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

Abstract

The invention provides a communication method and device of a pipe gallery active monitoring and early warning system, a terminal and a medium. The pipe gallery comprises various municipal pipelines, the active monitoring and early warning system for the pipe gallery comprises a plurality of monitoring nodes and a detection vehicle, the monitoring nodes are arranged on the municipal pipelines respectively, and the detection vehicle is used for actively detecting the municipal pipelines at an active detection time point; the method comprises the steps of determining a data uploading mode based on a time interval between a current time point and a next active detection time point; for each monitoring node, comparing the monitoring data of the monitoring node with historical monitoring data and monitoring data of adjacent nodes, and determining the abnormal degree of each monitoring node; and based on the abnormal degree of each monitoring node in each group, determining a data compression packaging mode and an uploading frequency of each monitoring node in the group so as to upload the monitoring data of each monitoring node. According to the invention, the energy utilization and monitoring and early warning effects of the active monitoring and early warning system for the pipe gallery can be optimized.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a communication method, device, terminal and medium of a pipeline gallery active monitoring and early warning system. Background Art

[0002] The underground comprehensive pipeline corridor may include various municipal pipelines such as communications, electricity, water supply, drainage, gas, and heat. In order 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 of various municipal pipelines, and upload the monitoring data to accurately understand the operating status of the pipeline and perform maintenance in a timely manner.

[0003] In order to improve the monitoring effect and efficiency, an active monitoring system for pipeline corridors has been proposed. It uses fixed monitoring nodes and movable detection vehicles to actively detect the status information of various parts of the entire pipeline at specific time points and operating conditions, so as to obtain more targeted monitoring effects. However, due to the morphological characteristics of the pipeline, it is necessary to set up multiple monitoring points on each pipeline to achieve continuous monitoring of the pipeline system. This monitoring method will generate a large amount of monitoring data, which poses a great challenge to the data processing capacity of the monitoring system, resulting in the processing and early warning of monitoring data being not targeted, and it is difficult to upload abnormal data and conduct early warning analysis in a timely manner. Summary of the invention

[0004] The embodiments of the present invention provide a communication method, device, terminal and medium of an active monitoring and early warning system for a pipe gallery, so as to solve the problem of improving the real-time performance of early warning analysis of monitoring data of the active monitoring and early warning system for a pipe gallery.

[0005] In a first aspect, an embodiment of the present invention provides a communication method for an active monitoring and early warning system of a pipe gallery, wherein the pipe gallery includes a plurality of municipal pipelines, and the active monitoring and early warning system of the pipe gallery includes a plurality of monitoring nodes and a detection vehicle, wherein each monitoring node is respectively arranged 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] Determine a 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 a monitoring node and uploading through a detection vehicle;

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

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

[0009] In a possible implementation, for each monitoring node, the monitoring data of the monitoring node is compared with the historical monitoring data and the monitoring data of the adjacent nodes to determine the abnormality degree of each monitoring node, including:

[0010] Calculate the difference between the current monitoring data and the historical monitoring data of the first monitoring node as the longitudinal deviation of the first monitoring node; wherein the first monitoring node is any monitoring node;

[0011] Calculate the difference between the current monitoring data of the first monitoring node and the current monitoring data of the second monitoring node as the lateral deviation of the first monitoring node; wherein the second monitoring node is an adjacent node of the first monitoring node;

[0012] The longitudinal deviation and the lateral deviation are weightedly summed to obtain the abnormality index value of the first monitoring node.

[0013] In a possible implementation, before determining the data compression and packaging method of each group of monitoring nodes based on the abnormality degree of each monitoring node in each group, the method further includes:

[0014] Clustering each monitoring node according to the abnormality index value and the group of each monitoring node to obtain multiple clusters;

[0015] According to the abnormality index value of each monitoring node in each group and the cluster it belongs to, the abnormality level of each group is divided;

[0016] Based on the abnormality level of each monitoring node in each group, determine the data compression and packaging method of each group of monitoring nodes, including:

[0017] The data compression and packaging method corresponding to the abnormality degree level is used as the data compression and packaging method of the group of monitoring nodes.

