Method and device for adaptively adjusting parameters of water purification equipment based on water quality changes

Abstract

The present invention provides a method and device for adaptively adjusting parameters of water purification equipment based on water quality changes, which relates to the technical field of equipment parameter adjustment, including: obtaining raw water quality information through water quality detection equipment; interactive water purification equipment collaborative system to obtain historical water treatment data set; performing water quality parameter aggregation to obtain multiple standard water quality indicators; obtaining multiple equipment performance parameter sets, performing node association analysis based on performance weights, and generating a collaborative water purification response network; performing collaborative water purification equipment positioning to obtain multiple spare collaborative water purification equipment; performing control parameter optimization to obtain a target collaborative water purification equipment group and a target collaborative water purification control parameter group; performing equipment parameter control after equipment activation to perform raw water treatment. The present invention solves the technical problem that the water treatment in the prior art usually adopts fixed parameters, which is difficult to cope with water quality changes or unforeseen pollution events, resulting in poor water treatment effect.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment parameter adjustment, and in particular to a method and device for adaptively adjusting parameters of water purification equipment based on water quality changes. Background Art

[0002] The water treatment process includes multiple physical and chemical treatment steps, such as coarse filtration, flocculation reaction, sedimentation, filtration, sterilization, automatic backwashing, etc., which are designed to improve the quality and safety of water supply. However, traditional water treatment usually adopts fixed parameters and is designed based on specific water quality standards and treatment goals. However, due to seasonal changes in water quality, source changes, or unforeseen pollution events, these fixed treatment parameters cannot always guarantee the optimal water treatment effect. For example, when the water quality deteriorates, pollutants in the water may not be effectively treated, or when the water quality is good, over-treatment may be performed, resulting in waste of resources and energy. Therefore, due to the lack of adaptive mechanisms, traditional water treatment methods usually result in poor treatment effects or excessive operating costs when faced with atypical water quality conditions. Summary of the invention

[0003] The present application provides a method and device for adaptively adjusting parameters of water purification equipment based on water quality changes, aiming to solve the technical problem that water treatment in the prior art usually adopts fixed parameters, which makes it difficult to cope with water quality changes or unforeseen pollution events, resulting in poor water treatment effects.

[0004] The first aspect disclosed in the present application provides a method for adaptively adjusting parameters of water purification equipment based on water quality changes, the method comprising: obtaining raw water quality information through water quality detection equipment, wherein the raw water quality information includes a variety of water quality parameters; interacting with a water purification equipment collaborative system to obtain a historical water treatment data set, wherein the water purification equipment collaborative system includes a plurality of collaborative water purification equipment; aggregating water quality parameters of the historical water treatment data set to obtain a variety of standard water quality indicators; obtaining a plurality of equipment performance parameter sets of the plurality of collaborative water purification equipment, using the plurality of standard water quality indicators and the plurality of collaborative water purification equipment as topological nodes, and combining The multiple equipment performance parameter sets are subjected to node association analysis based on performance weights to generate a collaborative water purification response network; the raw water quality information is synchronized to the collaborative water purification response network to locate the collaborative water purification equipment and obtain multiple standby collaborative water purification equipment with the multiple water quality parameters; the control parameters of the multiple standby collaborative water purification equipment are optimized through the multiple water quality parameters to obtain a target collaborative water purification equipment group and a target collaborative water purification control parameter group; after activating the equipment in the water purification equipment collaborative system based on the target collaborative water purification equipment group, the target collaborative water purification control parameter group is used to control equipment parameters and perform raw water treatment.

[0005] The second aspect disclosed in the present application provides a device for adaptively adjusting parameters of water purification equipment based on water quality changes, and the device is used for the above-mentioned method for adaptively adjusting parameters of water purification equipment based on water quality changes, and the device includes: a water quality information acquisition module, used to obtain raw water quality information through water quality detection equipment, wherein the raw water quality information includes a variety of water quality parameters; a water treatment data acquisition module, used to interact with the water purification equipment collaborative system to obtain a historical water treatment data set, wherein the water purification equipment collaborative system includes a plurality of collaborative water purification equipment; a quality parameter aggregation module, used to aggregate water quality parameters of the historical water treatment data set to obtain a variety of standard water quality indicators; a node association analysis module, used to obtain a plurality of equipment performance parameter sets of the plurality of collaborative water purification equipment, and to obtain a plurality of standard water quality indicators The target and the multiple collaborative water purification equipment are used as topological nodes, and node association analysis based on performance weights is performed in combination with the multiple equipment performance parameter sets to generate a collaborative water purification response network; a collaborative water purification equipment positioning module is used to synchronize the raw water quality information to the collaborative water purification response network, perform collaborative water purification equipment positioning, and obtain multiple backup collaborative water purification equipment with the multiple water quality parameters; a control parameter optimization module is used to optimize the control parameters of the multiple backup collaborative water purification equipment through the multiple water quality parameters to obtain a target collaborative water purification equipment group and a target collaborative water purification control parameter group; a raw water treatment module is used to use the target collaborative water purification control parameter group to perform equipment parameter control and perform raw water treatment after the target collaborative water purification equipment group is activated in the water purification equipment collaborative system.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] Through water quality detection technology, a variety of water quality parameters in the raw water are obtained to ensure that there is detailed water quality information as the basis for processing. Historical water treatment data is aggregated to form standard water quality indicators, which represent the water quality characteristics under normal operating conditions and provide a benchmark for subsequent equipment and parameter selection. The aggregated data and equipment performance parameters are used to build a collaborative water purification response network. This network optimizes the collaborative operation of the entire system by analyzing the performance of different equipment and their impact on water quality. The available equipment combination and operation mode are determined through network analysis to adapt to the changing water quality conditions. Real-time water quality information is synchronized to the response network, and the appropriate backup equipment combination is automatically located and selected to optimize the operation of the equipment according to the current water quality conditions. By optimizing the control parameters of the equipment combination, it is ensured that each group of equipment can operate in the best state, thereby improving the treatment efficiency and effect. The selected equipment combination is activated in the system, and the optimized control parameters are applied to start the actual water treatment process, ensuring that the entire water purification process is not only responsive and adaptable, but also able to effectively handle various water quality conditions, and ultimately produce clean water that meets the requirements, thereby improving the level of automation and intelligence in water treatment and improving the ability to respond to water quality changes.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of a method for adaptively adjusting parameters of a water purification device based on water quality changes provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of the structure of a device for adaptively adjusting parameters of a water purification device based on water quality changes provided in an embodiment of the present application.

