Digital intelligent water affair cloud application platform

Abstract

The invention relates to the technical field of digital intelligent water affair cloud application platforms, and discloses a digital intelligent water affair cloud application platform, which comprises a data acquisition module, a data base module, an intelligent analysis module, an application development framework module, a user interaction module, a system management module and an application module. According to the digital intelligent water affair cloud application platform, efficient collection, primary processing and transmission of real-time and historical data are performed through a standardized protocol, the timeliness and accuracy of the data are ensured, the data collection efficiency is improved, errors in the data transmission process are reduced, a machine learning algorithm is adopted, a prediction model is constructed based on a high-quality data set, and the prediction efficiency is improved. According to the method, a Docker containerization technology and a Kubernetes cluster management system are utilized, a GitLab CI / CD tool chain is matched, an accurate prediction result is output, a threshold value is set for comparison, optimization suggestions are provided, the method not only can early warn potential problems in advance, but also can continuously improve system performance, intelligent conversion from data to decision is achieved, and rapid creation of an application program is achieved by utilizing the Docker containerization technology and the Kubernetes cluster management system in cooperation with the GitLab CI / CD tool chain.

Description

Technical Field

[0001] The present invention relates to the technical field of water service cloud applications, specifically a digital and intelligent water service cloud application platform. Background Art

[0002] The digital and intelligent water service cloud application platform is an integrated cloud computing solution, aiming to comprehensively improve the intelligent level of water service management through advanced technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and blockchain.

[0003] However, in traditional water service management systems, the distribution of data collection points is limited and the collection frequency is low, resulting in insufficient data coverage. Data is prone to loss or delay during the transmission process, affecting the accuracy and timeliness of data analysis. Moreover, traditional water service management relies on empirical judgment, lacks intelligent prediction tools based on big data and machine learning, and cannot provide early warnings for potential problems or optimization suggestions. At the same time, the traditional application development process is cumbersome, the deployment and iteration speed are slow, it is difficult to quickly respond to changes in business requirements, and the management and maintenance of application programs require a large amount of manpower and material resources. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] To achieve the above object, the present invention provides the following technical solutions: A digital and intelligent water service cloud application platform, comprising: a data collection module, a data base module, an intelligent analysis module, an application development framework module, a user interaction module, a system management module, and an application module; The data collection module is used to collect real-time and historical data of water services, perform preliminary processing and transmission through a standardized protocol, and obtain a data stream; The data base module is used to integrate the data stream and perform preprocessing to obtain a high-quality data set; The intelligent analysis module constructs a prediction model based on the high-quality data set and machine learning algorithms, inputs the high-quality data set into the prediction model, outputs a prediction result, and compares it with a set threshold to obtain an optimization suggestion; The application development framework module creates, deploys, and integrates application programs for water service application scenarios using a self-developed development environment method, forms an application ecosystem, obtains customized water service management tools, and iterates the customized water service management tools using a continuous integration process; The user interaction module provides a friendly and intuitive operation interface for end-users by adopting responsive design and user experience optimization methods, and receives optimization suggestions and continuously records user usage information; The system management module encrypts and monitors the platform by adopting encryption measures, and at the same time conducts periodic reviews on the effectiveness of the encryption measures by using the audit tracking method, records the results, and continuously optimizes the system; The application module includes self-developed applications and other applications, and is used to implement real-time monitoring, data analysis, early warning systems, device management, user services, and decision support.

[0006] As a further solution of the present invention: The method for collecting real-time and historical water service data is preliminarily processed and transmitted through a standardized protocol to obtain a data stream. The specific steps are as follows: Deploy sensors at multiple monitoring points, collect water service data at preset time intervals, and package it into a message format conforming to the MQTT protocol; Use the MQTT library to send the message format to the MQTT broker server on the cloud platform; The MQTT broker server receives, validates, and routes the messages, and distributes the message format to the processing nodes according to the subscription relationship; Each message format contains a unique topic identifier to obtain a data stream.

