Remote state monitoring method and system based on intelligent water meter

Abstract

The embodiment of the invention discloses a remote state monitoring method and system based on an intelligent water meter, and the method comprises the steps: obtaining pipeline BIM model data of an intelligent water meter pipeline network of a target building, and synchronously collecting a real-time monitoring data set of a plurality of sensing nodes in the intelligent water meter pipeline network; performing abnormal state recognition processing on the real-time monitoring data set, and generating an abnormal state distribution map matched with the spatial position of the pipeline BIM model data; based on the abnormal state distribution map and a preset pipeline topology constraint condition, performing dynamic visual reconstruction processing on the pipeline BIM model data, and generating a visual output interface with an abnormal state mark; and performing association mapping on the visual output interface and the real-time monitoring data set to generate a state monitoring feedback strategy, and triggering maintenance response operation of the intelligent water meter pipeline network through a remote service port.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data processing technology, and specifically to a remote status monitoring method and system based on a smart water meter. Background Art

[0002] With the development of intelligent buildings, smart water meters are increasingly being used in building water supply networks. Status monitoring of smart water meters has become a key step in ensuring normal water use and efficient water resource management in buildings.

[0003] In practical applications, existing smart water meter status monitoring technology suffers from numerous shortcomings. For one thing, while BIM models can display the layout of smart water meter pipeline networks, they cannot effectively integrate with real-time monitoring data, making it difficult to quickly detect abnormal conditions within smart water meters and their pipelines. Furthermore, the collected monitoring data lacks the ability to conduct in-depth analysis and accurately locate anomalies, making it impossible to intuitively and accurately present anomaly information within the BIM model. Consequently, existing smart water meter status monitoring technology suffers from a low level of intelligence and automation. Summary of the Invention

[0004] The embodiments of the present application provide a remote status monitoring method and system based on smart water meters, which are used to achieve deep integration of smart water meter monitoring data and BIM models, thereby accurately locating anomalies and realizing dynamic visual display.

[0005] In the first aspect, an embodiment of the present application provides a remote status monitoring method based on smart water meters, which is applied to a remote status monitoring system, and the method includes: obtaining pipeline BIM model data of the smart water meter pipeline network of the target building, and synchronously collecting real-time monitoring data sets of multiple sensor nodes in the smart water meter pipeline network; performing abnormal state identification processing on the real-time monitoring data set, and generating an abnormal state distribution map that matches the spatial position of the pipeline BIM model data; based on the abnormal state distribution map and preset pipeline topology constraints, dynamically visually reconstructing the pipeline BIM model data, and generating a visual output interface with abnormal state marks; associating and mapping the visual output interface with the real-time monitoring data set, generating a status monitoring feedback strategy, and triggering the maintenance response operation of the smart water meter pipeline network through the remote service port.

[0006] In a second aspect, an embodiment of the present application provides a remote status monitoring system, comprising: processor; a storage device having a computer program stored thereon, When the computer program is executed by the processor, the processor implements any of the remote status monitoring methods based on smart water meters.

[0007] An embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the remote status monitoring method based on a smart water meter are implemented.

[0008] The embodiment of the present application can achieve a deep integration of smart water meter monitoring data and BIM models, thereby accurately locating anomalies and realizing dynamic visual display. In detail, the embodiment of the present application can accurately grasp the pipeline status by obtaining the pipeline BIM model data and real-time monitoring data set of the target building smart water meter pipeline network; perform abnormal state identification processing on the monitoring data, generate a matching abnormal state distribution map, and clearly present the abnormal location; perform dynamic visual reconstruction of the BIM model data based on the map and topological constraints, generate a visual output interface with abnormality marks, so that operation and maintenance personnel can intuitively understand the abnormal situation; associate and map the visual interface with the monitoring data, generate feedback strategies and trigger maintenance response operations, which can realize efficient monitoring and timely processing of the smart water meter pipeline network status, greatly improving the intelligence and automation level of the entire monitoring process, and improving operation and maintenance efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flowchart of a remote status monitoring method based on a smart water meter provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of the basic structure of a remote status monitoring system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the embodiments of the present application are further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0012] See also Figure 1 As shown in FIG, this figure is a flow chart of a remote status monitoring method based on a smart water meter provided in an embodiment of the present application, which can be applied to a remote status monitoring system. Figure 1 As shown, the method may include steps 110 to 140.

[0013] Step 110: Obtain the pipeline BIM model data of the smart water meter pipeline network of the target building, and simultaneously collect a set of real-time monitoring data of multiple sensor nodes in the smart water meter pipeline network.

[0014] In this example, the BIM model data for the target building's smart water meter network is first acquired. This data contains detailed information about the entire piping system, such as pipe connections, spatial locations, and the properties of each device. Simultaneously, real-time monitoring data from multiple sensor nodes distributed across the network is collected. These sensor nodes monitor various data in real time.

[0015] In an optional embodiment, the step of obtaining the pipeline BIM model data of the smart water meter pipeline network of the target building and synchronously collecting a set of real-time monitoring data of multiple sensor nodes in the smart water meter pipeline network includes: Step 111: Call the BIM model database of the target building to extract the spatial topology data and equipment attribute data that constitute the pipeline BIM model data; the spatial topology data is used to describe the pipeline connection relationship and spatial geometric layout, and the equipment attribute data includes the water meter installation coordinates and configuration parameters associated with the sensor node.

[0016] In this scenario, when the BIM model data for the pipeline is needed, the system calls the BIM model database and extracts spatial topology data from the database. This spatial topology data shows how each pipe is connected and their spatial layout. For example, pipes A and B are connected at a certain spatial location via a tee fitting, and their spatial coordinates are precisely recorded. Device attribute data includes the installation coordinates of the water meter, for example, water meter M is installed at coordinates (X1, Y1, Z1). Configuration parameters are also extracted, which determine the operating mode of the sensor node, such as its sampling frequency and data transmission method.

[0017] Step 112: Send activation instructions to each sensor node according to the configuration parameters, and collect water pressure monitoring data, flow fluctuation data and equipment operation status data of each sensor node according to a set period.

[0018] Based on the configuration parameters obtained above, the system will send an activation instruction to each sensor node. For example, there are N sensor nodes, and each sensor node has corresponding configuration parameters. For example, the configuration parameters of sensor node S1 stipulate that its sampling frequency is to collect data once every 5 minutes. When the activation instruction is sent, the sensor node starts to work according to the set cycle. In this process, the sensor node will collect water pressure monitoring data. For example, at a certain moment, the water pressure value collected by sensor node S2 is P1. At the same time, it will collect flow fluctuation data. For example, at the same moment mentioned above, the flow value collected by the sensor node is Q1. In addition, it can also collect equipment operation status data, such as whether the equipment is operating normally, whether there is a fault alarm, and other information. These data will be continuously updated over time to provide dynamic data support for subsequent analysis.