[0011] Explanation of the accompanying drawings: water quality information acquisition module 10, water treatment data acquisition module 20, quality parameter aggregation module 30, node association analysis module 40, collaborative water purification equipment positioning module 50, control parameter optimization module 60, raw water treatment module 70. DETAILED DESCRIPTION

[0012] The embodiments of the present application provide a method and device for adaptively adjusting parameters of a water purification device based on water quality changes, thereby solving the technical problem that water treatment in the prior art usually adopts fixed parameters, which makes it difficult to cope with water quality changes or unforeseen pollution events, resulting in poor water treatment effects.

[0013] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a method for adaptively adjusting parameters of a water purification device based on water quality changes, the method comprising:

[0015] The raw water quality information is obtained through water quality testing equipment, wherein the raw water quality information includes a variety of water quality parameters.

[0016] Install and configure water quality testing equipment that can measure and record the physical, chemical and biological parameters in water in real time. The equipment detects a variety of water quality parameters, such as turbidity, pH value, dissolved oxygen, heavy metal content, organic matter content, etc. These data are crucial for judging the degree of water pollution. Integrate the data collected by the testing equipment to obtain raw water quality information. Obtaining real-time raw water quality information can support the system to automatically adjust the treatment equipment and treatment parameters according to different water quality conditions. This method is particularly suitable for changing raw water conditions, such as township water plants or urban water supply systems, thereby optimizing the water treatment process.

[0017] An interactive water purification equipment collaborative system obtains a historical water treatment data set, wherein the water purification equipment collaborative system includes multiple collaborative water purification equipment.

[0018] All collaborative water purification equipment are connected to a centralized interactive water purification equipment collaborative system through the network. This system can receive and store the operation data from each device in real time. The system not only monitors in real time, but also records the historical operation data of the equipment, such as the water quality input and output of each treatment step, equipment operating status, maintenance records, etc. By interacting with the water purification equipment collaborative system, the historical water treatment data set of all collaborative water purification equipment can be obtained for data analysis and decision support for subsequent treatment steps.

[0019] The water quality parameters are aggregated on the historical water treatment data set to obtain a variety of standard water quality indicators.

[0020] Key water quality parameters such as pH, turbidity, hardness, organic matter content, microbial count, etc. are extracted from historical water treatment data, and statistical analysis is performed on the extracted data to identify the average, median, extreme values, etc. of each parameter at different times, with different equipment and under different treatment conditions. This aggregation process is used to determine the typical quality characteristics of water bodies under normal operating conditions, and the aggregated data are integrated into a variety of standard water quality indicators that summarize the typical behaviors and change patterns of water quality parameters under normal operating conditions.

[0021] Acquire multiple equipment performance parameter sets of the multiple collaborative water purification devices, use the multiple standard water quality indicators and the multiple collaborative water purification devices as topological nodes, perform node association analysis based on performance weights in combination with the multiple equipment performance parameter sets, and generate a collaborative water purification response network.

[0022] The key performance parameters of multiple collaborative water purification devices are collected, such as flow capacity, energy consumption, filtration efficiency, maintenance frequency, etc. Each device and standard water quality index is defined as a node in the network. These nodes include the operating performance of the equipment and the water quality indicators associated with these performances. A network model of these nodes is established, and node association analysis based on performance weights is performed. Specifically, different weights are given to each device according to the degree of influence of the device on water quality control and the reliability of the performance. The interactions and dependencies between the nodes are analyzed, such as which devices are more effective in dealing with specific water quality problems. Based on the above analysis, a collaborative water purification response network is formed, which can guide how to dynamically adjust the operation of each device in response to water quality changes, thereby achieving a more efficient and economical water treatment process.

[0023] The raw water quality information is synchronized to the collaborative water purification response network to locate the collaborative water purification equipment, and multiple backup collaborative water purification equipment with the multiple water quality parameters are obtained.

[0024] The raw water quality information is synchronized to the collaborative water purification response network. This information includes the physical, chemical and biological parameters of the water. Based on the synchronized water quality information, the response network analyzes which collaborative water purification equipment is most suitable for treating the current water quality. This process involves comparing equipment characteristics and water quality requirements, selecting equipment that can most effectively remove pollutants or adjust water quality, and based on the equipment positioning results, obtaining multiple backup collaborative water purification equipment that can effectively treat multiple water quality parameters of the current raw water.

