Mine wastewater softening and recycling method and device, electronic equipment and readable medium

Abstract

The embodiment of the invention discloses a mine wastewater softening and recycling method and device, electronic equipment and a readable medium. A specific embodiment of the method comprises the following steps: acquiring a wastewater quality data set and a wastewater component data set based on a wastewater data acquisition equipment group; based on the wastewater component data set, determining at least one chemical equilibrium data corresponding to the mine wastewater to obtain a chemical equilibrium data set; based on the chemical equilibrium data set, generating first medicament delivery information; performing fuzzification processing on each piece of wastewater quality data in the wastewater quality data set to generate a fuzzy putting rule base; on the basis of a fuzzy delivery rule base, correcting the first medicament delivery information to generate corrected medicament delivery information; generating a medicament putting instruction corresponding to the corrected medicament putting information, and controlling an associated medicament putting device to execute medicament putting operation corresponding to the medicament putting instruction. According to the embodiment, membrane pollution is avoided, and the situation of membrane resource waste is avoided.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, and more specifically to a method, apparatus, electronic device, and readable medium for softening and reusing mine wastewater. Background Technology

[0002] Mine wastewater softening is a crucial step in treating highly mineralized mine water, affecting not only water reuse efficiency but also the operational stability and economic costs of the treatment system. Currently, the common method for softening mine wastewater is through chemical reactions such as the double-alkali process, which causes calcium and magnesium ions to precipitate and be removed, thus achieving the goal of softening and reusing the mine wastewater.

[0003] However, when using the above methods to soften mine wastewater, the following technical problems often arise: When calcium and magnesium ions are removed by chemical reactions such as the double alkali method to form precipitates, incomplete reactions can lead to the precipitates redissolving or forming overly fine, non-settling particles that can deposit or crystallize on the membrane surface, causing membrane fouling and wasting membrane resources.

[0004] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of this disclosure provide methods, apparatus, electronic devices, and computer-readable media for softening and reusing mine wastewater to address one or more of the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide a method for softening and reusing mine wastewater. The method includes: in response to detecting a discharge operation targeting mine wastewater, acquiring a wastewater quality dataset and a wastewater composition dataset based on a wastewater data acquisition device group; determining at least one chemical equilibrium data corresponding to the mine wastewater based on the wastewater composition dataset and wastewater quality dataset, obtaining a chemical equilibrium dataset; generating first reagent dosing information based on the chemical equilibrium dataset, wherein the first reagent dosing information represents the dosage of a reagent used to react with free ions corresponding to each wastewater composition data; performing fuzzification processing on the chemical equilibrium data of each wastewater quality data in the wastewater quality dataset and chemical equilibrium dataset to generate a fuzzy dosing rule base; performing correction processing on the first reagent dosing information based on the fuzzy dosing rule base to generate corrected reagent dosing information, wherein the corrected reagent dosing information represents the corrected dosage of each reagent; generating a reagent dosing instruction corresponding to the corrected reagent dosing information; and controlling an associated reagent dosing device to execute the reagent dosing operation corresponding to the reagent dosing instruction.

[0008] Secondly, some embodiments of this disclosure provide a mine wastewater softening and reuse device, the device comprising: an acquisition unit configured to acquire a wastewater quality dataset and a wastewater composition dataset based on a wastewater data acquisition device group in response to monitoring a discharge operation of mine wastewater; a determination unit configured to determine at least one chemical equilibrium data corresponding to the mine wastewater based on the aforementioned wastewater composition dataset, thereby obtaining a chemical equilibrium dataset; and a generation unit configured to generate first reagent dosing information based on the aforementioned chemical equilibrium dataset, wherein the first reagent dosing information represents the dosing of reagents corresponding to each wastewater composition data. The system includes: a dosage of reagents for reacting with free ions; a fuzzification unit configured to fuzzify each wastewater quality data in the aforementioned wastewater quality dataset to generate a fuzzy dosing rule base; a correction unit configured to correct the first reagent dosing information based on the aforementioned fuzzy dosing rule base to generate corrected reagent dosing information, wherein the corrected reagent dosing information characterizes the corrected dosage of each reagent; and a control unit configured to generate reagent dosing instructions corresponding to the corrected reagent dosing information and control associated reagent dosing devices to execute the reagent dosing operation corresponding to the reagent dosing instructions.