[0007] As a further solution of the present invention: The method for integrating the data stream and performing preprocessing to obtain a high-quality data set, the specific steps are as follows: Adopt Apache Kafka as the message queue system and Apache Flink as the real-time data processing framework; After receiving the data stream forwarded by the MQTT broker server, first perform data buffering and preliminary filtering through Apache Kafka to remove obviously incorrect data packets; Use Apache Flink to perform preprocessing operations on the filtered data stream, including timestamp correction, data cleaning, and data standardization; Obtain a high-quality data set.

[0008] As a further solution of the present invention: The obtained high-quality data set includes: Adopt a data quality scoring method to evaluate the data set. The expression is: ; Among them, represents integrity, indicating whether the data fields are complete without omission, is the weight, which is different weight values set according to the importance and source of the data points, For timeliness, that is, the time interval from data generation to being processed, For variability, which measures the degree of change of data points relative to the historical average, is the data quality score; The value range is 0 < ≤ 1. When is close to 1, it indicates that the data point has high quality.

[0009] 5. The digital intelligent water service cloud application platform according to claim 1, characterized in that: a prediction model is constructed based on a high-quality data set and a machine learning algorithm, and the high-quality data set is input into the prediction model to output a prediction result. The specific steps are as follows: Use XGBoost to perform feature engineering processing on the data set, including feature selection and importance scoring, to identify key features; Adopt a long short-term memory network as the core machine learning algorithm, and construct a prediction model based on the long short-term memory network; Train the prediction model through key features and adjust the hyperparameters. Input the high-quality data set into the prediction model to calculate the prediction result. The expression is: ; Among them, is the preprocessed high-quality data set, is the parameter set of the LSTM model, The long short-term memory network function is used to process sequence data and generate a prediction output, is the prediction result.

[0010] As a further solution of the present invention: Set a threshold for comparison to obtain optimization suggestions. The specific steps are as follows: For each prediction result , calculate the error between the actual value and the predicted value. The expression is: ; Among them, is the prediction error at time is the actual value at time is the bias correction factor to avoid the case where the denominator is zero, ; Set a threshold , and judge whether the prediction deviation is within the acceptable range; If , an alarm mechanism is triggered to prompt the user of the existing problems and generate optimization suggestions, including adjusting model parameters, adding more training data, optimizing the model architecture, improving the feature selection strategy, and correcting external factors.

[0011] 7. As a further solution of the present invention: The method of using a self-developed development environment is used to create, deploy, and integrate application programs for water service application scenarios to form an application ecosystem, and a customized water service management tool is obtained. The continuous integration process is used to iterate the customized water service management tool. The specific steps are as follows: Use Dockerfile to define the operating environment and dependent libraries required for each water service management tool, and all components are built and run under the same conditions; Use the GitLab repository to host the source code and set up the CI / CD pipeline. When new code is committed, it triggers testing, building an image, and pushing it to a private Docker repository; Deploy the image to the production environment through the Kubernetes orchestration tool Helm, and dynamically adjust the resource allocation according to the actual load situation.

[0012] As a further solution of the present invention: The image is deployed to the production environment through the Kubernetes orchestration tool Helm, and the resource allocation is dynamically adjusted according to the actual load situation. The specific steps are as follows: Set the goals for each application service level, including the maximum response time and the minimum availability; Collect the current application load data, including CPU usage, memory usage, and the number of requests; Use a formula to calculate the resource adjustment factor , and adjust the number of Pods in the Kubernetes cluster, and update the deployment configuration through HelmChart to achieve dynamic resource adjustment. The expression is: ; Among them, is the proportional error, indicating the gap between the current load and the target load, is the integral gain, used to accumulate historical errors, is the differential gain, is the error function, is the time variable, is the resource adjustment factor; The value range is , when >0, it means that resource allocation needs to be increased, <0 then reduce resources.

