Water quality monitoring method and related device based on edge computing

Abstract

The embodiment of the present invention provides a water quality monitoring method and related devices based on edge computing, which belong to the field of data prediction technology. The method includes obtaining initial parameter data of the target water environment under the target sensor and target spatial information under the positioning sensor; determining the measurement reliability of the target sensor based on the initial parameter data, and adjusting the initial parameter data based on the measurement reliability to obtain target parameter data; performing data fusion on the target parameter data to obtain target fusion data of the target sensor; sending the target fusion data, target parameter data, and target spatial information to a cloud server that is communicatively connected to the edge device, so that the cloud server performs water quality prediction on the target fusion data, target parameter data, and target spatial information according to a water quality prediction model to obtain a target prediction result of the target water environment; receiving the target prediction result sent by the cloud server, and determining the target monitoring result of the target water environment based on the target prediction result.

Description

Technical Field

[0001] The present invention relates to the field of data prediction technology, and in particular to a water quality monitoring method and related devices based on edge computing. Background Art

[0002] Water environment monitoring is a vital part of water resource protection and management. With the development of artificial intelligence technology, by combining neural network models with sensor data, researchers can better analyze water environment conditions and make water quality predictions, thus providing a scientific basis for decision-making. However, although neural network models have demonstrated powerful capabilities in data processing and analysis, their prediction results are still highly dependent on the quality of the data collected by the sensors. If there are errors in the sensor measurements, such errors may be caused by equipment aging, environmental changes, or improper maintenance, resulting in deviations between the sensor information and the actual water quality conditions. Therefore, the methods for water quality monitoring in related technologies still have the problem of inaccurate predictions. Summary of the Invention

[0003] The main purpose of the embodiments of the present invention is to provide a water quality monitoring method and related devices based on edge computing, aiming to solve the problem in the related art of inaccurate prediction of water quality monitoring results due to errors in sensor measurement data.

[0004] In a first aspect, an embodiment of the present invention provides a water quality monitoring method based on edge computing, comprising:

[0005] Obtaining initial parameter data corresponding to the target water environment under the target sensor and target space information corresponding to the target water environment under the positioning sensor;

[0006] Determining a measurement reliability corresponding to the target sensor according to the initial parameter data, and adjusting the initial parameter data according to the measurement reliability to obtain target parameter data;

[0007] Performing data fusion on the target parameter data to obtain target fusion data corresponding to the target sensor;

[0008] The target fusion data, the target parameter data, and the target spatial information are sent to a cloud server in communication with the edge device, so that the cloud server performs water quality prediction on the target fusion data, the target parameter data, and the target spatial information according to a water quality prediction model to obtain a target prediction result corresponding to the target water environment;

[0009] Receive the target prediction result sent by the cloud server, and determine the target monitoring result corresponding to the target water environment according to the target prediction result.

[0010] In a second aspect, an embodiment of the present invention provides a water quality monitoring device based on edge computing, comprising:

[0011] A data acquisition module is used to obtain initial parameter data corresponding to the target water environment under the target sensor and target space information corresponding to the target water environment under the positioning sensor;

[0012] a data analysis module, configured to determine a measurement reliability corresponding to the target sensor based on the initial parameter data, and adjust the initial parameter data according to the measurement reliability to obtain target parameter data;

[0013] A data fusion module, configured to perform data fusion on the target parameter data to obtain target fusion data corresponding to the target sensor;

[0014] a data prediction module, configured to send the target fusion data, the target parameter data, and the target spatial information to a cloud server communicatively connected to the edge device, so that the cloud server performs water quality prediction on the target fusion data, the target parameter data, and the target spatial information according to a water quality prediction model, and obtains a target prediction result corresponding to the target water environment;

[0015] The water quality monitoring module is used to receive the target prediction result sent by the cloud server and determine the target monitoring result corresponding to the target water environment according to the target prediction result.

[0016] In a third aspect, an embodiment of the present invention further provides a terminal device, comprising a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the computer program is executed by the processor, the steps of any one of the edge computing-based water quality monitoring methods provided in the specification of the present invention are implemented.

[0017] In a fourth aspect, an embodiment of the present invention further provides a storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of the edge computing-based water quality monitoring methods provided in the specification of the present invention.

[0018] Embodiments of the present invention provide a water quality monitoring method and related apparatus based on edge computing. The method comprises: obtaining initial parameter data of a target water environment from a target sensor and corresponding target spatial information from a positioning sensor. By analyzing the initial parameter data, the measurement reliability of the target sensor can be evaluated. Based on the measurement reliability, the initial parameter data can be adjusted accordingly to obtain more accurate target parameter data. This process effectively reduces the impact of sensor errors on water quality monitoring results, providing important support for the accuracy of subsequent water quality monitoring result predictions. The target parameter data is then fused to generate target fused data for the target sensor. The generated target fused data, target parameter data, and target spatial information are then transmitted to a cloud server communicatively connected to an edge device. The cloud server performs a comprehensive analysis of the target fused data, target parameter data, and target spatial information based on a water quality prediction model to derive a target prediction result for the target water environment. The application of artificial intelligence in this process can further improve the accuracy of the target prediction result, while reducing the computing power requirements of the edge device and optimizing system resource utilization. Finally, the target prediction result received from the cloud server is used to determine the target monitoring result corresponding to the target water environment. This method effectively solves the problem of inaccurate prediction of water quality monitoring results due to errors in sensor measurement data in related technologies, improves the overall accuracy of water quality monitoring predictions, and provides a good foundation and support for subsequent real-time monitoring and related processing of the target water environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 A schematic diagram of a water quality monitoring method based on edge computing provided in an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of the module structure of a water quality monitoring device based on edge computing provided by an embodiment of the present invention;

[0022] Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0025] It should be understood that the terms used in this specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0026] Embodiments of the present invention provide a water quality monitoring method and related apparatus based on edge computing. The edge computing-based water quality monitoring method can be applied to terminal devices, such as tablet computers, laptop computers, desktop computers, personal digital assistants, and wearable devices. The terminal device can also be a server or a server cluster.