[0009] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0010] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0011] The above embodiments of this disclosure have the following beneficial effects: the mine wastewater softening and reuse method of some embodiments of this disclosure avoids membrane fouling and waste of membrane resources. Specifically, the causes of membrane fouling and waste of membrane resources are: when removing calcium and magnesium ions by chemical reactions such as the double alkali method, due to incomplete reactions, the precipitate redissolves or forms excessively fine, non-settling particles that deposit or crystallize on the membrane surface, leading to membrane fouling and thus waste of membrane resources. Based on this, the mine wastewater softening and reuse method of some embodiments of this disclosure firstly, in response to the detection of a discharge operation of mine wastewater, acquires a wastewater quality dataset and a wastewater composition dataset based on a wastewater data acquisition device group. Thus, water quality data and composition data of the mine wastewater can be obtained. Secondly, based on the aforementioned wastewater composition dataset, at least one chemical equilibrium data corresponding to the mine wastewater is determined, resulting in a chemical equilibrium dataset. Thus, the chemical equilibrium data of the wastewater can be determined. Then, based on the aforementioned chemical equilibrium dataset, first reagent dosing information is generated. Thus, initial reagent dosing information can be generated. Next, the wastewater quality data in the aforementioned wastewater quality dataset are fuzzified to generate a fuzzy dosing rule base. This allows for the generation of fuzzy rules corresponding to the wastewater quality data. Then, based on this fuzzy dosing rule base, the initial reagent dosing information is corrected to generate corrected reagent dosing information. This allows for the correction of the initial reagent dosing information using fuzzy rules, resulting in accurate reagent dosing data. Finally, a reagent dosing instruction corresponding to the corrected reagent dosing information is generated, and the associated reagent dosing device is controlled to execute the corresponding reagent dosing operation. This ensures complete chemical precipitation, preventing the precipitate from redissolving or forming excessively fine, non-settling particles, thus avoiding membrane fouling and wasting membrane resources. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0013] Figure 1 This is a flowchart of some embodiments of the mine wastewater softening and reuse method according to the present disclosure; Figure 2These are schematic diagrams of some embodiments of the mine wastewater softening and reuse device according to this disclosure; Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure; Figure 4 This is a schematic diagram of equipment deployment according to some embodiments of the mine wastewater softening and reuse method disclosed herein. Detailed Implementation

[0014] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0015] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0016] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0017] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0018] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0019] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Figure 1 A flow chart 100 of some embodiments of a mine wastewater softening and reuse method according to the present disclosure is shown. The mine wastewater softening and reuse method includes the following steps: Step 101: In response to the detection of a discharge operation targeting mine wastewater, acquire wastewater quality dataset and wastewater composition dataset based on the wastewater data acquisition equipment group.

[0021] In some embodiments, the entity executing the mine wastewater softening and reuse method (e.g., a server) can, in response to monitoring a discharge operation of mine wastewater, acquire a wastewater quality dataset and a wastewater composition dataset based on a wastewater data acquisition device group. The wastewater data acquisition devices in the aforementioned wastewater data acquisition device group may include: ion-selective electrodes, pH meters, samplers, turbidimeters, and conductivity meters. The wastewater quality data in the aforementioned wastewater quality dataset can characterize the water quality of the mine wastewater. The aforementioned wastewater quality data may include, but is not limited to, pH, conductivity, and turbidity. The wastewater composition data in the aforementioned wastewater composition dataset can characterize various free ions in the mine wastewater. As an example, the aforementioned wastewater composition data may include, but is not limited to, calcium ion, magnesium ion, and carbonate / bicarbonate electrodes.

[0022] In practice, in response to monitored discharge operations of mine wastewater, the following steps can be taken to acquire wastewater quality and composition datasets based on a wastewater data acquisition device group: The first step is to acquire the water flow rate and level value of the target mine in real time based on preset flow meters and level switches. These preset flow meters and level switches can be flow meters and level switches pre-installed in the wastewater discharge pipes of the target frame.

[0023] The second step involves controlling each wastewater data acquisition device in the wastewater data acquisition device group to collect wastewater data from the target mine, in response to the aforementioned water flow rate and liquid level meeting preset discharge conditions. The preset discharge conditions can be pre-defined wastewater discharge conditions. For example, the preset discharge conditions could be that the water flow rate is greater than or equal to a preset threshold and the liquid level exceeds a set value.