[0013] As a further solution of the present invention: The user interaction module provides a friendly and intuitive operation interface for end-users by adopting responsive design and user experience optimization methods, and receives optimization suggestions, continuously recording user usage information. The specific steps are as follows: Use React.js as the front-end development framework and combine it with the Tailwind CSS framework to achieve a responsive layout; Define different breakpoints through media queries in the Tailwind CSS framework, including 320px, 768px, and 1024px, and apply specific style rules according to the device width; Integrate a "feedback" pop-up window in the application. The background system receives and processes the feedback information, forms work orders, and assigns them to departments for follow-up; Use the Google Analytics analysis tool to track user behavior data, including page views, dwell time, and conversion rate metrics, and identify problem areas based on the data analysis results to formulate optimization measures; Deploy the ELK Stack log management platform to record user operation behaviors, including click, swipe, and input events. At the same time, mark abnormal situations; The optimization suggestions are passed to the end-users through the user interaction module.

[0014] As a further solution of the present invention: Encryption measures are adopted to encrypt and monitor the platform. At the same time, an audit tracking method is used to conduct periodic reviews on the effectiveness of the encryption measures, record the results, and continuously optimize the system. The specific steps are as follows: Use AES-256 as the symmetric encryption algorithm for encrypting sensitive information, and RSA as the asymmetric encryption algorithm for key exchange and digital signature. Adopt the TLS / SSL protocol to protect network communication; Deploy a key management system responsible for generating, storing, distributing, and destroying encryption keys; Enable transparent data encryption at the database level. For data processing within the application, use APIs to encrypt interface calls; Monitor the status of encryption operations, including encryption failures and abnormal decryption attempts, by integrating a security information and event management system; Create an immutable audit log for each critical operation, set a review period, and have an independent security team evaluate the effectiveness of the encryption measures; Generate a report after each review, pointing out the problems found and improvement suggestions.

[0015] Compared with the prior art, the beneficial effects of the present invention are: Efficient collection, preliminary processing and transmission of real-time and historical data through standardized protocols ensure the timeliness and accuracy of the data. This not only improves the data collection efficiency but also reduces errors during data transmission. Machine learning algorithms are used to build a prediction model based on a high-quality dataset, output accurate prediction results, and set thresholds for comparison to provide optimization suggestions. This method can not only early warn of potential problems but also continuously improve the system performance, realizing the intelligent transformation from data to decision-making. Using Docker containerization technology and Kubernetes cluster management system, combined with the GitLab CI / CD toolchain, it realizes the rapid creation, deployment, and iteration of applications. The self-developed development environment method not only improves the development efficiency but also enhances the flexibility and scalability of the system, enabling it to quickly respond to changes in business requirements. By adopting responsive design and user experience optimization methods, it provides a friendly and intuitive operation interface for end-users. The built-in feedback mechanism and data analysis tools help developers continuously improve the interface design and service functions, ensuring the maximization of user satisfaction. Brief Description of the Drawings

[0016] Figure 1 It is a system schematic diagram of the digital intelligent water service cloud application platform. Detailed Embodiments

[0017] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made in conjunction with the accompanying drawings of the specification.

[0018] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0019] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments.

[0020] Embodiment 1 Please refer to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a digital intelligent water service cloud application platform, including: a data collection module, a data base module, an intelligent analysis module, an application development framework module, a user interaction module, and a system management module; The data collection module is used to collect real-time and historical data of water services, perform preliminary processing and transmission through a standardized protocol, and obtain a data stream; Deploy sensors at multiple monitoring points to collect water utility data at preset time intervals and package it into a message format compliant with the MQTT protocol; Use the MQTT library to send the message format to the MQTT broker server on the cloud platform; The MQTT broker server receives, validates, and routes the messages and distributes the message format to the processing nodes according to the subscription relationship; Each message format contains a unique topic identifier to obtain a data stream.

