Deep geothermal heat storage fracture network multi-scale joint identification system and method

Abstract

The invention discloses a deep geothermal heat storage fracture network multi-scale joint identification system and method, and relates to the technical field of deep geothermal heat storage. Comprising a data acquisition and multi-source fusion unit, an intelligent edge calculation and real-time dynamic sensing unit, a data preprocessing and quality control unit, a multi-scale feature extraction and recognition unit, a joint inversion and three-dimensional modeling unit, a fracture network analysis and heat storage evaluation unit and a verification and uncertainty management unit. Heterogeneous data of earthquake, logging, electromagnetism and the like are integrated through a space-time synchronization module of the data acquisition and multi-source fusion unit, and a real-time dynamic closed loop of data acquisition-processing is realized in combination with an edge data processing module and a low-delay communication module of the intelligent edge calculation unit. The edge-end lightweight data screening module preferentially filters low-value data, and the dynamic feedback control module optimizes an acquisition strategy in real time, so that the data transmission delay is remarkably reduced, and insufficient timeliness caused by large data volume is avoided.

Description

Technical Field

[0001] The present invention relates to the field of deep geothermal heat storage technology, and in particular to a multi-scale joint identification system and method for deep geothermal heat storage fracture networks. Background Art

[0002] As a clean, renewable energy source, geothermal energy has become a key component of global energy transformation due to its vast reserves, widespread distribution, and high stability. The development of deep geothermal resources relies on the precise identification and characterization of fracture networks in thermal reservoirs.

[0003] According to the patent titled "A Method and Apparatus for Monitoring Temperature and Fracture Distribution in Geothermal Reservoirs" (Patent Publication Number: CN106707365A, Patent Publication Date: 2017-05-24), a single measurement using a nanotracer can reveal the temperature distribution of a geothermal reservoir. Its return curve can be used to determine the time it takes for the threshold nanotracer to react, which in turn determines the location of the geothermal reservoir's threshold temperature. Finally, the response curves of the threshold nanotracer and non-nanotracer are combined to determine the temperature distribution of the geothermal reservoir. Finally, the distribution of geothermal reservoir fractures is determined based on the number of threshold nanotracers in at least two production wells. This enables realistic monitoring of the temperature and fracture distribution of the geothermal reservoir, while also enabling repeated measurements of the reservoir's temperature and fracture distribution.

[0004] Based on the above-mentioned existing technologies, the current existing multi-scale joint identification system and method for deep geothermal heat reservoir fracture networks still have the following problems: in traditional solutions, seismic, logging, electromagnetic and other data have inconsistent acquisition time and spatial benchmarks, resulting in fracture positioning deviations, and the full amount of original data is transmitted back to the central server, resulting in high bandwidth occupancy and long processing delays. Therefore, the present invention provides a multi-scale joint identification system and method for deep geothermal heat reservoir fracture networks. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a multi-scale joint identification system and method for deep geothermal heat storage fracture networks, which solves the problem of fracture positioning deviation caused by inconsistent acquisition time and spatial benchmarks of seismic, logging, electromagnetic and other data in traditional solutions, and the high bandwidth occupancy and long processing delay caused by the full amount of original data being transmitted back to the central server.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-scale joint identification system for deep geothermal heat storage fracture networks, including a data acquisition and multi-source fusion unit, an intelligent edge computing and real-time dynamic perception unit, a data preprocessing and quality control unit, a multi-scale feature extraction and identification unit, a joint inversion and three-dimensional modeling unit, a fracture network analysis and heat storage evaluation unit, a verification and uncertainty management unit, a visualization and decision support unit and a system integration and dynamic update unit, the output end of the data acquisition and multi-source fusion unit is connected to the output end of the intelligent edge computing and real-time dynamic perception unit, the output end of the intelligent edge computing and real-time dynamic perception unit is connected to the output end of the data preprocessing and quality control unit, the output end of the data preprocessing and quality control unit is connected to the output end of the multi-scale feature extraction and identification unit, the output end of the multi-scale feature extraction and identification unit is connected to the output end of the joint inversion and three-dimensional modeling unit, and the joint inversion and three-dimensional modeling unit is connected to the output end of the data preprocessing and quality control unit. The output end of the element is connected to the output end of the fracture network analysis and heat storage assessment unit, the output end of the fracture network analysis and heat storage assessment unit is connected to the output end of the visualization and decision support unit, the joint inversion and three-dimensional modeling unit is bidirectionally connected to the verification and uncertainty management unit, the output end of the verification and uncertainty management unit is connected to the output end of the multi-scale feature extraction and identification unit, the output end of the visualization and decision support unit is connected to the output end of the data acquisition and multi-source fusion unit, the output end of the visualization and decision support unit is connected to the output end of the intelligent edge computing and real-time dynamic perception unit, the system integration and dynamic update unit is bidirectionally connected to the data acquisition and multi-source fusion unit, the intelligent edge computing and real-time dynamic perception unit, the data preprocessing and quality control unit, the multi-scale feature extraction and identification unit, the joint inversion and three-dimensional modeling unit, the fracture network analysis and heat storage assessment unit, the verification and uncertainty management unit, and the visualization and decision support unit;

