Tail water purification treatment method and system for geothermal recharge

Abstract

The invention provides a tail water purification treatment method and system for geothermal recharge. The tail water purification treatment method comprises the steps that geothermal tail water recharge standards of an underground geothermal reservoir are determined; collecting tail water pollutant distribution information and tail water treatment volume information of target geothermal tail water to be recharged; a tail water purification treatment line is determined, and the tail water purification treatment line comprises a plurality of purification treatment nodes; based on the tail water pollutant distribution information and the tail water treatment volume information, performing treatment parameter optimization of the plurality of purification treatment nodes, and generating a plurality of node optimal treatment parameters; performing purification treatment on the target geothermal tail water at the plurality of purification treatment nodes according to the optimal treatment parameters of the plurality of nodes, and performing recharge permission verification according to a geothermal tail water recharge standard; and after the recharge permission verification is passed, the treated geothermal tail water is reinjected to the underground geothermal reservoir through a recharge well. According to the method, it is ensured that the treated tail water meets the recharge standard, and the technical effect of reducing the pollution risk to the underground geothermal reservoir is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of sewage treatment, and in particular to a tail water purification method and system for geothermal reinjection. Background Art

[0002] In the development and utilization of geothermal resources, geothermal tail water reinjection is of vital importance, and the tail water purification effect directly affects the sustainability of geothermal reservoirs.

[0003] Currently, geothermal tailwater purification and treatment technologies face numerous challenges. For example, traditional treatment methods often rely on simple sedimentation and filtration, failing to fully consider the varying pollutant distributions and treatment capacity requirements of different geothermal tailwaters. These methods struggle to accurately remove pollutants from tailwaters with complex compositions and diverse treatment standards, resulting in treated tailwater failing to meet recharge standards and potentially contaminating underground geothermal reservoirs. Furthermore, traditional treatment equipment lacks the ability to optimize treatment parameters, resulting in low treatment efficiency. Summary of the Invention

[0004] In order to solve the problem that the existing geothermal tail water purification treatment method cannot meet the precise treatment requirements of geothermal tail water reinjection, the present invention provides a tail water purification treatment method and system for geothermal reinjection.

[0005] To achieve the above object, the present invention provides the following technical solutions: The present invention proposes a tail water purification method for geothermal recharge, comprising the following steps: Determine geothermal tailwater recharge standards for underground geothermal reservoirs; Collect tailwater pollutant distribution information and tailwater treatment volume information of the target geothermal tailwater to be recharged; Determining a tailwater purification treatment line, wherein the tailwater purification treatment line includes a plurality of purification treatment nodes; Based on the tailwater pollutant distribution information and the tailwater treatment volume information, optimizing the processing parameters of the multiple purification processing nodes to generate multiple optimal processing parameters for the nodes; Purifying the target geothermal tail water at the plurality of purification processing nodes using the plurality of node optimal processing parameters, and performing reinjection permit verification according to the geothermal tail water reinjection standard; After the reinjection permit is verified and passed, the treated geothermal tail water is reinjected into the underground geothermal reservoir through the reinjection well.

[0006] Preferably, the collecting of tail water pollutant distribution information and tail water treatment volume information of the target geothermal tail water to be recharged includes: Determining multiple detection indicators based on the geothermal tailwater reinjection standard; Configuring a multi-source water quality sensor based on a plurality of the detection indicators; Based on the use of multi-source water quality sensors to collect multi-point data of the target geothermal tailwater, the tailwater pollutant distribution information is obtained by fusion; The volume of the target geothermal tail water is collected to generate the tail water treatment volume information.

[0007] Preferably, the method of collecting multi-point data of the target geothermal tail water by using multi-source water quality sensors and fusing the tail water pollutant distribution information includes: Utilizing the multi-source water quality sensor to collect multi-point data and generate a multi-point water quality monitoring data set; Performing multi-point pollutant distribution uniformity identification on the multi-point water quality monitoring data set to generate a multi-point uniformity coefficient; Determine whether the multi-point uniformity coefficient meets the preset uniformity coefficient threshold. If the multi-point uniformity coefficient meets the preset uniformity coefficient threshold, perform multi-position mean calculation of various water quality sensing parameters based on the multi-point water quality monitoring data set to generate tail water pollutant distribution information; if not, generate a uniform optimization instruction, and send the uniform optimization instruction to the control terminal of the uniform mixing equipment for uniform mixing control.

[0008] Preferably, performing multi-point pollutant distribution uniformity identification on the multi-point water quality monitoring data set to generate a multi-point uniformity coefficient further includes: Based on the concentration data of each pollutant at different sampling points in the multi-point water quality monitoring data set, the mean and standard deviation are calculated; The coefficient of variation is calculated based on the standard deviation and mean, that is, the multi-point uniformity coefficient is obtained.

[0009] Preferably, the optimizing of the processing parameters of the plurality of purification processing nodes based on the tailwater pollutant distribution information and the tailwater treatment volume information to generate the optimal processing parameters of the plurality of nodes includes: Conduct historical processing data mining on multiple purification processing nodes and build multiple parameter optimization libraries; Based on the tailwater pollutant distribution information and the tailwater treatment volume information, screening a plurality of node initial treatment parameters that match the tailwater characteristics in the plurality of parameter optimization libraries; Mutual influence correction of the plurality of purification processing nodes is performed based on the plurality of node initial processing parameters to generate the plurality of node optimal processing parameters.

[0010] Preferably, historical processing data mining is performed on multiple purification processing nodes to construct multiple parameter optimization libraries, including: Comprehensively collect historical data of multiple purification treatment nodes in past geothermal tailwater purification treatment; Clean the collected historical data to obtain preprocessed data; The association rules are mined on the pre-processed data using the Apriori algorithm to obtain the association rules; The mined association rules are sorted and classified, and multiple parameter optimization libraries are constructed.

[0011] Preferably, performing mutual influence correction of the plurality of purification processing nodes based on the plurality of initial processing parameters of the nodes to generate the plurality of optimal processing parameters of the nodes includes: Collecting a plurality of historical purification monitoring data sets of the plurality of purification processing nodes; Based on the multiple historical purification monitoring data sets, analyzing the influence relationship of any node processing parameter on the pollutant indicators of other nodes except its own pollutant indicator, and generating multiple influence relationship topologies; Mutual influence correction is performed on the initial processing parameters of the multiple nodes using the multiple influence relationship topologies to generate optimal processing parameters of the multiple nodes.

[0012] Preferably, the purifying treatment of the target geothermal tail water at the plurality of purification treatment nodes using the plurality of node optimal treatment parameters and performing reinjection permit verification according to the geothermal tail water reinjection standard include: Based on the optimal treatment parameters, multiple purification treatment nodes are operated to purify the target geothermal tail water; Detect the purified target geothermal tail water and obtain water quality test results; Determine whether the water quality test result meets the geothermal tail water reinjection standard and obtain a judgment result.