[0021] The data base module is used to integrate the data stream and perform preprocessing to obtain a high-quality data set; Adopt Apache Kafka as the message queue system and Apache Flink as the real-time data processing framework; After receiving the data stream forwarded by the MQTT broker server, first perform data buffering and preliminary filtering through Apache Kafka to remove obviously incorrect data packets; Use Apache Flink to perform preprocessing operations on the filtered data stream, including timestamp correction, data cleaning, and data standardization; Obtain a high-quality data set; Adopt a data quality scoring method to evaluate the data set. The expression is: ; where is integrity, indicating whether the data fields are complete without omission, is the weight, with different weight values set according to the importance and source of the data points, is timeliness, that is, the time interval from data generation to being processed, is variability, measuring the degree of change of the data point relative to the historical average value, is the data quality score; The value range is 0 < ≤ 1. When is close to 1, it indicates that the data point has high quality.

[0022] The intelligent analysis module constructs a prediction model based on the high-quality data set and machine learning algorithms, inputs the high-quality data set into the prediction model, outputs the prediction results, sets a threshold for comparison, and obtains optimization suggestions; Use XGBoost to perform feature engineering processing on the data set, including feature selection and importance scoring, to identify key features; Adopt the long short-term memory network as the core machine learning algorithm and construct a prediction model based on the long short-term memory network; Train a prediction model with key features and adjust hyperparameters. Input a high-quality dataset into the prediction model to calculate the prediction result. The expression is: ; where is the preprocessed high-quality dataset, is the parameter set of the LSTM model, is the long short-term memory network function, which is used to process sequence data and generate prediction outputs, is the prediction result; For each prediction result , calculate the error between the actual value and the predicted value. The expression is: ; where is the prediction error at time is the actual value at time is the bias correction factor to avoid the denominator being zero, ; Set a threshold to determine whether the prediction deviation is within the acceptable range; If , trigger an alarm mechanism to prompt the user of the existing problems and generate optimization suggestions, including adjusting model parameters, adding more training data, optimizing the model architecture, improving the feature selection strategy, and correcting external factors.

[0023] The application development framework module uses the self-developed development environment method to create, deploy, and integrate application programs for water service application scenarios, form an application ecosystem, obtain customized water management tools, and iterate the customized water management tools using a continuous integration process; Use Dockerfile to define the running environment and dependent libraries required for each water management tool, and all components are built and run under the same conditions; Use the GitLab repository to host the source code and set up a CI / CD pipeline. When new code is committed, trigger tests, build images, and push them to a private Docker repository; Deploy the images to the production environment through the Kubernetes orchestration tool Helm, and dynamically adjust resource allocation according to the actual load situation; Set the goals for each application service level, including the maximum response time and the minimum availability; Collect the current application load data, including CPU usage, memory usage, and the number of requests; Calculate the resource adjustment factor using the formula and adjust the number of Pods in the Kubernetes cluster, update the deployment configuration through the Helm Chart to achieve dynamic resource adjustment. The expression is: ; Among them, is the proportional error, indicating the gap between the current load and the target load. is the integral gain, used to accumulate historical errors. is the derivative gain. is the error function. is the time variable. is the resource adjustment factor. The value range is When > 0, it means that resource allocation needs to be increased. <0 means reducing resources.

[0024] The user interaction module uses responsive design and user experience optimization methods to provide a friendly and intuitive operation interface for end-users, receive optimization suggestions, and continuously record user usage information. Use React.js as the front-end development framework and combine it with the Tailwind CSS framework to achieve a responsive layout. Define different breakpoints through media queries in the Tailwind CSS framework, including 320px, 768px, and 1024px, and apply specific style rules according to the device width. Integrate a "feedback" pop-up window in the application. The background system receives and processes the feedback information, forms a work order, and assigns it to the department for follow-up. Use the Google Analytics analysis tool to track user behavior data, including page views, dwell time, and conversion rate metrics, and identify problem areas based on the data analysis results to formulate optimization measures. Deploy the ELK Stack log management platform to record user operation behaviors, including click, swipe, and input events. At the same time, mark abnormal situations. Optimization suggestions are passed to end-users through the user interaction module.

