Water quality monitoring system for secondary water supply intelligent pump house

Abstract

The invention discloses a secondary water supply intelligent pump room water quality monitoring system, belongs to the technical field of pump room water quality monitoring, and solves the problem that when a water quality monitoring system in any secondary intelligent pump room is damaged, the water quality condition in the secondary intelligent pump room cannot be fed back. Comprising a water quality data acquisition module, a water quality safety analysis module and a polluted area analysis module, the water quality data acquisition module is used for acquiring pump room water quality data and feeding back to the water quality safety analysis module after preprocessing; the water quality safety analysis module is used for analyzing whether the water quality is polluted according to the water quality data of part of the pump rooms, and if yes, the analysis result is fed back to the polluted area analysis module. The system can randomly monitor the water quality of part of the secondary water supply smart pump rooms in real time and predict the water pollution of the remaining undetected secondary water supply smart pump rooms, so that even if part of monitoring equipment breaks down, the system can still maintain the continuity and efficiency of water quality monitoring.

Description

Technical Field

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

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

[0003] By integrating an advanced remote monitoring system, the secondary smart pump room can achieve real-time monitoring and management of pump room equipment. Operators can obtain pump room operation data at any time through computers or mobile devices to promptly discover and deal with 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 reduces operating costs. The pump room control system can optimize the operation strategy of the water pump through an automated scheduling algorithm to ensure the stability of water supply pressure and flow, while reducing energy consumption and maintenance costs.

[0004] In addition, a water quality monitoring system is also needed in the secondary intelligent pump room, so that the water quality of the tap water transported by the pump room can be effectively monitored. At present, each pump room has a one-to-one water quality monitoring system, and it is impossible to unify and coordinate the water quality monitoring data of the secondary intelligent pump rooms in the entire area. Therefore, when the water quality monitoring system in any secondary smart pump room is damaged, it is impossible to provide feedback on the water quality in the secondary smart pump room. Similarly, if multiple sets of water are damaged, there will be a large number of secondary smart pump rooms where the water quality cannot be detected, which will ultimately affect the output water quality and cannot be monitored and guaranteed.

[0005] Therefore, a secondary water supply smart pump room water quality monitoring system is proposed to solve or alleviate the above problems. Summary of the invention

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

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: A secondary water supply smart pump house water quality monitoring system, including 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 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 according to the water quality data of some pump rooms. If so, the analysis result is fed back to the pollution area analysis module; if not, the analysis result is directly fed back; The polluted area analysis module is used to analyze and confirm the pump room that may be polluted according to the analysis results of the water quality safety analysis module and through the prediction model, and then the water quality data collection module of the pump room that may be polluted is used to collect the water quality data of the pump room. The water quality safety analysis module analyzes whether the water quality is polluted and directly feeds back the analysis results.

[0008] Preferably, the number of the water quality data acquisition modules is several, each of which separately collects water quality data of a single pump room and feeds it back to the water quality safety analysis module after preprocessing. The water quality data of the pump room includes turbidity, total organic carbon TOC, total nitrogen TN, total phosphorus TP, ammonia nitrogen NH 3 .

[0009] Preferably, the collecting of pump room water quality data and feeding back to the water quality safety analysis module after pre-processing comprises 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 module is arranged to collect the pump room water quality data.

[0010] Preferably, the use of the Fisher-Yates shuffling algorithm to shuffle the water quality data acquisition module identifiers in the list comprises 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.

[0011] Preferably, the process of collecting pump room water quality data and feeding it 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.

[0012] Preferably, the step of analyzing whether water quality is polluted according to 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.

[0013] Preferably, 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, extract its water quality data as a feature vector: ; Extract the color information of each pumping station from the atlas as a feature vector: ; Use cosine similarity to calculate the similarity of water quality characteristics between two pumping stations: ,in, is the dot product of the eigenvectors, and is the norm of the vector; 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.

[0014] Preferably, the step of analyzing and confirming the pump room that may be contaminated based on the analysis result of the water quality safety analysis module and using a prediction model comprises the following steps: Convert the pump room data collected by the water quality data collection module into a real-time map, and extract the water quality data collected by the pump room by the water quality data collection module; Extract features from the water quality data collected by the real-time graph and water quality data collection module in the pump room; Import the above features into the prediction model and output a graph of historical water quality data of all pump houses in the same area during a certain period with the highest similarity; Combined with the output map, the historical water quality data of the remaining pump rooms that have not been collected are exported to identify the pump rooms that may be contaminated.

