Intelligent collection and analysis system for geothermal energy heat supply data in severe cold area

Abstract

The invention relates to the technical field of geothermal energy heat supply management, in particular to an intelligent collection and analysis system for geothermal energy heat supply data in a severe cold area. Through cooperative closed-loop work of the data acquisition module, the state evaluation module, the efficiency analysis module and the decision regulation and control module, comprehensive intelligent monitoring of the geothermal energy heat supply system in the severe cold area is achieved. The thermal output stability, the heat exchange efficiency and the hydraulic equilibrium state can be evaluated in real time, and an accurate and targeted cooperative regulation and control instruction set can be generated based on multi-parameter fusion analysis. Therefore, the problems of low energy efficiency, hydraulic unbalance and the like of the geothermal heat supply system in the severe cold area are effectively solved, and the operation efficiency and stability of the system and the heat supply quality are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of geothermal energy heating management technology, specifically to an intelligent data acquisition and analysis system for geothermal energy heating in frigid regions. Background Technology

[0002] Extremely cold regions experience prolonged periods of low temperatures, significant diurnal temperature variations, and thus place high demands on the stability and quality of heating systems. Heating networks must maintain high-load operation under sustained low temperatures, and this low-temperature environment directly impacts heat loss and the dynamic imbalance between heat source output and user load. Therefore, continuous monitoring of the heating system is essential.

[0003] Existing monitoring methods often focus independently on the geothermal source and user sides, lacking comprehensive data collection and integrated analysis across all three. For example, they only focus on instantaneous temperature differences at the geothermal source without quantifying the volatility of its heat output, making it difficult to effectively predict the risk of heat source attenuation. Furthermore, they fail to consider the impact of geothermal source fluctuations and ambient temperature changes on heat exchange losses. Moreover, when uneven heating occurs at the user side, it is difficult to accurately identify the critical path of hydraulic imbalance from multiple branches and determine the cause of the hydraulic imbalance. This results in a lack of targeted and precise control, affecting the overall energy efficiency and heating quality of the heating system. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art, realize the synchronous collection and fusion analysis of data from the geothermal source side, the heating network side and the user side, quantify the stability of heat output, accurately evaluate the heat exchange efficiency, accurately identify the hydraulically unbalanced branches and their imbalance types, and finally provide precise and targeted control instructions.

[0005] The technical solution adopted by the present invention to solve its technical problem is: an intelligent data acquisition and analysis system for geothermal energy heating in frigid regions, comprising: a data acquisition module for acquiring the inlet and outlet water temperatures on the geothermal source side and the user side, as well as the supply and return water temperatures and circulation flow rates on the heating network side.

[0006] The status assessment module is used to obtain the heat production stability index based on the time series data of the temperature difference between the inlet and outlet water on the geothermal source side.

[0007] The performance analysis module is used to correct the supply and return water temperature difference based on the heat production stability index to obtain the effective heat exchange temperature difference, and calculate the relative heat exchange performance index in combination with the circulation flow rate; at the same time, it calculates the temperature difference variation coefficient of inlet and outlet water at different user sides within the same time period, and identifies the critical path of hydraulic imbalance and its imbalance type in combination with the circulation flow rate.

[0008] The decision-making and control module is used to integrate and analyze the heat output stability index, relative heat exchange performance index, and user-side temperature difference variation coefficient to obtain the comprehensive energy efficiency, and generate a set of coordinated control instructions based on the imbalance type.

[0009] Compared with existing technologies, this invention has the following beneficial effects: 1. This invention achieves comprehensive intelligent monitoring of geothermal energy heating systems in frigid regions through the collaborative closed-loop operation of data acquisition modules, status assessment modules, efficiency analysis modules, and decision control modules. It can not only assess heat output stability, heat exchange efficiency, and hydraulic balance in real time, but also generate precise and targeted collaborative control instruction sets based on multi-parameter fusion analysis. This effectively solves problems such as low energy efficiency and hydraulic imbalance that easily occur in geothermal heating systems in frigid regions, significantly improving the system's operational efficiency, stability, and heating quality.