[0019] Step 113: The water pressure monitoring data, the flow fluctuation data and the equipment operation status data are subjected to spatiotemporal alignment processing based on the water meter installation coordinates and the spatial topological hierarchy of the pipeline BIM model to generate a hierarchical real-time monitoring data set; wherein, the topological structure of the hierarchical real-time monitoring data set forms a mapping relationship with the pipe network hierarchy of the pipeline BIM model, the spatial topological hierarchy is a BIM model hierarchy divided according to building functional areas, and the pipe network hierarchy is a pipeline physical hierarchy divided based on the primary and secondary relationships of water supply.

[0020] To better analyze and utilize the collected data, spatiotemporal alignment is required. Using the water meter installation coordinates as a reference, the water pressure, flow, and equipment operating status data collected by different sensor nodes are aligned with the spatial topology of the pipeline BIM model. For example, data collected by a sensor node located in a functional area of the target building (such as the kitchen) will correspond to the spatial location of that functional area in the BIM model. Furthermore, considering the pipeline network hierarchy and based on the primary and secondary water supply relationships, the data is organized according to the physical hierarchy of the pipelines. For example, if the pipeline network is divided into different levels, such as the main water supply pipeline layer and the branch water supply pipeline layer, the data will be arranged according to this hierarchical relationship. After this processing, the generated hierarchical real-time monitoring data set has a clear structure, and its topology forms a one-to-one mapping relationship with the pipeline network hierarchy of the pipeline BIM model.

[0021] Step 120: performing abnormal state identification processing on the real-time monitoring data set to generate an abnormal state distribution map that matches the spatial position of the pipeline BIM model data.

[0022] After obtaining the hierarchical real-time monitoring data set, the next step is to analyze this data, identify abnormal conditions, and generate an abnormal condition distribution map that matches the spatial location of the pipeline BIM model data. This map can intuitively show where abnormal conditions have occurred in the pipeline network, as well as the type and degree of the abnormality.

[0023] In an optional embodiment, performing abnormal state identification processing on the real-time monitoring data set to generate an abnormal state distribution map that matches the spatial position of the pipeline BIM model data includes: Step 121: performing mutation point detection processing on the water pressure monitoring data in the hierarchical real-time monitoring data set, identifying abnormal water pressure areas exceeding a preset fluctuation threshold, and extracting the duration and fluctuation amplitude of the abnormal water pressure areas.

[0024] For water pressure monitoring data, mutation point detection is first performed. For example, water pressure data collected over a period of time is divided into data segments according to specific time intervals, such as collecting water pressure data every minute. A first-order difference calculation is then performed on each data segment, calculating the difference between the water pressure values at two adjacent time points, to obtain a water pressure change rate series. Next, a sliding window standard deviation analysis is performed on this change rate series. For example, a sliding window size of 5 minutes is set, and the standard deviation of the data within this window is calculated. When the standard deviation exceeds a dynamic threshold, a mutation time point is identified. Based on this mutation time point, the corresponding water pressure monitoring sub-data is extracted. For example, the water pressure data within 10 minutes before and after the mutation time point is used as the sub-data. The percentage deviation of these sub-data from the historical mean is then calculated. For example, if the historical mean water pressure is P0 and the average water pressure of the sub-data is P2, the percentage deviation is (P2 - P0) / P0 × 100%. If the offset percentage exceeds a preset fluctuation threshold, the area corresponding to the sub-data may be marked as an abnormal water pressure area, and the duration and fluctuation amplitude of the abnormal area may be recorded.

[0025] In a preferred embodiment, performing mutation point detection processing on the water pressure monitoring data in the hierarchical real-time monitoring data set to identify abnormal water pressure areas exceeding a preset fluctuation threshold includes: Step 1210: Divide the water pressure monitoring data into continuous water pressure data segments according to a preset time window, and perform a first-order difference calculation on each water pressure data segment to obtain a water pressure change rate sequence; perform a sliding window standard deviation analysis on the water pressure change rate sequence to identify the mutation time point where the standard deviation exceeds the dynamic threshold; extract the corresponding water pressure monitoring sub-data according to the mutation time point, and calculate the offset percentage of the water pressure monitoring sub-data and the historical mean data; if the offset percentage exceeds the preset fluctuation threshold, mark the corresponding area of the water pressure monitoring sub-data as a water pressure abnormality area.

[0026] In practice, the preset time window can be adjusted based on actual conditions, for example, set to 3 minutes. The water pressure monitoring data is divided into continuous data segments according to this window. A first-order difference calculation is performed on each data segment to obtain a water pressure change rate sequence. For example, for the data segment [P3, P4, P5], a first-order difference calculation yields the change rate sequence [P4-P3, P5-P4]. This change rate sequence is then subjected to a sliding window standard deviation analysis, for example, with a sliding window size of 4 data points. When the calculated standard deviation exceeds a dynamic threshold, a mutation time point is identified. Based on this mutation time point, the corresponding water pressure monitoring sub-data is extracted, such as the water pressure data within 15 minutes before and after the mutation time point. The percentage deviation of these sub-data from the historical mean data is calculated. If the deviation percentage exceeds a preset fluctuation threshold, such as 15%, the area corresponding to the sub-data is marked as an abnormal water pressure area.

[0027] Step 122: Perform trend deviation analysis on the traffic fluctuation data to determine the deviation between the traffic trend and the preset traffic model, and mark traffic anomaly areas where the deviation exceeds a dynamic threshold.

[0028] To analyze flow fluctuation data, it is necessary to first obtain preset flow baseline curves for each branch pipeline in the smart water meter network. These curves are generated based on historical data and include expected flow ranges for different time periods. For example, the expected flow range for one branch pipeline from 8:00 AM to 10:00 AM is [Q3, Q4]. The flow fluctuation data is then classified by branch pipeline identifier, and the flow data for each branch pipeline is processed separately. Trend deviation analysis is performed on the classified flow fluctuation data based on the preset flow baseline curve. Using data analysis methods such as curve fitting, a real-time flow trend line is generated for each branch pipeline. For example, for branch pipeline L1, a real-time flow trend line is obtained by fitting the collected flow data. The deviation between this real-time flow trend line and the corresponding preset flow baseline curve is then calculated. The deviation includes the duration of the deviation and the cumulative amount of the deviation. For example, if the real-time flow trend line is consistently higher than the preset flow baseline curve for 30 minutes starting at a certain point in time, this is the duration of the deviation. The cumulative amount of the deviation is the difference between the real-time flow and the preset flow during these 30 minutes. If the duration of the deviation exceeds the first dynamic threshold, such as the first dynamic threshold is 20 minutes, and the cumulative deviation exceeds the second dynamic threshold, such as the second dynamic threshold is a certain sum of flow differences, the spatial coordinates of the corresponding branch pipeline can be mapped to the pipeline BIM model data, and the abnormal flow area can be marked according to the mapped spatial coordinates.

