Communication method and device of pipe gallery active monitoring and early warning system, terminal and medium

Abstract

This invention provides a communication method, device, terminal, and medium for an active monitoring and early warning system for utility tunnels. The utility tunnel includes various municipal pipelines, and the active monitoring and early warning system includes multiple monitoring nodes and a detection vehicle. Each monitoring node is installed on a specific municipal pipeline, and the detection vehicle is used to actively detect each municipal pipeline at active detection time points. The method includes: determining the data upload method based on the time interval between the current time point and the next active detection time point; for each monitoring node, comparing the monitoring data of that node with historical monitoring data and monitoring data from adjacent nodes to determine the degree of anomaly for each monitoring node; and based on the degree of anomaly of each monitoring node in each group, determining the data compression and packaging method and upload frequency for each monitoring node in that group, so as to upload the monitoring data of each monitoring node. This invention can optimize the energy utilization and monitoring and early warning effect of the active monitoring and early warning system for utility tunnels.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a communication method, device, terminal and medium for an active monitoring and early warning system for utility tunnels. Background Technology

[0002] Underground utility tunnels can include various municipal pipelines such as communications, electricity, water supply, drainage, gas, and heating. To ensure the long-term stable operation of the pipeline system and protect the environment and public safety, it is necessary to conduct safety monitoring and maintenance on all types of municipal pipelines, and upload the monitoring data to accurately understand the operating status of the pipelines and carry out maintenance in a timely manner.

[0003] To improve monitoring effectiveness and efficiency, a proactive monitoring system for utility tunnels has been proposed. This system utilizes fixed monitoring nodes and a mobile inspection vehicle to proactively detect the status information of various parts along the entire pipeline at specific times and under specific operating conditions, thus achieving more targeted monitoring results. However, due to the morphological characteristics of pipelines, multiple monitoring points need to be set up on each pipeline to achieve continuous monitoring of the pipeline system. This monitoring method generates a large amount of monitoring data, posing a significant challenge to the data processing capabilities of the monitoring system. Consequently, the processing and early warning of monitoring data lack specificity, and it is difficult to promptly upload and analyze abnormal data. Summary of the Invention

[0004] This invention provides a communication method, device, terminal, and medium for an active monitoring and early warning system for utility tunnels, in order to solve the problem of improving the real-time performance of early warning analysis of monitoring data from the active monitoring and early warning system for utility tunnels.

[0005] In a first aspect, embodiments of the present invention provide a communication method for an active monitoring and early warning system for a utility tunnel. The utility tunnel includes various municipal pipelines, and the active monitoring and early warning system includes multiple monitoring nodes and a detection vehicle. Each monitoring node is respectively installed on each municipal pipeline, and the detection vehicle is used to actively detect each municipal pipeline at an active detection time point. The method includes:

[0006] The data upload method is determined based on the time interval between the current time point and the next active detection time point; the data upload method includes uploading through monitoring nodes and uploading through detection vehicles;

[0007] For each monitoring node, the monitoring data of that node is compared with historical monitoring data and monitoring data of adjacent nodes to determine the degree of anomaly of each monitoring node;

[0008] Based on the degree of anomaly of each monitoring node in each group, the data compression and packaging method and upload frequency of each monitoring node in that group are determined in order to upload the monitoring data of each monitoring node; among them, each data compression and packaging method has different compression speed and reversibility.

[0009] In one possible implementation, for each monitoring node, the monitoring data of that node is compared with historical monitoring data and monitoring data of neighboring nodes to determine the degree of anomaly for each monitoring node, including:

[0010] The difference between the current monitoring data and the historical monitoring data of the first monitoring node is calculated as the longitudinal deviation of the first monitoring node; where the first monitoring node is any monitoring node.

[0011] The difference between the current monitoring data of the first monitoring node and the current monitoring data of the second monitoring node is calculated as the lateral deviation of the first monitoring node; wherein, the second monitoring node is the neighboring node of the first monitoring node.

[0012] The longitudinal and lateral deviations are weighted and summed to obtain the anomaly index value of the first monitoring node.

