Urban road ponding real-time monitoring management system based on wireless communication technology

Abstract

The invention discloses an urban road ponding real-time monitoring management system based on a wireless communication technology, and relates to the technical field of urban road intelligent monitoring. The system collects data of ponding liquid level, wireless communication quality, battery voltage and timestamp at low-lying and easy-ponding points of an urban road; calculating a ponding depth anomaly coefficient and a ponding development trend coefficient, and realizing graded evaluation of a ponding state and an evolution trend thereof; and when the ponding risk is aggravated, further comprehensively evaluating the stability of the communication link and the residual energy of the equipment, adaptively adjusting a sampling and communication strategy according to an evaluation result, caching key data and reporting risk information. The urban road waterlogging monitoring system can realize continuous and reliable monitoring of urban road waterlogging under the condition of no external power supply, improves the timeliness of waterlogging risk identification and the stability of system operation, and is suitable for urban road waterlogging prevention and safety management scenes.

Description

Technical Field

[0001] With the continuous advancement of urbanization, the paved area of ​​urban roads is constantly increasing, putting significant pressure on drainage systems under conditions of short-term heavy rainfall. Low-lying areas, underpasses, and sections of roads with insufficient drainage capacity are prone to flooding. Road flooding not only affects the normal passage of vehicles and pedestrians but can also lead to traffic accidents, damage to electrical facilities, and other safety hazards such as urban waterlogging. Therefore, real-time, continuous, and reliable monitoring and management of urban road flooding has become a crucial technical requirement for smart city construction and urban flood control management.

[0002] Currently, the main methods for monitoring urban road flooding include manual patrols, video surveillance, and sensor-based online monitoring. Manual patrols suffer from slow response times and limited coverage; video surveillance is significantly affected by lighting conditions, weather, and obstructions, making it difficult to accurately quantify water depth; while sensor-based online monitoring can directly obtain water level information, it still has several technical shortcomings in practical applications.

[0003] Most existing road waterlogging monitoring systems typically focus only on a single water level threshold, that is, they determine whether there is a risk of waterlogging by simply comparing the current water level with a preset warning water level. They fail to comprehensively consider the differences between the rate of water level change, short-term fluctuations and continuous development trends, which can easily lead to misjudgments in the early stages of rainfall or under short-term disturbances. It is difficult to achieve graded identification and dynamic assessment of the evolution process of waterlogging risk.

[0004] Road flood monitoring terminals deployed without external power supply typically rely on battery power, and their operational capabilities are significantly constrained by communication link quality and remaining energy status. In existing technologies, water level monitoring and equipment operational status assessment are often independent, lacking a mechanism to link flood risk levels with equipment operational reliability. This makes them prone to situations where equipment power is depleted, communication becomes unstable, or even the terminal goes offline when flood risk intensifies, leading to the loss of critical monitoring data and reducing the overall reliability of the monitoring system.

[0005] Furthermore, existing road flooding monitoring systems mostly employ a fixed sampling cycle design, failing to dynamically adjust the sampling density based on changes in flooding conditions. When flooding rises rapidly or enters a high-risk phase, the fixed sampling cycle struggles to capture detailed water level changes in a timely manner; while when flooding is stable or in a low-risk state, continuous high-frequency sampling results in unnecessary energy waste, which is detrimental to the long-term stable operation of the system. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a real-time monitoring and management system for urban road waterlogging based on wireless communication technology, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a real-time monitoring and management system for urban road waterlogging based on wireless communication technology, comprising:

[0008] The data acquisition module is used to monitor the water status of low-lying and easily flooded points on urban roads in real time, periodically collect the raw value of the water level (Hraw); collect the wireless link quality between the monitoring terminal and the remote platform, and obtain the signal strength indicator value (RSSI); collect the terminal power supply and time reference status, and obtain the battery voltage (Vbat); and collect the sampling timestamp (tstamp).