[0013] Preferably, after the reinjection permit verification is passed, the treated geothermal tail water is reinjected into the underground geothermal reservoir through the reinjection well, comprising: Determine the location and parameters of the recharge well based on the geological survey data of the site; Arrange reinjection equipment and a pipeline system at the determined location of the reinjection well based on the parameters of the reinjection well, and perform reinjection using the reinjection equipment and the pipeline system; After the reinjection is completed, the reinjection effect is monitored over a long period of time.

[0014] The present invention proposes a tail water purification system for geothermal reinjection, which is used to implement the above-mentioned tail water purification method for geothermal reinjection, comprising: a reinjection standard determination module configured to determine a geothermal tailwater reinjection standard for an underground geothermal reservoir; a tailwater information collection module configured to collect tailwater pollutant distribution information and tailwater treatment volume information of target geothermal tailwater; a processing line determination module, configured to determine a tailwater purification processing line, wherein the tailwater purification processing line includes a plurality of purification processing nodes; A processing parameter generation module is configured to optimize the processing parameters of multiple purification processing nodes based on tailwater pollutant distribution information and tailwater treatment volume information, and generate optimal processing parameters for multiple nodes; a recharge permission verification module configured to purify target geothermal tail water at multiple purification processing nodes using multiple node optimal processing parameters, and to perform recharge permission verification based on geothermal tail water recharge standards; The permit verification module is configured to reinject the treated geothermal tail water into the underground geothermal reservoir through the reinjection well after the reinjection permit verification is passed.

[0015] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention proposes a tail water purification method for geothermal reinjection. This method determines the geothermal tail water reinjection standard of the underground geothermal reservoir, clarifies the purification treatment target, collects the pollutant distribution information and treatment volume information of the target geothermal tail water, provides a basis for treatment, determines a tail water purification treatment line including multiple purification treatment nodes, optimizes the treatment parameters of each node based on the collected information, purifies the target geothermal tail water at multiple purification treatment nodes with the optimized parameters, and verifies according to the reinjection standard. After the verification is passed, the treated tail water is reinjected into the underground geothermal reservoir, achieving accurate and efficient purification of the geothermal tail water, ensuring that the reinjected tail water meets the standard, and reducing pollution to the geothermal reservoir. The method achieves the technical effect of accurately treating the tail water according to the characteristics of the geothermal tail water and the reinjection standard, effectively improving the tail water purification effect, ensuring that the treated tail water meets the reinjection standard, and reducing the pollution risk to the underground geothermal reservoir.

[0016] Furthermore, this method achieves the technical effect of accurately obtaining tailwater pollutant distribution and treatment volume information by determining detection indicators based on geothermal tailwater reinjection standards, configuring sensors to collect data and performing volume collection, providing a key basis for subsequent determination of purification treatment plans.

[0017] Furthermore, this method uses multi-source water quality sensors to collect multi-point data on the target geothermal tail water and then fuses it. The tail water pollutant distribution information obtained can accurately reflect the actual conditions of various pollutants in the tail water, providing a key basis for the subsequent formulation of accurate and effective tail water purification and treatment plans, ensuring that the geothermal tail water meets the recharge standards.

[0018] Furthermore, this method achieves the technical effects of recycling geothermal tail water, maintaining stable geothermal reservoir pressure, reducing environmental pollution caused by tail water discharge, and ensuring the sustainable development and utilization of geothermal resources by reinjecting the qualified treated geothermal tail water into the underground geothermal reservoir through the reinjection well. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic flow chart of a tail water purification method for geothermal reinjection provided by the present invention; Figure 2 This is a structural schematic diagram of a tail water purification system for geothermal reinjection provided by the present invention. DETAILED DESCRIPTION

[0020] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0021] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0022] The present invention proposes a tail water purification method for geothermal recharge, such as Figure 1 As shown, the following steps are included: Step A100: Determine the geothermal tailwater recharge standard of the underground geothermal reservoir.

[0023] In the embodiments of the present application, the geothermal reservoir is a geological body that stores geothermal resources underground and is the place where geothermal energy is stored and transported. Geothermal tailwater recharge is the process of re-injecting utilized geothermal tailwater into the underground geothermal reservoir.

[0024] Specifically, a detailed geological survey of the underground geothermal reservoir is conducted to analyze the reservoir's rock properties and pore structure. For example, core samples are obtained through geological drilling and subjected to microstructural analysis to determine the range of tailwater composition variations that the reservoir can withstand. Simultaneously, the potential impact of various pollutants in the geothermal tailwater on the surrounding soil and groundwater is monitored.

[0025] Based on the standards of relevant industry associations or regulatory agencies and with reference to international standards and local actual conditions, tailwater reinjection standards are formulated for different types of geothermal reservoirs. For example, for carbonate geothermal reservoirs, the total dissolved solids content is required to be less than 1000 mg / L and the iron ion content is required to be less than 0.3 mg / L, etc., to ensure that tailwater reinjection will not have a negative impact on the reservoir and the surrounding environment.

[0026] By determining the geothermal tail water reinjection standard, we have achieved the technical effect of providing clear goals and specifications for subsequent tail water purification treatment, and ensuring that tail water reinjection does not cause pollution to the reservoir and the surrounding environment.

[0027] Step A200: Collect tailwater pollutant distribution information and tailwater treatment volume information of the target geothermal tailwater.

[0028] In this embodiment, tailwater pollutant distribution information is generated by determining multiple detection indicators based on geothermal tailwater recharge standards, configuring multiple water quality sensors to collect multi-point data on the target geothermal tailwater, and then fusing the data. Tailwater treatment volume information is generated by collecting the volume of the target geothermal tailwater.

[0029] Specifically, multiple detection indicators are determined based on the geothermal tail water reinjection standard, and multi-source water quality sensors are configured according to the multiple detection indicators. The multi-source water quality sensors are used to collect multi-point data of the target geothermal tail water, and the collected multi-point data are fused to obtain the tail water pollutant distribution information. At the same time, the volume of the target geothermal tail water is collected to generate the tail water treatment volume information.

[0030] Step A300: Determine a tail water purification treatment line, wherein the tail water purification treatment line includes a plurality of purification treatment nodes.

[0031] In the embodiment of the present application, the tail water purification treatment line is a process line for purifying the target geothermal tail water of geothermal reinjection, and is a framework for achieving orderly and efficient purification of the target geothermal tail water.

[0032] In one embodiment of the present application, a large amount of historical processing data of different types of geothermal tail water is first collected, the historical processing data is grouped, and multiple arrays are selected for analysis, that is, more than 100 groups of geothermal tail water cases from different regions, different temperatures, and different compositions are analyzed to understand the common types of pollutants, such as calcium and magnesium ions, heavy metal ions (such as mercury, cadmium, lead, etc.), suspended matter, and dissolved gases, etc., and at the same time determine the concentration range of these pollutants in the tail water.

