Intelligent operation management system for geothermal energy in severe cold region

Abstract

The application belongs to the technical field of ground source heat operation management, and specifically discloses an intelligent operation management system for geothermal energy in severe cold regions. The system comprises a data acquisition and verification module, a soil dynamic analysis module, a thermal imbalance risk prediction module and a control priority determination module. The application collects and verifies geothermal operation data in real time, calculates key indicators such as heating power, cumulative soil heat extraction and heat recovery rate, drives a soil heat transfer model in combination with meteorological prediction data to perform forward-looking simulation, and outputs a thermal imbalance risk index. Based on the risk index and its change trend, the control priority of heating quality and soil thermal imbalance is dynamically determined and output, so that the soil heat recovery capacity is effectively maintained while meeting the heating demand, and the priority management direction between the heating quality guarantee and the soil thermal imbalance prevention and control is clarified, thereby achieving effective balance between maintaining user heating demand and protecting geothermal sustainability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of ground source heat operation management, and particularly relates to an intelligent operation management system for ground heat in a severe cold region. BACKGROUND

[0002] In the severe cold region, the winter is long and the extreme low temperature is outstanding, the heating demand is large and the continuous period is long, and long-term heat extraction can easily cause soil heat imbalance and even abnormal freezing and thawing of permafrost. Under this background, the importance of operation management of ground heat is self-evident.

[0003] The prior art such as the full-automatic ground heat production operation management system, method and storage medium disclosed in Chinese patent application publication No. CN119692637A collects environmental information and on-site production operation information through a control end, analyzes and decides, generates control instructions and feeds back to the ground heat end for execution, and forms a self-regulating closed loop.

[0004] Since the severe cold region has problems such as soil heat imbalance and fluctuation of ground heat supply capacity, the prior art only stays at how to adjust the production and operation of ground heat, without comprehensively considering the long-term impact on the soil environment, which can easily lead to imbalance between short-term heating demand and long-term soil sustainability, and further affect the final long-term heat extraction effect.

[0005] Secondly, the current control is only performed at the ground heat end, without analyzing the control priority of heating quality and heat imbalance according to the risk degree of soil heat imbalance, so that the corresponding soil heat imbalance risk situation cannot be responded and handled in time.

[0006] Finally, the operation management of the target ground heat is mainly based on the environmental information and other data collected by the control end, and the current collected data is not reasonably verified, so that the subsequent analysis and decision-making have certain errors, and the decision-making based on the wrong data cannot be self-aware. SUMMARY

[0007] In view of this, in order to solve the above problems, an intelligent operation management system for ground heat in a severe cold region is provided.

[0008] The purpose of the application can be achieved by the following technical scheme: the application provides an intelligent operation management system for ground heat in a severe cold region, comprising: a collected data verification module, which reasonably verifies real-time collected ground heat operation data of a target region, adjusts the monitoring frequency setting of ground heat operation data that fails to pass the verification, re-collects the corresponding operation data, and continues until the verification passes.

[0009] A soil dynamic analysis module, based on the verified ground heat operation data, calculates the heating power in real time, combines with the historical operation data sequence to calculate the cumulative heat extraction amount, and calculates the heat recovery rate through a preset soil heat transfer numerical model.

[0010] a thermal imbalance risk prediction module, in combination with meteorological prediction of a future prediction time window and real-time heating rate, soil cumulative heat extraction amount and heat recovery rate, driving the soil heat transfer numerical model to perform forward-looking simulation, outputting a thermal imbalance risk index.

[0011] a control priority determination module, determining and outputting the control priority of heating quality and thermal imbalance based on the thermal imbalance risk index.

[0012] Compared with the prior art, the beneficial effects of the present application are as follows: (1) The present application ensures the integrity and accuracy of the collected data by reasonably checking and monitoring frequency self-adaptive adjustment of the real-time collected geothermal operation data, providing a reliable foundation for subsequent processing, thereby avoiding the accumulation of decision-making errors caused by abnormal data.

[0013] (2) The present application realizes continuous monitoring of the soil heat balance state by real-time calculation of heating power and soil heat dynamic parameters according to the verified data, combined with historical operation data sequences and soil heat transfer models, providing a quantitative basis for evaluating the long-term heat extraction impact.

[0014] (3) The present application can identify the temperature drop and duration that may occur at key positions of the soil in the future time period in advance by driving the soil model to perform forward-looking simulation in combination with meteorological prediction of a future prediction time window, thereby realizing early identification and warning of potential risks, enhancing the ability to respond to emergencies, and at the same time, by generating a thermal imbalance risk index, fully evaluating the severity of potential hazards and providing a reliable basis for priority decision-making.

[0015] (4) The present application dynamically determines the priority order of heating quality and thermal imbalance control based on the thermal imbalance risk index, optimizes the operation and management strategy, ensures that the soil heat recovery capability is effectively maintained while meeting the heating demand, and at the same time, clarifies the priority management direction between heating quality guarantee and soil thermal imbalance prevention and control, thereby achieving effective balance between maintaining user heating demand and protecting geothermal sustainability, avoiding long-term operation risks caused by single target optimization, and enhancing the effect of geothermal operation and management in complex climate conditions in cold regions. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used for the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0017] Figure 1 It is a schematic diagram of the system modules of the present application.