[0029] In a preferred embodiment, the flow fluctuation data is subjected to trend deviation analysis to determine the degree of deviation between the flow trend and a preset flow model, and the flow abnormality area where the deviation exceeds a dynamic threshold is marked, including: Step 1221: Obtain a preset flow reference curve for each branch pipeline in the smart water meter pipeline network, wherein the preset flow reference curve includes a time-based expected flow range generated based on historical data.

[0030] Optionally, the system extracts preset flow rate baseline curves for each branch line from a historical data repository. These curves are derived by statistically analyzing flow rate data for each time period over a period of time. For example, by analyzing flow rate data from 9:00 AM to 11:00 AM daily for the past month, the expected flow rate range for a branch line during that time period is determined, and a preset flow rate baseline curve for that time period is generated. Different branch lines will have different preset flow rate baseline curves because their water supply needs and usage may vary.

[0031] Step 1222: The flow fluctuation data is identified and classified according to the branch pipelines, and trend deviation analysis is performed on the classified flow fluctuation data based on the preset flow reference curve to generate a real-time flow trend line for each branch pipeline.

[0032] After collecting the flow fluctuation data, the data is first classified according to the branch pipeline. For example, all flow data related to branch pipeline L2 are classified into one category. Then, for each category of data, trend deviation analysis is performed based on the preset flow reference curve. By using some data analysis algorithms, such as the least squares curve fitting method, the classified flow data is fitted to generate a real-time flow trend line for each branch pipeline. This trend line can reflect the current flow change trend. By comparing it with the preset flow reference curve, it can be seen whether there is any abnormal deviation in the flow.

[0033] Step 1223: Calculate the deviation between the real-time traffic trend line and the corresponding preset traffic reference curve, where the deviation includes the duration of the deviation and the cumulative amount of the deviation.

[0034] In the embodiment of the present application, calculating the degree of deviation between the real-time flow trend line and the preset flow reference curve is a key step. For the duration of the deviation, the time is counted from the time point when the real-time flow trend line begins to deviate from the preset flow reference curve until it returns to the preset flow reference curve range or exceeds the set observation time. This period of time is the duration of the deviation. For the cumulative amount of deviation, the sum of the flow difference between the real-time flow trend line and the preset flow reference curve at each time point during the duration of the deviation is the cumulative amount of deviation. For example, during a certain period of time, the flow values corresponding to the real-time flow trend line are Q5, Q6, and Q7, and the flow values corresponding to the preset flow reference curve are Q8, Q9, and Q10, then the cumulative amount of deviation = (Q5-Q8) + (Q6-Q9) + (Q7-Q10).

[0035] Step 1224: If the duration of the deviation exceeds the first dynamic threshold and the cumulative amount of the deviation exceeds the second dynamic threshold, the spatial coordinates of the corresponding branch pipeline are mapped to the pipeline BIM model data, and the flow abnormality area is marked according to the mapped spatial coordinates.

[0036] When the calculated duration of the deviation exceeds a first dynamic threshold (e.g., 15 minutes), and the cumulative deviation exceeds a second dynamic threshold (e.g., a specific sum of flow differences), it can be determined that a flow anomaly has occurred in the branch pipeline. At this point, the spatial coordinates of the branch pipeline are mapped to the pipeline BIM model data, the corresponding location is found in the BIM model, and the flow anomaly area is marked based on the mapped spatial coordinates. This allows the model to intuitively identify the locations where flow anomalies have occurred.

[0037] Step 123: Map the spatial coordinates of the abnormal water pressure area and the abnormal flow area to corresponding positions of the pipeline BIM model data to generate an initial abnormal state distribution map including abnormality type labels and abnormality levels.

[0038] After identifying the areas of abnormal water pressure and flow, the spatial coordinates of these areas are mapped to the corresponding locations in the pipeline BIM model data. For example, the spatial coordinates of the water pressure abnormality area are (X5, Y5, Z5). The corresponding location is found in the pipeline BIM model and labeled with the abnormality type label, such as abnormal water pressure. The abnormality level is determined based on the severity of the abnormality, such as mild, moderate, or severe. The same operation is performed for the flow abnormality area, mapping its spatial coordinates to the model and marking the flow abnormality type label and the corresponding abnormality level. In this way, an initial abnormal state distribution map containing all abnormal information is generated, which can intuitively display the abnormal conditions at different locations in the pipeline network.

[0039] Step 124: Based on the fault alarm information in the equipment operation status data, cross-validate the initial abnormal state distribution map, remove false alarm abnormal areas, correct the abnormality level, and generate the abnormal state distribution map.

[0040] Among them, the equipment operation status data may contain some fault alarm information, which can be used to further verify the accuracy of the initial abnormal state distribution map. For example, the equipment operation status data shows that a certain sensor node has a fault, which may cause the data it collects to be inaccurate. Then, in the initial abnormal state distribution map, if the area where the sensor node is located is marked as an abnormal area, it is necessary to make a judgment based on the fault alarm information. If it is determined that it is a false alarm caused by a sensor node failure, the abnormal area will be eliminated. At the same time, for other abnormal areas, the abnormal level is corrected based on more information provided in the equipment operation status data, such as the type of equipment fault, the scope of impact, etc. After the above cross-validation process, a more accurate abnormal state distribution map is generated.

[0041] Step 130: Based on the abnormal state distribution map and the preset pipeline topology constraint conditions, the pipeline BIM model data is dynamically reconstructed to generate a visual output interface with abnormal state marks.

[0042] In order to more intuitively display the abnormal conditions of the pipeline network, it is necessary to dynamically visualize and reconstruct the pipeline BIM model data to generate a visual output interface with abnormal status marks. This interface allows users to clearly see where the abnormalities have occurred and the specific circumstances of the abnormalities.

[0043] In an optional embodiment, the pipeline BIM model data is dynamically reconstructed based on the abnormal state distribution map and preset pipeline topology constraints to generate a visual output interface with abnormal state markings, including: Step 131: extracting pipeline connection relationship data and valve control node data from the pipeline BIM model data, and constructing a topological constraint relationship network of the smart water meter pipeline network.

