A water quality monitoring system for a secondary water supply intelligent pump house

Abstract

The present invention discloses a water quality monitoring system for a secondary water supply intelligent pump house, belonging to the technical field of pump house water quality monitoring, and solving the problem that when the water quality monitoring system in any one of the secondary intelligent pump houses is damaged, the water quality condition in the secondary intelligent pump house cannot be fed back. It includes a water quality data acquisition module, a water quality safety analysis module, and a pollution area analysis module; the water quality data acquisition module is used to collect pump house water quality data, preprocess it, and then feed it back to the water quality safety analysis module; the water quality safety analysis module is used to analyze whether there is pollution in the water quality based on the water quality data of some pump houses, and if there is, it will feed the analysis result back to the pollution area analysis module. The system of the present invention can randomly monitor the water quality of some secondary water supply intelligent pump houses in real time and predict the water pollution of the remaining untested secondary water supply intelligent pump houses, so that even if some monitoring devices fail, the system can still maintain the continuity and effectiveness of water quality monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality monitoring in pump houses, and particularly to a water quality monitoring system for intelligent pump houses of secondary water supply. Background Art

[0002] The intelligent secondary pump house is a modern water treatment facility designed to enhance the intelligence and efficiency of urban water supply systems. Its main function is to ensure the stability and safety of the water supply system through intelligent control and management of pumps. The intelligent secondary pump house is equipped with advanced automation equipment and control systems, including intelligent pumps, sensors, frequency converters, and remote monitoring systems. The intelligent pump can automatically adjust its operating state according to real-time water demand, achieving energy conservation and emission reduction. The sensors are used to monitor the operating parameters of the pump in real time, such as pressure, flow rate, and temperature, to ensure that the pump house operates in the best state. In addition, the frequency converter can adjust the speed of the pump to adapt to different water demands, further improving energy efficiency.

[0003] By integrating an advanced remote monitoring system, the intelligent secondary pump house can achieve real-time monitoring and management of pump house equipment. Operators can obtain the operating data of the pump house at any time through a computer or mobile device, and promptly discover and handle potential problems. This remote monitoring not only improves the operation and maintenance efficiency of the equipment, but also reduces the need for manual intervention and lowers the operation cost. The control system of the pump house can optimize the operation strategy of the pump through an automated scheduling algorithm to ensure the stability of water supply pressure and flow rate, while reducing energy consumption and maintenance costs.

[0004] In addition, a water quality monitoring system is also required in the intelligent secondary pump house to effectively monitor the water quality of the tap water transported by the pump house. Currently, each pump house has a one-to-one water quality monitoring system, and it is impossible to uniformly coordinate the water quality monitoring data of the intelligent secondary pump houses in the entire area. Therefore, when the water quality monitoring system in any intelligent secondary pump house is damaged, the water quality situation in that intelligent secondary pump house cannot be fed back. Similarly, if multiple sets are damaged, there will be a problem that the water quality in a large number of intelligent secondary pump houses cannot be detected, ultimately affecting the monitored and guaranteed water quality of the output.

[0005] Therefore, a water quality monitoring system for intelligent pump houses of secondary water supply is proposed to solve or alleviate the above problems. Summary of the Invention

[0006] The purpose of the present invention is to solve the deficiencies in the prior art and propose a water quality monitoring system for intelligent pump houses of secondary water supply.

[0007] To achieve the above purpose, the present invention adopts the following technical solutions:

[0008] A water quality monitoring system for a secondary water supply intelligent pump house, comprising a water quality data acquisition module, a water quality safety analysis module, and a pollution area analysis module;

[0009] The water quality data acquisition module is used to collect the water quality data of the pump house, preprocess it, and then feedback it to the water quality safety analysis module;

[0010] The water quality safety analysis module is used to analyze whether there is pollution in the water quality according to the water quality data of some pump houses. If there is pollution, it will feedback the analysis result to the pollution area analysis module. If there is no pollution, it will directly feedback the analysis result;

[0011] The pollution area analysis module is used to analyze and confirm the pump houses that may be polluted according to the analysis result of the water quality safety analysis module and through a prediction model. Then, it controls the water quality data acquisition module of the pump houses that may be polluted to collect the water quality data of the pump houses, and then the water quality safety analysis module analyzes whether there is pollution in the water quality and directly feedbacks the analysis result.