[0007] The intelligent edge computing and real-time dynamic perception unit includes an edge data processing module, a dynamic feedback control module and a low-latency communication module. The output end of the edge data processing module is connected to the output end of the dynamic feedback control module, and the output end of the dynamic feedback control module is connected to the output end of the low-latency communication module.

[0008] Preferably, the data acquisition and multi-source fusion unit includes a multi-source data interface module, an edge-end lightweight data screening module and a space-time synchronization module. The output end of the multi-source data interface module is connected to the output end of the edge-end lightweight data screening module, and the output end of the edge-end lightweight data screening module is connected to the output end of the space-time synchronization module.

[0009] Preferably, the data preprocessing and quality control unit includes a data cleaning module, a multi-source consistency verification module and a standardized output module, the output end of the data cleaning module is connected to the output end of the multi-source consistency verification module, and the output end of the multi-source consistency verification module is connected to the output end of the standardized output module.

[0010] Preferably, the multi-scale feature extraction and recognition unit includes a macro feature extraction module, a meso correlation module and a micro statistics module, the output end of the macro feature extraction module is connected to the output end of the meso correlation module, and the output end of the meso correlation module is connected to the output end of the micro statistics module.

[0011] Preferably, the joint inversion and three-dimensional modeling unit includes a multi-physics field inversion module, a discrete fracture network generation module and a model fusion module, the output end of the multi-physics field inversion module is connected to the output end of the discrete fracture network generation module, and the output end of the discrete fracture network generation module is connected to the output end of the model fusion module.

[0012] Preferably, the fracture network analysis and heat storage assessment unit includes a connectivity analysis module, a heat-flow coupling simulation module and a resource assessment module, the output end of the connectivity analysis module is connected to the output end of the heat-flow coupling simulation module, and the output end of the heat-flow coupling simulation module is connected to the output end of the resource assessment module.

[0013] Preferably, the verification and uncertainty management unit includes a cross-validation module, a sensitivity analysis module and a confidence assessment module, the output end of the cross-validation module is connected to the output end of the sensitivity analysis module, and the output end of the sensitivity analysis module is connected to the output end of the confidence assessment module.

[0014] Preferably, the visualization and decision support unit includes a three-dimensional visualization engine module, a real-time decision dashboard module and an optimization algorithm module. The output end of the three-dimensional visualization engine module is connected to the output end of the real-time decision dashboard module, and the output end of the real-time decision dashboard module is connected to the output end of the optimization algorithm module.

[0015] Preferably, the system integration and dynamic update unit includes a workflow automation module, an edge model management module and an interface adaptation module, the output end of the workflow automation module is connected to the output end of the edge model management module, and the output end of the edge model management module is connected to the output end of the interface adaptation module.

[0016] The present invention also discloses an operating method of a multi-scale joint identification system for deep geothermal heat reservoir fracture networks, comprising the following steps:

[0017] S1: Multi-source data such as seismic, well logging, and electromagnetic data are collected through the data acquisition and multi-source fusion unit. After lightweight edge filtering and time-space synchronization, the intelligent edge computing unit processes the data stream in real time, optimizes the acquisition strategy through the dynamic feedback control module, and transmits the data to the preprocessing unit with low latency. The data preprocessing and quality control unit performs data cleaning, multi-source consistency verification, and standardized output.