[0027] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0028] Please refer to Figure 1 , Figure 1 A flow chart of a water quality monitoring method based on edge computing provided in an embodiment of the present invention.

[0029] like Figure 1 As shown, the water quality monitoring method based on edge computing includes steps S101 to S105.

[0030] Step S101: obtaining initial parameter data corresponding to a target water environment under a target sensor and target space information corresponding to the target water environment under a positioning sensor.

[0031] For example, target sensors include, but are not limited to, sensors that measure water quality parameters such as temperature, solubility, pH, turbidity, conductivity, and ammonia nitrogen corresponding to the target water environment. Initial parameter data corresponding to the target water environment is collected in real time using the target sensors. Furthermore, a positioning sensor, such as a GPS device, measures the position of the target water environment to obtain target spatial information corresponding to the target water environment.

[0032] Step S102: determining the measurement reliability corresponding to the target sensor according to the initial parameter data, and adjusting the initial parameter data according to the measurement reliability to obtain target parameter data.

[0033] Exemplarily, historical measurement data corresponding to the target sensor is obtained, and then data fitting is performed on the measurement data of the target sensor based on the historical measurement data to obtain a target fitting function corresponding to the target sensor, thereby obtaining the corresponding predicted parameter data at the current moment based on the target fitting function, and then calculating the difference between the predicted parameter data and the initial parameter data. When the difference is less than a preset value, it is determined that the measurement reliability corresponding to the target sensor is high; when the difference is greater than or equal to the preset value, it is determined that the measurement reliability corresponding to the target sensor is low.

[0034] Exemplarily, when the measurement reliability is high, the initial parameter data is determined as the target parameter data; when the measurement reliability is low, the initial parameter data corresponding to the target sensor is re-obtained until the measurement reliability corresponding to the initial parameter data is high.

[0035] In some embodiments, the target sensor includes at least a first sensor and a second sensor, the initial parameter data includes at least first parameter data corresponding to the first sensor at the target moment and second parameter data corresponding to the second sensor at the target moment, and determining the measurement reliability corresponding to the target sensor based on the initial parameter data includes: obtaining first historical data corresponding to the first sensor and second historical data corresponding to the second sensor; determining a first distribution function corresponding to the first sensor based on the first historical data and determining a second distribution function corresponding to the second sensor based on the second historical data; determining a target correlation value between the first parameter data and the second parameter data based on the first distribution function, the second distribution function, the first parameter data and the second parameter data; and determining the measurement reliability corresponding to the first sensor and the second sensor based on the target correlation value.

[0036] Exemplarily, the target sensor includes at least a first sensor and a second sensor, and the initial parameter data includes at least first parameter data corresponding to the first sensor at the target time and second parameter data corresponding to the second sensor at the target time. That is, if the target sensor is a plurality of sensors, the initial parameter data includes measurement data corresponding to the plurality of sensors.

[0037] Exemplarily, at least first historical data corresponding to a first sensor and second historical data corresponding to a second sensor are obtained from a database, and then, based on the data distribution characteristics corresponding to the first and second historical data, a statistical analysis method is determined to estimate a data distribution function. Distribution functions include, but are not limited to, normal distribution, uniform distribution, exponential distribution, and the like.

[0038] For example, a distribution function is determined based on the distribution characteristics of the first historical data, and then a first parameter corresponding to the first historical data is obtained by fitting using statistical software such as R or Python's SciPy library. Furthermore, a first distribution function corresponding to the first historical data is determined based on the first parameter. Similarly, a distribution function is determined based on the distribution characteristics of the second historical data, and then a second parameter corresponding to the second historical data is obtained by fitting using statistical software such as R or Python's SciPy library. Furthermore, a second distribution function corresponding to the second historical data is determined based on the second parameter.

[0039] Exemplarily, the first data distance corresponding to the first sensor and the second sensor at the target time is calculated based on the first parameter data and the second parameter data, and the second data distance corresponding to the first sensor and the second sensor at different times is calculated based on the first distribution function and the second distribution function, thereby calculating the ratio between the first data distance and the second data distance, and determining the ratio result as the target correlation value between the first parameter data and the second parameter data. The closer the target correlation value is to 1, the closer the distribution between the first parameter data and the second parameter data is to the distribution between the first distribution function and the second distribution function; the further the target correlation value is from 1, the further the distribution between the first parameter data and the second parameter data is from the distribution between the first distribution function and the second distribution function, that is, the greater the measurement error between the first parameter data and the second parameter data.

[0040] Exemplarily, a preset value is determined, and the absolute value of the difference between the target correlation value and 1 is calculated to obtain an absolute error. When the absolute error is less than or equal to the preset value, the corresponding measurement reliability between the first sensor and the second sensor is high, that is, the first sensor and the second sensor can prove to each other that the reliability of each other's sensor measurement results is high; when the absolute error is greater than the preset value, the corresponding measurement reliability between the first sensor and the second sensor is low, that is, the first sensor and the second sensor cannot prove to each other that the reliability of each other's sensor measurement results is high; in other words, there is a problem of large measurement error between the first parameter data and the second parameter data.

[0041] Specifically, by fitting distribution functions with historical data, the sensor's measurement characteristics can be more accurately described, thereby improving the accuracy of current data analysis. Analyzing the target correlation value between the first and second parameter data using the first and second distribution functions effectively quantifies the measurement reliability of the first and second sensors, providing support for subsequent comprehensive and accurate water quality monitoring information.

