Dam monitoring method based on custom monitoring points, edge equipment and medium
By using a method of customizing monitoring points and intelligent configuration of edge devices, the problem of difficulty in adjusting traditional dam monitoring systems when monitoring needs change has been solved. This has enabled efficient and flexible monitoring using multiple types of sensors, improving the convenience of dam monitoring and the accuracy of data collection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-01
- Publication Date
- 2026-03-17
Smart Images
Figure CN121691384A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of dam monitoring, and in particular to a dam monitoring method, edge device, and medium based on custom monitoring points. Background Technology
[0002] In the field of dam safety monitoring, data acquisition devices are one of the core components of a dam safety monitoring system. Traditional methods mainly rely on sensors with fixed locations and ranges for data acquisition. These sensors are typically pre-positioned at key locations, and the signal from each sensor is transmitted to the monitoring center through a corresponding channel to monitor factors such as dam seepage, leakage, displacement, and strain. When an anomaly occurs in the dam, the sensors capture relevant data and send it to the monitoring center to support professional analysis and decision-making.
[0003] However, when monitoring needs change, such as adding monitoring areas or changing sensor types, the corresponding channels of the sensors need to be readjusted due to different channel standards. This makes the process of adjusting the data acquisition system time-consuming and labor-intensive, and difficult to meet diverse monitoring needs. Summary of the Invention
[0004] To improve the convenience of dam monitoring, this application provides a dam monitoring method, edge device, and medium based on custom monitoring points.
[0005] Firstly, this application provides a dam monitoring method based on custom monitoring points, employing the following technical solution: A dam monitoring method based on custom monitoring points, executed by an edge device, includes: Obtain the signal type code sent by the signal type detection circuit embedded in the signal access interface of the acquisition channel; Query the local database based on the signal type code to determine whether the signal type corresponding to the signal type code exists; If so, the configuration file corresponding to the signal type will be retrieved. Otherwise, a prompt message will be generated to upload the configuration file for the new signal type; or, Obtain the user's action information regarding the type selection of the signal type icon; In response to the type selection operation information, obtain the configuration file corresponding to the signal type; The acquisition channel is automatically configured according to the configuration file.
[0006] By adopting the above technical solution, and combining hardware detection with user interaction, intelligent configuration of the acquisition channels is achieved. The system automatically identifies signal types and matches configuration files, reducing manual operations and improving channel deployment efficiency. When a new signal type is introduced, the system proactively prompts for configuration upload, ensuring system compatibility and scalability. Users can manually select signal types, balancing the convenience of automatic identification with flexibility in special scenarios. Furthermore, the automatic application of configuration files ensures accurate adaptation of channel parameters, avoiding errors from manual configuration, improving data acquisition accuracy and system reliability, meeting the diverse monitoring needs of multiple types of sensors in dam monitoring, and enhancing the convenience of dam monitoring.
[0007] Furthermore, the method also includes: Obtain the actual monitoring point coordinates input by the user or the selected location information on the pre-established 3D model of the dam body; Virtual monitoring points are determined on the dam's three-dimensional model by combining the actual monitoring point coordinates or based on the location information. The actual monitoring points are the locations where sensors are installed on the dam, and the coordinates of the virtual monitoring points are consistent with the coordinates of the actual monitoring points. The virtual monitoring points are digital identifiers of the actual monitoring points in the dam's three-dimensional model. Obtain the sensors, acquisition channels, gateways, and cloud servers associated with the actual monitoring points corresponding to the virtual monitoring points; Automatically generate a topology network including node sets, edge sets, and attribute information; the node set includes: sensors, acquisition channels, gateways, cloud servers, and edge devices; the edge set includes: wired connection links between sensors and acquisition channels, wired connection links between acquisition channels and gateways, wireless connection links between gateways and sensors and / or acquisition channels and / or cloud servers covered by their wireless signals, wired connection links between acquisition channels and edge devices, and wireless connection links between edge devices and cloud servers; attribute information includes: sensor type, sampling frequency of acquisition channels, bandwidth between sensors and acquisition channels, bandwidth between acquisition channels and gateways, and bandwidth between acquisition channels and edge devices; The topology and its real-time status are displayed in a visual manner. Based on custom rules, the comprehensive output index of the monitoring area is obtained from the acquisition channel data of at least one actual monitoring point within the monitoring area.