[0012] Preferably, the number of the water quality data acquisition modules is several. Each water quality data acquisition module separately collects the water quality data of a single pump house, preprocesses it, and then feedbacks it to the water quality safety analysis module. The water quality data of the pump house includes the turbidity, total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH 3 .

[0013] Preferably, the step of collecting the water quality data of the pump house, preprocessing it, and then feedbacking it to the water quality safety analysis module includes the following steps:

[0014] Determine that the total number of water quality data acquisition modules is n;

[0015] Calculate the number k of water quality data acquisition modules to be selected, where k = n / 2. If n is odd, then k is rounded up;

[0016] Create a list containing all the identifiers of the water quality data acquisition modules ;

[0017] Use the Fisher-Yates shuffle algorithm to shuffle the identifiers of the water quality data acquisition modules in the list;

[0018] According to the shuffled list, select the identifiers of the first k water quality data acquisition modules as the selected water quality data acquisition modules;

[0019] Only arrange the selected water quality data acquisition modules to collect the water quality data of the pump house.

[0020] Preferably, the step of using the Fisher-Yates shuffle algorithm to shuffle the identifiers of the water quality data acquisition modules in the list includes the following steps:

[0021] For each index from i to 1, typically i starts from n - 1, where n is the length of the list: , , where denotes generating a random integer within the closed interval . denotes the elements at positions a and b in the list.

[0022] Preferably, after collecting and preprocessing the water quality data in the collection pump house and feeding it back to the water quality safety analysis module, the following steps are further included:

[0023] Collect several water quality data of the pump house on the same time axis for the selected water quality data collection module;

[0024] Traverse all data values of each water quality data in the water quality dataset to obtain the maximum and minimum values;

[0025] Perform standardization processing on the data value x of each water quality data through Formula 1 to obtain the standardized data value , and Formula 1 is , and finally form a water quality data matrix.

[0026] Preferably, for analyzing whether there is pollution in the water quality based on the water quality data of some pump houses, if there is pollution, the analysis result is fed back to the pollution area analysis module, and if there is no pollution, the analysis result is directly fed back, including the following steps:

[0027] Construct a rule set, each rule corresponding to a feature, and the rule form is: if the feature value is greater than the threshold, it is considered that the water quality index represented by this feature is polluted;

[0028] Apply the following rules to the water quality data matrix fed back by the selected water quality data collection module , and output the analysis result, where represents the data value of the j-th water quality feature of the i-th pump house, represents the threshold of the j-th water quality feature.

[0029] Preferably, the method for establishing the prediction model includes the following steps:

[0030] Collect the historical water quality data of all pump houses in a certain area to form a map library containing polluted pump houses, and require that there is at least one pump house with water quality pollution during the same period;

[0031] Record the pollution degree of the water quality data of each pump house compared with the threshold at the same time, and associate it with the color in the map, and the pollution degree is proportional to the color;

[0032] For each pumping station, extract its water quality data as a feature vector: ;

[0033] Extract the color information of each pumping station from the spectrum as a feature vector: ;

[0034] Use cosine similarity to calculate the water quality feature similarity between two pumping stations: , where is the dot product of the feature vectors, and are the norms of the vectors;

[0035] For the pumping stations with known pollution, use the water quality features and color features to train the model, and mark the polluted pumping stations as 1 and the non-polluted pumping stations as 0;

[0036] Import the support vector machine for training to find the optimal hyperplane to classify whether the pumping station is polluted, and finally output the prediction model.