[0018] Figure 2 The whole implementation process of the present application is shown in the schematic diagram.

[0019] Figure 3 The control priority determination process of the heat supply quality and heat imbalance of the present application is shown in the schematic diagram. 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 part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application.

[0021] The embodiments of the present application provide an intelligent operation management system for geothermal energy in severe cold regions, which is used to solve the problem of soil heat imbalance caused by long-term and high-intensity heat extraction in severe cold regions, while ensuring the quality of heating. The following will be described in detail in combination with Figure 1 and Figure 2 the specific implementation process.

[0022] Preferably, the intelligent operation management system for geothermal energy in severe cold regions comprises a collected data verification module, a soil dynamic analysis module, a heat imbalance risk prediction module and a control priority determination module.

[0023] In the above, the soil dynamic analysis module is connected with the collected data verification module and the heat imbalance risk prediction module, and the heat imbalance risk prediction module is connected with the control priority determination module. The collected data verification module outputs the verified geothermal operation data to the soil dynamic analysis module. The soil dynamic analysis module outputs the heating power, the cumulative heat extraction amount and the heat recovery rate to the heat imbalance risk prediction module. The heat imbalance risk prediction module outputs the heat imbalance risk index to the control priority determination module.

[0024] Before the collected data verification module starts, first, the sensors installed in the geothermal wellhead, the transmission and distribution pipeline, the heat exchanger and the specially buried monitoring well in the target area start to work, and the geothermal operation data is collected according to the initial set monitoring frequency, wherein the geothermal operation data at least includes the temperature and flow of the geothermal side and the user side circulating medium.

[0025] Exemplarily, the geothermal side circulation medium is a hot fluid extracted from the ground, and in the geothermal side circulation medium, a temperature sensor and a vortex flow meter are installed on the outlet pipe of the geothermal well to collect the output temperature and flow rate of the geothermal fluid, and similar sensors are installed on the wellhead return pipe to collect the temperature and flow rate of the circulation medium returned from the user side to the geothermal side after completing heat exchange and ready to be re-injected into the ground for heating. In the user side circulation medium, a temperature sensor and an electromagnetic flow meter are installed on the user side inlet pipe and outlet pipe of the plate heat exchanger, respectively, to collect the temperature and flow rate of the heating water supply and the heating return water.

[0026] Considering the low temperature, high humidity and freeze-thaw cycle environment in cold regions, it will directly cause the measurement accuracy of the sensor to drift, and the cold environment may exacerbate signal interference or transmission link interruption, resulting in data packet loss or damage, therefore, the present application starts the collection data verification module after completing the geothermal operation data collection of the target area.

[0027] The collection data verification module reasonably verifies the real-time collection of the geothermal operation data of the target area, adjusts the monitoring frequency setting of the geothermal operation data that fails to pass the verification, and re-collects the corresponding operation data until the verification passes.

[0028] Specifically, the specific implementation process of the reasonableness verification includes receiving the collected geothermal operation data, specifically including receiving the temperature and flow rate of the geothermal side and user side circulation medium.

[0029] For the received temperature and flow rate of the geothermal side circulation medium, first determine whether the total number of data points collected reaches the expected number of collected data points, if the total number of collected data points of the temperature or flow rate does not reach, it is determined that the geothermal side reasonableness verification fails.

[0030] If both reach, determine whether the temperature and flow rate of the geothermal side circulation medium are both within their reasonable ranges, if both are within, it is determined that the geothermal side reasonableness verification passes, otherwise it is determined that the geothermal side reasonableness verification fails.

[0031] As a preferred example, the reasonable range of the temperature of the geothermal side circulation medium is determined by the following method: obtaining the original soil temperature before the geothermal well is first put into operation, then, filtering out the heating season operation period from the historical operation data sequence in which the soil temperature can recover to close to the original soil temperature. For each heating season operation period, extract all the recharging temperature data recorded by the sensor on the recharging pipe of the geothermal side during this period, calculate the average recharging temperature value of the whole period, and select the minimum value from all the average recharging temperature values of the heating season operation period as the minimum recharging temperature empirical value. The original soil temperature is taken as the upper limit of the reasonable range, and the minimum recharging temperature empirical value is taken as the lower limit of the reasonable range, thereby forming a dynamic reasonable range of the temperature of the geothermal side circulation medium.

[0032] As another preferred example, the reasonable range of the corresponding flow rate of the geothermal side circulating medium is determined in the following manner: the heating season operation period during which the soil temperature can be restored to close to the original soil temperature is recorded as an eligible period, the rated flow rate of the geothermal system design is taken as a reference, then for each eligible period, all the flow rate data recorded by the downhole circulating pump outlet flow rate sensor during the entire heating season are extracted, the average flow rate value during its operation is calculated, and the distribution of the average flow rate values of all the eligible periods is counted, the 95th percentile of the average flow rate value distribution is taken as the upper limit of the reasonable range to prevent overload, and the 5th percentile of the average flow rate value distribution is taken as the lower limit of the reasonable range to ensure the circulation power and heat exchange efficiency, thereby forming the dynamic reasonable range of the corresponding flow rate of the geothermal side circulating medium.