[0008] By adopting the above technical solutions, digital identifiers for monitoring points are constructed through virtual-real mapping, and the device associations and status are visualized in combination with the topology network, enabling global control of the monitoring system. Regional comprehensive indicators are generated based on custom rules, thereby improving monitoring efficiency and decision-making accuracy.
[0009] Furthermore, the visualization of the network topology and its real-time status includes: Different colors are used to indicate the real-time status of each connection link, with green indicating normal status, yellow indicating load warning status, and red indicating fault status. The real-time bandwidth utilization of the link is represented by the changes in the thickness of dynamic lines. When the status of a node or link changes, an animation prompt effect is triggered and the status timestamp is updated; In response to user clicks on a node, the system displays the node's detailed operating parameters and historical status curves.
[0010] By adopting the above technical solution, the status of the link can be intuitively displayed by setting different color labels and dynamic line thickness. The animation prompts when nodes or links change can facilitate timely notification of status changes to staff.
[0011] Furthermore, the method also includes: The engineering value is obtained from the sensor data by a pre-set signal-specific processing algorithm within an independent computing unit integrated within the acquisition channel; Display the project values; The engineering values from each acquisition channel are input into a pre-set deep learning-based anomaly monitoring model to obtain a dam structural health report. The sensor data and engineering values are stored locally, and the engineering values are uploaded to the cloud server.
[0012] By adopting the above technical solutions, precise engineering values are obtained through dedicated algorithms, which are displayed in real time for easy monitoring. The combination of deep learning models improves the accuracy of anomaly identification, timely generates health reports to ensure dam safety, local storage facilitates data traceability, and cloud uploading facilitates remote management, thereby improving overall monitoring efficiency and safety.
[0013] Furthermore, the local storage of the sensor data and engineered values includes: A hierarchical storage strategy is adopted to store the raw sensor data and engineering values in different storage partitions of the edge device, respectively. A first storage period is set for the original data, and a second storage period is set for the engineering value, wherein the first storage period < the second storage period; When the storage capacity reaches a preset threshold, the earliest expiring data will be automatically deleted according to the storage period priority.
[0014] By adopting the above technical solutions, tiered storage makes data management clearer, facilitates rapid retrieval, and allows differentiated storage periods to match data value. Raw data is stored for a short period to save space, while engineering values are stored for a long period to meet demand. When the capacity is full, expired data is automatically deleted according to priority, ensuring continuous and effective storage and improving the storage efficiency and rationality of edge devices.
[0015] Furthermore, the method also includes: Test pulses are injected into the acquisition channel at preset intervals; Acquire the response signal and response time of the acquisition channel after the input test pulse; If the response signal is discontinuous or the response time is greater than the time threshold, the acquisition channel is marked as abnormal, and redundant acquisition channels are enabled.
[0016] By adopting the above technical solution, the status of the acquisition channel can be actively detected by injecting test pulses at regular intervals. Anomalies can be accurately identified by response signals and time, and redundant channels can be marked and activated in a timely manner to avoid data interruption caused by faults, ensuring the continuity and reliability of acquisition and providing stable data support for dam health monitoring.
[0017] Furthermore, enabling redundant acquisition channels includes: Obtain a preset priority list of redundant acquisition channels, wherein the priority list is determined based on the idle status of the redundant acquisition channels, the physical distance to the abnormal acquisition channels, and the stability of historical data transmission. Activation commands are sent to the redundant acquisition channels sequentially according to the priority list until a ready response signal is received from the redundant acquisition channels. Switch the sensor data transmission link associated with the abnormal acquisition channel to the activated redundant acquisition channel; Extract the configuration file of the abnormal acquisition channel and send it to the redundant acquisition channel so that the redundant acquisition channel can be configured according to the configuration file; The system receives parameter verification results from redundant acquisition channels. If the verification passes, the configuration is confirmed to be complete. If the verification fails, the configuration file is resent and the number of failures is recorded. When the number of failures exceeds a threshold, a manual configuration prompt message is generated.