[0013] In one possible implementation, before determining the data compression and packaging method for each group of monitoring nodes based on the anomaly level of each monitoring node in each group, the following is also included:

[0014] Based on the anomaly index value and the group of each monitoring node, multiple clusters are obtained;

[0015] Based on the anomaly index values ​​of each monitoring node in each group and the cluster they belong to, the anomaly level of each group is divided.

[0016] Based on the degree of anomaly of each monitoring node in each group, the data compression and packaging method for each group of monitoring nodes is determined, including:

[0017] The data compression and packaging method corresponding to the anomaly level will be used as the data compression and packaging method for this group of monitoring nodes.

[0018] In one possible implementation, the anomaly severity levels include high anomaly severity, medium anomaly severity, and low anomaly severity.

[0019] The data compression and packaging method corresponding to high anomalies is to use LZ4 or Snappy algorithm compression and package it in binary format.

[0020] For data with a moderate level of anomaly, the compression and packaging method is to use gzip or zlib algorithm compression and package it in JSON or XML format.

[0021] For data with low anomaly levels, the compression and packaging method is to use the bzip2 or xz compression algorithm and package it in text format.

[0022] In one possible implementation, the upload frequency of each monitoring node in each group is determined based on the degree of anomaly of each monitoring node in each group, including:

[0023] The basic upload frequency is determined based on the data upload method;

[0024] For each group, the basic upload frequency is adjusted based on the average value of the anomaly index of each monitoring node in that group to obtain the upload frequency for that group.

[0025] In one possible implementation, the method further includes:

[0026] For each group, the importance level of the group is determined based on the category of the municipal pipeline where each monitoring node in the group is located;

[0027] For each group, the upload priority of that group is determined based on its anomaly level and importance level, so that the detection vehicle can determine the upload order of the monitoring data for that group based on the upload priority corresponding to that group.

[0028] In one possible implementation, the method further includes:

[0029] The monitoring node with the largest remaining power in each group of monitoring nodes is selected as the upload node, and the monitoring data of the group of monitoring nodes is uploaded through the upload node.

[0030] Secondly, embodiments of the present invention provide a communication device for an active monitoring and early warning system for utility tunnels. The utility tunnel includes various municipal pipelines, and the active monitoring and early warning system includes multiple monitoring nodes and a detection vehicle. Each monitoring node is respectively installed on each municipal pipeline, and the detection vehicle is used to actively detect each municipal pipeline at active detection time points. The device includes:

[0031] The method determination module is used to determine the data upload method based on the time interval between the current time point and the next active detection time point; the data upload method includes uploading through monitoring nodes and uploading through detection vehicles;

[0032] The data comparison module is used to compare the monitoring data of each monitoring node with historical monitoring data and monitoring data of adjacent nodes to determine the degree of anomaly of each monitoring node.

[0033] The data compression and upload module is used to determine the data compression and uploading frequency of each monitoring node in each group based on the degree of anomaly of each monitoring node in that group, so as to upload the monitoring data of each monitoring node; among them, each data compression and uploading method has different compression speed and reversibility.

[0034] Thirdly, embodiments of the present invention provide a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation thereof.

[0035] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0036] This invention provides a communication method, device, terminal, and medium for an active monitoring and early warning system for utility tunnels. The method selects the data upload method based on the time interval between the current time point and the next active detection time point. When the time interval between the two time points is small, the monitoring data is retained until the detection vehicle arrives and then uploaded via the vehicle. In other cases, it is uploaded directly via wireless communication. The method also quantifies the degree of data anomaly at the monitoring nodes based on the monitoring data, determining an appropriate compression and uploading frequency. This allows for the allocation of different communication resources to monitoring data with different early warning values, optimizing energy utilization and monitoring and early warning effects of the active monitoring and early warning system for utility tunnels, and improving the real-time performance of abnormal data analysis and early warning. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating the implementation of a communication method for an active monitoring and early warning system for utility tunnels according to an embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram of the communication device of the active monitoring and early warning system for utility tunnels provided in an embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Detailed Implementation