[0033] For the received temperature and flow rate of the user side circulating medium, the same reasonableness check as the geothermal side is performed, and will not be described again.

[0034] The present application ensures the authenticity and reliability of the data from the source by real-time reasonableness check on the real-time collected geothermal operation data, thereby building a reliable data basis for all subsequent analysis and decision-making.

[0035] When the geothermal operation data collected by a certain sensor is determined to be not verified, it may be a transient interference or an early indication of sensor performance degradation. If the data is only marked as invalid without changing the monitoring strategy, on the one hand, signal loss may continue to exist, forming a monitoring blind area. On the other hand, it is difficult to distinguish between occasional abnormalities and progressive failures, resulting in failure to timely warn before the problem worsens. Therefore, the present application adjusts the monitoring frequency setting of the geothermal operation data that is not verified, and then can quickly collect more intensive data sequences, thereby providing more sufficient data basis for subsequent maintenance decisions, such as whether to need on-site repair or replace the sensor.

[0036] Specifically, the specific adjustment implementation process of adjusting the monitoring frequency setting of the geothermal operation data that is not verified includes: extracting the source sensor identification of the operation data that is not verified, and recording the source sensor as an adjustment sensor; and recording the monitoring frequency level currently set by the adjustment sensor as an initial frequency level.

[0037] The current data loss ratio of the adjustment sensor is counted, and the data loss ratio is the ratio of the number of lost data points to the number of expected set collected data points.

[0038] The values of each operation parameter item are extracted from the geothermal operation parameters collected from the adjustment sensor, the parameter items not located in their preset reasonable ranges are recorded as deviation parameter items, the ratio of the number of deviation parameter items to the total number of operation parameter items is counted, and is recorded as a deviation parameter item ratio.

[0039] determine whether to trigger any one of the following conditions: the data loss ratio or the deviation parameter item ratio exceeds the preset range.

[0040] It should be noted that the preset range of the data loss ratio can be set with reference to the theoretical packet success rate of the sensor communication protocol, such as Modbus or LoRaWAN, and the preset range of the data loss ratio can be preferably set to 2% to 10%. The deviation parameter item ratio is set based on the fault tolerance capability of the sensor set in the geothermal operation, for example, the allowed abnormal sensor ratio set in the geothermal operation is 20% to 30%, and the preset range of the deviation parameter item ratio can be set to 20% to 30%.

[0041] There is data loss in a plurality of time windows or a running parameter item exceeds a preset reasonable range in a plurality of time windows.

[0042] It should be noted that the plurality of time windows specifically refers to no less than two time windows.

[0043] If triggered, the emergency monitoring frequency level is started, otherwise, the next high monitoring frequency level is switched, wherein the emergency monitoring frequency level is the highest monitoring frequency level.

[0044] It should be further noted that when switching to the emergency monitoring frequency, if the sensor fails to pass the continuous verification under the emergency monitoring frequency level more than a preset number of times, an alarm is triggered and manual intervention is notified, and the data acquisition of the sensor is suspended, and the preset number of times is set to be greater than or equal to 2 times, and the preferred example of the present application is 3 times.

[0045] It should be understood that a high data loss ratio usually means a communication link interruption problem, and a high deviation parameter item ratio indicates that the sensor measurement is inaccurate, and either of the two cases indicates that the sensor has a major problem, therefore, the present application starts the fault warning instruction of adjusting the sensor to timely handle the problem of the sensor. When neither of the two cases occurs, a higher monitoring frequency is started to obtain more data to further confirm the sensor condition.

[0046] To ensure the effective operation of the sensor monitoring frequency adaptive adjustment mechanism, the present application preferably divides the monitoring frequency into three levels, which are represented by L1, L2 and L3, wherein the levels are in ascending order, L3 corresponds to the highest level, and L1 corresponds to the lowest level, wherein L1 level corresponds to the basic monitoring frequency range in normal operation, which can use the initial default setting of the sensor, for example, 10 minutes each time, L2 level is used when a slight or occasional abnormality is detected, for example, 5 minutes each time, and L3 corresponds to the emergency monitoring frequency level, which is used when a persistent abnormality is confirmed, wherein the monitoring frequency corresponding to L3 level is 1 minute each time.

[0047] It should be noted that the monitoring frequency value given above is only a preferred example, which can be dynamically adjusted according to the sensor model, or the existing experience value can be used.

[0048] The embodiment of the present application can ensure the integrity and accuracy of the collected data by reasonably checking the real-time collected geothermal operation data and adaptively adjusting the monitoring frequency, and provide a reliable basis for subsequent processing, thereby avoiding the accumulation of decision-making errors caused by abnormal data.

[0049] Since the prior art only focuses on short-term heating adjustment and ignores long-term soil heat balance, the geothermal system in severe cold regions faces the risk of heat imbalance, and the present application sets up a soil dynamic analysis module to realize quantitative evaluation of the long-term impact on the soil environment, and provides a decision basis for realizing dynamic balance between heating demand and maintaining soil sustainability.

