the target photovoltaic power station, extract the key structural parameters of the building information model of each photovoltaic support; the key structural parameters include: the cross-sectional dimensions of the support column, the angle of the diagonal bracing arrangement, the foundation constraint form, and the elastic modulus of the steel.

[0112] S4032, based on the key structural parameters of the building information model of each photovoltaic bracket, uses parametric modeling and finite element mesh generation methods to construct the corresponding finite element analysis model of the photovoltaic bracket.

[0113] Based on the building information model of each photovoltaic (PV) bracket, the cross-sectional dimensions of the bracket columns, the angle of the diagonal bracing, the form of the foundation constraint, and the elastic modulus of the steel are extracted in a targeted manner. Redundant information is eliminated while the core features of the bracket structure are accurately anchored to ensure the relevance and effectiveness of the basic data for modeling. Then, relying on parametric modeling and finite element mesh generation methods, the extracted key parameters are transformed into a finite element analysis model that fits the actual structural topology of the PV bracket, so as to achieve a high degree of matching between the constructed PV bracket finite element analysis model and the real structure of the PV bracket.

[0114] Based on the building information model of each photovoltaic (PV) bracket, key structural parameters of each PV bracket's building information model are extracted and constructed into corresponding PV bracket finite element analysis models. This ensures that the constructed PV bracket finite element analysis models are highly matched with the actual structural mechanical properties of the PV brackets. At the same time, corresponding and adapted finite element analysis models are quickly generated for different types of PV brackets in the power plant, avoiding the errors and inefficiencies of manual modeling. This facilitates the use of the constructed PV bracket finite element analysis models for subsequent parameter correction and coupled solution based on settlement field prediction results.

[0115] S404 uses the predicted results of each type of surface subsidence field in the area where the photovoltaic power station is located as boundaries. Real-time monitoring data from sensors in the target photovoltaic power station are used to correct the parameters of each preset finite element analysis model for the photovoltaic support. The corrected finite element analysis models are then coupled and solved to obtain the monitoring results of the photovoltaic support under the corresponding predicted surface subsidence field. These monitoring results include theoretical strain distribution cloud maps, key node displacements, structural stress ratios, and natural frequency variations. For details, please refer to [link to relevant documentation]. Figure 1 S103 of the illustrated embodiment will not be described again here.

[0116] This invention provides a method for monitoring photovoltaic power stations in coal mining subsidence areas. It utilizes synthetic aperture radar interferometry monitoring data from multiple satellites to acquire real-time settlement monitoring data, overcoming the limitations of single-satellite monitoring and enabling precise settlement monitoring of the area where the photovoltaic power station is located. A geological constitutive model is constructed by combining real-time settlement data with historical geological exploration data, and multiple settlement fields are simulated to establish the intrinsic correlation between surface subsidence and regional geological conditions in the coal mining subsidence area, achieving forward-looking prediction of settlement trends within a preset timeframe. Finally, using the settlement field prediction results as boundaries, the finite element model of the photovoltaic support is corrected using sensor data and coupled for solution, realizing quantitative mechanical coupling between geological subsidence and the structural response of the photovoltaic support. This facilitates precise monitoring of the structural safety of photovoltaic supports in coal mining subsidence areas.

[0117] This embodiment provides a method for monitoring photovoltaic power stations in coal mining subsidence areas, which can be used in the server terminal of photovoltaic power stations. Figure 5 This is a schematic diagram of the fifth process of the photovoltaic power station monitoring method in a coal mining subsidence area according to an embodiment of the present invention, as shown below. Figure 5 As shown, the process includes the following steps:

[0118] S501 uses synthetic aperture radar interferometry monitoring data of the target photovoltaic power station, acquired in real time by multiple satellites, and employs a multi-source data fusion algorithm to obtain real-time settlement monitoring data for the corresponding target photovoltaic power station. For details, please refer to [link to relevant documentation]. Figure 1 S101 of the illustrated embodiment will not be described again here.

