Water supply and drainage blockage and overflow detection method and system adopting Internet of Things

Abstract

The invention relates to the technical field of water supply and drainage intelligent detection, and discloses a water supply and drainage blockage and overflow detection method and system based on the Internet of Things, and the method comprises the steps: installing a sensor at a position corresponding to a facility, and obtaining data such as flow, liquid level and pressure; standardizing the data, transmitting the standardized data to a central control platform through an Internet of Things communication protocol, and displaying a real-time pipeline state; the flow condition, the pressure change, the liquid level monitoring and other information are compared with preset monitoring index standards, the difference between a read value and a set threshold value is compared in real time, the liquid level exceeds the preset upper limit (overflow) or the flow suddenly drops (blockage), an alarm is generated, and alarm information is pushed and sent to related personnel through a short message, an E-mail or an intelligent terminal. The system comprises a data acquisition module, an information display module and an alarm output module. Through application of the Internet of Things technology, intelligent management of the water supply and drainage system can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent water supply and drainage detection, and in particular to a water supply and drainage blockage and overflow detection method and system using the Internet of Things. Background Art

[0002] Water supply and drainage blockage and overflow detection are two important aspects of water supply and drainage system management, involving the flow state and safety of fluids in the pipeline system. Water supply and drainage blockage refers to the phenomenon that the flow of fluids is obstructed or completely interrupted in the pipeline system due to factors such as foreign matter, sediments, and plant roots. Blockage may cause a reduction in water flow or even cause system failure. Overflow refers to the phenomenon that fluids overflow from facilities such as pipelines and containers beyond their normal capacity. Overflow is usually caused by improper design of the pipeline system, equipment failure, or insufficient carrying capacity of the drainage system, which may cause environmental pollution and property losses. The establishment of a water supply and drainage blockage and overflow detection mechanism is crucial to ensure the safety and efficiency of the water supply and drainage system. However, the existing detection methods are too single, and it is impossible to accurately detect blockages and overflows by relying solely on equipment such as cameras, flow meters, and sensors, making the detection methods less intelligent.

[0003] Prior art 1, Chinese patent, application number: 202311331680.0 discloses a method and system for IoT point monitoring of sewage treatment using thresholds. The system includes monitoring stations arranged in the drainage network, a data analysis module for analyzing and processing the data of the monitoring stations, and a decision-making platform; the data analysis module analyzes the monitoring data, including vertically analyzing the liquid level threshold and water quality threshold of the same monitoring station with month-on-month fluctuations, and horizontally analyzing the delay time limit threshold of data fluctuations of different monitoring stations in the same time period, and performing correlation analysis with the actual test result data, and continuously iterating and updating, obtaining the liquid level threshold and water quality threshold and reporting them to the decision-making platform; the decision-making platform determines the potential areas with abnormal conditions such as private connection, mixed connection, rupture, blockage, and external water inflow, and issues task messages for maintenance. Although it reduces the operation and maintenance costs of the drainage network, it provides a guarantee for the monitoring operation of improving the quality and efficiency of the sewage system. However, the functions are complex and there is a lot of data to be processed, which reduces the efficiency of monitoring to a certain extent.

[0004] Prior art 2, Chinese patent, application number: 202310278316.6 discloses a method for predicting the state of a municipal drainage network based on graphs and deep learning, including the following steps: collecting the topological structure of the municipal drainage network in the target area, and obtaining the characteristics of each monitoring point in real time; using the above information to establish a graph set of spatiotemporal sequences; preprocessing the established spatiotemporal sequence graph model; combining graph convolutional neural networks and gated recurrent units to learn the established graph data and train related models; using the trained model to predict the state of the drainage network; reconstructing the prediction results into a graph to visualize the predicted overflow volume at each point; and finally using the established graph model and algorithm prediction results to troubleshoot the drainage network system and provide flood warnings. Although it can reduce the complexity of model establishment and improve the accuracy of drainage network system state prediction, it can accurately troubleshoot blocked faulty pipes and provide early warnings of areas where disasters may occur in the short term, effectively improving the city's disaster prevention and mitigation capabilities. However, the model relies too much on the reliability of data, resulting in a cumbersome model training process.

[0005] Prior art three, Chinese patent, application number: 202210554881.6 discloses a pipe network slow congestion area monitoring method and system, including the following steps: selecting monitoring nodes, installing online liquid level monitoring devices, and establishing a pipe network node liquid level monitoring network; combining urban pipe network design, construction surveying and mapping and geographic information basic data to complete coding, classification, spatial data collection, and establish a basic information database; constructing a spatial data isometric calculation model to screen difference nodes; judging overflow nodes; and on-site disposal. Although the network node monitoring based on the Internet of Things uses grid computing to determine the scope of slow flow and overflow areas of urban underground pipe networks, the existing problems of difficulty, high cost and long cycle in detecting blockages in urban underground pipe networks can be solved, which can greatly improve the transportation efficiency of urban underground drainage pipe networks and achieve the goal of improving the quality and efficiency of urban drainage pipe network supervision. However, the construction of the model relies too much on the basic information database, resulting in greater pressure on early data processing, and the data update speed cycle is long, which also affects the accuracy of the model.

[0006] At present, the existing technologies 1, 2 and 3 cannot accurately detect blockage and overflow, resulting in a low level of intelligence in the detection means. Therefore, the present invention provides a water supply and drainage blockage and overflow detection method and system using the Internet of Things. Summary of the invention

[0007] The main purpose of the present invention is to provide a water supply and drainage blockage and overflow detection method and system using the Internet of Things, so as to solve the problem that the prior art cannot accurately realize accurate detection of blockage and overflow, resulting in a low degree of intelligence of the detection means.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A water supply and drainage blockage and overflow detection method using the Internet of Things, the water supply and drainage blockage and overflow detection method using the Internet of Things comprising:

[0010] Obtain the location information of the water inlet, outlet, elbow, sewage well and sedimentation tank facilities of the water supply and drainage pipeline, install sensors at the corresponding locations of the facilities, and obtain flow, liquid level and pressure data through detection of different types of sensors;

[0011] Standardize the data and transmit it to the central control platform through the IoT communication protocol to display the real-time pipeline status, including flow, pressure changes and liquid level monitoring information;

[0012] The flow conditions, pressure changes and liquid level monitoring information are compared with the preset monitoring index standards, and the gap between the reading value and the set threshold is compared in real time. If the liquid level exceeds the preset upper limit or the flow drops suddenly, an alarm is generated and the alarm information is sent to relevant personnel via short message, email or smart terminal push.

[0013] As a further improvement of the present invention, sensors are installed at positions corresponding to the facilities, including:

[0014] Based on the topographic map and pipe network layout, determine the precise location of key facilities such as water inlets, outlets, elbows, sewage wells and sedimentation tanks; mark the geographic coordinates of each facility; and select the sensor type according to the monitoring target;

[0015] Connect the sensor power supply, conduct the wiring of the communication line, and perform preliminary calibration after the sensor is installed; attach a unique digital identification tag to each sensor, and perform device management and data association in the central control platform; the tag contains the device ID, working status information and installation date;

[0016] After all sensors are connected to the network, the central control platform confirms that the signal transmission is normal and performs preliminary data integration.

[0017] As a further improvement of the present invention, the real-time pipeline status is displayed, including:

[0018] The collected data is preprocessed to remove noise and outliers, and the preprocessed data is standardized, which includes unit unification and normalization. The normalized data is integrated into a structured format for transmission by the IoT communication protocol, and the real-time monitoring data of each facility is encapsulated into a data packet.

[0019] The central control platform confirms the successful receipt of data according to the receipt confirmation mechanism; if no confirmation is received, the central control platform implements the error handling strategy;

[0020] After receiving the standardized data packets, the central control platform presents them in the form of a real-time dashboard, including graphical flow monitoring, pressure change graphs and liquid level trend graphs, to intuitively understand the pipeline status.