[0050] The soil dynamic analysis module, based on the verified geothermal operation data, calculates the heating power in real time, and calculates the cumulative heat extraction amount in combination with the historical operation data sequence, and calculates the heat recovery rate through a preset soil heat transfer numerical model.

[0051] Specifically, the specific implementation process of the heating power includes: A1, real-time acquisition of the flow of the geothermal side circulating medium and the temperature of the medium at the inlet and outlet of the heat exchanger from the verified geothermal operation data, respectively denoted as and and , represents time.

[0052] The geothermal side heating power is calculated in real time through thermodynamic formula , , is the specific heat capacity of the geothermal side circulating medium at constant pressure.

[0053] A2, real-time acquisition of the flow of the user side circulating medium and the temperature of the medium at the inlet and outlet of the heat exchanger from the verified geothermal operation data, and real-time calculation of the user side heating power in the same way as the calculation of the geothermal side heating power.

[0054] A3, calculate the relative deviation value of the geothermal side heating power and the user side heating power, if the relative deviation value is within the historical reference range, the geothermal side heating power is taken as the final heating power.

[0055] Considering the heat transfer process from the geothermal side to the user side, there is inherent heat loss at both the pipe and the heat exchanger end. Under normal operating conditions, the relative deviation value of the geothermal side heating power and the user side heating power should be relatively stable. The greater the relative deviation value, the greater the abnormality of the current equipment operation.

[0056] It is worth noting that if there is no historical data, the design value or default value is used as the historical reference range. With the accumulation of running time, the historical reference range is determined by the distribution of the relative deviation value of the geothermal side heating power and the user side heating power during the normal operation period. The minimum value and the maximum value are selected from the distribution to form the historical reference range.

[0057] A4. If the relative deviation value exceeds the historical reference range, the credibility weight of the geothermal side heating power and the user side heating power is dynamically adjusted based on the deviation of the relative deviation value from the historical reference range.

[0058] Since the current default heating power is directly responsive to the power of the geothermal side, when the relative deviation value is within the historical reference range, the geothermal side heating power is assigned a higher credibility weight, and the initial credibility weight is set to the maximum value. Here, in order to unify the calculation dimension, it is set to 1. The user side heating power is usually used to verify the heating power of the geothermal side, so its initial credibility weight can be set to 0.

[0059] When it is not within the historical reference range, due to the increase in deviation, it usually means that the calculation of the geothermal side may be less reliable due to the complex downhole environment, so the credibility weight of the geothermal side heating power is reduced, and the credibility weight of the user side heating power is correspondingly increased. At this time, the absolute deviation value of the relative deviation value and the historical reference range is calculated, and the credibility weight of the geothermal side heating power is recorded as , and the credibility weight of the user side heating power is recorded as , where , , represents the upper limit of the historical reference range, represents the relative deviation value of the geothermal side heating power and the user side heating power.

[0060] A5. According to the credibility weight, the linear weighted sum of the geothermal side heating power and the user side heating power is taken as the final heating power , that is , and represent the geothermal side heating power and the user side heating power, respectively.

[0061] Since the heat exchange in the underground is a slow accumulation dynamic process, a single measurement cannot reflect the real heat load caused by long-term heat extraction, therefore, the application integrates the historical operation data to calculate the cumulative heat extraction, so as to accurately evaluate the heat bearing capacity of the soil. Meanwhile, the recovery capacity of the soil cannot be directly measured, the application inversely calculates the heat recovery rate by means of the preset soil heat transfer numerical model and the simulation environment of closing the heat source, so as to judge whether the natural heat recovery can balance the continuous heat extraction. In addition, since the underground temperature field monitoring points are limited, the soil heat transfer numerical model can effectively make up for the monitoring blind area and provide reliable basis for risk early warning.

[0062] It should be noted that the soil heat transfer numerical model is constructed with reference to the engineering technical specification of the ground source heat pump system, and is a two-dimensional or three-dimensional transient heat conduction model based on the finite element method or the finite volume method, and the model parameters are the thermal conductivity, density, constant-pressure specific heat capacity of the soil, and the specific heat capacity and thermal conductivity of the circulating medium.

[0063] The model parameters are directly determined through the field thermal response test in the on-site borehole, and the specific operation process and equipment requirements of the test comply with the related provisions in the engineering technical specification of the ground source heat pump system.

[0064] As a preferred embodiment of the application, the cumulative heat extraction of the soil includes: extracting the geothermal side heating power time sequence from the historical operation data sequence.

[0065] Based on the historical first starting time to the current time, the geothermal side heating power is time-integrated and calculated, and the cumulative heat extraction of the soil is output.

[0066] As another preferred embodiment of the application, the specific implementation process of calculating the heat recovery rate is as follows: first, the historical operation data sequence is extracted, including but not limited to the geothermal side heating power time sequence, the operation time sequence, and the monitoring well soil temperature time sequence at the first starting time, based on the monitoring well soil temperature time sequence, the soil initial temperature field is generated by using the cubic spline interpolation method.

[0067] It should be noted that the cubic spline interpolation method for generating the soil initial temperature field is a common temperature field generation method, which will not be described here.