[0119] S502, based on the real-time settlement monitoring data of the target photovoltaic power station and combined with the historical geological exploration data of the area where the target photovoltaic power station is located, a geological constitutive model is constructed and simulated to obtain prediction results of various surface subsidence fields in the area where the photovoltaic power station is located within a preset time period; the historical geological exploration data includes geological survey reports, groundwater level change data, and spatiotemporal distribution information of mining subsidence areas. For details, please refer to... Figure 1 S102 of the illustrated embodiment will not be described again here.

[0120] S503 uses the predicted results of each type of surface subsidence field in the area where the photovoltaic power station is located as boundaries. Real-time monitoring data from sensors in the target photovoltaic power station are used to correct the parameters of the preset finite element analysis model for each photovoltaic support structure. The corrected finite element analysis models are then coupled and solved to obtain the monitoring results of the photovoltaic support structure under the corresponding predicted surface subsidence field. These monitoring results include theoretical strain distribution cloud maps, key node displacements, structural stress ratios, and natural frequency variations. For details, please refer to [link to relevant documentation]. Figure 1 S103 of the illustrated embodiment will not be described again here.

[0121] S504. Based on the monitoring results of photovoltaic supports under the prediction results of each land subsidence field, and combined with the support type, service life, current health status and seasonal environmental factors of each photovoltaic support, the remaining service life assessment method is used to obtain the remaining service life of each photovoltaic support under the prediction results of the corresponding land subsidence field.

[0122] Specifically, the above S504 can be implemented as follows:

[0123] Based on integrated geological models, support structure BIM models, equipment information models, and monitoring data streams, a digital twin of the target photovoltaic power station is constructed. Then, by combining historical monitoring data and remaining life assessment methods, the remaining service life of the key structures of the photovoltaic support structure is predicted under different future settlement development scenarios, and the optimal maintenance time window is generated.

[0124] By combining the monitoring results of photovoltaic support structures under different subsidence field predictions with support type, service life, current health status and seasonal environmental factors, the remaining life assessment method is used to conduct targeted life analysis for each photovoltaic support, so as to achieve accurate quantitative assessment of the remaining service life of each photovoltaic support under different geological subsidence evolution trends.

[0125] This invention provides a method for monitoring photovoltaic power stations in coal mining subsidence areas. It utilizes synthetic aperture radar interferometry monitoring data from multiple satellites to acquire real-time settlement monitoring data, overcoming the limitations of single-satellite monitoring and enabling precise settlement monitoring of the area where the photovoltaic power station is located. A geological constitutive model is constructed by combining real-time settlement data with historical geological exploration data, and multiple settlement fields are simulated to establish the intrinsic correlation between surface subsidence and regional geological conditions in the coal mining subsidence area, achieving forward-looking prediction of settlement trends within a preset timeframe. Finally, using the settlement field prediction results as boundaries, the finite element model of the photovoltaic support is corrected using sensor data and coupled for solution, realizing quantitative mechanical coupling between geological subsidence and the structural response of the photovoltaic support. This facilitates precise monitoring of the structural safety of photovoltaic supports in coal mining subsidence areas.

[0126] This embodiment also provides a photovoltaic power station monitoring device for coal mining subsidence areas. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0127] This embodiment provides a monitoring device for a photovoltaic power station in a coal mining subsidence area, such as... Figure 6 As shown, the device includes:

[0128] The settlement monitoring module 610 is used to obtain real-time settlement monitoring data of the target photovoltaic power station based on the synthetic aperture radar interferometric monitoring data of the target photovoltaic power station collected in real time by multiple satellites and using a multi-source data fusion algorithm.

[0129] The subsidence field prediction module 620 is used to construct a geological constitutive model and simulate it based on the real-time subsidence monitoring data of the target photovoltaic power station and the historical geological exploration data of the area where the target photovoltaic power station is located, so as to obtain various surface subsidence field prediction results for the area where the photovoltaic power station is located within a preset time. The historical geological exploration data includes geological exploration reports, groundwater level change data and spatiotemporal distribution information of mining subsidence areas.

[0130] The coupling analysis module 630 is used to take the predicted results of each surface subsidence field of the photovoltaic power station as the boundary, use the real-time monitoring data of the sensors in the target photovoltaic power station to correct the parameters of the preset finite element analysis model of each photovoltaic support, and perform coupled solution on the corrected finite element analysis model of the photovoltaic support to obtain the monitoring results of the photovoltaic support under the corresponding surface subsidence field prediction results; the monitoring results include theoretical strain distribution cloud map, key node displacement, structural stress ratio and natural frequency change.