[0025] The system management module uses encryption measures to monitor the platform. At the same time, it uses the audit trail method to conduct periodic reviews on the effectiveness of the encryption measures, record the results, and continuously optimize the system. Use AES-256 as the symmetric encryption algorithm for encrypting sensitive information, RSA as the asymmetric encryption algorithm for key exchange and digital signature, and use the TLS / SSL protocol to protect network communication. Deploy a key management system responsible for generating, storing, distributing, and destroying encryption keys; Enable transparent data encryption at the database level. For internal data processing within the application, use APIs to encrypt interface calls; By integrating a security information and event management system, monitor the status of encryption operations, including encryption failures and abnormal decryption attempts; Create immutable audit logs for each critical operation, set a review period, and have an independent security team evaluate the effectiveness of encryption measures; Generate a report after each review, pointing out the discovered problems and improvement suggestions.

[0026] Application modules, including self-developed applications and other applications, are used to implement real-time monitoring, data analysis, early warning systems, device management, user services, and decision support; Real-time monitoring: Real-time monitor key parameters such as water quality, water level, and flow rate; Data analysis: Deeply analyze the collected data to provide trend prediction and anomaly detection; Early warning system: Based on the data analysis results, automatically trigger early warning notifications to remind relevant personnel to take actions; Device management: Remotely monitor and manage the device status of the water service system, including maintenance plans and fault alarms; User services: Provide users with self-service query, payment, and repair services; Decision support: Provide data-driven decision support tools for managers to help optimize water resource management and allocation.

[0027] In summary, through standardized protocols, efficient collection, preliminary processing, and transmission of real-time and historical data are ensured, guaranteeing the timeliness and accuracy of the data. This not only improves the data collection efficiency but also reduces errors during data transmission. Machine learning algorithms are used to build prediction models based on high-quality datasets, outputting accurate prediction results, and setting thresholds for comparison to provide optimization suggestions. The method can not only early warn of potential problems but also continuously improve the system performance, realizing the intelligent transformation from data to decision-making. By using Docker containerization technology and Kubernetes cluster management system, in conjunction with the GitLabCI / CD toolchain, rapid creation, deployment, and iteration of application programs are achieved. The self-developed development environment method not only improves the development efficiency but also enhances the flexibility and scalability of the system, enabling it to quickly respond to changes in business requirements. By adopting responsive design and user experience optimization methods, a friendly and intuitive operation interface is provided for end-users. The built-in feedback mechanism and data analysis tools help developers continuously improve the interface design and service functions, ensuring the maximization of user satisfaction.

[0028] Example 2 Referring to Table 1, this is the second embodiment of the present invention. To further verify the technical solution of the present invention, experimental simulation data of the digital intelligent water service cloud application platform is given.

[0029] To verify the effectiveness and superiority of the digital intelligent water service cloud application platform, a medium-sized urban water supply system was selected as the experimental object. This urban water supply system includes multiple water sources, water treatment plants, pumping stations, and an extensive water distribution network. The experiment aimed to evaluate the performance of the digital intelligent water service cloud application platform in improving data collection accuracy, optimizing resource allocation, enhancing user satisfaction, and strengthening security.

[0030] First, 20 key monitoring points were selected across the city, and high-precision sensors were deployed to collect real-time data such as water quality parameters (such as pH value, turbidity), flow rate, and pressure at a frequency of once every 5 minutes. The sensors packaged the data into message formats and sent them to the MQTT broker server on the cloud platform through the MQTT protocol. Each message contains a unique topic identifier, ensuring the security and reliability of data transmission. This not only increases the frequency of data collection but also guarantees the consistency and integrity of data transmission.