[0025] The control parameters of the multiple standby collaborative water purification equipment are optimized through the multiple water quality parameters to obtain a target collaborative water purification equipment group and a target collaborative water purification control parameter group.

[0026] According to the current water quality parameters, such as pollutant concentration and type, the control parameters of each backup equipment are adjusted and optimized, including adjusting the filtration rate, chemical dosage or UV lamp irradiation intensity, etc. A pre-established water treatment simulation model is used to test the treatment effects of different equipment under corresponding control parameters to determine which set of equipment and equipment parameters are most effective. Based on the simulation results, the equipment combination and control parameter combination that best meet the current water quality requirements are selected as the target collaborative water purification equipment group and target collaborative water purification control parameter group. In this way, not only can the raw water be effectively treated, but also the adaptability and flexibility of the entire water purification process can be improved to cope with changes in raw water quality and differences in equipment performance, ultimately improving the reliability and efficiency of the water treatment system.

[0027] After the target collaborative water purification equipment group is activated in the water purification equipment collaborative system, the target collaborative water purification control parameter group is used to control equipment parameters and perform raw water treatment.

[0028] In the water purification equipment collaborative system, activate the selected target collaborative water purification equipment group, including starting all related equipment and ensuring that they are in operation. According to the target collaborative water purification control parameter group determined in the previous evaluation, set the operating parameters of each device, including but not limited to flow setting, chemical dosage, filtration pressure, UV lamp intensity, etc. When the equipment and parameter settings are ready, start the raw water treatment process. In this process, the raw water will be processed by each activated equipment, including coarse filtration, flocculation reaction, sedimentation, filtration, sterilization, automatic backwashing and other processes to achieve efficient and reliable water quality treatment.

[0029] Furthermore, the method of obtaining raw water quality information by water quality detection equipment, wherein the raw water quality information includes a plurality of water quality parameters, includes:

[0030] By using the water quality physical parameter detection equipment of the water quality detection equipment, the raw water body is detected, and the detection results are screened based on the standard water body physical parameter set to obtain multiple unqualified water body physical parameters; by using the water quality chemical parameter detection equipment of the water quality detection equipment, the raw water body is detected, and the detection results are screened based on the standard water body chemical parameter set to obtain multiple unqualified water body chemical parameters; by using the water quality biological parameter detection equipment of the water quality detection equipment, the raw water body is detected, and the detection results are screened based on the standard water body biological parameter set to obtain multiple unqualified water body biological parameters; by using the water quality pollutant characteristic parameter detection equipment of the water quality detection equipment, the raw water body is detected to obtain multiple water body pollutant characteristic parameters; the multiple water body physical parameters, multiple water body chemical parameters, multiple water body biological parameters, and multiple water body pollutant characteristic parameters are integrated to obtain the multiple water body quality parameters.

[0031] Raw water is tested by water quality physical parameter testing equipment. These equipment can detect physical properties such as temperature, turbidity, color, conductivity and flow rate. The testing equipment collects physical data of the water body in real time and compares the collected physical data with a preset set of physical parameter standards. These standards define the qualified range of each parameter, such as the maximum acceptable turbidity or a specific conductivity range, and automatically identify parameters that exceed the standard range. Multiple unqualified water physical parameters are obtained, and these parameters require specific treatment measures to adjust the water quality to the qualified standards.

[0032] Similarly, water quality chemical parameter detection equipment is used to determine the chemical composition of raw water, such as pH, dissolved oxygen, hardness, organic matter content, heavy metals and other potential pollutants. These chemical parameters are measured by sensors and analytical instruments. Similar to physical parameters, chemical parameters are also compared with preset chemical standards. These standards specify the safety levels and water quality requirements of various chemical substances, and automatically identify any chemical parameters that do not meet the safety standards as unqualified multiple water chemical parameters for further processing.

[0033] Similarly, water quality biological parameter detection equipment is used to detect the presence of biological components in raw water, such as bacteria, viruses, algae and other microorganisms. Microbial contaminants can be identified and counted by using microscopes, culture methods or molecular biology techniques, and the detected biological parameter data is compared with preset biological water quality standards. These standards specify safety thresholds for different microorganisms, and biological contamination indicators that exceed the safety thresholds are identified as unqualified and require further treatment to reduce to a safe level.

[0034] Water quality pollutant characteristic parameter detection equipment includes high-precision analytical instruments, such as mass spectrometers, gas chromatographs, etc., which are used to detect specific pollutants in raw water, such as pesticides, drug residues, heavy metals, etc. These devices provide detailed water quality pollutant characteristic detection results, including the types and concentrations of various pollutants. These data help determine the specific types of pollutants present in the water so that targeted treatment can be carried out.

[0035] By integrating the detected multiple water physical parameters, multiple water chemical parameters, multiple water biological parameters, and multiple water pollutant characteristic parameters, a variety of water quality parameters are obtained, which reflect the overall quality status of the raw water. Through this detailed water quality analysis, various water quality problems can be more effectively identified and dealt with, thereby ensuring the safety of the effluent and meeting the high standards for domestic and industrial water use.

[0036] Furthermore, the method of optimizing the control parameters of the plurality of standby collaborative water purification devices by using the plurality of water quality parameters to obtain a target collaborative water purification device group and a target collaborative water purification control parameter group includes:

[0037] The multiple backup collaborative water purification devices are randomly combined to obtain a first backup collaborative water purification device group, wherein the first backup collaborative water purification device group includes multiple available collaborative water purification devices for processing the multiple water quality parameters; based on the multiple available collaborative water purification devices, multiple water purification simulation channels are located in a pre-built water purification simulation model; multiple water quality purification standard parameters of the multiple water quality parameters are obtained; the multiple water quality parameters and the multiple water quality purification standard parameters are input into the multiple water purification simulation channels to obtain multiple backup collaborative control parameters to form a first backup collaborative control parameter group; based on the first backup collaborative control parameter group, control fitness evaluation is performed to optimize the target collaborative water purification control parameter group, and the corresponding target collaborative water purification device group is obtained.