[0131] In some alternative implementations, the settlement monitoring module 610 includes:

[0132] The pre-selected satellite determination unit is used to determine multiple pre-selected satellites based on the weather forecast and current risk level of the target photovoltaic power station area, using satellite transit forecast and mission planning algorithms;

[0133] The data correction unit is used to obtain the corresponding synthetic aperture radar interferometry monitoring data to be fused from the synthetic aperture radar interferometry monitoring data of the target photovoltaic power station area acquired by each pre-selected satellite using the data correction method.

[0134] The data fusion unit is used to integrate synthetic aperture radar interferometry monitoring data from multiple pre-selected satellites and obtain real-time settlement monitoring data of the target photovoltaic power station using a multi-source data fusion algorithm.

[0135] In some optional implementations, the data correction unit includes:

[0136] The first correction subunit is used to obtain the first synthetic aperture radar interferometry monitoring data of the target photovoltaic power station area based on the synthetic aperture radar interferometry monitoring data of each pre-selected satellite, combined with the precise orbit data of the corresponding satellite, using the orbit fine correction method; the first synthetic aperture radar interferometry monitoring data includes the synthetic aperture radar interferometry monitoring data of the target photovoltaic power station area after removing orbital errors.

[0137] The second correction subunit is used to obtain the second synthetic aperture radar interferometry data of the corresponding pre-selected satellites based on the first synthetic aperture radar interferometry data of each pre-selected satellite, combined with external digital elevation model data, and using the terrain phase removal correction method; the second synthetic aperture radar interferometry data includes the first synthetic aperture radar interferometry data of the area where the target photovoltaic power station is located after removing terrain errors.

[0138] The third correction subunit is used to obtain the synthetic aperture radar interferometry monitoring data to be fused for the corresponding pre-selected satellites based on the second synthetic aperture radar interferometry monitoring data of each pre-selected satellite, combined with ERA5 atmospheric reanalysis data, and using the atmospheric phase screen estimation method; the synthetic aperture radar interferometry monitoring data to be fused includes the second synthetic aperture radar interferometry monitoring data of the area where the target photovoltaic power station is located after removing atmospheric delay errors.

[0139] In some alternative implementations, it also includes:

[0140] The model building module is used to extract the key structural parameters of the building information model of each photovoltaic support in the target photovoltaic power station and construct the corresponding finite element analysis model of the photovoltaic support.

[0141] In some alternative implementations, the model building module includes:

[0142] The parameter extraction unit is used to extract key structural parameters of the building information model of each photovoltaic support based on the three-dimensional geometric data, material property data, and node connection form data of the building information model of each photovoltaic support in the target photovoltaic power station. The key structural parameters include: the cross-sectional dimensions of the support column, the angle of the diagonal bracing arrangement, the foundation constraint form, and the elastic modulus of the steel.

[0143] The model building unit is used to construct the corresponding finite element analysis model of the photovoltaic support based on the key structural parameters of the building information model of each photovoltaic support using parametric modeling and finite element mesh generation methods.

[0144] In some alternative implementations, the settlement field prediction module 620 includes:

[0145] The modeling unit is used to construct an initial geological constitutive model based on real-time settlement monitoring data of the target photovoltaic power station and historical geological exploration data of the area where the target photovoltaic power station is located, using the rheological-elastoplastic coupling modeling method.

[0146] The model correction unit is used to calibrate the initial geological constitutive model using an inversion analysis method to obtain the corrected geological constitutive model.

[0147] The settlement prediction unit is used to simulate various geological deformation processes under different environmental disturbances based on the modified geological constitutive model, and to obtain various surface settlement prediction results for the photovoltaic power station area within a preset time. The surface settlement prediction results include: the magnitude of surface displacement, the direction of displacement, and the settlement rate.

[0148] In some alternative implementations, it also includes:

[0149] The life prediction module is used to obtain the remaining service life of each photovoltaic support under the corresponding land subsidence field prediction results based on the monitoring results of each photovoltaic support, combined with the support type, service life, current health status and seasonal environmental factors of each photovoltaic support, using the remaining life assessment method.