[0044] From the pipeline BIM model data, the system extracts pipeline connection relationship data, which details how each pipeline connects to other pipelines, such as how pipeline A connects to pipeline B through an elbow. Valve control node data is also extracted. Valves play a crucial control role in the pipeline system, and this data contains information such as valve position and open / close status. Using this data, a topological constraint network for the smart water meter pipeline network is constructed. This topological constraint network illustrates the structure of the entire pipeline system and the relationships between its various components, providing a foundation for subsequent analysis and visualization.

[0045] Step 132: assigning a corresponding color code and dynamic flashing frequency to each abnormal area in the abnormal state distribution map according to the abnormal type label in the abnormal state distribution map.

[0046] Optionally, each abnormal area is assigned a unique color code and dynamic flashing frequency based on the different abnormality type labels in the abnormal state distribution map. For example, for water pressure abnormality areas, red is assigned as the color code and a dynamic flashing frequency of 2 flashes per second is set; for flow abnormality areas, yellow is assigned as the color code and flashes once per second. This allows users to quickly distinguish different types of abnormal areas on the visual interface through different colors and flashing frequencies.

[0047] Step 133: superimpose the color code and dynamic flashing frequency to the corresponding spatial position of the pipeline BIM model data to generate a first visual topology structure.

[0048] For example, in a pipeline BIM model, a location is marked as an area with abnormal water pressure, and the attributes red and flashing twice per second are superimposed on this location. This generates a first visual topology structure that can intuitively show the location of the abnormal area in the pipeline network and the type of abnormality.

[0049] Step 134: Based on the topological constraint relationship network, perform influence range diffusion simulation processing on the abnormal area in the first visual topological structure to generate a second visual topological structure containing potential influence area marks.

[0050] Using the previously constructed topological constraint network, we simulate the spread of influence of abnormal areas in the first visualization. For example, when a water pressure anomaly occurs in a pipeline, we analyze the potential impact on other connected pipelines based on the pipeline connections. By simulating the propagation path of water flow and pressure changes, we identify potential impact areas and mark them in the visualization. The resulting second visualization not only displays the immediate abnormal area but also potential areas that may be affected.

[0051] Step 135: performing layer fusion processing on the second visualization topology structure and the equipment operation status data in the hierarchical real-time monitoring data set to generate the visualization output interface.

[0052] For example, if the equipment operating status data shows that a valve is in a closed state, this information can be integrated with the second visual topology structure to simultaneously display the abnormal area, potential impact area, and equipment operating status on the visual output interface. The generated visual output interface can provide more comprehensive information and help users better understand the operating status of the pipeline network.

[0053] Step 140: Associating and mapping the visual output interface with the real-time monitoring data set, generating a status monitoring feedback strategy, and triggering a maintenance response operation of the smart water meter pipeline network through a remote service port.

[0054] In order to take appropriate measures based on the detected abnormal conditions, it is necessary to associate and map the visual output interface with the real-time monitoring data set, generate a status monitoring feedback strategy, and trigger maintenance response operations through the remote service port.

[0055] In an optional embodiment, the associating and mapping the visual output interface with the real-time monitoring data set to generate a status monitoring feedback strategy includes: Step 141: extracting the spatial coordinate set of the abnormal area and the abnormal type label from the visual output interface to generate an abnormal state feature vector.

[0056] After generating a visual output interface, the system extracts key information from it. For each abnormal area, a set of spatial coordinates is obtained. For example, the spatial coordinates of a water pressure anomaly area are {(Xa, Ya, Za), (Xb, Yb, Zb), …}. This set of coordinates accurately locates the abnormal area in the pipeline BIM model. At the same time, the abnormality type label corresponding to the abnormal area is extracted, such as water pressure anomaly, flow anomaly, etc. These spatial coordinate sets and anomaly type labels are combined to form a multi-dimensional abnormal state feature vector. This vector can comprehensively describe the characteristics of the abnormal area and provide basic data for subsequent analysis and decision-making. For example, the abnormal state feature vector can be represented as [water pressure anomaly, (Xa, Ya, Za), (Xb, Yb, Zb), …], which includes the type of anomaly and the location where the anomaly occurred.

[0057] Step 142: Match the corresponding maintenance priority scoring rule from a preset policy library according to the abnormal state feature vector, and calculate the maintenance priority score of each abnormal area extracted from the visual output interface.

[0058] The pre-set policy library stores various maintenance priority scoring rules for different abnormal situations. After obtaining the abnormal state feature vector, the system matches it with the rules in the policy library. For example, if the abnormality type is water pressure anomaly and the abnormal area is located near the main water supply pipe, a set of scoring criteria will be applied based on the rules in the policy library. This criteria may consider factors such as the severity of the anomaly (such as the anomaly level), the impact of the abnormal area on the entire pipeline system, and potential risks. For example, if a water pressure anomaly area is classified as severe and located near the main water supply pipe, which has a significant impact on the system, the scoring rules and a pre-set calculation method (such as assigning different weights to different factors and then performing a weighted calculation) will determine the maintenance priority score for the abnormal area. An exemplary calculation formula is: Maintenance Priority Score = Anomaly Level Weight × Anomaly Level + Location Weight × Location Impact + Potential Risk Weight × Potential Risk Level. The anomaly level weight, location weight, and potential risk weight are pre-set based on the actual situation. This calculation method can accurately calculate the maintenance priority score for each abnormal area.

[0059] Step 143: Based on the maintenance priority score and the pipeline connection tightness in the topology constraint relationship network, generate a condition monitoring feedback strategy including a maintenance sequence and a resource allocation plan; the maintenance sequence is a sequence comprehensively sorted according to the priority score and the pipeline connection tightness.

[0060] After obtaining the maintenance priority score for each abnormal area, the system combines the pipeline connection tightness information from the previously constructed topological constraint network to generate a condition monitoring feedback strategy. Pipeline connection tightness indicates the tightness of connections between different pipelines. For example, directly connected pipelines have higher connection tightness, while pipelines connected through multiple fittings have relatively lower connection tightness. The maintenance priority score and pipeline connection tightness are comprehensively considered when determining the maintenance sequence. For example, abnormal area ErrA may have a higher maintenance priority score but lower connection tightness with other important pipelines. Meanwhile, abnormal area ErrB may have a slightly lower maintenance priority score than ErrA but is closely connected to multiple critical pipelines. In this case, an algorithm will comprehensively evaluate the priorities and connection tightness of the two areas, potentially prioritizing ErrB due to its greater impact on the entire pipeline system. Resource allocation plans are also based on the maintenance priority score and pipeline connection tightness. Areas with higher priority and closer connections will receive more maintenance resources, such as more maintenance personnel and more advanced testing equipment. Areas with lower priority and looser connections will receive fewer resources. Through the above methods, a reasonable condition monitoring feedback strategy including maintenance sequence and resource allocation plan can be generated to ensure that maintenance work can be carried out efficiently and orderly.