[0042] In some embodiments, determining the target association value between the first parameter data and the second parameter data based on the first distribution function, the second distribution function, the first parameter data, and the second parameter data includes: determining a first variance corresponding to the first sensor based on the first historical data and determining a second variance corresponding to the second sensor based on the second historical data; performing an integral operation on the first distribution function based on the first parameter data and the second parameter data to obtain a first association value between the first parameter data and the second parameter data; performing an integral operation on the second distribution function based on the first parameter data and the second parameter data to obtain a second association value between the first parameter data and the second parameter data; performing information fusion on the first association value and the second association value based on the first variance and the second variance to determine the target association value between the first parameter data and the second parameter data; wherein the target association value is obtained according to the following formula:

[0043] ;

[0044] in, represents the target association value between the first parameter data and the second parameter data, represents the second variance, represents the first variance, abs represents the absolute value, represents the second parameter data, represents the first parameter data, represents the first distribution function, represents the second distribution function, represents the differential, Integration symbol, x represents the integration variable, used to represent a first average distance between the first sensor and the second sensor corresponding to the first distribution function; The second average distance between the first sensor and the second sensor under the second distribution function is used to represent the second average distance between the first sensor and the second sensor.

[0045] For example, a first average value of the first sensor and a second average value of the second sensor are calculated based on the first historical data and the second historical data, respectively. A first difference value is obtained by performing a difference calculation between the first historical data and the first average value. The first difference value is then squared and then summed and averaged to obtain a first variance. Similarly, a second difference value is obtained by performing a difference calculation between the second historical data and the second average value. The second difference value is then squared and then summed and averaged to obtain a second variance.

[0046] Exemplarily, an integral operation is performed on the first distribution function according to the first parameter data and the second parameter data to obtain a first correlation value between the first parameter data and the second parameter data; wherein the first distribution function is used as the integral function, and the first parameter data and the second parameter data are used as the integral range, wherein the calculation formula of the first correlation value is as follows:

[0047]

[0048] in, represents a first association value between the first parameter data and the second parameter data, Represents the second parameter data, Represents the first parameter data, represents the first distribution function, represents the differential, Integration symbol, x represents the integration variable.

[0049] Exemplarily, performing an integral operation on the first distribution function within the range given by the first parameter data and the second parameter data can calculate the first correlation value corresponding to the range determined by the first parameter data and the second parameter data, thereby reflecting the first current distance between the first parameter data and the second parameter data at the target moment through the first correlation value.

[0050] Similarly, an integral operation is performed on the second distribution function according to the first parameter data and the second parameter data to obtain a second correlation value between the first parameter data and the second parameter data; wherein the second distribution function is used as the integral function, and the first parameter data and the second parameter data are used as the integral range, wherein the calculation formula of the second correlation value is as follows:

[0051]

[0052] in, represents a second association value between the first parameter data and the second parameter data, Represents the second parameter data, Represents the first parameter data, represents the second distribution function, represents the differential, Integration symbol, x represents the integration variable.

[0053] Exemplarily, performing an integral operation on the second distribution function within the range given by the first parameter data and the second parameter data can calculate the second correlation value corresponding to the range determined by the first parameter data and the second parameter data, thereby reflecting the second current distance between the first parameter data and the second parameter data at the target moment through the second correlation value.

[0054] Exemplarily, a first distribution function is integrated based on the first and second historical parameters at the same time in the first and second historical data to obtain a first historical correlation value, and the first historical correlation values ​​are then summed and averaged to obtain a first average distance. Furthermore, a second distribution function is integrated based on the first and second historical parameters at the same time in the first and second historical data to obtain a second historical correlation value, and the second historical correlation values ​​are then summed and averaged to obtain a second average distance.

[0055] Exemplarily, the target correlation value between the first parameter data and the second parameter data is determined by performing information fusion on the first correlation value and the second correlation value by using the first variance and the second variance according to the following formula:

[0056] ;

[0057] in, represents the target association value between the first parameter data and the second parameter data, represents the second variance, Indicates the first variance, abs indicates the absolute value, Represents the second parameter data, Represents the first parameter data, represents the first distribution function, represents the second distribution function, represents the differential, Integration symbol, x represents the integration variable, used to represent a first average distance between the first sensor and the second sensor corresponding to the first distribution function; The second average distance between the first sensor and the second sensor under the second distribution function is used to represent the second average distance between the first sensor and the second sensor.

[0058] For example, the first and second distribution functions, along with their corresponding first and second variances, are comprehensively considered to achieve more comprehensive information integration and optimize the target correlation value calculation process, making it more consistent with actual measurement conditions. Furthermore, the target correlation value calculation results can be dynamically adjusted using the first and second average distances, as well as the first and second variances, to better adapt to changes in environmental and operating conditions. This improves adaptability to different scenarios and makes subsequent water quality monitoring more flexible and accurate.

[0059] In some embodiments, adjusting the initial parameter data according to the measurement reliability to obtain target parameter data includes: performing abnormal screening on the target sensor according to the measurement reliability to obtain an abnormal sensor; obtaining third historical data corresponding to the abnormal sensor, and predicting the measurement value corresponding to the abnormal sensor at the target moment based on the third historical data to obtain a target estimated value corresponding to the abnormal sensor at the target moment; and adjusting the initial parameter data according to the target estimated value to obtain the target parameter data.

[0060] Exemplarily, the measurement reliability between the target sensors is analyzed. When the number of measurement reliability between any one of the target sensors and the other remaining sensors with high reliability is greater than a preset number, the sensor is a sensor with normal measurement; otherwise, the sensor is determined to be an abnormal sensor.

[0061] For example, the target sensors include a first sensor, a second sensor, a third sensor, etc. The measurement reliability between the first sensor and the second sensor is high, the measurement reliability between the first sensor and the third sensor is low, and the measurement reliability between the second sensor and the third sensor is high. Then, for the first sensor, only the second sensor can prove that the measurement reliability is high; for the second sensor, both the first sensor and the third sensor can prove that the measurement reliability is high; for the third sensor, only the second sensor can prove that the measurement reliability is high. Therefore, if the preset number is 2, the number of times when only the second sensor has high measurement reliability is 2, so the second sensor is the sensor with normal measurement, and the first sensor and the third sensor are both abnormal sensors.

