A water environment management system based on cloud computing

Abstract

The present invention discloses a water environment management system based on cloud computing, which relates to the field of environmental monitoring technology. In order to solve the problems in the prior art that water environment emergencies cannot be responded to in real time, the demand for rapid early warning cannot be met, and the regular manual monitoring data cannot achieve high frequency and large-scale coverage due to human, material and environmental conditions, thus affecting the continuity and integrity of the monitoring data; by real-time collection and transmission of monitoring data to a cloud computing platform, continuous and high-frequency monitoring of the water environment is achieved, and the instant analysis capability of the cloud computing platform can quickly identify water quality changes, accurately identify abnormal key monitoring points, and dynamically adjust the data collection and transmission time interval according to the severity of the abnormality and the scope of influence, which not only ensures the timely acquisition of key information, but also avoids unnecessary data transmission, saves resources, tracks and manages abnormal situations, and ensures that the problem is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a water environment management system based on cloud computing. Background Art

[0002] With the acceleration of industrialization and urbanization, water pollution is becoming increasingly serious, posing a great threat to human health and the ecological environment. For example, a Chinese patent with the announcement number CN113420845B discloses a method and system for fusing multi-source water environment monitoring data, including: automatic monitoring is performed every day within the evaluation period to obtain automatic water environment data, and the daily average of the automatic water environment data of each day is calculated; manual monitoring is performed on one day within the evaluation period to obtain manual water environment data; the number of days between each day of automatic monitoring and one day of manual monitoring is calculated to calculate the weight of each daily average value; based on the weight of the daily average value, the daily average value, the manual water environment data, the fusion weight of the automatic water environment data obtained in advance, and the fusion weight of the manual water environment data obtained in advance, the fusion data of the automatic water environment data and the manual water environment data are obtained.

[0003] Although the above patent can improve the accuracy of water environment monitoring and comprehensively reflect the overall situation of the monitoring section, there may be time delays in the fusion process, and it is impossible to respond to water environment emergencies in real time and meet the needs of rapid early warning. In addition, regular manual monitoring data is limited by human, material and environmental conditions, and cannot achieve high frequency and large-scale coverage, which affects the continuity and integrity of the monitoring data. Summary of the invention

[0004] The purpose of the present invention is to provide a water environment management system based on cloud computing, which realizes real-time data collection and analysis through a cloud computing platform, improves the accuracy of monitoring data, significantly improves the accuracy and real-time performance of monitoring data, simplifies the deployment and maintenance of the system, and monitors multiple water samples at the same time, meeting the needs of different application scenarios, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A water environment monitoring system based on cloud computing includes:

[0007] Water quality sensor, used to collect water quality parameters of water bodies in real time;

[0008] Environmental data collection equipment, used to monitor the water surface and surrounding environmental data in real time;

[0009] Cloud servers are used to build cloud computing platforms, obtain monitoring data collected by water quality sensors and environmental data acquisition equipment, process, store and analyze monitoring data, and perform water quality prediction, pollution source identification and risk assessment based on water environment quality models;

[0010] The user terminal is used to receive warning information from the cloud computing platform when water quality parameters exceed preset thresholds, obtain real-time monitoring data and water quality risk assessment reports based on the user interaction interface, and send control instructions to remotely control the operating status of water quality sensors and environmental data acquisition equipment;

[0011] The water environment management system also performs the following steps:

[0012] Step 1: Data collection: Automatic monitoring equipment is deployed at key monitoring points in the water environment to collect water quality parameters and environmental data in real time. The automatic monitoring equipment transmits the monitoring data to the cloud computing platform in real time;

[0013] Step 2: Data processing: The cloud computing platform pre-processes the acquired monitoring data and stores the pre-processed monitoring data in the water quality monitoring database according to the monitoring data type classification;

[0014] Step 3: Data analysis: Analyze the stored monitoring data based on the cloud computing platform, predict water quality trends, identify potential pollution sources and conduct risk assessment based on the water environment quality model;

[0015] Step 4: Monitoring and early warning: Based on the cloud computing platform, the acquired water quality parameter data and monitoring data analysis results are displayed in real time. When abnormal water quality is detected, the early warning mechanism is triggered, and early warning information is sent to the user terminal, and corresponding processing suggestions are provided.

[0016] Furthermore, the automatic monitoring equipment is deployed in step 1, specifically including:

[0017] Install automatic monitoring equipment at each key monitoring point, including water quality parameter sensors and environmental data collection equipment;

[0018] The water quality parameter sensor is installed underwater in direct contact with the water sample. The water quality parameter sensor actively collects water quality data at preset time intervals. At the same time, the environmental data collection device is installed on one side of the water quality parameter sensor collection range to monitor and record the water surface and surrounding environmental data in real time;

[0019] The automatic monitoring device performs data verification and standard unit conversion on the acquired monitoring data, and timestamps the verified and converted standard monitoring data to record the specific time of data collection.

[0020] Furthermore, the automatic monitoring equipment is deployed in step 1, further comprising:

[0021] Extract the straight-line distance between every two adjacent key monitoring points;

[0022] Compare the straight-line distance between each two adjacent key monitoring points with a preset straight-line distance threshold;

[0023] When the straight-line distance between each two adjacent key monitoring points exceeds a preset straight-line distance threshold, it is determined that an additional key monitoring point needs to be added between the two key monitoring points corresponding to the straight-line distance exceeding the preset straight-line distance threshold;

[0024] When it is determined that an additional key monitoring point is needed, two key monitoring points corresponding to the straight-line distance exceeding the preset straight-line distance threshold are set as the first key monitoring point and the second key monitoring point, and the key monitoring point to be added is used as the target key monitoring point;

[0025] Extracting the angle and the straight-line distance between the first key monitoring point and the second key monitoring point;

[0026] Extracting the water volume and water flow rate of the first key monitoring point;