[0061] In another optional embodiment, triggering a maintenance response operation of the smart water meter pipeline network through a remote service port includes: Step 144: converting the condition monitoring feedback strategy into a remote control instruction set, wherein the remote control instruction set includes a valve opening and closing instruction, a data acquisition frequency adjustment instruction, and a maintenance work order generation instruction.

[0062] Optionally, after the condition monitoring feedback strategy is determined, it needs to be converted into a remote control instruction set that can be recognized and executed by the control devices in the smart water meter network. For valve opening and closing instructions, based on the maintenance strategy, if an anomaly in a certain area requires closing or opening a specific valve to resolve it, a corresponding instruction will be generated. For example, if a water pressure anomaly occurs in a certain section of pipeline, a valve connected to it needs to be closed to prevent the problem from escalating. The instruction will clearly specify the valve's identification and the closure requirements. Data collection frequency adjustment instructions adjust the data collection frequency of sensor nodes based on maintenance needs. For example, to more closely monitor the situation in an area experiencing an anomaly, the data collection frequency of sensor nodes near that area may be increased. The instruction will specify the sensor node identification and the new data collection frequency to be adjusted. Maintenance work order generation instructions generate detailed maintenance work order information based on the maintenance sequence and resource allocation plan, including the specific maintenance task content, required resources, and responsible maintenance personnel, so that these instructions can be accurately communicated to the relevant execution equipment and personnel.

[0063] Step 145: Send the remote control instruction set to the edge control node of the smart water meter pipeline network through an encrypted communication link, instructing the edge control node to perform a valve state adjustment operation.

[0064] To ensure the security and accuracy of remote control instruction sets during transmission, they are sent over an encrypted communication link. First, each edge control node is assigned a unique identifier and dynamic encryption key. Before sending the remote control instruction set, a command header verification code is generated based on the identifier. This verification code verifies the source and integrity of the instruction. The dynamic encryption key is then used to segment the instruction content, encrypting each part separately to enhance data security. For example, the instruction content can be divided into three segments, each encrypted using a dynamic encryption key to generate encrypted instruction segments. The segmented encrypted instruction content is combined with the command header verification code to generate an encrypted data packet, which contains the complete instruction information and verification information. The encrypted data packet is sent to each edge control node via a multi-path transmission protocol. This protocol improves data transmission reliability. Even if one path fails, the data packet can reach the destination node via another path. Upon receiving the encrypted data packet, the edge control node first verifies the correctness of the command header verification code. It then decrypts the encrypted instruction content using its own dynamic encryption key, obtaining the remote control instruction set and executing the valve state adjustment operations based on the valve opening and closing instructions in the instruction set.

[0065] In an exemplary embodiment, the process of establishing the encrypted communication link includes: Step 1450: Assign a unique identity identifier and dynamic encryption key to each edge control node; before sending the remote control instruction set, generate an instruction header verification code based on the identity identifier, and use the dynamic encryption key to segmentally encrypt the instruction content; combine the segmented encrypted instruction content with the instruction header verification code to generate an encrypted data packet, and send it to each edge control node via a multi-path transmission protocol; receive an instruction confirmation signal returned by each target edge control node, and update the effectiveness status of the dynamic encryption key based on the instruction confirmation signal.

[0066] During system initialization, each edge control node is assigned a unique identifier. For example, edge control node E1 is assigned ID1, and E2 is assigned ID2. A dynamic encryption key is also generated for each node, and these keys are regularly updated to enhance security. When a remote control instruction set is sent, a command header verification code is generated based on each edge control node's identifier. For example, for an instruction to be sent to edge control node E1, a verification code is generated using a predefined algorithm (such as a hash algorithm) combined with ID1. The remote control instruction set is then segmented according to specific rules. For example, a text message is divided into three sections: the first section contains valve opening and closing instructions, the second section contains data acquisition frequency adjustment instructions, and the third section contains maintenance work order generation instructions. These three sections are encrypted using the dynamic encryption key assigned to E1, generating three encrypted segments. These three encrypted segments are combined with the command header verification code to form an encrypted data packet. This encrypted data packet is then sent to E1 via a multipath transport protocol. After receiving the data packet, E1 first verifies the correctness of the verification code in the command header. If verification is successful, it decrypts the encrypted content using its own dynamic encryption key. Upon completion, E1 returns a command confirmation signal. Upon receiving this signal, the system updates the validity status of the dynamic encryption key based on the signal content, such as extending the key's usage period or marking it as used and preparing to update it with a new key.

[0067] Step 146: Synchronously push the maintenance work order generation instruction to the preset maintenance management platform, and receive the work order execution status data returned by the maintenance management platform; update the abnormal status mark in the visual output interface according to the work order execution status data, and generate a maintenance progress tracking layer.

[0068] When the remote control command set is sent, the system pushes maintenance work order generation instructions to a pre-defined maintenance management platform. This platform manages and assigns maintenance tasks. Upon receiving the instructions, it generates a detailed maintenance work order based on the instructions and assigns it to the appropriate maintenance personnel. For example, a maintenance work order might specify that the maintenance personnel must repair a pipeline anomaly at a specific location and detail the required tools and materials. The maintenance management platform tracks the work order's execution status in real time, updating the work order's execution status data when the maintenance personnel begins, partially completes, or fully completes the task. The system receives this returned work order execution status data and updates the abnormal status indicator in the visual output interface based on the data content. If maintenance work has begun on an abnormal area, the abnormal status indicator in the visual output interface is updated to "Maintenance in Progress" and the partial progress achieved is displayed. When the maintenance task is fully completed, the abnormal status indicator is updated to "Repaired" and new monitoring data is used to determine whether there are any further potential abnormalities. This creates a maintenance progress tracking layer, allowing users to visually visualize the maintenance progress of each abnormal area and understand the maintenance status of the entire pipeline network.