[0041] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0042] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0043] See Figure 1 The document illustrates a flowchart of the communication method for the active monitoring and early warning system for utility tunnels provided in this embodiment of the invention, detailed below:

[0044] The utility tunnel includes various municipal pipelines. Its active monitoring and early warning system comprises multiple monitoring nodes and a detection vehicle. Each monitoring node is installed on a specific municipal pipeline, and the detection vehicle is used to actively inspect each pipeline at designated detection times. The method includes:

[0045] Step 101: Determine the data upload method based on the time interval between the current time point and the next active detection time point; the data upload method includes uploading through the monitoring node and uploading through the detection vehicle.

[0046] In this embodiment, multiple monitoring nodes are installed on each municipal pipeline. These monitoring nodes can be divided into one or more groups, and the monitoring data collected by each node in each group can be aggregated and uploaded in batches to facilitate the analysis and early warning of the status of each municipal pipeline. The monitoring nodes can communicate with each other via the Internet of Things, wired communication, or with an additional central control unit to achieve data aggregation.

[0047] The next active detection time point can be the moment when the detection vehicle arrives near a certain group. Since the detection vehicle is in a mobile state, the arrival time of each monitoring node is different. Therefore, each monitoring node can automatically select an appropriate data upload method according to the specific situation.

[0048] When the monitoring data of each monitoring node is relatively stable, this method can be used to select the data upload method. When the time interval between two time points is small, the monitoring data is retained until the inspection vehicle arrives and then uploaded through the inspection vehicle. Since the energy consumption of the processor in a unit of time is much lower than the energy consumption of data transmission, this method can achieve a certain energy-saving effect while ensuring the continuity of monitoring data upload.

[0049] When the monitoring data from a certain monitoring node is clearly abnormal, the system can switch to wireless communication to directly upload the monitoring data to the cloud-based early warning decision-making unit, depending on the severity of the anomaly. Alternatively, when uploading monitoring data to the inspection vehicle, priority tags can be assigned to the data based on the severity of the anomaly, ensuring that the inspection vehicle prioritizes uploading monitoring data with higher anomaly levels to the cloud-based early warning decision-making unit, thus guaranteeing the timeliness of proactive monitoring and early warning for the utility tunnel.

[0050] Step 102: For each monitoring node, compare the monitoring data of that monitoring node with historical monitoring data and monitoring data of adjacent nodes to determine the degree of anomaly of each monitoring node.

[0051] In this embodiment, the anomaly level is used to describe the severity of anomalies in the monitoring data collected by the monitoring node. The higher the anomaly level, the more likely the monitoring data corresponds to an abnormal pipeline state. Based on the anomaly level of the monitoring node, it can be determined whether communication resources should be prioritized for that monitoring node, thereby uploading the monitoring data collected by the node to the cloud-based early warning decision-making body for timely pipeline status early warning.

[0052] Step 103: Based on the degree of anomaly of each monitoring node in each group, determine the data compression and packaging method and upload frequency of each monitoring node in that group, so as to upload the monitoring data of each monitoring node; wherein, each data compression and packaging method has different compression speed and reversibility.

[0053] In this embodiment, when the time interval between two time points is large, monitoring data needs to be uploaded via wireless communication. In this case, the number of uploads can be planned based on the length of the time interval; the longer the time interval, the more uploads are needed.

[0054] Furthermore, the more severe the anomaly in the data of a monitoring node, the higher the urgency and importance of the monitoring data of that node. The monitoring data should be uploaded as quickly and completely as possible. At this time, a data compression and packaging method with high reversibility and fast compression speed should be selected to ensure the integrity of the data. The number of uploads should be adjusted based on the degree of anomaly to increase the upload frequency and promptly upload the rapidly changing monitoring data to the cloud-based early warning decision-making body.

