Deep geothermal cascade development system based on multi-source heat storage cooperative scheduling and control method

Abstract

The invention relates to the technical field of terrestrial heat control, and discloses a deep terrestrial heat cascade development system and control method based on multi-source heat storage cooperative scheduling, and the system comprises a multi-source heat storage perception and fusion subsystem, a dynamic heat flow scheduling subsystem and a cascade heat energy release subsystem. The method comprises the following steps: merging multi-source original heat storage distribution data into a heat storage state diagram containing real-time temperature, pressure and permeability parameters of heat storage; converting the heat storage state diagram into a real-time heat flow scheduling plan, wherein the heat flow scheduling plan specifies the rate, sequence and path of extracting heat energy from each heat storage; according to a real-time heat flow scheduling plan, heat energy is released step by step from high temperature to low temperature through a multi-stage heat exchange sequence, and heat energy of different grades is generated and output for power generation or heating. Accurate three-dimensional modeling of underground heat storage parameters is achieved, and the technical bottlenecks of low energy utilization efficiency, high stratum disturbance risk and fast productivity attenuation in traditional geothermal development are broken through.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geothermal control, in particular to a deep geothermal gradient development system based on multi-source heat reservoir collaborative scheduling and a control method. BACKGROUND

[0002] With the acceleration of global energy transformation, deep geothermal resources have become an important development direction in the field of clean energy due to their huge reserves, strong power supply stability and extremely low carbon emissions. Traditional geothermal development relies on a single heat reservoir, which has the problems of low energy extraction efficiency and rapid decline in production capacity during long-term operation. Especially in the field of deep geothermal resources with high temperature and high pressure, how to realize the collaborative use of heat energy across layers and extend the life of power stations has become a technical bottleneck that needs to be broken through. China has abundant middle and deep geothermal resources, but existing technologies cannot support their large-scale and efficient development. The current mainstream technologies mainly include three types: single-well closed cycle system: through artificial fracturing to enhance the permeability of a single heat reservoir, but it is easy to cause microseismic risk and the heat exchange area is limited; multi-layer segmented mining technology: deploying heat extraction wells at different depths, but each layer of heat energy is used independently, lacking system linkage; and heat and power cogeneration mode: using middle and low temperature geothermal for heating, which fails to realize efficient conversion of electric energy. The shortcomings of the above existing technologies are: independent development of heat reservoirs in different temperature ranges, leading to the separation of high-temperature electric energy conversion and direct use of middle and low temperature, with an overall energy utilization rate of less than 40%; concentrated mining of a single heat reservoir easily causes stress imbalance of rock mass, and a certain demonstration project once caused wellbore collapse due to pressure surge; lacking a cross-layer heat supplement mechanism, the output power of a typical EGS power station decays by 8%-12% per year; the cost of deep drilling accounts for more than 60% of the total investment of a project, and the single output mode is difficult to cover the whole life cycle cost.

[0003] The existing technology one, application number: CN202410021341.0 discloses a middle and low temperature geothermal gradient development and utilization device and method, which includes geothermal water extraction wells, a distributor, a geothermal power generation system, an indoor radiator, a heating floor and a hot spring connected in sequence, the hot spring is connected with the geothermal water extraction well, the distributor is also separately connected with the indoor radiator, the heating floor and the hot spring, and the distributor is used for regulating and controlling the direct connection of the geothermal water extraction well with the geothermal power generation system, the indoor radiator, the heating floor and the hot spring. Although it has the effect of improving the stability of geothermal utilization; but only through a simple distributor to realize the switching of the energy utilization end, lacking real-time perception and dynamic control ability of the underground heat reservoir state, and unable to optimize the mining strategy according to the change of reservoir parameters.

[0004] Prior art two, application number: CN 202010730110.9 discloses a power generation and central heating system and method for middle-low temperature geothermal gradient development and utilization, comprising a middle-deep water-heat geothermal well extraction and injection system, a geothermal power generation system, a geothermal plate heat exchanger heating system connected with the building indoor system, and each plate heat exchanger of the geothermal plate heat exchanger heating system is connected in order and in parallel with the building indoor system. The system further comprises a geothermal heat pump heating system with at least two geothermal heat pumps, and the geothermal heat pumps of the geothermal heat pump heating system correspond to the plate heat exchangers of the geothermal plate heat exchanger heating system one by one and are connected in series on the building indoor system. Although the five-stage gradient utilization of geothermal energy can be used according to the actual project geothermal water temperature characteristics, part of the gradient utilization can be used, but the overall energy utilization efficiency is more than 80%; However, the system architecture is fixed and cannot dynamically adjust the extraction order and rate according to the heat storage conditions, resulting in a significant decrease in efficiency when the reservoir pressure and temperature change.

[0005] Prior art three, application number: CN 202110557822.X discloses a high-efficiency energy-saving combustion steam device for middle-low temperature geothermal gradient development and utilization. The device includes a box body, a collection box, a water tank, and a water-vapor separator fixedly installed on the inner top of the box body, and a horizontal plate fixedly installed on the inner wall of one side of the box body. Although the connection between the air inlet pipe and the box body can be sealed to prevent accidents caused by steam eruption when the connection between the air inlet pipe and the box body loosens, the opening of the steam stop valve is facilitated for timely maintenance, which relatively reduces the waste of steam and improves the energy-saving effect of the device. The steam eruption gas energy can also be utilized to effectively enhance the energy-saving effect and practicality of the device. However, the safety and local energy-saving of the steam device are emphasized, and the multi-heat storage collaborative scheduling and overall system energy efficiency optimization are not involved.

[0006] The existing technologies one, two and three have the problem that the existing geothermal development system lacks real-time perception and dynamic response capability for multi-source heat storage state, and the fixed gradient utilization mode cannot adapt to the dynamic changes of underground heat storage, resulting in low energy extraction efficiency, poor system operation stability, and shortened reservoir life. Therefore, the present application provides a deep geothermal gradient development system and control method based on multi-source heat storage collaborative scheduling. SUMMARY

[0007] The main purpose of the present application is to provide a deep geothermal gradient development system and control method based on multi-source heat storage collaborative scheduling to solve the problem of lack of real-time perception and dynamic response capability for multi-source heat storage state in the prior art, and the fixed gradient utilization mode cannot adapt to the dynamic changes of underground heat storage, resulting in low energy extraction efficiency, poor system operation stability, and shortened reservoir life.

[0008] To achieve the above object, the present application provides the following technical solutions: A deep geothermal gradient development system based on multi-source heat reservoir collaborative scheduling, comprising: A dynamic heat flow scheduling subsystem for calculating a heat flow priority matrix of each heat source priority index based on parameters in a heat reservoir state diagram, converting the heat reservoir state diagram into a real-time heat flow scheduling plan through a dynamic heat flow scheduling algorithm, and specifying the rate, order and path of heat energy extraction from each heat reservoir; A gradient heat energy release subsystem for realizing gradient release of heat energy through a multi-stage heat exchange sequence, the multi-stage heat exchange sequence consisting of a series of temperature gradient heat exchange units, each heat exchange unit performing heat energy conversion for a temperature interval; according to the real-time heat flow scheduling plan, heat energy is released step by step through the multi-stage heat exchange sequence from high temperature to low temperature, producing different grade heat energy outputs for power generation or heating.