[0037] Preferably, analyzing and confirming the pump houses that may be polluted according to the analysis results of the water quality safety analysis module and through the prediction model includes the following steps

[0038] Convert the pump houses collected by the water quality data acquisition module into real-time spectra, and extract the water quality data of the pump houses collected by the water quality data acquisition module;

[0039] Extract features from the real-time spectra and the water quality data of the pump houses collected by the water quality data acquisition module;

[0040] Import the above features into the prediction model and output the spectra of the historical water quality data of all pump houses in the same area at a certain period with the highest similarity;

[0041] Combine the output spectra to export the historical water quality data of the remaining uncollected pump houses to confirm the pump houses that may be polluted.

[0042] The present invention has the following beneficial effects:

[0043] The present invention provides a system capable of real-time local monitoring and predicting water quality pollution in intelligent pump houses for secondary water supply. Through intelligent data collection, analysis and prediction, the accuracy and reliability of water quality monitoring are improved. Even in the case of damage to some monitoring devices, the continuity and effectiveness of water quality monitoring can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as a limitation of the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0045] Figure 1 This is the structural block diagram of the present invention. Specific embodiments

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0047] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0048] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0049] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship when the product of this invention is usually placed, or the orientation or positional relationship commonly understood by those skilled in the art. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0050] In addition, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0051] In the description of the present invention, it should also be noted that unless otherwise clearly specified and defined, the terms "set", "installed", "connected", and "linked" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0052] A water quality monitoring system for a secondary water supply intelligent pump house, as Figure 1 shown, includes a water quality data acquisition module, a water quality safety analysis module, and a pollution area analysis module;

[0053] The water quality data acquisition module is used to collect the water quality data of the pump house, preprocess it, and then feedback it to the water quality safety analysis module;

[0054] The water quality safety analysis module is used to analyze whether there is pollution in the water quality according to the water quality data of some pump houses. If there is pollution, the analysis result will be feedback to the pollution area analysis module. If there is no pollution, the analysis result will be directly feedback;

[0055] The pollution area analysis module is used to analyze and confirm the pump houses that may be polluted according to the analysis result of the water quality safety analysis module and through a prediction model, and then control the water quality data acquisition module of the pump houses that may be polluted to collect the water quality data of the pump houses, and then the water quality safety analysis module analyzes whether there is pollution in the water quality and directly feedbacks the analysis result.

[0056] This system collects key water quality parameters through the water quality data acquisition module, such as turbidity, total organic carbon, total nitrogen, total phosphorus, ammonia nitrogen, etc. These parameters are key indicators for evaluating water quality. Based on modular design, the water quality monitoring function of this system is divided into three modules: data acquisition, safety analysis, and pollution area analysis. The data acquisition module is responsible for collecting and preprocessing water quality data in real time, and then transmitting it to the water quality safety analysis module. The water quality safety analysis module uses preset rules and thresholds to analyze the collected data to determine whether there is pollution. Once pollution is detected, the system will activate the pollution area analysis module. This module analyzes the pump houses that may be polluted through a prediction model and guides the corresponding data acquisition module to conduct further water quality detection.

[0057] In this way, not only the accuracy and response speed of monitoring are improved, but also the robustness and reliability of the system are enhanced through the collaborative work between modules. Even if some monitoring devices fail, the system can still continue to perform monitoring tasks through other normally working modules to ensure water supply safety.

[0058] Preferably, the number of water quality data acquisition modules is several. Each water quality data acquisition module separately acquires the water quality data of a single pump house, preprocesses it, and then feeds it back to the water quality safety analysis module. The water quality data of the pump house includes the turbidity, total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH 3 .

[0059] By deploying multiple water quality data acquisition modules, each responsible for collecting the water quality data of a single pump house, including key indicators such as turbidity, total organic carbon, total nitrogen, total phosphorus, and ammonia nitrogen, the system can obtain more detailed water quality information and provide accurate data support for subsequent water quality safety analysis in this way.