[0021] As a further improvement of the present invention, the normalized data is integrated into a structured format for transmission by the Internet of Things communication protocol, including:

[0022] Perform complete normalized data collection and initialization to create a data structure that represents the information of a monitoring point, including the facility unique identifier and the acquisition timestamp; for each data point, convert the raw signal to a normalized value, and bundle the normalized flow, pressure, and level data to form a comprehensive data set;

[0023] Combine the flow, pressure and level data into a dictionary with clear and consistent naming for each field, and use a version control mechanism to identify different versions of the data packet format when the data structure changes; use a custom serialization function to encode each field of the data object in a structured manner; perform character encoding processing;

[0024] After serialization is completed, the checksum of the data is calculated, and a unique check code is generated by operating on the byte stream and attached to the end of the data packet; a data packet header is attached to the front end of the data packet, which contains metadata such as the protocol version, sender information, and content length.

[0025] As a further improvement of the present invention, each field of the data object is encoded in a structured manner, including:

[0026] Recursively traverse the dictionary, access each layer of fields one by one, extract the corresponding value of each field and perform corresponding encoding processing according to its data type; if the field type is integer, directly convert it to byte stream; if the field type is string, character encoding is performed first;

[0027] During the serialization process, a new byte stream buffer is created as a container for the final output; whenever a field is accessed, its encoded result is appended to the end of the byte stream in sequence; identification field information and type information are also included in the byte stream to form the metadata header of each field;

[0028] After the byte stream is constructed, unified character encoding processing is performed.

[0029] As a further improvement of the present invention, generating a unique verification code comprises:

[0030] Confirm the elements of input data: the byte stream of the data packet, that is, the result after serialization; the various information in the header of the data packet, including the protocol version, sender information, and content length; the timestamp in the data packet, which records the time when the data was generated; convert the byte stream and header information into a unified format, and splice all related byte streams, protocol information, and timestamps into a long string;

[0031] Encrypt the concatenated string once to generate a 256-bit hash value; weight each byte, select an irregular prime number for weighting, and then take the modulus of the weighted sum;

[0032] For the generated check code, perform another XOR operation, compare each byte of the entire byte stream with the check code, and generate a final check code; the generated final check code is appended to the end of the data packet.

[0033] As a further improvement of the present invention, a 256-bit hash value is generated, including:

[0034] When the input concatenated string arrives at the processing node, a serialization mapping is performed on each byte of the string and converted into a weighted bit granularity in the corresponding binary format to form an information spectrum;

[0035] The information spectrum is divided into several sub-spectra. The bits in each sub-spectra are grouped according to the built-in Hamming distance standard. The combination of each group of bits is symmetrical and has local pseudo-randomness. The weighted index table is dynamically adjusted between and within groups. Each bit group uses the previous weighted index table to exchange the internal permutations and combinations for multiple rounds. After each overlapping transformation, the newly generated bit set is brought into the feedback loop.

[0036] The adjusted bit set is fed into a 256-bit fixed-length hash value generated by simulating the interaction formed by microscopic particle dynamics, and the generated 256-bit hash value is mapped and recorded in the holographic storage module.

[0037] As a further improvement of the present invention, the gap judgment overflow and blockage includes:

[0038] The central control platform establishes dynamic thresholds based on historical operating data. The dynamic thresholds are adjusted based on the normal operating parameters of the equipment and environmental changes. The normal range of flow and liquid level is set and forms a baseline data set.

[0039] In each data collection cycle, the real-time read flow, level and pressure data are compared with the corresponding preset dynamic thresholds;

[0040] Identification of potential overflow or blockage issues generates appropriate alarms through built-in decision logic.

[0041] As a further improvement of the present invention, the state information of the sensor is combined with the current operating conditions to give the alarm a specific context.

[0042] To achieve the above object, the present invention also provides the following technical solutions:

[0043] A water supply and drainage blockage and overflow detection system using the Internet of Things, which is applied to the water supply and drainage blockage and overflow detection method using the Internet of Things, and the water supply and drainage blockage and overflow detection system using the Internet of Things includes:

[0044] The data acquisition module is used to obtain the location information of the water inlet, outlet, elbow, sewage well and sedimentation tank facilities of the water supply and drainage pipeline, install sensors at the corresponding locations of the facilities, and obtain flow, liquid level and pressure data through detection of different types of sensors;

[0045] The information display module is used to standardize the data and transmit the standardized data to the central control platform through the Internet of Things communication protocol to display the real-time pipeline status, including flow conditions, pressure changes and liquid level monitoring information;

[0046] The alarm output module is used to compare the flow conditions, pressure changes and liquid level monitoring information with the preset monitoring index standards, and compare the gap between the read value and the set threshold in real time. If the liquid level exceeds the preset upper limit or overflows, or the flow suddenly drops or is blocked, an alarm is generated, and the alarm information is sent to relevant personnel via short messages, emails or smart terminals.

[0047] The sensor deployment and location information acquisition of the present invention installs various types of sensors (flow, liquid level, pressure) at key locations of the water supply and drainage system (such as water inlets, outlets, elbows, sewage wells and sedimentation tanks) to achieve comprehensive monitoring coverage; through different types of sensors, the flow, liquid level and pressure data of the pipeline can be accurately obtained to form a real-time data source. Significance: Through real-time data monitoring of key nodes of the pipeline, the system status can be timely grasped, the water flow and water treatment efficiency can be optimized; problems in the facilities, especially potential blockage and overflow risks, can be discovered in a timely manner, providing basic data support for subsequent management and maintenance. Data standardization and transmission, through standardized processing, ensure that the data collected by the sensor adopts a unified format (such as JSON), which is convenient for subsequent data transmission and analysis; the Internet of Things communication protocol is adopted to ensure that the data can be quickly and securely transmitted to the central control platform to maintain real-time performance. Significance: The transmission and processing of real-time data provides decision makers with accurate and timely information support, which helps to make management decisions quickly; the use of standardized data formats makes subsequent technical expansion and equipment integration more convenient and efficient, and supports future upgrades and maintenance of the system. Abnormal monitoring and alarm generation, compare the flow, liquid level and pressure data with the preset monitoring indicators in real time, and dynamically monitor the system status; once the liquid level exceeds the preset upper limit (overflow) or the flow suddenly drops (blockage), the system can quickly generate an alarm and notify relevant personnel through multiple channels (such as short messages, emails, mobile phone push). Significance: Timely alarm mechanisms enable operation and maintenance personnel to take quick action to deal with potential overflow or blockage problems, thereby reducing losses and impacts; given that blockages and overflows may cause more serious environmental and safety problems, the rapid response mechanism provides an important guarantee for public health and environmental safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic flow chart of the steps of an embodiment of a method for detecting water supply and drainage blockage and overflow using the Internet of Things of the present invention;

[0049] Figure 2 A schematic flow chart of the steps of installing sensors at locations corresponding to facilities in one embodiment of a method for detecting water supply and drainage blockage and overflow using the Internet of Things according to the present invention;

[0050] Figure 3 A schematic flow chart showing the steps of real-time pipeline status according to an embodiment of a method for detecting blockage and overflow of water supply and drainage using the Internet of Things of the present invention;

[0051] Figure 4 This is a schematic flow chart of the steps of judging overflow and blockage according to the gap in one embodiment of a water supply and drainage blockage and overflow detection method using the Internet of Things of the present invention;

[0052] Figure 5This is a functional module diagram of an embodiment of a water supply and drainage blockage and overflow detection system using the Internet of Things of the present invention;

[0053] Figure 6 It is a structural schematic diagram of an embodiment of an electronic device of the present invention;

[0054] Figure 7 It is a schematic structural diagram of an embodiment of the storage medium of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] The terms "first", "second" and "third" in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0057] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0058] like Figure 1As shown, this embodiment provides an embodiment of a water supply and drainage blockage and overflow detection method using the Internet of Things. In this embodiment, the water supply and drainage blockage and overflow detection method using the Internet of Things specifically includes the following steps:

[0059] Step S1: Obtain the location information of water inlets, outlets, elbows, sewage wells, sedimentation tanks and other facilities of water supply and drainage pipelines, install sensors at the corresponding locations of the facilities, and obtain data such as flow, liquid level and pressure through different types of sensors;

[0060] Step S2: Standardize the data and transmit the standardized data to the central control platform through the Internet of Things communication protocol to display the real-time pipeline status, including flow conditions, pressure changes, liquid level monitoring and other information;

[0061] Step S3: Compare the flow conditions, pressure changes, and liquid level monitoring information with the preset monitoring index standards, and compare the gap between the read value and the set threshold in real time. If the liquid level exceeds the preset upper limit (overflow) or the flow suddenly drops (blockage), an alarm is generated, and the alarm information is sent to relevant personnel via short message, email or smart terminal push.