[0009] As a further improvement of the present application, the dynamic heat flow scheduling subsystem comprises: A heat source potential assessment processing component for extracting temperature, pressure and permeability parameters of each heat source candidate point from the heat reservoir state diagram, obtaining a quantitative priority index by linear combination and normalization of the parameter values of each heat source point with its corresponding weight; and the priority indexes of all heat source points together constitute a heat source priority index set; A scheduling sequence generation component for selecting the heat source point with the highest heat source priority index as the starting point of the sequence, and forming a preliminary heat flow scheduling sequence that is spatially dispersed and optimal in energy priority by iterative selection when selecting the next heat source point; A flow allocation and path planning processing component for taking the preliminary heat flow scheduling sequence as the framework; in the flow allocation stage, the total extracted heat energy share is allocated in proportion according to the priority index of each heat source point in the preliminary heat flow scheduling sequence and its current temperature parameter, and the rate of heat energy extraction from each heat source is determined; in the path planning stage, the permeability parameter in the heat reservoir state diagram is used to treat the permeability distribution map as a path unobstructedness map, and for each heat source point with allocated flow, a path with the highest permeability parameter cumulative value, i.e. the lowest flow resistance, is found between the heat source point and the common flow channel; the allocated extraction rate, the scheduled extraction order and the calculated optimal path are integrated to form a real-time heat flow scheduling plan.

[0010] As a further improvement of the present application, the flow allocation and path planning processing component specifically comprises: The start point and end point defining subassembly is used to pass the preliminary heat flow scheduling sequence through the path start point and end point defining process to obtain a path calculation framework. The path start point and end point defining process is based on the heat source point positions in the preliminary heat flow scheduling sequence and the positions of the public confluence channels defined by the system. The public confluence channels serve as the common end points of all paths. A start point coordinate is assigned to each heat source point, and the permeability parameters of these points are extracted from the heat reservoir state diagram, and the inlet area of the public confluence channel is defined; The permeability accumulation path subassembly is used to start from the start point of each heat source point, step by step search the permeability parameters of the adjacent grids in the heat reservoir state diagram, select the grid point with the highest permeability value as the next node of the path, and repeat the process until the end point of the public confluence channel is reached. The scheduling plan integration processing subassembly is used to integrate the extraction rate and extraction sequence in the preliminary heat flow scheduling sequence with the path information in the optimal path set of the heat source points. The extraction rate and sequence are imported from the preliminary heat flow scheduling sequence, and the optimal path of each heat source point is assigned. The path information includes the node sequence and the flow direction. All elements are integrated to form a real-time heat flow scheduling plan.

[0011] As a further improvement of the present application, the scheduling plan integration processing subassembly comprises: The scheduling element extraction processing module is used to separate three core elements from the preliminary heat flow scheduling sequence based on the operation sequence analysis method: a list of heat source point identifiers arranged in sequence, a heat energy extraction rate value corresponding to each heat source point, and an extraction time sequence implied by the list sequence. The three core elements together constitute a structured data set, namely a structured scheduling element set. The path-rate binding processing module is used to one-to-one correspond the operation instructions of each heat source point with its physical path. Each heat source point identifier in the structured scheduling element set is matched with the same-named path in the optimal path set of the heat source points. The three pieces of information of each heat source point that match successfully: the extraction rate, the extraction sequence, and the corresponding detailed node sequence and flow direction are packaged into an indivisible independent operation unit. After all the independent operation units of the heat source points are collected, a bound scheduling unit set is formed. The space-time scheduling matrix generation processing module is used to globally arrange all the units in the bound scheduling unit set in accordance with the extraction sequence embedded therein to form an execution sequence. The execution sequence completely specifies when, from where, at what rate, and along which specific path to extract heat energy. Finally, all the information is integrated and packaged into a unified document, namely a real-time heat flow scheduling plan.

[0012] As a further improvement of the present application, the path-rate binding processing module comprises: The identification list extraction processing submodule is configured to separate a heat source point identification list field from the structured scheduling element set and normalize each identification to obtain a standardized identification list. The path list extraction processing submodule is configured to scan the entry metadata of each path in the heat source point optimal path set, and the entry metadata contains the source heat source point identification corresponding to the optimal path; extract the source heat source point identification of all paths and perform standardization processing to form a path identification index. The matching and mapping processing submodule is configured to compare each identification in the standardized identification list with each identification in the path identification index; when and only when the two identification strings are completely identical, it is determined that the matching is successful; each successful matching pair, that is, a heat source point identification and its corresponding unique path entity, is recorded as a mapping entry; and all mapping entries constitute an identification-path mapping table.

[0013] As a further improvement of the present application, the path list extraction processing submodule comprises: The metadata scanning processing unit is configured to traverse each path entity in the heat source point optimal path set one by one, and accurately read the source heat source point identification field in the position of the entry metadata block; all read identifiers are temporarily stored as an initial set, that is, an original identification set. The identification normalization processing unit is configured to perform two core operations on each identification string in the original identification set: first, remove all non-alphanumeric characters; and second, convert all symbol characters to a unified uppercase form. The index structure generation processing unit is configured to establish a one-to-one mapping relationship between each identifier in the standardized identification list and the complete path entity corresponding to the identifier in the heat source point optimal path set, and organize the mapping relationship in a data structure that allows efficient retrieval to generate a path identification index.

[0014] As a further improvement of the present application, the index structure generation processing unit comprises: The association pair generation processing subunit is configured to traverse each identifier in the standardized identification list to use it as a query basis to reversely scan the entry metadata of all path entities in the heat source point optimal path set to find the source identifier after the same normalization processing; when a completely identical source identifier is found, the standardized identifier and the complete path entity corresponding to the identifier are combined into an association pair; and all such association pairs constitute a preliminary association pair set. The association pair verification processing subunit is used for checking each association pair in the preliminary association pair set, confirming that the source identifier contained in the entry metadata of the path entity in the pair is absolutely consistent with the normalized identifier in the association pair after the same normalization processing, and confirming that each normalized identifier only appears in a unique association pair; the association pair passing the verification is reserved to form a verified association pair set, thereby ensuring the uniqueness and accuracy of the mapping; The index structure assembly processing subunit is used for organizing the normalized identifier in each association pair as a queryable key and the corresponding complete path entity as an associated value in a single data structure instance on the basis of the verified association pair set; the data structure instance, i.e., the path identification index, supports quick retrieval and acquisition of the unique corresponding complete path entity through the normalized identifier.

