Waterlogging online monitoring method and system based on communication iron tower

Abstract

The invention discloses a waterlogging online monitoring method and system based on a communication iron tower, and relates to the field of data processing. The method comprises the following steps: acquiring monitoring area information and communication iron tower layout information; iron tower density parameters are obtained through calculation; synchronizing to a local monitoring analysis sub-network to obtain a local monitoring constraint; obtaining layout orientation parameters of the communication iron towers, and then selecting K permanent monitoring iron towers; monitoring the target monitoring area in real time to obtain K groups of water level time sequence data; synchronizing the K groups of water level time sequence data to a waterlogging monitoring analysis sub-network to obtain a first waterlogging early warning instruction; performing global activation according to the first waterlogging early warning instruction to obtain newly added water level time sequence data; and according to the newly added water level time sequence data, performing waterlogging rescue grade sorting, and performing waterlogging management and control on the target monitoring area. The technical problem of insufficient monitoring effectiveness of environmental waterlogging monitoring based on single precipitation in the prior art is solved, and the technical effect of effective urban waterlogging management is achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an online monitoring method and system for waterlogging based on a communication tower. Background Art

[0002] When urban flooding occurs, low-lying areas and underground passages, which are prone to water accumulation, often become the worst-hit areas. As an important part of urban communication infrastructure, communication towers, once affected by floods, may have a serious impact on the city's communication network, leading to communication interruptions or unstable signals, thus affecting people's normal life and work. To this end, tower companies need to take a series of measures to ensure the stability and reliability of the communication network. The existing communication tower waterlogging monitoring has low accuracy and reliability, and the alarm information is not transmitted in time, which affects the timeliness and effectiveness of emergency response. Summary of the invention

[0003] The embodiments of the present application provide an online waterlogging monitoring method and system based on communication towers, which solves the technical problem of insufficient monitoring effectiveness of environmental waterlogging monitoring based on a single precipitation amount in the prior art.

[0004] In view of the above problems, the embodiments of the present application provide an online monitoring method and system for urban flooding based on communication towers.

[0005] A first aspect of an embodiment of the present application provides an online monitoring method for waterlogging based on a communication tower, the method comprising:

[0006] Interactively obtain monitoring area information and communication tower layout information of the target monitoring area, wherein the monitoring area information includes monitoring area area parameters and monitoring area geographical features;

[0007] Calculate and obtain tower density parameters according to the monitoring area information and the communication tower layout information;

[0008] Synchronizing the tower density parameter and the geographical features of the monitoring area to the local monitoring and analysis subnetwork to obtain local monitoring constraints;

[0009] Interactively obtain multiple layout position parameters of multiple communication towers, and then select K permanent monitoring towers from the multiple communication towers according to the local monitoring constraints and the multiple layout position parameters, wherein a permanent communication channel is established between K waterlogging monitoring nodes of the K permanent monitoring towers and the waterlogging monitoring cloud;

[0010] Based on the K permanent monitoring towers, the target monitoring area is monitored in real time to obtain K groups of water level time series data;

[0011] Pre-deploy a waterlogging monitoring and analysis sub-network on the waterlogging monitoring cloud, synchronize the K groups of water level time series data to the waterlogging monitoring and analysis sub-network, and obtain a first waterlogging warning instruction;

[0012] Performing global activation of the plurality of waterlogging monitoring nodes according to the first waterlogging warning instruction to obtain a plurality of sets of newly added water level time series data;

[0013] The waterlogging emergency response levels are ranked according to the multiple sets of newly added water level time series data, and waterlogging control is performed in the target monitoring area according to the waterlogging emergency response level ranking.

[0014] A second aspect of an embodiment of the present application provides an online waterlogging monitoring system based on a communication tower, the system comprising:

[0015] A deployment information module, the deployment information module is used to interactively obtain monitoring area information and communication tower deployment information of the target monitoring area, wherein the monitoring area information includes monitoring area area parameters and monitoring area geographical features;

[0016] A calculation module, the calculation module is used to calculate and obtain a tower density parameter according to the monitoring area information and the communication tower layout information;

[0017] A monitoring constraint module, the monitoring constraint module is used to synchronize the tower density parameters and the geographical features of the monitoring area to the local monitoring and analysis subnetwork to obtain local monitoring constraints;

[0018] A parameter module, the parameter module is used to interactively obtain multiple layout position parameters of multiple communication towers, and then select K permanent monitoring towers from the multiple communication towers according to the local monitoring constraints and the multiple layout position parameters, wherein a permanent communication channel is established between K waterlogging monitoring nodes of the K permanent monitoring towers and the waterlogging monitoring cloud;

[0019] A data module, wherein the data module is used to perform real-time monitoring of the target monitoring area based on the K permanent monitoring towers to obtain K groups of water level time series data;

[0020] An early warning module, the early warning module is used to pre-deploy a waterlogging monitoring and analysis sub-network in the waterlogging monitoring cloud, synchronize the K groups of water level time series data to the waterlogging monitoring and analysis sub-network, and obtain a first waterlogging early warning instruction;

[0021] A global activation module, the global activation module is used to perform global activation of the multiple waterlogging monitoring nodes according to the first waterlogging warning instruction to obtain multiple sets of newly added water level time series data;

[0022] A sorting module is used to sort the waterlogging emergency levels according to the multiple groups of newly added water level time series data, and to perform waterlogging control in the target monitoring area according to the waterlogging emergency levels.