[0010] 2. This invention corrects the supply and return water temperature difference by using the heat output stability index and the ambient temperature factor, thereby eliminating the interference of internal factors of the geothermal source and the influence of the external environment, so that the relative heat exchange performance index can truly reflect the heat exchange efficiency of the heating network and ensure that precise control can be performed subsequently.

[0011] 3. This invention obtains an imbalance tendency score by integrating the user-side temperature difference variation coefficient and heat exchange efficiency deviation, thereby accurately locating the critical path of hydraulic imbalance and clearly distinguishing between inefficient heat exchange imbalance caused by insufficient flow and overflow imbalance caused by excessive flow. This provides a clear basis for subsequent targeted regulation and avoids blind or incorrect adjustment that could exacerbate heating system problems. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the system module connections of the present invention.

[0014] Figure 2 This is a flowchart illustrating the process of identifying critical paths of hydraulic imbalance according to the present invention.

[0015] Figure 3 This is a schematic diagram of the coordinated adjustment process of the present invention.

[0016] Figure 4 This diagram illustrates the connection status of the geothermal source, branch lines, and users in this project. Detailed Implementation

[0017] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. Furthermore, it should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale.

[0018] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

[0019] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent data acquisition and analysis system for geothermal energy heating in frigid regions provided by this invention.

[0022] Please see Figure 1 The diagram illustrates a module connection diagram of an intelligent data acquisition and analysis system for geothermal energy heating in frigid regions, provided by an embodiment of the present invention. Specifically, it includes a data acquisition module, a status assessment module, an efficiency analysis module, and a decision control module.

[0023] The data acquisition module is connected to both the status assessment module and the performance analysis module. The status assessment module is also connected to both the performance analysis module and the decision-making and control module. The performance analysis module is connected to the decision-making and control module. Based on this module connection diagram, it can be seen that in this system, data starts from the data acquisition module, passes sequentially through the status assessment module and the performance analysis module, and is finally used by the decision-making and control module to generate a collaborative control instruction set.

[0024] Please see Figure 4 The data acquisition module is used to collect the inlet and outlet water temperatures on the geothermal source side and the user side, as well as the supply and return water temperatures and circulation flow rates on the heating network side.

[0025] Specifically, temperature sensors are installed at the inlet and outlet of the heating medium on both the geothermal source and user sides, continuously collecting the inlet and outlet water temperatures at a fixed sampling frequency. Simultaneously, temperature sensors and flow meters are installed on the main supply and return water pipelines on the heating network side, also continuously collecting the supply and return water temperatures and circulation flow rates at corresponding fixed sampling frequencies.

[0026] Considering the rapid temperature changes in frigid regions, the sampling frequency for the geothermal source and heating network sides can be set to once every 1 minute, while the sampling frequency for the user side, due to relatively stable demand, can be set to once every 5 minutes.

[0027] The state assessment module is used to obtain the heat production stability index based on the time series data of the temperature difference between the inlet and outlet water on the geothermal source side.

[0028] As the source of the entire heating system, the stability of geothermal energy output directly affects the heat exchange efficiency of the pipeline network and the heating quality for users. Therefore, the state assessment module of this invention comprehensively analyzes and quantifies the stability of heat output based on temperature difference time-series data, considering both average level and fluctuation amplitude.

[0029] The specific process is as follows: After the data acquisition module continuously collects the inlet and outlet water temperatures of the geothermal source side at a fixed sampling frequency, based on the inlet and outlet water temperature data of the geothermal source side, the difference between the inlet and outlet water temperatures of the geothermal source side in different regions during the current monitoring period is first calculated.

[0030] Next, all the obtained differences are arranged in chronological order to construct a time series of inlet and outlet water temperature differences on the heat source side of each region, and the average value of each inlet and outlet water temperature difference time series is calculated as the current average temperature difference.

[0031] Then, historical operating data of the heat source side in various regions are obtained as a reference benchmark. Based on the above process of obtaining the current average temperature difference, the average temperature difference of the monitoring period in the same period in history is obtained in the same way, and arranged in chronological order to form the historical temperature difference sequence of the heat source side in various regions.