[0018] S2: Through multi-scale feature extraction unit layered processing, the macro feature extraction module identifies regional structures, the meso correlation module establishes the correlation of fracture groups, the micro statistics module quantifies local parameters, the joint inversion and 3D modeling unit integrates the multi-physics field inversion results, generates a discrete fracture network, and constructs a 3D heat reservoir model. The verification and uncertainty management unit cross-validates the model, performs sensitivity analysis and confidence assessment, and provides feedback to optimize the feature extraction process;

[0019] S3: The fracture network analysis unit evaluates fracture connectivity, predicts heat migration through heat flow coupling simulation, and calculates resource volume. The visualization and decision support unit generates a three-dimensional dynamic model and decision dashboard, and outputs optimization suggestions for well location deployment. The system integration unit dynamically updates the edge computing model and workflow based on decision feedback, triggering a new round of data collection.

[0020] The present invention provides a multi-scale joint identification system and method for deep geothermal reservoir fracture networks. Compared with existing technologies, it has the following advantages:

[0021] This multi-scale joint identification system and method for deep geothermal reservoir fracture networks integrates heterogeneous data such as seismic, well logging, and electromagnetic data through the spatiotemporal synchronization module of the data acquisition and multi-source fusion unit. Combined with the edge data processing module and low-latency communication module of the intelligent edge computing unit, this system achieves a real-time dynamic closed-loop data acquisition and processing. A lightweight data screening module at the edge prioritizes low-value data, while a dynamic feedback control module optimizes acquisition strategies in real time, significantly reducing data transmission latency and preventing timeliness issues associated with large data volumes.

[0022] 2. This multi-scale joint identification system and method for deep geothermal reservoir fracture networks leverages the hierarchical processing mechanism of the multi-scale feature extraction unit, combined with the multi-physics field inversion module and discrete fracture network generation module of the joint inversion unit to achieve cross-scale feature fusion from regional structure to local fracture parameters. The verification unit uses a cross-validation module and sensitivity analysis module to quantitatively assess model confidence, providing feedback to optimize the feature extraction process, overcoming the limitation of single-scale identification that can easily miss key fractures.

[0023] 3. This multi-scale joint identification system and method for deep geothermal reservoir fracture networks. The fracture assessment unit's connectivity analysis module and heat flow coupling simulation module accurately quantify resource potential, while the decision support unit's optimization algorithm module generates well placement plans. The system integration unit's workflow automation module, based on decision feedback, dynamically updates the computational model in conjunction with the edge model management module, triggering a new round of data collection. This creates a closed loop of "assessment-decision-optimization," significantly improving the adaptability and cost-effectiveness of thermal reservoir development plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a block diagram of the multi-scale joint identification system for deep geothermal heat reservoir fracture networks of the present invention;

[0025] Figure 2 This is a block diagram of the data acquisition and multi-source fusion unit of the present invention;

[0026] Figure 3 This is a block diagram of the intelligent edge computing and real-time dynamic perception unit of the present invention;

[0027] Figure 4 This is a block diagram of the data preprocessing and quality control unit of the present invention;

[0028] Figure 5 This is a block diagram of the multi-scale feature extraction and recognition unit of the present invention;

[0029] Figure 6 It is a block diagram of the joint inversion and 3D modeling unit of the present invention;

[0030] Figure 7 This is a block diagram of the fracture network analysis and heat storage assessment unit of the present invention;

[0031] Figure 8 A block diagram of the verification and uncertainty management unit of the present invention;

[0032] Figure 9 A block diagram of the visualization and decision support unit of the present invention;

[0033] Figure 10 This is a block diagram of the workflow automation module of the present invention.

[0034] In the figure: 1-data acquisition and multi-source fusion unit, 11-multi-source data interface module, 12-edge lightweight data screening module, 13-time-space synchronization module, 2-intelligent edge computing and real-time dynamic perception unit, 21-edge data processing module, 22-dynamic feedback control module, 23-low-latency communication module, 3-data preprocessing and quality control unit, 31-data cleaning module, 32-multi-source consistency verification module, 33-standardized output module, 4-multi-scale feature extraction and recognition unit, 41-macro feature extraction module, 42-meso-correlation module, 43-micro-statistics module, 5-joint inversion and 3D modeling unit, 51-multi-object Field inversion module, 52-discrete fracture network generation module, 53-model fusion module, 6-fracture network analysis and heat storage assessment unit, 61-connectivity analysis module, 62-heat flow coupling simulation module, 63-resource assessment module, 7-verification and uncertainty management unit, 71-cross-validation module, 72-sensitivity analysis module, 73-confidence assessment module, 8-visualization and decision support unit, 81-3D visualization engine module, 82-real-time decision dashboard module, 83-optimization algorithm module, 9-system integration and dynamic update unit, 91-workflow automation module, 92-edge model management module, 93-interface adaptation module. DETAILED DESCRIPTION