[0018] By adopting the above technical solutions, redundant channels are activated according to priority to ensure the selection of the optimal channel; links are quickly switched, and configuration and verification are performed synchronously to ensure continuous data transmission; in case of failure, retrying or prompting manual intervention improves fault tolerance, reduces monitoring interruptions, and enhances system stability and reliability.
[0019] Secondly, this application provides an edge device including multiple acquisition channels, each acquisition channel including an independent signal access interface and a signal processing circuit, the signal access interface being connected to the signal processing circuit, the signal access interface having an embedded signal type detection circuit, and the signal processing circuit including a programmable gain amplifier, a multiplexer switch and an analog-to-digital converter connected to each other; Also includes: At least one processor, and each of the signal processing circuits is connected to the processor; At least one memory; At least one computer program, wherein the at least one computer program is stored in the memory and configured to be executed by the at least one processor, the at least one computer program being configured to: perform a dam monitoring method based on custom monitoring points as described in any one of the first aspects.
[0020] By adopting the above technical solutions, multiple acquisition channels are equipped with independent interfaces and processing circuits. Combined with components such as programmable gain amplifiers, the independence and accuracy of signal processing are improved. The signal type detection circuit automatically identifies the signal type and matches the configuration file, or supports users to manually select the type to obtain the configuration, realizing automatic configuration of the acquisition channels. This greatly reduces manual operation costs and configuration errors, speeds up system deployment and debugging efficiency, flexibly adapts to diverse sensor signal types, enhances adaptability to different monitoring points and different monitoring needs of the dam, ensures the reliability and timeliness of monitoring data, and provides efficient and stable technical support for dam safety monitoring.
[0021] Furthermore, each acquisition channel is connected to a ceramic coupler or digital isolator between its power ground and signal ground.
[0022] By adopting the above technical solutions, ceramic or digital isolators can be installed between the power ground and the signal ground to effectively block ground loop interference and electromagnetic and radio frequency interference, avoid signal crosstalk between different channels, ensure the purity of signals in each acquisition channel, improve data acquisition accuracy and stability, ensure that multiple channels do not interfere with each other when working in parallel, and enhance the reliable operation capability of edge devices in complex environments.
[0023] Thirdly, this application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in any one of the first aspects, a dam monitoring method based on custom monitoring points.
[0024] By adopting the above technical solution, the processor executes a computer program in a computer-readable storage medium. Through a combination of hardware detection and user interaction, it achieves intelligent configuration of the acquisition channels, automatically identifies signal types and matches configuration files, reduces manual operation, improves channel deployment efficiency, and proactively prompts users to upload configurations when new signal types are introduced, ensuring system compatibility and scalability. It also supports users manually selecting signal types, balancing the convenience of automatic identification with flexibility in special scenarios. Furthermore, the automatic application of configuration files ensures accurate adaptation of channel parameters, avoids errors from manual configuration, improves data acquisition accuracy and system reliability, meets the diverse monitoring needs of multiple types of sensors in dam monitoring, and enhances the convenience of dam monitoring.
[0025] In summary, this application includes at least one of the following beneficial technical effects: 1. Automatically identify signal types and match configuration files, reducing manual operations, improving channel deployment efficiency, meeting the diverse monitoring needs of multiple types of sensors in dam monitoring, improving the convenience of dam monitoring, actively prompting to upload configuration when new signal types are introduced, ensuring system compatibility and scalability, supporting users to manually select signal types, and balancing the convenience of automatic identification with the flexibility in special scenarios. 2. Automatic application of configuration files ensures accurate adaptation of channel parameters, avoids errors from manual configuration, and improves the accuracy of data acquisition and the reliability of the system; 3. By combining topology network visualization to present device relationships and status, a global control of the monitoring system can be achieved; 4. Sensor data is processed to obtain engineering values, which are then combined with deep learning models to improve the accuracy of anomaly identification and generate health reports in a timely manner to ensure dam safety; 5. Local storage facilitates data traceability, while cloud uploading enables remote management, thus improving overall monitoring efficiency and security; 6. Timed injection of test pulses can actively detect the status of the acquisition channel. In case of abnormality, redundant channels can be activated in time to avoid data interruption caused by faults and ensure the continuity and reliability of acquisition. Attached Figure Description
[0026] Figure 1 This is a structural block diagram of the edge device in the embodiments of this application.