[0015] The present invention has the following beneficial effects: The present invention provides a system capable of real-time local monitoring and prediction of water quality pollution in a secondary water supply smart pump room. Through intelligent data collection, analysis and prediction, the accuracy and reliability of water quality monitoring are improved, and the continuity and effectiveness of water quality monitoring can be ensured even when some monitoring equipment is damaged. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a structural block diagram of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, 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.

[0019] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0021] In the description of the present invention, it should be understood that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the product of the invention is conventionally placed when in use, or are the orientations or positional relationships conventionally understood by those skilled in the art. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0022] Furthermore, the terms “first”, “second”, “third”, etc. are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0023] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" 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 a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] A secondary water supply smart pump room water quality monitoring system, such as Figure 1 As shown, it includes a water quality data acquisition module, a water quality safety analysis module, and a pollution area analysis module; The water quality data collection module is 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 according to the analysis results of the water quality safety analysis module and through the prediction model. The water quality data collection module of the pump room that may be polluted is used to collect the water quality data of the pump room. The water quality safety analysis module analyzes whether the water quality is polluted and directly feeds back the analysis results.

[0025] The system collects key water quality parameters such as turbidity, total organic carbon, total nitrogen, total phosphorus, ammonia nitrogen, etc. through the water quality data acquisition module. These parameters are key indicators for evaluating water quality. Based on modular design, this system divides water quality monitoring functions into three modules: data acquisition, safety analysis and pollution area analysis. The data acquisition module is responsible for real-time collection and pre-processing of water quality data, and then transmits 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, which analyzes the pump room that may be polluted through a predictive model and guides the corresponding data acquisition module to conduct further water quality testing.

[0026] This not only improves the accuracy and response speed of monitoring, but also enhances the robustness and reliability of the system through the collaborative work between modules. Even if some monitoring equipment fails, the system can still continue to perform monitoring tasks through other normally working modules to ensure water supply safety.

[0027] Preferably, there are several water quality data acquisition modules, each of which collects water quality data of a single pump room and feeds it back to the water quality safety analysis module after preprocessing. The water quality data of the pump room includes turbidity, total organic carbon TOC, total nitrogen TN, total phosphorus TP, ammonia nitrogen NH 3 .

[0028] Deploy multiple water quality data collection modules, each module is responsible for collecting water quality data of a single pump room, including key indicators such as turbidity, total organic carbon, total nitrogen, total phosphorus, and ammonia nitrogen. In this way, the system can obtain more detailed water quality information and provide accurate data support for subsequent water quality safety analysis.

[0029] Preferably, collecting the pump room water quality data and feeding it back to the water quality safety analysis module after pre-processing 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 module is arranged to collect the pump room water quality data.

[0030] Through the above method, the water quality data collection process is optimized to ensure the representativeness of the data and the robustness of the system. The total number of water quality data collection modules is determined, the number of modules that need to be selected is calculated, a list containing all module identifiers is created, and the order of the list is randomly disrupted using the Fisher-Yates shuffle algorithm, so as to select some modules for water quality data collection. This method improves the randomness and fairness of data collection through a randomized selection mechanism and avoids the deviation that may be caused by a fixed collection mode. At the same time, by collecting data on the same time axis for the selected modules, obtaining the maximum and minimum values, and performing data standardization, an accurate water quality data matrix is ​​formed, which provides high-quality input data for subsequent water quality safety analysis. This process not only enhances the system's anti-interference ability, but also ensures that when some monitoring equipment is damaged, the system can still perform effective water quality monitoring through other modules that are working normally.

[0031] Preferably, the Fisher-Yates shuffling algorithm is used to shuffle the water quality data acquisition module identifiers in the list, including 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.

[0032] Through the above steps, the accuracy and reliability of the water quality monitoring system are enhanced. The principle of this scheme is to use the Fisher-Yates shuffle algorithm to randomly sort the identifiers of the water quality data collection modules, thereby ensuring that the module selection for each data collection is random, avoiding the deviation that may be introduced by selecting modules in a fixed order.

[0033] Preferably, collecting the pump room water quality data and feeding it back to the water quality safety analysis module after pre-processing, further 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.

[0034] Through the above steps, the accuracy and availability of water quality monitoring data are improved, and data consistency and comparability are ensured through standardization. Standardization not only makes data from different pump rooms comparable, but also enhances the model's sensitivity to water quality changes, thereby improving the water quality monitoring system's ability to identify potential pollution.

[0035] Preferably, the water quality data of some pump rooms are used to analyze whether the water quality is polluted. If so, the analysis result is fed back to the pollution area analysis module. If not, the analysis result is directly fed back, including 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.