[0062] Exemplarily, the third historical data of the abnormal sensor is extracted from the database, and then the third historical data is fitted and analyzed using a machine learning or deep learning algorithm. The target fitting function of the abnormal sensor is obtained by data fitting. Next, the target moment is input into the target fitting function as a time parameter to obtain the target estimated value of the abnormal sensor at the target moment, and then the abnormal parameter data corresponding to the abnormal sensor in the initial parameter data is compared with the target estimated value. If the difference between the abnormal parameter data and the target estimated value exceeds a preset threshold, it means that the existing abnormal parameter data may be inaccurate and needs to be re-collected. Once the re-collected parameter data is obtained, it is replaced with the original abnormal parameter data to obtain the updated target parameter data. On the contrary, if the difference between the abnormal parameter data and the target estimated value is less than or equal to the preset threshold, there is no need to modify the parameter data of the abnormal sensor.

[0063] In some embodiments, predicting the measurement value corresponding to the abnormal sensor at the target time based on the third historical data to obtain the target estimated value corresponding to the abnormal sensor at the target time includes: using a Kalman filter algorithm to determine the state equation, gain matrix and observation equation corresponding to the abnormal sensor based on the third historical data; determining the state prediction value, gain prediction value and observation prediction value corresponding to the abnormal sensor at the target time based on the state equation, the gain matrix and the observation equation; using an error prediction model to determine the target error corresponding to the abnormal sensor at the target time based on the state prediction value, the gain prediction value and the observation prediction value; determining the target prediction value corresponding to the abnormal sensor at the target time based on the state prediction value, the gain prediction value and the observation prediction value; optimizing the target prediction value based on the target error to obtain the target estimated value corresponding to the abnormal sensor at the target time.

[0064] Exemplarily, a Kalman filter algorithm is used to determine the state equation, gain matrix, and observation equation corresponding to the abnormal sensor based on the third historical data. The state equation describes how the abnormal sensor's state changes over time. The gain matrix represents the relationship between variables during the abnormal sensor's state change and is calculated using the time update step in the Kalman filter algorithm. The observation equation describes the relationship between the observed value and the state of the abnormal sensor.

[0065] Exemplarily, the state equation and the third historical data are used to perform a time update to predict the state of the abnormal sensor at the target time to obtain a predicted state value. The gain matrix of the abnormal sensor at the target time is predicted using a calculation formula in the Kalman filter algorithm to obtain a predicted gain value. The observation equation and the predicted state value are used to predict the predicted observation value of the abnormal sensor at the target time.

[0066] Exemplarily, the error prediction model is a neural network model, and the historical state prediction value, historical gain prediction value and historical observation prediction value at each moment are obtained based on the third historical data, and the true error between the observation value at that moment and the true value is obtained, so that the historical state prediction value, historical gain prediction value and historical observation prediction value are used as inputs of the error prediction model, and the true error is used as output for model training to obtain the error prediction model.

[0067] For example, the state prediction value, gain prediction value, and observation prediction value are used as inputs to the error prediction model. Based on this, the target error of the abnormal sensor at the target time is obtained. Then, a Kalman filter is used to combine the state prediction value, gain prediction value, and observation prediction value to calculate the target prediction value of the abnormal sensor at the target time. The target prediction value is then optimized based on the target error to obtain a more accurate target estimate.

[0068] Specifically, the Kalman filter algorithm is effectively used to predict and optimize the data of abnormal sensors, thereby improving the accuracy and reliability of the target estimation value.

[0069] Step S103: performing data fusion on the target parameter data to obtain target fusion data corresponding to the target sensor.

[0070] Exemplarily, the actual measurement error corresponding to the target sensor is obtained, and the fusion weight corresponding to the target parameter data is determined according to the actual measurement error, and then the target parameter data is fused according to the fusion weight to obtain the target fusion data corresponding to the target sensor.

[0071] In some embodiments, the data fusion of the target parameter data to obtain target fusion data corresponding to the target sensor includes: selecting any one of the target sensors to be determined as the third sensor corresponding to the current analysis, and obtaining third parameter data corresponding to the third sensor from the target parameter data; obtaining a parameter prediction value corresponding to the third sensor at the same time as the third parameter data; calculating the mean square error corresponding to the parameter prediction value and the third parameter data, and determining the target weight corresponding to the data fusion of the third parameter data based on the mean square error; data fusing the third parameter data based on the target weight to obtain parameter fusion data corresponding to the third sensor; determining the sensor weight corresponding to the third sensor, and performing data fusion based on the sensor weight and the parameter fusion data to obtain the target fusion data corresponding to the target sensor.

[0072] Exemplarily, the target sensor includes multiple sensors, then any one of the target sensors is selected to be determined as the third sensor corresponding to the current analysis, and third parameter data corresponding to the third sensor is obtained from the target parameter data, wherein the third parameter data includes measurement data corresponding to the third sensor at multiple moments.

[0073] Exemplarily, a parameter prediction value corresponding to the third sensor at the same time as the third parameter data is obtained according to a parameter prediction model corresponding to the third sensor. The parameter prediction model can be a prediction model obtained through machine learning or deep learning based on historical parameter data corresponding to the third sensor.

[0074] Exemplarily, the mean square error between the parameter prediction value and the third parameter data is calculated, and the inverse of all the mean square errors is taken to obtain the inverse result, and then the inverse results are summed to obtain the inverse sum, and then the ratio between the inverse value of each mean square error and the inverse sum is determined as the target weight.

[0075] Exemplarily, the third parameter data at different times are weightedly fused according to the target weight to obtain parameter fusion data corresponding to the third sensor.

[0076] Exemplarily, an actual measurement error corresponding to the third sensor is obtained, thereby determining a sensor weight corresponding to the third sensor based on the actual measurement error, and then performing weighted summation on the parameter fusion data based on the sensor weight to obtain target fusion data corresponding to the target sensor.

[0077] Step S104: Send the target fusion data, the target parameter data, and the target spatial information to a cloud server that is communicatively connected to the edge device, so that the cloud server performs water quality prediction on the target fusion data, the target parameter data, and the target spatial information according to a water quality prediction model to obtain a target prediction result corresponding to the target water environment.