[0062] Preferably, in step S1 of this embodiment, the sensor deployment and location information acquisition are carried out by installing various types of sensors (flow, liquid level, pressure) at key locations of the water supply and drainage system (such as water inlet, outlet, elbow, sewage well and sedimentation tank) to achieve comprehensive monitoring coverage; through different types of sensors, the flow, liquid level and pressure data of the pipeline are accurately obtained to form a real-time data source. Significance: Through real-time data monitoring of key nodes of the pipeline, the system status can be grasped in time, and the water flow and water treatment efficiency can be optimized; problems in the facilities, especially potential blockage and overflow risks, can be discovered in time, providing basic data support for subsequent management and maintenance. Step S2 data standardization and transmission, through standardized processing, ensure that the data collected by the sensor adopts a unified format (such as JSON) to facilitate subsequent data transmission and analysis; adopt the Internet of Things communication protocol to ensure that the data can be quickly and securely transmitted to the central control platform to maintain real-time performance. Significance: The transmission and processing of real-time data provides decision makers with accurate and timely information support, which helps to make management decisions quickly; the use of standardized data formats makes subsequent technical expansion and equipment integration more convenient and efficient, and supports future upgrades and maintenance of the system. Step S3: Abnormal monitoring and alarm generation. The flow, liquid level and pressure data are compared with the preset monitoring indicators in real time to dynamically monitor the system status. Once the liquid level exceeds the preset upper limit (overflow) or the flow suddenly drops (blockage), the system can quickly generate an alarm and notify relevant personnel through multiple channels (such as short messages, emails, and mobile phone push). Significance: Timely alarm mechanisms enable operation and maintenance personnel to take quick action to deal with potential overflow or blockage problems, thereby reducing losses and impacts. Given that blockages and overflows may cause more serious environmental and safety problems, rapid response mechanisms provide important protection for public health and environmental safety.

[0063] In summary, this embodiment can realize intelligent management of water supply and drainage systems through the application of Internet of Things technology, improve the operational efficiency and response capability of the system, reduce the cost of manual monitoring, and ensure the safety and stability of water supply and drainage. At the same time, based on the analysis and feedback of real-time data, it provides a data basis for long-term system optimization and promotes the development direction of intelligent water services.

[0064] Furthermore, if Figure 2 As shown, the process of installing the sensor at the location corresponding to the facility in step S1 specifically includes the following steps:

[0065] Step S11: Based on the topographic map and the pipe network layout map, determine the precise location of key facilities such as water inlets, outlets, elbows, sewage wells and sedimentation tanks; mark the geographic coordinates of each facility; and select the sensor type according to the monitoring target;

[0066] Step S12: Connect the sensor power supply, carry out the wiring of the communication line, and perform preliminary calibration after the sensor is installed; attach a unique digital identification tag to each sensor, and perform device management and data association in the central control platform; the tag contains the device ID, working status information and installation date, etc.;

[0067] Step S13: After all sensors are connected to the network, the central control platform confirms that the signal transmission is normal and performs preliminary data integration;

[0068] In the sensor networking, set the following parameters:

[0069] S i represents the state of the i-th sensor, S i ∈0,1, where 0 means offline and 1 means online. i (t) represents the data value sent by the i-th sensor at time t, W i represents the bandwidth of the i-th sensor, R i (t) represents the transmission rate of the i-th sensor at time t, N represents the total number of sensors, T represents the data transmission delay, and P ij represents the channel state from sensor i to sensor j, P ij ∈[0,1] represents the availability and stability of the channel, and A(t) represents the overall data received by the central control platform at time t;

[0070] Then the expression of sensor networking can be written as:

[0071]

[0072] The expression describes how the data A(t) received by the central control platform at time t is integrated with the data of each online sensor S i The state and data value D sent i (t), transmission rate R i (t), bandwidth W i and the channel state p ij Result;

[0073] Introduce data quality factors into the sensor analog signal reception and define data quality Q i (t) represents the quality of sensor i when sending data, and the expression is updated as follows:

[0074]

[0075] Q i (t) can be further defined as:

[0076] Q i (t) = α iSignal-to-Noise Ratio (SNR) i ·Error Correction Factor(ECF) i

[0077] Where: α i is the characteristic parameter of the i-th sensor, ensuring its output quality.

[0078] SNR is expressed as:

[0079]

[0080] Where P signal is the signal power, P noise is the noise power, ECF i is the error correction factor used to quantify the data integrity protection during data transmission;

[0081] The final sensor networking expression is:

[0082]

[0083] Preferably, step S11 of this embodiment determines the facility location and sensor selection, which can accurately identify the location of various facilities in the water supply and drainage system and ensure that the sensors are installed at key nodes; according to different locations and monitoring requirements (such as flow, liquid level and pressure), select the appropriate sensor type to ensure that each monitoring target has appropriate equipment support. Significance: Accurate installation location selection can improve the comprehensiveness of monitoring and ensure that data collection covers all key areas; selecting appropriate sensors can avoid unnecessary waste of resources, reduce later maintenance and replacement costs, and thus improve the cost performance of the entire system. Step S12 sensor installation and cable layout, connecting the sensor power supply and communication line wiring work to ensure that the sensor can continue to work and transmit data in real time, improve the reliability of the equipment; by attaching a unique digital identification label to each sensor, effective management and tracking of the equipment is achieved. Significance: Sensors that have been preliminarily calibrated will improve the accuracy of the measurement data, which is conducive to subsequent data analysis and decision-making; affixing a unique identification label makes the information of each sensor clear at a glance, which is convenient for maintenance personnel to conduct daily inspections, troubleshooting and data management. Step S13: Sensor signal confirmation and data integration, ensuring that all sensors can be connected to the Internet normally, communicate effectively with the central control platform, and transmit monitoring data in real time; perform preliminary integration of data collected from each sensor to ensure data integrity and consistency. Significance: Ensuring normal signal transmission and data integration improves the stability and reliability of the overall monitoring system, laying a good foundation for subsequent monitoring and early warning; real-time data integration and analysis capabilities promote timely understanding of the status of the water supply and drainage system, and provide data support for managers, enabling them to respond quickly to potential problems.

[0084] The expression of this embodiment combines all factors that improve the quality of data integration, making the performance evaluation more comprehensive and systematic. It ensures that the information obtained by the central control platform is not only integrated in quantity, but also guaranteed in quality, and deepens the understanding of the status in the network; these formulas quantitatively analyze and evaluate the data of the sensors to ensure the reliability and effectiveness of the data during the acquisition, transmission and integration process, thereby enhancing the stability, flexibility and adaptability of the entire sensor networking system; it provides effective support for monitoring and control tasks, and improves the response capability and efficiency of the overall system.

[0085] In summary, this embodiment can systematically realize the effective installation and management of sensors, ensuring that the monitoring and data integration of the water supply and drainage system achieve the expected results. It not only improves the operating efficiency of the system, but also provides a solid foundation for subsequent fault warnings, and ultimately realizes the intelligent management and maintenance of the system.