[0027] Extracting the water volume and water flow rate of the second key monitoring point;

[0028] Obtaining a first distance setting parameter using the angle between the first key monitoring point and the second key monitoring point combined with the water volume and water flow rate at the first key monitoring point;

[0029] The first distance setting parameter is obtained by the following formula:

[0030]

[0031] Among them, P 01 represents the first distance setting parameter; θ represents the angle between the first key monitoring point and the second key monitoring point; Q 01 and Q 02 Respectively represent the water volume at the first key monitoring point and the water volume at the second key monitoring point; V 01 and V 02 Denote the water flow rate at the first key monitoring point and the water flow rate at the second key monitoring point respectively; D z represents the median distance corresponding to the straight-line distance between all two adjacent key monitoring points; k represents the adjustment coefficient, and the value range of k is 1.2-1.7; D p It represents the average distance between all two adjacent key monitoring points.

[0032] Obtain a second distance setting parameter using the straight-line distance between the first key monitoring point and the second key monitoring point in combination with the water volume and water flow rate of the second key monitoring point;

[0033] The second distance setting parameter is obtained by the following formula:

[0034]

[0035] Among them, P 02 represents the second distance setting parameter; D represents the straight-line distance between the first key monitoring point and the second key monitoring point;

[0036] The first distance setting parameter and the second distance setting parameter are used to set the distance range between the target key monitoring point and the first key monitoring point and the second key monitoring point.

[0037] Further, using the first distance setting parameter and the second distance setting parameter to set the distance range between the target key monitoring point and the first key monitoring point and the second key monitoring point includes:

[0038] Extracting a first distance setting parameter and a second distance setting parameter;

[0039] Extract the straight-line distance between every two adjacent key monitoring points;

[0040] Acquire the altitude difference between the first key monitoring point and the second key monitoring point using the altitude of the first key monitoring point and the altitude of the second key monitoring point;

[0041] Obtaining a first distance range between the target key monitoring point and the first key monitoring point by using the straight-line distance between each two adjacent key monitoring points, the altitude difference between the first key monitoring point and the second key monitoring point, and combining the first distance setting parameter and the second distance setting parameter;

[0042] The first distance range is obtained by the following formula:

[0043]

[0044]

[0045] Among them, L up and L down Indicates the upper and lower limits of the first distance range; P 02 , P 02represents the first distance setting parameter and the second distance setting parameter respectively; D represents the straight-line distance between the first key monitoring point and the second key monitoring point; H represents the altitude difference between the first key monitoring point and the second key monitoring point; n represents the number of groups of each adjacent two key monitoring points except the first key monitoring point and the second key monitoring point; D i represents the straight-line distance between the i-th group of key monitoring points;

[0046] Using the first distance range in combination with the straight-line distance between the first key monitoring point and the second key monitoring point, a second distance range between the target key monitoring point and the first key monitoring point is obtained;

[0047] The second distance range is obtained by the following formula:

[0048]

[0049] Among them, S up and S down Indicates the upper limit and lower limit of the second distance range;

[0050] The first distance range and the second distance range are used as constraint conditions to set a target key monitoring point so that the distance between the target key monitoring point and the first key monitoring point and the second key monitoring point satisfies the first distance range and the second distance range.

[0051] Furthermore, the water quality parameter sensor actively collects water quality data at preset time intervals, specifically including:

[0052] Acquire water quality parameter data collected based on preset time intervals, traverse the water quality parameter data according to parameter characteristics, identify and extract abnormal data that does not meet the parameter characteristics, and record the collection time point of the corresponding abnormal data;

[0053] Determine whether the time interval and number of abnormal data collection time points meet the preset minimum normal standard. If so, remove the abnormal data from the water quality parameter data to obtain normal water quality parameter data. Otherwise, determine that the water quality parameter sensor is abnormal and issue a terminal abnormal alarm reminder.

[0054] The normal water quality parameter data are classified to obtain sensor parameters and water quality parameters, wherein the sensor parameters include sensor operation data and operation temperature, and the water quality parameters include pH value, dissolved oxygen, turbidity and heavy metal content parameters of the water body.

[0055] Furthermore, the step 1: data collection, further includes:

[0056] Build a data interaction channel between the automatic monitoring equipment and the cloud computing platform, and transmit the data collected by the automatic monitoring equipment to the cloud computing platform through the data interaction channel;

[0057] Obtain the monitoring data analysis results of the cloud computing platform based on the data interaction channel, determine the key monitoring points with abnormalities, and adjust the data collection and transmission time interval of the automatic monitoring equipment according to the severity and possible impact of the abnormal key monitoring points;

[0058] The abnormal key monitoring points will be marked as abnormal. After the abnormal situation is resolved, the abnormal mark of the key monitoring point will be cancelled, and the data collection and transmission time interval of the automatic monitoring equipment will be restored to the preset time interval.

[0059] Furthermore, in step 2, the pre-processed monitoring data is classified and stored in the water quality monitoring database according to the monitoring data type, specifically including:

[0060] Integrate the pre-processed monitoring data received from the automatic monitoring equipment and identify data types in the pre-processed monitoring data, including water quality parameter types and environmental data types;

[0061] The water quality parameter type includes the sub-parameter types of pH value, dissolved oxygen, turbidity, and heavy metal content of the water body, and the environmental data type includes the sub-data types of camera monitoring video data and meteorological data;

[0062] Classify and store the pre-processed monitoring data into different tables or fields in the water quality monitoring database according to the identified data types;

[0063] Among them, one or more tables are created based on the water quality parameter type to store the sub-parameter type, and a corresponding vector space is created based on the environmental data and stored according to the sub-data type.