[0078] For example, a suitable communication protocol is selected to implement data transmission between the edge device and the cloud server. Communication protocols include but are not limited to HTTP, MQTT, CoAP, etc. The optimal protocol needs to be selected based on the specific application scenario and network conditions.

[0079] For example, the target fusion data, target parameter data, and target spatial information are packaged according to the requirements of the communication protocol to obtain a request instruction, which is then sent to the cloud server. Upon receiving the request instruction, the cloud server parses the request instruction to obtain the target fusion data, target parameter data, and target spatial information. The cloud server then performs a water quality prediction on the target fusion data, target parameter data, and target spatial information based on a water quality prediction model, obtaining a target prediction result corresponding to the target water environment. The water quality prediction model can be a classification model or a water quality score prediction model corresponding to a neural network or machine learning.

[0080] For example, if the water quality prediction model is a neural network-based classification model, the water quality prediction model can determine the target prediction result corresponding to the target water environment as one of excellent water quality, good water quality, or poor water quality based on the target fusion data, target parameter data, and target spatial information. If the water quality prediction model is a neural network-based water quality scoring model, the water quality prediction model can determine the target prediction result corresponding to the target water environment as a score based on the target fusion data, target parameter data, and target spatial information. A higher score indicates better water quality in the target water environment.

[0081] Exemplarily, the cloud server performs water quality prediction on the target fusion data, target parameter data and target spatial information according to the water quality prediction model. After obtaining the target prediction results corresponding to the target water environment, the cloud server packages the target prediction results according to the communication protocol and sends them to the edge device.

[0082] Step S105: receiving the target prediction result sent by the cloud server, and determining the target monitoring result corresponding to the target water environment according to the target prediction result.

[0083] Exemplarily, the target prediction result sent by the cloud server is received, and then the target prediction result is compared with the preset information. When the target prediction result meets the preset information, the target monitoring result corresponding to the target water environment is determined to be qualified water quality; when the target prediction result does not meet the preset information, the target monitoring result corresponding to the target water environment is determined to be unqualified water quality.

[0084] For example, when the target prediction result is one of excellent water quality, good water quality, or poor water quality, and the preset information is one of excellent water quality and good water quality, then when the target prediction result is one of excellent water quality and good water quality, the target monitoring result is qualified water quality; otherwise, the target monitoring result corresponding to the target water environment is determined to be unqualified water quality. When the target prediction result is a score, the preset information is a preset value; when the target prediction result is greater than or equal to the preset value, the target monitoring result is qualified water quality; otherwise, the target monitoring result corresponding to the target water environment is determined to be unqualified water quality.

[0085] In some embodiments, after obtaining the target monitoring result, the method further includes: obtaining a manual verification result corresponding to the target monitoring result, and sending the manual verification result to the cloud server, so that the cloud server updates the water quality prediction model according to the manual verification result to obtain the updated water quality prediction model.

[0086] For example, the acquired target monitoring results are submitted to a professional water quality analyst or expert in the relevant field for manual verification to obtain manual verification results. If the manual verification results contain modifications to the target monitoring results, that is, if the water quality prediction model predicts an error, the manual verification results are sent to the cloud server. After receiving the manual verification results, the cloud server performs data analysis and storage. When the number of manual verification results collected reaches a certain number, the cloud server invokes the water quality prediction model update mechanism. The water quality prediction model is then updated based on the manual verification results to obtain an updated water quality prediction model, and the cloud server then obtains an updated water quality prediction model. The updated water quality prediction model will more accurately reflect the water quality of the target water environment and enable more reliable future water quality predictions. In addition, the cloud server also needs to deploy the updated water quality prediction model to the cloud server for subsequent edge devices to perform water quality predictions. By effectively utilizing the manual verification results to continuously optimize the water quality prediction model, the accuracy and reliability of the water quality prediction model can be further improved.

[0087] See also Figure 2 , Figure 2An edge computing-based water quality monitoring device 200 is provided in an embodiment of the present application. The edge computing-based water quality monitoring device 200 includes a data acquisition module 201, a data analysis module 202, a data fusion module 203, a data prediction module 204, and a water quality monitoring module 205. The data acquisition module 201 is used to obtain initial parameter data corresponding to the target water environment under the target sensor and target space information corresponding to the target water environment under the positioning sensor; the data analysis module 202 is used to determine the measurement reliability corresponding to the target sensor based on the initial parameter data, and adjust the initial parameter data according to the measurement reliability to obtain target parameter data; The data fusion module 203 is used to perform data fusion on the target parameter data to obtain the target fusion data corresponding to the target sensor; the data prediction module 204 is used to send the target fusion data, the target parameter data and the target spatial information to a cloud server that is communicatively connected to the edge device, so that the cloud server performs water quality prediction on the target fusion data, the target parameter data and the target spatial information according to the water quality prediction model to obtain the target prediction result corresponding to the target water environment; the water quality monitoring module 205 is used to receive the target prediction result sent by the cloud server, and determine the target monitoring result corresponding to the target water environment based on the target prediction result.

[0088] In some embodiments, the target sensor includes at least a first sensor and a second sensor, and the initial parameter data includes at least first parameter data corresponding to the first sensor at a target time and second parameter data corresponding to the second sensor at the target time. In the process of determining the measurement reliability corresponding to the target sensor based on the initial parameter data, the data analysis module 202 performs:

[0089] Obtaining first historical data corresponding to the first sensor and second historical data corresponding to the second sensor;

[0090] determining a first distribution function corresponding to the first sensor according to the first historical data and determining a second distribution function corresponding to the second sensor according to the second historical data;

[0091] determining a target correlation value between the first parameter data and the second parameter data according to the first distribution function, the second distribution function, the first parameter data, and the second parameter data;

[0092] The corresponding measurement reliability between the first sensor and the second sensor is determined according to the target correlation value.