[0055] This invention selects the data upload method based on the time interval between the current time point and the next active detection time point. When the time interval between the two time points is small, the monitoring data is retained until the detection vehicle arrives and then uploaded through the detection vehicle. In other cases, it is directly uploaded via wireless communication. Based on the monitoring data, the degree of data anomaly of the monitoring nodes is quantitatively evaluated to determine the appropriate compression and packaging method and upload frequency. This allows for the allocation of different communication resources to monitoring data with different early warning values, thereby optimizing the energy utilization and monitoring and early warning effect of the utility tunnel active monitoring and early warning system and improving the real-time performance of abnormal data analysis and early warning.

[0056] In one possible implementation, for each monitoring node, the monitoring data of that node is compared with historical monitoring data and monitoring data of neighboring nodes to determine the degree of anomaly for each monitoring node, including:

[0057] The difference between the current monitoring data and the historical monitoring data of the first monitoring node is calculated as the longitudinal deviation of the first monitoring node; where the first monitoring node is any monitoring node.

[0058] The difference between the current monitoring data of the first monitoring node and the current monitoring data of the second monitoring node is calculated as the lateral deviation of the first monitoring node; wherein, the second monitoring node is the neighboring node of the first monitoring node.

[0059] The longitudinal and lateral deviations are weighted and summed to obtain the anomaly index value of the first monitoring node.

[0060] In this embodiment, the first monitoring node and the second monitoring node should be in the same group. Since the pipeline is a monolithic structure, the monitoring data of each monitoring node on the same pipeline exhibit a certain correlation when they change. Taking four monitoring nodes A, B, C, and D evenly distributed along a pipeline as an example, the correlation of changes in certain types of pipeline monitoring data, such as vibration signals, can be reflected in the step-like changes in the monitoring data a, b, c, and d of the four adjacent monitoring nodes A, B, C, and D. For example, d = c + e = b + 2e = a + 3e, where e is the difference between c and d. When the monitoring nodes are not evenly distributed, the differences in the monitoring data of each monitoring node vary, but generally follow the above pattern. Simultaneously, there are also certain types of monitoring data where the correlation is reflected in the fact that when the pipeline is in normal condition, the difference in the monitoring data of adjacent monitoring nodes should be less than a certain threshold.

[0061] The vertical deviation of the first monitoring node is the difference between the current monitoring data and the historical monitoring data. A larger vertical deviation indicates a more abnormal current monitoring data. The horizontal deviation is the difference between the current monitoring data and the current monitoring data of the second monitoring node. A smaller horizontal deviation indicates that the difference between the current monitoring data of the second monitoring node and its historical monitoring data is also larger, which confirms that the current monitoring data of the first monitoring node is highly abnormal. Therefore, the weight of the vertical deviation can be set to a positive value, and the weight of the horizontal deviation can be set to a negative value.

[0062] In addition, monitoring nodes may have multiple sensors, each of which collects a type of monitoring data. The numerical ranges of different types of monitoring data are different, and different weights can be set for different types of monitoring data to accurately assess the degree of anomaly of the monitoring node.

[0063] In one possible implementation, before determining the data compression and packaging method for each group of monitoring nodes based on the anomaly level of each monitoring node in each group, the following is also included:

[0064] Based on the anomaly index value and the group of each monitoring node, multiple clusters are obtained;

[0065] Based on the anomaly index values ​​of each monitoring node in each group and the cluster they belong to, the anomaly level of each group is divided.

[0066] Based on the degree of anomaly of each monitoring node in each group, the data compression and packaging method for each group of monitoring nodes is determined, including:

[0067] The data compression and packaging method corresponding to the anomaly level will be used as the data compression and packaging method for this group of monitoring nodes.

[0068] In this embodiment, the monitoring nodes are clustered according to the anomaly index value and the group of each monitoring node. Nodes with similar anomaly levels can be grouped into one category, and the original groups are preserved as much as possible. This allows for the classification of the anomaly levels of the monitoring nodes. Monitoring nodes in the same anomaly level group can use the same data compression and packaging method.

[0069] Specifically, each group can first be regarded as a first cluster, that is, a cluster based on the location of the monitoring node. Then, based on the first cluster, the anomaly index values ​​of the monitoring nodes are clustered to obtain multiple second clusters. Each monitoring node in each second cluster corresponds to an anomaly index value level, thereby realizing the anomaly index value level of each monitoring node. Finally, the anomaly index value levels corresponding to each monitoring node in each first cluster are counted. The anomaly index value level with the most occurrences is taken as the anomaly level corresponding to the first cluster, or the anomaly index value level with the highest level is taken as the anomaly level corresponding to the first cluster.