[0031] The received data stream was buffered and initially filtered by Apache Kafka to remove significantly incorrect data packets. Subsequently, Apache Flink was used to perform preprocessing operations on the filtered data stream, including timestamp correction, data cleaning, and standardization. After preprocessing, a high-quality data set was formed. A data quality scoring method was used to evaluate the quality of the data set, where different weight values were assigned to integrity, timeliness, and variability. The final data quality score obtained was 0.95, which is much higher than the average score of 0.78 of traditional systems.

[0032] Based on the high-quality data set, XGBoost was used for feature engineering processing to identify key features, and a long short-term memory network (LSTM) was used to construct a prediction model. By adjusting the hyperparameters, the model was successfully trained and performed excellently in practical applications. For each prediction result, the error between the actual value and the predicted value was calculated, and a threshold of 0.05 was set. When the error exceeded the threshold, an alarm mechanism was triggered to prompt relevant personnel to take actions. The results showed that the prediction accuracy of the model reached 96%, significantly superior to 85% of the existing technologies.

[0033] To meet the requirements of different scenarios, the development team used Dockerfile to define the running environments and dependent libraries required for multiple water management tools. All components were built and run under the same conditions, ensuring consistency. The source code was hosted in a GitLab repository, and a CI / CD pipeline was set up to achieve automated testing, building images, and pushing them to a private Docker repository. The images were deployed to the production environment through the Kubernetes orchestration tool Helm, and the resource allocation was dynamically adjusted according to the actual load. When the CPU usage rate was detected to exceed 80%, the number of Pods was automatically increased to maintain service performance.

[0034] For front-end development, React.js was combined with the Tailwind CSS framework to achieve a responsive layout that adapts to various screen sizes. Media queries defined three breakpoints at 320px, 768px, and 1024px, and specific style rules were applied according to different device widths. An "Opinion Feedback" pop-up window was integrated, and the back-end system could receive and process feedback information, form work orders, and assign them to relevant departments for follow-up. At the same time, the Google Analytics analysis tool was used to track user behavior data, recording metrics such as page views, dwell time, and conversion rates. The ELK Stack log management platform recorded user operation behaviors, including click, swipe, and input events, and marked abnormal situations. By continuously collecting user feedback, the interface design and service functions were continuously improved, and the user satisfaction rate increased from 70% to 90%.

[0035] In terms of security measures, AES-256 was used as the symmetric encryption algorithm, RSA as the asymmetric encryption algorithm, and the TLS / SSL protocol was used to protect network communications. A key management system was deployed to be responsible for generating, storing, distributing, and destroying encryption keys. Transparent data encryption was enabled at the database level, and data processing within the application also encrypted API calls. In addition, the status of encryption operations, including encryption failures and decryption attempt anomalies, was monitored by integrating a security information and event management system. An immutable audit log was created for each critical operation, and a monthly review cycle was set. An independent security team comprehensively evaluated the effectiveness of the encryption measures, and a report was generated after each review, pointing out the discovered problems and improvement suggestions, ensuring the long-term security of the system.

[0036] Table 1 Data analysis of the comparison between the digital intelligent water cloud application platform and the existing technology As can be seen from the above table, the digital intelligent water cloud application platform shows obvious superiority in many aspects: In the prior art, the data collection frequency was only 12 times per hour, while the present invention achieves high-frequency collection of 120 times per hour. A higher collection frequency means finer-grained data coverage, which helps to capture instantaneous changes and provides a more accurate analysis basis.

[0037] The data transmission success rate in the prior art was approximately 90%, while the present invention, with the support of the MQTT protocol and the cloud platform, has increased this ratio to 99.5%. The extremely high transmission success rate ensures the integrity and availability of data, reducing data loss or delay problems caused by transmission failures.

[0038] The data quality score in the prior art was 0.78, while the present invention has increased the score to 0.95 through a series of preprocessing operations. The high-quality data set not only improves the accuracy of subsequent analysis but also provides a solid basis for intelligent decision-making.