[0009] The water depth anomaly assessment module is used to obtain effective water level parameters Hc, historical safe water level benchmark Href, and water level change based on the original water level value Hraw and the sampling timestamp tstamp. and adjacent sampling time interval The abnormal water depth coefficient JSY is calculated and compared with the abnormal water depth threshold Jth to determine whether the current urban road monitoring point is in a normal water accumulation state. If it is in an abnormal water accumulation state, the water accumulation risk enhancement assessment mechanism is activated.

[0010] The waterlogging development trend assessment module is used to activate the enhanced waterlogging risk assessment mechanism and construct a key monitoring water level change data between multiple adjacent sampling groups. Sampling time interval adjacent to the corresponding key monitoring The trend assessment dataset is used to calculate the water accumulation trend coefficient FQX and compare it with the water accumulation trend threshold Fth to determine whether the water accumulation at the current urban road monitoring points has an upward trend. If so, the equipment operation guarantee assessment mechanism is activated.

[0011] The equipment operation assurance assessment module is used to initiate the equipment operation assurance assessment mechanism. By acquiring the stable communication quality parameter Rc, the communication quality reference value Rref, the remaining energy characterization parameter Ec, and the battery energy reference value Eref, it calculates the equipment operation assurance coefficient SYB and compares it with the equipment operation assurance threshold Sth to determine whether the equipment operation status is qualified. If it is not qualified, it automatically adjusts the sampling and communication strategy, caches key data, and reports risk information to reduce power consumption, ensure continuous monitoring, and extend the equipment working cycle.

[0012] Preferably, the data acquisition module includes a water accumulation status acquisition unit, a communication link acquisition unit, and a power supply status acquisition unit;

[0013] The water accumulation status acquisition unit is used to monitor the water status and environmental conditions at water accumulation points on urban roads in real time; by installing water level probes at low-lying and easily flooded locations on urban roads, the water level is periodically collected to obtain the original water level value Hraw.

[0014] The communication link acquisition unit is used to monitor the wireless communication conditions between the water accumulation monitoring point and the remote platform in real time; through the communication component installed inside the monitoring terminal, the signal strength indicator value RSSI of the current wireless communication link is obtained in real time during the data acquisition process.

[0015] The power supply status acquisition unit is used to acquire the energy status and data time reference of the monitoring equipment under conditions without external power supply; through the power management circuit installed inside the monitoring terminal, the current battery voltage of the equipment is acquired in real time to obtain battery voltage data Vbat; through the real-time clock RTC integrated inside the terminal, the corresponding timestamp tstamp is generated and acquired when the equipment is woken up and performs sampling operation.

[0016] Preferably, the water depth anomaly assessment module includes a water level time parameter extraction unit, a first calculation unit, and a first analysis unit;

[0017] The water level time parameter extraction unit is used to obtain the effective accumulated water level parameter Hc based on the original liquid level sequence Hraw collected by the water level probe by averaging multiple consecutive samples and zero-point offset calibration; based on two consecutive calibrated effective water levels Hc(k) and Hc(k−1) at the same monitoring point, the water level change between the two adjacent samples is calculated and obtained using differential operation. Based on the two sampling timestamps tstamp(k) and tstamp(k−1) generated by the real-time clock (RTC), the time interval between adjacent sampling is obtained by calculating the timestamp difference. Based on historical observation data synchronized from the cloud platform or on-site historical database during equipment initialization, the historical safe water level benchmark value Href of the road location is read and obtained by adopting long-term historical statistics and quantile value selection method.

[0018] Preferably, the first calculation unit is used to calculate the effective accumulated water level parameter Hc, the historical safe water level reference value Href, and the water level change. and adjacent sampling time interval After dimensionless processing, the anomaly coefficient JSY for water depth is calculated and obtained.