[0060] Preferably, after collecting and preprocessing the water quality data of the pump house and feeding it back to the water quality safety analysis module, the following steps are included:

[0061] Determine that the total number of water quality data acquisition modules is n;

[0062] Calculate the number of water quality data acquisition modules k to be selected, where k = n / 2. If n is odd, then k is rounded up;

[0063] Create a list containing all the identifiers of the water quality data acquisition modules ;

[0064] Use the Fisher-Yates shuffle algorithm to shuffle the identifiers of the water quality data acquisition modules in the list;

[0065] According to the shuffled list, select the first k identifiers of the water quality data acquisition modules as the selected water quality data acquisition modules;

[0066] Only arrange the selected water quality data acquisition modules to collect the water quality data of the pump house.

[0067] Through the above method, the water quality data acquisition process is optimized to ensure the representativeness of the data and the robustness of the system. Determine the total number of water quality data acquisition modules, calculate the number of modules to be selected, create a list containing all module identifiers, and use the Fisher-Yates shuffle algorithm to randomly shuffle the list order, so as to select some modules for water quality data acquisition. This method improves the randomness and fairness of data acquisition through a randomized selection mechanism, avoiding the bias that may be brought by a fixed acquisition mode. At the same time, by collecting data on the selected modules on the same time axis, obtaining the maximum and minimum values, and performing data standardization processing, an accurate water quality data matrix is formed, providing high-quality input data for subsequent water quality safety analysis. This process not only enhances the anti-interference ability of the system but also ensures that when some monitoring devices are damaged, the system can still effectively monitor the water quality through other normally working modules.

[0068] Preferably, the Fisher-Yates shuffle algorithm is used to shuffle the identification numbers of the water quality data acquisition modules in the list, including the following steps:

[0069] For each index from i to 1, typically i starts from n - 1, where n is the length of the list: , , where denotes generating a random integer within the closed interval , denotes the elements at positions a and b in the list.

[0070] Through the above steps, the accuracy and reliability of the water quality monitoring system are enhanced. The principle of this solution is to randomly sort the identification numbers of the water quality data acquisition modules using the Fisher-Yates shuffle algorithm, thereby ensuring that the module selection for each data acquisition is random and avoiding biases that may be introduced by selecting modules in a fixed order.

[0071] Preferably, after collecting and preprocessing the water quality data in the pump house, it is fed back to the water quality safety analysis module, and the following steps are also included:

[0072] Collect several water quality data of the pump house at the same time axis for the selected water quality data acquisition module;

[0073] Traverse all data values of each water quality data in the water quality dataset to obtain the maximum and minimum values;

[0074] Perform standardization processing on the data value x of each water quality data through Formula 1 to obtain the standardized data value , and Formula 1 is , and finally form a water quality data matrix.

[0075] Through the above steps, the accuracy and availability of the water quality monitoring data are improved. By standardization processing, data consistency and comparability are ensured. Standardization processing not only makes the data from different pump houses comparable but also enhances the sensitivity of the model to water quality changes, thereby improving the ability of the water quality monitoring system to identify potential pollution.

[0076] Preferably, based on the water quality data of some pump houses, analyze whether there is pollution in the water quality. If there is, feedback the analysis result to the pollution area analysis module; if not, directly feedback the analysis result, including the following steps:

[0077] Construct a rule set, where each rule corresponds to a feature, and the rule form is: If the feature value is greater than the threshold, it is considered that the water quality index represented by this feature is polluted;

[0078] Apply the following rules to the water quality data matrix fed back by the selected water quality data acquisition module and output the analysis result, where, represents the logical "OR", represents the data value of the j-th water quality characteristic of the i-th pump house, represents the threshold value of the j-th water quality characteristic.