[0070] In addition, the characteristics of the monitoring data of each monitoring node in a certain group can be matched with the characteristics of abnormal data, and the level of abnormality can be directly judged based on the similarity.

[0071] In one possible implementation, the anomaly severity levels include high anomaly severity, medium anomaly severity, and low anomaly severity.

[0072] The data compression and packaging method corresponding to high anomalies is to use LZ4 or Snappy algorithm compression and package it in binary format.

[0073] For data with a moderate level of anomaly, the compression and packaging method is to use gzip or zlib algorithm compression and package it in JSON or XML format.

[0074] For data with low anomaly levels, the compression and packaging method is to use the bzip2 or xz compression algorithm and package it in text format.

[0075] In this embodiment, using LZ4 or Snappy compression algorithms can reduce latency, and using binary format packaging has higher efficiency;

[0076] Using gzip or zlib algorithms results in higher compression ratios, and packaging in JSON or XML format makes it easy to read and parse.

[0077] Using bzip2 or xz compression algorithms can save storage space, and using text format for packaging facilitates data recording and logging.

[0078] In one possible implementation, the upload frequency of each monitoring node in each group is determined based on the degree of anomaly of each monitoring node in each group, including:

[0079] The basic upload frequency is determined based on the data upload method;

[0080] For each group, the basic upload frequency is adjusted based on the average value of the anomaly index of each monitoring node in that group to obtain the upload frequency for that group.

[0081] In this embodiment, the base upload frequency corresponding to each data upload method can be preset.

[0082] When data is uploaded via a mobile monitoring vehicle, the vehicle is continuously moving during active monitoring. The positional relationship between the vehicle and each monitoring node in a given group might be: entering the communication range of monitoring node A—entering the communication range of monitoring node B…entering the communication range of monitoring node N—leaving the communication range of monitoring node N. During this period, all monitoring data collected in that group can be uploaded to the vehicle. This data upload method has low energy consumption and can utilize a higher base upload frequency, improving monitoring and early warning effectiveness.

[0083] When the data is uploaded via monitoring node, this method consumes a lot of energy. A lower base upload frequency can be used to save system energy.

[0084] When the anomaly index values ​​of each monitoring node in a group are low—for example, if the average anomaly index value of a certain group accounts for the bottom 50% of the average anomaly index values ​​of all monitoring nodes in all groups—then the basic upload frequency is not adjusted. When the anomaly index values ​​of each monitoring node in a group are high, the average anomaly index value of the monitoring nodes in that group can be multiplied by the basic upload frequency to obtain the upload frequency for that group. This upload frequency can then be used to determine the upload times for the monitoring data of that group, appropriately increasing the number of uploads of anomaly monitoring data without excessively increasing system communication energy consumption.

[0085] In addition, after the abnormal monitoring data is uploaded, the cloud-based early warning decision-maker processes and analyzes the abnormal monitoring data to determine whether the data indicates an abnormal pipeline status and issues a corresponding data upload command. Subsequently, each monitoring node can perform subsequent uploads based on the data upload method and upload frequency in the data upload command.

[0086] In one possible implementation, the method further includes:

[0087] For each group, the importance level of the group is determined based on the category of the municipal pipeline where each monitoring node in the group is located;

[0088] For each group, the upload priority of that group is determined based on its anomaly level and importance level, so that the detection vehicle can determine the upload order of the monitoring data for that group based on the upload priority corresponding to that group.

[0089] In this embodiment, the severity of a failure varies for each type of municipal pipeline. The importance level of each type of municipal pipeline can be pre-set based on information such as the pipeline's category, location, and operating characteristics. The higher the importance level value, the greater the severity of the failure. When the monitoring nodes deployed on the municipal pipeline collect abnormal monitoring data, they should be uploaded first.