[0027] Figure 2 This is a flowchart illustrating the dam monitoring method based on custom monitoring points in this application embodiment. Detailed Implementation
[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0029] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.
[0030] This application provides an edge device, with reference to... Figure 1It includes multiple acquisition channels, and the number of acquisition channels can be set according to actual needs. This application sets 64 acquisition channels, and each acquisition channel corresponds to one monitoring point.
[0031] Each acquisition channel includes an independent signal access interface and signal processing circuit. The signal access interface and signal processing circuit are connected. The signal processing circuit includes a programmable gain amplifier, a multiplexer, an analog-to-digital converter, and an independent computing unit.
[0032] The signal access interface is embedded with a signal type detection circuit, which can automatically monitor the signals connected to the acquisition channel and obtain the signal type code.
[0033] Programmable gain amplifiers and multiplexers can be configured to enable plug-and-play support for various types of sensors in the acquisition channels, with each acquisition channel supporting multiple signal switching.
[0034] Each acquisition channel is connected to a ceramic coupler or digital isolator between its power ground and signal ground to prevent crosstalk from high-voltage channels to low-voltage channels.
[0035] The edge device also includes a processor, a memory, and a display screen. The memory and display screen are both connected to the processor, such as via a bus. Optionally, the edge device may also include a transceiver. It should be noted that in practical applications, the transceiver is not limited to one unit, and the structure of this edge device does not constitute a limitation on the embodiments of this application.
[0036] The processor can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.
[0037] A bus can include a pathway for transmitting information between the aforementioned components. The bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc.
[0038] The memory may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited to these.
[0039] The memory stores application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor executes the application code stored in the memory to implement the content shown in the foregoing method embodiments.
[0040] Figure 1 The edge device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.
[0041] This application discloses a dam monitoring method based on custom monitoring points. (Refer to...) Figure 1 This is executed by an edge device, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, desktop computer, etc., but is not limited to these.
[0042] The edge device can automatically configure the acquisition channel, and also supports manual configuration by staff. During automatic configuration, simply connect the sensor to the signal input interface, and then execute steps S111 to S114 before proceeding to step S200. Step S111: Obtain the signal type code sent by the signal type detection circuit embedded in the signal access interface of the acquisition channel.
[0043] Specifically, when the sensor is connected to the signal access interface, the signal type detection circuit automatically obtains the signal type code, loads preset parameters such as range and sampling rate, and sends the signal type code and preset parameters to the edge device.
[0044] Step S112: Query the local database based on the signal type code to determine if a signal type corresponding to the signal type code exists; if so, proceed to step S113: query and obtain the configuration file corresponding to the matching signal type; otherwise, proceed to step S114: generate a prompt message to upload the configuration file of the new signal type.
[0045] Specifically, the edge device's local database stores various signal type codes, the signal types corresponding to the signal type codes, and the configuration files corresponding to the signal types. The configuration files include gain and filtering parameters, etc.
[0046] Therefore, the edge device first queries the local database based on the signal type code. If a corresponding record exists, it retrieves the configuration file for that signal type. If neither the corresponding signal type nor the configuration file is stored, a prompt message is generated, reminding staff to upload a configuration file for the new signal type.
[0047] Once the edge device determines the configuration file, step S200 is executed: automatically configure the acquisition channel according to the configuration file.
[0048] When the staff manually configures the acquisition channel, step S121-S122 is executed, followed by step S200: Step S121: Obtain the user's type selection operation information for the signal type icon.
[0049] Step S122: In response to the type selection operation information, obtain the configuration file corresponding to the signal type.
[0050] Specifically, the edge device sets a corresponding icon for each signal type on the display interface. When the staff clicks the icon, the corresponding signal type selection operation information is generated. The edge device responds to the type selection operation information, obtains the configuration file corresponding to the signal type, and then executes step S105 to automatically configure the acquisition channel according to the configuration file.