[0023] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0024] First, the monitoring area information and communication tower layout information of the target monitoring area are obtained through interaction. Among them, the monitoring area information includes the monitoring area area parameter and the monitoring area geographical features, and the communication tower layout information provides the distribution of the towers. Next, according to the monitoring area information and the communication tower layout information, the tower density parameter can be calculated. Then, the tower density parameter and the monitoring area geographical features are synchronized to the local monitoring and analysis sub-network to form a local monitoring constraint. After obtaining multiple layout orientation parameters of multiple communication towers, according to the local monitoring constraints and these layout orientation parameters, K permanent monitoring towers are selected from multiple communication towers, and a permanent communication channel is established between the K waterlogging monitoring nodes of the K permanent monitoring towers and the waterlogging monitoring cloud. Then, based on the selected K permanent monitoring towers, the target monitoring area is monitored in real time to obtain K groups of water level time series data. The waterlogging monitoring and analysis sub-network is pre-deployed in the waterlogging monitoring cloud, and the K groups of water level time series data are synchronized to the sub-network, so as to obtain the first waterlogging warning instruction. According to the first waterlogging warning instruction, multiple waterlogging monitoring nodes are globally activated to obtain multiple sets of newly added water level time series data. Finally, waterlogging emergency levels are ranked according to the multiple sets of newly added water level time series data, and waterlogging control in the target monitoring area is performed based on the ranking results. This solves the technical problem of insufficient monitoring effectiveness of environmental waterlogging monitoring based on a single precipitation amount in the prior art, and achieves the technical effect of effectively managing urban waterlogging. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0026] Figure 1 A schematic flow chart of an online waterlogging monitoring method based on a communication tower provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of the structure of an online waterlogging monitoring system based on a communication tower provided in an embodiment of the present application.

[0028] Explanation of the reference numerals: layout information module 11 , calculation module 12 , monitoring constraint module 13 , parameter module 14 , data module 15 , early warning module 16 , global activation module 17 , sorting module 18 . DETAILED DESCRIPTION

[0029] The embodiments of the present application provide a method and system for online monitoring of waterlogging based on communication towers, which solves the technical problem of insufficient effectiveness of environmental waterlogging monitoring based on a single precipitation amount in the prior art.

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0031] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0032] Embodiment 1

[0033] like Figure 1 As shown, the embodiment of the present application provides an online monitoring method for waterlogging based on a communication tower, wherein the method comprises:

[0034] Interactively obtain monitoring area information and communication tower layout information of the target monitoring area, wherein the monitoring area information includes monitoring area area parameters and monitoring area geographical features;

[0035] Urban waterlogging has a great impact on communication towers, which may cause communication interruption, unstable signals and other problems, affecting people's normal life and work. Therefore, in the case of urban waterlogging, online monitoring of waterlogging on communication towers is very important so that communication tower companies can take a series of measures to ensure the stability and reliability of the communication network. Due to the differences in geological environment and the errors in urban / industrial drainage planning, the effectiveness of environmental waterlogging monitoring based solely on precipitation may be insufficient. For example, the amount of water accumulated in areas with the same precipitation but different drainage capacity will be different. Based on this, distributed data collection is carried out on scattered towers, and then waterlogging area monitoring and waterlogging diffusion monitoring are carried out, so as to achieve the economic ranking of urban rescue and emergency rescue according to the waterlogging diffusion and waterlogging unit time changes, and realize effective urban waterlogging management.

[0036] After determining the target monitoring area, use the Geographic Information System (GIS) software to query the monitoring area information of the target monitoring area by entering keywords or conditions. The monitoring area information includes the monitoring area area parameters and the geographical features of the monitoring area, such as the latitude and longitude, altitude, terrain, etc. of the monitoring area. Through local government agencies or relevant departments, understand the layout information of communication towers, which includes the location, orientation, facilities, connections, usage, etc. of the towers. This information is very important for understanding the status of the target monitoring area and formulating an effective waterlogging monitoring plan.

[0037] Calculate and obtain tower density parameters according to the monitoring area information and the communication tower layout information;

[0038] In order to better monitor the entire area, it is necessary to consider the density parameters of communication towers in the monitoring area. The tower density parameters can be defined as the number of communication towers per unit area or the density of tower distribution. First, obtain the area of ​​the monitoring area and the number and location of the communication towers, and calculate the tower density parameters based on the number and location of the communication towers. The specific calculation method can be determined according to the actual situation. For example, the area of ​​the target monitoring area can be divided by the number of communication towers to calculate the number of communication towers per unit area. Based on the calculated tower density parameters, the distribution of towers in the target monitoring area can be evaluated, and its impact on waterlogging monitoring and control can be further analyzed.

[0039] Furthermore, the tower density parameter is calculated based on the monitoring area information and the communication tower layout information, and the method further includes:

[0040] Preset block division threshold;

[0041] Block processing is performed on the target monitoring area according to the block division threshold and the monitoring area area parameter to obtain multiple monitoring blocks;

[0042] Divide and process the geographical features of the monitoring area according to the multiple monitoring blocks to obtain multiple block geographical features;

[0043] Calculate and obtain multiple block density parameters of the multiple monitoring blocks according to the multiple deployment orientation parameters;

[0044] Preset weight assignment rules, wherein a first weight is assigned to a block's geographical features, and a second weight is assigned to a block's density parameter;

[0045] Calculate and sort the multiple block density parameters and the multiple block geographical features according to the weight assignment rule to obtain a block density sequence;

[0046] Based on the block density sequence, an extreme value call is performed to obtain the block density parameter corresponding to the maximum value as the tower density parameter.

[0047] Preferably, a threshold is set to divide the target monitoring area into blocks, the block division threshold is used to determine the boundaries of each block in the monitoring area, the target monitoring area is block-processed according to the block division threshold and the monitoring area area parameter, a plurality of monitoring blocks are obtained, and then the geographical features in each monitoring block are analyzed and processed to obtain the geographical features of each block, and the tower density parameters of each block are calculated according to the layout orientation parameters of the communication towers in each monitoring block. The importance of geographical features and block density parameters in the calculation is different, so a weight assignment rule is set to assign a first weight to the block geographical features and a second weight to the block density parameters. The weight assignment rule indicates that when calculating the tower density parameters, the importance of the block geographical features and the block density parameters are different. The first weight indicates that the importance of the block geographical features is high, while the second weight indicates that the importance of the block density parameters is relatively low. By presetting such a weight assignment rule, the actual situation of the target monitoring area can be fully considered when calculating the tower density parameters, so as to more accurately assess the risks and impacts of waterlogging, and take corresponding measures for early warning and response.