[0064] Furthermore, the step three: data analysis specifically includes:

[0065] Extract monitoring data from the water quality monitoring database, and input the corresponding monitoring data into the trained water environment quality model based on the data type of the monitoring data;

[0066] Based on the water environment quality model, the water quality parameters in the monitoring data are analyzed in time series, the changing trends of water quality parameters are identified, the changing patterns of water quality parameters in different seasons and time periods are analyzed, and water quality parameters are predicted based on the changing patterns of water quality parameters;

[0067] Analyze the spatial distribution characteristics of water quality parameters in the monitoring area based on the water environment quality model, determine the distribution of water quality parameters in the monitoring area based on the spatial distribution characteristics, and identify potential pollution areas based on the differences in water quality parameters between key monitoring points;

[0068] Analyze the impact of different pollution sources on water quality, determine the specific location and type of pollution sources based on the changing trends of water quality parameters and their distribution within the monitoring area, assess water quality risks, and generate a water quality risk assessment report.

[0069] Furthermore, the step 4: monitoring and early warning, further includes:

[0070] Based on the changing trend of water quality parameters and the prediction results of water quality parameters, it is judged whether the water quality parameters exceed the preset safety threshold. When the water quality parameters exceed the preset safety threshold, an early warning mechanism is triggered;

[0071] Based on the early warning mechanism, a water quality risk assessment report corresponding to the water quality parameters is obtained to generate early warning information, which includes detailed information on water quality anomalies, possible impact ranges, and recommended emergency measures;

[0072] At the same time, corresponding monitoring data is obtained based on the water quality parameters, corresponding key monitoring points are determined, corresponding management personnel are matched based on the key monitoring points, and the early warning information is transmitted to the management personnel user terminal based on the Internet of Things.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] By collecting and transmitting monitoring data to the cloud computing platform in real time, continuous and high-frequency monitoring of the water environment is achieved. The cloud computing platform's real-time analysis capabilities can quickly identify changes in water quality, accurately identify abnormal key monitoring points, and dynamically adjust data collection and transmission time intervals according to the severity of the abnormality and the scope of impact. This not only ensures the timely acquisition of key information, but also avoids unnecessary data transmission, saves resources, tracks and manages abnormal situations, and ensures that problems are effectively resolved. By intelligently adjusting the data collection frequency, unnecessary energy consumption and network resource usage are reduced, long-term operating costs are reduced, the utilization rate of monitoring equipment is improved, the service life of the equipment is extended, and maintenance and replacement costs are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 This is a flow chart of the water environment management method based on cloud computing of the present invention. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0077] In order to solve the technical problems that the existing technology cannot respond to water environment emergencies in real time, cannot meet the needs of rapid warning, and the regular manual monitoring data is limited by human, material and environmental conditions and cannot achieve high frequency and large-scale coverage, thus affecting the continuity and integrity of the monitoring data, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0078] A water environment monitoring system based on cloud computing and a water environment management system based on cloud computing include:

[0079] Water quality sensor, used to collect water quality parameters of water bodies in real time;

[0080] Environmental data collection equipment, used to monitor the water surface and surrounding environmental data in real time;

[0081] Cloud servers are used to build cloud computing platforms, obtain monitoring data collected by water quality sensors and environmental data acquisition equipment, process, store and analyze monitoring data, and perform water quality prediction, pollution source identification and risk assessment based on water environment quality models;

[0082] The user terminal is used to receive warning information from the cloud computing platform when water quality parameters exceed preset thresholds, obtain real-time monitoring data and water quality risk assessment reports based on the user interaction interface, and send control instructions to remotely control the operating status of water quality sensors and environmental data acquisition equipment;

[0083] The water environment management system also performs the following steps:

[0084] Step 1: Data collection: Automatic monitoring equipment is deployed at key monitoring points in the water environment to collect water quality parameters and environmental data in real time. Water quality parameters include pH value, dissolved oxygen, turbidity, heavy metal content and other parameters of the water body. Environmental data includes real-time monitoring of the water surface and surrounding environment. The automatic monitoring equipment transmits the monitoring data to the cloud computing platform in real time.

[0085] In this embodiment, the step 1: data collection, further includes:

[0086] Build a data interaction channel between the automatic monitoring equipment and the cloud computing platform, and transmit the data collected by the automatic monitoring equipment to the cloud computing platform through the data interaction channel;

[0087] Obtain the monitoring data analysis results of the cloud computing platform based on the data interaction channel, determine the key monitoring points with abnormalities, and adjust the data collection and transmission time interval of the automatic monitoring equipment according to the severity and possible impact of the abnormal key monitoring points;

[0088] The abnormal key monitoring points will be marked as abnormal. After the abnormal situation is resolved, the abnormal mark of the key monitoring point will be cancelled, and the data collection and transmission time interval of the automatic monitoring equipment will be restored to the preset time interval;

[0089] In this embodiment, the cloud computing platform performs real-time analysis on the transmitted data, and can quickly identify changes in water quality. By analyzing the monitoring data, it can accurately identify abnormal key monitoring points, and dynamically adjust the data collection and transmission time interval according to the severity and impact range of the abnormality, which not only ensures the timely acquisition of key information, but also avoids unnecessary data transmission, saves resources, tracks and manages abnormal situations, ensures that problems are effectively solved, intelligently adjusts the data collection frequency, reduces unnecessary energy consumption and network resource occupation, reduces long-term operating costs, improves the utilization rate of monitoring equipment, extends the service life of equipment, and reduces maintenance and replacement costs;

[0090] Step 2: Data processing: The cloud computing platform pre-processes the acquired monitoring data, including data cleaning (removing invalid and erroneous data), normalization (unifying data format and dimension), and data calibration (adjusting monitoring data according to environmental parameters), and stores the pre-processed monitoring data in the water quality monitoring database according to the monitoring data type classification;

[0091] Step 3: Data analysis: Data analysis is performed on the stored monitoring data based on the cloud computing platform, including time series analysis, spatial distribution analysis, outlier detection, etc. Water quality change trend prediction, identification of potential pollution sources and risk assessment are performed based on the water environment quality model;

[0092] Step 4: Monitoring and early warning: Based on the cloud computing platform, the acquired water quality parameter data and monitoring data analysis results are displayed in real time. When abnormal water quality is detected, the early warning mechanism is triggered, and early warning information is sent to the user terminal, and corresponding processing suggestions are provided. The cloud computing platform provides a user interaction interface, allowing managers to query historical data, customize reports, set early warning thresholds, etc., and provide data support for decision-making.