[0039] The prediction accuracy rate in the prior art was 85%, while the present invention, by introducing advanced machine learning algorithms, especially the LSTM model, has achieved a prediction accuracy rate of 96%. This significant improvement enables water utility managers to issue early warnings of potential problems and formulate more effective response strategies.

[0040] The security review cycle in the prior art was 6 months, while the present invention has shortened it to once a month. More frequent security reviews ensure the effectiveness and compliance of encryption measures, timely detect potential security hazards, and reduce the likelihood of risks occurring.

[0041] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. Digital water cloud application platform, characterized by: include: Data acquisition module, data base module, intelligent analysis module, application development framework module, user interaction module, system management module and application module; The data acquisition module is used to collect real-time and historical data of water affairs, perform preliminary processing and transmission through standardized protocols, and obtain data streams; The data base module is used to integrate data streams and pre-process them to obtain high-quality data sets; The intelligent analysis module builds a prediction model based on high-quality data sets and machine learning algorithms, inputs the high-quality data sets into the prediction model, outputs prediction results, sets thresholds for comparison, and obtains optimization suggestions; The application development framework module uses a self-developed development environment method to create, deploy and integrate applications for water application scenarios, form an application ecosystem, obtain customized water management tools, and use a continuous integration process to iterate the customized water management tools; The user interaction module adopts responsive design and user experience optimization methods to provide a friendly and intuitive operation interface for end users, receive optimization suggestions, and continuously record user usage information; The system management module adopts encryption measures to monitor the platform, and adopts audit tracking methods to periodically review the effectiveness of encryption measures, record the results, and continuously optimize the system; The application modules, including self-developed applications and other applications, are used to achieve real-time monitoring, data analysis, early warning systems, equipment management, user services and decision support.

2. The digital water cloud application platform according to claim 1 is characterized by: The method for collecting real-time and historical data of water affairs is used to perform preliminary processing and transmission through a standardized protocol to obtain a data stream. The specific steps are as follows: Deploy sensors at multiple monitoring points to collect water service data at preset time intervals and package them into a message format that complies with the MQTT protocol; Use the MQTT library to send the message format to the MQTT proxy server on the cloud platform; The MQTT proxy server receives, verifies and routes messages, and distributes the message format to processing nodes based on the subscription relationship; Each message format contains a unique topic identifier, resulting in a data stream.

3. The digital water cloud application platform according to claim 1 is characterized by: The method is used to integrate data streams and preprocess them to obtain high-quality data sets. The specific steps are as follows: Use Apache Kafka as the message queue system and Apache Flink as the real-time data processing framework; After receiving the data stream forwarded by the MQTT proxy server, Apache Kafka is first used for data buffering and preliminary filtering to remove obviously erroneous data packets; Use Apache Flink to perform preprocessing operations on the filtered data stream, including timestamp correction, data cleaning, and data standardization; Get high-quality data sets.

4. The digital water cloud application platform according to claim 3 is characterized by: The high-quality data set obtained includes: The data quality scoring method is used to evaluate the data set, and the expression is: ； in, For completeness, it indicates whether the data field is complete. is the weight, which is set according to the importance and source of the data point. Timeliness is the time interval from data generation to data processing. Variability is a measure of how much a data point changes relative to its historical average. Score data quality; The value range is 0< ≤1, when When it is close to 1, it means the data point has higher quality.

5. The digital water cloud application platform according to claim 1 is characterized by: The prediction model is constructed based on high-quality data sets and machine learning algorithms, and the high-quality data sets are input into the prediction model to output prediction results. The specific steps are as follows: Use XGBoost to perform feature engineering on the dataset, including feature selection and importance scoring, to identify key features; Adopting long short-term memory network as the core machine learning algorithm, and building a prediction model based on long short-term memory network; By training the prediction model with key features and adjusting the hyperparameters, high-quality datasets Input into the prediction model to calculate the prediction result, the expression is: ； in, For a high-quality preprocessed dataset, is the parameter set of the LSTM model, Long short-term memory network functions, which process sequence data and generate prediction outputs, For the prediction results.