[0150] The photovoltaic power station monitoring device for coal mining subsidence areas provided in this embodiment of the invention can execute the photovoltaic power station monitoring method for coal mining subsidence areas provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0151] Figure 7This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0152] The following is a detailed reference. Figure 7 This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 701, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 702 or a program loaded from memory 708 into random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device. The processor 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0153] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 7 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0154] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 709, or installed from a memory 708, or installed from a ROM 702. When the computer program is executed by the processor 701, it performs the functions defined in the photovoltaic power station monitoring method for coal mining subsidence areas according to embodiments of the present invention.

[0155] Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0156] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the photovoltaic power station monitoring method for coal mining subsidence areas shown in the above embodiments is implemented.

[0157] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0158] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for monitoring photovoltaic power stations in coal mining subsidence areas, characterized in that, The method includes: Based on the synthetic aperture radar interferometric monitoring data of the target photovoltaic power station collected in real time by multiple satellites, the real-time settlement monitoring data of the corresponding target photovoltaic power station is obtained by using a multi-source data fusion algorithm. Based on the real-time settlement monitoring data of the target photovoltaic power station, combined with the historical geological exploration data of the area where the target photovoltaic power station is located, a geological constitutive model is constructed and simulated to obtain the prediction results of various surface settlement fields in the area where the photovoltaic power station is located within a preset time period; the historical geological exploration data includes geological exploration reports, groundwater level change data, and spatiotemporal distribution information of mining subsidence areas; Each predicted land subsidence field in the area where the photovoltaic power station is located is used as a boundary. The parameters of each preset finite element analysis model of the photovoltaic support are corrected using real-time monitoring data from sensors in the target photovoltaic power station. The corrected finite element analysis model of the photovoltaic support is then coupled and solved to obtain the monitoring results of the photovoltaic support under the corresponding predicted land subsidence field. The monitoring results include theoretical strain distribution cloud map, key node displacement, structural stress ratio and natural frequency change.

2. The method according to claim 1, characterized in that, The synthetic aperture radar interferometric monitoring data of the target photovoltaic power station, acquired in real time by multiple satellites, is used to obtain real-time settlement monitoring data of the corresponding target photovoltaic power station using a multi-source data fusion algorithm, including: Based on the weather forecast and current risk level of the area where the target photovoltaic power station is located, multiple pre-selected satellites are determined using satellite transit forecast and mission planning algorithms; Based on the synthetic aperture radar interferometry monitoring data of each preselected satellite for the area where the target photovoltaic power station is located, the synthetic aperture radar interferometry monitoring data to be fused for the corresponding preselected satellite is obtained by using the data correction method. By integrating synthetic aperture radar interferometry monitoring data from multiple pre-selected satellites, and using a multi-source data fusion algorithm, real-time settlement monitoring data of the target photovoltaic power station is obtained.

3. The method according to claim 2, characterized in that, The process involves obtaining synthetic aperture radar interferometry (SAR) monitoring data for the target photovoltaic power station area from each pre-selected satellite using a data correction method, and then merging the corresponding SAR data for the pre-selected satellites. This includes: Based on the synthetic aperture radar interferometry monitoring data of each pre-selected satellite over the area where the target photovoltaic power station is located, and combined with the precise orbit data of the corresponding satellite, the first synthetic aperture radar interferometry monitoring data of the corresponding pre-selected satellite is obtained by using the orbital fine correction method; the first synthetic aperture radar interferometry monitoring data includes the synthetic aperture radar interferometry monitoring data of the area where the target photovoltaic power station is located after removing orbital errors. Based on the first synthetic aperture radar interferometry monitoring data of each pre-selected satellite, combined with external digital elevation model data, the second synthetic aperture radar interferometry monitoring data of the corresponding pre-selected satellite is obtained using the terrain phase removal and correction method; the second synthetic aperture radar interferometry monitoring data includes the first synthetic aperture radar interferometry monitoring data of the target photovoltaic power station area after removing terrain errors. Based on the second synthetic aperture radar interferometry monitoring data of each preselected satellite, combined with ERA5 atmospheric reanalysis data, the corresponding synthetic aperture radar interferometry monitoring data to be fused for the preselected satellites is obtained using the atmospheric phase screen estimation method; the synthetic aperture radar interferometry monitoring data to be fused includes the second synthetic aperture radar interferometry monitoring data of the area where the target photovoltaic power station is located after removing atmospheric delay errors.