[0093] In some embodiments, during the process of determining the target correlation value between the first parameter data and the second parameter data based on the first distribution function, the second distribution function, the first parameter data, and the second parameter data, the data analysis module 202 performs:

[0094] determining a first variance corresponding to the first sensor based on the first historical data and determining a second variance corresponding to the second sensor based on the second historical data;

[0095] Performing an integral operation on the first distribution function according to the first parameter data and the second parameter data to obtain a first correlation value between the first parameter data and the second parameter data;

[0096] Performing an integral operation on the second distribution function according to the first parameter data and the second parameter data to obtain a second correlation value between the first parameter data and the second parameter data;

[0097] Performing information fusion on the first correlation value and the second correlation value according to the first variance and the second variance to determine the target correlation value between the first parameter data and the second parameter data;

[0098] The target correlation value is obtained according to the following formula:

[0099] ;

[0100] in, represents the target association value between the first parameter data and the second parameter data, represents the second variance, represents the first variance, abs represents the absolute value, represents the second parameter data, represents the first parameter data, represents the first distribution function, represents the second distribution function, represents the differential, Integration symbol, x represents the integration variable, used to represent a first average distance between the first sensor and the second sensor corresponding to the first distribution function; The second average distance between the first sensor and the second sensor under the second distribution function is used to represent the second average distance between the first sensor and the second sensor.

[0101] In some embodiments, during the process of adjusting the initial parameter data according to the measurement reliability to obtain the target parameter data, the data analysis module 202 performs:

[0102] Performing abnormal screening on the target sensor according to the measurement reliability to obtain an abnormal sensor;

[0103] Obtaining third historical data corresponding to the abnormal sensor, and predicting a measurement value corresponding to the abnormal sensor at a target time based on the third historical data to obtain a target estimated value corresponding to the abnormal sensor at the target time;

[0104] The target parameter data is obtained by adjusting the initial parameter data according to the target estimated value.

[0105] In some embodiments, the data analysis module 202 performs, during the process of predicting the measurement value corresponding to the abnormal sensor at the target time based on the third historical data and obtaining the target estimated value corresponding to the abnormal sensor at the target time:

[0106] Determine, using a Kalman filter algorithm based on the third historical data, a state equation, a gain matrix, and an observation equation corresponding to the abnormal sensor;

[0107] Determine the state prediction value, gain prediction value and observation prediction value corresponding to the abnormal sensor at the target time according to the state equation, the gain matrix and the observation equation;

[0108] Determine a target error corresponding to the abnormal sensor at the target time according to the state prediction value, the gain prediction value, and the observation prediction value using an error prediction model;

[0109] Determine a target prediction value corresponding to the abnormal sensor at the target time according to the state prediction value, the gain prediction value and the observation prediction value;

[0110] The target prediction value is optimized according to the target error to obtain a target estimated value corresponding to the abnormal sensor at the target time.

[0111] In some embodiments, during the process of fusing the target parameter data to obtain the target fusion data corresponding to the target sensor, the data fusion module 203 executes:

[0112] Select any one of the target sensors to be determined as the third sensor corresponding to the current analysis, and obtain third parameter data corresponding to the third sensor from the target parameter data;

[0113] Obtaining a parameter prediction value corresponding to the third sensor at the same time as the third parameter data;

[0114] Calculating the corresponding mean square error between the parameter prediction value and the third parameter data, and determining the corresponding target weight when the third parameter data is subjected to data fusion according to the mean square error;

[0115] Performing data fusion on the third parameter data according to the target weight to obtain parameter fusion data corresponding to the third sensor;

[0116] A sensor weight corresponding to the third sensor is determined, and data fusion is performed according to the sensor weight and the parameter fusion data to obtain the target fusion data corresponding to the target sensor.

[0117] In some embodiments, after obtaining the target monitoring result, the water quality monitoring module 205 further performs:

[0118] Obtain a manual verification result corresponding to the target monitoring result, and send the manual verification result to the cloud server, so that the cloud server updates the water quality prediction model according to the manual verification result to obtain the updated water quality prediction model.

[0119] In some embodiments, the edge computing-based water quality monitoring device 200 can be applied to a terminal device.

[0120] It should be noted that, those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working process of the water quality monitoring device 200 based on edge computing described above can refer to the corresponding process in the aforementioned embodiment of the water quality monitoring method based on edge computing, and will not be repeated here.

[0121] See also Figure 3 , Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention.

[0122] like Figure 3 As shown, the terminal device 300 includes a processor 301 and a memory 302 , and the processor 301 and the memory 302 are connected via a bus 303 , such as an I 2 C (Inter-Integrated Circuit) bus.

[0123] Specifically, processor 301 is used to provide computing and control capabilities to support the operation of the entire terminal device. Processor 301 can be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0124] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0125] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the embodiment of the present invention, and does not constitute a limitation on the terminal device to which the embodiment of the present invention is applied. The specific server may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0126] The processor is used to run a computer program stored in a memory, and implement any one of the edge computing-based water quality monitoring methods provided in an embodiment of the present invention when executing the computer program.

[0127] In one embodiment, the processor is configured to run a computer program stored in the memory, and implement the following steps when executing the computer program:

[0128] Obtaining initial parameter data corresponding to the target water environment under the target sensor and target space information corresponding to the target water environment under the positioning sensor;

[0129] Determining a measurement reliability corresponding to the target sensor according to the initial parameter data, and adjusting the initial parameter data according to the measurement reliability to obtain target parameter data;

[0130] Performing data fusion on the target parameter data to obtain target fusion data corresponding to the target sensor;

[0131] The target fusion data, the target parameter data, and the target spatial information are sent to a cloud server in communication with the edge device, so that the cloud server performs water quality prediction on the target fusion data, the target parameter data, and the target spatial information according to a water quality prediction model to obtain a target prediction result corresponding to the target water environment;

[0132] Receive the target prediction result sent by the cloud server, and determine the target monitoring result corresponding to the target water environment according to the target prediction result.