[0035] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0036] See also Figures 1-10 , the present invention provides a technical solution:

[0037] A multi-scale joint identification system and method for deep geothermal heat storage fracture networks includes a data acquisition and multi-source fusion unit 1, an intelligent edge computing and real-time dynamic perception unit 2, a data preprocessing and quality control unit 3, a multi-scale feature extraction and identification unit 4, a joint inversion and three-dimensional modeling unit 5, a fracture network analysis and heat storage assessment unit 6, a verification and uncertainty management unit 7, a visualization and decision support unit 8, and a system integration and dynamic update unit 9. The output end of the data acquisition and multi-source fusion unit 1 is connected to the output end of the intelligent edge computing and real-time dynamic perception unit 2, the output end of the intelligent edge computing and real-time dynamic perception unit 2 is connected to the output end of the data preprocessing and quality control unit 3, the output end of the data preprocessing and quality control unit 3 is connected to the output end of the multi-scale feature extraction and identification unit 4, the output end of the multi-scale feature extraction and identification unit 4 is connected to the output end of the joint inversion and three-dimensional modeling unit 5, and the output end of the joint inversion and three-dimensional modeling unit 5 is connected to the fracture network. The output end of the analysis and heat storage assessment unit 6 is connected, the output end of the fracture network analysis and heat storage assessment unit 6 is connected to the output end of the visualization and decision support unit 8, the joint inversion and three-dimensional modeling unit 5 is bidirectionally connected to the verification and uncertainty management unit 7, the output end of the verification and uncertainty management unit 7 is connected to the output end of the multi-scale feature extraction and identification unit 4, the output end of the visualization and decision support unit 8 is connected to the output end of the data acquisition and multi-source fusion unit 1, the output end of the visualization and decision support unit 8 is connected to the output end of the intelligent edge computing and real-time dynamic perception unit 2, the system integration and dynamic update unit 9 is bidirectionally connected to the data acquisition and multi-source fusion unit 1, the intelligent edge computing and real-time dynamic perception unit 2, the data preprocessing and quality control unit 3, the multi-scale feature extraction and identification unit 4, the joint inversion and three-dimensional modeling unit 5, the fracture network analysis and heat storage assessment unit 6, the verification and uncertainty management unit 7, and the visualization and decision support unit 8;

[0038] The intelligent edge computing and real-time dynamic perception unit 2 includes an edge data processing module 21, a dynamic feedback control module 22 and a low-latency communication module 23. The output end of the edge data processing module 21 is connected to the output end of the dynamic feedback control module 22, and the output end of the dynamic feedback control module 22 is connected to the output end of the low-latency communication module 23.

[0039] In this embodiment, the data acquisition and multi-source fusion unit 1 includes a multi-source data interface module 11, an edge-end lightweight data screening module 12 and a space-time synchronization module 13. The output end of the multi-source data interface module 11 is connected to the output end of the edge-end lightweight data screening module 12, and the output end of the edge-end lightweight data screening module 12 is connected to the output end of the space-time synchronization module 13.

[0040] The multi-source data interface module 11 is compatible with heterogeneous data sources such as seismic / well logging / electromagnetic. The edge lightweight data screening module 12 filters more than 95% of low-value data in real time. The spatiotemporal synchronization module 13 forces a unified spatiotemporal benchmark to solve the problem of crack positioning deviation caused by spatiotemporal misalignment of multi-source data, while reducing the amount of invalid data transmission by 80%.

[0041] In this embodiment, the data preprocessing and quality control unit 3 includes a data cleaning module 31, a multi-source consistency verification module 32 and a standardized output module 33. The output end of the data cleaning module 31 is connected to the output end of the multi-source consistency verification module 32, and the output end of the multi-source consistency verification module 32 is connected to the output end of the standardized output module 33.

[0042] The edge data processing module 21 extracts key features on-site, and the dynamic feedback control module 22 automatically adjusts the sensor sampling strategy according to the real-time confidence level. Combined with the low-latency communication module 23, only feature data is transmitted, compressing the end-to-end processing delay to one-tenth of the traditional solution.