[0018] In a possible implementation, the abnormality level levels include high abnormality level, medium abnormality level and low abnormality level;

[0019] The data compression and packaging method corresponding to the high abnormality level is compressed using the LZ4 or Snappy algorithm and packaged in binary format;

[0020] The data compression and packaging method corresponding to the medium abnormality level is to use gzip or zlib algorithm compression and package in JSON or XML format;

[0021] The data compression and packaging method corresponding to the low abnormality level is to use the bzip2 or xz compression algorithm and package it in text format.

[0022] In a possible implementation, based on the abnormality level of each monitoring node in each group, the upload frequency of each group of monitoring nodes is determined, including:

[0023] Determine the basic upload frequency based on the data upload method;

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

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

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

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

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

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

[0030] In a second aspect, an embodiment of the present invention provides a communication device for an active monitoring and early warning system for a pipe gallery, wherein the pipe gallery includes a plurality of municipal pipelines, and the active monitoring and early warning system for the pipe gallery includes a plurality of monitoring nodes and a detection vehicle, wherein each monitoring node is respectively 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:

[0031] A method determination module is used to determine a 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 a monitoring node and uploading through a detection vehicle;

[0032] A data comparison module is used to compare the monitoring data of each monitoring node with the historical monitoring data and the monitoring data of adjacent nodes to determine the abnormality degree of each monitoring node;

[0033] The packaging and uploading module is used to determine the data compression packaging method and upload frequency of each monitoring node in each group based on the abnormality level of each monitoring node in the group, so as to upload the monitoring data of each monitoring node; wherein each data compression packaging method has different compression speed and reversibility.

[0034] In a third aspect, an embodiment of the present invention provides a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation manner of the first aspect are implemented.

[0035] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0036] The embodiment of the present invention provides a communication method, device, terminal and medium of an active monitoring and early warning system for a utility corridor. The method for uploading monitoring data is selected according to 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 uploaded by the detection vehicle. In other cases, the data is directly uploaded through wireless communication. The degree of data anomaly of the monitoring node is quantitatively evaluated based on the monitoring data, and the appropriate compression packaging method and upload frequency are determined, so that different communication resources are allocated to monitoring data with different early warning values. This can optimize the energy utilization and monitoring and early warning effects of the active monitoring and early warning system of the utility corridor, and improve the real-time performance of abnormal data analysis and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0038] Figure 1 It is a flow chart for implementing a communication method of a pipe gallery active monitoring and early warning system provided by an embodiment of the present invention;

[0039] Figure 2 It is a structural schematic diagram of a communication device of a pipe gallery active monitoring and early warning system provided by an embodiment of the present invention;

[0040] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0042] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0043] See also Figure 1 , which shows a flow chart of the implementation of the communication method of the active monitoring and early warning system of the pipe gallery provided by an embodiment of the present invention, which is described in detail as follows:

[0044] The pipe gallery includes a variety of municipal pipelines. The active monitoring and early warning system of the pipe gallery includes a plurality of monitoring nodes and a detection vehicle. Each monitoring node is respectively arranged on each municipal pipeline. The detection vehicle is used to actively detect each municipal pipeline at an active detection time point. The method includes:

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

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

[0047] The next active detection time point can be the time when the detection vehicle arrives near a certain group. Since the detection vehicle is in a mobile state, the time point of reaching each group of monitoring nodes is different, so each group of monitoring nodes can automatically select the 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 will be retained until the inspection vehicle arrives and uploaded through the inspection vehicle. Since the energy consumption of the processor calculated per unit 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 of a monitoring node is obviously abnormal, the monitoring data can be directly uploaded to the cloud-based early warning decision-maker through wireless communication according to the degree of abnormality. Or when uploading the monitoring data to the inspection vehicle, priority tags can be assigned to the monitoring data based on the degree of abnormality, so that the inspection vehicle will prioritize uploading the monitoring data with high abnormality to the cloud-based early warning decision-maker, ensuring the timeliness of active monitoring and early warning of the corridor.