[0024] The third step involves preprocessing the wastewater data in the collected wastewater dataset to generate a preprocessed wastewater dataset. This preprocessing may include, but is not limited to, outlier removal and missing value compensation. Outlier removal can be performed by testing 10 consecutive sampling points from the sensor using the Grubbs criterion; sampling points with deviations from the mean exceeding three standard deviations are removed. Missing value compensation can be achieved by using a weighted moving average method to compensate for the removed sampling points.

[0025] The fourth step is to perform time alignment on the pretreated wastewater dataset to generate a wastewater quality dataset. In practice, the transit time of water flow in the wastewater discharge pipeline can be determined, and the data can be time-shifted and aligned.

[0026] The fifth step involves controlling the sampler to sample the mine wastewater and controlling the associated component analysis equipment to perform component analysis on the sampled mine wastewater in order to generate a wastewater component dataset.

[0027] In practice, the above pretreated wastewater dataset can be time-aligned using the following sub-steps to generate a wastewater quality dataset: The first sub-step involves selecting pretreated wastewater data that meets preset conditions from the aforementioned pretreated wastewater dataset as target data. These preset conditions may include selecting pretreated wastewater data with stable water quality parameters and sensitivity to reagent addition or mixing reactions. For example, the target data could be conductivity.

[0028] The second sub-step is to determine the cross-correlation function corresponding to the target data.

[0029] The third sub-step involves determining the wastewater flow transmission time based on the aforementioned cross-correlation function. In practice, this can be achieved by iterating through the parameters representing the delay time in the cross-correlation function and determining the delay time corresponding to the maximum value of the cross-correlation function, which can then be used as the wastewater flow transmission time.

[0030] The fourth sub-step involves performing time alignment processing on the pretreated wastewater dataset based on the aforementioned wastewater flow transmission time to generate a wastewater quality dataset.

[0031] Step 102: Based on the wastewater composition dataset, determine at least one chemical equilibrium data corresponding to the mine wastewater to obtain a chemical equilibrium dataset.

[0032] In some embodiments, the aforementioned executing entity may determine at least one chemical equilibrium data corresponding to the mine wastewater based on the aforementioned wastewater composition dataset, thereby obtaining a chemical equilibrium dataset.

[0033] In practice, the chemical equilibrium dataset can be obtained by determining at least one chemical equilibrium data point for mine wastewater based on the wastewater composition dataset through the following steps: The first step is to determine at least one complexation reaction formula corresponding to the target mine. In practice, firstly, this can be achieved by identifying the ions in the mine wastewater that need to be treated, as the target ions. Secondly, the complexation reaction formula for the target ions is determined. As an example, the complexation reaction formula could be Ca²⁺. + +SO4² - CaSO4(aq).

[0034] The second step involves generating the ion concentration of each free ion in the mine wastewater based on the aforementioned wastewater composition dataset and at least one complexation reaction formula, serving as a chemical equilibrium dataset. In practice, the ion concentration of each free ion is determined using the Newton-Raphson iteration method based on the mass conservation equation and the chemical equilibrium equation set. As an example, the aforementioned free ions may include, but are not limited to, Ca²⁺. + Mg²+ CO3² - OH - .

[0035] Step 103: Generate the first agent dosing information based on the chemical equilibrium dataset.

[0036] In some embodiments, the executing entity may generate first reagent dosing information based on the aforementioned chemical equilibrium dataset. This first reagent dosing information represents the dosage of the reagent used to react with the free ions corresponding to each wastewater component data.

[0037] In addressing the technical problems mentioned in the background section, and considering the application scenario, the use of chemical precipitation methods such as micro-sand flocculation and heavy medium rapid sedimentation presents the following challenges: competing ions in the wastewater may affect the effectiveness of coagulants (such as PAC and PFC) and flocculants (such as PAM); some free ions may interfere with floc formation or alter their settling characteristics, thus preventing complete softening of the mine wastewater. Given the specific requirements of this application scenario—the complete removal of target ions—we have decided to adopt the following solution: In some optional implementations of certain embodiments, the first reagent dosing information can be generated based on a chemical equilibrium dataset through the following steps: The first step, for each chemical equilibrium data point in the above chemical equilibrium dataset, is to perform the following generation steps: The first generation step involves determining the target concentration value corresponding to the aforementioned chemical equilibrium data. This target concentration value can be a pre-defined concentration of a specific free ion in the treated mine wastewater.

[0038] The second generation step involves generating at least one reaction pathway corresponding to the aforementioned chemical equilibrium data, resulting in a set of reaction pathways. These reaction pathways can be chemical reaction pathways that a particular free ion can undertake. For example, in response to the aforementioned free ion being Ca²⁺… + Therefore, the above reaction pathway set may include Ca² + +CO3² - →CaCO3 and Ca² + +2OH - →Ca(OH)2.