[0033] At the same time, according to the established geothermal tailwater reinjection standards (determined by relevant industry specifications, geological research and environmental protection requirements), for example, in a certain area, the local reinjection standards stipulate that the content of heavy metal mercury in the tailwater must be lower than 0.001 mg / L, and the suspended matter content must be lower than 50 mg / L, etc. Based on this geothermal tailwater reinjection standard, the key pollutants that need to be removed and their compliance limits are clearly defined, providing a clear target for the screening of subsequent treatment technologies.

[0034] Based on the key pollutants to be removed and their compliance limits, as well as the geothermal tail water reinjection standards, a series of treatment technologies that may be suitable for geothermal tail water purification have been preliminarily screened out, such as sedimentation, filtration, ion exchange, membrane separation, etc. Sedimentation technology, as a basic treatment method, uses gravity to settle suspended particles in the tail water and remove larger suspended particles; filtration technology, depending on the pore size of the filter, can effectively intercept impurities of different particle sizes, such as high-efficiency filtration technology, which can achieve a suspended matter removal rate of more than 95%; ion exchange technology, through the exchange reaction between ion exchange resin and ions in the tail water, can achieve a removal rate of 80%-90% for specific heavy metal ions; membrane separation technologies, such as reverse osmosis membranes, ultrafiltration membranes, etc., can achieve precise separation of tiny particles, ions and molecules.

[0035] Then, based on the characteristics and scope of application of different treatment technologies, a reasonable combination is made in combination with the composition, concentration, and treatment volume of specific geothermal tail water. For example, for geothermal tail water containing high concentrations of suspended matter and heavy metals, precipitation technology will be used first. By adding flocculants and other agents, the suspended matter will be quickly settled and most of the suspended matter will be removed, generally reducing the suspended matter content by 70%-80%. Then, ion exchange technology is used to remove specific heavy metal ions, effectively reducing the concentration of heavy metal ions. Finally, membrane separation technology is used for further fine filtration to remove the remaining tiny particles and ions, ensuring that the various indicators of the tail water meet the recharge standards, thereby constructing a tail water purification treatment line containing multiple purification treatment nodes to achieve efficient purification treatment of geothermal tail water.

[0036] Step A400: Based on the tailwater pollutant distribution information and the tailwater treatment volume information, optimize the treatment parameters of multiple purification treatment nodes to generate the optimal treatment parameters of multiple nodes.

[0037] In the embodiment of the present application, the node optimal processing parameters are ideal processing parameters for multiple purification processing nodes.

[0038] Specifically, the historical processing data of multiple purification treatment nodes are mined to construct multiple parameter optimization libraries. Then, based on the tailwater pollutant distribution information and tailwater treatment volume information, the initial treatment parameters of multiple nodes are screened and matched in the parameter optimization library. Finally, based on the initial treatment parameters of the nodes, the mutual influence correction of multiple purification treatment nodes is performed to obtain the optimal treatment parameters of multiple nodes.

[0039] Step A500: Based on the optimal processing parameters of multiple nodes, the target geothermal tail water is purified at multiple purification processing nodes, and the reinjection permission is verified according to the geothermal tail water reinjection standard.

[0040] In the embodiment of the present application, the re-injection permission verification is a process of purifying the target geothermal tail water using the optimal processing parameters of multiple nodes, conducting water quality testing on the treated tail water, generating water quality test results, and then judging whether the results meet the geothermal tail water re-injection standards.

[0041] In one embodiment of the present application, after the target geothermal tail water is purified at multiple purification processing nodes using multiple node optimal processing parameters, the treated tail water is subjected to water quality testing to generate a test result, and then the result is compared with the geothermal tail water reinjection standard. If the standard is met, the reinjection permit verification is passed.

[0042] Step A600: After the reinjection permit verification is passed, the treated geothermal tail water is reinjected into the underground geothermal reservoir through the reinjection well.

[0043] In one embodiment of the present application, the location and parameters of the recharge well are determined based on the address geological exploration data. For example, in a geothermal development project in a certain area, through detailed survey of the underground rock structure, it is determined that the recharge well should be located in an area with higher permeability above the geothermal reservoir. Generally, the permeability needs to reach 50-100 millidarcy to ensure that the tail water can be smoothly injected.

[0044] Based on the parameters of the reinjection well, reinjection equipment and pipeline systems are arranged at the determined location of the reinjection well. Among them, the reinjection equipment includes a high-pressure pump, etc., and its pressure parameters are set according to the reservoir pressure. Based on construction experience data, for example, the general reinjection pressure needs to be 2-5 MPa higher than the reservoir pressure to ensure that the tail water can overcome the resistance and enter the reservoir. The pipeline system needs to have good corrosion resistance to prevent the tail water from corroding the pipeline. For example, corrosion-resistant steel pipes are used, and their inner walls are specially treated to effectively resist corrosion from chemical substances in geothermal tail water.

[0045] For reinjection, flow sensors and pressure sensors are installed on the pipeline to monitor the reinjection flow and pressure in real time. Generally, the reinjection flow is controlled at 5-10 cubic meters per hour to ensure the stability of reinjection. At the same time, the water quality of the tail water is continuously monitored by multi-source water quality sensors installed on the pipeline to ensure that the tail water quality does not change during the reinjection process.

[0046] After reinjection is complete, long-term monitoring of the reinjection effect is necessary. Monitoring wells are set up around the reinjection wells, and water samples are regularly collected to analyze changes in composition. For example, the heavy metal content in the tailwater is monitored to see whether it diffuses through the reservoir over time. If the diffusion range exceeds a certain standard (e.g., the increase in heavy metal content within a 50-meter radius of the reinjection well must not exceed 10%), the reinjection strategy must be adjusted or subsequent purification measures strengthened to ensure that the underground geothermal reservoir is not contaminated during the reinjection process and maintain its sustainable utilization capacity.

[0047] This method achieves the technical effects of recycling geothermal tail water, maintaining stable geothermal reservoir pressure, reducing environmental pollution caused by tail water discharge, and ensuring the sustainable development and utilization of geothermal resources by reinjecting qualified treated geothermal tail water into the underground geothermal reservoir through the reinjection well.

[0048] Furthermore, step A200 of the method provided in the embodiment of the present application, collecting tailwater pollutant distribution information and tailwater treatment volume information of the target geothermal tailwater, includes: A210: Determine multiple detection indicators based on geothermal tailwater recharge standards.

[0049] A220: Configure multi-source water quality sensors based on multiple detection indicators.

[0050] A230: Use multi-source water quality sensors to collect multi-point data on the target geothermal tail water and then fuse them to obtain tail water pollutant distribution information.

[0051] A240: Collect the volume of target geothermal tail water and generate the tail water treatment volume information.