[0015] As a further improvement of the application, the stepped heat energy release subsystem comprises: The hot flow sequential import processing component is used for extracting a sequential list of heat energy extraction and a corresponding extraction rate from the real-time hot flow scheduling plan; the heat energy flows from different paths are arranged in a time sequence according to the sequential list, thereby ensuring that high heat energy flows are preferentially imported; meanwhile, the flow proportion of each heat source point is adjusted according to the extraction rate to form a sequential hot flow. The temperature interval matching release processing component is used for sequentially importing the sequential hot flow to high-temperature heat exchange, converting heat energy for a preset high-temperature interval, absorbing heat through a conduction medium of the high-temperature heat exchange to generate high-temperature heat energy output; the remaining heat energy is then imported to medium-temperature heat exchange, converting heat energy for a medium-temperature interval, releasing heat through a heat exchange surface of the medium-temperature heat exchange to generate medium-temperature heat energy output; finally, the remaining heat energy enters low-temperature heat exchange, and the low-temperature heat exchange converts heat energy for a low-temperature interval, diffuses heat through an extended area of the low-temperature heat exchange to generate low-temperature heat energy output. The heat energy output modulation processing component is used for proportionally adjusting each level of heat energy value in the hierarchical heat energy value set according to the external energy application demand; the adjusted heat energy value is integrated into a unified output flow.

[0016] As a further improvement of the application, the multi-source heat reservoir perception and fusion subsystem is further provided, which is used for non-invasively detecting an underground heat reservoir through geothermal resonance scanning to generate original heat reservoir distribution data; the original heat reservoir distribution data is subjected to heat reservoir feature fusion processing to combine the multi-source original heat reservoir distribution data into a heat reservoir state map containing real-time temperature, pressure and permeability parameters of the heat reservoir.

[0017] To achieve the above object, the application further provides the following technical scheme: A deep geothermal gradient development control method based on multi-source heat reservoir collaborative scheduling is applied to a deep geothermal gradient development system based on multi-source heat reservoir collaborative scheduling, and the deep geothermal gradient development control method based on multi-source heat reservoir collaborative scheduling comprises the following steps: The underground heat reservoir is non-invasively detected by geothermal resonance scanning to generate original heat reservoir distribution data; the original heat reservoir distribution data is subjected to heat reservoir feature fusion processing, and the multi-source original heat reservoir distribution data is combined into a heat reservoir state diagram containing real-time temperature, pressure and permeability parameters of the heat reservoir; A heat flow priority matrix for calculating the priority index of each heat source based on the parameters in the heat reservoir state diagram is adopted, and a real-time heat flow scheduling plan is converted from the heat reservoir state diagram through a dynamic heat flow scheduling algorithm, and the heat flow scheduling plan specifies the rate, sequence and path of heat energy extraction from each heat reservoir; The multi-stage heat exchange sequence is used to realize the gradient release of heat energy, and the multi-stage heat exchange sequence is composed of a series of temperature gradient heat exchange units, and each heat exchange unit is used for heat energy conversion in a specific temperature interval; according to the real-time heat flow scheduling plan, the heat energy is released step by step from high temperature to low temperature through the multi-stage heat exchange sequence, and different grade heat energy outputs are generated for power generation or heating.

[0018] The present application realizes the accurate three-dimensional modeling of the underground heat reservoir parameters through the multi-source heat reservoir perception and fusion subsystem, constructs the heat source priority matrix and generates the optimal exploitation path by means of the dynamic heat flow scheduling subsystem, and realizes the step-by-step energy conversion of the temperature interval through the gradient heat energy release subsystem, thereby breaking through the technical bottlenecks of low energy utilization efficiency, high risk of stratum disturbance and rapid production capacity decline in traditional geothermal development, forming a whole-chain collaborative control from resource exploration, intelligent scheduling to energy output, and significantly improving the system comprehensive energy utilization rate and prolonging the life cycle of the geothermal field. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 It is a functional module schematic diagram of the deep geothermal gradient development system based on multi-source heat reservoir collaborative scheduling of the present application; Figure 2 It is a functional module schematic diagram of the multi-source heat reservoir perception and fusion subsystem of the deep geothermal gradient development system based on multi-source heat reservoir collaborative scheduling of the present application; Figure 3 It is a functional module schematic diagram of the dynamic heat flow scheduling subsystem of the deep geothermal gradient development system based on multi-source heat reservoir collaborative scheduling of the present application; Figure 4 It is a functional module schematic diagram of the gradient heat energy release subsystem of the deep geothermal gradient development system based on multi-source heat reservoir collaborative scheduling of the present application; Figure 5 It is a step flow schematic diagram of one embodiment of the deep geothermal gradient development control method based on multi-source heat reservoir collaborative scheduling of the present application; Figure 6 Structure diagram of an embodiment of an electronic device of the present application; Figure 7 Structure diagram of an embodiment of a storage medium of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0021] The terms “first”, “second”, “third” in the present application are only for descriptive purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with “first”, “second”, “third” can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of “plurality” is at least two, for example, two, three, etc., unless otherwise explicitly and specifically limited. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application are only used to explain the relative position relationship, movement condition, etc. between the components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly. In addition, the terms “include” and “have” and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0022] In this document, reference to “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase “in an embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. Those skilled in the art will appreciate that embodiments described herein can be combined with other embodiments.

[0023] As Figure 1 shown, the present embodiment provides an embodiment of a deep geothermal cascade development system based on multi-source heat reservoir collaborative scheduling, in which the deep geothermal cascade development system based on multi-source heat reservoir collaborative scheduling includes, which are electrically connected in sequence: a multi-source heat reservoir perception and fusion subsystem, a dynamic heat flow scheduling subsystem, and a cascade heat energy release subsystem. The multi-source geothermal reservoir perception and fusion subsystem is configured to perform non-invasive detection on the underground geothermal reservoir through geothermal resonance scanning to generate original geothermal reservoir distribution data; the original geothermal reservoir distribution data is subjected to geothermal reservoir feature fusion processing to combine the multi-source original geothermal reservoir distribution data into a geothermal reservoir state diagram containing real-time temperature, pressure and permeability parameters of the geothermal reservoir. The dynamic heat flow scheduling subsystem is configured to calculate a heat flow priority matrix of each heat source based on the parameters in the geothermal reservoir state diagram, and convert the geothermal reservoir state diagram into a real-time heat flow scheduling plan through a dynamic heat flow scheduling algorithm, the heat flow scheduling plan specifying the rate, sequence and path of heat energy extraction from each geothermal reservoir. The cascade heat energy release subsystem is configured to realize cascade release of heat energy through a multi-stage heat exchange sequence, the multi-stage heat exchange sequence being composed of a series of heat exchange units with temperature gradients, each heat exchange unit being configured to convert heat energy for a specific temperature interval; according to the real-time heat flow scheduling plan, heat energy is released step by step through the multi-stage heat exchange sequence from high temperature to low temperature to generate heat energy outputs of different grades for power generation or heating.