[0039] The third generation step involves determining the competing ions corresponding to the chemical equilibrium data based on the aforementioned reaction pathway set, thus obtaining a set of competing ions.

[0040] As an example, the aforementioned mine wastewater also contains Mg²⁺. + , while Mg² + It can also be used with CO3²- Precipitation occurs, therefore, Mg²⁺ can be precipitated. + It was identified as a competing ion.

[0041] The fourth step involves generating a reaction efficiency factor corresponding to the aforementioned chemical equilibrium data, based on the aforementioned competing ion set. In practice, the reaction efficiency factor corresponding to the aforementioned chemical equilibrium data can be generated using the Tilly modulus and the first-order irreversible reaction formula.

[0042] The fifth generation step involves determining the loss factor for each competing ion in the aforementioned competitive ion set. This loss factor can be determined using a fitting algorithm. For example, the fitting algorithm may include, but is not limited to, particle swarm optimization and hybrid genetic algorithms.

[0043] The sixth step involves constructing a chemical optimization equation based on the aforementioned reaction efficiency factor and the determined loss factors. In practice, the chemical optimization equation can be constructed with the goal of minimizing the total reagent cost.

[0044] The seventh step involves generating the corresponding water quality constraints based on the target concentration value. In practice, a pre-defined set of constraint rules can be used to determine the various water quality data ranges for the target concentration value, which can then be used as ion constraint conditions.

[0045] The eighth step involves solving the chemical optimization equation based on the objective optimization algorithm and the aforementioned water quality constraints to generate reagent dosing information. Here, a nonlinear optimization algorithm can be used to search for dosage combinations under the constraints, serving as the reagent dosing information. This nonlinear optimization algorithm can be a sequential quadratic programming algorithm.

[0046] The second step is to combine the generated drug delivery information into the first drug delivery information.

[0047] The first and second steps described above, as an inventive point of this disclosure, combined with step "106" below, solve the technical problem that "competing ions in wastewater may affect the effectiveness of coagulants and flocculants, and some free ions may interfere with the formation of flocs or change their settling characteristics, thus making it impossible to completely soften mine wastewater." The reasons for the inability to completely soften mine wastewater are as follows: competing ions in wastewater may affect the effectiveness of coagulants and flocculants, and some free ions may interfere with the formation of flocs or change their settling characteristics, thus making it impossible to completely soften mine wastewater. If the above factors are resolved, the effect of completely softening mine wastewater can be achieved. To achieve this effect, this disclosure, firstly, for each chemical equilibrium data in the above chemical equilibrium dataset, performs the following generation steps: First, determine the target concentration value corresponding to the above chemical equilibrium data. This allows the determination of the target ion concentration, providing a data basis for subsequent generation of reagent dosing information. Second, generate at least one reaction path corresponding to the above chemical equilibrium data, obtaining a reaction path set. This allows the determination of each reaction path for free ions. Third, based on the aforementioned reaction pathway set, the competing ions corresponding to the aforementioned chemical equilibrium data are determined, resulting in a set of competing ions. This allows for the identification of ions that compete with the aforementioned free ions for the reaction. Fourth, based on the aforementioned set of competing ions, a reaction efficiency factor corresponding to the aforementioned chemical equilibrium data is generated; for each competing ion in the aforementioned set of competing ions, a loss factor corresponding to that ion is determined; based on the aforementioned reaction efficiency factor and the determined loss factors, a chemical optimization equation is constructed; using a target optimization algorithm, the aforementioned chemical optimization equation is solved to generate reagent dosing information. This allows for the generation of reagent dosing information corresponding to the aforementioned free ions. Finally, the generated reagent dosing information is combined into first reagent dosing information. This allows for the determination of accurate reagent dosing data. Combined with step "Step 106" below, a reagent dosing instruction corresponding to the aforementioned corrected reagent dosing information is generated, and the associated reagent dosing device is controlled to execute the reagent dosing operation corresponding to the aforementioned reagent dosing instruction. This allows for the accurate dosing of chemical reagents for the reaction, thereby enabling complete softening of mine wastewater.

[0048] Step 104: Fuzzify each wastewater quality data in the wastewater quality dataset to generate a fuzzy application rule base.

[0049] In some embodiments, the aforementioned execution entity may perform fuzzification processing on each wastewater quality data in the aforementioned wastewater quality dataset to generate a fuzzy delivery rule base.