[0052] In the embodiment of the present application, the multi-source water quality sensor is a device used to collect multi-point data of the target geothermal tail water to determine the distribution of tail water pollutants.

[0053] Specifically, a multi-source water quality sensor is configured based on the geothermal tail water recharge standard determined in step A100; the multi-source water quality sensor may include a variety of sensor units for detecting different pollutants, such as an ion selective electrode unit for detecting heavy metal ions, a pH sensor unit for detecting acidity and alkalinity, etc.

[0054] The configured multi-source water quality sensor is used to collect multi-point data of the target geothermal tail water; in actual operation, multiple sampling points are set at the starting end, middle end and end end of the tail water discharge pipeline to ensure the representativeness of the collected data.

[0055] Then, multi-source water quality sensors are used to collect multi-point data of the target geothermal tail water to obtain a multi-point water quality monitoring data set. The multi-point pollutant distribution uniformity of the multi-point water quality monitoring data set is identified to generate a multi-point uniformity coefficient. It is judged whether the multi-point uniformity coefficient meets the preset uniformity coefficient threshold. If the multi-point uniformity coefficient meets the preset uniformity coefficient threshold, the multi-location mean calculation of various water quality sensing parameters is performed based on the multi-point water quality monitoring data set to generate tail water pollutant distribution information.

[0056] In addition to obtaining information on the distribution of tailwater pollutants, the volume of the target geothermal tailwater also needs to be collected. This is typically done using equipment such as electromagnetic flowmeters. By accurately measuring the induced electromotive force, the electromagnetic flowmeter can calculate the tailwater flow rate. Combined with the cross-sectional area of ​​the pipe, the tailwater flow rate can be calculated. In practical applications, the electromagnetic flowmeter is installed on the tailwater transmission pipeline and records tailwater flow data at regular intervals (e.g., 1 minute). By continuously recording the changes in tailwater flow over time and multiplying the flow data obtained from each measurement by the corresponding time interval, the volume of the tailwater over different time periods can be calculated.

[0057] By determining detection indicators based on geothermal tailwater reinjection standards, configuring sensors to collect data and conduct volume collection, we have achieved the technical effect of accurately obtaining tailwater pollutant distribution and treatment volume information, providing a key basis for the subsequent determination of purification treatment plans.

[0058] Furthermore, step A230 of the method provided in the embodiment of the present application uses a multi-source water quality sensor to collect and fuse multi-point data of the target geothermal tail water to obtain tail water pollutant distribution information, including: A231: Use multi-source water quality sensors to collect multi-point data and generate multi-point water quality monitoring datasets; A232: Identify the uniformity of pollutant distribution at multiple points for multi-point water quality monitoring data sets and generate multi-point uniformity coefficients; A233: Determine whether the multi-point uniformity coefficient meets a preset uniformity coefficient threshold; if so, calculate the multi-location mean of each water quality sensing parameter based on the multi-point water quality monitoring data set to generate the tailwater pollutant distribution information; If not, generate uniform optimization instructions; The uniform optimization instruction is sent to the control terminal of the uniform stirring device to perform uniform stirring control.

[0059] In this embodiment, the multi-point water quality monitoring dataset is a data set generated by collecting multi-point data from target geothermal tailwater using multiple water quality sensors. The multi-point uniformity coefficient is a coefficient generated by identifying the uniformity of multi-point pollutant distribution within the multi-point water quality monitoring dataset. The preset uniformity coefficient threshold is a pre-set standard value. If it is met, tailwater pollutant distribution information is generated; if it is not met, a uniformity optimization instruction is issued.

[0060] Alternatively, first, based on geothermal tailwater recharge standards, determine the types of pollutants that need to be monitored, such as heavy metals (mercury, cadmium, etc.), pH, and dissolved solids. Multi-source water quality sensors are then selected and configured for these pollutants. For example, a multi-source water quality sensor could be composed of an ion-selective electrode sensor capable of detecting multiple heavy metal ions and a pH sensor for detecting pH.

[0061] Then, these multi-source water quality sensors are installed at different locations (starting end, middle end and end end) of the target geothermal tail water to collect multi-point data. For example, 10 sampling points are set up and data is collected every certain time (such as 30 minutes). After a period of continuous collection (such as 24 hours), a multi-point water quality monitoring data set containing different locations and times is generated. This data set covers the concentration changes of various pollutants at different sampling points.

[0062] Next, a data processing algorithm was used to identify the uniformity of pollutant distribution at multiple points within the multi-point water quality monitoring dataset. The multi-point uniformity coefficient was calculated using the coefficient of variation method. First, the mean and standard deviation of each pollutant concentration at different sampling points within the multi-point water quality monitoring dataset were calculated. The coefficient of variation was then calculated based on the standard deviation and mean. This coefficient, calculated by dividing the standard deviation by the mean, is the multi-point uniformity coefficient. When the calculated multi-point uniformity coefficient meets a preset uniformity threshold (assumed to be 0.15, determined by technicians in this field based on analysis of a large amount of historical geothermal tailwater sample data, combined with multiple factors such as the tailwater purification treatment objectives, the characteristics of the treatment process, and the requirements of the reinjection standard), it indicates that the pollutant distribution in the tailwater is relatively uniform.

[0063] After the uniformity condition is met (i.e., the multi-point uniformity coefficient is less than or equal to 0.15), the multi-location mean of each water quality sensing parameter is calculated based on the multi-point water quality monitoring data set. For example, for mercury ion content, the mercury ion concentration detected at 10 sampling points is added together and then divided by the number of sampling points, 10, to obtain the average mercury ion content. Similarly, the average content of each pollutant is calculated, thus generating tailwater pollutant distribution information containing the content of each pollutant.

[0064] The tailwater pollutant distribution information obtained through the above steps can accurately reflect the actual conditions of various pollutants in the tailwater, provide a key basis for the subsequent formulation of accurate and effective tailwater purification and treatment plans, and ensure that geothermal tailwater meets the recharge standards.

[0065] In the embodiment of the present application, the uniform optimization instruction is used to improve the uniformity of the tail water pollutant distribution when it is determined that the multi-point uniformity coefficient is not satisfied. The uniform stirring device is a device that stirs the target geothermal tail water to make the tail water pollutant distribution more uniform.

[0066] Specifically, when the multi-point uniformity coefficient exceeds 0.15 (not meeting the preset uniformity coefficient threshold), measures must be taken to achieve a more even distribution of tailwater pollutants. First, the tailwater purification equipment used for geothermal reinjection generates a uniformity optimization instruction. This instruction is generated based on a logic judgment program set within the equipment. This program uses a preset uniformity coefficient threshold as a benchmark. Once the multi-point uniformity coefficient exceeds this threshold, the instruction generation mechanism is immediately triggered.

