Water source monitoring system based on intelligent matching and inspection management

Abstract

The invention relates to the technical field of water source monitoring systems, in particular to a water source monitoring system based on intelligent matching and inspection management. The cloud platform module is wirelessly connected with the user side module; based on the user data and the water source information, intelligent matching and scheduling assignment are executed; the intelligent matching module is in data connection with the cloud platform module and encodes the water source position based on the coherence group theory of topology; quantizing a water source state by using a Bes sel function; constructing an intelligent matching scoring model by using the Lie group and Lie algebra theories; performing optimal matching selection by applying an elliptic curve theory; a matching result is optimized by using a Toeplitz matrix; the inspection management module is used for generating and assigning inspection tasks; recording and analyzing an inspection result; updating water source state information in real time; the data analysis module is in data connection with the cloud platform module and the inspection management module; and a water source use report and early warning information are generated, so that water source matching can be quickly and accurately performed.

Description

Technical Field

[0001] The present invention relates to the technical field of water source monitoring systems, and particularly to a water source monitoring system based on intelligent matching and patrol management. Background Art

[0002] With the acceleration of the urbanization process and the continuous increase in water resource demand, water source management has become a key issue in modern urban operation. Traditional water source monitoring and management methods mainly rely on manual inspections at fixed intervals and simple data recording, which are inadequate in dealing with the increasingly complex water source management requirements.

[0003] In recent years, some cities have begun to attempt to introduce information technology means to improve water source management. For example, using remote sensors to monitor water quality data in real time, or adopting a patrol strategy based on priorities. These methods have improved the efficiency of water source management to a certain extent, but there are still many problems. First of all, these systems are often isolated and lack overall consideration, resulting in insufficient information sharing and low decision-making efficiency. Secondly, existing intelligent attempts mainly focus on the data collection link, and the degree of intelligence in data analysis and decision support is insufficient, unable to fully utilize the value of data.

[0004] In addition, the current water source matching methods usually adopt simple distance priority or capacity priority principles, which cannot adapt to the complex and changeable actual needs. In terms of patrol management, although there is a priority-based scheduling, there is still a lack of in-depth analysis of historical data and real-time status, making it difficult to achieve true intelligent and precise management.

[0005] These deficiencies in the prior art have led to a series of problems: low water source utilization efficiency, untimely problem discovery, weak emergency response ability, and serious resource waste. Especially in the face of emergencies or complex environments, the limitations of existing systems are more obvious, and it is difficult to provide fast and accurate decision support. Summary of the Invention

[0006] The present invention aims to solve the above technical problems and provides a water source monitoring system based on intelligent matching and patrol management. Through innovative algorithm design and modular architecture, this system realizes the full intelligence and refinement of water source management.

[0007] The present invention proposes a water source monitoring system based on intelligent matching and patrol management, including:

[0008] A user terminal module, used for:

[0009] Implementing user registration, login, and management functions;

[0010] Providing water source information maintenance and equipment maintenance functions;

[0011] A cloud platform module, wirelessly connected to the client module, for:

[0012] Receiving user data and water source information sent by the client module;

[0013] Based on the user data and water source information, performing intelligent matching and scheduling assignment;

[0014] An intelligent matching module, data-connected to the cloud platform module, for:

[0015] Encoding the water source location based on the homology group theory of topology;

[0016] Quantifying the water source status using Bessel functions;

[0017] Constructing an intelligent matching scoring model using Lie group and Lie algebra theories;

[0018] Applying elliptic curve theory for optimal matching selection;

[0019] Optimizing the matching result using a Toeplitz matrix;

[0020] An inspection management module, data-connected to the intelligent matching module and the client module, for:

[0021] Generating and dispatching inspection tasks;

[0022] Recording and analyzing inspection results;

[0023] Real-time updating of water source status information;

[0024] A data analysis module, data-connected to the cloud platform module and the inspection management module, for:

[0025] Statistically analyzing historical inspection data;

[0026] Generating a water source usage report and warning information.

[0027] Preferably, the intelligent matching module includes:

[0028] A location encoding unit for implementing a water source location encoding algorithm:

[0029] f 1 (x,y,z) = [H 0 (X), H 1 (X), H 2 (X)],

[0030] where (x,y,z) are the three-dimensional coordinates of the water source, and H n (X) is the n-dimensional homology group;

[0031] A state quantization unit, which is used to implement a water source state quantization algorithm:

[0032] f 2 (H) = J α (βH),

[0033] where J α is the Bessel function of order α, H is the homology group vector output by the position encoding unit, and β is the undetermined parameter scoring calculation unit, which is used to implement an intelligent matching scoring algorithm:

[0034] f 3 (J) = tr(ad g (J)),

[0035] where J is the Bessel function value output by the state quantization unit, ad g is the adjoint representation of the Lie algebra g, and tr represents the trace of a matrix;

[0036] An optimal selection unit, which is used to implement an optimal matching selection algorithm:

[0037] f 4 (s) = [k]P,

[0038] where s is the score output by the scoring calculation unit, P is the base point on the elliptic curve E; A result optimization unit, which is used to implement a matching result optimization algorithm:

[0039] f 5 (Q) = T -1 Q,

[0040] where Q is the elliptic curve point output by the optimal selection unit, and T -1 is the inverse matrix of the Toeplitz matrix

[0041] Preferably, the user terminal module includes:

[0042] A user interface unit, which is used to provide a graphical operation interface;

[0043] A data acquisition unit, which is used to acquire information such as the water source location and status;

[0044] A communication unit, which is used to perform data interaction with the cloud platform module;

[0045] where the user interface unit, the data acquisition unit, and the communication unit are interconnected through an internal bus.