[0051] Therefore, this application can accurately allocate channel resources and flexibly adjust them according to the needs of different sensors, avoiding channel idleness or conflicts, significantly improving channel resource utilization, reducing monitoring costs, and allowing for customization of sensor placement and signal types in the monitoring channels. It can quickly respond to diverse structural monitoring needs; whether adding monitoring areas or replacing sensors, it can be achieved through simple settings, greatly improving the flexibility and efficiency of data acquisition. Furthermore, the edge devices facilitate the addition of monitoring points or the expansion of functions without large-scale modifications to the system architecture. Only corresponding configurations on the devices are required to achieve flexible expansion of the monitoring system, meeting the ever-evolving needs of dam structure monitoring technology.
[0052] Furthermore, edge devices can generate a topology diagram based on the relationship between sensors and acquisition channels. This diagram can be presented graphically to allow managers to quickly grasp the overall system architecture, avoid management blind spots caused by information dispersion, and significantly reduce the complexity of system maintenance, including steps S11 to S16.
[0053] Step S11: Obtain the actual monitoring point coordinates input by the user or the selected location information on the pre-established 3D model of the dam.
[0054] Specifically, users can directly input the coordinates of the actual monitoring point, or select the location of the monitoring point on the interactive interface of the edge device. The interactive interface is like a 3D model of the dam body, and the 3D model of the dam body corresponds one-to-one with the actual structure of the dam body.
[0055] Step S12: Combine the actual monitoring point coordinates or the location information to determine the virtual monitoring points on the dam's 3D model. The actual monitoring points are the locations where sensors are installed on the dam. The coordinates of the virtual monitoring points are consistent with the coordinates of the actual monitoring points and are digital identifiers of the actual monitoring points in the dam's 3D model.
[0056] Specifically, the edge device can find the corresponding virtual monitoring point in the 3D model of the dam body based on each actual monitoring point.
[0057] Step S13: Obtain the sensors, acquisition channels, gateways, and cloud servers associated with the actual monitoring points corresponding to the virtual monitoring points.
[0058] Step S14: Automatically generate a topology network including node sets, edge sets, and attribute information; the node set includes: sensors, acquisition channels, gateways, cloud servers, and edge devices; the edge set includes: wired connection links between sensors and acquisition channels, wired connection links between acquisition channels and gateways, wireless connection links between gateways and sensors and / or acquisition channels and / or cloud servers covered by their wireless signals, wired connection links between acquisition channels and edge devices, and wireless connection links between edge devices and cloud servers; the attribute information includes: sensor type, sampling frequency of acquisition channels, bandwidth between sensors and acquisition channels, bandwidth between acquisition channels and gateways, and bandwidth between acquisition channels and edge devices.
[0059] Step S15: Visualize the network topology and its real-time status.
[0060] Specifically, when displaying the real-time status of the network topology, the status of each node or link can be reflected in real time through colors, dynamic effects, etc. For example, different colors can be used to indicate the real-time status of each connection link, with green indicating a normal state, yellow indicating a load warning state, and red indicating a fault state; the thickness of dynamic lines can represent the real-time bandwidth utilization of the links; when the status of a node or link changes, an animated prompt effect is triggered and the status timestamp is updated; in response to user clicks on node operation information, detailed operating parameters and historical status curves of the node are displayed. This allows staff to intuitively grasp the system's operating status.
[0061] Step S16: Based on custom rules, obtain the comprehensive output index of the monitoring area according to the acquisition channel data of at least one actual monitoring point within the monitoring area.
[0062] Specifically, staff define custom rules based on monitoring needs, such as setting monitoring areas and the weights and thresholds of data from each monitoring point within those areas. They then summarize and analyze the data from each actual monitoring point to calculate a comprehensive indicator, such as a regional health score or risk level, to assess the overall monitoring status of the monitoring area.
[0063] Furthermore, in order to reduce data transmission pressure, this application also includes steps S21 to S24: Step S21: Obtain the engineering value calculated by the signal-specific processing algorithm pre-set in the independent computing unit integrated in the acquisition channel based on the sensor data.