[0079] Through the above steps, it is possible to accurately analyze and identify the pump houses with water quality pollution, improving the efficiency and accuracy of water quality monitoring. By constructing a rule set, each rule sets a threshold for a water quality characteristic. When the water quality characteristic value exceeds the threshold, the system determines that the water quality index represented by this characteristic is polluted. The system applies the collected water quality data matrix to these rules, combines the comparison results of the water quality characteristic data values of each pump house and the corresponding thresholds through the logical "OR" operator, and outputs the analysis result. This method enables the system to quickly respond to water quality changes. Once signs of pollution are detected, the analysis result is immediately fed back to the pollution area analysis module, so as to take corresponding measures.

[0080] Preferably, the method for establishing a prediction model includes the following steps,

[0081] Collect the historical water quality data of all pump houses in a certain area to form a map library containing polluted pump houses, and require that there is at least one pump house with water quality pollution during the same period;

[0082] Record the pollution degree of the water quality data of each pump house compared with the threshold value at the same time, and associate it with the color in the map. The pollution degree is proportional to the color;

[0083] For each pumping station, extract its water quality data as a feature vector: ;

[0084] Extract the color information of each pumping station from the map as a feature vector: ;

[0085] Use the cosine similarity to calculate the water quality feature similarity between two pumping stations: , where, is the dot product of the feature vectors, and are the norms of the vectors;

[0086] For the pumping stations with known pollution, use the water quality characteristics and color characteristics to train the model, and mark the polluted pumping stations as 1 and the non-polluted pumping stations as 0;

[0087] Import the support vector machine for training to find the optimal hyperplane to classify whether the pumping station is polluted, and finally output the prediction model;

[0088] According to the analysis result of the water quality safety analysis module and through the prediction model, analyze and confirm the pump houses that may be polluted, including the following steps,

[0089] Convert the pump house collected by the water quality data acquisition module into a real-time atlas, and extract the water quality data of the pump house collected by the water quality data acquisition module;

[0090] Extract features from the real-time atlas and the water quality data of the pump house collected by the water quality data acquisition module;

[0091] Import the above features into the prediction model and output the atlas of the historical water quality data of all pump houses in the same area at a certain period with the highest similarity;

[0092] Combine the output atlas to export the historical water quality data of the remaining uncollected pump houses to confirm the pump houses that may be polluted.

[0093] Collect and analyze the historical water quality data of all pump houses in the same area. By associating the water quality data with the color information in the atlas, construct a feature vector, and use the cosine similarity calculation method to evaluate the water quality feature similarity between pump stations. Subsequently, through the support vector machine training model, find the optimal hyperplane for distinguishing polluted and non-polluted pump houses, so as to establish a prediction model. Using this prediction model, by extracting the features of the real-time collected water quality data and historical data, import them into the prediction model for analysis to determine the pump houses that may be polluted. This step involves converting the real-time water quality data into an atlas, extracting features, and comparing them with historical data to identify and warn of potential water quality problems. In this way, all pump houses can jointly apply the same set of water quality monitoring systems, which can not only monitor the water quality in real time, but also discover and respond to possible water quality pollution events in advance through the prediction model, thus ensuring the safety of water supply.