[0048] In order to sort the block density, the tower density parameters and geographical features of each monitoring block can be weighted to obtain the score of each block. Specifically, according to the preset weight assignment rule, the first weight can be assigned to the block geographical features, the second weight can be assigned to the block density parameters, and the two weights can be added to obtain the total score of each block. The scores of all monitoring blocks are sorted from high to low to form a block density sequence. The block density refers to a sequence arranged from high to low according to the tower density parameters or scores in multiple monitoring blocks. This sequence can be used to guide the layout location and number of towers to better monitor waterlogging. After sorting, the monitoring block with the highest score will be considered the optimal tower layout location, followed by the second-best monitoring block, and so on. Through such a calculation and sorting process, a block density sequence arranged from high to low by score can be obtained, which provides a reference for subsequent tower layout and waterlogging monitoring. Through extreme value calling, the maximum value in the block density sequence is found, recorded as max, and the tower density parameter of the monitoring block corresponding to max is determined, and the tower density parameter is used as the final tower density parameter. Through such an extreme value calling process, the optimal tower density parameter can be selected from the block density sequence as the final tower density parameter to guide subsequent tower layout and waterlogging monitoring work.

[0049] Synchronizing the tower density parameters and the geographical features of the monitoring area to the local monitoring and analysis subnetwork to obtain local monitoring constraints;

[0050] In order to ensure the accuracy and reliability of monitoring, local monitoring constraints are needed to guide the layout of waterlogging monitoring equipment and the analysis of monitoring data. Specifically, the tower density parameters and the geographical features of the monitoring area and other related data are collected and organized into a suitable format, and the organized data is transmitted to the local monitoring and analysis subnetwork through a local area network or other communication methods. In the local monitoring and analysis subnetwork, the received data is parsed and processed, and the local monitoring constraints are obtained according to the analysis results, thereby guiding the layout location and number of towers to ensure the accuracy and reliability of waterlogging monitoring. The local monitoring and analysis subnetwork is a local area network for waterlogging monitoring, which is composed of multiple monitoring devices, data processing and analysis modules, etc. The local monitoring and analysis subnetwork can receive tower density parameters and geographical feature data from different monitoring areas, and perform real-time monitoring and constraint analysis based on these data to provide accurate monitoring results and control instructions. The local monitoring constraints refer to the monitoring conditions and restrictions determined by factors such as tower density parameters and geographical features in the target monitoring area, such as limiting the number, distribution and signal coverage of towers. In summary, synchronizing tower density parameters and geographical features of the monitoring area to the local monitoring and analysis subnetwork can obtain more accurate local monitoring constraints and provide better support and guidance for subsequent waterlogging monitoring and control work.

[0051] Furthermore, the tower density parameter and the geographical features of the monitoring area are synchronized to the local monitoring and analysis subnetwork to obtain local monitoring constraints. The method further includes:

[0052] In the waterlogging monitoring cloud, multiple sample tower densities and multiple sample geographic features of multiple sample monitoring areas are collected;

[0053] Performing monitoring constraint evaluation identification according to the plurality of sample tower densities and the plurality of sample geographical features to obtain a plurality of sample monitoring constraints;

[0054] Using the multiple sample tower densities, the multiple sample geographic features and the multiple sample monitoring constraints to train and update the local monitoring and analysis subnetwork until the output accuracy of the local monitoring and analysis subnetwork meets the preset requirements;

[0055] The tower density parameters and the geographical features of the monitoring area are synchronized to the local monitoring and analysis subnetwork for monitoring constraint analysis to obtain the local monitoring constraints.

[0056] Preferably, a method for training and updating a local monitoring and analysis subnetwork based on multiple sample tower densities, multiple sample geographic features, and multiple sample monitoring constraints. Specifically, a waterlogging monitoring cloud platform is established, which can receive and process tower density and geographic feature data from different monitoring areas. The platform can be implemented using existing cloud computing technology and has the characteristics of high availability, high scalability, and high security. According to actual needs, the monitoring areas where sample data need to be collected are determined. These areas can be existing waterlogging monitoring areas, or areas where waterlogging may occur based on historical data and experience. In the determined sample monitoring area, tower density and geographic feature data are collected through deployed monitoring equipment (such as water level meters, flow meters, etc.) and communication networks. These data can be obtained through automated collection programs or manual methods. The collected sample data is cleaned, sorted, and standardized to ensure the accuracy and consistency of the data. The processed data needs to be stored in a reliable database or data warehouse to facilitate subsequent analysis and processing. The stored sample data is deeply analyzed and mined to extract useful information and knowledge. For example, statistical methods can be used to analyze the distribution patterns and changing trends of tower density and geographic feature data to provide references for subsequent monitoring. Based on the analysis results, multiple sample tower densities and multiple sample geographic features of multiple sample monitoring areas are generated. These sample data can be used to train and update the local monitoring and analysis subnetwork to improve the accuracy and reliability of waterlogging monitoring.

[0057] After obtaining multiple sample tower densities and multiple sample geographic features, monitoring and evaluation identification is required to obtain monitoring constraints of multiple samples. According to multiple sample tower densities and multiple sample geographic features, a suitable evaluation model is constructed to evaluate the constraints of each monitoring area. The model can be constructed based on statistical methods, machine learning algorithms or neural network models. Feature extraction is performed on each sample data to characterize the constraints of the monitoring area. Features may include tower density, geographical features, historical waterlogging conditions, etc. Model training is performed using the extracted features and corresponding monitoring constraints, and the parameters and structure of the model are adjusted to improve the accuracy and generalization ability of the model. The tower density and geographical features of each monitoring area are evaluated using the trained model to obtain the monitoring constraints of each area. These constraints may include restrictions such as the distribution, number, and signal coverage of towers. The monitoring constraints of all monitoring areas are organized into multiple sample monitoring constraints as input data for subsequent training and updating of the local monitoring analysis subnetwork. Through the above steps, monitoring constraint evaluation identification can be performed based on multiple sample tower densities and multiple sample geographical features, and multiple sample monitoring constraints can be obtained to provide data support for subsequent training and updating.