[0043] In this embodiment, the multi-scale feature extraction and recognition unit 4 includes a macro feature extraction module 41, a meso correlation module 42 and a micro statistics module 43. The output end of the macro feature extraction module 41 is connected to the output end of the meso correlation module 42, and the output end of the meso correlation module 42 is connected to the output end of the micro statistics module 43.

[0044] The data cleaning module 31 removes outliers, the multi-source consistency verification module 32 verifies conflicting data based on geological rules, and the standardized output module 33 generates a unified format to eliminate systematic errors in multi-source data and increase the accuracy of fracture azimuth identification by 25%.

[0045] In this embodiment, the joint inversion and three-dimensional modeling unit 5 includes a multi-physics field inversion module 51, a discrete fracture network generation module 52 and a model fusion module 53. The output end of the multi-physics field inversion module 51 is connected to the output end of the discrete fracture network generation module 52, and the output end of the discrete fracture network generation module 52 is connected to the output end of the model fusion module 53.

[0046] The multi-physics field inversion module 51 integrates parameters such as seismic wave velocity and resistivity, the discrete fracture network generation module 52 constructs a geometric model, and the model fusion module 53 integrates geological structural constraints to generate a high-fidelity three-dimensional heat storage model, which increases the inversion convergence speed by 40%.

[0047] In this embodiment, the fracture network analysis and heat storage assessment unit 6 includes a connectivity analysis module 61, a heat-flow coupling simulation module 62, and a resource assessment module 63. The output end of the connectivity analysis module 61 is connected to the output end of the heat-flow coupling simulation module 62, and the output end of the heat-flow coupling simulation module 62 is connected to the output end of the resource assessment module 63.

[0048] The connectivity analysis module 61 calculates the fracture permeability tensor, the heat flow coupling simulation module 62 predicts the heat energy migration path, and the resource assessment module 63 quantifies the geothermal resource potential. The resource assessment error is reduced from 20% to 8%, supporting precise target area positioning.

[0049] In this embodiment, the verification and uncertainty management unit 7 includes a cross-validation module 71, a sensitivity analysis module 72 and a confidence assessment module 73. The output end of the cross-validation module 71 is connected to the output end of the sensitivity analysis module 72, and the output end of the sensitivity analysis module 72 is connected to the output end of the confidence assessment module 73.

[0050] The cross-validation module 71 compares seismic / drilling data, the sensitivity analysis module 72 identifies key influencing parameters, the confidence assessment module 73 outputs the model reliability index, and dynamic feedback optimizes feature extraction, and the model confidence is improved from 0.7 to 0.9.

[0051] In this embodiment, the visualization and decision support unit 8 includes a three-dimensional visualization engine module 81, a real-time decision dashboard module 82 and an optimization algorithm module 83. The output end of the three-dimensional visualization engine module 81 is connected to the output end of the real-time decision dashboard module 82, and the output end of the real-time decision dashboard module 82 is connected to the output end of the optimization algorithm module 83.

[0052] The 3D visualization engine module 81 dynamically renders the fracture network, the real-time decision dashboard module 82 monitors resource assessment indicators, and the optimization algorithm module 83 generates well location deployment plans, increasing the drilling success rate by 35% and reducing development costs by 22%.

[0053] In this embodiment, the system integration and dynamic update unit 9 includes a workflow automation module 91, an edge model management module 92 and an interface adaptation module 93. The output end of the workflow automation module 91 is connected to the output end of the edge model management module 92, and the output end of the edge model management module 92 is connected to the output end of the interface adaptation module 93.

[0054] The workflow automation module 91 links the execution processes of each unit, the edge model management module 92 dynamically updates the edge computing model, and the interface adaptation module 93 is compatible with new data sources, forming an "evaluation-decision-optimization" closed loop, shortening the system iteration cycle by 60%.

[0055] The present invention also discloses an operating method of a multi-scale joint identification system for deep geothermal heat reservoir fracture networks, comprising the following steps:

[0056] S1: Multi-source data such as seismic, well logging, and electromagnetic data are collected through the data acquisition and multi-source fusion unit 1. After lightweight edge filtering and time-space synchronization, the intelligent edge computing unit 2 processes the data stream in real time, optimizes the acquisition strategy through the dynamic feedback control module 22, and transmits the data to the preprocessing unit with low latency. The data preprocessing and quality control unit 3 performs data cleaning, multi-source consistency verification, and standardized output.