[0046] Preferably, the cloud platform module includes:

[0047] A data storage unit, which is used to store user data, water source information, and inspection records;

[0048] A task scheduling unit for generating and dispatching inspection tasks;

[0049] An API interface unit for providing an access interface for external systems;

[0050] Among them, the data storage unit, the task scheduling unit, and the API interface unit are interconnected through a cloud service bus.

[0051] Preferably, the inspection management module includes:

[0052] A task generation unit for generating inspection tasks based on water source distribution and inspection cycles;

[0053] A result recording unit for recording the inspection data uploaded by inspection personnel;

[0054] A status update unit for real-time updating of water source status according to inspection results;

[0055] Among them, the task generation unit, the result recording unit, and the status update unit are interconnected through an internal data bus.

[0056] Preferably, the data analysis module includes:

[0057] A data preprocessing unit for cleaning and standardizing raw data;

[0058] A statistical analysis unit for performing statistical analysis on the preprocessed data;

[0059] A report generation unit for generating analysis reports and visualization charts;

[0060] An early warning judgment unit for generating early warning information based on analysis results;

[0061] Among them, the data preprocessing unit, the statistical analysis unit, the report generation unit, and the early warning judgment unit are interconnected through a data analysis bus.

[0062] Preferably, it further includes:

[0063] A GIS integration module, which is data-connected to the cloud platform module and the user terminal module, and is used for:

[0064] Providing a visual display of water source geographical information;

[0065] Supporting an intelligent matching function based on geographical location.

[0066] Preferably, it further includes:

[0067] A permission management module, which is data-connected to the user terminal module and the cloud platform module, and is used for:

[0068] Implement role-based dynamic permission allocation;

[0069] Control users' access rights to system functions and data.

[0070] Preferably, data transmission between the user terminal module and the cloud platform module is carried out through an encrypted HTTPS protocol to ensure the security of data transmission.

[0071] Preferably, the intelligent matching module further includes an adaptive optimization unit for:

[0072] Dynamically adjust the parameters of the intelligent matching algorithm based on historical matching data;

[0073] Continuously improve the matching accuracy through machine learning methods.

[0074] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0075] The core of the present invention lies in its unique intelligent matching algorithm and adaptive patrol management mechanism. The intelligent matching algorithm integrates advanced mathematical tools such as topology, Lie group theory, and elliptic curve cryptography, which can not only quickly and accurately perform water source matching but also adapt to complex and changeable actual situations. The adaptive patrol management mechanism continuously optimizes the patrol strategy through machine learning, greatly improving the problem discovery rate and patrol efficiency.

[0076] These two core functions complement each other, forming a self-optimizing closed-loop system. The accurate data provided by intelligent matching provides a decision-making basis for patrol management, while the real-time data collected by patrol management in turn optimizes the matching algorithm. This synergistic effect enables the system to continuously improve its performance and adapt to various complex scenarios.

[0077] In addition, the data analysis module of the present invention adopts an advanced anomaly detection algorithm, which can detect potential problems early and greatly improve the early warning ability of the system. The GIS integration module provides intuitive spatial information support for decision-making, making resource allocation more reasonable and efficient.

[0078] Generally speaking, the system of the present invention significantly outperforms the prior art in multiple key indicators. It not only improves the accuracy and response speed of water source matching but also greatly enhances the problem discovery rate and resource utilization efficiency. The adaptive characteristics of the system enable it to continuously optimize its performance and maintain efficient operation in the long term.

[0079] More importantly, the present invention provides a comprehensive and integrated solution for water source management. It breaks the limitation of information silos in traditional water source management and realizes the full-process intelligence from data collection, analysis to decision support. This holistic solution not only improves the daily management efficiency but also greatly enhances the system's ability to respond to emergencies.

[0080] Generally speaking, the water source monitoring system based on intelligent matching and inspection management of the present invention represents a major technological breakthrough in the field of water source management. It provides strong technical support for the sustainable utilization of urban water resources and is expected to generate significant economic and social benefits in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is the logic block diagram of the overall system of the present invention.

[0082] Figure 2 It is the logic block diagram of the user terminal module and the cloud platform module of the present invention.

[0083] Figure 3 It is the logic block diagram of the intelligent matching module of the present invention.

[0084] Figure 4 It is the logic block diagram of the inspection management module of the present invention.

[0085] Figure 5 It is the logic block diagram of the data analysis module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0086] Refer to Figures 1-5 , the present invention discloses a water source monitoring system based on intelligent matching and inspection management. The system includes a user terminal module 1, a cloud platform module 2, an intelligent matching module 3, an inspection management module 4, and a data analysis module 5.

[0087] The user terminal module 1 is used to implement user registration, login, and management functions, and provide water source information maintenance and equipment maintenance functions. Preferably, the user terminal module 1 can be a mobile application or a web interface, enabling users to conveniently access system functions. For example, users can report the water source status in real time or view inspection tasks through the application on a mobile device.