[0064] Specifically, raw sensor data, such as high-frequency oscillation signals from vibrating wire sensors and minute voltage changes from resistance sensors, is typically massive in volume and contains redundant information. Performing signal processing locally within the acquisition channel, such as converting frequency signals into stress values, allows for the direct output of engineering values like "MPa" or "℃," significantly reducing the amount of data uploaded to edge devices or the cloud and lowering transmission bandwidth usage. Therefore, edge devices integrate independent computing units within each acquisition channel. These independent computing units employ dedicated threshold signal processing algorithms to calculate engineering values from the sensor-acquired data.
[0065] Step S22: Display project values.
[0066] Step S23: Input the engineering values from each acquisition channel into the preset deep learning-based anomaly monitoring model to obtain a dam structure health report.
[0067] Specifically, in order to conduct in-depth analysis of the dam's condition, edge devices pre-train anomaly monitoring models based on deep learning, such as anomaly monitoring models trained using the LSTM algorithm, which can automatically analyze and output a dam structural health report based on the input engineering values.
[0068] Step S24: Locally store sensor data and engineering values, and upload the engineering values to the cloud server.
[0069] Specifically, once the engineering values are uploaded to the cloud server, staff can remotely log in to the cloud server to obtain the sensor engineering values at any time and learn about the monitoring status of the dam.
[0070] When storing sensor data and engineering values locally, this includes: A hierarchical storage strategy is adopted, storing raw sensor data and engineering values in different storage partitions of the edge device respectively; a first storage period is set for the raw data and a second storage period is set for the engineering values, wherein the first storage period is less than the second storage period; when the storage capacity reaches a preset threshold, the earliest expired data is automatically deleted according to the storage period priority.
[0071] Raw sensor data is typically large in volume but requires high timeliness, while processed engineering data is smaller in volume and needs long-term traceability. By partitioning storage and setting different expiration dates, the storage needs of engineering data with higher long-term value can be prioritized within the limited storage capacity of edge devices, avoiding resource waste. When storage capacity is insufficient, the earliest expiring data is automatically deleted according to the expiration date, without manual intervention, reducing operation and maintenance costs. Especially in dam monitoring scenarios where edge devices are deployed in a dispersed manner and manual maintenance is difficult, this can reduce the risk of data loss due to storage overflow.
[0072] Furthermore, to maintain the health of the acquisition channel, the edge device periodically monitors the acquisition channel itself, including steps S311 to S33: Step S31: Inject test pulses into the acquisition channel at preset intervals.
[0073] Specifically, periodically injecting test pulses is a proactive monitoring method that can detect potential problems, such as poor contact or signal attenuation, before the acquisition channel completely fails. Compared to passively waiting for a fault to occur before taking action, this method can provide early warning, reduce data loss caused by sudden channel failures, and ensure the continuity of critical monitoring data for the dam.
[0074] Step S32: Obtain the response signal and response time of the acquisition channel after the input test pulse.
[0075] Specifically, by analyzing the continuity and response time of the response signal, the real-time performance of the acquisition channel can be objectively quantified. For example, a longer response time may indicate a decrease in channel transmission efficiency, and signal discontinuity may suggest interference or loss in the link. These quantitative indicators make anomaly detection more accurate and avoid misjudgment or missed detection.
[0076] Step S33: If the response signal is discontinuous or the response time is greater than the time threshold, mark the acquisition channel as abnormal and enable redundant acquisition channels.
[0077] Specifically, once an anomaly is detected, the system can automatically activate redundant channels without manual intervention, significantly shortening fault recovery time. For scenarios in dam monitoring that require real-time data support, such as flood season water level monitoring and early warning of structural stress mutations, this rapid switching can minimize the safety risks caused by data interruption.
[0078] Furthermore, when enabling redundant acquisition channels, steps S331 to S335 are included: Step S331: Obtain a preset priority list of redundant acquisition channels, wherein the priority list is determined based on the idle status of the redundant acquisition channels, the physical distance from the abnormal acquisition channels, and the stability of historical data transmission.