[0090] Specifically, the product of the anomaly level and importance level of each group can be calculated. The larger the product, the higher the upload priority of that group, and the detection vehicle will prioritize uploading the monitoring data of that group.

[0091] In one possible implementation, the method further includes:

[0092] The monitoring node with the largest remaining power in each group of monitoring nodes is selected as the upload node, and the monitoring data of the group of monitoring nodes is uploaded through the upload node.

[0093] In this embodiment, to ensure the sustainability of the active monitoring and early warning system for the utility tunnel, the monitoring node with the largest remaining power in each group of monitoring nodes can be used as the upload node to bear the energy consumption of data upload and extend the running time of the other monitoring nodes.

[0094] In addition, when the data is uploaded via the inspection vehicle, the node closest to the inspection vehicle in each group of monitoring nodes can be set as the upload node to complete the upload as early as possible.

[0095] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0096] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0097] Figure 2 A schematic diagram of the communication device of the active monitoring and early warning system for utility tunnels provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0098] like Figure 2 As shown, the utility tunnel includes various municipal pipelines. The active monitoring and early warning system for the utility tunnel includes multiple monitoring nodes and an inspection vehicle. Each monitoring node is installed on each municipal pipeline, and the inspection vehicle is used to actively inspect each municipal pipeline at the designated inspection time. The communication device 2 of the active monitoring and early warning system for the utility tunnel includes:

[0099] The method determination module 21 is used to determine the data upload method based on the time interval between the current time point and the next active detection time point; wherein, the data upload method includes uploading through the monitoring node and uploading through the detection vehicle;

[0100] The data comparison module 22 is used to compare the monitoring data of each monitoring node with historical monitoring data and monitoring data of adjacent nodes to determine the degree of abnormality of each monitoring node.

[0101] The packaging and uploading module 23 is used to determine the data compression and packaging method and upload frequency of each monitoring node in each group based on the degree of anomaly of each monitoring node in that group, so as to upload the monitoring data of each monitoring node; wherein, each data compression and packaging method has different compression speed and reversibility.

[0102] In one possible implementation, the data comparison module 22 is specifically used for:

[0103] The difference between the current monitoring data and the historical monitoring data of the first monitoring node is calculated as the longitudinal deviation of the first monitoring node; where the first monitoring node is any monitoring node.

[0104] The difference between the current monitoring data of the first monitoring node and the current monitoring data of the second monitoring node is calculated as the lateral deviation of the first monitoring node; wherein, the second monitoring node is the neighboring node of the first monitoring node.

[0105] The longitudinal and lateral deviations are weighted and summed to obtain the anomaly index value of the first monitoring node.

[0106] In one possible implementation, the data comparison module 22 is also used for:

[0107] Before determining the data compression and packaging method for each monitoring node based on the anomaly level of each monitoring node in each group, the monitoring nodes are clustered according to the anomaly level index value and the group of each monitoring node to obtain multiple clusters;

[0108] Based on the anomaly index values ​​of each monitoring node in each group and the cluster they belong to, the anomaly level of each group is divided.

[0109] The package upload module 23 is specifically used for:

[0110] The data compression and packaging method corresponding to the anomaly level will be used as the data compression and packaging method for this group of monitoring nodes.

[0111] In one possible implementation, the anomaly severity levels include high anomaly severity, medium anomaly severity, and low anomaly severity.

[0112] The data compression and packaging method corresponding to high anomalies is to use LZ4 or Snappy algorithm compression and package it in binary format.

[0113] For data with a moderate level of anomaly, the compression and packaging method is to use gzip or zlib algorithm compression and package it in JSON or XML format.

[0114] For data with low anomaly levels, the compression and packaging method is to use the bzip2 or xz compression algorithm and package it in text format.

[0115] In one possible implementation, the package upload module 23 is specifically used for:

[0116] The basic upload frequency is determined based on the data upload method;

[0117] For each group, the basic upload frequency is adjusted based on the average value of the anomaly index of each monitoring node in that group to obtain the upload frequency for that group.