[0088] The cloud platform module 2 is wirelessly connected to the user terminal module 1 and is used to receive user data and water source information sent by the user terminal module 1, and perform intelligent matching and scheduling assignment based on these data. In an embodiment of the present invention, the cloud platform module 2 adopts a distributed architecture to ensure the high availability and scalability of the system. The cloud platform module 2 can use a secure data transmission protocol, such as HTTPS, to protect the transmission of sensitive information.

[0089] The data connection between the intelligent matching module 3 and the cloud platform module 2 is one of the core innovations of the present invention. This module adopts a series of advanced mathematical methods and algorithms to achieve efficient and accurate water source matching. Specifically, the intelligent matching module 3 includes the following functions: encoding the water source location based on the homology group theory of topology; quantifying the water source state using Bessel functions; constructing an intelligent matching scoring model using Lie group and Lie algebra theories; applying elliptic curve theory for optimal matching selection; and optimizing the matching result using Toeplitz matrices.

[0090] The inspection management module 4 is data-connected to the intelligent matching module 3 and the user terminal module 1, and is used to generate and dispatch inspection tasks, record and analyze inspection results, and update the water source state information in real time. In a preferred embodiment of the present invention, the inspection management module 4 can dynamically adjust the inspection frequency according to the importance of the water source and historical inspection records. For example, for water sources with high importance or more problems in history, the inspection frequency can be increased; for water sources with stable states, the inspection frequency can be appropriately reduced to optimize resource utilization.

[0091] The data analysis module 5 is data-connected to the cloud platform module 2 and the inspection management module 4, and is used to statistically analyze historical inspection data and generate water source usage reports and warning information. In an embodiment of the present invention, the data analysis module 5 can use machine learning algorithms, such as time series analysis or anomaly detection algorithms, to predict possible problems of the water source. For example, the ARIMA model can be used to predict the change trend of water source water quality, and when the prediction result exceeds the preset threshold, the system will automatically generate warning information.

[0092] The system of the present invention improves the efficiency and accuracy of water source scheduling through intelligent matching algorithms, enhances the quality of water source monitoring through scientific inspection management, and at the same time provides strong support for water source management decisions through data analysis. This comprehensive solution can significantly improve the efficiency and effectiveness of water source management and is of great significance for ensuring the sustainable utilization of water resources.

[0093] In a preferred embodiment of the present invention, the intelligent matching module 3 includes a location encoding unit 31, a state quantification unit 32, a scoring calculation unit 33, an optimal selection unit 34, and a result optimization unit 35. These units work together to achieve efficient and accurate intelligent matching of water sources.

[0094] In a large water source monitoring network, there are multiple water source points distributed in different geographical locations. It is necessary to effectively identify and classify these water source points for subsequent data processing and analysis. Through the homology group theory in topology, the spatial relationships and distribution patterns between water source points can be captured, which is very important for understanding and predicting the change trend of water quality.

[0095] The location encoding unit 31 is used to implement the water source location encoding algorithm. This algorithm is based on the homology group theory of topology and can effectively capture the spatial structure characteristics of the water source distribution.

[0096] First, the homology group theory in topology is used to encode the water source locations. This method can effectively capture the spatial structure characteristics of the water source distribution. The homology group is an important concept in topology, used to describe the connectivity and hole structure of space. For water source monitoring, it can be used to represent the relative positions and connection methods of water source points in the network.

[0097]

[0098] Among them, H n (X) is the n-dimensional homology group, is the kernel of the n-dimensional boundary operator, is the image of the n + 1-dimensional boundary operator.

[0099] Specifically, the location encoding function is defined as follows:

[0100] f 1 (x, y, z) = [H 0 (X), H 1 (X), H 2 (X)],

[0101] where (x, y, z) are the three-dimensional coordinates of the water source, and H n (X) is the n-dimensional homology group. This encoding method can better reflect the topological relationship between water sources compared with the traditional coordinate representation. For example, for two adjacent water sources, although their physical coordinates may be quite different, they may have similar characteristics in the homology group representation, thus better reflecting their spatial relationship. Being able to better understand the spatial relationship between water source points helps to discover potential pollution paths or the propagation patterns of water quality changes. According to the distribution characteristics of water source points, reasonably arrange the inspection routes and equipment deployment to improve the monitoring efficiency.

[0102] The water quality data collected by the sensors (such as pH value, conductivity, dissolved oxygen, etc.) need to be converted into a unified standard form for subsequent comparison and analysis. The choice of Bessel function is to simulate certain types of fluctuation phenomena, which is very useful for capturing the changes of water quality parameters over time and space.

[0103] The state quantization unit 32 is responsible for implementing the water source state quantization algorithm. The present invention uses the Bessel function to quantify the water source state because the Bessel function has excellent properties in describing fluctuation phenomena and is suitable for representing the dynamic characteristics of water sources. The state quantization function is defined as follows:

[0104] f2 J(H) = J α (βH),

[0105] where J α is the Bessel function of order α, H is the homology group vector output by the position encoding unit, and β is a parameter to be determined. In practical applications, α can be selected according to the water source type. For example, for surface water sources, α = 0 can be selected, and for groundwater sources, α = 1 can be selected. The β parameter can be obtained through training with historical data, and the initial value can be set to 1. This state quantization method can effectively capture the dynamic changes of the water source state and provide a reliable basis for subsequent matching scoring.

[0106] Through the Bessel function, the changing trend of water quality parameters can be represented more accurately, providing a more refined state assessment. Utilizing the frequency domain characteristics of the Bessel function, abnormal water quality fluctuations can be more easily identified, and potential pollution events can be timely warned.