[0093] In this embodiment, through the efficient data interaction between the automatic monitoring equipment and the cloud computing platform, the accuracy and timeliness of the monitoring data are improved, and the monitoring data are collected and transmitted to the cloud computing platform in real time, realizing continuous and high-frequency monitoring of the water environment. The real-time analysis capability of the cloud computing platform can quickly identify water quality changes, accurately identify abnormal key monitoring points, and dynamically adjust the data collection and transmission time intervals according to the severity of the abnormality and the scope of impact, thereby improving the utilization rate of the monitoring equipment. Data processing and analysis improve the availability of data, providing a scientific basis for water quality change trend prediction, pollution source identification and risk assessment, effectively ensuring the safety of the water environment, and sending early warning information and treatment suggestions to management personnel in a timely manner, thereby enhancing the predictability and emergency response capabilities of water environment management.

[0094] In this embodiment, the automatic monitoring equipment is deployed in step 1, specifically including:

[0095] Install automatic monitoring equipment at each key monitoring point, including water quality parameter sensors and environmental data collection equipment;

[0096] Among them, the key monitoring points include: water inlets: the main water inlets of rivers, lakes and other water bodies, used to monitor external pollution sources; water outlets: the outlet locations of sewage treatment plants and industrial discharge outlets, used to monitor the quality of treated water; water source protection areas: the core areas of drinking water sources, to ensure drinking water safety; water quality fragile areas: ecologically sensitive areas, such as wetlands and protected areas, to monitor the impact of water quality changes on the ecology; densely populated areas: waters around cities, focusing on the impact of human activities on water quality; industrial concentration areas: waters near factories, monitoring the impact of industrial emissions on water quality; agricultural irrigation areas: irrigation water sources near farmland, monitoring the impact of agricultural activities on water quality;

[0097] The water quality parameter sensor is installed underwater in direct contact with the water sample. The water quality parameter sensor actively collects water quality data at preset time intervals. At the same time, the environmental data collection device is installed on one side of the water quality parameter sensor collection range to monitor and record the water surface and surrounding environmental data in real time;

[0098] The automatic monitoring device performs data verification and standard unit conversion on the acquired monitoring data, and timestamps the verified and converted standard monitoring data to record the specific time of data collection.

[0099] Specifically, the automatic monitoring equipment is deployed in step 1, and further includes:

[0100] Extract the straight-line distance between every two adjacent key monitoring points;

[0101] Compare the straight-line distance between each two adjacent key monitoring points with a preset straight-line distance threshold;

[0102] When the straight-line distance between each two adjacent key monitoring points exceeds a preset straight-line distance threshold, it is determined that an additional key monitoring point needs to be added between the two key monitoring points corresponding to the straight-line distance exceeding the preset straight-line distance threshold;

[0103] When it is determined that an additional key monitoring point is needed, two key monitoring points corresponding to the straight-line distance exceeding the preset straight-line distance threshold are set as the first key monitoring point and the second key monitoring point, and the key monitoring point to be added is used as the target key monitoring point;

[0104] Extracting the angle and the straight-line distance between the first key monitoring point and the second key monitoring point;

[0105] Extracting the water volume and water flow rate of the first key monitoring point;

[0106] Extracting the water volume and water flow rate of the second key monitoring point;

[0107] Obtaining a first distance setting parameter using the angle between the first key monitoring point and the second key monitoring point combined with the water volume and water flow rate at the first key monitoring point;

[0108] The first distance setting parameter is obtained by the following formula:

[0109]

[0110] Among them, P 01 represents the first distance setting parameter; θ represents the angle between the first key monitoring point and the second key monitoring point; Q 01 and Q 02 Respectively represent the water volume at the first key monitoring point and the water volume at the second key monitoring point; V 01 and V 02 Denote the water flow rate at the first key monitoring point and the water flow rate at the second key monitoring point respectively; D z represents the median distance corresponding to the straight-line distance between all two adjacent key monitoring points; k represents the adjustment coefficient, and the value range of k is 1.2-1.7; D p It represents the average distance between all two adjacent key monitoring points.

[0111] Obtain a second distance setting parameter using the straight-line distance between the first key monitoring point and the second key monitoring point in combination with the water volume and water flow rate of the second key monitoring point;

[0112] The second distance setting parameter is obtained by the following formula:

[0113]

[0114] Among them, P02 represents the second distance setting parameter; D represents the straight-line distance between the first key monitoring point and the second key monitoring point;

[0115] The first distance setting parameter and the second distance setting parameter are used to set the distance range between the target key monitoring point and the first key monitoring point and the second key monitoring point.