[0024] Preferably, the embodiment realizes accurate three-dimensional modeling of geothermal reservoir parameters through the multi-source geothermal reservoir perception and fusion subsystem, constructs a heat source priority matrix and generates an optimal extraction path with the aid of the dynamic heat flow scheduling subsystem, and realizes step-by-step energy conversion of temperature intervals through the cascade heat energy release subsystem, thereby breaking through the technical bottlenecks of low energy utilization efficiency, high risk of stratum disturbance and rapid production capacity decline in traditional geothermal development, forming a full-chain collaborative control from resource detection, intelligent scheduling to energy output, and significantly improving the system's comprehensive energy utilization rate and extending the life cycle of the geothermal field.

[0025] Further, as shown in Figure 2 The multi-source geothermal reservoir perception and fusion subsystem specifically includes: The consistency checking component is configured to perform data consistency checking processing on the original geothermal reservoir distribution data, identify and eliminate abnormal values caused by detection interference or geological mutations by calculating the deviation degree of each data point from adjacent data, and obtain consistent geothermal reservoir data. In the consistency checking component, the process of identifying abnormal values by calculating the deviation degree of each data point from adjacent data is based on the spatial consistency principle and derived from the adjacent correlation theory in geological data statistics. First, a set of adjacent data is defined for each data point, the set of adjacent data being determined by a distance range in a three-dimensional space, the distance range being set based on the resolution of geothermal reservoir detection. Then, the average deviation of the parameter values of each data point from its adjacent data points is calculated, the deviation degree being quantified by the ratio of the absolute value of the difference between the parameter values to the number of adjacent data points. If the deviation degree exceeds a preset fault tolerance threshold, which is adjusted according to the stability of historical detection data, the data point is identified as an abnormal value and eliminated. Finally, the obtained consistent geothermal reservoir data eliminates inconsistent points and only retains spatially consistent data points for input for weighted aggregation.

[0026] a weighted aggregation component configured to calculate weights according to information entropy of each data source, and to assign higher weights to data sources with higher information entropy to ensure that high-reliability data dominates in the fusion; after the weighted aggregation, the preliminary fusion thermal reservoir data are integrated to form a unified parameter set by calculating weighted average values of temperature, pressure and permeability; a data conversion component configured to convert the discrete preliminary fusion thermal reservoir data into a continuous spatial distribution map by parameter space diffusion; to fill data gaps by using a gradual change trend of adjacent parameter values to ensure smooth transition of temperature, pressure and permeability in the three-dimensional space; and to generate a thermal reservoir state map in a grid form, each grid point of which contains real-time temperature, pressure and permeability parameters.

[0027] Preferably, the embodiment removes abnormal data by data consistency verification to ensure the reliability of the basic data, uses information entropy weighted aggregation to strengthen the dominant position of high-credibility data in the fusion to form a unified parameter set, and finally converts the discrete data into a continuous spatial distribution map by parameter space diffusion to finally construct a thermal reservoir state map that can accurately reflect the three-dimensional parameter distribution of the underground thermal reservoir.

[0028] Further, the data conversion component specifically includes: a grid framework construction processing sub-component configured to obtain an initial grid framework by performing grid framework construction processing on the preliminary fusion thermal reservoir data; the grid framework construction processing is based on the spatial distribution range of the preliminary fusion thermal reservoir data, defines a three-dimensional grid structure, and determines the grid resolution according to the data point density and the geological horizon characteristics, each grid unit being initialized as an unfilled state; and the initial grid framework provides a spatial basis for parameter diffusion. a parameter diffusion calculation processing sub-component configured to obtain a parameter-filled grid by performing parameter diffusion calculation processing on the initial grid framework; the parameter diffusion calculation processing is based on the principle of adjacent influence, which is derived from the attenuation effect in the spatial interpolation theory, for each grid point, the preliminary fusion thermal reservoir data points adjacent thereto are found, the adjacent range being controlled by a distance threshold of the grid point and the data point; then, distance-weighted contribution values are calculated according to the parameter values and distances of the adjacent data points, the weighted contribution values decreasing with the increase of the distance, and the temperature, pressure and permeability parameter values of the grid point are obtained by summing the weighted contribution values of all the adjacent data points; and the parameter-filled grid eliminates data gaps and realizes preliminary spatial distribution of the parameters.

[0029] The smoothing optimization subcomponent is used to smooth the parameter-filled grid to obtain the reservoir state map. This smoothing optimization is based on the principle of local consistency, derived from the theory of geological property smoothness. For each grid point in the parameter-filled grid, its parameter gradient with neighboring grid points is calculated. By adjusting the parameter values ​​of the grid points, the gradient change is minimized, ensuring a smooth transition of temperature, pressure, and permeability in three-dimensional space. The final generated reservoir state map is presented in grid form, with each grid point containing real-time temperature, pressure, and permeability parameters, providing input for the dynamic heat flow scheduling subsystem.

[0030] Preferably, this embodiment establishes a spatial computing foundation by constructing a three-dimensional mesh framework, uses a distance-weighted interpolation algorithm to transform discrete data into a continuous parameter field, and then achieves a smooth spatial transition of geological attributes through gradient minimization optimization, ultimately generating a high-precision geothermal reservoir state map, providing a reliable spatial parameter basis for subsequent resource scheduling.

[0031] Furthermore, such as Figure 3 As shown, the dynamic heat flow scheduling subsystem specifically includes: The heat source potential assessment and processing component is used to extract the temperature, pressure, and permeability parameters of each heat source candidate point from the reservoir state diagram. Based on the concept of energy grade in thermodynamics and the recoverability principle in reservoir engineering, dynamic weights are assigned to the three parameters: the temperature parameter has the highest weight, representing its energy grade; the permeability parameter is second, representing the ease of heat energy extraction; and the pressure parameter serves as an adjustment factor. By linearly combining and normalizing the parameter values ​​of each heat source point with their corresponding weights, a quantified priority index is obtained. The priority indices of all heat source points together constitute the heat source priority index set. The scheduling sequence generation component is used to select the heat source with the highest heat source priority index as the starting point of the sequence. When selecting the next heat source, its own heat source priority index is considered, and its spatial distance from the selected heat source is also evaluated. Through iterative selection, a preliminary heat flow scheduling sequence that is spatially dispersed and optimal in terms of energy priority is formed. The flow allocation and path planning processing component is used to form a preliminary heat flow scheduling sequence as the framework. In the flow allocation stage, based on the priority index and current temperature parameter of each heat source point in the preliminary heat flow scheduling sequence, the total share of extracted heat energy is allocated proportionally to determine the rate of heat energy extraction from each heat source. In the path planning stage, the permeability parameter in the heat storage state diagram is used to treat the permeability distribution map as a path unobstructedness map. For each heat source point with allocated flow, a path with the highest cumulative permeability parameter value, i.e., the lowest flow resistance, is found between it and the common confluence channel. The allocated extraction rate, the scheduled extraction order, and the calculated optimal path are integrated to form a real-time heat flow scheduling plan.