[0107] When selecting the most suitable matching object from multiple candidate water source points, the state information and historical records of each water source point must be considered; the theory of Lie groups and Lie algebras provides powerful tools for modeling dynamic change processes, which is particularly effective for capturing the evolution of water source states.

[0108] The scoring calculation unit 33 implements an intelligent matching scoring algorithm. The system of the present invention constructs a scoring model using the theory of Lie groups and Lie algebras. This method can effectively capture the dynamic change characteristics of the water source state. The scoring function is defined as follows:

[0109] f 3 (J) = tr(ad g (J)),

[0110] where J is the Bessel function value output by the state quantization unit, ad g is the adjoint representation of the Lie algebra g, and tr represents the trace of the matrix.

[0111] Define the Lie algebra g:

[0112]

[0113] where M n (R) is the n-dimensional real matrix space, and G is the corresponding Lie group.

[0114] This scoring method takes into account the dynamic changes of the water source state and can more accurately reflect the real-time availability of the water source. For example, for water sources with large water volume fluctuations, their scores will be quickly adjusted over time, thus better reflecting their actual available states.

[0115] Lie groups are a type of continuous transformation group, and Lie algebras are the local linearizations of Lie groups. They have extensive applications in describing dynamic systems and cybernetics. It is possible to adjust the scoring criteria in real time according to the changes in the water source state to ensure the accuracy and timeliness of the matching results. The mathematical framework provided by Lie groups and Lie algebras can effectively handle complex nonlinear dynamic systems and is applicable to the changing environments in water source monitoring.

[0116] After the scoring is completed, it is necessary to select the optimal one or several from numerous candidate matching objects. This process requires high efficiency and security, especially when dealing with sensitive data. The elliptic curve theory not only has important applications in cryptography but can also be used to quickly calculate discrete logarithm problems, thus accelerating the process of selecting the optimal match.

[0117] The optimal selection unit 34 is responsible for implementing the optimal matching selection algorithm. The system of the present invention applies the elliptic curve theory for optimal matching selection, which improves the calculation efficiency while ensuring security. The selection function is defined as follows:

[0118] f 4 (s ) = [ k ] P,

[0119] where s is the score output by the scoring calculation unit, P is the base point on the elliptic curve E.

[0120] Define the elliptic curve E:

[0121] E: y 2 =x 3 +ax + b (mod p),

[0122] where a and b are the elliptic curve parameters, and p is a prime number.

[0123] The elliptic curve parameters can be selected according to the system security requirements. For example, the P-256 curve recommended by NIST can be used. This selection method can not only quickly determine the optimal match but also ensure the security of the selection process and prevent malicious attacks.

[0124] An elliptic curve is a plane algebraic curve, and the points on it form an Abelian group. Elliptic curve cryptography (ECC) is famous for its high security. The operations on elliptic curves are usually more efficient than traditional integer factorization or discrete logarithm problems and can complete a large number of matching calculations in a shorter time. Using elliptic curve cryptography, it is possible to select the optimal match while ensuring data security and preventing information leakage.

[0125] The matching results may be affected by noise or other factors. Therefore, it is necessary to further optimize the results to ensure their stability and reliability. The Toeplitz matrix has a special structure that can well capture the patterns in time series data, which is very beneficial for optimizing the matching results.

[0126] The result optimization unit 35 is used to implement the matching result optimization algorithm. The system of the present invention uses a Toeplitz matrix to optimize the matching results. This method can effectively capture the time series characteristics of water source matching. The optimization function is defined as follows:

[0127] f 5 (Q) = T -1 Q,

[0128] Define the Toeplitz matrix T,

[0129]

[0130] where Q is the elliptic curve point output by the optimal selection unit, and T -1 is the inverse matrix of the Toeplitz matrix. The size of the Toeplitz matrix can be selected according to the length of the time series of the system. For example, a 10×10 matrix can be selected to consider the last 10 matching results. This optimization method can consider historical matching results, thereby providing a more stable and reliable final matching result.

[0131] By using a Toeplitz matrix to optimize the matching results, noise interference can be effectively filtered out, and the accuracy of the results can be improved. This matrix structure is very suitable for processing time series data and can help us better understand the historical trends and future predictions of water quality changes.

[0132] In another embodiment of the present invention, the user terminal module 1 includes a user interface unit 11, a data acquisition unit 12, and a communication unit 13. These units are interconnected through an internal bus to jointly implement the various functions of the user terminal.

[0133] The user interface unit 11 is responsible for providing a graphical operation interface. Preferably, the interface can be a responsive design developed based on Web technology and can adapt to different device screen sizes. The interface can include function modules such as water source information display, inspection task management, and data analysis reports, enabling users to operate the system intuitively and conveniently.

[0134] The data acquisition unit 12 is used to collect information such as the location and status of water sources. The system of the present invention can integrate multiple data acquisition methods, such as manual input, automatic sensor acquisition, image recognition, etc. For example, for the water source location information, GPS positioning technology can be used to automatically obtain it; for water quality data, a water quality sensor can be connected to collect it in real time. This diversified data acquisition method can ensure the comprehensiveness and accuracy of the data.

[0135] The communication unit 13 is responsible for data interaction with the cloud platform module 2. The system of the present invention adopts an encrypted data transmission protocol, such as HTTPS, to ensure the security of data transmission. At the same time, the communication unit 13 also implements data compression and resume - interrupted transfer functions to cope with unstable network conditions and improve the reliability of data transmission.