[0067] The uniformity optimization command is then sent to the control terminal of the uniform mixing equipment via a communication line. Common communication methods include wired communication (such as Ethernet, RS485 bus, etc.) and wireless communication (such as Wi-Fi, Bluetooth, etc.). Those skilled in the art will choose the right method based on the layout of the field equipment and environmental conditions. For example, if the field equipment is relatively concentrated and interference is low, the lower-cost RS485 bus can be used for communication; if the equipment is dispersed and wiring is difficult, Wi-Fi can be used for wireless transmission.

[0068] The mixing equipment begins operation upon receiving the command. It typically consists of a motor, agitating blades, and a control system. The motor powers the agitating blades, enabling them to operate at a preset speed and stirring pattern. Different types of mixing equipment are suitable for tailwater mixing of varying scales and properties. For large-scale geothermal tailwater, a high-power paddle agitator with a larger diameter blade can be used, generating greater stirring force and promoting rapid and uniform mixing of the tailwater. For smaller-scale tailwater, a turbine agitator may be used, providing a more refined mixing effect. During the mixing process, a speed sensor monitors the tailwater's stirring status in real time, such as the stirring speed and temperature (for example, maintaining the stirring speed at 30-50 revolutions per minute and the temperature within a certain range to prevent excessive heat generation from affecting the tailwater's properties). This ensures a stable and efficient mixing process. After a period of stirring (e.g., 15-30 minutes), the pollutants in the tailwater become more evenly distributed.

[0069] The above steps provide good conditions for subsequent accurate acquisition of tailwater pollutant distribution information and further purification treatment.

[0070] Furthermore, step A400 of the method provided in the embodiment of the present application optimizes the processing parameters of the plurality of purification processing nodes based on the tailwater pollutant distribution information and the tailwater treatment volume information, and generates the optimal processing parameters of the plurality of nodes, including: A410: Obtain historical processing data from multiple purification processing nodes and build multiple parameter optimization libraries.

[0071] A420: Based on the tailwater pollutant distribution information and the tailwater treatment volume information, multiple node initial treatment parameters that match the tailwater characteristics are screened in the multiple parameter optimization libraries.

[0072] A430: Perform mutual influence correction of the plurality of purification processing nodes based on the initial processing parameters of the plurality of nodes to generate optimal processing parameters for the plurality of nodes.

[0073] In the embodiments of this application, the parameter optimization library contains a large amount of data and information related to tailwater purification. Initial node treatment parameters are selected from multiple parameter optimization libraries based on tailwater pollutant distribution information and tailwater treatment volume information. These parameters are matched to the current tailwater characteristics and are used for subsequent interaction correction to generate optimal treatment parameters for multiple nodes.

[0074] Specifically, historical processing data of multiple purification processing nodes is obtained, and multiple parameter optimization libraries are constructed, including: Comprehensively collect historical data from multiple purification nodes on geothermal tailwater purification processes. This data covers various tailwater characteristics, such as pollutant type (heavy metals, microorganisms, etc.), concentration, pH, temperature, and corresponding treatment parameters, such as reagent dosage, reaction time, filtration speed, and equipment operating power. Furthermore, data on treatment effectiveness, such as the removal rate of various pollutants and the final water quality indicators achieved by the tailwater, are also collected. This data, which may come from treatment projects at different times and locations, forms the basis for building a parameter optimization library.

[0075] The collected historical data is cleaned to remove duplicate, erroneous, or incomplete data records to obtain preprocessed data. For example, the data is checked for outliers. For example, if some monitoring data exceeds a reasonable range, it needs to be corrected or eliminated. For missing values, methods such as mean filling and regression prediction are used to supplement them based on the characteristics and distribution of the data. At the same time, the data is standardized to convert data of different dimensions into a unified dimensionless form for subsequent analysis. For example, data of different units such as the dosage of the chemical and the tailwater flow rate are converted into numerical values ​​between 0 and 1 to ensure that different data are comparable in the mining algorithm.

[0076] The association rules are mined on the preprocessed data using the Apriori algorithm (Apriori association rule learning algorithm) to obtain the association rules.

[0077] Specifically, the support and confidence thresholds for association rule mining are determined based on actual needs and data characteristics. Support indicates the proportion of a specific item set (various combinations of treatment parameters, tailwater characteristics, and treatment effects) within a dataset (historical data from multiple purification treatment nodes), while confidence measures the probability that a transaction containing the preceding item also contains the subsequent item. For example, setting a support threshold of 0.1 means that at least 10% of all data records must satisfy a given association rule; a confidence threshold of 0.8 indicates that when the preceding item appears, the subsequent item appears with at least an 80% probability.

[0078] When determining the support threshold, the scale and characteristics of the data must be considered. If the data volume is large, the support threshold can be appropriately increased to reduce redundant frequent itemsets. For example, when processing massive amounts of historical geothermal tailwater purification data, given the rich data, the support threshold can be set between 0.15 and 0.2 to filter out more representative frequent itemsets. If the data volume is small, an excessively low support threshold may result in the mining of a large number of low-frequency, meaningless itemsets. In this case, the support threshold can be set to around 0.05-0.1.

[0079] Determining the confidence threshold also needs to be based on actual needs. In the geothermal tailwater purification scenario, if technicians have strict requirements for treatment results and hope to find association rules with high certainty, then the confidence threshold should be set higher, such as 0.85-0.95. This means that only when there is a high probability of association between a treatment parameter and tailwater characteristics and treatment effect will it be considered a valid rule. Conversely, if the focus is on exploring potential relationships, the confidence threshold can be appropriately lowered, such as 0.7-0.8, to discover more possible association rules, but at the same time, its reliability will need to be further verified later.

[0080] After setting a support threshold (which determines the minimum frequency at which an item set can appear in the dataset), the algorithm searches for frequent item sets in the preprocessed data. Starting with a single item, the algorithm gradually generates frequent item sets containing multiple items. For example, it first identifies frequently occurring item sets for a single treatment parameter (such as the dosage of a particular agent) or tailwater characteristic (such as the concentration of a particular pollutant). These items are then combined to generate frequent item sets containing multiple treatment parameters and tailwater characteristics. During this generation process, items are filtered based on the support threshold, and only those with support greater than the set threshold are retained, such as the frequently occurring combination of "specific agent dosage + pollutant concentration."

[0081] Based on the generated frequent item sets, we further mine association rules. By calculating confidence levels, we filter out rules that meet the confidence threshold. For example, if we discover an association rule like "When the heavy metal concentration in the tailwater is high and the reaction time reaches a certain length, the heavy metal removal rate is high," and its confidence level meets the set threshold of 0.8, we retain it.