[0133] In some embodiments, the target sensor includes at least a first sensor and a second sensor, the initial parameter data includes at least first parameter data corresponding to the first sensor at a target moment and second parameter data corresponding to the second sensor at the target moment, and the processor 301, in determining the measurement reliability corresponding to the target sensor based on the initial parameter data, executes:

[0134] Obtaining first historical data corresponding to the first sensor and second historical data corresponding to the second sensor;

[0135] determining a first distribution function corresponding to the first sensor according to the first historical data and determining a second distribution function corresponding to the second sensor according to the second historical data;

[0136] determining a target correlation value between the first parameter data and the second parameter data according to the first distribution function, the second distribution function, the first parameter data, and the second parameter data;

[0137] The corresponding measurement reliability between the first sensor and the second sensor is determined according to the target correlation value.

[0138] In some embodiments, during the process of determining the target association value between the first parameter data and the second parameter data according to the first distribution function, the second distribution function, the first parameter data, and the second parameter data, the processor 301 executes:

[0139] determining a first variance corresponding to the first sensor based on the first historical data and determining a second variance corresponding to the second sensor based on the second historical data;

[0140] Performing an integral operation on the first distribution function according to the first parameter data and the second parameter data to obtain a first correlation value between the first parameter data and the second parameter data;

[0141] Performing an integral operation on the second distribution function according to the first parameter data and the second parameter data to obtain a second correlation value between the first parameter data and the second parameter data;

[0142] Performing information fusion on the first correlation value and the second correlation value according to the first variance and the second variance to determine the target correlation value between the first parameter data and the second parameter data;

[0143] The target correlation value is obtained according to the following formula:

[0144] ;

[0145] in, represents the target association value between the first parameter data and the second parameter data, represents the second variance, represents the first variance, abs represents the absolute value, represents the second parameter data, represents the first parameter data, represents the first distribution function, represents the second distribution function, represents the differential, Integration symbol, x represents the integration variable, used to represent a first average distance between the first sensor and the second sensor corresponding to the first distribution function; The second average distance between the first sensor and the second sensor under the second distribution function is used to represent the second average distance between the first sensor and the second sensor.

[0146] In some implementations, during the process of adjusting the initial parameter data according to the measurement reliability to obtain target parameter data, the processor 301 executes:

[0147] Performing abnormal screening on the target sensor according to the measurement reliability to obtain an abnormal sensor;

[0148] Obtaining third historical data corresponding to the abnormal sensor, and predicting a measurement value corresponding to the abnormal sensor at a target time based on the third historical data to obtain a target estimated value corresponding to the abnormal sensor at the target time;

[0149] The target parameter data is obtained by adjusting the initial parameter data according to the target estimated value.

[0150] In some embodiments, during the process of predicting the measurement value corresponding to the abnormal sensor at the target time based on the third historical data and obtaining the target estimated value corresponding to the abnormal sensor at the target time, the processor 301 executes:

[0151] Determine, using a Kalman filter algorithm based on the third historical data, a state equation, a gain matrix, and an observation equation corresponding to the abnormal sensor;

[0152] Determine the state prediction value, gain prediction value and observation prediction value corresponding to the abnormal sensor at the target time according to the state equation, the gain matrix and the observation equation;

[0153] Determine a target error corresponding to the abnormal sensor at the target time according to the state prediction value, the gain prediction value, and the observation prediction value using an error prediction model;

[0154] Determine a target prediction value corresponding to the abnormal sensor at the target time according to the state prediction value, the gain prediction value and the observation prediction value;

[0155] The target prediction value is optimized according to the target error to obtain a target estimated value corresponding to the abnormal sensor at the target time.

[0156] In some implementations, during the process of performing data fusion on the target parameter data to obtain target fusion data corresponding to the target sensor, the processor 301 executes:

[0157] Select any one of the target sensors to be determined as the third sensor corresponding to the current analysis, and obtain third parameter data corresponding to the third sensor from the target parameter data;

[0158] Obtaining a parameter prediction value corresponding to the third sensor at the same time as the third parameter data;

[0159] Calculating the corresponding mean square error between the parameter prediction value and the third parameter data, and determining the corresponding target weight when the third parameter data is subjected to data fusion according to the mean square error;

[0160] Performing data fusion on the third parameter data according to the target weight to obtain parameter fusion data corresponding to the third sensor;

[0161] A sensor weight corresponding to the third sensor is determined, and data fusion is performed according to the sensor weight and the parameter fusion data to obtain the target fusion data corresponding to the target sensor.

[0162] In some embodiments, after obtaining the target monitoring result, the processor 301 further executes:

[0163] Obtain a manual verification result corresponding to the target monitoring result, and send the manual verification result to the cloud server, so that the cloud server updates the water quality prediction model according to the manual verification result to obtain the updated water quality prediction model.

[0164] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific working process of the terminal device described above can refer to the corresponding process in the aforementioned edge computing-based water quality monitoring method embodiment, and will not be repeated here.

[0165] An embodiment of the present invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any water quality monitoring method based on edge computing provided in the description of the embodiment of the present invention.

[0166] The storage medium may be an internal storage unit of the terminal device described in the aforementioned embodiment, such as a hard disk or memory of the terminal device. The storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the terminal device.

[0167] Those skilled in the art will appreciate that all or some of the steps, systems, and functional modules / units in the methods, systems, and devices disclosed above may be implemented as software, firmware, hardware, or any combination thereof. In hardware embodiments, the division between functional modules / units described above does not necessarily correspond to the division between physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media encompasses both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0168] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or system. In the absence of further limitations, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system that includes the element.

[0169] The serial numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be based on the scope of protection of the claims.