[0136] In a preferred embodiment of the present invention, the cloud platform module 2 includes a data storage unit 21, a task scheduling unit 22, and an API interface unit 23. These units are interconnected through a cloud service bus to form the core processing platform of the system.

[0137] The data storage unit 21 is used to store user data, water source information, and inspection records. The system of the present invention adopts a distributed database technology, such as Apache Cassandra, to achieve high availability and scalability. The data storage adopts a multi - replica mechanism to ensure the security and reliability of the data. At the same time, the system also implements a data hierarchical storage strategy, storing hot data in high - speed storage devices and transferring cold data to low - cost storage devices to optimize storage costs and access performance.

[0138] The task scheduling unit 22 is responsible for generating and dispatching inspection tasks. The system of the present invention adopts an intelligent scheduling algorithm, considering various factors such as the importance of water sources, the frequency of historical problems, the location of inspection personnel, etc., to dynamically generate an optimal inspection plan. For example, for water sources with frequent problems recently, the system will automatically increase their inspection frequency; for multiple nearby water sources, the system will try to arrange them on the same inspection route to improve inspection efficiency.

[0139] The API interface unit 23 is used to provide an access interface for external systems. The system of the present invention adopts a RESTful API design to provide a standardized interface for external system integration. The API interface supports multiple authentication methods, such as OAuth 2.0, to ensure the security of interface calls. At the same time, the system also implements an API flow - limiting and circuit - breaking mechanism to prevent malicious calls or system overload.

[0140] Through these well-designed modules and units, the water source monitoring system based on intelligent matching and inspection management of the present invention can achieve efficient and accurate water source management, and provide strong support for the sustainable use of water resources. In one embodiment of the present invention, the inspection management module 4 includes a task generation unit 41, a result recording unit 42 and a status update unit 43. These units are interconnected through an internal data bus to jointly achieve efficient inspection management functions.

[0141] The task generation unit 41 is used to generate inspection tasks based on the distribution of water sources and the inspection cycle. The system of the present invention uses an intelligent algorithm to optimize the generation process of inspection tasks. For example, the system will consider the geographical distribution of water sources and arrange water sources with similar geographical locations in the same inspection route to reduce the movement time of inspection personnel. At the same time, the system will dynamically adjust the inspection cycle according to the importance of the water source and the frequency of historical problems. For water sources with high importance or frequent problems, the system will automatically increase the inspection frequency; for water sources with stable status, the inspection interval may be appropriately extended. This intelligent task generation method can significantly improve the inspection efficiency while ensuring that key water sources receive adequate attention.

[0142] The result recording unit 42 is responsible for recording the inspection data uploaded by the inspectors. The system of the present invention supports a variety of data recording methods, including text description, numerical entry, photo uploading, etc. Preferably, the system also integrates voice recognition technology, and the inspectors can quickly record the inspection results through voice input. In order to ensure the accuracy of the data, the system will perform preliminary verification on the uploaded data, such as checking whether the value is within a reasonable range, whether the photo is clear and discernible, etc. If an abnormality is found, the system will immediately remind the inspectors to review. This diversified data recording method with a verification mechanism not only improves the inspection efficiency, but also ensures the data quality.

[0143] The status update unit 43 is used to update the water source status in real time according to the inspection results. When new inspection data is uploaded, the system of the present invention will immediately trigger the status update process. The update process not only includes directly recorded data, but also conducts a comprehensive analysis in combination with historical data. For example, the system will compare the current data with historical trends, and if a significant deviation is found, it will be marked as a potential abnormality. The updated status information will be immediately synchronized to other modules of the system, especially the intelligent matching module 3, to ensure that the matching results are always based on the latest water source status. This real-time update mechanism ensures that the system always has the latest situation of the water source and provides timely and accurate information support for decision-making.

[0144] In another embodiment of the present invention, the data analysis module 5 includes a data preprocessing unit 51, a statistical analysis unit 52, a report generation unit 53 and an early warning judgment unit 54. These units are interconnected through a data analysis bus to jointly realize a powerful data analysis function.

[0145] The data preprocessing unit 51 is used to clean and standardize the original data. The system of the present invention employs a variety of data cleaning techniques, including outlier detection, missing value handling, duplicate data removal, etc. For example, for numerical data, the system uses the box plot method to detect outliers; for missing values, appropriate imputation methods are selected according to the data characteristics, such as mean imputation, regression imputation, etc. The data standardization process converts data from different sources and in different formats into a unified standard format, facilitating subsequent analysis. This strict data preprocessing process can significantly improve the accuracy and reliability of subsequent analysis.

[0146] The statistical analysis unit 52 is responsible for performing statistical analysis on the preprocessed data. The system of the present invention integrates a variety of statistical analysis methods, including descriptive statistics, correlation analysis, time series analysis, etc. For example, the system automatically calculates descriptive statistics such as the mean, standard deviation, maximum value, minimum value, etc. of each indicator; for time series data, the system uses the ARIMA model for trend analysis and prediction. These statistical analysis results provide a solid data basis for water source management decisions.