[0086] Furthermore, if Figure 3 As shown, the process of displaying the real-time pipeline status in step S2 specifically includes the following steps:

[0087] Step S21: pre-processing the collected data to remove noise and outliers, and standardizing the pre-processed data, which includes unit unification and normalization; integrating the normalized data into a structured format for transmission by the Internet of Things communication protocol, and encapsulating the real-time monitoring data of each facility into a data packet;

[0088] Step S22: The central control platform confirms that the data is successfully received according to the reception confirmation mechanism; if no confirmation is received, the central control platform executes the error handling strategy;

[0089] Step S23: After the central control platform receives the standardized data packet, it presents it in the form of a real-time dashboard, including graphical flow monitoring, pressure change graph and liquid level trend graph, etc., to intuitively understand the pipeline status.

[0090] Preferably, the data preprocessing and structured packaging in step S21 of this embodiment can eliminate data fluctuations caused by external environmental interference through noise removal, thereby improving the accuracy of data collection; the detection and processing of abnormal values ​​ensure that the transmitted data reflects the real pipeline status; ensure that the input data is in the same dimension, making subsequent analysis and comparison feasible; by mapping the data to the same range, different parameters can be directly compared, enhancing the availability of data; the preprocessed and standardized data is integrated into a structured format that can be recognized by the Internet of Things communication protocol, facilitating the convenient transmission and analysis of data. Significance: Ensure the authenticity, accuracy and reliability of the monitoring data, and provide a solid data foundation for subsequent analysis and decision-making; simplify the data transmission process through standardization and packaging, and improve the efficiency of the entire monitoring system. Step S22 data reception confirmation mechanism, through the feedback mechanism, the central control platform can promptly confirm whether the data is successfully received, thereby ensuring the integrity of the monitoring information; when no confirmation is received, the central control platform executes the error handling strategy to ensure that data loss caused by factors such as network fluctuations can be corrected in a timely manner. Significance: Improve the fault tolerance of the system so that the accuracy of data transmission is guaranteed in an unstable network environment; ensure the timeliness and accuracy of monitoring information so that operation managers can obtain the latest data for quick response and decision-making. Step S23 real-time dashboard display: visualize the data through the real-time dashboard, use charts and trend charts to vividly display the changes in flow, pressure and liquid level, so that the monitoring data is not only easy to understand but also intuitive; the real-time dashboard allows users to interact and quickly obtain the required information through switching, zooming and filtering functions. Significance: Enable decision makers and operators to instantly grasp the operating status of the pipeline, enhance the ability to identify potential problems early; improve the efficiency of data analysis, speed up response time, support effective resource management and emergency response decisions, and improve prevention and control capabilities.

[0091] In summary, the entire monitoring system of this embodiment realizes closed-loop effective management from data collection to display. Each step ensures the accuracy, timeliness and operability of real-time pipeline status monitoring, providing strong guarantees for subsequent decision support, fault warning and resource optimization. It not only improves the monitoring capability of the water supply and drainage system, but also lays the foundation for the construction of smart cities and smart infrastructure.

[0092] Furthermore, the process of integrating the normalized data into a structured format for transmission by the Internet of Things communication protocol in step S21 specifically includes the following steps:

[0093] Step S211: Perform complete normalized data collection and initialization, create a data structure to represent the information of a monitoring point, including the facility unique identifier and the acquisition timestamp; for each data point, convert the original signal into a normalized value, and bundle the normalized flow, pressure and liquid level data to form a comprehensive data set;

[0094] The data structure contains:

[0095] Multi-dimensional identity integration layer:

[0096] DeviceIdentity: It consists of a composite identifier with five key fields:

[0097] GlobalID: An internationally standardized unique identifier that ensures the uniqueness of a device across the entire system.

[0098] ZoneCode: Specifies the code of the geographical area or functional zone where the device is located, used to quickly locate and classify the device;

[0099] AssetTag: It is the local tag of the equipment, which simplifies equipment identification and helps to perform efficient on-site maintenance;

[0100] Timestamp: the time when the data record is generated, ensuring the relevance and accuracy of the time series;

[0101] DataVersion: version number, providing a tracking identifier for the evolution of the data structure;

[0102] The change record and time sequence monitoring layer focuses on the dynamic update of time records and the fluidity of data, and ensures real-time performance by binding each data item to time:

[0103] DataCaptureTime: records the specific time when data is collected, using a high-precision timestamp;

[0104] DataRefreshTime: A time record automatically generated each time data is updated, giving a single data item a traceable operation history;

[0105] Data samples and historical difference layer. In order to ensure the persistence and change monitoring of flow, level and pressure data, this layer no longer contains only current values, but introduces a complete set of historical data for easy comparison and analysis:

[0106] FlowMetrics:

[0107] CurrentFlow: real-time flow value;

[0108] HistoricFlow: flow history, time periods and threshold changes for access to information points;

[0109] FlowVariance: flow fluctuation indicator, monitoring the magnitude and trend of flow changes;

[0110] LevelMetrics:

[0111] CurrentLevel: instant reading of the current liquid level value;

[0112] HistoricLevel: A historical record of the liquid level, which facilitates the examination of long-term trends and fluctuations;

[0113] LevelDynamics: measurement of liquid level fluctuation frequency and rate of change;

[0114] Pressure Readings:

[0115] CurrentPressure: instant pressure value;

[0116] HistoricPressure: past pressure records, data that supports retrospective analysis;

[0117] PressureDrift: monitors the trend of pressure changes, focusing on the comparison between normal values ​​and abnormal conditions;

[0118] Independent status monitoring and alarm mechanism, create status feedback and alarm modules, adopt active monitoring and risk management to respond to potential failures in a timely manner:

[0119] OperationalStatus: Dynamic status indicator, reflecting the operating status of the device, giving the following status indications:

[0120] Normal: The device operates at an ideal standard;

[0121] AttentionRequired: The device requires attention and has not reached full normal performance.

[0122] Critical: The equipment is operating abnormally and requires urgent intervention;

[0123] AlertLog:

[0124] AlertType: describes the type of status alert;

[0125] Timestamp: corresponds to the time when the alarm occurs, ensuring problem tracking;

[0126] SeverityLevel: The severity of the alarm is marked from low to high, making it easy to communicate and identify priorities;

[0127] Step S212: merge the flow, pressure and liquid level data into a dictionary, with each field being clear and named consistently, and adopting a version control mechanism that identifies different versions of the data packet format when the data structure changes; using a custom serialization function to encode each field of the data object in a structured manner; and performing character encoding processing;

[0128] Among them, the custom serialization function is:

[0129] Input: a data object (such as a dictionary or a list);

[0130] Output: a byte stream (buffer) containing the serialized result;

[0131] Initialization: Create an empty byte stream buffer to store the serialization results;

[0132] Recursive function serialize_item(item):

[0133] If item is a dictionary:

[0134] Loop through each key-value pair (key, value) in the dictionary:

[0135] Extract the field name key, encode it in UTF-8 and append it to buffer;

[0136] Check the type of value. If it is an integer, convert it directly into a byte stream and append it to the buffer.

[0137] If value is a string:

[0138] First encode the characters and append them to the buffer;

[0139] The length of the string can be recorded here, for example: a byte stream with the length of the string appended to the buffer;

[0140] For a list, append its length to buffer and then recursively call serialize_item for each element;

[0141] For nested dictionaries, repeat this process, recursively calling serialize_item;

[0142] If item is a list:

[0143] Record the length of the list, append it to buffer, and then recursively call serialize_item for each element in the list;

[0144] Step S213: After serialization is completed, the checksum of the data is calculated, and a unique check code is generated by performing a specific algorithm on the byte stream and appended to the end of the data packet; a data packet header is appended to the front end of the data packet, which contains necessary meta-information such as the protocol version, sender information, and content length.