[0032] Preferably, the dynamic heat flow scheduling subsystem of the present embodiment dynamically weights and normalizes the temperature, pressure and permeability parameters of the heat source candidate points in the thermal reservoir through a heat source potential evaluation processing component, generating a set of quantified heat source priority indices to provide basic data support for scheduling. Based on the priority indices and spatial distribution characteristics, a preliminary heat flow scheduling sequence that takes into account both energy priority and geographical dispersion is constructed by a scheduling sequence generation component, realizing the ordered arrangement of heat source activation. The flow rate allocation and path planning processing component dynamically allocates the extraction rate of each heat source in combination with the priority index and temperature parameter, and generates a transport path with the lowest flow resistance based on the permeability distribution map, ultimately forming a real-time heat flow scheduling plan that integrates extraction rate, execution order and transport path. The overall system realizes efficient development of geothermal resources, optimizes heat energy extraction efficiency and transport stability through multi-module collaboration, reduces energy loss and flow resistance during the extraction process, and improves the overall operating efficiency of the geothermal system.

[0033] Further, the flow rate allocation and path planning processing component specifically includes: The start and end point definition sub-component is used to define the path calculation framework by defining the start and end points of the preliminary heat flow scheduling sequence. The path start and end point definition process is based on the heat source point positions in the preliminary heat flow scheduling sequence and the defined common convergence channel position. The common convergence channel serves as the common endpoint of all paths. Each heat source point is assigned a start point coordinate, and the permeability parameters of these points are extracted from the thermal reservoir state map. The inlet area of the common convergence channel is also defined. The output path calculation framework clearly defines the start and end point ranges of each heat source point, providing spatial constraints for path calculation. The permeability accumulation path sub-component is used to search for the permeability parameters of adjacent grids in the thermal reservoir state map starting from the start point of each heat source point. The grid point with the highest permeability value is selected as the next node on the path, and this process is repeated until the common convergence channel endpoint is reached. The quality of the path is evaluated by the cumulative value of the permeability parameters of all grid points on the path, with the highest cumulative value indicating the lowest flow resistance. The path set of all heat source points constitutes the optimal path set of heat source points, ensuring the efficiency of heat energy extraction paths.

[0034] The scheduling plan integration processing sub-component is used to integrate the extraction rate, extraction order and path information in the optimal path set of heat source points in the preliminary heat flow scheduling sequence. The extraction rate and order are imported from the preliminary heat flow scheduling sequence, and each heat source point is assigned its corresponding optimal path. The path information includes node sequence and flow direction. All elements are integrated to form a real-time heat flow scheduling plan that clearly specifies the rate, order and specific path of heat energy extraction from each heat source.

[0035] Preferably, the flow distribution and path planning processing components of the present embodiment achieve overall optimization of the thermal energy transport network through systematic synergy. The spatial constraint framework established by the origin and destination definition subcomponent provides precise coordinate boundaries and permeability parameter benchmarks for path searching. The permeability accumulation path subcomponent dynamically constructs transport paths based on geological parameters, forming thermal energy transmission channels with minimal flow resistance through a continuous optimization node selection mechanism. The scheduling plan integration processing subcomponent deeply integrates time control parameters and spatial path characteristics, generating a thermal flow control scheme that combines time-dimension scheduling logic and space-dimension path planning. Ultimately, it achieves the technical effects of improving thermal energy extraction efficiency, minimizing flow resistance, and adaptively matching scheduling instructions with geological conditions, ensuring stable operation and optimal energy efficiency of the geothermal resource development system in complex geological environments.

[0036] Further, the scheduling plan integration processing subcomponent specifically includes: A scheduling element extraction processing module is used to separate three core elements from the preliminary thermal flow scheduling sequence based on the operation sequence analysis method: a list of heat source point identifiers arranged in order, a thermal energy extraction rate value corresponding to each heat source point, and a strict extraction time sequence implied by the list order; the three core elements together form a structured data set, i.e., a structured scheduling element set; A path-rate binding processing module is used to one-to-one correspond each heat source point's operation instruction with its physical path; match each heat source point identifier in the structured scheduling element set with the same-named path in the heat source point optimal path set; encapsulate the three pieces of information of each heat source point that match successfully: extraction rate, extraction order, and corresponding detailed node sequence and flow direction; as an indivisible independent operation unit; after all heat source point independent operation units are collected, a bound scheduling unit set is formed; A space-time scheduling matrix generation processing module is used to globally arrange all units in the bound scheduling unit set according to their embedded extraction order, forming an execution sequence; the execution sequence completely specifies when, from where, at what rate, and along which specific path to extract thermal energy, and finally integrates all information into a unified document, i.e., a real-time thermal flow scheduling plan.

[0037] Preferably, the scheduling plan integration processing subcomponent of the embodiment realizes accurate conversion and execution of scheduling instructions through modular cooperation; the hierarchical data structure established by the scheduling element extraction processing module systematically separates the time sequence control parameters and spatial coordinates. The operation unit formed by the path-rate binding processing module realizes a stable correspondence between physical paths and dynamic parameters. The serialized execution framework constructed by the space-time scheduling matrix generation processing module integrates discrete operation units into continuous control processes with strict space-time correlation. Finally, an integrated scheduling scheme with clear execution timing, accurate spatial positioning, and controllable extraction rate is formed, realizing the orderliness in the time dimension and the accuracy in the spatial dimension of the heat energy extraction process, ensuring the stable operation and dynamic response capability of the geothermal resource development system under complex working conditions.

[0038] Further, the path-rate binding processing module specifically comprises: The identification list extraction processing submodule is used to separate the hot source point identification list field from the structured scheduling element set, and to standardize the format of each identification, eliminating non-standard characters or spaces in the name, to obtain a standardized identification list; The path list extraction processing submodule is used to scan the entry metadata of each path in the hot source point optimal path set, which contains the source hot source point identification corresponding to the optimal path; the source hot source point identifications of all paths are extracted and standardized to form a path identification index; The matching and mapping processing submodule is used to completely match each identification in the standardized identification list with each identification in the path identification index; only when the two identification strings are completely identical, it is determined that the matching is successful; each successful matching pair, i.e. a hot source point identification and its corresponding unique path entity, is recorded as a mapping entry; the collection of all mapping entries constitutes an identification-path mapping table.

[0039] Preferably, the path-rate binding processing module of the embodiment realizes the accurate association of hot source points and optimal paths through the cooperative operation of each submodule. The identification list extraction processing submodule standardizes and cleans the hot source point identification, ensuring the naming specification and uniformity; the path list extraction processing submodule extracts the source identification from the path metadata and establishes an index to form a searchable path identification set; the matching and mapping processing submodule establishes a strict correspondence between the hot source point identification and the path entity through the string complete matching mechanism. The finally generated identification-path mapping table realizes the bidirectional traceability of the hot source point and the path entity, provides accurate data basis for rate calculation, eliminates path matching errors caused by non-standard naming, and improves system processing efficiency and data consistency.