[0079] Specifically, the priority list sorting rules ensure the adaptability of redundant channels from multiple dimensions: idle states avoid resource conflicts, close physical distances reduce signal attenuation, and high historical stability reduces the probability of secondary failures, avoiding blind monitoring of all redundant resources during a failure. Activating channels sequentially by priority allows for the fastest identification of available and higher-performing redundant channels, minimizing data interruption time.
[0080] Step S332: Send activation commands to the redundant acquisition channels in sequence according to the priority list until a ready response signal is received from the redundant acquisition channels.
[0081] Step S333: Switch the sensor data transmission link associated with the abnormal acquisition channel to the activated redundant acquisition channel.
[0082] Step S334: Extract the configuration file of the abnormal acquisition channel and send it to the redundant acquisition channel so that the redundant acquisition channel can be configured according to the configuration file.
[0083] Step S335: Receive the parameter verification result returned by the redundant acquisition channel. If the verification passes, the configuration is confirmed to be complete. If the verification fails, the configuration file is resent and the number of failures is recorded. When the number of failures exceeds the threshold, a manual configuration prompt message is generated.
[0084] Specifically, the entire process of enabling redundant acquisition channels is mainly automated, including automatic activation, automatic configuration, and automatic verification, reducing manual intervention; manual prompts are only triggered when configuration fails multiple times, which improves the system's autonomy and ensures traceability of problems in extreme cases.
[0085] This application embodiment also provides a computer-readable storage medium storing a computer program. When executed by a processor, this program implements the dam monitoring method based on custom monitoring points provided in the above embodiments. The processor executes the computer program in the computer-readable storage medium to achieve intelligent configuration of the acquisition channels through a combination of hardware detection and user interaction. It automatically identifies signal types and matches configuration files, reducing manual operations and improving channel deployment efficiency. When a new signal type is detected, it actively prompts for configuration upload, ensuring system compatibility and scalability. It supports users to manually select signal types, balancing the convenience of automatic identification with flexibility in special scenarios. Furthermore, the automatic application of configuration files ensures accurate adaptation of channel parameters, avoiding errors from manual configuration, improving data acquisition accuracy and system reliability, meeting the diverse monitoring needs of multiple types of sensors in dam monitoring, and improving the convenience of dam monitoring.
[0086] In this embodiment, the computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.
[0087] The computer program in this embodiment includes program code for performing all the aforementioned methods. The program code may include instructions corresponding to the method steps provided in the above embodiments. The computer program can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network). The computer program can be executed entirely on the user's computer as a standalone software package.
[0088] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.
[0089] Additionally, it should be understood that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
Claims
1. A dam monitoring method based on a self-defined monitoring point, characterized in that, The method comprises the following steps: acquiring a signal type code sent by a signal type detection circuit embedded in a signal access interface of a collection channel; querying a local database according to the signal type code to determine whether a signal type corresponding to the signal type code exists; if yes, a configuration file corresponding to the signal type is obtained; otherwise, prompt information for uploading a configuration file of the new signal type is generated; or, acquiring type selection operation information of a user on a signal type icon; in response to the type selection operation information, a configuration file corresponding to the signal type is acquired; and automatically configuring the collection channel according to the configuration file.
2. The method of claim 1, wherein, The method further comprises the following steps: acquiring actual monitoring point coordinates input by a user or position information selected on a pre-established dam body three-dimensional model; determining a virtual monitoring point on the dam body three-dimensional model in combination with the actual monitoring point coordinates or according to the position information, wherein the actual monitoring point is a position where a sensor is installed on a dam, and the virtual monitoring point has the same coordinates as the actual monitoring point coordinates and is a digitalized identification of the actual monitoring point on the dam body three-dimensional model; acquiring a sensor associated with an actual monitoring point corresponding to the virtual monitoring point, a collection channel, a gateway, and a cloud server; automatically generating a topology network including a node set, an edge set, and attribute information; the node set includes the sensor, the collection channel, the gateway, the cloud server, and the edge device; the edge set includes a wired connection link between the sensor and the collection channel, a wired connection link between the collection channel and the gateway, a wireless connection link between the gateway and the sensor and / or the collection channel and / or the cloud server covered by a wireless signal of the gateway, a wired connection link between the collection channel and the edge device, and a wireless connection link between the edge device and the cloud server; and the attribute information includes a sensor type, a sampling frequency of the collection channel, a bandwidth between the sensor and the collection channel, a bandwidth between the collection channel and the gateway, and a bandwidth between the collection channel and the edge device; visually displaying the topology network and a real-time state; based on a self-defined rule, acquiring an output comprehensive index of a monitoring area according to collection channel data of at least one actual monitoring point in the monitoring area.