[0019] Preferably, the first analysis unit is used to obtain a first evaluation result by comparing the water depth anomaly coefficient JSY with the water depth anomaly threshold Jth using a preset water depth anomaly threshold Jth:

[0020] When the abnormal water depth coefficient JSY < the abnormal water depth threshold Jth, it is determined that the water level at the current urban road monitoring point is within a controllable range and is a normal water accumulation state, and continuous monitoring is required.

[0021] When the water depth anomaly coefficient JSY is greater than or equal to the water depth anomaly threshold Jth, the water level at the current urban road monitoring point is determined to be outside the controllable range, indicating an abnormal water accumulation state. This triggers the first early warning instruction and generates the first strategy: marking the current urban road monitoring point as a key monitoring target with an abnormal water accumulation risk; initiating the enhanced water accumulation risk assessment mechanism; and shortening the sampling cycle to increase the real-time monitoring density of the current point.

[0022] Preferably, the water accumulation trend assessment module includes a second calculation unit and a second analysis unit;

[0023] The second calculation unit is used to activate the enhanced water accumulation risk assessment mechanism when the first early warning instruction is received; based on the enhanced current monitoring density, it obtains a continuous water level data sequence and constructs a key monitoring water level change data containing multiple sets of adjacent samples. Sampling time interval adjacent to the corresponding key monitoring The trend assessment dataset was processed to be dimensionless, and the water accumulation trend coefficient FQX was calculated and obtained.

[0024] Preferably, the second analysis unit is used to obtain a second evaluation result by comparing the water accumulation trend coefficient FQX with the water accumulation trend threshold Fth, based on a preset water accumulation trend threshold Fth:

[0025] When the water accumulation trend coefficient FQX < the water accumulation trend threshold Fth, it is determined that the water accumulation at the current urban road monitoring point has no upward trend, and monitoring continues.

[0026] When the water accumulation trend coefficient FQX is greater than or equal to the water accumulation trend threshold Fth, it is determined that the water accumulation at the current urban road monitoring point is showing an upward trend, and there is a risk that the water accumulation will deepen further, affecting road traffic or causing safety accidents. This triggers a second warning instruction and generates a second strategy: mark the current monitoring point as a high-risk water accumulation point; and activate the equipment operation guarantee assessment mechanism to comprehensively assess the stability of the communication link and the remaining energy of the equipment.

[0027] Preferably, the equipment operation assurance assessment module includes a communication quality parameter extraction unit, a third calculation unit, and a third analysis unit;

[0028] The communication quality parameter extraction unit is used to extract and obtain stable communication quality parameter Rc based on the wireless signal strength sequence RSSI periodically collected by the communication module, using sliding window outlier elimination and interval normalization mapping; based on the signal strength of historical statistics from the cloud at the same monitoring point, a long-term median comparison method is used to obtain the communication quality reference value Rref; based on the battery terminal voltage sequence Vbat periodically collected by the power management circuit, a sampling smoothing and segmented mapping method is used to obtain the remaining energy characterization parameter Ec; and through the battery's factory nominal value, a baseline calculation method combining nominal capacity and temperature correction is used to obtain the battery energy reference value Eref.

[0029] Preferably, the third calculation unit is used to calculate and obtain the equipment operation guarantee coefficient SYB by dimensionlessly processing the obtained stable communication quality parameter Rc, communication quality reference value Rref, remaining energy characterization parameter Ec, and battery energy reference value Eref.

[0030] Preferably, the third analysis unit is used to obtain a third evaluation result by comparing the equipment operation guarantee coefficient SYB with the equipment operation guarantee threshold Sth using a preset equipment operation guarantee threshold Sth:

[0031] When the equipment operation guarantee coefficient SYB ≥ the equipment operation guarantee threshold Sth, it indicates that the equipment operation status is qualified and should be continuously monitored.