[0038] Multiple devices are randomly selected from multiple backup collaborative water purification devices as a group, as the first backup collaborative water purification equipment group, to meet the current water treatment needs. This random combination method facilitates the subsequent traversal and combination of all backup collaborative water purification devices, thereby improving the comprehensiveness of equipment selection.

[0039] The water purification simulation model is built based on past water treatment data and performance records of different equipment. The model can simulate various physical, chemical and biological reactions in the water treatment process. In the model, a simulation channel is configured for each device. These channels represent different water treatment paths and can simulate the cleaning process of water passing through various equipment. According to the selected multiple available collaborative water purification equipment, the same equipment is matched in the water purification simulation model to locate multiple water purification simulation channels corresponding to multiple available collaborative water purification equipment.

[0040] Water quality purification standard parameters are the water quality purification standards that need to be achieved in the water treatment process. These standards are set according to regulatory requirements, safety indicators or specific use needs, such as drinking water standards, industrial water standards, etc. Various water quality purification standard parameters are collected from relevant departments or standard documents, including but not limited to chemical composition, biological pollutant limits, physical properties, etc.

[0041] A variety of water quality parameters and a variety of water quality purification standard parameters are input into multiple water purification simulation channels. Through simulation operations, the effects of different treatment technologies and equipment combinations under these set parameters are evaluated to determine which control parameters can effectively meet these standards. Based on the simulation results, multiple alternative coordinated control parameters are generated, which are used to adjust equipment performance in actual operations.

[0042] Perform fitness evaluation on the first backup collaborative control parameter group, analyze the performance of these parameters in historical applications, such as processing efficiency, cost-effectiveness, energy consumption, etc., to obtain the first fitness of the first backup collaborative control parameter group, and traverse all equipment combinations based on the fitness evaluation to select the equipment combination with the highest fitness as the target collaborative water purification control parameter group, and locate the target collaborative water purification equipment group according to the target collaborative water purification control parameter group as the solution that best meets the current water quality conditions and treatment requirements. Through this process, a high degree of adaptability and optimization is achieved, ensuring that the predetermined water quality purification standards can be efficiently achieved under various water quality conditions, which not only improves the efficiency of water treatment, but also ensures the economy and sustainability of the treatment process.

[0043] Furthermore, the water purification simulation model is pre-built, and the method includes:

[0044] Based on the water purification equipment collaborative system, a first collaborative water purification equipment is extracted, wherein the first collaborative water purification equipment is any one of a plurality of collaborative water purification equipment; according to the first collaborative water purification equipment, first historical water treatment data is extracted from a historical water treatment data set, wherein the first historical water treatment data includes a plurality of first inlet water quality parameters, a plurality of first outlet water quality parameters, and a plurality of first water purification control parameters; the plurality of first inlet water quality parameters, the plurality of first outlet water quality parameters, and the plurality of first water purification control parameters are subjected to supervised training to obtain a first water purification simulation channel; and so on, multiple water purification simulation channels of the plurality of collaborative water purification equipment are obtained by training, and the water purification simulation model is obtained by combining them.

[0045] Based on the water purification equipment collaborative system, an analysis object is randomly selected from multiple collaborative water purification equipment as the first collaborative water purification equipment. This random selection facilitates subsequent traversal analysis of all collaborative water purification equipment.

[0046] According to the selected first collaborative water purification equipment, relevant water treatment data are extracted from the historical water treatment data set, and the obtained first historical water treatment data include multiple first inlet water quality parameters, multiple first outlet water quality parameters, and multiple first water purification control parameters, wherein the first inlet water quality parameters are the quality parameters of the water before the equipment is treated, such as pH value, turbidity, chemical oxygen demand, etc., the first outlet water quality parameters are the quality parameters of the water after treatment, which show the effect of the water purification process, and the first water purification control parameters are the operating parameters used in the equipment, such as flow control, chemical dosage, backwash frequency, etc.

[0047] Use machine learning algorithms, such as decision trees, random forests or neural networks, to train the data. During this training process, the input data is the first inlet water quality parameter and the first outlet water quality parameter, and the target output data is the first water purification control parameter. Through this training, the model learns how to predict the control parameters of the equipment based on the input inlet water quality and outlet water quality. After the training is completed, the obtained model is the first water purification simulation channel, which can simulate the actual water treatment process and predict the water purification control parameters of the equipment.

[0048] By analogy, all the multiple collaborative water purification devices are trained to obtain multiple water purification simulation channels, and the multiple water purification simulation channels are combined to obtain the water purification simulation model. By using historical data for machine learning training, the system is supported to better understand the impact of treatment effects on operating parameters, thereby achieving more refined and adaptive control, effectively improving the accuracy and intelligence of water treatment.

[0049] Furthermore, the method of performing supervised training to obtain the first water purification simulation channel also includes:

[0050] During the supervised training process, the number of training times is counted, and whenever the number of training times reaches a preset number, a clean water simulation loss coefficient is obtained; when the clean water simulation loss coefficient is less than the clean water simulation loss threshold, the first clean water simulation channel is generated.