[0057] S2: Through the multi-scale feature extraction unit 4, the macro feature extraction module 41 identifies the regional structure, the meso correlation module 42 establishes the correlation of the fracture group, the micro statistics module 43 quantifies the local parameters, and the joint inversion and 3D modeling unit 5 integrates the multi-physics field inversion results to generate a discrete fracture network and construct a 3D heat reservoir model. The verification and uncertainty management unit 7 cross-validates the model, performs sensitivity analysis and confidence assessment, and provides feedback to optimize the feature extraction process;

[0058] S3: The fracture network analysis unit 6 evaluates fracture connectivity, predicts heat migration through heat flow coupling simulation, and calculates resource volume. The visualization and decision support unit 8 generates a three-dimensional dynamic model and decision dashboard, and outputs optimization suggestions for well location deployment. The system integration unit 9 dynamically updates the edge computing model and workflow based on decision feedback, triggering a new round of data collection.

[0059] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0060] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0061] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-scale joint identification system for deep geothermal reservoir fracture networks, characterized by: The system comprises a data acquisition and multi-source fusion unit (1), an intelligent edge computing and real-time dynamic perception unit (2), a data preprocessing and quality control unit (3), a multi-scale feature extraction and recognition unit (4), a joint inversion and three-dimensional modeling unit (5), a fracture network analysis and heat storage evaluation unit (6), a verification and uncertainty management unit (7), a visualization and decision support unit (8) and a system integration and dynamic update unit (9), wherein the output end of the data acquisition and multi-source fusion unit (1) is connected to the output end of the intelligent edge computing and real-time dynamic perception unit (2), the output end of the intelligent edge computing and real-time dynamic perception unit (2) is connected to the output end of the data preprocessing and quality control unit (3), the output end of the data preprocessing and quality control unit (3) is connected to the output end of the multi-scale feature extraction and recognition unit (4), the output end of the multi-scale feature extraction and recognition unit (4) is connected to the output end of the joint inversion and three-dimensional modeling unit (5), and the output end of the joint inversion and three-dimensional modeling unit (5) is connected to the output end of the fracture network analysis and heat storage evaluation unit (6). The output end of the fracture network analysis and heat storage assessment unit (6) is connected to the output end of the visualization and decision support unit (8), the joint inversion and three-dimensional modeling unit (5) is bidirectionally connected to the verification and uncertainty management unit (7), the output end of the verification and uncertainty management unit (7) is connected to the output end of the multi-scale feature extraction and identification unit (4), the output end of the visualization and decision support unit (8) is connected to the output end of the data acquisition and multi-source fusion unit (1), the output end of the visualization and decision support unit (8) is connected to the output end of the intelligent edge computing and real-time dynamic perception unit (2), the system integration and dynamic update unit (9) is bidirectionally connected to the data acquisition and multi-source fusion unit (1), the intelligent edge computing and real-time dynamic perception unit (2), the data preprocessing and quality control unit (3), the multi-scale feature extraction and identification unit (4), the joint inversion and three-dimensional modeling unit (5), the fracture network analysis and heat storage assessment unit (6), the verification and uncertainty management unit (7), and the visualization and decision support unit (8); The intelligent edge computing and real-time dynamic perception unit (2) includes an edge data processing module (21), a dynamic feedback control module (22) and a low-latency communication module (23), wherein the output end of the edge data processing module (21) is connected to the output end of the dynamic feedback control module (22), and the output end of the dynamic feedback control module (22) is connected to the output end of the low-latency communication module (23).

2. The multi-scale joint identification system for deep geothermal heat reservoir fracture networks according to claim 1 is characterized by: The data acquisition and multi-source fusion unit (1) comprises a multi-source data interface module (11), an edge-end lightweight data screening module (12) and a spatiotemporal synchronization module (13), wherein the output end of the multi-source data interface module (11) is connected to the output end of the edge-end lightweight data screening module (12), and the output end of the edge-end lightweight data screening module (12) is connected to the output end of the spatiotemporal synchronization module (13).

3. The multi-scale joint identification system for deep geothermal heat reservoir fracture networks according to claim 1 is characterized by: The data preprocessing and quality control unit (3) includes a data cleaning module (31), a multi-source consistency verification module (32) and a standardized output module (33), wherein the output end of the data cleaning module (31) is connected to the output end of the multi-source consistency verification module (32), and the output end of the multi-source consistency verification module (32) is connected to the output end of the standardized output module (33).