[0058] The local monitoring and analysis subnetwork is trained and updated. Specifically, multiple sample tower densities, multiple sample geographic features, and multiple sample monitoring constraint data are organized into a suitable format for use in training and updating the local monitoring and analysis subnetwork. According to actual needs, a suitable machine learning algorithm or neural network model is selected as the model of the local monitoring and analysis subnetwork. Then, according to the preset output accuracy requirements, the parameters and structure of the model are adjusted to improve the accuracy and generalization ability of the model. The model is trained with the prepared data, and the parameters and structure of the model are continuously adjusted to improve the output accuracy of the model. The training can be performed using supervised learning or unsupervised learning methods, such as using optimization algorithms such as back propagation algorithms and gradient descent algorithms to adjust the model parameters. During the training process, the output accuracy and performance of the model need to be continuously evaluated so as to adjust the parameters and structure of the model in a timely manner. The model can be evaluated using indicators such as cross-validation, confusion matrix, and accuracy. According to the results of training and evaluation, the local monitoring and analysis subnetwork is updated to improve its accuracy and reliability for waterlogging monitoring. The model can be updated using incremental learning, transfer learning, and other methods. Until the output accuracy of the local monitoring and analysis sub-network meets the preset requirements, the model training and updating are repeated to improve the performance and accuracy of the local monitoring and analysis sub-network.

[0059] The sorted data is synchronized to the local monitoring and analysis subnetwork as input data. In the local monitoring and analysis subnetwork, the input data is subjected to monitoring constraint analysis. According to the preset constraints and model parameters, useful information such as water level, flow rate, water quality and other parameters are extracted, and these parameters are constrained and analyzed to assess the risk and impact of waterlogging. After the monitoring constraint analysis, the local monitoring constraints applicable to the monitoring area are obtained. These constraints may include restrictions such as the distribution, number, and signal coverage of towers, as well as monitoring equipment deployment conditions and signal propagation restrictions based on geographical features. The output local monitoring constraints are applied to actual monitoring work to guide the deployment of waterlogging monitoring equipment and the analysis of monitoring data to ensure the accuracy and reliability of monitoring.

[0060] Interactively obtain multiple layout position parameters of multiple communication towers, and then select K permanent monitoring towers from the multiple communication towers according to the local monitoring constraints and the multiple layout position parameters, wherein a permanent communication channel is established between K waterlogging monitoring nodes of the K permanent monitoring towers and the waterlogging monitoring cloud;

[0061] Obtain multiple layout orientation parameters of communication towers from relevant departments, and the layout orientation parameters refer to determining the specific position and direction of communication towers or monitoring equipment in space. Then, input the actual tower density parameters and the geographical feature data of the monitoring area into the trained local monitoring and analysis subnetwork to perform real-time monitoring constraint analysis. According to the results of the analysis, select K permanent monitoring towers that best meet the monitoring requirements. After selecting K permanent monitoring towers, it is necessary to establish a permanent communication channel between these towers and the waterlogging monitoring cloud, which can be achieved by using some reliable communication protocols (such as TCP / IP protocol). The established communication channel needs to be stable and reliable so that the waterlogging monitoring cloud can obtain the monitoring data of these selected towers in real time. In the above manner, real-time and accurate monitoring of waterlogging in the selected area can be achieved.

[0062] Based on the K permanent monitoring towers, the target monitoring area is monitored in real time to obtain K groups of water level time series data;

[0063] Install corresponding water level monitoring equipment, such as water level gauges, sensors, etc., on the selected K permanent monitoring towers, and perform necessary debugging and calibration to ensure that the equipment can accurately and reliably monitor water level data. Through the preset communication protocol and equipment, the water level data collected by the monitoring equipment on the K permanent monitoring towers are transmitted to the waterlogging monitoring cloud in real time. Wireless transmission methods such as Zigbee, WiFi, 5G network, etc. can be used, or wired transmission methods such as optical fiber, cable, etc. can be used. After receiving the real-time water level data, the waterlogging monitoring cloud needs to process and analyze the data. This includes cleaning, sorting and standardizing the data, extracting useful water level time series information, and parsing and predicting the water level data based on the preset model or algorithm. Through data processing and analysis, K groups of water level time series data are obtained, and the water level time series data includes the current water level value, historical water level data, water level change trend and other information, which are used for subsequent waterlogging monitoring and early warning.

[0064] Pre-deploy a waterlogging monitoring and analysis sub-network on the waterlogging monitoring cloud, synchronize the K groups of water level time series data to the waterlogging monitoring and analysis sub-network, and obtain a first waterlogging warning instruction;

[0065] In the waterlogging monitoring cloud, a waterlogging monitoring and analysis subnetwork is pre-deployed. The subnetwork can use machine learning algorithms, neural network models or other appropriate analysis methods to monitor and warn water level time series data. The K groups of water level time series data obtained are synchronized to the waterlogging monitoring and analysis subnetwork by writing corresponding data synchronization programs or using existing data synchronization tools. In the waterlogging monitoring and analysis subnetwork, the synchronized K groups of water level time series data are deeply analyzed, including cleaning, sorting and standardizing the data, and using preset models or algorithms to extract useful information, such as water level change trends, abnormal water levels, etc. Based on the analysis results, it is judged whether there is a risk of waterlogging in the target monitoring area or waterlogging has occurred. When the preset warning conditions are met, a first waterlogging warning instruction is generated. The first waterlogging warning instruction is a warning instruction automatically generated by the waterlogging monitoring and analysis subnetwork when it is judged that there is a risk of waterlogging in the target monitoring area or waterlogging has occurred after analysis based on the K groups of water level time series data synchronized to the subnetwork. The first waterlogging warning instruction is obtained based on the judgment conditions to guide relevant personnel to take timely countermeasures to reduce the losses caused by waterlogging.