[0051] During the supervised training process, the number of model training iterations is tracked and recorded. The number of training times is related to the convergence of the algorithm. More training can support the model to better understand complex data relationships. The preset number of times is set according to specific needs, for example, 10 times or 20 times. Whenever the number of training times reaches the preset number, a loss value is calculated. This value represents the error between the current model's predicted result and the actual result. The loss coefficient can be calculated using mean square error, cross entropy loss or other related loss functions. The goal of the loss coefficient is to gradually reduce it through back propagation and parameter adjustment.

[0052] Set a water purification simulation loss threshold, which is a standard for judging the quality of model training. Only when the model's loss coefficient is lower than this threshold, the model is judged to be good enough to accurately predict the water treatment results. When the model's loss coefficient is lower than the set threshold, the model is considered to be stable and reliable enough. At this time, the first water purification simulation channel is generated. This channel has been verified to be able to accurately simulate the water treatment process under predetermined parameters.

[0053] This training method improves the accuracy and reliability of the water purification simulation channel in real-world applications. Through such a training and verification process, the efficiency and accuracy of the simulation channel in predicting water treatment results can be ensured.

[0054] Furthermore, the method of performing control fitness evaluation and optimization based on the first standby collaborative control parameter group to obtain the target collaborative water purification control parameter group includes:

[0055] In the historical water treatment data set, search for the first equipment operation efficiency parameter set for water treatment using the first standby collaborative water purification equipment group to operate the first standby collaborative control parameter group, wherein the first equipment operation efficiency parameter set includes water purification control time, water purification control energy consumption, agent consumption, and backwashing frequency; perform control fitness evaluation based on the first equipment operation efficiency parameter set to generate a first control fitness; traverse all random combinations of the multiple standby collaborative water purification equipment to obtain multiple standby collaborative water purification equipment groups, and obtain multiple standby collaborative control parameter groups; perform control fitness evaluation on the multiple standby collaborative control parameter groups according to the first control fitness to generate multiple control fitnesses; and locate the target collaborative water purification control parameter group in the multiple standby collaborative control parameter groups based on the maximum value of the multiple control fitnesses.

[0056] In the historical water treatment data set, operation data related to the first backup collaborative water purification equipment group and its corresponding first backup collaborative control parameter group are searched. These data specifically involve various efficiency parameters of the equipment in actual operation, including water purification control time, water purification control energy consumption, chemical consumption, and backwashing frequency. Among them, the water purification control time is the length of time required for each treatment cycle, the water purification control energy consumption is the energy consumption required to complete a certain amount of water treatment, the chemical consumption is the amount of chemical agents used in the water treatment process, and the backwashing frequency is the number of backwashing times required to maintain the filtration efficiency.

[0057] The operational fitness of the first standby collaborative water purification equipment group is evaluated using the first equipment operating efficiency parameter set. The purpose of the evaluation is to determine whether the performance of these equipment and control parameters in actual operation meets the expected results and whether they are economical and efficient. Through a comprehensive analysis of the equipment operating efficiency parameters, a first control fitness is generated, which reflects the performance of the first standby collaborative water purification equipment group in water treatment under the first standby collaborative control parameter group. The control fitness evaluation can support the determination of which equipment and parameter settings can best achieve the goals of energy saving, high efficiency and cost-effectiveness, thereby improving the economy and environmental sustainability of the entire water treatment process.

[0058] All available equipment combinations of multiple standby collaborative water purification equipment are traversed, similar to the first standby collaborative water purification equipment group, and all equipment combinations are analyzed to obtain corresponding multiple standby collaborative control parameter groups, which define specific settings for equipment operation.

[0059] Similar to the process of obtaining the first control fitness, the efficiency and effectiveness of each backup equipment combination and its corresponding control parameter group are evaluated, including evaluating its energy consumption, processing efficiency, and chemical use efficiency. After the evaluation, multiple control fitness are generated. These fitness are used to compare which combination performs best in actual operation.

[0060] Among all the alternative combinations, the parameter group with the highest control fitness is selected as the target collaborative water purification control parameter group. This is a selection based on comprehensive analysis, which makes the selected combination most suitable for current and expected future conditions, thereby ensuring that the water treatment process always operates in the best state.

[0061] Furthermore, the method further comprises:

[0062] An interactive water purification equipment collaborative system obtains a first equipment stability parameter set of the first standby collaborative water purification equipment group, wherein the first equipment stability parameter set includes equipment operation failure rate, equipment key component aging rate, equipment life cycle, and equipment continuous operation time; performs equipment stability evaluation based on the first equipment stability parameter set to obtain a first equipment stability coefficient; and compensates the first control fitness with the first equipment stability coefficient.

[0063] Utilize the water purification equipment collaborative system to query the equipment stability related data in the first standby collaborative water purification equipment group. These data reflect the reliability and durability of the equipment in actual operation, including equipment operation failure rate, equipment key component aging rate, equipment life cycle, and equipment continuous operation time. Among them, the equipment operation failure rate is the frequency of equipment failures during operation, the key component aging rate is the rate of loss or degradation of key performance components, the equipment life cycle is the expected effective service life of the equipment, and the continuous operation time is the maximum time the equipment can operate continuously without maintenance.

[0064] The collected stability parameters are analyzed to evaluate the overall stability of each device. Based on the stability evaluation, a coefficient representing the stability of the device is generated, called the first device stability coefficient. This coefficient can quantify the long-term operating capability and reliability of the device.