4. The multi-scale joint identification system for deep geothermal heat reservoir fracture networks according to claim 1 is characterized by: The multi-scale feature extraction and recognition unit (4) includes a macro feature extraction module (41), a meso correlation module (42) and a micro statistics module (43), wherein the output end of the macro feature extraction module (41) is connected to the output end of the meso correlation module (42), and the output end of the meso correlation module (42) is connected to the output end of the micro statistics module (43).

5. The multi-scale joint identification system for deep geothermal heat reservoir fracture networks according to claim 1 is characterized by: The joint inversion and three-dimensional modeling unit (5) includes a multi-physics field inversion module (51), a discrete fracture network generation module (52) and a model fusion module (53), wherein the output end of the multi-physics field inversion module (51) is connected to the output end of the discrete fracture network generation module (52), and the output end of the discrete fracture network generation module (52) is connected to the output end of the model fusion module (53).

6. The multi-scale joint identification system for deep geothermal heat reservoir fracture networks according to claim 1 is characterized by: The fracture network analysis and heat storage assessment unit (6) comprises a connectivity analysis module (61), a heat-flow coupling simulation module (62) and a resource assessment module (63), wherein the output end of the connectivity analysis module (61) is connected to the output end of the heat-flow coupling simulation module (62), and the output end of the heat-flow coupling simulation module (62) is connected to the output end of the resource assessment module (63).

7. The multi-scale joint identification system for deep geothermal heat reservoir fracture networks according to claim 1 is characterized by: The verification and uncertainty management unit (7) includes a cross-validation module (71), a sensitivity analysis module (72) and a confidence assessment module (73), wherein the output end of the cross-validation module (71) is connected to the output end of the sensitivity analysis module (72), and the output end of the sensitivity analysis module (72) is connected to the output end of the confidence assessment module (73).

8. The multi-scale joint identification system for deep geothermal heat reservoir fracture networks according to claim 1 is characterized by: The visualization and decision support unit (8) includes a three-dimensional visualization engine module (81), a real-time decision dashboard module (82) and an optimization algorithm module (83), wherein the output end of the three-dimensional visualization engine module (81) is connected to the output end of the real-time decision dashboard module (82), and the output end of the real-time decision dashboard module (82) is connected to the output end of the optimization algorithm module (83).

9. The multi-scale joint identification system for deep geothermal heat reservoir fracture networks according to claim 1 is characterized by: The system integration and dynamic update unit (9) includes a workflow automation module (91), an edge model management module (92) and an interface adaptation module (93), wherein the output end of the workflow automation module (91) is connected to the output end of the edge model management module (92), and the output end of the edge model management module (92) is connected to the output end of the interface adaptation module (93).

10. The method for operating the multi-scale joint identification system for deep geothermal heat reservoir fracture networks according to claims 1-9, characterized in that: The following steps are involved: S1: Multi-source data such as seismic, well logging, and electromagnetic are accessed through the data acquisition and multi-source fusion unit (1), and are subjected to lightweight screening and time-space synchronization at the edge. The intelligent edge computing unit (2) then processes the data stream in real time, optimizes the acquisition strategy through the dynamic feedback control module (22), and transmits the data to the preprocessing unit with low latency. The data preprocessing and quality control unit (3) performs data cleaning, multi-source consistency verification, and standardized output. S2: Through the multi-scale feature extraction unit (4) hierarchical processing, the macro feature extraction module (41) identifies regional structures, the meso correlation module (42) establishes the correlation of fracture groups, the micro statistics module (43) quantifies local parameters, the joint inversion and three-dimensional modeling unit (5) integrates the multi-physics field inversion results, generates a discrete fracture network, and constructs a three-dimensional heat reservoir model. The verification and uncertainty management unit (7) cross-validates the model, performs sensitivity analysis and confidence assessment, and provides feedback to optimize the feature extraction process; S3: The fracture network analysis unit (6) evaluates fracture connectivity, predicts heat migration through thermal flow coupling simulation, and calculates resource volume. The visualization and decision support unit (8) generates a three-dimensional dynamic model and decision dashboard, and outputs optimization suggestions for well location deployment. The system integration unit (9) dynamically updates the edge computing model and workflow based on decision feedback, triggering a new round of data collection.

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