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

[0051] In this embodiment, the abnormality degree is used to describe the severity of the abnormality of the monitoring data collected by the monitoring node. The greater the abnormality degree, the more likely the monitoring data corresponds to the abnormal state of the pipeline. Based on the abnormality degree of the monitoring node, it can be determined whether it is necessary to prioritize the allocation of communication resources for the monitoring node, so as to upload the monitoring data collected by the monitoring node to the early warning decision-making party in the cloud, and timely issue a pipeline status early warning.

[0052] Step 103, based on the abnormality level of each monitoring node in each group, determine the data compression packaging method and upload frequency of each monitoring node in the group to upload the monitoring data of each monitoring node; wherein each data compression packaging method has different compression speed and reversibility.

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

[0054] In addition, the more serious the data anomaly of the monitoring node, the higher the urgency and importance of the monitoring data of the 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, the upload frequency should be increased, and the rapidly changing monitoring data should be uploaded to the early warning decision-making party in the cloud in a timely manner.

[0055] The embodiment of the present invention selects the uploading method of monitoring data according to 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 uploaded by the detection vehicle. In other cases, the data is directly uploaded through wireless communication. The data abnormality degree of the monitoring node is quantitatively evaluated based on the monitoring data, and the appropriate compression packaging method and upload frequency are determined, so that different communication resources are allocated to monitoring data with different warning values. This can optimize the energy utilization and monitoring and warning effects of the active monitoring and early warning system of the pipeline corridor, and improve the real-time analysis and early warning of abnormal data.

[0056] In a possible implementation, for each monitoring node, the monitoring data of the monitoring node is compared with the historical monitoring data and the monitoring data of the adjacent nodes to determine the abnormality degree of each monitoring node, including:

[0057] Calculate the difference between the current monitoring data and the historical monitoring data of the first monitoring node as the longitudinal deviation of the first monitoring node; wherein the first monitoring node is any monitoring node;

[0058] Calculate the difference between the current monitoring data of the first monitoring node and the current monitoring data of the second monitoring node as the lateral deviation of the first monitoring node; wherein the second monitoring node is an adjacent node of the first monitoring node;

[0059] The longitudinal deviation and the lateral deviation are weightedly summed to obtain the abnormality 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 an integral structure, the monitoring data of each monitoring node on the same pipeline has a certain correlation when it changes. Taking the monitoring nodes A, B, C, and D, which are uniformly arranged on a pipeline in sequence, as an example, the correlation of the changes in certain categories of pipeline monitoring data, such as vibration signals, can be reflected in that the monitoring data a, b, c, and d of the four adjacent monitoring nodes A, B, C, and D change in a step-by-step manner, for example, d=c+e=b+2e=a+3e, where e is the difference between c and d. When the monitoring nodes are non-uniformly arranged, there are differences in the difference in the monitoring data of each monitoring node, but they basically follow the above rules. At the same time, there are also certain categories of monitoring data, the correlation of which is reflected in that the difference in the monitoring data of adjacent monitoring nodes should be less than a certain threshold when the pipeline state is normal.

[0061] The longitudinal deviation of the first monitoring node is the difference between the current monitoring data and the historical monitoring data. The larger the longitudinal deviation, the more abnormal the current monitoring data. The transverse deviation is the difference between the current monitoring data and the current monitoring data of the second monitoring node. When the transverse deviation is small, it means that the difference between the current monitoring data of the second monitoring node and its historical monitoring data is also large, which can confirm that the current monitoring data of the first monitoring node is more abnormal. Therefore, the weight of the longitudinal deviation can be set to a positive value, and the weight of the transverse deviation can be set to a negative value.

[0062] In addition, a monitoring node may have multiple sensors, each of which collects a category of monitoring data. Different categories of monitoring data have different numerical ranges. Different weights can be set for different categories of monitoring data to accurately assess the degree of abnormality of the monitoring node.

[0063] In a possible implementation, before determining the data compression and packaging method of each group of monitoring nodes based on the abnormality degree of each monitoring node in each group, the method further includes:

[0064] Clustering each monitoring node according to the abnormality index value and the group of each monitoring node to obtain multiple clusters;

[0065] According to the abnormality index value of each monitoring node in each group and the cluster it belongs to, the abnormality level of each group is divided;

[0066] Based on the abnormality level of each monitoring node in each group, determine the data compression and packaging method of each group of monitoring nodes, including:

[0067] The data compression and packaging method corresponding to the abnormality degree level is used as the data compression and packaging method of the group of monitoring nodes.