[0082] The association rules mined are organized and classified to construct multiple parameter optimization libraries. These libraries are divided according to different tailwater characteristics (such as different major pollutant types, different pH ranges, etc.), treatment stages (such as primary purification, deep purification, etc.), or treatment objectives (such as removing specific pollutants, meeting specific recharge standards, etc.). For example, a parameter optimization library is constructed for heavy metal-contaminated tailwater, which contains treatment parameter combinations that can achieve high removal rates at different heavy metal concentrations and different treatment scales. Another example is to construct a primary treatment parameter optimization library and a deep treatment parameter optimization library based on the treatment stage, respectively storing the effective treatment parameter association rules for the corresponding stages. Each parameter optimization library contains the potential relationship between treatment parameters, tailwater characteristics, and treatment effects.

[0083] Based on the currently acquired tailwater pollutant distribution information and tailwater treatment volume information, multiple node initial treatment parameters that match the tailwater characteristics are screened in multiple parameter optimization libraries. For example, the main pollutants in the current tailwater are heavy metal ions (such as lead ion concentration of 5 mg / L, mercury ion concentration of 0.1 mg / L), and the treatment volume is 50 cubic meters / hour. This information is input into the parameter optimization library for retrieval. Based on the existing data association relationship in the optimization library, the treatment parameter combination that has a good effect in past treatments for similar tailwater characteristics (such as lead ion concentration of 4-6 mg / L, mercury ion concentration of 0.05-0.15 mg / L, and treatment volume of 40-60 cubic meters / hour) is screened out as the initial treatment parameters for multiple nodes.

[0084] When generating the optimal processing parameters for multiple nodes, multiple historical purification monitoring data sets of multiple purification processing nodes are first collected. Then, based on these data sets, the influence of any node processing parameter on the pollutant indicators of other nodes is analyzed to generate multiple influence relationship topologies. Finally, these topologies are used to perform mutual influence correction on the initial processing parameters of multiple nodes, thereby obtaining the optimal processing parameters for multiple nodes.

[0085] Through the above steps, the entire purification process is ensured to be efficient and stable, and the tail water meets the re-injection standards.

[0086] Furthermore, step A430 of the method provided in the embodiment of the present application, performing mutual influence correction of the multiple purification processing nodes based on the multiple initial processing parameters of the nodes to generate optimal processing parameters for the multiple nodes, includes: A431: Collect multiple historical purification monitoring data sets for multiple purification processing nodes.

[0087] A432: Based on multiple historical purification monitoring data sets, analyze the impact of any node processing parameters on the pollutant indicators of other nodes except its own pollutant indicators, and generate multiple impact relationship topologies.

[0088] A433: Perform mutual influence correction on the initial processing parameters of the multiple nodes using multiple influence relationship topologies to generate optimal processing parameters for the multiple nodes.

[0089] In this embodiment, the historical purification monitoring dataset is a collection of data sets generated by multiple purification processing nodes during past geothermal tailwater purification processes. The impact relationship topology is generated by analyzing the impact of any node's processing parameters on the pollutant indicators of other nodes, in addition to its own pollutant indicators, based on these multiple historical purification monitoring datasets.

[0090] Specifically, the first step is to collect multiple historical purification monitoring data sets from multiple purification treatment nodes. These data include the treatment parameters of each node (such as flocculant dosage, disinfectant dosage, etc.) and pollutant indicators (such as dissolved organic matter removal rate, disinfection effect, etc.). In the past geothermal tailwater purification process, each purification treatment node will continue to generate a large amount of data. These data cover treatment information at different times and under different tailwater water quality conditions, such as temperature, pressure, and changes in the concentration of various pollutants during the treatment process. For example, a system with three purification treatment nodes has recorded data every hour every day during the past year of operation. Then, each node will generate approximately 8,760 data items, and the three nodes will accumulate more than 26,000 historical purification monitoring data items in total.

[0091] Next, taking the first node as the flocculant dosage, the second node as the removal rate of a certain type of dissolved organic matter, and the third node as the disinfectant dosage as an example, the flocculant dosage is grouped and counted according to an increase of 10%. In each group of data, the change in the removal rate of this type of dissolved organic matter at the second node and the change in the disinfectant dosage at the third node to achieve the same disinfection effect are calculated.

[0092] For example, in a statistical analysis of 1,000 data sets, 200 of them showed a 10% increase in flocculant dosage. Within these 200 data sets, it was found that the second node improved the removal rate of this type of dissolved organic matter by an average of 15%, while the third node required an average 20% increase in disinfectant dosage to achieve the same disinfection effect. Through this simple statistical counting and average calculation method, the impact of each node's treatment parameters was determined.

[0093] Then, a topological diagram is drawn based on these influence relationships. Nodes are used to represent each purification treatment node, such as the first flocculation and sedimentation node, the second dissolved organic matter treatment node, and the third disinfection node. Directed edges are used to represent the influence relationships between nodes. The edge from the first node to the second node is labeled "A 10% increase in flocculant dosage increases the dissolved organic matter removal rate by 15%." The edge from the first node to the third node is labeled "A 10% increase in flocculant dosage requires a 20% increase in disinfectant dosage to achieve the same disinfection effect." This generates an influence relationship topology, visually demonstrating the interrelationships between the treatment parameters of each node.

[0094] Finally, multiple influence relationship topologies are used to perform mutual influence correction on the initial treatment parameters of multiple nodes to generate multiple optimal treatment parameters for the nodes. For example, according to the influence relationship topology generated previously, after determining the pollutant distribution information and treatment volume information of the current tail water, multiple matching initial treatment parameters of the nodes are screened out, and the initial determination of the first node flocculant dosage is preset to be X, the second node removal rate of a certain type of dissolved organic matter is Y, and the third node disinfectant dosage is Z. However, according to the influence relationship topology, it can be seen that the change in the flocculant dosage of the first node will affect the treatment effect of the subsequent nodes. Therefore, these initial parameters are adjusted using the topological relationship, the flocculant dosage of the first node is appropriately reduced, and the second removal rate of a certain type of dissolved organic matter and the disinfectant dosage of the third node are adjusted accordingly. After multiple iterative calculations and optimizations, the optimal treatment parameters of multiple nodes that can achieve the best treatment effect for the entire purification treatment system are finally obtained, ensuring that the tail water can meet the geothermal tail water recharge standard after purification.

[0095] By generating an influence relationship topology and using it to perform mutual influence correction on the initial processing parameters of the nodes, the technical effect of generating optimal processing parameters for multiple nodes is achieved, improving the geothermal tail water purification effect and making it more in line with the reinjection standards.

[0096] Furthermore, step A500 of the method provided in the embodiment of the present application purifies the target geothermal tail water at multiple purification processing nodes using multiple node optimal processing parameters, and verifies the reinjection permission according to the geothermal tail water reinjection standard, including: A510: After the target geothermal tail water is purified, water quality testing is performed to generate water quality test results.

[0097] A520: Determine whether the water quality test results meet the geothermal tail water recharge standards. If so, the recharge permit verification is passed.