[0032] Furthermore, the percentile of the current average temperature difference within the historical temperature difference series can be calculated using linear interpolation. Percentiles provide a clear picture of the current heat output level compared to the historical average for the same period.

[0033] It should be noted that linear interpolation is an existing calculation method, and its specific formulas and calculation formulas will not be elaborated in this invention.

[0034] Meanwhile, the standard deviation of each inlet and outlet water temperature difference time series is calculated, and the geothermal source temperature difference fluctuation coefficient is obtained by dividing the standard deviation by its average value. The geothermal source temperature difference fluctuation coefficient reflects the relative fluctuation range of the temperature difference. The smaller the value, the more stable the inlet and outlet water temperature difference time series.

[0035] Finally, considering that percentiles and geothermal source temperature difference fluctuation coefficients have different dimensions, they need to be normalized first. The normalization process can be carried out using the Min-Max normalization method, which maps the values ​​of the two to the interval 0-1.

[0036] Considering the need to reflect both average level and fluctuation range, the normalized percentile and the geothermal source temperature difference fluctuation coefficient are multiplied together, and this product is used as the heat production stability index. When the percentile is high and the geothermal source temperature difference fluctuation coefficient is low, the closer the heat production stability index is to 1, the higher the geothermal source heat production stability.

[0037] Please see Figure 2 The performance analysis module is used to correct the supply and return water temperature difference based on the heat production stability index to obtain the effective heat exchange temperature difference, and calculate the relative heat exchange performance index in combination with the circulation flow rate; at the same time, it calculates the temperature difference variation coefficient of inlet and outlet water at different user sides within the same time period, and identifies the critical path of hydraulic imbalance and its imbalance type in combination with the circulation flow rate.

[0038] To avoid misjudging heat exchange efficiency due to fluctuations in geothermal heat output, it is necessary to correct the supply and return water temperature difference based on the heat output stability index.

[0039] Specifically, the difference between the current supply water temperature and the return water temperature is calculated as the current supply and return water temperature difference. Similarly, the supply and return water temperature data of the heating network side during the same historical monitoring period are retrieved, and the average value of the supply and return water temperature difference is calculated as the historical benchmark supply and return water temperature difference.

[0040] Considering that ambient temperature has a significant impact on the heat exchange efficiency of heating networks in frigid regions, this invention introduces an ambient temperature factor, which reflects the impact of ambient temperature changes on heat exchange losses.

[0041] The ambient temperature is collected by temperature sensors installed within the heating area, and the average ambient temperature of the same historical monitoring period is calculated. Then, the ratio of the current ambient temperature to this average value is calculated to obtain the ambient temperature factor. The smaller the ambient temperature factor value, the greater the heat exchange loss on the heating network side.

[0042] Next, considering that the heat production stability index and the ambient temperature factor are the internal and external influencing factors of the heat transfer temperature difference, respectively, the product of the heat production stability index and the ambient temperature factor is used as the weight. .

[0043] According to weight Regarding the current supply and return water temperature difference and historical baseline supply and return water temperature difference Weighted calculations are performed to obtain the effective heat exchange temperature difference. The weighted calculation formula is as follows: .

[0044] Based on the effective heat exchange temperature difference obtained above, the relative heat exchange performance index is further calculated by combining the circulation flow rate. Specifically, the density and specific heat capacity of the heating medium are first obtained, and then the two are multiplied by the effective heat exchange temperature difference and the current circulation flow rate to obtain the current heat exchange quantity.

[0045] Then, the heat exchange volume during the same historical monitoring period is obtained and the average value is calculated to obtain the historical baseline heat exchange volume; finally, the ratio of the current heat exchange volume to the historical baseline heat exchange volume is used as the relative heat exchange performance index.

[0046] Relative heat exchange performance directly reflects the change in current heat exchange efficiency relative to historical levels. When its value is greater than 1, it indicates that the heat exchange efficiency on the heating network side has improved, and when its value is less than 1, it indicates that the heat exchange efficiency on the heating network side has decreased.