[0116] The technical effect of the above technical solution is: by accurately calculating the first distance setting parameter and the second distance setting parameter, the solution can more accurately determine the location of the target key monitoring point, thereby improving the accuracy of the monitoring network. Considering multiple factors (such as angle, water volume, water flow rate, and straight-line distance, etc.) to calculate the distance setting parameters, the layout of the monitoring points is more in line with the actual water flow characteristics, and the accuracy of the monitoring data is further improved. By comparing the straight-line distance between two adjacent key monitoring points with the preset straight-line distance threshold, and adding key monitoring points when necessary, the solution can optimize the layout of the monitoring network to make it more reasonable and efficient. The optimized monitoring network can cover the monitoring area more comprehensively, reduce monitoring blind spots, and improve monitoring efficiency. The adjustment coefficient k in the solution allows adjustment within a certain range to adapt to different water flow conditions and monitoring requirements. This flexibility enables the monitoring network to be optimized for different situations, improving its adaptability and practicality. By comprehensively considering multiple factors to calculate the distance setting parameters, the solution can more flexibly cope with complex and changeable water flow environments. By accurately calculating the location of the target key monitoring points, the solution can reduce unnecessary monitoring point settings, thereby reducing monitoring costs. The optimized monitoring network reduces operation and maintenance costs and improves cost-effectiveness while maintaining high accuracy and comprehensive coverage. By ensuring the appropriate distance between the target key monitoring points and adjacent key monitoring points, the solution can maintain the continuity and integrity of monitoring data. Continuous monitoring data is of great significance for analyzing water flow changes and predicting extreme events such as floods, which helps to improve the decision-making level of water resources management and environmental protection.

[0117] In summary, the technical effects of this technical solution in terms of performance indicators are mainly reflected in the improvement of monitoring accuracy, optimization of monitoring network, enhancement of adaptability and flexibility, improvement of cost-effectiveness, and maintenance of data continuity and integrity. These technical effects make this solution have broad application prospects and important practical value in the fields of water resources management and environmental protection.

[0118] Specifically, using the first distance setting parameter and the second distance setting parameter to set the distance range between the target key monitoring point and the first key monitoring point and the second key monitoring point includes:

[0119] Extracting a first distance setting parameter and a second distance setting parameter;

[0120] Extract the straight-line distance between every two adjacent key monitoring points;

[0121] Acquire the altitude difference between the first key monitoring point and the second key monitoring point using the altitude of the first key monitoring point and the altitude of the second key monitoring point;

[0122] Obtaining a first distance range between the target key monitoring point and the first key monitoring point by using the straight-line distance between each two adjacent key monitoring points, the altitude difference between the first key monitoring point and the second key monitoring point, and combining the first distance setting parameter and the second distance setting parameter;

[0123] The first distance range is obtained by the following formula:

[0124]

[0125]

[0126] Among them, L up and L down Indicates the upper and lower limits of the first distance range; P 02 represents the second distance setting parameter; D represents the straight-line distance between the first key monitoring point and the second key monitoring point; H represents the altitude difference between the first key monitoring point and the second key monitoring point; n represents the number of groups of each adjacent two key monitoring points except the first key monitoring point and the second key monitoring point; D i represents the straight-line distance between the i-th group of key monitoring points;

[0127] Using the first distance range in combination with the straight-line distance between the first key monitoring point and the second key monitoring point, a second distance range between the target key monitoring point and the first key monitoring point is obtained;

[0128] The second distance range is obtained by the following formula:

[0129]

[0130] Among them, S up and S down Indicates the upper limit and lower limit of the second distance range;

[0131] The first distance range and the second distance range are used as constraint conditions to set a target key monitoring point so that the distance between the target key monitoring point and the first key monitoring point and the second key monitoring point satisfies the first distance range and the second distance range.

[0132] The technical effect of the above technical solution is: by comprehensively considering the first distance setting parameter, the second distance setting parameter, the straight-line distance between two adjacent key monitoring points and the altitude difference, the solution can accurately calculate the first distance range and the second distance range between the target key monitoring point and the first key monitoring point. This precise calculation method helps to ensure that the location setting of the target key monitoring point is more reasonable, thereby improving the accuracy of the monitoring data. The solution introduces a variety of factors (such as straight-line distance, altitude difference, water volume, water flow rate, etc.) to calculate the distance setting parameters and distance ranges. These factors jointly reflect the water flow characteristics and terrain conditions, which help to more comprehensively evaluate the location of the target key monitoring point. By considering the altitude difference, the solution can better adapt to complex terrain conditions and ensure that the monitoring network can maintain high accuracy and stability under different terrains. Considering the influence of water volume and water flow rate on water flow characteristics, the solution can flexibly adjust the location of the target key monitoring point according to different water flow conditions, thereby ensuring the reliability and effectiveness of the monitoring data. By accurately calculating the location of the target key monitoring point, the solution can optimize the layout of the monitoring network, reduce unnecessary monitoring point settings, and thus improve monitoring efficiency. The optimized monitoring network can reduce operation and maintenance costs and improve cost-effectiveness while maintaining high accuracy and comprehensive coverage. By ensuring the appropriate distance between the target key monitoring points and adjacent key monitoring points, the solution can maintain the continuity and integrity of the monitoring data, which is helpful for analyzing water flow changes and predicting extreme events such as floods. The optimized monitoring network can cover the monitoring area more comprehensively, reduce monitoring blind spots, and improve the comprehensiveness and reliability of monitoring data.

[0133] In summary, the technical effects of this technical solution in terms of performance indicators are mainly reflected in improved accuracy, enhanced adaptability, improved monitoring efficiency, and data continuity and integrity assurance. These technical effects work together on the monitoring network, enabling it to more accurately reflect water flow characteristics and terrain conditions, and provide strong technical support for water resources management and environmental protection.

[0134] In this embodiment, the water quality parameter sensor actively collects water quality data at preset time intervals, specifically including:

[0135] Acquire water quality parameter data collected based on preset time intervals, traverse the water quality parameter data according to parameter characteristics, identify and extract abnormal data that does not meet the parameter characteristics, and record the collection time point of the corresponding abnormal data;

[0136] Determine whether the time interval and number of abnormal data collection time points meet the preset minimum normal standard. If so, remove the abnormal data from the water quality parameter data to obtain normal water quality parameter data. Otherwise, determine that the water quality parameter sensor is abnormal and issue a terminal abnormal alarm reminder.