[0147] The report generation unit 53 is used to generate analysis reports and visualization charts. The system of the present invention can automatically generate various types of reports, including daily reports, weekly reports, monthly reports, and annual reports, etc. The reports not only contain detailed data analysis results but also automatically generate various visualization charts, such as line charts, bar charts, scatter plots, etc., to intuitively display data trends and patterns. Preferably, the system also supports interactive reports, and users can perform operations such as data filtering and drilling down on the report interface to deeply explore the data of interest. This intelligent report generation function greatly reduces the workload of manual data collation and at the same time provides a more intuitive and understandable data display method.

[0148] The early warning judgment unit 54 is responsible for generating early warning information based on the analysis results. The system of the present invention adopts a multi-level early warning mechanism, including threshold early warning, trend early warning, and pattern recognition early warning, etc. For example, for water quality indicators, the system sets multiple early warning thresholds, and when the indicators approach or exceed these thresholds, the system issues early warnings at different levels; for water volume changes, the system analyzes historical trends and triggers early warnings when abnormal changes are found; the system also uses machine learning algorithms to identify abnormal patterns to detect potential problems early. This comprehensive early warning mechanism can help managers timely discover and solve water source problems and effectively reduce risks.

[0149] The system of the present invention further includes a GIS integration module 6, which is data-connected to the cloud platform module 2 and the user terminal module 1. The main function of the GIS integration module 6 is to provide a visual display of water source geographical information and support a location-based intelligent matching function.

[0150] In a preferred embodiment of the present invention, the GIS integration module 6 adopts a hierarchical design, including a basic map layer, a water source distribution layer, a real-time status layer, etc. The basic map layer provides detailed topographic and geomorphic information; the water source distribution layer shows the specific locations of each water source; the real-time status layer dynamically displays the current status of the water source, such as water volume, water quality and other information. Users can freely explore the map through operations such as zooming and panning, and can also click on specific water sources to view detailed information.

[0151] The GIS integration module 6 also supports a variety of spatial analysis functions. For example, the system can automatically calculate the available water sources within a certain range around a given location, providing geographical information support for intelligent matching. In addition, the system can also perform spatial clustering analysis to help identify the patterns and characteristics of water source distribution, providing a reference for water source management decisions.

[0152] Another important component of the present invention is the permission management module 7, which is data-connected to the user terminal module 1 and the cloud platform module 2. The main function of the permission management module 7 is to implement role-based dynamic permission allocation and control users' access rights to system functions and data.

[0153] In an embodiment of the present invention, the permission management module 7 adopts the RBAC (Role-Based Access Control) model. The system predefines multiple roles, such as system administrators, inspection personnel, data analysts, etc., and each role has a specific set of permissions. When a new user registers, the system administrator can assign one or more roles to them. In addition, the system also supports fine-grained permission control, and access permissions can be set for specific function modules or data objects.

[0154] The permission management module 7 also implements a dynamic permission adjustment function. For example, when an emergency occurs at a certain water source, the system can temporarily elevate the permissions of relevant personnel to quickly handle the problem. At the same time, the system will record all permission change operations for subsequent auditing.

[0155] Through this flexible and strict permission management mechanism, the system of the present invention can not only ensure data security and operation specifications, but also adapt to the complex and changeable actual work requirements. In a preferred embodiment of the present invention, the data transmission between the user terminal module 1 and the cloud platform module 2 adopts the encrypted HTTPS protocol to ensure the security of data transmission. This security mechanism is crucial for protecting sensitive water source information and user data.

[0156] The HTTPS protocol adds an SSL / TLS encryption layer on the basis of the HTTP protocol, which can effectively prevent data from being eavesdropped, tampered with or forged during transmission. The system of the present invention adopts the latest TLS1.3 protocol when implementing HTTPS, and this protocol has higher security and faster handshake speed compared with previous versions.

[0157] To further enhance security, the system of the present invention also adopts a two-way authentication mechanism. Not only does the server need to provide a certificate to the client, but the client also needs to provide a certificate to the server. This two-way authentication mechanism can effectively prevent man-in-the-middle attacks and ensure that the identities of both communication parties are true and trustworthy.

[0158] In addition, the system of the present invention also implements the certificate pinning technology. This technology directly embeds the expected server certificate or public key into the client application. When performing an HTTPS connection, the client will check whether the certificate provided by the server matches the pre-embedded certificate. This mechanism can effectively prevent certificate hijacking attacks.

[0159] During the data transmission process, the system of the present invention also adopts data compression technology. On the premise of not affecting security, the data to be transmitted is compressed through algorithms such as gzip, which can significantly reduce the amount of data transmitted and improve the transmission efficiency. This is particularly important for mobile users or regions with poor network conditions.

[0160] Preferably, the system of the present invention also implements the function of resume data transfer. When the data transmission is interrupted due to network problems, the system can resume the transmission from the breakpoint without having to start over. This mechanism greatly improves the reliability of data transmission, especially in the case of transmitting a large amount of data or unstable network conditions.

[0161] Through these comprehensive security measures and optimization technologies, the system of the present invention can provide efficient and reliable data transmission services while ensuring data security, providing a solid foundation for the stable operation of the water source monitoring system.

[0162] In another embodiment of the present invention, the intelligent matching module 3 further includes an adaptive optimization unit 36. The main function of this unit is to dynamically adjust the parameters of the intelligent matching algorithm based on historical matching data and continuously improve the matching accuracy through machine learning methods.

[0163] The adaptive optimization unit 36 adopts an innovative hybrid optimization algorithm, combining the advantages of genetic algorithms and reinforcement learning. The core idea of this algorithm is to regard the matching problem as a multi-objective optimization problem, and the objectives include matching accuracy, response time, and resource utilization efficiency, etc.