[0047] In heating systems in frigid regions, hydraulic imbalance is a common and significant problem. The cause of hydraulic imbalance is often the mismatch between the flow distribution between heating network branches and the actual heat load demand of each user.

[0048] Hydraulic imbalance often manifests as insufficient heating on some user sides and overheating on others, resulting in increased overall energy consumption and reduced heat source efficiency in the heating system.

[0049] Therefore, it is necessary to quantitatively identify and locate hydraulic imbalances. Based on this, the present invention comprehensively considers the user-side temperature difference variation coefficient and circulation flow rate.

[0050] Regarding the coefficient of variation of temperature difference on the user side: First, based on the inlet and outlet water temperature data of each user side within the same time period, calculate the difference between the inlet and outlet water temperatures of each user side within the same time period, which is used as the real-time temperature difference.

[0051] Then, the average of all real-time temperature differences is calculated as the average temperature difference; subsequently, the standard deviation of all real-time temperature differences is calculated, and the ratio of the standard deviation to the average temperature difference is used as the coefficient of variation of the user-side temperature difference.

[0052] The coefficient of variation of temperature difference on the user side directly reflects the stability and uniformity of the heating effect for the corresponding user during the monitoring period. The higher the value, the greater the fluctuation in the heating effect of the branch, and the more serious the uneven heating and cooling phenomenon among users, which is a direct manifestation of hydraulic imbalance.

[0053] In order to accurately locate the hydraulic imbalance, it is necessary to further identify the key branches that cause the hydraulic imbalance.

[0054] Specifically: First, each user's pipeline branch is used as an analysis unit, and the corresponding real-time temperature difference of the user is obtained as the branch's equivalent temperature difference. Then, based on the process of obtaining the user-side temperature difference variation coefficient, the variation coefficient of the equivalent temperature difference of each branch is calculated similarly, and this variation coefficient reflects the temperature difference difference between branches.

[0055] Simultaneously, the current heat exchange rate at each user's side is compared with the real-time flow rate of its corresponding branch to obtain the heat exchange rate per unit flow rate. The average heat exchange rate per unit flow rate of all these values ​​is then calculated as the average heat exchange rate per unit flow rate.

[0056] Then, the relative deviation between the heat exchange per unit flow rate of each branch and the average heat exchange per unit flow rate is calculated as the deviation of heat exchange efficiency.

[0057] A negative value for the heat exchange efficiency deviation indicates that the heat exchange efficiency of that branch is lower than the average level of the heating system, possibly due to insufficient flow or fouling of the heat exchange surface; a positive value indicates that there may be excessive flow. The larger the absolute value of the heat exchange efficiency deviation, the lower the match between the flow distribution of that branch and the actual heat load demand of the user.

[0058] Finally, the coefficient of variation of the equivalent temperature difference of each branch is weighted and fused with the deviation of heat transfer efficiency to obtain the imbalance tendency score. The calculation formula is as follows: Next, all imbalance tendency scores are sorted in descending order, and the branch with the highest score is identified as the critical path for hydraulic imbalance. If multiple branches have the same and highest imbalance tendency score, then all of these branches are identified as critical paths for hydraulic imbalance.

[0059] in, Let m be the branch number, which is 1, 2, ..., m, where m is the total number of branches; For the first The coefficient of variation of the equivalent temperature difference of each branch. For the first Deviation in heat exchange efficiency of each branch For the first The imbalance tendency score of each branch, This is a weighting coefficient, and for heating systems in frigid regions, its value can range from 1.2 to 1.5.

[0060] It should be noted that the following is adopted: The absolute value of the deviation in heat exchange efficiency is because both insufficient and excessive flow can lead to hydraulic imbalance, and the magnitude of the absolute value indicates the severity of the hydraulic imbalance.

[0061] Furthermore, the rationality of flow distribution is the root cause of the temperature uniformity problem. Therefore, by assigning... By adjusting the weights, the system of this invention can more accurately identify branches with relatively mild temperature fluctuations but distorted flow rates and a high risk of hydraulic imbalance, thereby enabling timely intervention and regulation.