[0050] In practice, the following steps can be used to fuzzify the individual chemical equilibrium data in the chemical equilibrium dataset to generate a fuzzy application rule base: The first step is to generate an initial rule base. This initial rule base includes at least one initial rule. This initial rule can be a pre-defined chemical reaction principle.

[0051] The second step is to perform fuzzification processing on each wastewater quality data point in the aforementioned wastewater quality dataset to generate a fuzzy dataset. In practice, for each wastewater quality data point in the dataset, a preset number of fuzzy ranges can be configured. The corresponding fuzzy range is determined based on the data values ​​of the wastewater quality data.

[0052] The third step is to update each initial rule in the initial rule base based on the fuzzy dataset to generate updated initial rules, thus obtaining the fuzzy delivery rule base.

[0053] Step 105: Based on the fuzzy dosing rule base, the first drug dosing information is corrected to generate corrected drug dosing information.

[0054] In some embodiments, the executing entity can perform correction processing on the first drug delivery information based on the aforementioned fuzzy delivery rule base to generate corrected drug delivery information. In practice, firstly, an adjustment factor corresponding to each drug delivery information in the first drug delivery information can be generated using a fuzzy inference algorithm. The fuzzy inference algorithm can be a TSK (Takagi-Sugeno-Kang) fuzzy system. Secondly, based on the aforementioned adjustment factors, the first drug delivery information is corrected using a weighted average formula to generate corrected drug delivery information. The corrected drug delivery information represents the corrected delivery dosage of each drug.

[0055] Step 106: Generate a drug dispensing instruction corresponding to the calibration drug dispensing information, and control the associated drug dispensing device to execute the drug dispensing operation corresponding to the drug dispensing instruction.

[0056] In some embodiments, the aforementioned executing entity can generate a drug dispensing instruction corresponding to the aforementioned calibration drug dispensing information, and control an associated drug dispensing device to execute the drug dispensing operation corresponding to the aforementioned drug dispensing instruction. For example... Figure 4 The diagram shown illustrates the equipment deployment of some embodiments of the mine wastewater softening and reuse method of this disclosure. Figure 4 As can be seen, in some embodiments of this disclosure, wastewater from the wastewater pool flows out through a pipeline, and wastewater quality data and wastewater composition data are collected by the wastewater data acquisition equipment group in the wastewater data acquisition equipment group and sent to the server. The server generates a reagent dosing instruction through the above steps 101-106 and controls the reagent dosing device to dosing the reagent into the sedimentation tank through the pipeline in order to soften and recycle the mine wastewater.

[0057] In addressing the technical problems mentioned in the background section, and considering the application scenario of softening mine wastewater in a mine shaft, the following technical issues arise: the inability to monitor the status of the reagent dosing device and pipelines in a timely manner within the mine, and issues such as valve jamming and pipe scaling leading to discrepancies between the actual reagent dosage and the command, resulting in incomplete wastewater softening. To address the specific requirements of this application scenario, including precise reagent dosing, we have decided to adopt the following solution: In some optional implementations of certain embodiments, the aforementioned execution entity may generate a drug delivery instruction corresponding to the correction drug delivery information and control the associated drug delivery device to execute the drug delivery operation corresponding to the drug delivery instruction through the following steps: The first step is to determine the water flow transmission delay value based on the water flow transmission simulation model. This simulation model can be used to simulate the flow of chemicals in a pipeline. For example, it can be a pipeline hydraulic model. Here, the time from the start of chemical administration to the addition of chemicals to the mine wastewater can be simulated using this simulation model and used as the water flow transmission delay value.

[0058] The second step involves transforming the above-mentioned corrective agent dosing information based on the model predictive control algorithm, the aforementioned wastewater quality dataset, and the aforementioned water flow transmission delay value to generate a dosing instruction. In practice, the water flow transmission delay value can be discretized into time-varying steps, constraints can be generated using the model predictive control algorithm, optimization can be performed using the interior point method, and a simulated signal or pulse frequency can be generated as the dosing instruction.

[0059] The third step is to control the associated drug dispensing device to execute the drug dispensing operation corresponding to the above-mentioned drug dispensing command.

[0060] The fourth step is to control the pre-installed current sensor to collect the dispensing current of the above-mentioned drug dispensing device.

[0061] In practice, the pre-installed current sensor can be controlled through the following sub-steps to collect the dispensing current of the above-mentioned drug dispensing device: The first sub-step involves oversampling the current at a preset frequency to generate an oversampled current.