[0164] The main steps of the algorithm are as follows:

[0165] 1. Initialization: Generate a set of initial parameter sets according to historical matching data.

[0166] 2. Evaluation: Use the current parameter set for matching and calculate the scores of each objective.

[0167] 3. Selection: Based on the Pareto optimization principle, select the non-dominated solutions as the excellent individuals.

[0168] 4. Crossover and Mutation: Perform genetic operations on the selected excellent individuals to generate new parameter sets.

[0169] 5. Reinforcement Learning: Use the Q-learning algorithm to further optimize the parameters according to the feedback of the matching results.

[0170] 6. Iteration: Repeat steps 2 - 5 until the preset number of iterations or performance metrics are reached.

[0171] This adaptive optimization mechanism enables the intelligent matching algorithm to continuously adapt to new data patterns and business requirements, and always maintain high - efficiency and accurate matching performance.

[0172] Preferably, the adaptive optimization unit 36 also implements an online learning function. The system can continuously learn new matching patterns without interrupting the service. For example, when it is found that the matching accuracy of a certain type of water source decreases, the system will automatically increase the weight of this type of sample and quickly adjust the model parameters.

[0173] In addition, to cope with possible extreme situations or emergencies, the adaptive optimization unit 36 also designs a set of rapid response mechanisms. When abnormal fluctuations in the matching results are detected, the system will immediately start an emergency optimization program to quickly adjust the algorithm parameters to minimize the impact.

[0174] Through this advanced adaptive optimization mechanism, the system of the present invention can not only maintain long - term high performance, but also flexibly cope with various complex actual scenarios, providing strong and reliable technical support for the intelligent matching of water sources. This continuous optimization ability makes the water source monitoring system of the present invention have stronger adaptability and sustainability in practical applications, and can better meet the changing water source management needs.

[0175] To verify the superiority of the water source monitoring system of the present invention based on intelligent matching and inspection management, a typical urban water source management scenario was selected for the example test, and it was compared with two traditional methods. The test scenario involved 100 scattered water sources in a medium - sized city, and the test period was 3 months.

[0176] Example: Adopt the complete system of the present invention, including an intelligent matching module, an inspection management module, and a data analysis module.

[0177] Comparative Example 1: Adopt the traditional fixed - cycle inspection method, and inspect all water sources once a week.

[0178] Comparative Example 2: Adopt a simple priority - based inspection method, and allocate the inspection frequency according to the importance of the water source.

[0179] The test indicators and methods are as follows:

[0180] 1. Matching accuracy rate: Randomly select 1000 water usage demands, and calculate the proportion of the correct number of intelligent matches to the total number of times.

[0181] 2. Average response time: Record the average time from receiving the water usage demand to completing the water source matching.

[0182] 3. Problem discovery rate: The number of water source problems actually discovered divided by the total number of problems discovered through a comprehensive manual inspection.

[0183] 4. Inspection efficiency: The number of effective inspections completed per working day.

[0184] 5. Resource utilization rate: The proportion of the water source capacity actually used to the total water source capacity.

[0185] 6. System reliability: The proportion of the normal operation time of the system to the total operation time.

[0186] The test results are shown in the following table:

[0187]

[0188]

[0189] Analysis and discussion are as follows:

[0190] 1. Matching accuracy rate and average response time: The system of the present invention demonstrates an extremely high matching accuracy rate (98.5%) and a fast response time (0.5 seconds). This is mainly due to the innovative algorithm in the intelligent matching module, especially the matching method based on topology and Lie group theory. This highly efficient and accurate matching ability is particularly important in emergency situations, as it can quickly find the most suitable water source, greatly improving the emergency response ability.

[0191] 2. Problem discovery rate: The system of the present invention is significantly superior to traditional methods in terms of the problem discovery rate (95% vs. 75% and 82%). This is attributed to the combination of intelligent inspection management and real-time data analysis. The system can dynamically adjust the inspection strategy based on historical data and real-time status, and more targeted to discover potential problems.

[0192] 3. Inspection efficiency: The system of the present invention can complete 30 effective inspections per day, which is 20 - 50% higher than traditional methods. This improvement in efficiency stems from the intelligent scheduling ability of the system, which can optimize the inspection route and reduce unnecessary repeated inspections.

[0193] 4. Resource utilization rate: The system of the present invention increases the resource utilization rate to 85%, which is 13 - 20 percentage points higher than the traditional method. This is mainly due to the intelligent matching algorithm, which can better balance the use of each water source and avoid the situation where some water sources are overused while others are idle.

[0194] 5. System reliability: Although the system of the present invention is slightly lower in reliability than the simple traditional method (99.9% vs. 100%), considering the complexity and richness of functions of the system, this reliability level is still very excellent. The 0.1% downtime is mainly used for system upgrades and maintenance, which can improve the overall performance and reliability of the system in the long run.

[0195] In summary, the water source monitoring system based on intelligent matching and inspection management of the present invention is significantly superior to the traditional method in multiple key indicators. It not only improves the efficiency and accuracy of water source management but also greatly enhances the resource utilization rate. The intelligent and adaptive characteristics of the system enable it to better cope with complex and changing actual situations, providing a comprehensive and efficient solution for urban water source management.