[0062] After identifying the critical path of hydraulic imbalance, the type of imbalance is further determined: if the heat exchange per unit flow of the critical path of hydraulic imbalance is lower than the heat exchange per unit flow of the average, it indicates that the critical path of hydraulic imbalance may have insufficient flow due to reasons such as pipe blockage or insufficient valve opening, resulting in low heat exchange efficiency. Therefore, it is determined that there is an inefficient heat exchange type of imbalance.

[0063] If the heat exchange per unit flow rate is higher than the average heat exchange per unit flow rate, it indicates that there is excess flow in the critical path of hydraulic imbalance, leading to heat waste. Therefore, it is determined that there is an overflow-type imbalance.

[0064] In actual heating regulation, the heat output stability index, relative heat exchange performance index, and user-side temperature difference variation coefficient are not isolated but are interconnected and mutually restrictive.

[0065] For example, if the total circulating flow of the pipeline is increased simply because of the high coefficient of variation of the temperature difference on the user side, it may incorrectly adjust a critical path of hydraulic imbalance caused by overflow, which may exacerbate heat waste.

[0066] Therefore, this invention obtains a comprehensive energy efficiency by integrating and analyzing the heat output stability index, relative heat exchange performance index, and user-side temperature difference variation coefficient through the decision control module, and generates a collaborative control instruction set in combination with the imbalance type.

[0067] Regarding the overall energy efficiency: the percentiles of the heat output stability index, the relative heat exchange performance index, and the user-side temperature difference variation coefficient in the historical operating data of the system of this invention were calculated respectively.

[0068] However, since the user-side temperature difference variation coefficient is a negative indicator, while the heat output stability index and the relative heat exchange performance index are both positive indicators, it is necessary to positively process the user-side temperature difference variation coefficient. Specifically, the original percentile is converted into the final percentile using the conversion formula: final percentile = 100% - original percentile. In this way, the three percentiles used for subsequent calculation of comprehensive energy efficiency are all positive indicators, ensuring that the data fusion direction is consistent.

[0069] Then, in order to eliminate the differences in the dimensions and numerical ranges of different indicators, the percentiles of the heat output stability index and the relative heat exchange performance index, as well as the final percentile of the user-side temperature difference variation coefficient, need to be normalized and mapped to the interval 0-1 to obtain standardized parameter values; then the geometric mean of the three standardized parameter values ​​is calculated, and the geometric mean is used as the comprehensive energy efficiency.

[0070] It should be noted that when the standardized parameter values ​​of one or more of the following—heat output stability index, relative heat exchange performance index, and user-side temperature difference variation coefficient—are closer to 0, the overall energy efficiency will be lower. Using the geometric mean value can help the system of this invention to more sensitively monitor this situation and thus prioritize corresponding adjustments.

[0071] Please see Figure 3 After obtaining the comprehensive energy efficiency, the system of the present invention further combines the imbalance type to generate a set of coordinated control instructions.

[0072] Specifically: First, compare the standardized parameter values ​​of the user-side temperature difference variation coefficient, heat output stability index, and relative heat exchange performance index, and take the system part corresponding to the minimum value as the control object; where the system part is the geothermal source side, the heating network side, or the user side.

[0073] Next, based on the overall energy efficiency , benchmark adjustment range coefficient The control amplitude is obtained by combining the standardized parameter values ​​corresponding to the control object. The calculation formula is: .

[0074] in, This represents the difference between the current overall energy efficiency and 1 (ideal overall energy efficiency). The larger the deviation in energy efficiency, the greater the adjustment required. For the standardized parameter values ​​corresponding to the controlled object, This indicates the severity of the problems within the entity being regulated. The higher the value, the more serious the problem is in the object being regulated.

[0075] In this invention, The load can be determined based on the ambient temperature within the heating area; the lower the ambient temperature, the higher the load on the heating system. The smaller the value, the better. For example, when the ambient temperature is below -20 degrees Celsius, The value can range from 5% to 8%, but 6% is a more specific value; when the ambient temperature is between -20 degrees Celsius and -8 degrees Celsius... The value can range from 8% to 13%, but can specifically be 9%.