[0040] Further, the path list extraction processing submodule specifically comprises: a metadata scanning processing unit, configured to traverse each path entity in the set of optimal heat source point paths one by one, and accurately read a source heat source point identifier field at a specific position in an entry metadata block of the path entity; an identifier normalization processing unit, configured to perform two core operations on each identifier string in the original identifier set: first, remove all non-alphanumeric characters such as underscores or hyphens; second, convert all punctuation characters to a uniform uppercase form; an index structure generating processing unit, configured to establish a one-to-one mapping relationship between each identifier in the normalized identifier list and the complete path entity corresponding to the identifier in the set of optimal heat source point paths, and organize the mapping relationship in a data structure that allows efficient retrieval, to generate a path identifier index.

[0041] Preferably, the path list extraction processing submodule of the embodiment realizes the function of extracting and standardizing source identifiers from the path set through the cooperative operation of the three processing units. The metadata scanning processing unit systematically traverses the path set and accurately obtains source heat source point identifiers to form an original data set. The identifier normalization processing unit eliminates the format differences of identifiers through character filtering and format unification operations, ensuring the reliability of the matching operation. The index structure generating processing unit establishes an accurate mapping relationship between the standardized identifiers and the complete path entities, and constructs an efficient retrieval data structure. The submodule as a whole realizes the complete extraction, format unification, and fast retrieval capability of path source identifiers, provides standardized input for the identifier matching operation, and simultaneously optimizes the system processing efficiency through preprocessing.

[0042] Further, the index structure generating processing unit specifically comprises: an associated pair generating processing subunit, configured to traverse each identifier in the normalized identifier list, use the identifier as a query basis, and reverse scan the entry metadata of all path entities in the set of optimal heat source point paths to find source identifiers that have undergone the same normalization processing; when a completely identical source identifier is found, the normalized identifier and the complete path entity corresponding to the normalized identifier are combined into an associated pair; all such associated pairs constitute a preliminary associated pair set; an associated pair verifying processing subunit, configured to check each associated pair in the preliminary associated pair set, confirm that the source identifier contained in the entry metadata of the path entity in the associated pair is absolutely identical to the normalized identifier in the associated pair after undergoing the same normalization processing, and confirm that each normalized identifier only appears in a unique associated pair; the associated pairs that pass the verification are retained to form a verified associated pair set, ensuring the uniqueness and accuracy of the mapping; The index structure assembly processing subunit is used to systematically organize the normalized identifier in each association pair as a queryable key and the corresponding complete path entity as the association value, based on the verified set of association pairs, into a single data structure instance; the data structure instance is the path identifier index, which supports fast retrieval and retrieval of its unique corresponding complete path entity through the normalized identifier.

[0043] Preferably, the index structure generation processing unit in this embodiment achieves precise mapping between path entities and identifiers through a multi-layered processing mechanism. The association pair generation processing subunit establishes a preliminary identifier-path entity correspondence by traversing identifiers and reverse-scanning path metadata, forming a preliminary association pair set. The association pair verification processing subunit performs double verification on the preliminary association pairs to ensure absolute consistency between the source identifier and the normalized identifier, while enforcing identifier uniqueness constraints to eliminate duplicate mappings and data ambiguity, generating a set of verified association pairs with strict uniqueness. The index structure assembly processing subunit integrates the verified association pairs into a unified data structure, constructing a path identifier index with normalized identifiers as keys and complete path entities as values, ultimately enabling rapid retrieval of corresponding path entities using normalized identifiers. The overall technical solution achieves high accuracy and optimized retrieval efficiency in the mapping relationship between identifiers and path entities through step-by-step verification and structured storage.

[0044] Furthermore, such as Figure 4 As shown, the cascaded heat release subsystem specifically includes: The sequential heat flow import processing component is used to extract the order list of heat energy extraction and the corresponding extraction rate from the real-time heat flow scheduling plan; according to the order list, the heat energy flows from different paths are arranged in time sequence to ensure that high heat energy flows are imported first; at the same time, the flow rate ratio of each heat source point is adjusted according to the extraction rate to form a continuous heat energy flow with well-defined parameters; the sequential heat flow input includes the initial temperature distribution of heat energy, flow rate, and the starting point for entering the multi-stage heat exchange sequence, providing a basis for staged release; The temperature range matching and release processing component guides the sequential heat flow input first to a high-temperature heat exchanger. Heat is converted within a preset high-temperature range, absorbing heat through the heat exchanger's conductive medium to generate high-temperature heat output, while the temperature of remaining heat energy decreases. The remaining heat energy is then introduced to a medium-temperature heat exchanger, where it is converted within a medium-temperature range. Heat is released through the heat exchanger's surface, generating medium-temperature heat output and further reducing the heat energy temperature. Finally, the remaining heat energy enters a low-temperature heat exchanger, where it is converted within a low-temperature range. Heat diffusion is achieved using the extended area of ​​the low-temperature heat exchanger, generating low-temperature heat output. The output heat energy value of each heat exchanger is recorded independently, and all outputs together constitute a graded heat energy value set, containing heat energy values ​​for different temperature ranges. The heat energy output modulation processing component is configured to proportionally adjust each level of heat energy value in the set of hierarchical heat energy values according to external energy application requirements (e.g., high-temperature heat energy required for power generation or medium-low-temperature heat energy required for heating); for example, increasing the proportion of high-temperature heat energy values to improve power generation efficiency, or balancing medium-low-temperature heat energy values to meet regional heating requirements; the adjusted heat energy values are integrated into a unified output stream with variable grade characteristics, which is directly used for power generation or heating applications.

[0045] Preferably, the embodiment arranges and regulates the flow of multiple heat sources through a heat flow sequential introduction processing component to form a continuous controllable heat energy input flow. The temperature interval matching release processing component realizes the layered extraction and conversion of heat energy according to temperature gradients through the series connection of high, medium, and low temperature heat exchange, generating a set of hierarchical heat energy values with clear temperature intervals. The heat energy output modulation processing component dynamically adjusts the proportion of heat energy in each temperature zone according to the terminal application requirements, and finally integrates to form a unified output stream with adjustable grade characteristics. The overall technical solution realizes the time-sequential optimization distribution, temperature interval utilization, and output grade modulation according to demand of multiple heat sources, effectively improving the comprehensive utilization efficiency of heat energy and the adaptability of the system.

[0046] As shown in Figure 5 The embodiment also provides an embodiment of a deep geothermal gradient development control method based on collaborative scheduling of multiple heat reservoirs. In this embodiment, the deep geothermal gradient development control method based on collaborative scheduling of multiple heat reservoirs is applied to the deep geothermal gradient development system based on collaborative scheduling of multiple heat reservoirs in the above embodiment. The deep geothermal gradient development control method based on collaborative scheduling of multiple heat reservoirs specifically includes the following steps: Step S1: non-invasively detecting the underground heat reservoir through geothermal resonance scanning to generate original heat reservoir distribution data; the original heat reservoir distribution data is subjected to heat reservoir feature fusion processing to combine the multiple original heat reservoir distribution data into a heat reservoir state diagram containing real-time temperature, pressure, and permeability parameters of the heat reservoir; Step S2: calculating a heat flow priority matrix of each heat source priority index based on the parameters in the heat reservoir state diagram, and converting the heat reservoir state diagram into a real-time heat flow scheduling plan through a dynamic heat flow scheduling algorithm, wherein the heat flow scheduling plan specifies the rate, order, and path of heat energy extraction from each heat reservoir; Step S3: realizing the gradient release of heat energy through a multi-stage heat exchange sequence, wherein the multi-stage heat exchange sequence is composed of a series of heat exchange units with temperature gradients, and each heat exchange unit is configured to convert heat energy for a specific temperature interval; according to the real-time heat flow scheduling plan, the heat energy is released step by step from high temperature to low temperature through the multi-stage heat exchange sequence to produce heat energy outputs with different grades, which are used for power generation or heating.