[0068] In this embodiment, each monitoring node is clustered according to the abnormality index value and the group of each monitoring node. Nodes with similar abnormality levels can be clustered into one category, and the original groups are retained as much as possible, so that the groups of monitoring nodes are graded according to the abnormality level. Each group of monitoring nodes with the same abnormality level can use the same data compression packaging method.

[0069] Specifically, each group can be first regarded as a first clustering cluster, that is, a clustering cluster based on the location of the monitoring node, and then the abnormality index values ​​of the monitoring nodes are clustered on the basis of the first clustering cluster to obtain multiple second clustering clusters. Each monitoring node in each second clustering cluster corresponds to an abnormality index value level, and the abnormality index value level of each monitoring node is realized. Finally, the abnormality index value level corresponding to each monitoring node in each first clustering cluster is counted, and the abnormality index value level with the largest number is used as the abnormality level corresponding to the first clustering cluster, or the abnormality index value level with the highest level is used as the abnormality level corresponding to the first clustering 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 the abnormal data for similarity, and the abnormal level can be directly determined based on the similarity.

[0071] In a possible implementation, the abnormality level levels include high abnormality level, medium abnormality level and low abnormality level;

[0072] The data compression and packaging method corresponding to the high abnormality level is compressed using the LZ4 or Snappy algorithm and packaged in binary format;

[0073] The data compression and packaging method corresponding to the medium abnormality level is to use gzip or zlib algorithm compression and package in JSON or XML format;

[0074] The data compression and packaging method corresponding to the low abnormality level is to use the bzip2 or xz compression algorithm and package it in text format.

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

[0076] The gzip or zlib algorithm compression has a higher compression ratio, and the JSON or XML format packaging is easy to read and parse;

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

[0078] In a possible implementation, based on the abnormality level of each monitoring node in each group, the upload frequency of each group of monitoring nodes is determined, including:

[0079] Determine the basic upload frequency based on the data upload method;

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

[0081] In this embodiment, a basic uploading frequency corresponding to each data uploading mode may be preset.

[0082] When the data is uploaded through the detection vehicle, since the detection vehicle is in a continuous moving state during the active detection process, the position relationship between the detection vehicle and each monitoring node in a certain group may 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, the monitoring data collected by the group can be uploaded to the detection vehicle. This data upload method has low energy consumption and can adopt a higher basic upload frequency to improve the monitoring and early warning effect.

[0083] When the data upload method is monitoring node upload, the energy consumption of this data upload method is relatively high. A lower basic upload frequency can be used to save system energy consumption.

[0084] When the abnormality index value of each monitoring node in a group is low, for example, the average value of the abnormality index value of each monitoring node in a certain group accounts for the latter 50% of the average value of the abnormality index value of each monitoring node in each group, the basic upload frequency will not be adjusted. When the abnormality index value of each monitoring node in a group is high, the average value of the abnormality index value of each monitoring node in the group can be multiplied by the basic upload frequency as the upload frequency of the group. Then, the upload time of the group of monitoring data can be determined based on the upload frequency, and the number of uploads of abnormal monitoring data can be appropriately increased without excessively increasing the system communication energy consumption.

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

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

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

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

[0089] In this embodiment, the severity of each municipal pipeline failure is different. The importance level of each type of municipal pipeline can be pre-set according to the type, location, operation characteristics and other information of the municipal pipeline. The larger the value of the importance level, the greater the severity of the municipal pipeline failure. When the monitoring node deployed on the municipal pipeline collects abnormal monitoring data, it should be uploaded as a priority.

[0090] Specifically, the product of the abnormality level and the importance level of each group can be calculated. The larger the product is, the higher the upload priority of the group is, and the inspection vehicle will give priority to uploading the monitoring data of the group.

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

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

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

[0094] In addition, when the data is uploaded via a detection vehicle, the node closest to the detection vehicle in each group of monitoring nodes can also be set as the uploading node, so as to complete the upload as soon as possible.