[0076] After obtaining the control range, a set of coordinated control instructions is generated based on the control range, the control object, and the imbalance type, and then sent to the execution end of the control object. The specific coordinated control process is as follows: When the control object is the geothermal source side, the pump speed on the geothermal source side and the opening of the main valve on the heating network side are coordinated and adjusted according to the control range.

[0077] For example, when the heat output stability index is low, the adjustment direction is to reduce the geothermal source pump speed to stabilize the heat source output. Simultaneously, this should be combined with reducing the opening of the main valves in the heating network to maintain network pressure balance and prevent hydraulic imbalance on the user side. The pump speed change can be calculated using the formula: The change in the opening degree of the main valve on the heating network side can be determined using the formula: Obtain; among them, The preset proportionality coefficient can be 0.5-1.5 in this invention, and can specifically be 1.1.

[0078] In a preferred embodiment of the present invention, while adjusting the opening of the main valve on the heating network side, the total supply and return water pressure is monitored in real time. If the current total supply and return water pressure deviates from the target value determined during the design of the heating system and continues to deviate, the system of the present invention automatically adjusts the opening of the main valve repeatedly with small adjustments based on the current total supply and return water pressure deviation, using 1%-2% of the total stroke of the main valve from fully closed to fully open as the single adjustment amount, until the total supply and return water pressure difference stabilizes within the allowable error range of the target value.

[0079] For example, for a heating system with a total supply and return water pressure target of 400 kPa, its allowable error range can be set as follows: That is, 390kPa-410kPa.

[0080] When the control target is the heating network side, the total circulating flow of the network is adjusted according to the control range.

[0081] For example, the flow rate can be precisely adjusted by controlling the speed of the main circulating pump using a frequency converter. Assuming the adjustment range is 0.15 and the current speed of the main circulating pump is 2500 RPM, the change in speed is: RPM.

[0082] When the target of regulation is the user side, the absolute value of the regulation amplitude is used as the regulation amount, and the opening direction of the regulating valve on the critical path of hydraulic imbalance is determined according to the type of imbalance: if the type of imbalance is inefficient heat exchange imbalance, the regulation direction is to increase the opening, thereby increasing the flow rate of the critical path of hydraulic imbalance and improving the heat exchange of the corresponding user.

[0083] If the imbalance type is an overflow type imbalance, the adjustment direction is to reduce the opening, thereby limiting the excess flow in the critical path of the hydraulic imbalance and balancing the flow distribution of the heating system.

[0084] In a preferred embodiment of the present invention, to avoid problems such as overshoot and drastic pressure fluctuations caused by a single large adjustment, and to ensure that the heating system maintains stable and efficient operation in frigid environments, the present invention adopts a gradual adjustment strategy, that is, decomposing the adjustment range into N consecutive single adjustment amounts.

[0085] Furthermore, after each single adjustment, the system of the present invention needs to wait for a period of time, which can be set to 10-15 minutes, and then recalculate the current comprehensive energy efficiency. If the difference between the current comprehensive energy efficiency and the comprehensive energy efficiency before this adjustment is greater than 0, the next adjustment will continue; if it is less than 0, it means that the adjustment is invalid, and the system of the present invention will stop subsequent adjustments.

[0086] Taking the precise adjustment of flow rate by controlling the main circulation pump speed via a frequency converter as an example, the current speed of the main circulation pump is 2500 RPM, and the adjustment range is 0.15. The adjustment range is divided into 3 parts, each time... RPM was used as the control variable and adjusted a total of 3 times.

[0087] In summary, this invention achieves comprehensive intelligent monitoring of geothermal energy heating systems in frigid regions through the collaborative closed-loop operation of data acquisition, status assessment, efficiency analysis, and decision control modules. It not only enables real-time evaluation of heat output stability, heat exchange efficiency, and hydraulic balance, but also generates precise and targeted collaborative control command sets based on multi-parameter fusion analysis. This effectively solves problems such as low energy efficiency and hydraulic imbalance that commonly occur in geothermal heating systems in frigid regions, significantly improving system efficiency, stability, and heating quality.