[0062] The second sub-step involves down-converting the oversampled current using a digital decimation filter to generate the applied current. This improves the current signal-to-noise ratio and effective resolution.

[0063] Fifth, based on the Kalman filter algorithm, determine at least one device status information of the aforementioned drug delivery device to obtain a device status information set. The device status information in this set can be information characterizing the status of the drug delivery device. This device status information may include, but is not limited to, diaphragm displacement and outlet pressure.

[0064] Step 6: Based on the device status information in the aforementioned device status information set, generate at least one device anomaly information corresponding to the aforementioned drug delivery device. In practice, device anomaly information corresponding to any preset anomaly condition in the preset anomaly condition set can be generated in response to the aforementioned device status information satisfying any preset anomaly condition. As an example, in response to the diaphragm displacement being greater than or equal to a preset threshold, device anomaly information indicating blockage of the drug delivery device can be generated. Here, the aforementioned device anomaly information can also indicate wear of the drug delivery pipeline.

[0065] Step 7: For each of the at least one device malfunction messages mentioned above, execute the corresponding malfunction recovery operation. In practice, for each device malfunction message, a corresponding malfunction recovery operation can be executed. As an example, in response to the device malfunction message indicating device blockage, an associated mobile robot can be controlled to replace the drug delivery device.

[0066] Steps one through seven above, as an inventive point of this disclosure, solve the technical problem: "Because the status of the agent dosing device and pipeline cannot be observed in a timely manner in the mine, and due to valve jamming, pipeline scaling, etc., the actual agent dosage does not match the instruction, thus making it impossible to completely soften the wastewater." The reasons for the inability to completely soften the mine wastewater are as follows: Because the status of the agent dosing device and pipeline cannot be observed in a timely manner in the mine, and due to valve jamming, pipeline scaling, etc., the actual agent dosage does not match the instruction, thus making it impossible to completely soften the wastewater. If the above factors are solved, the effect of completely softening the mine wastewater can be achieved. To achieve this effect, this disclosure firstly determines the water flow transmission delay value based on a water flow transmission simulation model. This allows the determination of the time delay of agent transmission in the pipeline. Secondly, based on the model predictive control algorithm, the aforementioned wastewater quality dataset, and the aforementioned water flow transmission delay value, the aforementioned corrected agent dosing information is converted to generate an agent dosing instruction. This allows the generation of an agent dosing instruction according to the transmission time delay. Third, control the associated reagent dispensing device to execute the reagent dispensing operation corresponding to the above-mentioned reagent dispensing command; control the pre-installed current sensor to collect the dispensing current of the above-mentioned reagent dispensing device. This allows for real-time acquisition of the device's current status during reagent dispensing. Fourth, based on the Kalman filter algorithm, determine at least one device status information of the above-mentioned reagent dispensing device to obtain a device status information set. This allows for determination of the device's status information through the current status. Fifth, based on the device status information in the above-mentioned device status information set, generate at least one device abnormality information corresponding to the above-mentioned reagent dispensing device; for each of the at least one device abnormality information, execute the corresponding abnormality recovery operation. This allows for the execution of the corresponding abnormality recovery operation when a device abnormality is detected, thereby avoiding situations where the actual reagent dispensing amount does not match the command due to valve jamming, pipeline scaling, etc., resulting in the inability to completely soften the mine wastewater.

[0067] The above embodiments of this disclosure have the following beneficial effects: the mine wastewater softening and reuse method of some embodiments of this disclosure avoids membrane fouling and waste of membrane resources. Specifically, the causes of membrane fouling and waste of membrane resources are: when removing calcium and magnesium ions by chemical reactions such as the double alkali method, due to incomplete reactions, the precipitate redissolves or forms excessively fine, non-settling particles that deposit or crystallize on the membrane surface, leading to membrane fouling and thus waste of membrane resources. Based on this, the mine wastewater softening and reuse method of some embodiments of this disclosure firstly, in response to the detection of a discharge operation of mine wastewater, acquires a wastewater quality dataset and a wastewater composition dataset based on a wastewater data acquisition device group. Thus, water quality data and composition data of the mine wastewater can be obtained. Secondly, based on the aforementioned wastewater composition dataset, at least one chemical equilibrium data corresponding to the mine wastewater is determined, resulting in a chemical equilibrium dataset. Thus, the chemical equilibrium data of the wastewater can be determined. Then, based on the aforementioned chemical equilibrium dataset, first reagent dosing information is generated. Thus, initial reagent dosing information can be generated. Next, the wastewater quality data in the aforementioned wastewater quality dataset are fuzzified to generate a fuzzy dosing rule base. This allows for the generation of fuzzy rules corresponding to the wastewater quality data. Then, based on this fuzzy dosing rule base, the initial reagent dosing information is corrected to generate corrected reagent dosing information. This allows for the correction of the initial reagent dosing information using fuzzy rules, resulting in accurate reagent dosing data. Finally, a reagent dosing instruction corresponding to the corrected reagent dosing information is generated, and the associated reagent dosing device is controlled to execute the corresponding reagent dosing operation. This ensures complete chemical precipitation, preventing the precipitate from redissolving or forming excessively fine, non-settling particles, thus avoiding membrane fouling and wasting membrane resources.