[0068] Then, the three-dimensional unsteady heat conduction equation based on the finite volume method is used as the control equation of the soil heat transfer numerical model: .

[0069] Wherein, represents the density of the soil, represents the constant-pressure specific heat capacity of the circulating medium, represents the partial derivative of the soil temperature with respect to time, thermal conductivity of the circulating medium, soil temperature gradient vector.

[0070] heat flux vector, calculated according to Fourier's law, divergence of the heat flux, representing the net inflow of heat per unit volume, corresponding to the net heat conduction due to the temperature gradient, internal heat source intensity, when represents heat supply, injecting heat into the soil, when represents heat extraction, extracting heat from the soil, when represents no internal heat source, a pure heat conduction process. Wherein the thermal conductivity of the soil can be measured on site, and the specific heat capacity and thermal conductivity of the circulating medium can be artificially introduced by the geothermal operation and management personnel.

[0071] The target area is meshed by using the finite volume method to obtain each soil unit, wherein the meshing follows the following principles: the near-well area is meshed densely, the minimum grid size is not greater than 0.5 m, the outer boundary of the calculation area is not less than 50 m away from the well group center, and the vertical layering is not less than 10 layers.

[0072] The extracted operation time, soil initial temperature field and geothermal side heating power are integrated into a structured input parameter set, and the input parameter set is loaded into the soil heat transfer numerical model.

[0073] The soil initial temperature field is taken as the model initial condition, the geothermal side heating power is taken as the heat flux density condition of the downhole heat exchanger boundary in the model, the change process of the soil temperature field from the initial state to the current state is simulated, and the current soil temperature field is obtained.

[0074] After obtaining the current soil temperature field, the heat extraction source term is turned off in the model, that is, the is set to 0, the recovery process of the soil temperature field in the natural condition within a predetermined period of time is simulated, and the total heat recovered by the soil is recorded. .

[0075] wherein, represents the number of the divided soil unit, , represents the soil density of the th soil unit, the soil specific heat capacity at constant pressure of the th grid, represents the temperature of the th soil unit at the beginning of the heat recovery process, represents the temperature of the a temperature of a soil unit, representing a volume of a soil unit.

[0076] Exemplarily, the preset period can be set as 30 days.

[0077] The total heat recovered by the soil is divided by the length of time corresponding to the preset period to calculate a heat recovery rate of the soil.

[0078] The embodiment of the present application realizes continuous monitoring of the soil heat balance state by calculating the heating power and the soil heat dynamic parameters in real time according to the verified data, in combination with the historical operation data sequence and the soil heat transfer model, and provides a quantitative basis for evaluating the long-term heat extraction impact.

[0079] Since the soil heat imbalance is a gradual change process jointly caused by long-term heat extraction accumulation and future climate conditions, potential risks cannot be predicted only by relying on current or historical operation data, and therefore, future meteorological forecasts must be combined, based on which, after the heating power, the cumulative heat extraction amount and the heat recovery rate are calculated, the heat imbalance risk prediction module is started.

[0080] The heat imbalance risk prediction module combines the meteorological prediction of the future prediction time window and the real-time heating rate, the cumulative heat extraction amount of the soil and the heat recovery rate to drive the soil heat transfer numerical model to perform forward-looking simulation and output a heat imbalance risk index.

[0081] It is worth noting that if the meteorological prediction of the future prediction time window is unavailable or incomplete, the historical same-period meteorological data is used as a replacement for the incomplete or unavailable part.

[0082] In a specific embodiment, the heat imbalance risk index is output by the following steps: B1, taking the current real-time heating rate as a constant heat flux density condition of the downhole heat exchanger boundary in the future prediction time window.

[0083] B2, receiving an environmental temperature prediction sequence in the future prediction time window as a heat exchange condition of the upper boundary of the soil heat transfer numerical model.

[0084] B3, loading the cumulative heat extraction amount of the soil and the heat recovery rate as initial conditions into the soil heat transfer numerical model to simulate the spatio-temporal evolution process of the soil temperature field in the future prediction time window and output simulation results containing the change of the soil temperature distribution over time.

[0085] ​B4, the wall surface of the downhole heat exchanger and the central point position at different depths of the monitoring well are taken as key soil monitoring positions, the minimum soil temperature, the maximum value of the cumulative duration of the soil temperature being lower than the preset risk threshold and the temperature drop in the future prediction time window at each key soil monitoring position are extracted from the simulation result, the cumulative duration is recorded as the low-temperature duration, and the temperature drop is obtained by subtracting the minimum soil temperature from the maximum soil temperature.

[0086] The preset risk soil low-temperature threshold is determined according to the soil pore water freezing critical temperature, for example, for typical cold region soil, the threshold is set to 0-5 DEG C.

[0087] B5, the minimum soil temperature, the low-temperature duration and the temperature drop of each key soil monitoring position are respectively subjected to minimum-maximum normalization processing.

[0088] B6, linear weighted summation is performed on the normalized index values, the local thermal imbalance risk index at each key soil monitoring position is calculated, the final thermal imbalance risk index is determined based on the spatial distribution characteristics of the local thermal imbalance risk indexes, and the final thermal imbalance risk index is output.