[0118] In one possible implementation, the data comparison module 22 is also used for:

[0119] For each group, the importance level of the group is determined based on the category of the municipal pipeline where each monitoring node in the group is located;

[0120] For each group, the upload priority of that group is determined based on its anomaly level and importance level, so that the detection vehicle can determine the upload order of the monitoring data for that group based on the upload priority corresponding to that group.

[0121] In one possible implementation, the device further includes:

[0122] The node selection module is used to select the monitoring node with the largest remaining power in each group of monitoring nodes as the upload node, and upload the monitoring data of the group of monitoring nodes through the upload node.

[0123] This invention selects the data upload method based on the time interval between the current time point and the next active detection time point. When the time interval between the two time points is small, the monitoring data is retained until the detection vehicle arrives and then uploaded through the detection vehicle. In other cases, it is directly uploaded via wireless communication. Based on the monitoring data, the degree of data anomaly of the monitoring nodes is quantitatively evaluated to determine the appropriate compression and packaging method and upload frequency. This allows for the allocation of different communication resources to monitoring data with different early warning values, thereby optimizing the energy utilization and monitoring and early warning effect of the utility tunnel active monitoring and early warning system and improving the real-time performance of abnormal data analysis and early warning.

[0124] Figure 3 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Figure 3 As shown, the terminal 3 in this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, it implements the steps in the communication method embodiments of the various pipe gallery active monitoring and early warning systems described above, for example... Figure 1Steps 101 to 103 are shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of modules / units 21 to 23 shown.

[0125] For example, the computer program 32 can be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 32 in the terminal 3. For example, the computer program 32 can be divided into... Figure 2 Modules / units 21 to 23 are shown.

[0126] The terminal 3 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of terminal 3 and does not constitute a limitation on terminal 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0127] The processor 30 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0128] The memory 31 can be an internal storage unit of the terminal 3, such as a hard disk or memory of the terminal 3. The memory 31 can also be an external storage device of the terminal 3, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal 3. Furthermore, the memory 31 can include both internal storage units and external storage devices of the terminal 3. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0129] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0130] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0131] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0132] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0133] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0134] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0135] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the communication method embodiments of the above-described active monitoring and early warning systems for utility tunnels. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0136] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A communication method of a pipe gallery active monitoring and early warning system, characterized in that, The pipe gallery comprises a plurality of municipal pipelines, and the active monitoring and early warning system of the pipe gallery comprises a plurality of monitoring nodes and a detection vehicle, each monitoring node is arranged on each municipal pipeline, and the detection vehicle is used for actively detecting each municipal pipeline at an active detection time point; the method comprises: determining a data uploading mode based on a time interval between a current time point and a next active detection time point; wherein the data uploading mode comprises uploading through the monitoring nodes and uploading through the detection vehicle; the method for determining the data uploading mode based on the time interval between the current time point and the next active detection time point comprises: when the time interval between the two time points is small, retaining the monitoring data until the detection vehicle arrives, and then uploading the monitoring data through the detection vehicle; for each monitoring node, comparing the monitoring data of the monitoring node with historical monitoring data and monitoring data of adjacent nodes to determine an abnormality degree of each monitoring node; based on the abnormality degree of each monitoring node in each group, determining a data compression and packaging mode and an uploading frequency of each monitoring node in the group to upload the monitoring data of each monitoring node; wherein each data compression and packaging mode has different compression speeds and reversibilities; before the step of determining the data compression and packaging mode of each monitoring node in each group based on the abnormality degree of each monitoring node in each group, the method further comprises: clustering the monitoring nodes according to the abnormality degree index value and the group of each monitoring node to obtain a plurality of clustering clusters; dividing an abnormality degree level of each group according to the abnormality degree index value of each monitoring node in each group and the clustering cluster where each monitoring node is located; the step of determining the data compression and packaging mode of each monitoring node in each group based on the abnormality degree of each monitoring node in each group comprises: taking the data compression and packaging mode corresponding to the abnormality degree level as the data compression and packaging mode of the monitoring node in the group.