[0137] The normal water quality parameter data are classified to obtain sensor parameters and water quality parameters, wherein the sensor parameters include sensor operation data and operation temperature, and the water quality parameters include pH value, dissolved oxygen, turbidity and heavy metal content parameters of the water body.

[0138] In this embodiment, by installing equipment at multiple key monitoring points and traversing the data, the water environment conditions can be fully understood, the accuracy and reliability of data collection can be improved, real-time identification and processing of abnormal data can be achieved, the self-diagnosis capability of the monitoring system can be effectively improved, the intelligence and automation level of the water environment management system can be enhanced, and normal water quality parameter data can be classified and processed, providing a clear and orderly information basis for subsequent data analysis and decision-making.

[0139] In this embodiment, the step 2 stores the pre-processed monitoring data in the water quality monitoring database according to the monitoring data type classification, which specifically includes:

[0140] Integrate the pre-processed monitoring data (including water quality parameters and environmental data) received from the automatic monitoring equipment, and identify the data types in the pre-processed monitoring data, including water quality parameter types and environmental data types;

[0141] Water quality parameter types include sub-parameter types of pH value, dissolved oxygen, turbidity, and heavy metal content of water bodies, which directly reflect the physical and chemical state of water bodies; environmental data types include sub-data types of camera monitoring video data and meteorological data (such as temperature, humidity, wind speed, etc.), which provide background information of the water surface and surrounding environment;

[0142] According to the identified data types, the preprocessed monitoring data are classified and stored in different tables or fields in the water quality monitoring database. Among them, one or more tables are created based on the water quality parameter type to store the sub-parameter type, such as pH value table, dissolved oxygen table, etc. Each table contains the corresponding monitoring data, timestamp, monitoring point location and other information; based on the environmental data, corresponding vector spaces are created according to the sub-data types (such as images, meteorological data) for storage, such as image databases or meteorological data tables.

[0143] In this embodiment, by classifying the preprocessed monitoring data according to the data type and storing it in the water quality monitoring database, efficient organization, rapid query and accurate analysis of the data are achieved, the integrity of the information is ensured, the storage space is optimized, and data security is enhanced, thereby significantly improving the availability and management efficiency of water environment monitoring data and providing comprehensive and scientific data support for water environment management.

[0144] In this embodiment, the step three: data analysis, specifically includes:

[0145] Extract monitoring data from the water quality monitoring database, and input the corresponding monitoring data into the trained water environment quality model based on the data type of the monitoring data;

[0146] Based on the water environment quality model, the water quality parameters in the monitoring data are analyzed in time series, including seasonal decomposition and trend analysis, to identify the changing trends of water quality parameters, analyze the changing patterns of water quality parameters in different seasons and time periods, and predict water quality parameters based on the changing patterns of water quality parameters, taking into account seasonal factors and trend changes;

[0147] Analyze the spatial distribution characteristics of water quality parameters in the monitoring area based on the water environment quality model, determine the distribution of water quality parameters in the monitoring area based on the spatial distribution characteristics, and identify potential pollution areas based on the differences in water quality parameters between key monitoring points;

[0148] Analyze the impact of different pollution sources on water quality, determine the specific location and type of pollution sources based on the changing trends of water quality parameters and their distribution within the monitoring area, assess water quality risks, and generate a water quality risk assessment report, including risk levels, possible impact ranges, and recommended management measures;

[0149] In this embodiment, the water environment quality model can predict future water quality changes, evaluate the impact of different pollution sources on water quality, and provide a scientific basis for water environment management. The water environment quality model outputs key information including predicted values ​​of water quality parameters, pollution source identification results, and water quality risk assessment reports. The analysis results are presented to decision makers in the form of easy-to-understand charts and reports to ensure that decision makers can understand the meaning and limitations of the analysis results, so as to formulate corresponding environmental management policies and measures.

[0150] In this embodiment, the step 4: monitoring and early warning, further includes:

[0151] Based on the changing trend of water quality parameters and the prediction results of water quality parameters, it is judged whether the water quality parameters exceed the preset safety threshold. When the water quality parameters exceed the preset safety threshold, an early warning mechanism is triggered;

[0152] Based on the early warning mechanism, the water quality risk assessment report corresponding to the water quality parameters is obtained to generate early warning information, which includes detailed information on water quality anomalies, possible impact ranges, and recommended emergency measures. The early warning information will be notified to management personnel and relevant departments in a timely manner through SMS, email, application push, etc., to ensure the accuracy and timeliness of information transmission;

[0153] At the same time, corresponding monitoring data is obtained based on the water quality parameters, corresponding key monitoring points are determined, corresponding management personnel are matched based on the key monitoring points, and the early warning information is transmitted to the management personnel user terminal based on the Internet of Things.

[0154] In this embodiment, potential risks are identified in a timely manner through preset safety thresholds to ensure water quality safety. Detailed early warning information and emergency measures are provided to enhance the ability to respond to sudden water pollution incidents. Multi-channel notification of managers ensures the timeliness and accuracy of information transmission, achieves accurate push of early warning information, and ensures that managers can take prompt action, thereby effectively protecting water environment safety and public health.

[0155] In order to better demonstrate the implementation of the water environment monitoring method based on cloud computing, the present invention provides a water environment management system based on cloud computing, including:

[0156] Water quality sensors are used to collect water quality parameters such as pH value, dissolved oxygen, turbidity, and heavy metal content in real time. They are in direct contact with water samples underwater and have a stable and reliable installation method to avoid damage caused by water flow or external interference.