[0098] In one embodiment, the target geothermal tailwater is first purified by operating multiple purification nodes based on the optimal treatment parameters generated in step A433. For example, if the first purification node is the flocculation and sedimentation process, the optimal treatment parameters are used to determine the precise dosage of flocculant at 5 kg per cubic meter of tailwater, with a reaction time of 30 minutes. The second node is the filtration process, where the parameters are used to control the filtration rate at 10 cubic meters per hour. Through the synergistic effects of these nodes, the tailwater's various pollutant indicators gradually approach the recharge standard.

[0099] After the purification process is complete, water quality testing is immediately performed. Using professional water quality testing equipment and methods, the treated tailwater is thoroughly tested to obtain the water quality test results. For example, an atomic absorption spectrometer is used to measure the content of heavy metal ions in the tailwater, and a chemical oxygen demand (COD) meter is used to measure the content of organic pollutants. Suppose the treated tailwater is found to contain a heavy metal lead ion concentration of 0.05 mg / L and a COD value of 30 mg / L.

[0100] These water quality test results are then compared with the geothermal tailwater reinjection standards (see step A100 for specific standards). For example, if the reinjection standards for a particular geothermal reservoir require a lead ion concentration below 0.1 mg / L and a COD value below 50 mg / L, the reinjection permit verification will be passed because both the lead ion concentration and COD values ​​of the treated tailwater meet these standards. This closely linked series of steps, through precise control of purification parameters, rigorous water quality testing, and comparison with the reinjection standards, ensures that the treated geothermal tailwater meets reinjection requirements, enabling safe reinjection of the geothermal tailwater and protecting the environment and resources of the geothermal reservoir.

[0101] By testing the water quality of the target geothermal tail water after purification and comparing it with the reinjection standard, the technical effect of judging whether the treated tail water meets the reinjection requirements and ensuring the safe reinjection of geothermal tail water is achieved.

[0102] In summary, the tail water purification method for geothermal reinjection provided in the embodiments of the present application has the following technical effects: This application determines the geothermal tail water recharge standard of the underground geothermal reservoir, collects the tail water pollutant distribution information and tail water treatment volume information of the target geothermal tail water, determines the tail water purification treatment line containing multiple purification treatment nodes, optimizes the treatment parameters based on the above information to generate the optimal treatment parameters of multiple nodes, purifies the target geothermal tail water at multiple purification treatment nodes and verifies the recharge standard, and after the recharge permit verification is passed, the treated geothermal tail water is re-injected into the underground geothermal reservoir. In the process of collecting information, the detection index is determined based on the recharge standard, and multi-source water quality sensors are configured to perform multi-point data collection and fusion operations; when optimizing the processing parameters, historical data is mined to build a parameter optimization library, the initial processing parameters are screened and mutual influence correction is performed; in the purification and verification stages, water quality is tested after treatment and compared with the recharge standard to ensure the safe recharge of geothermal tail water, achieving the technical effect of accurately treating tail water according to the characteristics of geothermal tail water and recharge standard, effectively improving the tail water purification effect, ensuring that the treated tail water meets the recharge standard, and reducing the pollution risk to the underground geothermal reservoir.

[0103] The present invention also proposes a tail water purification system for geothermal recharge, which is used to implement a tail water purification method for geothermal recharge, such as Figure 2 As shown, it includes a recharge standard determination module 1, a tailwater information collection module 2, a treatment line determination module 3, a treatment parameter generation module 4, a recharge permission verification module 5 and a permission verification pass module 6: The reinjection standard determination module 1 is configured to determine the geothermal tail water reinjection standard of the underground geothermal reservoir; The tailwater information collection module 2 is configured to collect tailwater pollutant distribution information and tailwater treatment volume information of the target geothermal tailwater to be recharged; a processing line determination module 3, configured to determine a tailwater purification processing line, wherein the tailwater purification processing line includes a plurality of purification processing nodes; The processing parameter generation module 4 is configured to optimize the processing parameters of multiple purification processing nodes based on the tailwater pollutant distribution information and the tailwater treatment volume information, and generate the optimal processing parameters of the multiple nodes; The recharge permission verification module 5 is configured to purify the target geothermal tail water at multiple purification processing nodes using multiple node optimal processing parameters, and perform recharge permission verification based on the geothermal tail water recharge standard; The permit verification module 6 is configured to reinject the treated geothermal tail water into the underground geothermal reservoir through the reinjection well after the reinjection permit verification is passed.

[0104] Specifically, the tail water information collection module 2 is further configured to: determine multiple detection indicators based on the geothermal tail water recharge standard; configure a multi-source water quality sensor with multiple detection indicators; use the multi-source water quality sensor to collect multi-point data of the target geothermal tail water and then fuse them to generate tail water pollutant distribution information; collect the volume of the target geothermal tail water to generate the tail water treatment volume information; use the multi-source water quality sensor to collect multi-point data to generate a multi-point water quality monitoring data set; identify the uniformity of multi-point pollutant distribution on the multi-point water quality monitoring data set to generate a multi-point uniformity coefficient; determine whether the multi-point uniformity coefficient meets the preset uniformity coefficient threshold, and if so, perform multi-position mean calculation of various water quality sensor parameters based on the multi-point water quality monitoring data set to generate tail water pollutant distribution information; if not, generate a uniform optimization instruction; and send the uniform optimization instruction to the control terminal of the uniform mixing equipment for uniform mixing control.

[0105] The processing parameter generation module 4 is further configured to: perform historical processing data mining on multiple purification processing nodes and construct multiple parameter optimization libraries; based on the tailwater pollutant distribution information and the tailwater treatment volume information, screen multiple node initial processing parameters that match the tailwater characteristics in the multiple parameter optimization libraries; perform mutual influence correction on multiple purification processing nodes based on the multiple node initial processing parameters to generate multiple node optimal processing parameters; collect multiple historical purification monitoring data sets of multiple purification processing nodes; based on the multiple historical purification monitoring data sets, analyze the influence relationship of any node processing parameter on the pollutant indicators of other nodes except its own pollutant indicators, and generate multiple influence relationship topologies; perform mutual influence correction on the initial processing parameters of multiple nodes using multiple influence relationship topologies to generate multiple node optimal processing parameters.

[0106] The recharge permission verification module 5 is further configured to: perform water quality testing on the target geothermal tail water after purification treatment and generate water quality test results; determine whether the water quality test results meet the geothermal tail water recharge standards, and if so, the recharge permission verification is passed.

[0107] A tail water purification system for geothermal reinjection provided by an embodiment of the present invention can execute the tail water purification method for geothermal reinjection provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0108] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from all points of view, the embodiments should be regarded as illustrative and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and range of equivalents of the claims are included in the present invention. Any reference signs in the claims should not be construed as limiting the claim to which they relate.