[0047] Preferably, the embodiment realizes real-time acquisition of multi-dimensional parameters of underground heat reservoirs and fusion into dynamic state diagrams, generates optimal exploitation strategies based on a heat source priority matrix, realizes heat energy graded conversion through a heat exchange sequence matched with a temperature gradient, and finally forms a closed-loop control from resource detection, intelligent scheduling to energy output, significantly improving the energy extraction efficiency, operation stability and resource utilization rate of the geothermal system under complex geological conditions.

[0048] As shown in Figure 6 The embodiment provides an electronic device, and in the embodiment, the electronic device includes a processor and a memory coupled with the processor.

[0049] The memory stores program instructions for implementing the deep geothermal graded development control based on multi-source heat reservoir collaborative scheduling of any of the above embodiments.

[0050] The processor is configured to execute the program instructions stored in the memory to perform the deep geothermal graded development based on multi-source heat reservoir collaborative scheduling.

[0051] The processor can also be referred to as a CPU (Central Processing Unit). The processor can be an integrated circuit chip having a processing capability of signals. The processor can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0052] Further, Figure 7 The storage medium of the embodiment of the present application has a structure as shown in the structural schematic diagram of the storage medium of the embodiment of the present application. The storage medium of the embodiment of the present application stores program instructions capable of implementing all the methods described above. The program instructions can be stored in the storage medium in the form of a software product, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes, or a computer, a server, a mobile phone, a tablet, and other terminal devices.

[0053] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative, for example, the division of units is only a logical function division, and actual implementation can have other division manners, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0054] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit. The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation using the content of the present application specification and drawings, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the present application.

[0055] The specific embodiments of the application are described in detail above, but they are only examples. The present application is not limited to the specific embodiments described above. Any equivalent modification or substitution made by those skilled in the art to the present application is also within the scope of the present application, and therefore, any equivalent transformation, modification, improvement, etc. made without departing from the spirit and principle range of the present application should be included in the scope of the present application.

Claims

1. A deep geothermal gradient step development system based on multi-source heat reservoir collaborative scheduling, characterized in that, The deep geothermal gradient development system based on multi-source heat reservoir collaborative scheduling comprises: a dynamic heat flow scheduling subsystem, configured to calculate a heat flow priority matrix of each heat source by using parameters in a heat reservoir state diagram, and convert the heat reservoir state diagram into a real-time heat flow scheduling plan by using a dynamic heat flow scheduling algorithm, the heat flow scheduling plan specifying a rate, sequence and path of heat energy extraction from each heat reservoir; a gradient heat energy release subsystem, configured to realize gradient release of heat energy by using a multi-stage heat exchange sequence, the multi-stage heat exchange sequence comprising a series of heat exchange units with temperature gradients, each heat exchange unit being configured to convert heat energy in a temperature interval, and the heat energy being released in stages from high temperature to low temperature by using the multi-stage heat exchange sequence according to the real-time heat flow scheduling plan, thereby generating heat energy outputs with different grades for power generation or heating.

2. The deep geothermal gradient step development system based on multi-source heat reservoir collaborative scheduling according to claim 1, characterized in that, The dynamic heat flow scheduling subsystem comprises: a heat source potential evaluation processing component, configured to extract temperature, pressure and permeability parameters of each heat source candidate point from the heat reservoir state diagram, and obtain a quantitative priority index by linearly combining and normalizing the parameter values of each heat source point with corresponding weights; a scheduling sequence generation component, configured to select a heat source point with the highest priority index as a starting point of a sequence, and form a preliminary heat flow scheduling sequence that is spatially dispersed and optimal in energy priority by iteratively selecting a next heat source point; a flow distribution and path planning processing component, configured to use the preliminary heat flow scheduling sequence as a framework, and perform flow distribution and path planning in the following manners:

3. The deep geothermal gradient development system based on multi-source heat reservoir collaborative scheduling according to claim 2, characterized in that, in a flow distribution stage, the flow distribution and path planning processing component is configured to proportionally distribute a share of total extracted heat energy according to the priority index of each heat source point in the preliminary heat flow scheduling sequence and a current temperature parameter of the heat source point, and determine a rate of heat energy extraction from each heat source; in a path planning stage, the flow distribution and path planning processing component is configured to use permeability parameters in the heat reservoir state diagram, regard a permeability distribution map as a path unobstructedness map, and find a path with the highest cumulative permeability parameter value, i.e., the lowest flow resistance, between each heat source point with distributed flow and a common flow channel, and integrate the distributed extraction rate, the scheduled extraction sequence and the calculated optimal path to form a real-time heat flow scheduling plan. The flow distribution and path planning processing component specifically comprises: a starting point and ending point definition subcomponent, configured to perform starting point and ending point definition processing on the preliminary heat flow scheduling sequence to obtain a path calculation framework, the starting point and ending point definition processing being based on positions of heat source points in the preliminary heat flow scheduling sequence and a position of a common flow channel defined by the system, the common flow channel serving as a common ending point of all paths, each heat source point being assigned a starting point coordinate, and permeability parameters of these points being extracted from the heat reservoir state diagram, and an inlet region of the common flow channel being defined; a permeability accumulation path subcomponent, configured to, for each heat source point, start from the starting point, search permeability parameters of adjacent grids in the heat reservoir state diagram step by step, select a grid point with the highest permeability value as a next node of the path, and repeat the process until the common flow channel ending point is reached. The scheduling plan integration processing subcomponent is configured to integrate the extraction rate and sequence in the preliminary heat flow scheduling sequence with the path information in the optimal path set of the heat source points; the extraction rate and sequence are imported from the preliminary heat flow scheduling sequence, and each heat source point is assigned with a corresponding optimal path; the path information includes a node sequence and a flow direction; and all elements are integrated to form a real-time heat flow scheduling plan.

4. The deep geothermal gradient step development system based on multi-source heat reservoir collaborative scheduling according to claim 3, characterized in that, The scheduling plan integration processing subcomponent comprises: The scheduling element extraction processing module is configured to separate three core elements from the preliminary heat flow scheduling sequence based on an operation sequence analysis method: a heat source point identifier list arranged in sequence, a heat energy extraction rate value corresponding to each heat source point, and an extraction time sequence implied by the list sequence; the three core elements together constitute a structured data set, i.e., a structured scheduling element set; The path-rate binding processing module is configured to one-to-one correspond the operation instruction of each heat source point with its physical path; match each heat source point identifier in the structured scheduling element set with a same-named path in the optimal path set of the heat source points; encapsulate the three pieces of information of each heat source point that match successfully: the extraction rate, the extraction sequence, and the corresponding detailed node sequence and flow direction, as an indivisible independent operation unit; and after all the independent operation units of the heat source points are collected, a bound scheduling unit set is formed. The space-time scheduling matrix generation processing module is configured to globally arrange all the units in the bound scheduling unit set in accordance with the extraction sequence embedded therein to form an execution sequence; the execution sequence completely specifies when, from where, at what rate, and along which specific path to extract heat energy, and finally integrates and encapsulates all the information in a unified document, i.e., a real-time heat flow scheduling plan.