[0095] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean 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 embodiment of the present invention.

[0096] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0097] Figure 2 The structure diagram of the communication device of the active monitoring and early warning system of the pipe gallery provided by the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0098] like Figure 2 As shown, the pipe gallery includes a variety of municipal pipelines, and the active monitoring and early warning system of the pipe gallery includes multiple monitoring nodes and detection vehicles. Each monitoring node is respectively arranged on each municipal pipeline, and the detection vehicle is used to actively detect each municipal pipeline at the active detection time point; the communication device 2 of the active monitoring and early warning system of the pipe gallery includes:

[0099] A method determination module 21 is used to determine a 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 a monitoring node and uploading through a detection vehicle;

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

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

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

[0103] Calculate the difference between the current monitoring data and the historical monitoring data of the first monitoring node as the longitudinal deviation of the first monitoring node; wherein the first monitoring node is any monitoring node;

[0104] Calculate the difference between the current monitoring data of the first monitoring node and the current monitoring data of the second monitoring node as the lateral deviation of the first monitoring node; wherein the second monitoring node is an adjacent node of the first monitoring node;

[0105] The longitudinal deviation and the lateral deviation are weightedly summed to obtain the abnormality index value of the first monitoring node.

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

[0107] Before determining the data compression and packaging method of each group of monitoring nodes based on the abnormality degree of each monitoring node in each group, clustering each monitoring node according to the abnormality degree index value and the group of each monitoring node to obtain multiple clusters;

[0108] According to the abnormality index value of each monitoring node in each group and the cluster it belongs to, the abnormality 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 abnormality degree level is used as the data compression and packaging method of the group of monitoring nodes.

[0111] In a possible implementation, the abnormality level levels include high abnormality level, medium abnormality level and low abnormality level;

[0112] The data compression and packaging method corresponding to the high abnormality level is compressed using the LZ4 or Snappy algorithm and packaged in binary format;

[0113] The data compression and packaging method corresponding to the medium abnormality level is to use gzip or zlib algorithm compression and package in JSON or XML format;

[0114] The data compression and packaging method corresponding to the low abnormality level is to use the bzip2 or xz compression algorithm and package it in text format.

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

[0116] Determine the basic upload frequency based on the data upload method;

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

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

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

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

[0121] In a 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 uploading node, and upload the monitoring data of the group of monitoring nodes through the uploading node.

[0123] The embodiment of the present invention selects the uploading method of monitoring data according to 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 uploaded by the detection vehicle. In other cases, the data is directly uploaded through wireless communication. The data abnormality degree of the monitoring node is quantitatively evaluated based on the monitoring data, and the appropriate compression packaging method and upload frequency are determined, so that different communication resources are allocated to monitoring data with different warning values. This can optimize the energy utilization and monitoring and warning effects of the active monitoring and early warning system of the pipeline corridor, and improve the real-time analysis and early warning of abnormal data.

[0124] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. Figure 3 As shown, the terminal 3 of 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, the steps in the communication method embodiments of the above-mentioned active monitoring and early warning system of the pipe gallery are implemented, for example Figure 1Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the above-mentioned device embodiments are realized, for example, Figure 2 Functions of modules / units 21 to 23 are shown.

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

[0126] The terminal 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of terminal 3 and does not constitute a limitation on terminal 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and 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 (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) 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, etc.

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

[0129] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by 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 embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0130] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0132] In the embodiments provided by the present 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 only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

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

[0134] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0135] If the integrated module / unit is implemented in the form of 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, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the communication method embodiments of the above-mentioned active monitoring and early warning system of the corridor can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0136] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A communication method for an active monitoring and early warning system for a pipe gallery, characterized in that: The pipe gallery includes a variety of municipal pipelines, and the active monitoring and early warning system of the pipe gallery includes a plurality of monitoring nodes and a detection vehicle, each monitoring node is respectively arranged 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: Determine a 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 a monitoring node and uploading through the detection vehicle; For each monitoring node, the monitoring data of the monitoring node is compared with the historical monitoring data and the monitoring data of the adjacent nodes to determine the abnormality degree of each monitoring node; Based on the abnormality degree of each monitoring node in each group, the data compression packaging method and upload frequency of each monitoring node in the group are determined to upload the monitoring data of each monitoring node; wherein each data compression packaging method has different compression speed and reversibility.