[0065] The first control fitness calculated previously is compensated using the first equipment stability coefficient. This means that when selecting equipment and parameter settings, in addition to considering efficiency and cost, the long-term operation and maintenance requirements of the equipment must also be considered. Compensation may cause the original efficient but less stable equipment combination to be lowered in priority in the evaluation, while a more stable equipment combination is promoted.

[0066] Through this approach, when selecting the appropriate combination of equipment and control parameters, not only the immediate treatment efficiency and cost-effectiveness are considered, but also the long-term stability and reliability of the equipment are fully considered. This comprehensive analysis ensures the continued efficient operation of water treatment, reduces long-term operating costs, and reduces the risk of unexpected downtime.

[0067] In summary, the method for adaptively adjusting parameters of a water purification device based on water quality changes provided in the embodiments of the present application has the following technical effects:

[0068] Through water quality detection technology, a variety of water quality parameters in the raw water are obtained to ensure that there is detailed water quality information as the basis for processing. Historical water treatment data is aggregated to form standard water quality indicators, which represent the water quality characteristics under normal operating conditions and provide a benchmark for subsequent equipment and parameter selection. The aggregated data and equipment performance parameters are used to build a collaborative water purification response network. This network optimizes the collaborative operation of the entire system by analyzing the performance of different equipment and their impact on water quality. The available equipment combination and operation mode are determined through network analysis to adapt to the changing water quality conditions. Real-time water quality information is synchronized to the response network, and the appropriate backup equipment combination is automatically located and selected to optimize the operation of the equipment according to the current water quality conditions. By optimizing the control parameters of the equipment combination, it is ensured that each group of equipment can operate in the best state, thereby improving the treatment efficiency and effect. The selected equipment combination is activated in the system, and the optimized control parameters are applied to start the actual water treatment process, ensuring that the entire water purification process is not only responsive and adaptable, but also able to effectively handle various water quality conditions, and ultimately produce clean water that meets the requirements, thereby improving the level of automation and intelligence in water treatment and improving the ability to respond to water quality changes.

[0069] Embodiment 2 is based on the same inventive concept as the method for adaptively adjusting parameters of a water purification device based on water quality changes in the aforementioned embodiment. Figure 2 As shown, the embodiment of the present application provides a device for adaptively adjusting parameters of a water purification device based on water quality changes, the device comprising:

[0070] The water quality information acquisition module 10 is used to obtain raw water quality information through water quality detection equipment, wherein the raw water quality information includes a variety of water quality parameters; the water treatment data acquisition module 20 is used to interact with the water purification equipment collaborative system to obtain a historical water treatment data set, wherein the water purification equipment collaborative system includes a plurality of collaborative water purification equipment; the quality parameter aggregation module 30 is used to aggregate the water quality parameters of the historical water treatment data set to obtain a variety of standard water quality indicators; the node association analysis module 40 is used to obtain a plurality of equipment performance parameter sets of the plurality of collaborative water purification equipment, and use the plurality of standard water quality indicators and the plurality of collaborative water purification equipment as topological nodes, combined with the plurality of equipment performance parameter sets to perform A collaborative water purification response network is generated based on node association analysis of performance weights; a collaborative water purification equipment positioning module 50 is used to synchronize the raw water quality information to the collaborative water purification response network, locate the collaborative water purification equipment, and obtain multiple standby collaborative water purification equipment of the multiple water quality parameters; a control parameter optimization module 60 is used to optimize the control parameters of the multiple standby collaborative water purification equipment through the multiple water quality parameters to obtain a target collaborative water purification equipment group and a target collaborative water purification control parameter group; a raw water treatment module 70 is used to perform equipment parameter control based on the target collaborative water purification equipment group after the equipment is activated in the water purification equipment collaborative system, and to perform raw water treatment using the target collaborative water purification control parameter group.

[0071] Furthermore, the device also includes a plurality of water quality parameter acquisition modules to perform the following operation steps:

[0072] By using the water quality physical parameter detection equipment of the water quality detection equipment, the raw water body is detected, and the detection results are screened based on the standard water body physical parameter set to obtain multiple unqualified water body physical parameters; by using the water quality chemical parameter detection equipment of the water quality detection equipment, the raw water body is detected, and the detection results are screened based on the standard water body chemical parameter set to obtain multiple unqualified water body chemical parameters; by using the water quality biological parameter detection equipment of the water quality detection equipment, the raw water body is detected, and the detection results are screened based on the standard water body biological parameter set to obtain multiple unqualified water body biological parameters; by using the water quality pollutant characteristic parameter detection equipment of the water quality detection equipment, the raw water body is detected to obtain multiple water body pollutant characteristic parameters; the multiple water body physical parameters, multiple water body chemical parameters, multiple water body biological parameters, and multiple water body pollutant characteristic parameters are integrated to obtain the multiple water body quality parameters.

[0073] Furthermore, the device also includes an optimization module to perform the following operation steps:

[0074] The multiple backup collaborative water purification devices are randomly combined to obtain a first backup collaborative water purification device group, wherein the first backup collaborative water purification device group includes multiple available collaborative water purification devices for processing the multiple water quality parameters; based on the multiple available collaborative water purification devices, multiple water purification simulation channels are located in a pre-built water purification simulation model; multiple water quality purification standard parameters of the multiple water quality parameters are obtained; the multiple water quality parameters and the multiple water quality purification standard parameters are input into the multiple water purification simulation channels to obtain multiple backup collaborative control parameters to form a first backup collaborative control parameter group; based on the first backup collaborative control parameter group, control fitness evaluation is performed to optimize the target collaborative water purification control parameter group, and the corresponding target collaborative water purification device group is obtained.