5. The multi-source heat storage collaborative scheduling based deep geothermal gradient development system according to claim 4, characterized in that, The path-rate binding processing module comprises: The identifier list extraction processing submodule is configured to separate the heat source point identifier list field from the structured scheduling element set and normalize the format of each identifier to obtain a standardized identifier list; The path list extraction processing submodule is configured to scan the entry metadata of each path in the optimal path set of the heat source points, and the entry metadata contains the source heat source point identifier corresponding to the optimal path; extract the source heat source point identifiers of all the paths and perform standardization processing to form a path identifier index; The matching and mapping processing submodule is configured to completely compare each identifier in the standardized identifier list with each identifier in the path identifier index; when and only when the two identifier strings are completely identical, it is determined that the matching is successful; each successful matching pair, i.e., a heat source point identifier and its corresponding unique path entity, is recorded as a mapping entry; and a collection of all the mapping entries constitutes an identifier-path mapping table.

6. The multi-source heat reservoir based coordinated dispatching deep geothermal gradient development system according to claim 5, characterized in that, The path list extraction processing submodule comprises: The metadata scanning processing unit is configured to traverse each path entity in the optimal path set of the heat source points one by one and accurately read the source heat source point identifier field in the position of the entry metadata block; all the read identifiers are temporarily stored as an initial set, i.e., an original identifier set; The identification normalization processing unit is configured to perform two core operations on each identification string in the original identification set: first, remove all non-alphanumeric characters; second, convert all symbol characters to a uniform uppercase form. The index structure generation processing unit is configured to establish a one-to-one mapping relationship between each identifier in the normalized identification list and the complete path entity corresponding to the identifier in the heat source point optimal path set, and organize the mapping relationship in a data structure that allows efficient retrieval to generate a path identifier index.

7. The multi-source heat reservoir based coordinated dispatching deep geothermal gradient development system according to claim 6, characterized in that, The index structure generation processing unit comprises: The association pair generation processing subunit is configured to traverse each identifier in the normalized identification list to use it as a query basis to reverse scan the entry metadata of all path entities in the heat source point optimal path set to find the source identifier after the same normalization processing; when a completely consistent source identifier is found, the normalized identifier and the complete path entity corresponding to the normalized identifier are combined into an association pair; all such association pairs constitute a preliminary association pair set; The association pair verification processing subunit is configured to check each association pair in the preliminary association pair set to confirm that the source identifier contained in the path entity in the association pair in the entry metadata is absolutely consistent with the normalized identifier in the association pair after the same normalization processing; at the same time, it is confirmed that each normalized identifier only appears in a unique association pair; the association pairs that pass the verification form a verified association pair set to ensure the uniqueness and accuracy of the mapping; The index structure assembly processing subunit is configured to use the verified association pair set as a basis to organize each normalized identifier in the association pair as a queryable key and its corresponding complete path entity as an associated value in a single data structure instance; the data structure instance is a path identifier index that supports fast retrieval and acquisition of the unique corresponding complete path entity through the normalized identifier.

8. The multi-source heat storage collaborative scheduling based deep geothermal step development system according to claim 1, characterized in that, The stepped heat energy release subsystem comprises: The hot stream sequential import processing component is configured to extract a sequential list and a corresponding extraction rate of heat energy extraction from the real-time hot stream scheduling plan; arrange the heat energy streams from different paths in time sequence according to the sequential list to ensure that high heat energy streams are imported first; at the same time, adjust the flow proportion of each heat source point according to the extraction rate to form a sequential hot stream; The temperature interval matching release processing component is configured to input the sequential hot stream to high-temperature heat exchange first, convert heat energy for the preset high-temperature interval, absorb heat through the conduction medium of the high-temperature heat exchange to generate high-temperature heat energy output; the remaining heat energy is then imported into the medium-temperature heat exchange, converted for the medium-temperature interval, and released through the heat exchange surface of the medium-temperature heat exchange to generate medium-temperature heat energy output; finally, the remaining heat energy enters the low-temperature heat exchange, which converts for the low-temperature interval and uses the extended area of the low-temperature heat exchange for heat diffusion to generate low-temperature heat energy output; The heat energy output modulation processing component is configured to proportionally adjust each level of heat energy value in the hierarchical heat energy value set according to the external energy application demand; the adjusted heat energy value is integrated into a unified output stream.

9. The multi-source heat reservoir based coordinated dispatching deep geothermal gradient development system according to claim 1, characterized in that, The method also comprises a multi-source geothermal reservoir sensing and fusion subsystem for non-intrusive detection of underground geothermal reservoirs through geothermal resonance scanning to generate raw geothermal reservoir distribution data; the raw geothermal reservoir distribution data is subjected to geothermal reservoir feature fusion processing to combine the multi-source raw geothermal reservoir distribution data into a geothermal reservoir state diagram containing real-time temperature, pressure and permeability parameters of the geothermal reservoir.

10. A deep geothermal gradient stepwise development control method based on multi-source heat reservoir collaborative scheduling, applied to the deep geothermal gradient stepwise development system based on multi-source heat reservoir collaborative scheduling according to any one of claims 1 to 9, characterized in that, The method for deep geothermal gradient development control based on multi-source geothermal reservoir collaborative scheduling comprises: The method also comprises a multi-source geothermal reservoir sensing and fusion subsystem for non-intrusive detection of underground geothermal reservoirs through geothermal resonance scanning to generate raw geothermal reservoir distribution data; the raw geothermal reservoir distribution data is subjected to geothermal reservoir feature fusion processing to combine the multi-source raw geothermal reservoir distribution data into a geothermal reservoir state diagram containing real-time temperature, pressure and permeability parameters of the geothermal reservoir. A heat flow priority matrix is calculated based on the parameters in the geothermal reservoir state diagram to calculate the priority index of each heat source, and a dynamic heat flow scheduling algorithm is used to convert the geothermal reservoir state diagram into a real-time heat flow scheduling plan, which specifies the rate, sequence and path of heat energy extraction from each geothermal reservoir. The method also comprises a multi-level heat exchange sequence for gradient release of heat energy, which is composed of a series of heat exchange units with temperature gradients, and each heat exchange unit is used for heat energy conversion in a temperature interval; according to the real-time heat flow scheduling plan, heat energy is released step by step through the multi-level heat exchange sequence from high temperature to low temperature to produce heat energy output of different grades for power generation or heating.

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