3. The method of claim 2, wherein, The visually displaying the topology network and the real-time state comprises the following steps: adopting different colors to identify real-time states of each connection link, wherein green represents a normal state, yellow represents a load warning state, and red represents a fault state; using changes in thickness of dynamic lines to represent real-time bandwidth occupancy rates of the links; when a state of a node or a link changes, triggering an animation prompt effect and updating a state timestamp; in response to operation information of a user clicking a node, displaying detailed running parameters and a historical state curve of the node.
4. The method of claim 1, wherein, The method further comprises the following steps: acquiring an engineering value calculated by a signal exclusive processing algorithm preinstalled in an independent computing unit integrated in the collection channel according to sensor data; displaying the engineering value; inputting the engineering value of each collection channel into a pre-set abnormal monitoring model based on deep learning to obtain a dam structure health report; locally storing the sensor data and the engineering value, and uploading the engineering value to a cloud server.
5. The method of claim 4, wherein, The local storage of the sensor data and engineering values comprises: Adopting a hierarchical storage strategy, the sensor raw data and engineering values are stored in different storage partitions of the edge device respectively; The first storage deadline is set for the raw data, and the second storage deadline is set for the engineering values, wherein the first storage deadline < the second storage deadline; When the storage capacity reaches a preset threshold, the earliest expired data is automatically deleted according to the storage deadline priority.
6. The method of claim 1, wherein, The method further comprises: Injecting a test pulse into the collection channel every preset time; Obtaining the response signal and response time of the collection channel after inputting the test pulse; If the response signal is discontinuous or the response time is greater than a time threshold, marking the collection channel as abnormal and enabling a redundant collection channel.
7. The method of claim 6, wherein, The enabled redundant collection channel comprises: Obtaining a preset redundant collection channel priority list, wherein the priority list is determined according to the idle state of the redundant collection channel, the physical location distance from the abnormal collection channel, and the historical data transmission stability; According to the priority list, the redundant collection channels are sequentially sent activation instructions until a ready response signal returned by the redundant collection channel is received; Switching the sensor data transmission link associated with the abnormal collection channel to the activated redundant collection channel; Extracting the configuration file of the abnormal collection channel and sending it to the redundant collection channel, so that the redundant collection channel is configured according to the configuration file; Receiving the parameter verification result returned by the redundant collection channel, if the verification passes, confirming that the configuration is complete; if the verification fails, resending the configuration file and recording the number of failures, when the number of failures exceeds a threshold, generating a manual configuration prompt information.
8. An edge device, characterized by Each collection channel corresponds to a set of independent signal access interfaces and signal processing circuits, the signal access interface is connected with the signal processing circuit, the signal access interface is embedded with a signal type detection circuit, and the signal processing circuit comprises a programmable gain amplifier, a multiplexing switch, an analog-to-digital converter and an independent computing unit connected with each other; Further comprising: At least one processor, each signal processing circuit is connected with the processor; At least one memory; At least one computer program, wherein the at least one computer program is stored in the memory and is configured to be executed by the at least one processor, and the at least one computer program is configured to execute a dam monitoring method based on a self-defined monitoring point according to any one of claims 1 to 7.
9. An edge device according to claim 8, characterized in that A porcelain coupling isolation or digital isolator is connected between the power supply ground and the signal ground of each collection channel.
10. A computer-readable storage medium, characterized in that, A computer program capable of being loaded and executed by the processor to execute a dam monitoring method based on a self-defined monitoring point according to any one of claims 1 to 7 is stored.