[0157] Environmental data collection equipment, including cameras, weather stations, etc., are used to monitor the water surface and surrounding environmental data in real time. The cameras monitor the water surface conditions in real time, such as floating objects, water quality and color changes, and surrounding environmental data, such as temperature, humidity, wind speed, etc. The cameras and weather stations are installed within the collection range of the water quality parameter sensors;

[0158] Cloud servers are used to build cloud computing platforms, obtain monitoring data collected by water quality sensors and environmental data acquisition equipment, process, store and analyze monitoring data, and perform water quality prediction, pollution source identification and risk assessment based on water environment quality models;

[0159] The user terminal is used to receive warning information from the cloud computing platform when water quality parameters exceed preset thresholds, obtain real-time monitoring data and water quality risk assessment reports based on the user interaction interface, and send control instructions to remotely control the operating status of water quality sensors and environmental data acquisition equipment.

[0160] In this embodiment, by integrating water quality sensors and environmental data acquisition equipment, the deployment and maintenance of the system are simplified, and multiple water samples are monitored at the same time to meet the needs of different application scenarios. It also works in conjunction with the cloud server and user terminals to ensure the real-time collection of water quality parameters and environmental data, and improves the timeliness and continuity of monitoring. The cloud server can process and store a large amount of monitoring data, and conduct in-depth analysis through the water environment quality model to improve the accuracy of water quality prediction and risk assessment. The user terminal can receive early warning information, so that managers can take prompt action to effectively prevent and respond to water pollution incidents. At the same time, it enhances the operational flexibility and management convenience of the monitoring equipment, which helps to ensure water environment safety and promote the sustainable management of water resources.

[0161] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A water environment management system based on cloud computing, characterized in that: include: Water quality sensor, used to collect water quality parameters of water bodies in real time; Environmental data collection equipment, used to monitor the water surface and surrounding environmental data in real time; Cloud servers are used to build cloud computing platforms, obtain monitoring data collected by water quality sensors and environmental data acquisition equipment, process, store and analyze monitoring data, and perform water quality prediction, pollution source identification and risk assessment based on water environment quality models; The user terminal is used to receive warning information from the cloud computing platform when water quality parameters exceed preset thresholds, obtain real-time monitoring data and water quality risk assessment reports based on the user interaction interface, and send control instructions to remotely control the operating status of water quality sensors and environmental data acquisition equipment; The water environment management system also performs the following steps: Step 1: Data collection: Automatic monitoring equipment is deployed at key monitoring points in the water environment to collect water quality parameters and environmental data in real time. The automatic monitoring equipment transmits the monitoring data to the cloud computing platform in real time; Step 2: Data processing: The cloud computing platform pre-processes the acquired monitoring data and stores the pre-processed monitoring data in the water quality monitoring database according to the monitoring data type classification; Step 3: Data analysis: Analyze the stored monitoring data based on the cloud computing platform, predict water quality trends, identify potential pollution sources and conduct risk assessment based on the water environment quality model; Step 4: Monitoring and early warning: Based on the cloud computing platform, the acquired water quality parameter data and monitoring data analysis results are displayed in real time. When abnormal water quality is detected, the early warning mechanism is triggered, and early warning information is sent to the user terminal, and corresponding treatment suggestions are provided; The automatic monitoring equipment is deployed in step 1, specifically including: Install automatic monitoring equipment at each key monitoring point, including water quality parameter sensors and environmental data collection equipment; The water quality parameter sensor is installed underwater in direct contact with the water sample. The water quality parameter sensor actively collects water quality data at preset time intervals. At the same time, the environmental data collection device is installed on one side of the water quality parameter sensor collection range to monitor and record the water surface and surrounding environmental data in real time; The automatic monitoring device performs data verification and standard unit conversion on the acquired monitoring data, and timestamps the verified and converted standard monitoring data to record the specific time of data collection; The automatic monitoring equipment is deployed in step 1, further comprising: Extract the straight-line distance between every two adjacent key monitoring points; Compare the straight-line distance between each two adjacent key monitoring points with a preset straight-line distance threshold; When the straight-line distance between each two adjacent key monitoring points exceeds a preset straight-line distance threshold, it is determined that an additional key monitoring point needs to be added between the two key monitoring points corresponding to the straight-line distance exceeding the preset straight-line distance threshold; When it is determined that an additional key monitoring point is needed, two key monitoring points corresponding to the straight-line distance exceeding the preset straight-line distance threshold are set as the first key monitoring point and the second key monitoring point, and the key monitoring point to be added is used as the target key monitoring point; Extracting the angle and the straight-line distance between the first key monitoring point and the second key monitoring point; Extracting the water volume and water flow rate of the first key monitoring point; Extracting the water volume and water flow rate of the second key monitoring point; Obtaining a first distance setting parameter using the angle between the first key monitoring point and the second key monitoring point combined with the water volume and water flow rate at the first key monitoring point; The first distance setting parameter is obtained by the following formula: Among them, P 01 represents the first distance setting parameter; θ represents the angle between the first key monitoring point and the second key monitoring point; Q 01 and Q 02 Respectively represent the water volume at the first key monitoring point and the water volume at the second key monitoring point; V 01 and V 02 Denote the water flow rate at the first key monitoring point and the water flow rate at the second key monitoring point respectively; D z represents the median distance corresponding to the straight-line distance between all two adjacent key monitoring points; k represents the adjustment coefficient, and the value range of k is 1.2-1.7; D p It represents the average distance between all the adjacent key monitoring points. Obtain a second distance setting parameter using the straight-line distance between the first key monitoring point and the second key monitoring point in combination with the water volume and water flow rate of the second key monitoring point; The second distance setting parameter is obtained by the following formula: Among them, P 02 represents the second distance setting parameter; D represents the straight-line distance between the first key monitoring point and the second key monitoring point; The first distance setting parameter and the second distance setting parameter are used to set the distance range between the target key monitoring point and the first key monitoring point and the second key monitoring point.