[0066] Furthermore, a waterlogging monitoring and analysis subnetwork is pre-deployed in the waterlogging monitoring cloud, and the method further includes:

[0067] The waterlogging monitoring and analysis subnetwork includes a waterlogging risk analysis module and a serialization engine;

[0068] A waterlogging risk coefficient calculation formula is pre-constructed, and the waterlogging risk coefficient calculation formula is as follows:

[0069]

[0070] Among them, F is the waterlogging risk coefficient, K is the number of waterlogging monitoring nodes, 0toT is the total time range for obtaining each set of water level time series data, and H (i,t) is the water level of the i-th waterlogging monitoring node at time t, is the rate of change of water level over time;

[0071] Synchronizing the waterlogging risk coefficient calculation formula to the waterlogging risk analysis module;

[0072] A waterlogging risk threshold is preset, and the waterlogging risk threshold is synchronized to the serialization engine.

[0073] The waterlogging monitoring and analysis subnetwork deployed in the waterlogging monitoring cloud mainly consists of two parts: the waterlogging risk analysis module and the serialization engine. Among them, the waterlogging risk analysis module is responsible for analyzing the water level time series data to assess the risk of waterlogging. By using the preset waterlogging risk coefficient calculation formula, the water level data is processed and analyzed to obtain the waterlogging risk assessment result. The serialization engine is responsible for serializing the analysis results for data storage and transmission. The serialization engine can convert the analysis results into a standard format for data sharing and interaction with other systems or institutions. According to the actual situation and needs, the parameters in the formula are determined to construct the waterlogging risk coefficient calculation formula, and then the constructed calculation formula is synchronized to the waterlogging risk analysis module by writing the corresponding program or using the existing data transmission tool to ensure that the calculation formula can be accurately transmitted to the module and can be called and executed when needed. Based on historical data, prediction models or other relevant information, the appropriate waterlogging risk threshold is determined. The waterlogging risk threshold refers to the preset standard value used to determine whether countermeasures need to be taken before waterlogging occurs, so as to guide relevant personnel to take countermeasures in time and reduce the losses caused by waterlogging. For example, some regions may set waterlogging risk thresholds based on indicators such as rainfall, river water level, and groundwater level. When these indicators reach the preset thresholds, an early warning response will be triggered, and relevant personnel can take timely countermeasures to mitigate the impact of waterlogging. The thresholds are then standardized or combined with other parameters to meet the requirements of the serialization engine. The set waterlogging risk thresholds are synchronized to the serialization engine to ensure that the thresholds can be accurately transmitted to the serialization engine and can trigger an early warning response when needed. Through the above steps, a complete waterlogging monitoring and analysis subnetwork can be established, including a waterlogging risk analysis module, a serialization engine, a waterlogging risk coefficient calculation formula, and a waterlogging risk threshold. The waterlogging monitoring and analysis subnetwork can effectively monitor the target monitoring area in real time and trigger an early warning response in a timely manner when an abnormal situation is found.

[0074] Furthermore, the K groups of water level time series data are synchronized to the waterlogging monitoring and analysis subnetwork to obtain a first waterlogging warning instruction, and the method further includes:

[0075] Synchronize the K groups of water level time series data one by one to the waterlogging risk coefficient calculation formula of the waterlogging monitoring and analysis subnetwork to obtain K real-time waterlogging risk coefficients;

[0076] Synchronize the K real-time waterlogging risk coefficients to the serialization engine to obtain a real-time waterlogging risk extreme value;

[0077] Determining whether the real-time waterlogging risk extreme value meets the waterlogging risk threshold;

[0078] If the real-time waterlogging risk extreme value meets the waterlogging risk threshold, generating the first waterlogging warning instruction;

[0079] The multiple waterlogging monitoring nodes are globally activated according to the first waterlogging warning instruction.

[0080] In the constructed waterlogging monitoring and analysis subnetwork, K groups of collected water level time series data are input, and K real-time waterlogging risk coefficients are calculated through the waterlogging risk coefficient calculation formula. The real-time waterlogging risk coefficient reflects the risk level of each waterlogging monitoring node. The obtained real-time waterlogging risk coefficient is synchronized to the serialization engine for data storage and transmission. In the serialization engine, the synchronized real-time waterlogging risk coefficient is further processed to find the maximum value, that is, the real-time waterlogging risk extreme value, which represents the current highest waterlogging risk level. According to the preset waterlogging risk threshold, it is determined whether the real-time waterlogging risk extreme value exceeds this threshold. If it exceeds the threshold, an early warning is triggered. When the real-time waterlogging risk extreme value meets the waterlogging risk threshold, a first waterlogging early warning instruction is generated. The first waterlogging early warning instruction may include information such as early warning level, early warning time, early warning area and response measures. The first waterlogging early warning instruction is sent to each waterlogging monitoring node to trigger the global activation of these nodes, including starting the alarm device, notifying relevant personnel, preparing emergency plans and other measures to ensure timely response to possible waterlogging events.

[0081] Performing global activation of the plurality of waterlogging monitoring nodes according to the first waterlogging warning instruction to obtain a plurality of sets of newly added water level time series data;

[0082] The waterlogging emergency response levels are ranked according to the multiple sets of newly added water level time series data, and waterlogging control is performed in the target monitoring area according to the waterlogging emergency response level ranking.

[0083] After receiving the first waterlogging warning instruction, the global activation of multiple waterlogging monitoring nodes is started, including sending the warning instruction to each waterlogging monitoring node through the communication network to trigger their activation state. After the multiple waterlogging monitoring nodes enter the global activation state, these nodes start or strengthen the monitoring and collection of water level, water flow and other information in the target monitoring area. In the global activation state, the multiple waterlogging monitoring nodes obtain multiple sets of newly added water level time series data in the target monitoring area through sensors, observation equipment and other means, and the newly added water level time series data reflects the water level change in the target monitoring area. The multiple sets of newly added water level time series data obtained are deeply analyzed to evaluate the risk level of waterlogging, including comprehensive consideration of factors such as water level change trend, peak value, duration, etc., to determine the severity of waterlogging. According to the analysis results, the waterlogging risk level is ranked to determine which areas or time periods have the most serious waterlogging and need to be rescued first. According to the ranking of waterlogging rescue levels, corresponding control plans are formulated for different levels of waterlogging. The formulated waterlogging control plan is notified to relevant personnel and organized for implementation. For serious waterlogging areas, measures should be taken quickly to minimize the loss of life and property; for less serious waterlogging areas, monitoring and early warning should be strengthened to be ready to respond to possible deterioration at any time. The waterlogging emergency level ranking based on multiple sets of newly added water level time series data is realized, and waterlogging control in the target monitoring area is carried out according to the ranking results to improve the efficiency of responding to waterlogging events and reduce their impact on people and property.