[0145] Preferably, in step S211 of this embodiment, data collection and initialization is performed by creating a data structure to organize and store the information of the monitoring points to ensure the orderliness of data integration; the raw data signal is normalized to uniformly convert it into a value within the standard range to ensure the comparability of different types of data; the normalized data of flow, pressure and liquid level are bound together to form a comprehensive data set for subsequent data processing and analysis. Significance: The normalized data can eliminate the influence of measurement errors and improve the quality and reliability of monitoring data; through the combination of data structures, the access logic of data is simplified to facilitate subsequent operations and processing; the consistency of the format after data integration is ensured, laying the foundation for the linkage analysis of multi-dimensional data in the later stage. Step S212 data merging and serialization, merge the data of flow, pressure and liquid level into a structured dictionary, allowing quick access to the required data through clear field names; add version control to the data format to ensure that when the data structure changes, the system can accurately identify different versions of data packets; encode the data object through a custom serialization function, and perform character encoding processing at the same time to ensure the compatibility of data during cross-platform transmission. Significance: The use of dictionary structure helps to quickly parse and retrieve data, improving the efficiency of data processing; the version control mechanism ensures that future expansion and modification of the data structure will not affect the operation of existing applications, enhancing the maintainability of the system; through serialization and character encoding, it ensures that data can be designed and distributed in a variety of environments, consistency and integrity are guaranteed, and errors caused by encoding problems are reduced. Step S213 Checksum and data encapsulation, calculate the serialized byte stream, generate a unique check code, and attach it to the end of the data packet so that the receiver can verify the integrity of the data; add necessary meta information including protocol version, sender information and content length to the front end of the data packet, so that the receiver can clearly understand the structure and source of the data packet. Significance: The use of checksum ensures that the receiver can verify that the data is not damaged or tampered with during transmission, enhancing the security of data transmission; the additional information of the data packet header provides the necessary context information, enabling the receiving system to intelligently process and parse the data, reducing the possibility of misunderstanding and errors; through the verification and tracking of the data status, it facilitates the subsequent system maintenance and troubleshooting, reducing the maintenance cost.

[0146] In summary, steps S211, S212 and S213 of this embodiment not only realize the effective integration and standardized processing of data, but also ensure the security and reliability of the data transmission process, thereby improving the stability and efficiency of the entire Internet of Things system. Each link is closely connected, supporting the quality and efficiency of data transmission, and providing a basis for subsequent data analysis and decision-making.

[0147] Furthermore, the process of encoding each field of the data object in a structured manner in step S212 specifically includes the following steps:

[0148] Step S2121: recursively traverse the dictionary, access the fields of each layer one by one, extract the corresponding value of each field and perform corresponding encoding processing according to its data type; if the field type is integer, directly convert it into a byte stream; if the field type is a string, character encoding is performed first;

[0149] Step S2122: during the serialization process, a new byte stream buffer is created as a container for the final output; whenever a field is accessed, its encoded result is appended to the end of the byte stream in sequence; identification field information and type information are also included in the byte stream to form a metadata header for each field;

[0150] Step S2123: After the byte stream is constructed, a unified character encoding process is performed.

[0151] Preferably, in step S2121 of this embodiment, field extraction and encoding processing are performed to recursively traverse the dictionary to ensure that the fields at each level are accessed, thereby avoiding missing any data; by identifying the field type (integer, string, etc.) to determine the corresponding encoding method, the accuracy and adaptability of the data in the conversion process are guaranteed; the integer field is directly converted into a byte stream, effectively reducing the space required for data storage and transmission, and improving the efficiency of data transmission. Significance: Ensure that all fields are effectively extracted, avoid information omission, and lay the foundation for subsequent encoding and transmission; select the appropriate encoding method according to the different field types, so that the system can flexibly respond to the processing requirements of various data formats and improve the versatility of the system; through efficient processing such as integer to byte stream, reduce the memory space and bandwidth occupied by data, and improve the overall performance of the system. Step S2122 byte stream creation and information inclusion, create a new byte stream buffer as the final output container, which realizes the storage and management of structured data; add the identification information and type information of the field in the byte stream to form a metadata header for each field, enhance the self-descriptiveness of the data, and facilitate subsequent parsing and decoding. Significance: By organizing the byte stream, the structured data can maintain consistency during storage and transmission, ensuring that each part of the information can be logically associated, which is convenient for the external system to parse; introducing metadata headers to provide structural information for each field, so that the receiver can quickly identify and parse the data content, reducing the complexity of understanding the structure; using such a structured byte stream design, the field can be easily expanded or modified in the future without affecting the parsing mechanism of the existing data, so as to adapt to changes in business needs. Step S2123 character encoding processing, after the byte stream is constructed, a unified character encoding processing is performed to ensure compatibility between different character sets; standardize the character representation of the data to ensure the consistency and readability of the data when it is transmitted between different systems or applications, and avoid errors caused by inconsistent encoding. Significance: Performing character encoding processing enables data to remain consistent between different operating systems and applications, thereby enhancing the interactive capabilities between systems; through standardized character encoding processing, errors caused by encoding are reduced, the reliability of data transmission is improved, and the robustness of the system is enhanced; ensure that the information in the transmission process will not be distorted due to changes in character encoding, and ensure that the data content maintains its original semantics and value during transmission.

[0152] In summary, through the gradual implementation of steps S2121, S2122 and S2123, the data encoding process in this embodiment becomes efficient, structured and self-describing, so that each part of the transmission meets the standardization requirements, ensuring the integrity, consistency and efficiency of the data. This not only optimizes the data processing process, but also provides a solid foundation for subsequent analysis and application.

[0153] Furthermore, the process of generating a unique verification code in step S213 specifically includes the following steps:

[0154] Step S2131: confirm the elements of the input data: the byte stream of the data packet, i.e. the result after serialization; various information in the header of the data packet, such as the protocol version, sender information, content length, etc.; the timestamp in the data packet, recording the time when the data was generated; convert the byte stream and header information into a unified format, and splice all related byte streams, protocol information, and timestamps into a long string;

[0155] Step S2132: encrypt the concatenated string once to generate a 256-bit hash value; weight each byte, select an irregular prime number for weighting, and then take the modulus of the weighted sum;

[0156] Step S2133: Perform another XOR operation on the generated check code, compare each byte of the entire byte stream with the check code, and generate a final check code; append the generated final check code to the end of the data packet.

[0157] Preferably, step S2131 of this embodiment confirms the elements of the input data, identifies and confirms all the input data required to generate the check code, including the byte stream, the header information of the data packet and the timestamp; by extracting this information, it lays the foundation for the subsequent generation of the check code; converts the byte stream and the header information into a long string in a unified format, making the subsequent processing simple and intuitive; standardizes a variety of information to ensure that all elements can be effectively spliced. Significance: Confirming all relevant data ensures that the generation of the check code takes into account the complete information content, can effectively reflect the characteristics of the data packet, and increases the credibility of the check code; by formatting the information uniformly, the flexibility and consistency of the subsequent processing are enhanced, which helps to avoid potential errors caused by inconsistent data formats. Step S2132 encrypts and weights the spliced ​​string, encrypts the spliced ​​string, and generates a 256-bit hash value. The hash value is a short, unique identifier, which greatly reduces the possibility of collision; performs byte weighting processing, selects irregular prime numbers for weighted calculation, and then takes the modulus of the sum, which increases the randomness and unpredictability of the final check code, and increases the possibility of different check codes generated by the same input data. Significance: The use of hash encryption effectively prevents third parties from tampering with data, while ensuring the uniqueness and accuracy of the check code to a certain extent; through weighting and modulo, the anti-collision ability of the check code is further improved, so that even for similar data packets, the generated check code can have a large difference, reducing the probability of data integrity review failure. Step S2133 generates and appends the final check code, and performs an XOR operation on the generated check code and each byte of the entire byte stream to form a final check code; a method of effectively combining the check code and the original data helps to generate an unpredictable final check code; the generated final check code is appended to the end of the data packet and becomes part of the data packet, which is conducive to subsequent integrity verification and verification. Significance: The appending of the generated final check code at the end of the data packet can be easily verified at the receiving end to ensure the integrity of the data during transmission; through the combination of XOR, the relationship between each byte and the check code makes the verification process more challenging, which promotes the receiving end to independently confirm the authenticity and integrity of the data.