[0088] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0089] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0090] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0091] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0092] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart data acquisition and analysis system for geothermal energy heating in frigid regions, characterized in that, include: The data acquisition module is used to collect the inlet and outlet water temperatures on the geothermal source side and the user side, as well as the supply and return water temperatures and circulation flow on the heating network side. The status assessment module is used to obtain the heat production stability index based on the time series data of the temperature difference between the inlet and outlet water on the geothermal source side. The performance analysis module is used to correct the supply and return water temperature difference based on the heat production stability index to obtain the effective heat exchange temperature difference, and calculate the relative heat exchange performance index in combination with the circulation flow rate; at the same time, it calculates the temperature difference variation coefficient of inlet and outlet water at different user sides within the same time period, and identifies the critical path of hydraulic imbalance and its imbalance type in combination with the circulation flow rate. The decision-making and control module is used to integrate and analyze the heat output stability index, relative heat exchange performance index, and user-side temperature difference variation coefficient to obtain the comprehensive energy efficiency, and generate a set of coordinated control instructions based on the imbalance type.

2. The intelligent data acquisition and analysis system for geothermal energy heating in frigid regions according to claim 1, characterized in that, The process of obtaining the heat production stability index based on the time-series data of the temperature difference between the inlet and outlet water on the geothermal source side is as follows: The inlet and outlet water temperatures at the geothermal source side are continuously collected at a fixed sampling frequency. Calculate the difference between the inlet and outlet water temperatures at the heat source side in each region during the current monitoring period, construct a time series of inlet and outlet water temperature differences at the heat source side in each region, and calculate the average value of each inlet and outlet water temperature difference time series as the current average temperature difference. Historical operating data of heat sources in various regions during the same period were obtained as a reference benchmark. Similarly, the average temperature difference during the same period of historical monitoring was obtained and arranged in chronological order to form a historical temperature difference sequence of heat sources in various regions. Calculate the percentile of the current average temperature difference in the historical temperature difference series; Calculate the standard deviation of each inlet and outlet water temperature difference time series, and divide the standard deviation by its average value to obtain the geothermal source temperature difference fluctuation coefficient; The percentile and the geothermal source temperature difference fluctuation coefficient are first normalized and then multiplied to obtain the heat production stability index.

3. The intelligent data acquisition and analysis system for geothermal energy heating in frigid regions according to claim 2, characterized in that, The effective heat exchange temperature difference is obtained by correcting the supply and return water temperature difference based on the heat production stability index, specifically as follows: Calculate the difference between the current supply water temperature and the return water temperature as the current supply and return water temperature difference. Similarly, obtain the supply and return water temperature difference during the same historical monitoring period and calculate the average value as the historical benchmark supply and return water temperature difference. Ambient temperature is collected by temperature sensors installed within the heating area; the ambient temperature during the same historical monitoring period is obtained and the average value is calculated. Calculate the ratio of the current ambient temperature to the average value to obtain the ambient temperature factor; The effective heat exchange temperature difference is obtained by weighting the current supply and return water temperature difference with the historical benchmark supply and return water temperature difference by multiplying the heat output stability index with the ambient temperature factor.

4. The intelligent data acquisition and analysis system for geothermal energy heating in frigid regions according to claim 1, characterized in that, The calculation of the relative heat exchange performance index based on the effective heat exchange temperature difference and circulation flow rate is as follows: Obtain the density and specific heat capacity of the heating medium, and multiply them by the effective heat exchange temperature difference and the current circulation flow rate to obtain the current heat exchange quantity; Obtain the heat exchange volume within the historical monitoring period and calculate the average value to obtain the historical baseline heat exchange volume; The ratio of the current heat exchange capacity to the historical baseline heat exchange capacity is used as the relative heat exchange performance index.