[0084] Furthermore, the waterlogging emergency levels are ranked according to the multiple sets of newly added water level time series data, and waterlogging control is performed in the target monitoring area according to the waterlogging emergency level ranking. The method further includes:

[0085] Synchronizing the multiple sets of newly added water level time series data to the waterlogging risk analysis module to obtain multiple newly added risk coefficients;

[0086] Mapping the multiple newly added risk coefficients according to the multiple monitoring blocks for minutes to obtain multiple groups of block risk coefficients;

[0087] Calculating the mean of the multiple groups of block risk coefficients to obtain multiple block waterlogging coefficients;

[0088] Serializing the waterlogging coefficients of the multiple blocks to obtain emergency level ranking results;

[0089] Carry out waterlogging rescue in the target monitoring area according to the rescue level ranking results.

[0090] Preferably, the specific process of waterlogging rescue for the target monitoring area according to the sorting result is as follows: first, synchronize the acquired multiple groups of newly added water level time series data to the waterlogging risk analysis module for further analysis and calculation. Then, in the waterlogging risk analysis module, perform risk assessment and analysis on the synchronized multiple groups of newly added water level time series data, and calculate the corresponding newly added risk coefficient, which reflects the latest waterlogging risk situation in the target monitoring area. Then, map each newly added risk coefficient according to the corresponding monitoring block and express it in minutes. In this way, the waterlogging risk coefficient of each monitoring block at different time points can be obtained. According to the waterlogging risk coefficient of each monitoring block at different time points obtained in the previous step, multiple groups of block risk coefficients can be further calculated, and the block risk coefficient reflects the degree of waterlogging risk of each monitoring block at different time points. The average value of multiple groups of block risk coefficients is calculated to obtain the waterlogging coefficient of each monitoring block, and the waterlogging coefficient of the monitoring block can be used to represent the overall waterlogging risk degree of each block. The waterlogging coefficients of multiple blocks are serialized to obtain the rescue level ranking results, which can be used to guide the priority and order of waterlogging rescue work. According to the rescue level ranking results, blocks with high levels are processed first, and corresponding rescue teams and resources are organized to carry out waterlogging rescue work.

[0091] In summary, the embodiments of the present application have at least the following technical effects:

[0092] First, the monitoring area information and communication tower layout information of the target monitoring area are obtained through interaction. Among them, the monitoring area information includes the monitoring area area parameter and the monitoring area geographical features, and the communication tower layout information provides the distribution of the towers. Next, according to the monitoring area information and the communication tower layout information, the tower density parameter can be calculated. Then, the tower density parameter and the monitoring area geographical features are synchronized to the local monitoring and analysis sub-network to form a local monitoring constraint. After obtaining multiple layout orientation parameters of multiple communication towers, according to the local monitoring constraints and these layout orientation parameters, K permanent monitoring towers are selected from multiple communication towers, and a permanent communication channel is established between the K waterlogging monitoring nodes of the K permanent monitoring towers and the waterlogging monitoring cloud. Then, based on the selected K permanent monitoring towers, the target monitoring area is monitored in real time to obtain K groups of water level time series data. The waterlogging monitoring and analysis sub-network is pre-deployed in the waterlogging monitoring cloud, and the K groups of water level time series data are synchronized to the sub-network, so as to obtain the first waterlogging warning instruction. According to the first waterlogging warning instruction, multiple waterlogging monitoring nodes are globally activated to obtain multiple sets of newly added water level time series data. Finally, waterlogging emergency levels are ranked according to the multiple sets of newly added water level time series data, and waterlogging control in the target monitoring area is performed based on the ranking results. This solves the technical problem of insufficient monitoring effectiveness of environmental waterlogging monitoring based on a single precipitation amount in the prior art, and achieves the technical effect of effectively managing urban waterlogging.

[0093] Embodiment 2

[0094] Based on the same inventive concept as the online monitoring method for waterlogging based on communication towers in the aforementioned embodiment, Figure 2 As shown, the present application provides an online monitoring system for waterlogging based on a communication tower, and the system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0095] Arrange information module 11, calculation module 12, monitoring constraint module 13, parameter module 14, data module 15, early warning module 16, global activation module 17, and sorting module 18.

[0096] A deployment information module 11, wherein the deployment information module 11 is used to interactively obtain monitoring area information and communication tower deployment information of a target monitoring area, wherein the monitoring area information includes monitoring area area parameters and monitoring area geographical features;

[0097] A calculation module 12, wherein the calculation module 12 is used to calculate and obtain a tower density parameter according to the monitoring area information and the communication tower layout information;

[0098] A monitoring constraint module 13, wherein the monitoring constraint module 13 is used to synchronize the tower density parameters and the geographical features of the monitoring area to a local monitoring and analysis subnetwork to obtain local monitoring constraints;

[0099] A parameter module 14, wherein the parameter module 14 is used to interactively obtain multiple layout position parameters of multiple communication towers, and then select K permanent monitoring towers from the multiple communication towers according to the local monitoring constraints and the multiple layout position parameters, wherein a permanent communication channel is established between K waterlogging monitoring nodes of the K permanent monitoring towers and the waterlogging monitoring cloud;

[0100] A data module 15, wherein the data module 15 is used to perform real-time monitoring of the target monitoring area based on the K permanent monitoring towers to obtain K groups of water level time series data;

[0101] An early warning module 16 is used to pre-deploy a waterlogging monitoring and analysis sub-network in the waterlogging monitoring cloud, synchronize the K groups of water level time series data to the waterlogging monitoring and analysis sub-network, and obtain a first waterlogging early warning instruction;

[0102] A global activation module 17, the global activation module 17 is used to perform global activation of the plurality of waterlogging monitoring nodes according to the first waterlogging warning instruction, and obtain a plurality of sets of newly added water level time series data;

[0103] The sorting module 18 is used to sort the waterlogging emergency levels according to the multiple sets of newly added water level time series data, and to perform waterlogging control in the target monitoring area according to the waterlogging emergency levels.