[0158] The process of generating a unique verification code in this embodiment specifically includes the following steps:

[0159] First, confirm the elements of the input data, including the byte stream of the data packet, the data packet header information, and the timestamp. The specific steps are as follows:

[0160] The byte stream of the data packet: denoted as B = b1, b2, ..., b n , where each b i Indicates the i-th byte;

[0161] Packet header information: includes multiple fields, such as protocol version V, sender information S, content length (L, which can be represented as a vector

[0162] Timestamp: denoted as T, which is the UTC timestamp when the data is generated;

[0163] Convert the byte stream and header information into a unified format, and concatenate all relevant byte streams, protocol information, and timestamps into a long string, which can be expressed as:

[0164] C=g(B,H,T)=concat(B,H,T)=b1|b2|…|b n |V|S|L|T

[0165] Next, encrypt the concatenated string C to generate a 256-bit hash value H C :

[0166]

[0167] Perform weighted processing on each byte and set an irregular prime number set P = p1, p2, ..., p k , each byte b i The weight w i Calculated by the following formula:

[0168]

[0169] Weight w i It depends not only on the prime number, but also on the index i of the byte to calculate the weighted sum W:

[0170]

[0171] Next, the weighted sum is modulo-operated to generate a preliminary check code C p :

[0172] C p =W mod m

[0173] Where m is a prime number selected to ensure uniform distribution when generating the check code;

[0174] For the generated preliminary verification code C p , perform an XOR operation to get the final check code C f :

[0175]

[0176] Where ⊕ represents an XOR operation, and max(B) represents the maximum byte value in the byte stream B, which ensures the complexity of the checksum and makes it difficult to be predicted;

[0177] Finally, the generated check code C f Append to the end of the data packet to form a complete data packet:

[0178] D=B|C f

[0179] Here, D is the complete data packet, including the original byte stream and the additional checksum C f .

[0180] In summary, the unique verification code generated by steps S2131, S2132 and S2133 in this embodiment can effectively improve the integrity and security of the data packet. The process of strengthening data security not only ensures that the information has not been tampered with, but also reduces the risk of conflicts and anomalies by introducing various randomness and complex calculation methods, thereby providing reliable protection in data transmission and subsequent verification. The entire process builds a stable, reliable and efficient data integrity verification mechanism to ensure the security of data interaction.

[0181] Furthermore, the process of generating a 256-bit hash value in step S2132 specifically includes the following steps:

[0182] Step S21321: When the input concatenated string arrives at the processing node, serialization mapping is performed on each byte of the string, and converted into a weighted bit granularity in a corresponding binary format to form an information spectrum;

[0183] Step S21322: Divide the information spectrum into several sub-spectra, and group the bits in each sub-spectra according to the built-in Hamming distance standard. The combination of each group of bits is symmetrical and has local pseudo-randomness. Dynamic adjustment is made between and within groups through weighted index tables. Each bit group uses the previous weighted index table to exchange the internal permutations and combinations for multiple rounds. After each overlapping transformation, the newly generated bit set is brought into the feedback loop.

[0184] Step S21323: The adjusted bit set is input, and a 256-bit fixed-length hash value is generated by simulating the interaction formed by the dynamics of microscopic particles. The generated 256-bit hash value is mapped and recorded in the holographic storage module.

[0185] Preferably, step S21321 of this embodiment forms a serialized mapping and information spectrum, converts the input concatenated string into a binary format, and assigns a specific weight to each byte; through weighted bit granularity, a more detailed information spectrum is formed. At this time, the value and position of each bit are carefully designed to ensure that the bit can reflect the characteristics of the input information as much as possible in subsequent processing; the information spectrum becomes a composite data expression form, which can reflect the overall relationship of the bits and their importance in information transmission. Significance: Through serialized mapping and weighting, it can be ensured that the details of the original information are not lost in subsequent processing, which improves the accuracy and reliability of the system; a higher complexity is introduced, so that the simple byte input becomes a highly structured bit set, which is convenient for further operation and processing. Step S21322: Grouping and dynamic adjustment of information spectrum. The information spectrum is grouped according to the standard of Hamming distance, which ensures the symmetry and pseudo-randomness of each group of bits. At this time, the dynamic adjustment within and between groups enhances the correlation between bit groups and reduces data redundancy. Using the pre-defined weighted index table, each bit group forms a new bit set after multiple rounds of interleaving, thereby creating an adaptive cyclic adjustment mechanism to optimize the arrangement and combination of bits in a timely manner. Significance: By grouping based on Hamming distance, the probability of similar inputs generating the same hash value is reduced, the uniqueness and security of the data are increased, and the occurrence of collisions is further prevented. Multiple rounds of exchange and feedback loops ensure that under different input conditions, the generated bit set can adapt to different data characteristics, enhancing the flexibility and responsiveness of the algorithm. Step S21323: Processing of bit sets and generation of 256-bit hash values. After the improved bit sets are sent to the processing system, a 256-bit hash value is generated by simulating the interaction between microscopic particles. The generated hash value includes all the features of the previous steps, and the randomness and uniqueness are enhanced through complex interactions. The generated 256-bit hash value is mapped and recorded in the holographic storage module to ensure fast access and verification. Moreover, with the characteristics of holographic storage, the storage process itself also helps to reduce the risk of data damage and loss. Significance: The 256-bit fixed-length hash value has extremely high security and uniqueness, can effectively prevent data replay and tampering attacks, and is used to confirm data integrity and identity authentication. Through the mapping and recording of holographic storage, each new hash value generated can reflect the overall changes, which is convenient for subsequent security review, data search and traceability, and enhances the maintainability of the system.

[0186] In summary, the data transformation and structure optimization achieved by the three steps in this embodiment not only improves the efficiency and security of data processing, but more importantly ensures its uniqueness and the characteristics of being difficult to reverse crack. The combination of these steps forms an interdependent framework, so that the 256-bit hash value finally generated has a high degree of security and reliability, and can effectively meet the challenges of modern information protection.

[0187] Furthermore, if Figure 4 As shown, the process of judging overflow and blockage by difference in step S3 specifically includes the following steps:

[0188] Step S31: The central control platform establishes dynamic thresholds based on historical operation data. The dynamic thresholds are adjusted based on the normal operating parameters of the equipment and environmental changes. The normal ranges of flow rate and liquid level are set and a baseline data set is formed.