[0075] Furthermore, the device also includes a water purification simulation model building module to perform the following operation steps:

[0076] Based on the water purification equipment collaborative system, a first collaborative water purification equipment is extracted, wherein the first collaborative water purification equipment is any one of a plurality of collaborative water purification equipment; according to the first collaborative water purification equipment, first historical water treatment data is extracted from a historical water treatment data set, wherein the first historical water treatment data includes a plurality of first inlet water quality parameters, a plurality of first outlet water quality parameters, and a plurality of first water purification control parameters; the plurality of first inlet water quality parameters, the plurality of first outlet water quality parameters, and the plurality of first water purification control parameters are subjected to supervised training to obtain a first water purification simulation channel; and so on, multiple water purification simulation channels of the plurality of collaborative water purification equipment are obtained by training, and the water purification simulation model is obtained by combining them.

[0077] Furthermore, the device also includes a training module to perform the following steps:

[0078] During the supervised training process, the number of training times is counted, and whenever the number of training times reaches a preset number, a clean water simulation loss coefficient is obtained; when the clean water simulation loss coefficient is less than the clean water simulation loss threshold, the first clean water simulation channel is generated.

[0079] Furthermore, the device further includes a target collaborative water purification control parameter group acquisition module to perform the following operation steps:

[0080] In the historical water treatment data set, search for the first equipment operation efficiency parameter set for water treatment using the first standby collaborative water purification equipment group to operate the first standby collaborative control parameter group, wherein the first equipment operation efficiency parameter set includes water purification control time, water purification control energy consumption, agent consumption, and backwashing frequency; perform control fitness evaluation based on the first equipment operation efficiency parameter set to generate a first control fitness; traverse all random combinations of the multiple standby collaborative water purification equipment to obtain multiple standby collaborative water purification equipment groups, and obtain multiple standby collaborative control parameter groups; perform control fitness evaluation on the multiple standby collaborative control parameter groups according to the first control fitness to generate multiple control fitnesses; and locate the target collaborative water purification control parameter group in the multiple standby collaborative control parameter groups based on the maximum value of the multiple control fitnesses.

[0081] Furthermore, the device further includes a fitness compensation module to perform the following operation steps:

[0082] An interactive water purification equipment collaborative system obtains a first equipment stability parameter set of the first standby collaborative water purification equipment group, wherein the first equipment stability parameter set includes equipment operation failure rate, equipment key component aging rate, equipment life cycle, and equipment continuous operation time; performs equipment stability evaluation based on the first equipment stability parameter set to obtain a first equipment stability coefficient; and compensates the first control fitness with the first equipment stability coefficient.

[0083] Through the above-mentioned detailed description of the method for adaptively adjusting parameters of water purification equipment based on water quality changes in this specification, those skilled in the art can clearly understand the device for adaptively adjusting parameters of water purification equipment based on water quality changes in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0084] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for adaptively adjusting parameters of water purification equipment based on water quality changes, characterized in that: The method comprises: Obtaining raw water quality information through water quality testing equipment, wherein the raw water quality information includes a variety of water quality parameters; An interactive water purification equipment collaborative system is used to obtain a historical water treatment data set, wherein the water purification equipment collaborative system includes a plurality of collaborative water purification equipment; Aggregating water quality parameters on the historical water treatment data set to obtain multiple standard water quality indicators; Acquire multiple equipment performance parameter sets of the multiple collaborative water purification equipment, use the multiple standard water quality indicators and the multiple collaborative water purification equipment as topological nodes, perform node association analysis based on performance weights in combination with the multiple equipment performance parameter sets, and generate a collaborative water purification response network; Synchronize the raw water quality information to the collaborative water purification response network, locate the collaborative water purification equipment, and obtain multiple backup collaborative water purification equipment with the multiple water quality parameters; Optimizing the control parameters of the plurality of standby collaborative water purification equipment by using the plurality of water quality parameters to obtain a target collaborative water purification equipment group and a target collaborative water purification control parameter group; After the target collaborative water purification equipment group is activated in the water purification equipment collaborative system, the target collaborative water purification control parameter group is used to control equipment parameters and perform raw water treatment.

2. The method for adaptively adjusting parameters of water purification equipment based on water quality changes according to claim 1, characterized in that: The method of obtaining raw water quality information by water quality detection equipment, wherein the raw water quality information includes a variety of water quality parameters, includes: Through the water quality physical parameter detection equipment of the water quality detection equipment, the raw water body is tested, and the test results are screened based on the standard water body physical parameter set to obtain a variety of unqualified water body physical parameters; The water quality chemical parameter detection equipment is used to detect the raw water body, and the detection results are screened based on the standard water body chemical parameter set to obtain a variety of unqualified water body chemical parameters; Through the water quality biological parameter detection equipment of the water quality detection equipment, the raw water body is tested, and the test results are screened based on the standard water body biological parameter set to obtain a variety of unqualified water body biological parameters; Through the water quality testing equipment, the water quality pollutant characteristic parameter testing equipment is used to test the raw water body and obtain various water pollutant characteristic parameters; The multiple water body physical parameters, multiple water body chemical parameters, multiple water body biological parameters, and multiple water body pollutant characteristic parameters are integrated to obtain the multiple water body quality parameters.