6. The digital water cloud application platform according to claim 1 is characterized by: The threshold is set for comparison to obtain optimization suggestions, and the specific steps are as follows: For each prediction result , calculate the error between the actual value and the predicted value, the expression is: ； in, for The prediction error at time for The actual value of the moment, is the bias correction factor to avoid the denominator being zero, ; Setting Thresholds , determine whether the prediction deviation is within the acceptable range; if , an alarm mechanism is triggered to inform the user of the problem and generate optimization suggestions, including adjusting model parameters, adding more training data, optimizing model architecture, improving feature selection strategies, and correcting external factors.

7. The digital water cloud application platform according to claim 1 is characterized by: The self-developed development environment method is used to create, deploy and integrate applications for water application scenarios to form an application ecosystem, obtain customized water management tools, and use a continuous integration process to iterate the customized water management tools. The specific steps are: Use Dockerfile to define the operating environment and dependency libraries required for each water management tool, so that all components are built and run under the same conditions; Use GitLab repository to host source code and set up CI / CD pipeline. When new code is submitted, trigger the test, build the image and push it to the private Docker repository. Deploy the image to the production environment through the Kubernetes orchestration tool Helm, and dynamically adjust resource allocation based on actual load conditions.

8. The digital water cloud application platform according to claim 7 is characterized by: The image is deployed to the production environment through the Kubernetes orchestration tool Helm, and resource allocation is dynamically adjusted according to the actual load situation. The specific steps are as follows: Set service-level goals for each application, including maximum response time and minimum availability; Collect current application load data, including CPU usage, memory usage, and number of requests; Calculate resource adjustment factors using formula , and adjust the number of Pods in the Kubernetes cluster, update the deployment configuration through HelmChart, and realize dynamic resource adjustment. The expression is: ； in, is the proportional error, which indicates the difference between the current load and the target load. is the integral gain, which is used to accumulate historical errors. is the differential gain, is the error function, is the time variable, is the resource adjustment factor; The value range is ,when >0, indicating the need to increase resource allocation, <0, reduce resources.

9. The digital water cloud application platform according to claim 1, characterized in that: The user interaction module adopts responsive design and user experience optimization methods to provide a friendly and intuitive operation interface for end users, receive optimization suggestions, and continuously record user usage information. The specific steps are as follows: Use React.js as the front-end development framework and combine it with Tailwind CSS framework to achieve responsive layout; Define different breakpoints, including 320px, 768px, and 1024px, through media queries in the Tailwind CSS framework, and apply specific style rules based on device width; Integrate a "Feedback" pop-up window in the application, and the backend system receives and processes the feedback information, forming a work order and assigning it to the department for follow-up; Use Google Analytics to track user behavior data, including page views, dwell time, and conversion rate indicators, and identify problem areas based on data analysis results and develop optimization measures; Deploy the ELK Stack log management platform to record user operations, including clicks, slides, and input events, and mark abnormal situations; Optimization suggestions are delivered to end users through the user interaction module.

10. The digital water cloud application platform according to claim 1, characterized in that: The encryption measures are used to monitor the platform, and the audit tracking method is used to periodically review the effectiveness of the encryption measures, record the results, and continuously optimize the system. The specific steps are: Use AES-256 as the symmetric encryption algorithm to encrypt sensitive information, RSA as the asymmetric encryption algorithm for key exchange and digital signature, and TLS / SSL protocol to protect network communications; Deploy a key management system responsible for generating, storing, distributing and destroying encryption keys; Enable transparent data encryption at the database level, and use APIs to encrypt interface calls for data processing within applications; Monitor the status of cryptographic operations, including encryption failures and decryption attempt anomalies, through integrated security information and event management systems; Create an unalterable audit log for each key operation, set a review cycle, and have an independent security team evaluate the effectiveness of encryption measures; A report is generated after each review, indicating the issues found and suggestions for improvement.

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