[0189] Step S32: In each data collection cycle, the real-time read flow, liquid level and pressure data are compared with the corresponding preset dynamic thresholds;

[0190] When the detection value of the liquid level sensor exceeds the upper threshold, the liquid level difference is calculated; if the difference exceeds the preset tolerance range, it is determined to be a possible overflow event; within the specified data acquisition cycle, the liquid level sensor records the current liquid level value in the system; then compares it with the upper limit of the dynamic threshold; calculates the difference between the current liquid level and the set upper threshold, which is recorded as the liquid level difference; compares the calculated liquid level difference with the preset tolerance range to determine whether the following conditions are met; if the liquid level difference > tolerance range, it means that the liquid level exceeds the safety range, and it is determined that the possibility of overflow is increased; confirm the speed of change of the liquid level. If the rising speed of the liquid level exceeds the predefined rate threshold, the overflow prediction will be further enhanced, and this state will be marked as "possible overflow";

[0191] For flow monitoring, if the detected flow value drops below the preset lower limit, the difference between the current flow and the normal operating flow is compared, and accompanied by a pressure drop, it is confirmed that a blockage has occurred; in the same data acquisition cycle, the flow and pressure sensors simultaneously obtain the corresponding values, the flow value is divided into "current flow" and "normal operating flow", and the pressure value is "current pressure value"; the difference between the current flow and the normal operating flow is calculated and recorded as the flow difference; check whether the current flow is lower than the preset lower limit threshold, the conditions are as follows: if the current flow < lower limit threshold, and the flow difference <0 (i.e., the flow drops), it indicates that there may be a problem; at the same time, check the current pressure value, and when the pressure sensor information feedback is as follows, further confirmation is performed: if the current pressure < the lower limit threshold of the normal operating pressure, and the magnitude of the flow value reduction meets a specific ratio, the risk of blockage is further confirmed; combined with the flow difference and pressure changes, if the conditions of a sharp drop in flow and a pressure lower than the normal value are met at the same time, it is finally confirmed that the system is blocked (if the conditions of a sharp drop in flow (i.e., the flow difference is obviously negative) and the pressure is lower than the normal threshold are met at the same time, it is determined that the system is at risk of blockage);

[0192] Step S33: Confirm that there is a potential overflow or blockage problem and generate a corresponding alarm through the built-in decision logic, combining the status information of the sensor with the current operating conditions to give the alarm a specific context.

[0193] Preferably, in step S31 of this embodiment, a dynamic threshold and a baseline data set are established. The dynamic threshold automatically generated by the central control platform according to the historical operation data of the equipment and combined with environmental changes adapts to the changing law of long-term operation, making the monitoring standard more accurate; by setting the normal range of flow, liquid level and pressure, a baseline data set is established, and the baseline data set provides an important reference basis for subsequent real-time data monitoring. Significance achieved: Through the establishment of dynamic thresholds, the system can respond more sensitively to changes in various environmental conditions, thereby improving the accuracy of monitoring data; the establishment of baseline data enables maintenance personnel to promptly discover operating characteristics that deviate from normal conditions, thereby achieving preventive maintenance and avoiding potential system crashes or serious failures. Step S32 real-time data reading and threshold comparison, in each data collection cycle, the real-time collected flow, liquid level and pressure data will be immediately compared with the dynamic threshold, and abnormal conditions can be discovered at the first time; the gap between parameters such as liquid level and flow and the preset threshold can be accurately calculated, and their changes can be monitored. Significance achieved: The automation of the real-time comparison process greatly improves the monitoring efficiency and eliminates the risk of delay due to manual intervention; by comparing the rapid confirmation of abnormal conditions, the system can promptly respond to potential overflow or blockage problems and reduce the risk of accidents. Step S33 alarm generation and context assignment, the built-in decision logic can automatically generate corresponding alarms according to the detected fault type (such as overflow or blockage) and provide specific status information; by combining the sensor status information with the current operation status, the alarm information is more detailed and actionable. Significance achieved: The generated alarm can be quickly transmitted to the relevant personnel, so that they can take immediate measures to prevent the situation from further deteriorating; combining the alarm with the context information enables maintenance personnel to judge the nature and severity of the problem more quickly, thereby optimizing the subsequent processing process and reducing the troubleshooting time.

[0194] Assume that in a water supply and drainage system, the following dynamic thresholds and collection conditions are set:

[0195] Liquid level dynamic threshold:

[0196] Upper threshold: 100cm, tolerance range: 5cm;

[0197] Traffic dynamic threshold:

[0198] Lower threshold: 50L / min, normal operating flow: 100L / min;

[0199] Real-time data in the current collection cycle:

[0200] Current liquid level: 105cm, current flow rate: 40L / min, normal operating flow rate: 100L / min, current pressure: 30kPa, lower threshold of normal operating pressure: 20kPa;

[0201] Overflow judgment

[0202] Liquid level difference calculation:

[0203] Calculate the difference between the current liquid level and the upper limit of the dynamic threshold: [liquid level difference = current liquid level - upper limit threshold = 105, cm - 100, cm = 5, cm]

[0204] Comparison of tolerance range:

[0205] Compare the liquid level difference with the tolerance range: Since the liquid level difference does not exceed the tolerance range, it is temporarily impossible to determine overflow based on this data.

[0206] Confirmation of the rate of change of the liquid level (assuming the rising speed data is supported): Assuming that the rate at which the liquid level rises to the current value is 6 cm / min, which exceeds the predefined rate threshold (assuming 5 cm / min), then this state can be further marked as "possible overflow";

[0207] Blockage judgment

[0208] Flow difference calculation:

[0209] Calculate the difference between the current flow rate and the normal operating flow rate: [Flow difference = current flow rate - normal operating flow rate = 40, L / min - 100, L / min = -60, L / min]

[0210] Lower threshold check:

[0211] Check whether the current flow rate is lower than the lower threshold: [40,L / min<50,L / min (true)}]

[0212] At the same time, check whether the flow difference is less than 0: [-60, L / min < 0, (true)] The downward trend of the flow here is very obvious, so it indicates that there may be a blockage.

[0213] Pressure value check:

[0214] Check whether the current pressure value is lower than the lower threshold of normal operating pressure: [30,kPa>20,kPa (not true)] Now, the current pressure is not lower than the lower threshold of the normal value, indicating that although the flow rate has dropped severely, the pressure is normal, so blockage cannot be confirmed.

[0215] Summary of results

[0216] Liquid level judgment: overflow is not confirmed because the liquid level difference does not exceed the tolerance range. Although the liquid level rises at a fast rate, it does not reach the alarm condition.

[0217] Flow rate determination: The initial determination was that there was a problem with the flow rate drop, but since the pressure was normal, the final confirmation of the blockage was not established;

[0218] To sum up, the entire gap judgment overflow and blockage process of this embodiment builds an efficient and agile pipeline monitoring system; it not only greatly improves the real-time monitoring capability of the water supply and drainage system; it also enhances the self-healing ability of the system through intelligent decision-making and alarm mechanisms, better protects the normal operation of the equipment and environmental safety, and ultimately promotes the rational use of resources and the improvement of management efficiency.

[0219] like Figure 5 As shown, this embodiment also provides an embodiment of a water supply and drainage blockage and overflow detection system using the Internet of Things. In this embodiment, the water supply and drainage blockage and overflow detection system using the Internet of Things is applied to the water supply and drainage blockage and overflow detection method using the Internet of Things in the above embodiment. The water supply and drainage blockage and overflow detection system using the Internet of Things includes a data acquisition module 1, an information display module 2, and an alarm output module 3 that are electrically connected in sequence;

[0220] Among them, the data acquisition module 1 is used to obtain the location information of facilities such as the water inlet, outlet, elbow, sewage well and sedimentation tank of the water supply and drainage pipeline, install sensors at the corresponding positions of the facilities, and obtain data such as flow, liquid level and pressure through different types of sensors; the information display module 2 is used to standardize the data, and transmit the standardized data to the central control platform through the Internet of Things communication protocol to display the real-time pipeline status, including flow conditions, pressure changes and liquid level monitoring information; the alarm output module 3 is used to compare the flow conditions, pressure changes and liquid level monitoring information with the preset monitoring index standards, and compare the gap between the read value and the set threshold in real time. If the liquid level exceeds the preset upper limit (overflow) or the flow suddenly drops (blockage), an alarm is generated, and the alarm information is sent to relevant personnel via short message, email or smart terminal push.

[0221] like Figure 6 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 4 includes a processor 41 and a memory 42 coupled to the processor 41.

[0222] The memory 42 stores program instructions for implementing the water supply and drainage blockage and overflow detection method using the Internet of Things according to any of the above-mentioned embodiments.

[0223] The processor 41 is used to execute program instructions stored in the memory 42 to perform water supply and drainage blockage and overflow detection using the Internet of Things.