3. The method for adaptively adjusting parameters of water purification equipment based on water quality changes according to claim 1, characterized in that: The method of optimizing the control parameters of the plurality of standby collaborative water purification devices by using the plurality of water quality parameters to obtain a target collaborative water purification device group and a target collaborative water purification control parameter group includes: Randomly combining the multiple standby collaborative water purification devices to obtain a first standby collaborative water purification device group, wherein the first standby collaborative water purification device group includes multiple available collaborative water purification devices for processing the multiple water quality parameters; Based on the plurality of available collaborative water purification devices, locating a plurality of water purification simulation channels in a pre-built water purification simulation model; Obtaining a plurality of water quality purification standard parameters of the plurality of water quality parameters; Input the multiple water quality parameters and the multiple water quality purification standard parameters into the multiple water purification simulation channels to obtain multiple standby coordinated control parameters to form a first standby coordinated control parameter group; Based on the first standby collaborative control parameter group, a control fitness evaluation is performed to obtain the target collaborative water purification control parameter group and the corresponding target collaborative water purification equipment group.

4. The method for adaptively adjusting parameters of water purification equipment based on water quality changes according to claim 3, characterized in that: Pre-constructing the water purification simulation model, the method comprises: Extracting a first coordinated water purification device based on the water purification device coordinated system, wherein the first coordinated water purification device is any one of the plurality of coordinated water purification devices; According to the first collaborative water purification device, extracting first historical water treatment data from the historical water treatment data set, wherein the first historical water treatment data includes a plurality of first inlet water quality parameters, a plurality of first outlet water quality parameters, and a plurality of first water purification control parameters; The plurality of first inlet water quality parameters, the plurality of first outlet water quality parameters, and the plurality of first water purification control parameters are subjected to supervised training to obtain a first water purification simulation channel; By analogy, multiple water purification simulation channels of the multiple collaborative water purification devices are obtained through training, and the water purification simulation model is obtained by combining them.

5. The method for adaptively adjusting parameters of water purification equipment based on water quality changes according to claim 4, characterized in that: The supervised training is performed to obtain a first water purification simulation channel, and the method further comprises: During the supervised training process, the number of training times is counted, and whenever the number of training times reaches the preset number, the water purification simulation loss coefficient is obtained; When the clean water simulation loss coefficient is less than the clean water simulation loss threshold, the first clean water simulation channel is generated.

6. The method for adaptively adjusting parameters of water purification equipment based on water quality changes according to claim 3, characterized in that: The method of performing control fitness evaluation and optimization based on the first standby collaborative control parameter group to obtain the target collaborative water purification control parameter group includes: In the historical water treatment data set, searching for a first equipment operation efficiency parameter set for water treatment using the first standby coordinated water purification equipment group to operate the first standby coordinated control parameter group, wherein the first equipment operation efficiency parameter set includes water purification control time, water purification control energy consumption, agent consumption, and backwashing frequency; Performing a control fitness evaluation based on the first equipment operation efficiency parameter set to generate a first control fitness; Traversing all random combinations of the plurality of standby cooperative water purification devices, obtaining a plurality of standby cooperative water purification device groups, and obtaining a plurality of standby cooperative control parameter groups; According to the first control fitness, performing control fitness evaluation on the multiple backup coordinated control parameter groups to generate multiple control fitness; Based on the maximum value of the multiple control fitnesses, the target collaborative water purification control parameter group is located in the multiple standby collaborative control parameter groups.

7. The method for adaptively adjusting parameters of water purification equipment based on water quality changes according to claim 6, characterized in that: The method further comprises: The interactive water purification equipment coordination system obtains a first equipment stability parameter set of the first standby coordinated water purification equipment group, wherein the first equipment stability parameter set includes equipment operation failure rate, equipment key component aging rate, equipment life cycle, and equipment continuous operation time; Performing device stability evaluation based on the first device stability parameter set to obtain a first device stability coefficient; The first control fitness is compensated by the first device stability coefficient.

8. A device for adaptively adjusting parameters of water purification equipment based on water quality changes, characterized in that: The method for adaptively adjusting parameters of a water purification device based on water quality changes according to any one of claims 1 to 7 comprises: A water quality information acquisition module, used to acquire raw water quality information through water quality detection equipment, wherein the raw water quality information includes a variety of water quality parameters; A water treatment data acquisition module, used to interact with the water purification equipment collaborative system to obtain a historical water treatment data set, wherein the water purification equipment collaborative system includes a plurality of collaborative water purification equipment; A quality parameter aggregation module, used to aggregate water quality parameters of the historical water treatment data set to obtain a variety of standard water quality indicators; A node association analysis module, used to obtain a plurality of equipment performance parameter sets of the plurality of collaborative water purification equipment, use the plurality of standard water quality indicators and the plurality of collaborative water purification equipment as topological nodes, and perform node association analysis based on performance weights in combination with the plurality of equipment performance parameter sets to generate a collaborative water purification response network; A collaborative water purification equipment positioning module, used to synchronize the raw water quality information to the collaborative water purification response network, perform collaborative water purification equipment positioning, and obtain multiple backup collaborative water purification equipment of the multiple water quality parameters; A control parameter optimization module, used to optimize the control parameters of the plurality of standby collaborative water purification devices through the plurality of water quality parameters, and obtain a target collaborative water purification device group and a target collaborative water purification control parameter group; The raw water treatment module is used to perform equipment parameter control and raw water treatment by using the target collaborative water purification control parameter group after the target collaborative water purification equipment group is activated in the water purification equipment collaborative system.

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