[0068] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a mine wastewater softening and reuse device, which are similar to... Figure 1 Corresponding to the method embodiments shown, this mine wastewater softening and reuse device can be specifically applied to various electronic devices.

[0069] like Figure 2As shown, a mine wastewater softening and reuse device 200 in some embodiments includes: an acquisition unit 201, a determination unit 202, a generation unit 203, a fuzzification unit 204, a correction unit 205, and a control unit 206. The acquisition unit 201 is configured to acquire a wastewater quality dataset and a wastewater composition dataset based on a wastewater data acquisition device group in response to monitoring a discharge operation of mine wastewater; the determination unit 202 is configured to determine at least one chemical equilibrium data corresponding to the mine wastewater based on the aforementioned wastewater composition dataset, thereby obtaining a chemical equilibrium dataset; the generation unit 203 is configured to generate first reagent dosing information based on the aforementioned chemical equilibrium dataset, wherein the first reagent dosing information characterizes the dosage of the reagent used to react with the free ions corresponding to each wastewater composition data; the fuzzification unit 204, the correction unit 205, and the control unit 206. The fuzzing unit 204 is configured to fuzzify each wastewater quality data in the aforementioned wastewater quality dataset to generate a fuzzy dosing rule base; the correction unit 205 is configured to correct the aforementioned first agent dosing information based on the aforementioned fuzzy dosing rule base to generate corrected agent dosing information, wherein the corrected agent dosing information represents the corrected dosage of each agent; the control unit 206 is configured to generate agent dosing instructions corresponding to the aforementioned corrected agent dosing information, and to control the associated agent dosing device to execute the agent dosing operation corresponding to the aforementioned agent dosing instructions.

[0070] It is understandable that the units described in the mine wastewater softening and reuse device 200 are similar to those in the reference. Figure 1 The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the mine wastewater softening and reuse device 200 and the units contained therein, and will not be repeated here.

[0071] The following is for reference. Figure 3 This document illustrates a structural schematic of an electronic device 300 suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0072] like Figure 3As shown, the electronic device 300 may include a processing unit 301 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0073] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0074] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0075] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0076] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0077] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: in response to detecting a discharge operation of mine wastewater, acquire a wastewater quality dataset and a wastewater composition dataset based on a wastewater data acquisition device group; determine at least one chemical equilibrium data corresponding to the mine wastewater based on the aforementioned wastewater composition dataset, obtaining a chemical equilibrium dataset; generate first reagent dosing information based on the aforementioned chemical equilibrium dataset, wherein the first reagent dosing information represents the dosage of the reagent used to react with the free ions corresponding to each wastewater composition data; perform fuzzification processing on each wastewater quality data in the aforementioned wastewater quality dataset to generate a fuzzy dosing rule base; and perform correction processing on the aforementioned first reagent dosing information based on the aforementioned fuzzy dosing rule base to generate corrected reagent dosing information, wherein the corrected reagent dosing information represents the corrected dosage of each reagent. Generate a drug dispensing instruction corresponding to the above-mentioned correction drug dispensing information, and control the associated drug dispensing device to execute the drug dispensing operation corresponding to the above-mentioned drug dispensing instruction.

[0078] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0079] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0080] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit, a determination unit, a generation unit, a fuzzification unit, a correction unit, and a control unit. The names of these units do not necessarily limit the specific unit itself; for example, the acquisition unit may also be described as "a unit that acquires a wastewater quality dataset based on a wastewater data acquisition device group in response to monitoring a discharge operation of mine wastewater."