[0094] The above is only the preferred embodiment of the present invention and is not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A secondary water supply intelligent pump room water quality monitoring system, characterized in that: Including water quality data collection module, water quality safety analysis module, and pollution area analysis module; The number of the water quality data acquisition modules is several, and the water quality data acquisition modules are used to collect pump room water quality data and feed it back to the water quality safety analysis module after pre-processing; The water quality safety analysis module is used to analyze whether the water quality is polluted based on the water quality data of some pump rooms. If so, the analysis results are fed back to the pollution area analysis module. If not, the analysis results are directly fed back; The pollution area analysis module is used to analyze and confirm the pump room that may be polluted by using the prediction model based on the analysis results of the water quality safety analysis module. The water quality data collection module that controls the pump room that may be polluted collects the water quality data of the pump room, and then the water quality safety analysis module analyzes whether the water quality is polluted and directly feeds back the analysis results. The feedback to the water quality safety analysis module includes the following steps: Determine the total number of water quality data acquisition modules as n; calculate the number of water quality data acquisition modules that need to be selected k=n / 2, if n is an odd number, k is rounded up; Create a list containing all water quality data collection module identifiers , use the Fisher-Yates shuffle algorithm to shuffle the water quality data collection module identifiers in the list; According to the shuffled list, the first k water quality data collection module identifiers are selected as the selected water quality data collection modules; only the selected water quality data collection modules are arranged to collect the water quality data of the pump room; The analysis and confirmation of the possible contamination of the pump room by the prediction model includes the following steps: Convert the pump room data collected by the selected water quality data collection module into a real-time map and provide the water quality data of the pump room; perform feature extraction on the real-time map and water quality data; Import the features into the prediction model and output the graph of historical water quality data of all pump houses in the same area during a certain period with the highest similarity; combine the output graph to derive the historical water quality data of the remaining pump houses that have not been collected to identify the pump houses that may be contaminated; The method for establishing the prediction model comprises the following steps: Collect historical water quality data of all pump houses in the same area to form a map library containing polluted pump houses, and require that there is at least one pump house with water pollution in the same period; Record the pollution degree of water quality data of each pump room compared with the threshold at the same time, and associate it with the color in the map. The pollution degree is proportional to the color. For each pumping station, its water quality data is extracted as a feature vector, the color information of each pumping station is extracted from the spectrum as a feature vector, and the cosine similarity is used to calculate the similarity of water quality characteristics between two pumping stations; For pumping stations with known pollution, the model is trained using water quality features and color features, and polluted pumping stations are marked as 1 and non-polluted pumping stations are marked as 0; The support vector machine is imported for training to find the best hyperplane to classify whether the pumping station is polluted, and finally the prediction model is output.

2. A secondary water supply intelligent pump room water quality monitoring system according to claim 1, characterized in that: Each of the water quality data acquisition modules separately collects water quality data of a single pump room and feeds it back to the water quality safety analysis module after pre-processing. The pump room water quality data includes turbidity Turbidity, total organic carbon TOC, total nitrogen TN, total phosphorus TP and ammonia nitrogen NH3 of water quality.

3. A secondary water supply intelligent pump room water quality monitoring system according to claim 1, characterized in that: The method of using the Fisher-Yates shuffling algorithm to shuffle the water quality data collection module identifiers in the list includes the following steps: For each index from i to 1, usually i starts at n-1, where n is the length of the list: ,in, , which means generating a closed interval A random integer within Represents the element at index a and b in the list.

4. A secondary water supply intelligent pump room water quality monitoring system according to claim 1, characterized in that: The method of collecting pump room water quality data and feeding back to the water quality safety analysis module after pre-processing also includes the following steps: The selected water quality data collection module collects a number of water quality data of the pump room on the same time axis; Traverse all data values ​​of each water quality data in the water quality data set to obtain the maximum and minimum values; Formula 1 is used to standardize the data value x of each water quality data to obtain the standardized data value , formula 1 is , and finally form a water quality data matrix.

5. A secondary water supply intelligent pump room water quality monitoring system according to claim 1, characterized in that: The method of analyzing whether the water quality is polluted according to the water quality data of some pump rooms, and if so, feeding back the analysis result to the polluted area analysis module, and if not, directly feeding back the analysis result, comprises the following steps: Construct a set of rules, each rule corresponds to a feature, and the rule form is: if the feature value is greater than the threshold, it is considered that the water quality indicator represented by the feature is polluted; Apply the following rules to the water quality data matrix fed back by the selected water quality data collection module: , and output the analysis results, where represents the logical "or", represents the data value of the jth water quality characteristic of the i-th pump room, represents the threshold of the j-th water quality feature.

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