Claims

1. A water quality monitoring method based on edge computing, characterized in that: The method comprises: Obtaining initial parameter data corresponding to the target water environment under the target sensor and target space information corresponding to the target water environment under the positioning sensor; Determining a measurement reliability corresponding to the target sensor according to the initial parameter data, and adjusting the initial parameter data according to the measurement reliability to obtain target parameter data; Performing data fusion on the target parameter data to obtain target fusion data corresponding to the target sensor; The target fusion data, the target parameter data, and the target spatial information are sent to a cloud server in communication with the edge device, so that the cloud server performs water quality prediction on the target fusion data, the target parameter data, and the target spatial information according to a water quality prediction model to obtain a target prediction result corresponding to the target water environment; receiving the target prediction result sent by the cloud server, and determining the target monitoring result corresponding to the target water environment according to the target prediction result; The target sensor includes at least a first sensor and a second sensor, the initial parameter data includes at least first parameter data corresponding to the first sensor at a target moment and second parameter data corresponding to the second sensor at the target moment, and determining the measurement reliability corresponding to the target sensor based on the initial parameter data includes: Obtaining first historical data corresponding to the first sensor and second historical data corresponding to the second sensor; determining a first distribution function corresponding to the first sensor according to the first historical data and determining a second distribution function corresponding to the second sensor according to the second historical data; determining a target correlation value between the first parameter data and the second parameter data according to the first distribution function, the second distribution function, the first parameter data, and the second parameter data; determining the corresponding measurement reliability between the first sensor and the second sensor according to the target correlation value; The determining a target correlation value between the first parameter data and the second parameter data according to the first distribution function, the second distribution function, the first parameter data, and the second parameter data includes: determining a first variance corresponding to the first sensor based on the first historical data and determining a second variance corresponding to the second sensor based on the second historical data; Performing an integral operation on the first distribution function according to the first parameter data and the second parameter data to obtain a first correlation value between the first parameter data and the second parameter data; Performing an integral operation on the second distribution function according to the first parameter data and the second parameter data to obtain a second correlation value between the first parameter data and the second parameter data; Performing information fusion on the first correlation value and the second correlation value according to the first variance and the second variance to determine the target correlation value between the first parameter data and the second parameter data; The target correlation value is obtained according to the following formula: ； in, represents the target association value between the first parameter data and the second parameter data, represents the second variance, represents the first variance, abs represents the absolute value, represents the second parameter data, represents the first parameter data, represents the first distribution function, represents the second distribution function, represents the differential, Integration symbol, x represents the integration variable, used to represent a first average distance between the first sensor and the second sensor corresponding to the first distribution function; The second average distance between the first sensor and the second sensor under the second distribution function is used to represent the second average distance between the first sensor and the second sensor.

2. The method according to claim 1, characterized in that The adjusting the initial parameter data according to the measurement reliability to obtain target parameter data includes: Performing abnormal screening on the target sensor according to the measurement reliability to obtain an abnormal sensor; Obtaining third historical data corresponding to the abnormal sensor, and predicting a measurement value corresponding to the abnormal sensor at a target time based on the third historical data to obtain a target estimated value corresponding to the abnormal sensor at the target time; The target parameter data is obtained by adjusting the initial parameter data according to the target estimated value.

3. The method according to claim 2, characterized in that The predicting, based on the third historical data, the measurement value corresponding to the abnormal sensor at the target time to obtain the target estimated value corresponding to the abnormal sensor at the target time includes: Determine, using a Kalman filter algorithm based on the third historical data, a state equation, a gain matrix, and an observation equation corresponding to the abnormal sensor; Determine the state prediction value, gain prediction value and observation prediction value corresponding to the abnormal sensor at the target time according to the state equation, the gain matrix and the observation equation; Determine a target error corresponding to the abnormal sensor at the target time according to the state prediction value, the gain prediction value, and the observation prediction value using an error prediction model; Determine a target prediction value corresponding to the abnormal sensor at the target time according to the state prediction value, the gain prediction value and the observation prediction value; The target prediction value is optimized according to the target error to obtain a target estimated value corresponding to the abnormal sensor at the target time.

4. The method according to claim 1, wherein The performing data fusion on the target parameter data to obtain target fusion data corresponding to the target sensor includes: Select any one of the target sensors to be determined as the third sensor corresponding to the current analysis, and obtain third parameter data corresponding to the third sensor from the target parameter data; Obtaining a parameter prediction value corresponding to the third sensor at the same time as the third parameter data; Calculating the corresponding mean square error between the parameter prediction value and the third parameter data, and determining the corresponding target weight when the third parameter data is subjected to data fusion according to the mean square error; Performing data fusion on the third parameter data according to the target weight to obtain parameter fusion data corresponding to the third sensor; A sensor weight corresponding to the third sensor is determined, and data fusion is performed according to the sensor weight and the parameter fusion data to obtain the target fusion data corresponding to the target sensor.

5. The method according to any one of claims 1 to 4, characterized in that After obtaining the target monitoring result, the method further includes: Obtain a manual verification result corresponding to the target monitoring result, and send the manual verification result to the cloud server, so that the cloud server updates the water quality prediction model according to the manual verification result to obtain the updated water quality prediction model.

6. A water quality monitoring device based on edge computing, applied to the water quality monitoring method based on edge computing according to any one of claims 1 to 5, characterized in that: include: A data acquisition module is used to obtain initial parameter data corresponding to the target water environment under the target sensor and target space information corresponding to the target water environment under the positioning sensor; a data analysis module, configured to determine a measurement reliability corresponding to the target sensor based on the initial parameter data, and adjust the initial parameter data according to the measurement reliability to obtain target parameter data; A data fusion module, configured to perform data fusion on the target parameter data to obtain target fusion data corresponding to the target sensor; a data prediction module, configured to send the target fusion data, the target parameter data, and the target spatial information to a cloud server communicatively connected to the edge device, so that the cloud server performs water quality prediction on the target fusion data, the target parameter data, and the target spatial information according to a water quality prediction model, and obtains a target prediction result corresponding to the target water environment; The water quality monitoring module is used to receive the target prediction result sent by the cloud server and determine the target monitoring result corresponding to the target water environment according to the target prediction result.

7. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer program and implement the edge computing-based water quality monitoring method according to any one of claims 1 to 5 when executing the computer program.

8. A computer storage medium for computer storage, characterized in that: The computer storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the edge computing-based water quality monitoring method according to any one of claims 1 to 5.

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