[0109] In addition, it should be understood that although this specification describes the embodiments, not every embodiment contains only one independent technical solution. This description is for clarity only. Those skilled in the art should consider the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for the purpose of illustrating the technical concept of the present invention and cannot be used to limit the scope of protection of the present invention. Any changes made based on the technical solution in accordance with the technical concept proposed by the present invention fall within the scope of protection of the claims of the present invention.

Claims

1. A tail water purification method for geothermal recharge, characterized in that: The following steps are involved: Determine geothermal tailwater recharge standards for underground geothermal reservoirs; Collect tailwater pollutant distribution information and tailwater treatment volume information of the target geothermal tailwater to be recharged; Determining a tailwater purification treatment line, wherein the tailwater purification treatment line includes a plurality of purification treatment nodes; Based on the tailwater pollutant distribution information and the tailwater treatment volume information, optimizing the processing parameters of the multiple purification processing nodes to generate multiple optimal processing parameters for the nodes; Purifying the target geothermal tail water at the plurality of purification processing nodes using the plurality of node optimal processing parameters, and performing reinjection permit verification according to the geothermal tail water reinjection standard; After the reinjection permit is verified and passed, the treated geothermal tail water is reinjected into the underground geothermal reservoir through the reinjection well.

2. The tail water purification method for geothermal recharge according to claim 1, characterized in that: The collection of tailwater pollutant distribution information and tailwater treatment volume information of the target geothermal tailwater to be recharged includes: Determining multiple detection indicators based on the geothermal tailwater reinjection standard; Configuring a multi-source water quality sensor based on a plurality of the detection indicators; Based on the use of multi-source water quality sensors to collect multi-point data of the target geothermal tailwater, the tailwater pollutant distribution information is obtained by fusion; The volume of the target geothermal tail water is collected to generate the tail water treatment volume information.

3. The tail water purification method for geothermal recharge according to claim 2, characterized in that: The method is based on collecting multi-point data of the target geothermal tail water using multi-source water quality sensors and fusing the tail water pollutant distribution information to obtain the following information: Utilizing the multi-source water quality sensor to collect multi-point data and generate a multi-point water quality monitoring data set; Performing multi-point pollutant distribution uniformity identification on the multi-point water quality monitoring data set to generate a multi-point uniformity coefficient; Determine whether the multi-point uniformity coefficient meets the preset uniformity coefficient threshold. If the multi-point uniformity coefficient meets the preset uniformity coefficient threshold, perform multi-position mean calculation of various water quality sensing parameters based on the multi-point water quality monitoring data set to generate tail water pollutant distribution information; if not, generate a uniform optimization instruction, and send the uniform optimization instruction to the control terminal of the uniform mixing equipment for uniform mixing control.

4. The tail water purification method for geothermal recharge according to claim 3, characterized in that: Performing multi-point pollutant distribution uniformity identification on the multi-point water quality monitoring data set to generate a multi-point uniformity coefficient also includes: Based on the concentration data of each pollutant at different sampling points in the multi-point water quality monitoring data set, the mean and standard deviation are calculated; The coefficient of variation is calculated based on the standard deviation and mean, that is, the multi-point uniformity coefficient is obtained.

5. The tail water purification method for geothermal recharge according to claim 1, characterized in that: The step of optimizing the processing parameters of the plurality of purification processing nodes based on the tailwater pollutant distribution information and the tailwater treatment volume information to generate the optimal processing parameters of the plurality of nodes includes: Conduct historical processing data mining on multiple purification processing nodes and build multiple parameter optimization libraries; Based on the tailwater pollutant distribution information and the tailwater treatment volume information, screening a plurality of node initial treatment parameters that match the tailwater characteristics in the plurality of parameter optimization libraries; Mutual influence correction of the plurality of purification processing nodes is performed based on the plurality of node initial processing parameters to generate the plurality of node optimal processing parameters.

6. The tail water purification method for geothermal recharge according to claim 5, characterized in that: Conduct historical processing data mining on multiple purification processing nodes and build multiple parameter optimization libraries, including: Comprehensively collect historical data of multiple purification treatment nodes in past geothermal tailwater purification treatment; Clean the collected historical data to obtain preprocessed data; The association rules are mined on the pre-processed data using the Apriori algorithm to obtain the association rules; The mined association rules are sorted and classified, and multiple parameter optimization libraries are constructed.

7. The tail water purification method for geothermal recharge according to claim 5, characterized in that: Performing mutual influence correction of the plurality of purification processing nodes based on the plurality of initial processing parameters of the nodes to generate optimal processing parameters of the plurality of nodes includes: Collecting a plurality of historical purification monitoring data sets of the plurality of purification processing nodes; Based on the multiple historical purification monitoring data sets, analyzing the influence relationship of any node processing parameter on the pollutant indicators of other nodes except its own pollutant indicator, and generating multiple influence relationship topologies; Mutual influence correction is performed on the initial processing parameters of the multiple nodes using the multiple influence relationship topologies to generate optimal processing parameters of the multiple nodes.

8. The tail water purification method for geothermal recharge according to claim 1, characterized in that: Purifying the target geothermal tail water at the plurality of purification processing nodes using the plurality of node optimal processing parameters, and performing reinjection permit verification according to the geothermal tail water reinjection standard, includes: Based on the optimal treatment parameters, multiple purification treatment nodes are operated to purify the target geothermal tail water; Detect the purified target geothermal tail water and obtain water quality test results; Determine whether the water quality test result meets the geothermal tail water reinjection standard and obtain a judgment result.

9. The tail water purification method for geothermal recharge according to claim 1, characterized in that: After the reinjection permit verification is passed, the treated geothermal tail water is reinjected into the underground geothermal reservoir through the reinjection well, including: Determine the location and parameters of the recharge well based on the geological survey data of the site; Arrange reinjection equipment and a pipeline system at the determined location of the reinjection well based on the parameters of the reinjection well, and perform reinjection using the reinjection equipment and the pipeline system; After the reinjection is completed, the reinjection effect is monitored over a long period of time.

10. A tail water purification system for geothermal recharge, used for implementing the tail water purification method for geothermal recharge according to any one of claims 1 to 9, characterized in that: include: a reinjection standard determination module configured to determine a geothermal tailwater reinjection standard for an underground geothermal reservoir; a tailwater information collection module configured to collect tailwater pollutant distribution information and tailwater treatment volume information of target geothermal tailwater; a processing line determination module, configured to determine a tailwater purification processing line, wherein the tailwater purification processing line includes a plurality of purification processing nodes; A processing parameter generation module is configured to optimize the processing parameters of multiple purification processing nodes based on tailwater pollutant distribution information and tailwater treatment volume information, and generate optimal processing parameters for multiple nodes; a recharge permission verification module configured to purify target geothermal tail water at multiple purification processing nodes using multiple node optimal processing parameters, and to verify the recharge permission using geothermal tail water recharge standards; The permit verification pass module is configured to reinject the treated geothermal tail water into the underground geothermal reservoir through the reinjection well after the reinjection permit verification is passed.

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