[0089] It should be noted that the minimum soil temperature represents whether the soil approaches the freezing critical point and reflects the immediate severity of the risk, is given the highest weight, the low-temperature duration measures the cumulative effect of the soil under low-temperature stress, the longer the duration, the more difficult to recover, and therefore is given a medium weight, and the temperature drop mainly reflects the severity of the temperature change and is an early signal of stability deterioration, and therefore is given a relatively low weight, and the sum of the weights of the three is 1, wherein, as a preferred example, the weights of the minimum soil temperature, the low-temperature duration and the temperature drop can be set to 0.5, 0.3 and 0.2 respectively.

[0090] It should be noted that the minimum soil temperature represents whether the soil approaches the freezing critical point and reflects the immediate severity of the risk, is given the highest weight, the low-temperature duration measures the cumulative effect of the soil under low-temperature stress, the longer the duration, the more difficult to recover, and therefore is given a medium weight, and the temperature drop mainly reflects the severity of the temperature change and is an early signal of stability deterioration, and therefore is given a relatively low weight, and the sum of the weights of the three is 1, wherein, as a preferred example, the weights of the minimum soil temperature, the low-temperature duration and the temperature drop can be set to 0.5, 0.3 and 0.2 respectively.

[0090] Considering that the soil thermal imbalance risk around the downhole heat exchanger of the geothermal well is essentially a non-uniform spatial problem. If only the average value is used, the early warning signal of the local high-risk point such as the end of the heat exchanger or the weak geological area will be seriously diluted, resulting in that the targeted intervention cannot be performed. If only the maximum value is concerned, the decision will be misled. Therefore, the final thermal imbalance risk index is determined by analyzing the spatial distribution ratio.

[0091] Further, the specific implementation process of determining the final thermal imbalance risk index comprises: calculating the average value of the local thermal imbalance risk indexes at all key soil monitoring positions.

[0092] The proportion of the monitoring positions with the local thermal imbalance risk index greater than or equal to the average value and the proportion of the monitoring positions with the local thermal imbalance risk index less than the average value are respectively recorded as the first position proportion and the second position proportion.

[0093] If the first position proportion is greater than the second position proportion, the maximum local thermal imbalance risk index is taken as the final thermal imbalance risk index, otherwise, the average value is taken as the final thermal imbalance risk index.

[0094] It should be noted that when the first position proportion is greater than the second position proportion, it indicates that the current high-risk point is in a local concentration state, and using the maximum local index can ensure focusing on the most serious local problem, and when the first position proportion is less than or equal to the second position proportion, at this time, the risk is more evenly distributed in space, indicating that the risk is global, and using the average value can more accurately reflect the overall risk level, avoiding overreaction to a single abnormal point. Further ensure that the final risk index can capture extreme local risk and represent overall trends.

[0095] The embodiment of the present application can identify the temperature drop and duration that may occur in the future period of time in the key position of the soil in advance by combining the meteorological prediction of the future prediction time window and driving the soil model to perform forward-looking simulation, thereby realizing early identification and warning of potential risks, enhancing the ability to respond to emergencies, and at the same time, by generating a thermal imbalance risk index, the severity of potential harm is fully evaluated, and a reliable basis is provided for priority decision-making.

[0096] There has been an inherent conflict between immediate heating demand and maintaining long-term thermal sustainability of soil in geothermal energy operation in cold regions, and if the heating quality is continuously prioritized and the accumulation of thermal imbalance risk is ignored, it will lead to irreversible decay of soil thermal recovery ability, so the control priority determination module needs to be started.

[0097] The control priority determination module determines and outputs the control priority of the heating quality and the thermal imbalance based on the thermal imbalance risk index.

[0098] Specifically, referring to Figure 3 As shown in the figure, determining and outputting the control priority of the heating quality and the thermal imbalance includes comparing the thermal imbalance risk index with a set risk threshold interval.

[0099] Exemplarily, the risk threshold interval is set in the following manner: collecting and sorting all recorded heat imbalance event cases during historical operation, extracting the imbalance risk level to which each event case belongs, classifying heat imbalance event cases belonging to the same imbalance risk level, extracting the geothermal operation data of heat imbalance event cases under each imbalance risk level, and performing the same analysis according to the analysis manner of the heat imbalance risk index of the present application, thereby obtaining the heat imbalance risk index distribution under each imbalance risk level, extracting the maximum value of the heat imbalance risk index distribution of the lowest imbalance risk level as the lower limit of the set risk threshold interval, and extracting the minimum value of the heat imbalance risk index distribution of the highest imbalance risk level as the upper limit of the set risk threshold interval. The upper limit and the lower limit are combined to obtain the set risk threshold interval.

[0100] If the heat imbalance risk index is lower than the lower limit of the set risk threshold interval, the control priority is heating quality priority.

[0101] If the heat imbalance risk index is within the set risk threshold interval, the heating quality and the heat imbalance have the same control priority.

[0102] If the heat imbalance risk index exceeds the upper limit of the set risk threshold interval, the control priority is heat imbalance priority.