4. The method according to claim 1, characterized in that, Also includes: Based on the building information model of each photovoltaic support in the target photovoltaic power station, the key structural parameters of the building information model of each photovoltaic support are extracted and constructed into the corresponding finite element analysis model of the photovoltaic support.

5. The method according to claim 4, characterized in that, The building information model (BIM) of each photovoltaic (PV) support within the target PV power plant is used to extract key structural parameters from the BIM model of each PV support, constructing a corresponding finite element analysis model for the PV support, including: Based on the 3D geometric data, material property data, and node connection data of the building information model of each photovoltaic support in the target photovoltaic power station, key structural parameters of the building information model of each photovoltaic support are extracted; the key structural parameters include: the cross-sectional dimensions of the support column, the angle of the diagonal bracing arrangement, the foundation constraint form, and the elastic modulus of the steel. Based on the key structural parameters of the building information model of each photovoltaic support, a corresponding finite element analysis model of the photovoltaic support is constructed using parametric modeling and finite element mesh generation methods.

6. The method according to claim 1, characterized in that, Based on the real-time settlement monitoring data of the target photovoltaic power station, combined with historical geological exploration data of the area where the target photovoltaic power station is located, a geological constitutive model is constructed and simulated to obtain various predicted surface settlement fields for the area where the photovoltaic power station is located within a preset time period, including: Based on real-time settlement monitoring data of the target photovoltaic power station and historical geological exploration data of the area where the target photovoltaic power station is located, an initial geological constitutive model is constructed using the rheological-elastoplastic coupling modeling method. Based on the initial geological constitutive model, the corrected geological constitutive model is obtained by calibrating it using inversion analysis. Based on the modified geological constitutive model, the simulation of geological deformation processes under various environmental disturbances is carried out to obtain multiple surface subsidence field prediction results for the photovoltaic power station area within a preset time period; the surface subsidence field prediction results include: surface displacement magnitude, displacement direction and subsidence rate.

7. The method according to claim 1, characterized in that, Also includes: Based on the monitoring results of the photovoltaic support under the prediction results of each land subsidence field, and combined with the support type, service life, current health status and seasonal environmental factors of each photovoltaic support, the remaining service life of each photovoltaic support under the prediction results of the corresponding land subsidence field is obtained by using the remaining service life assessment method.

8. A monitoring device for a photovoltaic power station in a coal mining subsidence area, characterized in that, The device includes: The settlement monitoring module is used to obtain real-time settlement monitoring data of the target photovoltaic power station based on the synthetic aperture radar interferometry monitoring data of the target photovoltaic power station collected in real time by multiple satellites and using a multi-source data fusion algorithm. The subsidence field prediction module is used to construct a geological constitutive model and simulate it based on the real-time subsidence monitoring data of the target photovoltaic power station and the historical geological exploration data of the area where the target photovoltaic power station is located, so as to obtain various surface subsidence field prediction results for the area where the photovoltaic power station is located within a preset time. The historical geological exploration data includes geological exploration reports, groundwater level change data and spatiotemporal distribution information of mining subsidence areas. The coupling analysis module is used to take the predicted results of each surface subsidence field of the photovoltaic power station as the boundary, use the real-time monitoring data of the sensors in the target photovoltaic power station to correct the parameters of the preset finite element analysis model of each photovoltaic support, and perform coupled solution on the corrected finite element analysis model of the photovoltaic support to obtain the monitoring results of the photovoltaic support under the corresponding surface subsidence field prediction results; the monitoring results include theoretical strain distribution cloud map, key node displacement, structural stress ratio and natural frequency change.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the photovoltaic power station monitoring method for coal mining subsidence areas as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the photovoltaic power station monitoring method for coal mining subsidence areas as described in any one of claims 1 to 7.

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