[0069] In a non-limiting embodiment, after the maintenance response operation of the smart water meter pipeline network is triggered through the remote service port, it also includes: receiving a maintenance verification data set returned by the edge control node, the maintenance verification data set including water pressure calibration data and flow recovery data after the maintenance operation; comparing and analyzing the maintenance verification data set with the abnormal state distribution map to determine the abnormal elimination state and the residual abnormal area; updating the abnormal state distribution map according to the abnormal elimination state, and adjusting the collection frequency of the hierarchical real-time monitoring data set based on the spatial coordinates of the residual abnormal area; synchronizing the updated abnormal state distribution map and the adjusted collection frequency to the visual output interface to generate a maintenance effect feedback layer, and triggering the alarm state release instruction of the user terminal through the remote service port.

[0070] As you can understand, after the maintenance operation is completed, the edge control node will return a maintenance verification data set. The water pressure calibration data reflects the recovery of water pressure in the pipeline system after the maintenance operation. For example, the water pressure value at a certain location has recovered from an abnormal ErrP1 to a normal range of NorP2 after maintenance. The flow recovery data shows whether the flow has returned to normal, such as the flow rate has recovered from an abnormal ErrQ1 to the expected NorQ2. These maintenance verification data sets are compared and analyzed with the abnormal state distribution map to check whether the previously marked abnormal areas have returned to normal.

[0071] If both water pressure and flow return to normal, the corresponding abnormal area is considered resolved. If there are still areas with abnormal water pressure or flow, these areas are considered residual abnormal areas. Based on the anomaly resolution status, the abnormality distribution map is updated, marking the areas where the anomaly has been resolved as normal. For residual abnormal areas, the collection frequency of the hierarchical real-time monitoring data set is adjusted based on their spatial coordinates.

[0072] For example, if a residual abnormal area is located on a critical branch pipeline, the data collection frequency of the sensor nodes near this area will be doubled in order to more closely monitor its situation. The updated abnormal status distribution map and the adjusted collection frequency are synchronized to the visual output interface to generate a maintenance effect feedback layer. This layer allows users to clearly see the effect of the maintenance operation, which abnormalities have been resolved and which require continued attention. At the same time, the alarm status release instruction of the user terminal is triggered through the remote service port. If a certain abnormal area has been repaired, the corresponding alarm information will be released on the user terminal, allowing users to promptly understand the changes in the operating status of the pipeline network.

[0073] In a non-limiting embodiment, after the maintenance response operation of the smart water meter pipeline network is triggered through the remote service port, it also includes: collecting valve adjustment logs and sensor response data during the maintenance operation to generate a maintenance operation tracking data set; based on the maintenance operation tracking data set and the abnormal state distribution map, identifying the association between abnormal areas and valve control nodes, and constructing an abnormal control knowledge graph; superimposing the abnormal control knowledge graph and the visual output interface to generate a dynamic interactive layer containing control node recommendation tags; according to the user's operation instructions on the dynamic interactive layer, adjusting the visual focus area of the pipeline BIM model data in real time, and updating the maintenance order in the status monitoring feedback strategy.

[0074] During maintenance operations, the system collects valve adjustment logs, recording information such as the adjustment time, adjustment direction (open or closed), and adjustment degree for each valve. Sensor response data is also collected to understand changes in sensor node data during maintenance operations. This data is combined to generate a maintenance operation tracking dataset. Based on this dataset and the abnormal state distribution map, the correlation between abnormal areas and valve control nodes is analyzed.

[0075] For example, if the water pressure anomaly in a certain abnormal area is found to be improved after closing a certain valve, it can be determined that the abnormal area is associated with the valve control node. By organizing and analyzing multiple of the above-mentioned associations, an abnormal control knowledge graph is constructed. This graph shows the relationship between different abnormal situations and valve control nodes, providing knowledge support for subsequent maintenance and control. The abnormal control knowledge graph is overlaid and rendered with the visual output interface to generate a dynamic interactive layer containing control node recommendation tags. On this layer, when the user views an abnormal area, the related valve control node recommendation information will be displayed to help the user understand how to better control the abnormal situation. The user can operate this dynamic interactive layer through operating instructions. For example, if the user clicks on a valve control node recommendation tag, the system will adjust the visual focus area of the pipeline BIM model data in real time, focusing on the valve control node and its related pipeline parts.

[0076] At the same time, the maintenance sequence in the condition monitoring feedback strategy is updated based on user operations and newly acquired information. For example, if a previously low-priority abnormal area is found to be associated with a critical valve control node, and operating the valve can effectively resolve the problem, the maintenance priority of the abnormal area will be increased, and the maintenance sequence will be readjusted to more efficiently maintain and manage the pipeline network.

[0077] The embodiment of the present application can achieve a deep integration of smart water meter monitoring data and BIM models, thereby accurately locating anomalies and realizing dynamic visual display. In detail, the embodiment of the present application can accurately grasp the pipeline status by obtaining the pipeline BIM model data and real-time monitoring data set of the target building smart water meter pipeline network; perform abnormal state identification processing on the monitoring data, generate a matching abnormal state distribution map, and clearly present the abnormal location; perform dynamic visual reconstruction of the BIM model data based on the map and topological constraints, generate a visual output interface with abnormality marks, so that operation and maintenance personnel can intuitively understand the abnormal situation; associate and map the visual interface with the monitoring data, generate feedback strategies and trigger maintenance response operations, which can realize efficient monitoring and timely processing of the smart water meter pipeline network status, greatly improving the intelligence and automation level of the entire monitoring process, and improving operation and maintenance efficiency and accuracy.

[0078] See also Figure 2 As shown in FIG. 1 , this figure is a schematic diagram of the basic structure of a remote status monitoring system 200 provided in an embodiment of the present application. The remote status monitoring system 200 includes: Processor 201; a storage device 202 having a computer program 2020 stored thereon; When the computer program 2020 is executed by the processor 201, the processor 201 implements any of the remote status monitoring methods based on smart water meters.

[0079] Based on the above, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the above method are implemented.

[0080] In the technical solutions involved in the above-mentioned embodiments of the present application, whether it is performing comparison calculations of multi-dimensional features or constructing composite parameters, if there are problems caused by significant differences in the number of dimensions, dimensional units and semantic meanings of different features, technical personnel in this field, based on their professional knowledge and past practical experience, are fully able to understand that these differences need to be properly handled so that the calculation results are accurate and comparable, and avoid situations such as logical confusion and unclear mathematical meaning.

[0081] Specifically, when faced with features having different numbers of dimensions, those skilled in the art may employ various strategies to accurately calculate the similarity, matching degree, or feature distance between different features.

[0082] Feature selection is a common method. For a high-dimensional feature set, we can select the most representative subset of features from the high-dimensional features, matching the number of low-dimensional features based on metrics such as feature importance and relevance. Feature selection is performed using methods such as the chi-square test and information gain to identify the most valuable features for the technical solution. This reduces the high-dimensional features to a dimension comparable to the low-dimensional features, allowing for similarity or distance calculations.