[0103] The embodiment of the present application dynamically determines the priority order of heating quality and heat imbalance control based on the heat imbalance risk index, optimizes the operation management strategy, ensures that the soil heat recovery capacity is effectively maintained while meeting the heating demand, and clearly defines the priority management direction between heating quality guarantee and soil heat imbalance prevention and control, thereby achieving effective balance between maintaining user heating demand and protecting geothermal sustainability, avoiding long-term operation risks caused by single target optimization, and enhancing the geothermal operation management effect in complex climate conditions in cold regions.

[0104] Further, the control priority determination module further comprises dynamically adjusting the control priority by monitoring the change trend of the heat imbalance risk index: monitoring the change trend of the heat imbalance risk index in the future prediction time window: when the heat imbalance risk index continuously rises in the future prediction time window, the adjustment is performed according to the following adjustment rules: if the control priority is heating quality priority, the adjustment is made to the same control priority.

[0105] If the control priority is the same control priority, the adjustment is made to heat imbalance priority.

[0106] If the current control priority is heat imbalance priority, a heat imbalance warning instruction is started.

[0107] When the heat imbalance risk index continuously decreases in the future prediction time window, the current control priority is directly locked as heating quality priority.

[0108] The application realizes gradual regulation of the control priority by tracking the change trend of the risk index in real time, and gradually increasing the control priority when the risk is on the rise, avoiding the impact of sharp switching on the stability of heat supply. When the risk trend is good, the heat supply priority mode is automatically locked to ensure energy utilization efficiency. Further, the lag problem of only focusing on the current risk state and ignoring the change trend is effectively solved, realizing the leap from after-response to pre-prevention, while ensuring the sustainability of soil heat and considering the stability of heat supply quality.

[0109] The above is only an example and description of the concept of the application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or replace them with similar ways without deviating from the concept of the application or exceeding the scope defined by the application.

Claims

1. A smart operation and management system for geothermal energy in frigid regions, characterized in that, The system includes: The data collection and verification module verifies the rationality of the geothermal operation data collected in real time in the target area, adjusts the monitoring frequency settings of geothermal operation data that fails verification, and re-collects the corresponding operation data until verification is successful. The soil dynamic analysis module calculates the heating power in real time based on the verified geothermal operation data, and calculates the cumulative heat extraction by combining the historical operation data sequence. At the same time, it calculates the heat recovery rate through a preset soil heat transfer numerical model. The thermal imbalance risk prediction module combines meteorological forecasts for future prediction time windows with real-time heating rates, cumulative soil heat extraction, and thermal recovery rates to drive the soil heat transfer numerical model to perform forward-looking simulations and output a thermal imbalance risk index. The control priority determination module determines and outputs the control priorities for heating quality and thermal imbalance based on the thermal imbalance risk index.

2. The intelligent operation and management system for geothermal energy in frigid regions as described in claim 1, characterized in that: The reasonableness verification includes: Geothermal operation data, including at least temperature and flow rate, is collected in real time by all sensors installed at the geothermal wellhead, distribution pipeline, heat exchanger, and buried in the monitoring well. Determine whether the geothermal operation data is complete and within a preset reasonable range; If the data is complete and within the preset reasonable range, then the reasonableness verification is passed. If the data is incomplete, or if the geothermal operation data exceeds the stated reasonable range, the reasonableness verification will fail.

3. The intelligent operation and management system for geothermal energy in frigid regions as described in claim 1, characterized in that: The process of adjusting the monitoring frequency setting includes: Extract the source sensor identifier of the operational data that fails verification, and mark the source sensor as the adjustment sensor, and mark the current monitoring frequency level of the adjustment sensor as the initial frequency level; The current data loss ratio of the sensor is statistically adjusted, where the data loss ratio is the ratio of the number of lost data points to the expected number of data points to be collected; Extract the values ​​of each operating parameter from the geothermal operating parameters collected by the adjustment sensor, and record the parameter items that are not within their preset reasonable range as deviation parameter items. Calculate the ratio of the number of deviation parameter items to the total number of operating parameter items, and record it as the deviation parameter item ratio. Determine if any of the following conditions are triggered: The proportion of data loss or the proportion of deviation parameters exceeds the preset range; Data loss occurs in multiple consecutive time windows, or a certain operating parameter exceeds the preset reasonable range for multiple consecutive time windows; If triggered, the emergency monitoring frequency level will be activated; otherwise, the system will switch to the next higher monitoring frequency level. Among them, the emergency monitoring frequency level is the highest monitoring frequency level.

4. The intelligent operation and management system for geothermal energy in frigid regions as described in claim 1, characterized in that: The calculation process for the heating power is as follows: The flow rate and temperature of the circulating medium on the geothermal side and the user side are obtained in real time from the verified geothermal operation data, and the heating power on the geothermal side and the heat gain power on the user side are calculated in real time using thermodynamic formulas. Calculate the relative deviation between the geothermal heating power and the user-side heating power. If the relative deviation is within the historical reference range, then the geothermal heating power is taken as the final heating power. If the relative deviation value exceeds the historical benchmark range, the reliability weights of the geothermal heating power and the user-side heat gain power are dynamically adjusted based on the degree of deviation between the relative deviation value and the historical benchmark range. Based on the credibility weight, the weighted sum of the geothermal heating power and the user-side heat power is used as the final heating power.