2. The communication method of the pipe gallery active monitoring and early warning system according to claim 1, characterized in that, the step of determining the abnormality degree of each monitoring node by comparing the monitoring data of the monitoring node with historical monitoring data and monitoring data of adjacent nodes for each monitoring node comprises: calculating a difference value between the current monitoring data and the historical monitoring data of a first monitoring node as a longitudinal deviation of the first monitoring node; wherein the first monitoring node is any monitoring node; calculating a difference value between the current monitoring data of the first monitoring node and the current monitoring data of a second monitoring node as a transverse deviation of the first monitoring node; wherein the second monitoring node is an adjacent node of the first monitoring node; performing weighted summation on the longitudinal deviation and the transverse deviation to obtain an abnormality degree index value of the first monitoring node.

3. The communication method of the pipe gallery active monitoring and early warning system according to claim 1, characterized in that, the abnormality degree level comprises a high abnormality degree, a medium abnormality degree and a low abnormality degree; the data compression and packaging mode corresponding to the high abnormality degree is to compress by using an LZ4 or Snappy algorithm and to package in a binary format; the data compression and packaging mode corresponding to the medium abnormality degree is to compress by using a gzip or zlib algorithm and to package in a JSON or XML format; the data compression and packaging mode corresponding to the low abnormality degree is to compress by using a bzip2 or xz compression algorithm and to package in a text format.

4. The communication method of the pipe gallery active monitoring and early warning system according to claim 2, characterized in that, determine an uploading frequency of each group of monitoring nodes based on the abnormality degree of each monitoring node in the group, including: determining a basic uploading frequency based on the data uploading manner; adjust the basic uploading frequency based on the average value of the abnormality degree index value of each monitoring node in the group to obtain the uploading frequency of the group.

5. The communication method of the pipe gallery active monitoring and early warning system according to claim 4, characterized in that, The method further includes: determining the importance degree level of the group based on the category of the municipal pipeline where each monitoring node in the group is located for each group; determining the uploading priority of the group based on the abnormality degree level and the importance degree level of the group for each group, so that the detection vehicle determines the uploading order of the monitoring data of the group based on the uploading priority corresponding to the group.

6. The communication method of the pipe gallery active monitoring and early warning system according to any one of claims 1 to 5, characterized in that, The method further includes: taking the monitoring node with the largest remaining power in each group of monitoring nodes as an uploading node, and uploading the monitoring data of the group of monitoring nodes through the uploading node.

7. A communication device of a pipe gallery active monitoring and early warning system, characterized in that, The pipe gallery includes various municipal pipelines, and the active monitoring and early warning system of the pipe gallery includes multiple monitoring nodes and a detection vehicle. Each monitoring node is arranged on each municipal pipeline, and the detection vehicle is used to actively detect each municipal pipeline at an active detection time point. The device includes: a manner determination module configured to determine a data uploading manner based on the time interval between the current time point and the next active detection time point, wherein the data uploading manner includes uploading through a monitoring node and uploading through the detection vehicle; The manner determination module is specifically configured to: when the time interval between the two time points is small, retain the monitoring data until the detection vehicle arrives, and then upload the monitoring data through the detection vehicle; a data comparison module configured to compare, for each monitoring node, the monitoring data of the monitoring node with historical monitoring data and monitoring data of adjacent nodes to determine the abnormality degree of each monitoring node; a packaging and uploading module configured to determine, based on the abnormality degree of each monitoring node in each group, the data compression and packaging manner and the uploading frequency of each monitoring node in the group to upload the monitoring data of each monitoring node, wherein each data compression and packaging manner has different compression speed and reversibility; The data comparison module is further configured to: before determining the data compression and packaging manner of each group of monitoring nodes based on the abnormality degree of each monitoring node in the group, cluster the monitoring nodes according to the abnormality degree index value and the group of each monitoring node to obtain multiple clustering clusters; divide the abnormality degree level of each group based on the abnormality degree index value and the clustering cluster of each monitoring node in the group; The packaging and uploading module is specifically configured to: take the data compression and packaging manner corresponding to the abnormality degree level as the data compression and packaging manner of the group of monitoring nodes.

8. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

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