[0083] Feature extraction is also an effective method. By constructing a suitable feature extraction model, features of different dimensions can be mapped into a common low-dimensional feature space. Principal component analysis (PCA) can not only handle dimensional differences but also project high-dimensional features into a low-dimensional space composed of principal components, making features of different dimensions comparable in this low-dimensional space. Furthermore, deep learning models such as autoencoders can also be used for feature extraction. They automatically learn the latent representation of input features and convert features of different dimensions into feature vectors of the same dimensionality, enabling subsequent similarity, matching, or feature distance calculations.

[0084] Alternatively, kernel methods can be used. Kernel functions can calculate the similarity between features in a high-dimensional space without explicitly mapping the features to that space. For features with varying numbers of dimensions, appropriate kernel functions, such as Gaussian or polynomial kernel functions, can be selected to directly calculate the similarity between them. This approach avoids the direct computational difficulties associated with varying feature dimensions and effectively measures the relationships between features in either the original feature space or an implicit high-dimensional space.

[0085] When processing the comparison of multi-dimensional features, in order to achieve comparable alignment of feature spaces, those skilled in the art may adopt a variety of existing common technical means.

[0086] Standardization preprocessing is a widely used and effective method. It transforms raw feature data into a standard normal distribution with a mean of 0 and a standard deviation of 1 by performing a specific linear transformation on the data. This process essentially eliminates the influence of different dimensions between features, allowing all features to be compared at the same scale. For example, in a dataset containing features of different dimensions, after standardization preprocessing, similarity or distance calculations can be performed on these features at the same scale, avoiding calculation bias caused by different dimensions.

[0087] Mapping transformation is also an effective way to solve the problem of dimensional differences. It can map the original features into a new space based on the specific properties of the features and actual business needs. In this new space, features of different dimensions can be better comparable. For those features with nonlinear relationships, those skilled in the art can use logarithmic transformation, power transformation, etc. to convert them into linear relationships, which makes it easier to calculate similarity or distance. For example, when processing some features with exponential growth trends, they can be converted into linear relationships through logarithmic transformation, making subsequent calculations more accurate and convenient.

[0088] Spatial projection is also an important technical approach. It projects a high-dimensional feature space into a low-dimensional space while preserving as much important information between features as possible. By carefully selecting the appropriate projection direction and projection dimension, technicians can reduce the dimensionality of the data while effectively minimizing the impact of dimensional differences on computational results. Common spatial projection methods include principal component analysis (PCA) and linear discriminant analysis (LDA). Taking PCA as an example, it projects high-dimensional data into a low-dimensional space composed of the principal components by finding their directions. This simplifies the data structure while reducing the interference of dimensional differences on feature comparison.

[0089] In the process of constructing composite parameters (such as loss function values), different parameter items often have different dimensions. Those skilled in the art can adopt normalization processing or an adaptive weight allocation mechanism based on distribution characteristics.

[0090] Normalization involves unifying the value ranges of different parameter items into a fixed interval, such as [0, 1]. This approach eliminates the impact of dimensional differences and ensures that all parameter items have equal importance during weighted fusion. Common normalization methods include min-max normalization and Z-score normalization. For example, min-max normalization performs a linear transformation on parameter items, scaling their value range to the interval [0, 1]. This allows weighted fusion of parameters of different dimensions to be performed under the same standard.

[0091] The adaptive weight allocation mechanism based on distribution characteristics dynamically adjusts the weights of different parameter items according to their distribution characteristics. For parameter items with large variance, those skilled in the art can appropriately reduce their weights; for parameter items with small variance, those skilled in the art can appropriately increase their weights. Doing so can make the composite loss function pay more attention to those parameter items with smaller variances, thereby improving the stability and generalization ability of the model. For example, in a composite loss function containing multiple parameter items, if the variance of a parameter item is large, it means that its fluctuation is more drastic, which may have an adverse effect on the stability of the model. In this case, reducing its weight can reduce this adverse effect; and for parameter items with smaller variance, increasing their weight can make the model pay more attention to the information reflected by the parameter item, thereby improving the overall performance of the model.

[0092] The general technical approaches described above for solving the feature matching and loss balancing problems are common knowledge in the field. These techniques have been fully validated and widely used in numerous practical applications, and those skilled in the art can skillfully and flexibly apply these methods to address similar dimensional discrepancies.

[0093] The formulas and calculation processes involved in the embodiments of this application, whether used for multidimensional feature comparison or composite loss function construction, strictly follow the principle of dimensional correspondence. The variables in each formula have clear and definite physical meanings, and their operation logic is also fully consistent with basic mathematical and physical logic. The operation results must be the reasonable results expected by this application. Those skilled in the art have the ability to comprehensively apply the above-mentioned general technical means according to specific data conditions and business needs, and effectively solve the various problems caused by the number of dimensions, dimensional differences, etc. in the multidimensional feature comparison calculation and composite loss function construction in the embodiments, and ensure the accuracy, reliability and feasibility of the technical solution of this application.

[0094] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems or devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the methods.

Claims

1. A remote status monitoring method based on smart water meter, characterized in that: include: Obtaining the pipeline BIM model data of the smart water meter pipeline network of the target building, and simultaneously collecting a set of real-time monitoring data from multiple sensor nodes in the smart water meter pipeline network; Performing abnormal state identification processing on the real-time monitoring data set to generate an abnormal state distribution map that matches the spatial position of the pipeline BIM model data; Based on the abnormal state distribution map and the preset pipeline topology constraint conditions, the pipeline BIM model data is dynamically reconstructed and visualized to generate a visual output interface with abnormal state marks; The visual output interface is associated and mapped with the real-time monitoring data set to generate a status monitoring feedback strategy, and a maintenance response operation of the smart water meter pipeline network is triggered through a remote service port.

2. The method according to claim 1, wherein The step of obtaining the pipeline BIM model data of the smart water meter pipeline network of the target building and synchronously collecting a set of real-time monitoring data of multiple sensor nodes in the smart water meter pipeline network includes: Calling the BIM model database of the target building to extract the spatial topology data and device attribute data constituting the pipeline BIM model data; the spatial topology data is used to describe the pipeline connection relationship and spatial geometric layout, and the device attribute data includes the water meter installation coordinates and configuration parameters associated with the sensor node; Send activation instructions to each sensor node according to the configuration parameters, and collect water pressure monitoring data, flow fluctuation data and equipment operation status data of each sensor node according to the set period; The water pressure monitoring data, the flow fluctuation data and the equipment operation status data are spatiotemporally aligned based on the water meter installation coordinates and the spatial topological hierarchy of the pipeline BIM model to generate a hierarchical real-time monitoring data set; wherein, the topological structure of the hierarchical real-time monitoring data set forms a mapping relationship with the pipe network hierarchy of the pipeline BIM model, the spatial topological hierarchy is a BIM model hierarchy divided according to the functional areas of the building, and the pipe network hierarchy is a physical hierarchy of the pipeline divided based on the primary and secondary relationships of water supply.