[0196] Based on the above test results, the system configuration can be further optimized to propose an optimal embodiment. In this embodiment, the parameters of the intelligent matching module are slightly adjusted, and the order α of the Bessel function is set to 0.5. This value shows the best balance in the test and can consider both the short-term fluctuations and long-term trends of the water source. At the same time, the learning rate of the adaptive optimization unit is slightly increased to enable the system to adapt to new data patterns faster.

[0197] In the inspection management module, a dynamic adjustment mechanism based on weather forecasting is added. The system will automatically adjust the inspection plan according to the weather forecast for the next few days. For example, it will increase the inspection frequency of water sources in flood-prone areas on days with more rainfall expected.

[0198] In addition, a deep learning-based anomaly detection algorithm is integrated into the data analysis module. This algorithm can learn the complex patterns of water source data and detect potential anomalies earlier.

[0199] These optimization measures make the system perform even better in practical applications, especially in complex and changing environments, showing stronger adaptability and prediction ability. This optimal embodiment can not only meet the current water source management needs but also lay a solid technical foundation for future smart city water resource management.

[0200] It should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. The water source monitoring system based on intelligent matching and inspection management is characterized by: include: The client module is used to: Implement user registration, login and management functions; Provide water source information maintenance and equipment repair functions; The cloud platform module is wirelessly connected to the user terminal module and is used to: Receiving user data and water source information sent by the user terminal module; Based on the user data and water source information, perform intelligent matching and scheduling assignment; The intelligent matching module is connected to the cloud platform module for: The location of water sources is encoded based on the homology group theory of topology; Use Bessel functions to quantify water source status; Use Lie group and Lie algebra theory to build an intelligent matching scoring model; Apply elliptic curve theory to select the best match; Use the Toeplitz matrix to optimize the matching results; The inspection management module is data-connected with the intelligent matching module and the user-end module, and is used to: Generate and assign inspection tasks; Record and analyze inspection results; Update water source status information in real time; The data analysis module is connected to the cloud platform module and the inspection management module for: Conduct statistical analysis on historical inspection data; Generate water usage reports and warning information.

2. The system according to claim 1, characterized in that The intelligent matching module includes: a position coding unit, which is used to implement a water source position coding algorithm: f1(x,y,z)=[H0(X),H1(X),H2(X)], Among them, (x, y, z) is the three-dimensional coordinate of the water source, H n (X) is the n-dimensional homology group; State quantization unit, used to implement water source state quantification algorithm: f2(H)=J α (βH), Among them, J α is the α-order Bessel function, H is the homology group vector output by the position encoding unit, and β is the undetermined parameter scoring calculation unit, which is used to implement the intelligent matching scoring algorithm: Where J is the Bessel function value output by the state quantization unit, Lie algebra The adjoint of is represented by, tr represents the trace of the matrix; The optimal selection unit is used to implement the optimal matching selection algorithm: f4(s)=[k]P, Among them, s is the score output by the score calculation unit, P is the base point on the elliptic curve E; the result optimization unit is used to implement the matching result optimization algorithm: f5(Q)=T -1 Q, Among them, Q is the elliptic curve point output by the optimal selection unit, T -1 is the inverse matrix of the Toeplitz matrix.

3. The system according to claim 1, characterized in that The user end module comprises: A user interface unit, used for providing a graphical operation interface; Data collection unit, used to collect information such as water source location and status; A communication unit, used for data interaction with the cloud platform module; Wherein, the user interface unit, the data acquisition unit and the communication unit are connected to each other via an internal bus.

4. The system according to claim 1, characterized in that The cloud platform module includes: Data storage unit, used to store user data, water source information and inspection records; Task scheduling unit, used to generate and dispatch inspection tasks; API interface unit, used to provide external system access interface; Wherein, the data storage unit, the task scheduling unit and the API interface unit are interconnected via a cloud service bus.

5. The system according to claim 1, characterized in that The inspection management module includes: A task generation unit, used to generate inspection tasks based on water source distribution and inspection cycle; Result recording unit, used to record the inspection data uploaded by the inspectors; A status update unit, used to update the water source status in real time according to the inspection results; Wherein, the task generating unit, the result recording unit and the status updating unit are connected to each other via an internal data bus.

6. The system according to claim 1, characterized in that The data analysis module includes: Data preprocessing unit, used to clean and standardize raw data; A statistical analysis unit, used for performing statistical analysis on the preprocessed data; A report generation unit, used to generate analysis reports and visualization charts; An early warning judgment unit, used to generate early warning information based on the analysis results; Wherein, the data preprocessing unit, the statistical analysis unit, the report generating unit and the early warning judgment unit are interconnected via a data analysis bus.

7. The system according to claim 1, characterized in that Also includes: The GIS integration module is connected to the cloud platform module and the user terminal module for: Provide visual display of water source geographical information; Supports intelligent matching function based on geographic location.

8. The system according to claim 1, characterized in that Also includes: The rights management module is data-connected with the user-side module and the cloud platform module, and is used to: Implement dynamic permission allocation based on roles; Control user access to system functions and data.

9. The system according to claim 1, characterized in that The user terminal module and the cloud platform module perform data transmission via an encrypted HTTPS protocol to ensure the security of data transmission.

10. The system according to claim 1, characterized in that The intelligent matching module also includes an adaptive optimization unit, which is used to: Dynamically adjust the parameters of the intelligent matching algorithm based on historical matching data; Continuously improve matching accuracy through machine learning methods.

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