[0081] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0082] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for softening and reusing mine wastewater, comprising: In response to the detection of operations involving the discharge of mine wastewater, wastewater quality datasets and wastewater composition datasets are acquired based on the wastewater data acquisition equipment group; Based on the wastewater composition dataset, at least one chemical equilibrium data point corresponding to the mine wastewater is determined to obtain a chemical equilibrium dataset. Based on the chemical equilibrium dataset, first reagent dosing information is generated, wherein the first reagent dosing information represents the dosage of the reagent used to react with the free ions corresponding to each wastewater component data. The wastewater quality data in the wastewater quality dataset are fuzzified to generate a fuzzy application rule base. Based on the fuzzy dosing rule base, the first drug dosing information is corrected to generate corrected drug dosing information, wherein the corrected drug dosing information represents the dosage of each drug after correction; Generate a drug dispensing instruction corresponding to the correction drug dispensing information, and control the associated drug dispensing device to execute the drug dispensing operation corresponding to the drug dispensing instruction.

2. The method according to claim 1, wherein, The wastewater data acquisition equipment in the wastewater data acquisition equipment group includes: ion-selective electrode, pH meter, sampler, turbidimeter and conductivity meter.

3. The method according to claim 1, wherein, The step of determining at least one chemical equilibrium data point corresponding to the mine wastewater based on the wastewater composition dataset, to obtain the chemical equilibrium dataset, includes: Determine at least one complexation reaction formula corresponding to the target mine; Based on the wastewater composition dataset and the at least one complexation reaction formula, the ion concentration of each free ion in the mine wastewater is generated as a chemical equilibrium dataset.

4. The method according to claim 1, wherein, The step of fuzzifying each wastewater quality data point in the wastewater quality dataset to generate a fuzzy application rule base includes: Generate an initial rule base, wherein the initial rule base includes at least one initial rule; The wastewater quality data in the wastewater quality dataset are fuzzified to generate a fuzzy dataset. Based on the fuzzy dataset, each initial rule in the initial rule base is updated to generate updated initial rules, thus obtaining the fuzzy delivery rule base.

5. The method according to claim 1, wherein, In response to the detection of a discharge operation targeting mine wastewater, the wastewater data acquisition equipment group acquires a wastewater quality dataset and a wastewater composition dataset, including: Based on preset flow meters and level switches, the water flow rate and level value of the target mine are obtained in real time. In response to the water flow rate and the liquid level value meeting the preset discharge conditions, each wastewater data acquisition device in the wastewater data acquisition device group is controlled to collect wastewater data from the target mine to obtain the collected wastewater dataset. Data preprocessing is performed on each piece of wastewater data in the collected wastewater dataset to generate a preprocessed wastewater dataset; The pretreated wastewater dataset is time-aligned to generate a wastewater quality dataset. The system controls a sampler to sample mine wastewater and controls associated component analysis equipment to perform component analysis on the sampled mine wastewater to generate a wastewater component dataset. The step of performing time alignment processing on the pretreated wastewater dataset to generate a wastewater quality dataset includes: Select pretreated wastewater data that meets preset conditions from the pretreated wastewater dataset as target data; Determine the cross-correlation function corresponding to the target data; Based on the cross-correlation function, the wastewater flow transmission time is determined; Based on the wastewater flow transmission time, the pretreated wastewater dataset is time-aligned to generate a wastewater quality dataset.

6. A mine wastewater softening and reuse device, comprising: The acquisition unit is configured to acquire wastewater quality datasets and wastewater composition datasets based on the wastewater data acquisition device group in response to the detection of a discharge operation targeting mine wastewater. The determining unit is configured to determine at least one chemical equilibrium data corresponding to the mine wastewater based on the wastewater composition dataset, thereby obtaining a chemical equilibrium dataset. The generation unit is configured to generate first reagent dosing information based on the chemical equilibrium dataset, wherein the first reagent dosing information characterizes the dosage of the reagents used to react with the free ions corresponding to each wastewater component data. The fuzzification unit is configured to fuzzify each wastewater quality data in the wastewater quality dataset to generate a fuzzy application rule base. The correction unit is configured to perform correction processing on the first drug dosing information based on the fuzzy dosing rule base to generate corrected drug dosing information, wherein the corrected drug dosing information represents the dosage of each drug after correction. The control unit is configured to generate a drug dispensing command corresponding to the correction drug dispensing information, and to control an associated drug dispensing device to perform the drug dispensing operation corresponding to the drug dispensing command.

7. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 5.

8. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 5.

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