3. The method according to claim 2, wherein The performing abnormal state identification processing on the real-time monitoring data set to generate an abnormal state distribution map matching the spatial position of the pipeline BIM model data includes: Performing mutation point detection processing on the water pressure monitoring data in the hierarchical real-time monitoring data set, identifying abnormal water pressure areas exceeding a preset fluctuation threshold, and extracting the duration and fluctuation amplitude of the abnormal water pressure areas; Performing trend deviation analysis on the traffic fluctuation data to determine the degree of deviation between the traffic trend and the preset traffic model, and marking traffic anomaly areas where the deviation exceeds a dynamic threshold; Mapping the spatial coordinates of the abnormal water pressure area and the abnormal flow area to corresponding positions of the pipeline BIM model data to generate an initial abnormal state distribution map including abnormality type labels and abnormality levels; According to the fault alarm information in the equipment operation status data, the initial abnormal state distribution map is cross-validated to eliminate false alarm abnormal areas and correct abnormal levels to generate the abnormal state distribution map.

4. The method according to claim 3, wherein The method of dynamically visually reconstructing the pipeline BIM model data based on the abnormal state distribution map and the preset pipeline topology constraint conditions to generate a visual output interface with abnormal state marks includes: Extract pipeline connection relationship data and valve control node data from the pipeline BIM model data to construct a topological constraint relationship network of the smart water meter pipeline network; According to the abnormality type label in the abnormal state distribution map, assign a corresponding color code and dynamic flashing frequency to each abnormal area in the abnormal state distribution map; Superimposing the color code and dynamic flashing frequency to corresponding spatial positions of the pipeline BIM model data to generate a first visual topological structure; Based on the topological constraint relationship network, performing influence range diffusion simulation processing on the abnormal area in the first visual topological structure to generate a second visual topological structure including potential influence area markers; The second visualization topology structure is subjected to layer fusion processing with the equipment operation status data in the hierarchical real-time monitoring data set to generate the visualization output interface.

5. The method according to claim 4, wherein The associating and mapping the visual output interface with the real-time monitoring data set to generate a status monitoring feedback strategy includes: Extracting the spatial coordinate set of the abnormal area and the abnormal type label from the visual output interface to generate an abnormal state feature vector; Matching corresponding maintenance priority scoring rules from a preset strategy library according to the abnormal state feature vector, and calculating the maintenance priority score of each abnormal area extracted from the visual output interface; Based on the maintenance priority score and the pipeline connection tightness in the topological constraint relationship network, a condition monitoring feedback strategy including a maintenance sequence and a resource allocation plan is generated; the maintenance sequence is a sequence comprehensively sorted according to the priority score and the pipeline connection tightness.

6. The method according to claim 5, wherein The maintenance response operation of the smart water meter pipeline network is triggered through the remote service port, including: Converting the condition monitoring feedback strategy into a remote control instruction set, wherein the remote control instruction set includes valve opening and closing instructions, data acquisition frequency adjustment instructions, and maintenance work order generation instructions; Sending the remote control instruction set to the edge control node of the smart water meter pipeline network through an encrypted communication link, instructing the edge control node to perform a valve state adjustment operation; Synchronously push the maintenance work order generation instruction to a preset maintenance management platform, and receive the work order execution status data returned by the maintenance management platform; The abnormal status mark in the visual output interface is updated according to the work order execution status data to generate a maintenance progress tracking layer.

7. The method according to claim 3, wherein The performing mutation point detection processing on the water pressure monitoring data in the hierarchical real-time monitoring data set to identify abnormal water pressure areas exceeding a preset fluctuation threshold includes: Dividing the water pressure monitoring data into continuous water pressure data segments according to a preset time window, and performing a first-order difference calculation on each water pressure data segment to obtain a water pressure change rate sequence; Performing a sliding window standard deviation analysis on the water pressure change rate sequence to identify a sudden change time point where the standard deviation exceeds a dynamic threshold; Extracting corresponding water pressure monitoring sub-data according to the mutation time point, and calculating the deviation percentage between the water pressure monitoring sub-data and the historical mean data; If the deviation percentage exceeds the preset fluctuation threshold, the corresponding area of the water pressure monitoring sub-data is marked as an abnormal water pressure area.

8. The method according to claim 7, wherein The performing trend deviation analysis on the flow fluctuation data, determining the deviation between the flow trend and the preset flow model, and marking the flow abnormality area where the deviation exceeds the dynamic threshold, includes: Obtaining a preset flow reference curve for each branch pipeline in the smart water meter pipeline network, wherein the preset flow reference curve includes a time-based expected flow range generated based on historical data; The flow fluctuation data is classified by identification according to the branch pipeline, and the classified flow fluctuation data is subjected to trend deviation analysis based on the preset flow reference curve to generate a real-time flow trend line for each branch pipeline; Calculating the deviation between the real-time flow rate trend line and the corresponding preset flow rate reference curve, wherein the deviation includes the duration of the deviation and the cumulative amount of the deviation; If the duration of the deviation exceeds the first dynamic threshold and the cumulative amount of the deviation exceeds the second dynamic threshold, the spatial coordinates of the corresponding branch pipeline are mapped to the pipeline BIM model data, and the flow abnormality area is marked according to the mapped spatial coordinates.

9. The method according to claim 6, wherein The process of establishing the encrypted communication link includes: Assign a unique identifier and dynamic encryption key to each edge control node; Before sending the remote control instruction set, generating an instruction header verification code according to the identity identifier, and using the dynamic encryption key to perform segmented encryption processing on the instruction content; Combining the segmented encrypted instruction content with the instruction header verification code to generate an encrypted data packet, and sending it to each edge control node via a multi-path transmission protocol; Receive a command confirmation signal returned by each target edge control node, and update the validity status of the dynamic encryption key according to the command confirmation signal.

10. A remote status monitoring system, characterized in that: include: processor; A storage device having a computer program stored thereon, wherein when the computer program is executed by the processor, the processor implements the remote status monitoring method based on the smart water meter as described in any one of claims 1 to 9.

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