2. The communication method of the active monitoring and early warning system of the utility tunnel according to claim 1 is characterized in that: For each monitoring node, the monitoring data of the monitoring node is compared with the historical monitoring data and the monitoring data of the adjacent nodes to determine the abnormality degree of each monitoring node, including: Calculate the difference between the current monitoring data and the historical monitoring data of the first monitoring node as the longitudinal deviation of the first monitoring node; wherein the first monitoring node is any monitoring node; Calculating a difference between current monitoring data of the first monitoring node and current monitoring data of a second monitoring node as a lateral deviation of the first monitoring node; wherein the second monitoring node is an adjacent node of the first monitoring node; The longitudinal deviation and the lateral deviation are weightedly summed to obtain an abnormality index value of the first monitoring node.

3. The communication method of the active monitoring and early warning system of the utility tunnel according to claim 2 is characterized in that: Before determining the data compression and packaging method of each group of monitoring nodes based on the abnormality degree of each monitoring node in each group, the method further includes: Clustering each monitoring node according to the abnormality index value and the group of each monitoring node to obtain multiple clusters; According to the abnormality index value of each monitoring node in each group and the cluster it belongs to, the abnormality level of each group is divided; The method of compressing and packaging the data of each group of monitoring nodes based on the abnormality degree of each monitoring node in each group includes: The data compression and packaging method corresponding to the abnormality degree level is used as the data compression and packaging method of the group of monitoring nodes.

4. The communication method of the active monitoring and early warning system of the utility tunnel according to claim 3 is characterized in that: The abnormality levels include high abnormality, medium abnormality and low abnormality; The data compression and packaging method corresponding to the high abnormality level is to compress using LZ4 or Snappy algorithm and package in binary format; The data compression and packaging method corresponding to the medium abnormality level is to compress using the gzip or zlib algorithm and package in JSON or XML format; The data compression and packaging method corresponding to the low abnormality level is to use bzip2 or xz compression algorithm and package it in text format.

5. The communication method of the active monitoring and early warning system of the utility tunnel according to claim 2 is characterized in that: Based on the abnormality level of each monitoring node in each group, determine the upload frequency of each group of monitoring nodes, including: Determining a basic upload frequency based on the data upload method; For each group, the basic upload frequency is adjusted based on the average value of the abnormality index value of each monitoring node in the group to obtain the upload frequency of the group.

6. The communication method of the active monitoring and early warning system of the utility corridor according to claim 5 is characterized in that: The method further comprises: For each group, based on the category of the municipal pipeline where each monitoring node in the group is located, determine the importance level corresponding to the group; For each group, the upload priority of the group is determined based on the abnormality level and importance level of the group, so that the detection vehicle determines the upload order of the monitoring data of the group based on the upload priority corresponding to the group.

7. The communication method of the active monitoring and early warning system of the utility gallery according to any one of claims 1 to 6, characterized in that: The method further comprises: The monitoring node with the largest remaining power in each group of monitoring nodes is used as an uploading node, and the monitoring data of the group of monitoring nodes is uploaded through the uploading node.

8. A communication device for an active monitoring and early warning system for a pipe gallery, characterized in that: The pipe gallery includes a variety of municipal pipelines, and the active monitoring and early warning system of the pipe gallery includes a plurality of monitoring nodes and a detection vehicle, each monitoring node is respectively 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 mode determination module, used to determine a data upload mode based on a time interval between a current time point and a next active detection time point; wherein the data upload mode includes uploading through a monitoring node and uploading through the detection vehicle; A data comparison module is used to compare the monitoring data of each monitoring node with the historical monitoring data and the monitoring data of adjacent nodes to determine the abnormality degree of each monitoring node; The packaging and uploading module is used to determine the data compression packaging method and upload frequency of each monitoring node in each group based on the abnormality level of each monitoring node in the group, so as to upload the monitoring data of each monitoring node; wherein each data compression packaging method has different compression speed and reversibility.

9. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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