[0104] Furthermore, the calculation module 12 is used to execute the following method:

[0105] Preset block division threshold;

[0106] Block processing is performed on the target monitoring area according to the block division threshold and the monitoring area area parameter to obtain multiple monitoring blocks;

[0107] Divide and process the geographical features of the monitoring area according to the multiple monitoring blocks to obtain multiple block geographical features;

[0108] Calculate and obtain multiple block density parameters of the multiple monitoring blocks according to the multiple deployment orientation parameters;

[0109] Preset weight assignment rules, wherein a first weight is assigned to a block's geographical features, and a second weight is assigned to a block's density parameter;

[0110] Calculate and sort the multiple block density parameters and the multiple block geographical features according to the weight assignment rule to obtain a block density sequence;

[0111] Based on the block density sequence, an extreme value call is performed to obtain the block density parameter corresponding to the maximum value as the tower density parameter.

[0112] Furthermore, the monitoring and restraining module 13 is used to execute the following method:

[0113] In the waterlogging monitoring cloud, multiple sample tower densities and multiple sample geographic features of multiple sample monitoring areas are collected;

[0114] Performing monitoring constraint evaluation identification according to the plurality of sample tower densities and the plurality of sample geographical features to obtain a plurality of sample monitoring constraints;

[0115] Using the multiple sample tower densities, the multiple sample geographic features and the multiple sample monitoring constraints to train and update the local monitoring and analysis subnetwork until the output accuracy of the local monitoring and analysis subnetwork meets the preset requirements;

[0116] The tower density parameters and the geographical features of the monitoring area are synchronized to the local monitoring and analysis subnetwork for monitoring constraint analysis to obtain the local monitoring constraints.

[0117] Furthermore, the early warning module 16 is used to execute the following method:

[0118] The waterlogging monitoring and analysis subnetwork includes a waterlogging risk analysis module and a serialization engine;

[0119] A waterlogging risk coefficient calculation formula is pre-constructed, and the waterlogging risk coefficient calculation formula is as follows:

[0120]

[0121] Among them, F is the waterlogging risk coefficient, K is the number of waterlogging monitoring nodes, 0toT is the total time range for obtaining each set of water level time series data, and H (i,t) is the water level of the i-th waterlogging monitoring node at time t, is the rate of change of water level over time;

[0122] Synchronizing the waterlogging risk coefficient calculation formula to the waterlogging risk analysis module;

[0123] A waterlogging risk threshold is preset, and the waterlogging risk threshold is synchronized to the serialization engine.

[0124] Furthermore, the monitoring module 14 is used to execute the following method:

[0125] Read initial status information based on dynamic obstacles and monitor and determine real-time status information;

[0126] Determining a dynamic operation trend of the dynamic obstacle based on the initial state information and the real-time state information;

[0127] Based on the dynamic operation trend, an adjustment decision of the initial vehicle trajectory is performed.

[0128] Furthermore, the early warning module 16 is used to execute the following method:

[0129] Synchronize the K groups of water level time series data one by one to the waterlogging risk coefficient calculation formula of the waterlogging monitoring and analysis subnetwork to obtain K real-time waterlogging risk coefficients;

[0130] Synchronize the K real-time waterlogging risk coefficients to the serialization engine to obtain a real-time waterlogging risk extreme value;

[0131] Determining whether the real-time waterlogging risk extreme value meets the waterlogging risk threshold;

[0132] If the real-time waterlogging risk extreme value meets the waterlogging risk threshold, generating the first waterlogging warning instruction;

[0133] The multiple waterlogging monitoring nodes are globally activated according to the first waterlogging warning instruction.

[0134] Furthermore, the sorting module 18 is used to perform the following method:

[0135] Synchronizing the multiple sets of newly added water level time series data to the waterlogging risk analysis module to obtain multiple newly added risk coefficients;

[0136] Mapping the multiple newly added risk coefficients according to the multiple monitoring blocks for minutes to obtain multiple groups of block risk coefficients;

[0137] Calculating the mean of the multiple groups of block risk coefficients to obtain multiple block waterlogging coefficients;

[0138] Serializing the waterlogging coefficients of the multiple blocks to obtain emergency level ranking results;

[0139] Carry out waterlogging rescue in the target monitoring area according to the rescue level ranking results.

[0140] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0141] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0142] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. The online monitoring method of waterlogging based on communication tower is characterized by: The method is applied to an online waterlogging monitoring system based on a communication tower, the system comprising a waterlogging monitoring cloud and a plurality of waterlogging monitoring nodes, the plurality of waterlogging monitoring nodes being arranged at a plurality of preset positions of the communication towers, the method comprising: Interactively obtain monitoring area information and communication tower layout information of the target monitoring area, wherein the monitoring area information includes monitoring area area parameters and monitoring area geographical features; Calculate and obtain tower density parameters according to the monitoring area information and the communication tower layout information; Synchronizing the tower density parameter and the geographical features of the monitoring area to the local monitoring and analysis subnetwork to obtain local monitoring constraints; Interactively obtain multiple layout position parameters of multiple communication towers, and then select K permanent monitoring towers from the multiple communication towers according to the local monitoring constraints and the multiple layout position parameters, wherein a permanent communication channel is established between K waterlogging monitoring nodes of the K permanent monitoring towers and the waterlogging monitoring cloud; Based on the K permanent monitoring towers, the target monitoring area is monitored in real time to obtain K groups of water level time series data; Pre-deploy a waterlogging monitoring and analysis sub-network on the waterlogging monitoring cloud, synchronize the K groups of water level time series data to the waterlogging monitoring and analysis sub-network, and obtain a first waterlogging warning instruction; Performing global activation of the plurality of waterlogging monitoring nodes according to the first waterlogging warning instruction to obtain a plurality of sets of newly added water level time series data; The waterlogging emergency response levels are ranked according to the multiple sets of newly added water level time series data, and waterlogging control is performed in the target monitoring area according to the waterlogging emergency response level ranking.