[0036] Through the above steps, it is possible to accurately analyze and identify pump rooms with water pollution, thereby improving the efficiency and accuracy of water quality monitoring. By constructing a set of rules, each rule sets a threshold for a water quality feature. When the water quality feature value exceeds the threshold, the system determines that the water quality index represented by the feature is polluted. The system applies the collected water quality data matrix to these rules, and outputs the analysis results by combining the comparison results of the water quality feature data values ​​of each pump room with the corresponding threshold through the logical "or" operator. This method enables the system to respond quickly to changes in water quality. Once signs of pollution are found, the analysis results are immediately fed back to the pollution area analysis module so that corresponding measures can be taken.

[0037] Preferably, 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, extract its water quality data as a feature vector: ; Extract the color information of each pumping station from the atlas as a feature vector: ; Use cosine similarity to calculate the similarity of water quality characteristics between two pumping stations: ,in, is the dot product of the eigenvectors, and is the norm of the vector; 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; Import support vector machine for training to find the best hyperplane to classify whether the pumping station is polluted, and finally output the prediction model; According to the analysis results of the water quality safety analysis module and the prediction model, the pump room that may be contaminated is analyzed and confirmed, including the following steps: Convert the pump room data collected by the water quality data collection module into a real-time map, and extract the water quality data collected by the pump room by the water quality data collection module; Extract features from the water quality data collected by the real-time graph and water quality data collection module in the pump room; Import the above features into the prediction model and output a graph of historical water quality data of all pump houses in the same area during a certain period with the highest similarity; Combined with the output map, the historical water quality data of the remaining pump rooms that have not been collected are exported to identify the pump rooms that may be contaminated.

[0038] Collect and analyze the historical water quality data of all pump houses in the same area, construct feature vectors by associating water quality data with color information in the graph, and use the cosine similarity calculation method to evaluate the similarity of water quality characteristics between pump houses. Subsequently, the support vector machine training model is used to find the optimal hyperplane that distinguishes polluted and non-polluted pump houses, so as to establish a prediction model. Using this prediction model, the real-time collected water quality data and historical data are extracted for features, and imported into the prediction model for analysis to determine the pump houses that may be polluted. This step involves converting real-time water quality data into a graph, extracting features, and comparing them with historical data to identify and warn of potential water quality problems. In this way, all pump houses can use the same water quality monitoring system, which can not only monitor water quality in real time, but also detect and respond to possible water pollution incidents in advance through the prediction model, thereby ensuring water supply safety.

[0039] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in 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 water quality data acquisition module is 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 according to the water quality data of some pump rooms. If so, the analysis result is fed back to the pollution area analysis module; if not, the analysis result is directly fed back; The polluted area analysis module is used to analyze and confirm the pump room that may be polluted according to the analysis results of the water quality safety analysis module and through the prediction model, and then the water quality data collection module of the pump room that may be polluted is used to collect the water quality data of the pump room. The water quality safety analysis module analyzes whether the water quality is polluted and directly feeds back the analysis results.

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

3. A secondary water supply intelligent pump room water quality monitoring system according to claim 1 or 2, characterized in that: The method of collecting pump room water quality data and feeding it back to the water quality safety analysis module after pre-processing 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 module is arranged to collect the pump room water quality data.

4. A secondary water supply intelligent pump room water quality monitoring system according to claim 3, 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.

5. A secondary water supply intelligent pump room water quality monitoring system according to claim 3, 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.

6. 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.

7. A secondary water supply intelligent pump room water quality monitoring system according to claim 1, characterized in that: 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, extract its water quality data as a feature vector: ; Extract the color information of each pumping station from the atlas as a feature vector: ; Use cosine similarity to calculate the similarity of water quality characteristics between two pumping stations: ,in, is the dot product of the eigenvectors, and is the norm of the vector; for pumping stations with known pollution, the model is trained using water quality features and color features, and the polluted pumping stations are marked as 1 and the 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.

8. A secondary water supply intelligent pump room water quality monitoring system according to claim 7, characterized in that: The method of analyzing and confirming the pump room that may be contaminated based on the analysis results of the water quality safety analysis module and using the prediction model includes the following steps: Convert the pump room data collected by the water quality data collection module into a real-time map, and extract the water quality data collected by the pump room by the water quality data collection module; Extract features from the water quality data collected by the real-time graph and water quality data collection module in the pump room; Import the above features into the prediction model and output a graph of historical water quality data of all pump houses in the same area during a certain period with the highest similarity; Combined with the output map, the historical water quality data of the remaining pump rooms that have not been collected are exported to identify the pump rooms that may be contaminated.

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