2. A water environment management system based on cloud computing as claimed in claim 1, characterized in that: Using the first distance setting parameter and the second distance setting parameter to set the distance range between the target key monitoring point and the first key monitoring point and the second key monitoring point includes: Extracting a first distance setting parameter and a second distance setting parameter; Extract the straight-line distance between every two adjacent key monitoring points; Acquire the altitude difference between the first key monitoring point and the second key monitoring point using the altitude of the first key monitoring point and the altitude of the second key monitoring point; Obtaining a first distance range between the target key monitoring point and the first key monitoring point by using the straight-line distance between each two adjacent key monitoring points, the altitude difference between the first key monitoring point and the second key monitoring point, and combining the first distance setting parameter and the second distance setting parameter; The first distance range is obtained by the following formula: Among them, L up and L down Indicates the upper and lower limits of the first distance range; P 01 , P 02 represents the first distance setting parameter and the second distance setting parameter respectively; D represents the straight-line distance between the first key monitoring point and the second key monitoring point; H represents the altitude difference between the first key monitoring point and the second key monitoring point; n represents the number of groups of each adjacent two key monitoring points except the first key monitoring point and the second key monitoring point; D i represents the straight-line distance between the i-th group of key monitoring points; Using the first distance range in combination with the straight-line distance between the first key monitoring point and the second key monitoring point, a second distance range between the target key monitoring point and the first key monitoring point is obtained; The second distance range is obtained by the following formula: Among them, S up and S down Indicates the upper limit and lower limit of the second distance range; The first distance range and the second distance range are used as constraint conditions to set a target key monitoring point so that the distance between the target key monitoring point and the first key monitoring point and the second key monitoring point satisfies the first distance range and the second distance range.

3. A water environment management system based on cloud computing as claimed in claim 2, characterized in that: The water quality parameter sensor actively collects water quality data at preset time intervals, specifically including: Acquire water quality parameter data collected based on preset time intervals, traverse the water quality parameter data according to parameter characteristics, identify and extract abnormal data that does not meet the parameter characteristics, and record the collection time point of the corresponding abnormal data; Determine whether the time interval and number of abnormal data collection time points meet the preset minimum normal standard. If so, remove the abnormal data from the water quality parameter data to obtain normal water quality parameter data. Otherwise, determine that the water quality parameter sensor is abnormal and issue a terminal abnormal alarm reminder. The normal water quality parameter data are classified to obtain sensor parameters and water quality parameters, wherein the sensor parameters include sensor operation data and operation temperature, and the water quality parameters include pH value, dissolved oxygen, turbidity and heavy metal content parameters of the water body.

4. A water environment management system based on cloud computing as claimed in claim 3, characterized in that: The step 1: data collection, further includes: Build a data interaction channel between the automatic monitoring equipment and the cloud computing platform, and transmit the data collected by the automatic monitoring equipment to the cloud computing platform through the data interaction channel; Obtain the monitoring data analysis results of the cloud computing platform based on the data interaction channel, determine the key monitoring points with abnormalities, and adjust the data collection and transmission time interval of the automatic monitoring equipment according to the severity and possible impact of the abnormal key monitoring points; The abnormal key monitoring points will be marked as abnormal. After the abnormal situation is resolved, the abnormal mark of the key monitoring point will be cancelled, and the data collection and transmission time interval of the automatic monitoring equipment will be restored to the preset time interval.

5. A water environment management system based on cloud computing as claimed in claim 4, characterized in that: The step 2 is to classify and store the pre-processed monitoring data into a water quality monitoring database according to the type of monitoring data, specifically including: Integrate the pre-processed monitoring data received from the automatic monitoring equipment and identify data types in the pre-processed monitoring data, including water quality parameter types and environmental data types; The water quality parameter type includes the sub-parameter types of pH value, dissolved oxygen, turbidity, and heavy metal content of the water body, and the environmental data type includes the sub-data types of camera monitoring video data and meteorological data; Classify and store the pre-processed monitoring data into different tables or fields in the water quality monitoring database according to the identified data types; Among them, one or more tables are created based on the water quality parameter type to store the sub-parameter type, and a corresponding vector space is created based on the environmental data and stored according to the sub-data type.

6. A water environment management system based on cloud computing as claimed in claim 5, characterized in that: The step three: data analysis, specifically includes: Extract monitoring data from the water quality monitoring database, and input the corresponding monitoring data into the trained water environment quality model based on the data type of the monitoring data; Based on the water environment quality model, the water quality parameters in the monitoring data are analyzed in time series, the changing trends of water quality parameters are identified, the changing patterns of water quality parameters in different seasons and time periods are analyzed, and water quality parameters are predicted based on the changing patterns of water quality parameters; Analyze the spatial distribution characteristics of water quality parameters in the monitoring area based on the water environment quality model, determine the distribution of water quality parameters in the monitoring area based on the spatial distribution characteristics, and identify potential pollution areas based on the differences in water quality parameters between key monitoring points; Analyze the impact of different pollution sources on water quality, determine the specific location and type of pollution sources based on the changing trends of water quality parameters and their distribution within the monitoring area, assess water quality risks, and generate a water quality risk assessment report.

7. A water environment management system based on cloud computing as claimed in claim 6, characterized in that: The step 4: monitoring and early warning, further includes: Based on the changing trend of water quality parameters and the prediction results of water quality parameters, it is judged whether the water quality parameters exceed the preset safety threshold. When the water quality parameters exceed the preset safety threshold, an early warning mechanism is triggered; Based on the early warning mechanism, a water quality risk assessment report corresponding to the water quality parameters is obtained to generate early warning information, which includes detailed information on water quality anomalies, possible impact ranges, and recommended emergency measures; At the same time, corresponding monitoring data is obtained based on the water quality parameters, corresponding key monitoring points are determined, corresponding management personnel are matched based on the key monitoring points, and the early warning information is transmitted to the management personnel user terminal based on the Internet of Things.

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