[0224] The processor 41 may also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip having the ability to process signals. The processor 41 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0225] Further, Figure 7 The schematic diagram of the structure of the storage medium of an embodiment of the present application is that the storage medium 5 of the embodiment of the present application stores program instructions 51 that can implement all the above methods, wherein the program instructions 51 can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0226] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0227] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units. The above is only an implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present invention.

[0228] The specific implementation methods of the invention are described in detail above, but they are only examples, and the invention is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the invention, therefore, the equalization, modification, improvement, etc. made without departing from the spirit and principle of the invention should be included in the scope of the invention.

Claims

1. A water supply and drainage blockage and overflow detection method using the Internet of Things, characterized in that: The water supply and drainage blockage and overflow detection method using the Internet of Things includes: Obtain the location information of the water inlet, outlet, elbow, sewage well and sedimentation tank facilities of the water supply and drainage pipeline, install sensors at the corresponding locations of the facilities, and obtain flow, liquid level and pressure data through detection of different types of sensors; Standardize the data and transmit it to the central control platform through the IoT communication protocol to display the real-time pipeline status, including flow, pressure changes and liquid level monitoring information; The flow conditions, pressure changes and liquid level monitoring information are compared with the preset monitoring index standards, and the gap between the reading value and the set threshold is compared in real time. If the liquid level exceeds the preset upper limit or the flow drops suddenly, an alarm is generated and the alarm information is sent to relevant personnel via short message, email or smart terminal push.

2. The water supply and drainage blockage and overflow detection method using the Internet of Things according to claim 1 is characterized in that: Install sensors at locations corresponding to the facility, including: Based on the topographic map and pipe network layout, determine the precise location of key facilities such as water inlets, outlets, elbows, sewage wells and sedimentation tanks; mark the geographic coordinates of each facility; and select the sensor type according to the monitoring target; Connect the sensor power supply, conduct the wiring of the communication line, and perform preliminary calibration after the sensor is installed; attach a unique digital identification tag to each sensor, and perform device management and data association in the central control platform; the tag contains the device ID, working status information and installation date; After all sensors are connected to the network, the central control platform confirms that the signal transmission is normal and performs preliminary data integration.

3. The water supply and drainage blockage and overflow detection method using the Internet of Things according to claim 1 is characterized in that: Displays real-time pipeline status, including: The collected data is preprocessed to remove noise and outliers, and the preprocessed data is standardized, which includes unit unification and normalization. The normalized data is integrated into a structured format for transmission by the IoT communication protocol, and the real-time monitoring data of each facility is encapsulated into a data packet. The central control platform confirms the successful receipt of data according to the receipt confirmation mechanism; if no confirmation is received, the central control platform implements the error handling strategy; After receiving the standardized data packets, the central control platform presents them in the form of a real-time dashboard, including graphical flow monitoring, pressure change graphs and liquid level trend graphs, to intuitively understand the pipeline status.

4. The water supply and drainage blockage and overflow detection method using the Internet of Things according to claim 3 is characterized in that: Integrate the normalized data into a structured format for IoT communication protocol transmission, including: Perform complete normalized data collection and initialization to create a data structure that represents the information of a monitoring point, including the facility unique identifier and the acquisition timestamp; for each data point, convert the raw signal to a normalized value, and bundle the normalized flow, pressure, and level data to form a comprehensive data set; Combine the flow, pressure and level data into a dictionary with clear and consistent naming for each field, and use a version control mechanism to identify different versions of the data packet format when the data structure changes; use a custom serialization function to encode each field of the data object in a structured manner; perform character encoding processing; After serialization is completed, the checksum of the data is calculated, and a unique check code is generated by operating on the byte stream and attached to the end of the data packet; a data packet header is attached to the front end of the data packet, which contains metadata such as the protocol version, sender information, and content length.

5. The water supply and drainage blockage and overflow detection method using the Internet of Things according to claim 4 is characterized in that: Encode each field of the data object in a structured manner, including: Recursively traverse the dictionary, access each layer of fields one by one, extract the corresponding value of each field and perform corresponding encoding processing according to its data type; if the field type is integer, directly convert it to byte stream; if the field type is string, character encoding is performed first; During the serialization process, a new byte stream buffer is created as a container for the final output; whenever a field is accessed, its encoded result is appended to the end of the byte stream in sequence; identification field information and type information are also included in the byte stream to form the metadata header of each field; After the byte stream is constructed, unified character encoding processing is performed.

6. The water supply and drainage blockage and overflow detection method using the Internet of Things according to claim 5 is characterized in that: Generate a unique verification code, including: Elements to confirm input data: the byte stream of the data packet, i.e. the result after serialization; various information in the packet header, including protocol version, sender information, content length; timestamp in the data packet, recording the time when the data was generated; Convert the byte stream and header information into a unified format, and concatenate all relevant byte streams, protocol information, and timestamps into a long string; Encrypt the concatenated string once to generate a 256-bit hash value; Weight each byte, select an irregular prime number for weighting, and then take the modulus of the weighted sum; For the generated check code, perform another XOR operation, compare each byte of the entire byte stream with the check code, and generate a final check code; the generated final check code is appended to the end of the data packet.

7. The water supply and drainage blockage and overflow detection method using the Internet of Things according to claim 6 is characterized in that: Generates a 256-bit hash value consisting of: When the input concatenated string arrives at the processing node, a serialization mapping is performed on each byte of the string and converted into a weighted bit granularity in the corresponding binary format to form an information spectrum; The information spectrum is divided into several sub-spectra. The bits in each sub-spectra are grouped according to the built-in Hamming distance standard. The combination of each group of bits is symmetrical and has local pseudo-randomness. The weighted index table is dynamically adjusted between and within groups. Each bit group uses the previous weighted index table to exchange the internal permutations and combinations for multiple rounds. After each overlapping transformation, the newly generated bit set is brought into the feedback loop. The adjusted bit set is fed into a 256-bit fixed-length hash value generated by simulating the interaction formed by microscopic particle dynamics, and the generated 256-bit hash value is mapped and recorded in the holographic storage module.

8. The water supply and drainage blockage and overflow detection method using the Internet of Things according to claim 1 is characterized in that: Gap determination for overflow and blockage, including: The central control platform establishes dynamic thresholds based on historical operating data. The dynamic thresholds are adjusted based on the normal operating parameters of the equipment and environmental changes. The normal range of flow and liquid level is set and forms a baseline data set. In each data collection cycle, the real-time read flow, level and pressure data are compared with the corresponding preset dynamic thresholds; Identification of potential overflow or blockage issues generates appropriate alarms through built-in decision logic.

9. The water supply and drainage blockage and overflow detection method using the Internet of Things according to claim 8, characterized in that: Combine sensor status information with current operating conditions to give alerts specific context.

10. A water supply and drainage blockage and overflow detection system using the Internet of Things, which is applied to the water supply and drainage blockage and overflow detection method using the Internet of Things as claimed in any one of claims 1 to 9, characterized in that: The water supply and drainage blockage and overflow detection system using the Internet of Things includes: The data acquisition module is used to obtain the location information of the water inlet, outlet, elbow, sewage well and sedimentation tank facilities of the water supply and drainage pipeline, install sensors at the corresponding locations of the facilities, and obtain flow, liquid level and pressure data through detection of different types of sensors; The information display module is used to standardize the data and transmit the standardized data to the central control platform through the Internet of Things communication protocol to display the real-time pipeline status, including flow conditions, pressure changes and liquid level monitoring information; The alarm output module is used to compare the flow conditions, pressure changes and liquid level monitoring information with the preset monitoring index standards, and compare the gap between the read value and the set threshold in real time. If the liquid level exceeds the preset upper limit or overflows, or the flow suddenly drops or is blocked, an alarm is generated, and the alarm information is sent to relevant personnel via short messages, emails or smart terminals.

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