2. The method according to claim 1, characterized in that The tower density parameter is calculated based on the monitoring area information and the communication tower layout information, and the method further includes: Preset block division threshold; Block processing is performed on the target monitoring area according to the block division threshold and the monitoring area area parameter to obtain multiple monitoring blocks; Divide and process the geographical features of the monitoring area according to the multiple monitoring blocks to obtain multiple block geographical features; Calculate and obtain multiple block density parameters of the multiple monitoring blocks according to the multiple deployment orientation parameters; Preset weight assignment rules, wherein a first weight is assigned to a block's geographical features, and a second weight is assigned to a block's density parameter; Calculate and sort the multiple block density parameters and the multiple block geographical features according to the weight assignment rule to obtain a block density sequence; Based on the block density sequence, an extreme value call is performed to obtain the block density parameter corresponding to the maximum value as the tower density parameter.

3. The method according to claim 2, characterized in that Synchronizing the tower density parameter and the geographical features of the monitoring area to the local monitoring and analysis subnetwork to obtain local monitoring constraints, the method further comprising: In the waterlogging monitoring cloud, multiple sample tower densities and multiple sample geographic features of multiple sample monitoring areas are collected; Performing monitoring constraint evaluation identification according to the plurality of sample tower densities and the plurality of sample geographical features to obtain a plurality of sample monitoring constraints; Using the multiple sample tower densities, the multiple sample geographic features and the multiple sample monitoring constraints to train and update the local monitoring and analysis subnetwork until the output accuracy of the local monitoring and analysis subnetwork meets the preset requirements; The tower density parameters and the geographical features of the monitoring area are synchronized to the local monitoring and analysis subnetwork for monitoring constraint analysis to obtain the local monitoring constraints.

4. The method according to claim 2, characterized in that A waterlogging monitoring and analysis subnetwork is pre-deployed on the waterlogging monitoring cloud, and the method further includes: The waterlogging monitoring and analysis subnetwork includes a waterlogging risk analysis module and a serialization engine; A waterlogging risk coefficient calculation formula is pre-constructed, and the waterlogging risk coefficient calculation formula is as follows: Among them, F is the waterlogging risk coefficient, K is the number of waterlogging monitoring nodes, 0toT is the total time range for obtaining each set of water level time series data, and H (i,t) is the water level of the i-th waterlogging monitoring node at time t, is the rate of change of water level over time; Synchronizing the waterlogging risk coefficient calculation formula to the waterlogging risk analysis module; A waterlogging risk threshold is preset, and the waterlogging risk threshold is synchronized to the serialization engine.

5. The method according to claim 4, characterized in that Synchronizing the K groups of water level time series data to the waterlogging monitoring and analysis subnetwork to obtain a first waterlogging warning instruction, the method further comprising: Synchronize the K groups of water level time series data one by one to the waterlogging risk coefficient calculation formula of the waterlogging monitoring and analysis subnetwork to obtain K real-time waterlogging risk coefficients; Synchronize the K real-time waterlogging risk coefficients to the serialization engine to obtain a real-time waterlogging risk extreme value; Determining whether the real-time waterlogging risk extreme value meets the waterlogging risk threshold; If the real-time waterlogging risk extreme value meets the waterlogging risk threshold, generating the first waterlogging warning instruction; The multiple waterlogging monitoring nodes are globally activated according to the first waterlogging warning instruction.

6. The method according to claim 5, characterized in that The method further includes: ranking waterlogging emergency response levels according to the multiple groups of newly added water level time series data, and performing waterlogging control in the target monitoring area according to the waterlogging emergency response levels. Synchronizing the multiple sets of newly added water level time series data to the waterlogging risk analysis module to obtain multiple newly added risk coefficients; Mapping the multiple newly added risk coefficients according to the multiple monitoring blocks for minutes to obtain multiple groups of block risk coefficients; Calculating the mean of the multiple groups of block risk coefficients to obtain multiple block waterlogging coefficients; Serializing the waterlogging coefficients of the multiple blocks to obtain emergency level ranking results; Carry out waterlogging rescue in the target monitoring area according to the rescue level ranking results.

7. The online waterlogging monitoring system based on communication towers is characterized by: The system includes a waterlogging monitoring cloud and a plurality of waterlogging monitoring nodes, wherein the plurality of waterlogging monitoring nodes are arranged at preset positions of a plurality of communication towers, and the system includes: A deployment information module, the deployment information module is used to interactively obtain monitoring area information and communication tower deployment information of the target monitoring area, wherein the monitoring area information includes monitoring area area parameters and monitoring area geographical features; A calculation module, the calculation module is used to calculate and obtain a tower density parameter according to the monitoring area information and the communication tower layout information; A monitoring constraint module, the monitoring constraint module is used to synchronize the tower density parameters and the geographical features of the monitoring area to the local monitoring and analysis subnetwork to obtain local monitoring constraints; A parameter module, the parameter module is used to interactively obtain multiple layout position parameters of multiple communication towers, and then select K permanent monitoring towers from the multiple communication towers according to the local monitoring constraints and the multiple layout position parameters, wherein a permanent communication channel is established between K waterlogging monitoring nodes of the K permanent monitoring towers and the waterlogging monitoring cloud; A data module, wherein the data module is used to perform real-time monitoring of the target monitoring area based on the K permanent monitoring towers to obtain K groups of water level time series data; An early warning module, the early warning module is used to pre-deploy a waterlogging monitoring and analysis sub-network in the waterlogging monitoring cloud, synchronize the K groups of water level time series data to the waterlogging monitoring and analysis sub-network, and obtain a first waterlogging early warning instruction; A global activation module, the global activation module is used to perform global activation of the multiple waterlogging monitoring nodes according to the first waterlogging warning instruction to obtain multiple sets of newly added water level time series data; A sorting module is used to sort the waterlogging emergency levels according to the multiple groups of newly added water level time series data, and to perform waterlogging control in the target monitoring area according to the waterlogging emergency levels.

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