5. The intelligent data acquisition and analysis system for geothermal energy heating in frigid regions according to claim 1, characterized in that, The process of calculating the coefficient of variation of temperature difference between influent and effluent water at different user sides within the same time period is as follows: Based on the inlet and outlet water temperature data of each user side within the same time period, the difference between the inlet and outlet water temperatures of each user side within the same time period is calculated as the real-time temperature difference. Calculate the average of all real-time temperature differences as the average temperature difference; Calculate the standard deviation of all real-time temperature differences, and use the ratio of the standard deviation to the average temperature difference as the coefficient of variation of the user-side temperature difference.

6. The intelligent data acquisition and analysis system for geothermal energy heating in frigid regions according to claim 5, characterized in that, The process of identifying critical paths of hydraulic imbalance based on the user-side temperature difference variation coefficient and circulation flow rate is as follows: Each user’s pipeline branch is used as an analysis unit, and the real-time temperature difference of the corresponding user is obtained as the equivalent temperature difference of the branch. Based on the process of obtaining the user-side temperature difference variation coefficient, the variation coefficient of the equivalent temperature difference of each branch is calculated similarly. The heat exchange rate per unit flow is obtained by comparing the current heat exchange rate of each user with the real-time flow rate of its corresponding branch. Calculate the average heat exchange per unit flow rate for all values, and use this as the average heat exchange per unit flow rate. The relative deviation between the heat exchange per unit flow rate of each branch and the average heat exchange per unit flow rate is calculated as the deviation of heat exchange efficiency. The coefficient of variation of the equivalent temperature difference of each branch is weighted and integrated with the deviation of heat exchange efficiency to obtain the imbalance tendency score; All imbalance tendency scores are sorted in descending order, and the branch with the highest score is identified as the critical path of hydraulic imbalance.

7. The intelligent data acquisition and analysis system for geothermal energy heating in frigid regions according to claim 6, characterized in that, The types of imbalance identified in the critical path of hydraulic imbalance include: If the heat transfer per unit flow rate of the critical path of hydraulic imbalance is lower than the average heat transfer per unit flow rate, then an inefficient heat transfer type imbalance is identified. If the heat exchange per unit flow rate is higher than the average heat exchange per unit flow rate, then an overflow imbalance is identified.

8. The intelligent data acquisition and analysis system for geothermal energy heating in frigid regions according to claim 1, characterized in that, The process of integrating and analyzing the heat output stability index, relative heat exchange performance index, and user-side temperature difference variation coefficient is as follows: Calculate the percentiles of the heat output stability index, relative heat exchange performance index, and user-side temperature difference variation coefficient in the historical number of system operations; The percentiles are normalized to obtain standardized parameter values; then the geometric mean of the three standardized parameter values ​​is calculated, and the geometric mean is used as the comprehensive energy efficiency.

9. The intelligent data acquisition and analysis system for geothermal energy heating in frigid regions according to claim 8, characterized in that, The specific process for generating the coordinated control instruction set is as follows: The standardized parameter values ​​of the user-side temperature difference variation coefficient, heat output stability index, and relative heat exchange performance index are compared, and the system part corresponding to the minimum value is taken as the control object; where the system part is the geothermal source side, the heating network side, or the user side. The control range is calculated by combining the comprehensive energy efficiency, the benchmark control range coefficient, and the standardized parameter values ​​corresponding to the control object. A set of coordinated control instructions is generated based on the control range, the control target, and the type of imbalance, and then sent to the execution end of the control target.

10. The intelligent data acquisition and analysis system for geothermal energy heating in frigid regions according to claim 9, characterized in that, The process of coordinated adjustment based on the magnitude of regulation, the object of regulation, and the type of imbalance is as follows: When the control target is the geothermal source side, the pump speed on the geothermal source side and the opening of the main valve on the heating network side are adjusted in coordination according to the control range. When the control target is the heating network side, the total circulating flow of the network is adjusted according to the control range; When the target of regulation is the user side, the absolute value of the regulation amplitude is used as the regulation amount, and the opening direction of the regulating valve on the critical path of hydraulic imbalance is determined according to the type of imbalance: if the type of imbalance is inefficient heat exchange type imbalance, the regulation direction is to increase the opening; if the type of imbalance is overflow type imbalance, the regulation direction is to decrease the opening.

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