5. The intelligent operation and management system for geothermal energy in frigid regions as described in claim 1, characterized in that: The calculation of the cumulative heat output includes: Extract the geothermal side heating power time series from the historical operation data sequence; Based on the time from the first historical start-up moment to the current moment, the geothermal side heating power is calculated by time integration, and the cumulative heat extracted from the soil is output.

6. The intelligent operation and management system for geothermal energy in frigid regions as described in claim 5, characterized in that: The soil heat transfer numerical model is a two-dimensional or three-dimensional transient heat conduction model constructed based on the finite element method or finite volume method. Its model parameters are the thermal conductivity, density, specific heat capacity at constant pressure of the soil, and the specific heat capacity and thermal conductivity of the circulating medium. The calculation of the heat recovery rate using a pre-defined soil heat transfer numerical model includes: Extract the operation time series and the monitoring well soil temperature time series from the historical operation data sequence, and generate the initial soil temperature field based on the monitoring well soil temperature time series; The operating time and initial soil temperature field are extracted from the historical operating data sequence, and an input parameter set is generated by combining it with the geothermal side heating power time series. The input parameter set is then loaded into a preset soil heat transfer numerical model. In the model, the geothermal side heating power is used as the heat flux density condition of the downhole heat exchanger boundary to simulate the change process of the soil temperature field from the initial state to the current state, and obtain the soil temperature field at the current moment. After obtaining the soil temperature field at the current moment, the heat source item is turned off in the model to simulate the recovery process of the soil temperature field within a preset time period under natural conditions, and the total heat of soil recovery is recorded. The soil thermal recovery rate is calculated by dividing the total heat of soil recovery by the duration of the preset time period.

7. The intelligent operation and management system for geothermal energy in frigid regions as described in claim 1, characterized in that: The thermal imbalance risk index is output through the following steps: The current real-time heating rate is used as the constant heat flux density condition for the downhole heat exchanger boundary within the future prediction time window. Receive the predicted environmental temperature sequence within the future prediction time window and use it as the heat exchange condition at the upper boundary of the soil heat transfer numerical model. The cumulative heat taken from the soil and the rate of thermal recovery are used as initial conditions and loaded into the soil heat transfer numerical model to simulate the spatiotemporal evolution of the soil temperature field within a future prediction time window and output simulation results. The center points at the wall of the downhole heat exchanger and at different depths of the monitoring well are taken as key soil monitoring locations. From the simulation results, the minimum soil temperature, the maximum cumulative duration of soil temperature below the preset risk threshold, and the temperature drop are extracted from each key soil monitoring location within the future prediction time window. The cumulative duration is recorded as the low temperature duration. The minimum soil temperature, duration of low temperature, and temperature drop at each key soil monitoring location were normalized. The normalized index values ​​are weighted and summed to calculate the local thermal imbalance risk index at each key soil monitoring location. Based on the spatial distribution characteristics of each local thermal imbalance risk index, the final thermal imbalance risk index is determined and output.

8. The intelligent operation and management system for geothermal energy in frigid regions as described in claim 7, characterized in that: The determination of the final thermal imbalance risk index includes: Calculate the average of the local thermal imbalance risk index at all key soil monitoring locations; The proportion of monitoring locations where the local thermal imbalance risk index is greater than or equal to the average value and the proportion of monitoring locations where the local thermal imbalance risk index is less than the average value are respectively denoted as the first location proportion and the second location proportion. If the proportion of the first position is greater than the proportion of the second position, the maximum local thermal imbalance risk index shall be used as the final thermal imbalance risk index; otherwise, the average value shall be used as the final thermal imbalance risk index.

9. The intelligent operation and management system for geothermal energy in frigid regions as described in claim 1, characterized in that: The determination and output of control priorities for heating quality and thermal imbalance include: Compare the thermal imbalance risk index with the set risk threshold range; If the heat imbalance risk index is lower than the lower limit of the set risk threshold range, the control priority is to prioritize heating quality. If the heat imbalance risk index is within the set risk threshold range, heating quality and heat imbalance are given equal control priority. If the heat imbalance risk index exceeds the upper limit of the set risk threshold range, the control priority is to prioritize heat imbalance.

10. The intelligent operation and management system for geothermal energy in frigid regions as described in claim 9, characterized in that: The control priority determination module also includes dynamically adjusting the control priority by monitoring the changing trend of the thermal imbalance risk index. Monitor the trend of the thermal imbalance risk index within the future forecast window: When the thermal imbalance risk index continues to rise within the future forecast window, it will be adjusted according to the following rules: If the control priority is heating quality first, then adjust it to the same control priority; If the control priority is the same as the control priority, then adjust to thermal imbalance priority; If the current control priority is thermal imbalance priority, then activate the thermal imbalance early warning command; When the heat imbalance risk index continues to decline within the future forecast time window, the current control priority will be directly locked to prioritize heating quality.

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