[0032] When the equipment operation guarantee coefficient SYB < the equipment operation guarantee threshold Sth, it indicates that the equipment is in an unqualified operating state. The current urban road waterlogging monitoring equipment is experiencing a decline in communication quality or power supply capacity. The equipment is at risk of reduced monitoring capabilities, delayed data reporting, or equipment offline due to communication link degradation or insufficient remaining energy. This triggers a third early warning instruction and generates a third strategy: automatically reducing the sampling frequency or extending the sampling interval to reduce the energy consumption of high-frequency sampling; limiting the number of retries for communication failures to avoid repeated reporting attempts under poor signal quality conditions, which would cause additional power consumption; retaining key waterlogging status data and caching it locally, and uploading it centrally after communication conditions improve; sending an equipment operation risk indicator to the cloud to prompt maintenance personnel to conduct inspections or arrange maintenance plans for the current equipment location; after the adjustment is completed, the equipment re-enters a low-power sleep mode, waiting for the next sampling wake-up, extending the equipment's available working cycle.

[0033] This invention provides a real-time monitoring and management system for urban road waterlogging based on wireless communication technology. It has the following beneficial effects:

[0034] (1) The real-time monitoring and management system for urban road waterlogging based on wireless communication technology analyzes the road waterlogging status from two dimensions: "whether the current water level is abnormal" and "whether the water level change shows an upward trend" by constructing a waterlogging depth anomaly assessment module and a waterlogging development trend assessment module. This avoids coarse-grained judgment based solely on a single water level threshold, thereby enabling more accurate differentiation between normal waterlogging, abnormal waterlogging, and high-risk waterlogging status, and improving the scientificity and reliability of waterlogging risk identification.

[0035] (2) The urban road waterlogging real-time monitoring and management system based on wireless communication technology introduces parameters of water level change and adjacent sampling time interval in the calculation of water depth anomaly coefficient and water development trend coefficient. This makes the evaluation results not only reflect the absolute water level height, but also reflect the water growth rate and trend. It can promptly identify the risk of sudden and accelerated waterlogging caused by short-term heavy rainfall or drainage obstruction, and improve the system's early warning timeliness.

[0036] (3) The real-time monitoring and management system for urban road water accumulation based on wireless communication technology further introduces an equipment operation guarantee assessment mechanism during the stage of increased water accumulation risk. By conducting a comprehensive quantitative analysis of communication quality and remaining energy, it can identify in advance the problems of equipment offline, data delay or monitoring interruption that may be caused by communication link degradation or insufficient power supply, so that the system can be expanded from "only monitoring water accumulation" to "simultaneously guaranteeing monitoring capabilities", which significantly improves the overall operational reliability.

[0037] (4) The real-time monitoring and management system for urban road waterlogging based on wireless communication technology automatically generates differentiated operation strategies for different evaluation results, including measures such as adjusting sampling frequency, limiting communication retry, local data caching, low-power sleep mode and operation and maintenance prompts. While ensuring the continuous acquisition of key waterlogging information, it effectively reduces equipment energy consumption and invalid communication overhead, extends the continuous working cycle of equipment, reduces the frequency of manual inspection and operation and maintenance costs, and has good engineering practicality and promotion value. Attached Figure Description

[0038] Figure 1 This is a flowchart of the urban road waterlogging real-time monitoring and management system based on wireless communication technology according to the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1

[0041] Please see Figure 1 This invention provides a real-time monitoring and management system for urban road waterlogging based on wireless communication technology, comprising:

[0042] The data acquisition module is used to monitor the water status of low-lying and easily flooded points on urban roads in real time, periodically collect the raw value of the water level (Hraw); collect the wireless link quality between the monitoring terminal and the remote platform, and obtain the signal strength indicator value (RSSI); collect the terminal power supply and time reference status, and obtain the battery voltage (Vbat); and collect the sampling timestamp (tstamp).

[0043] The water depth anomaly assessment module is used to obtain effective water level parameters Hc, historical safe water level benchmark Href, and water level change based on the original water level value Hraw and the sampling timestamp tstamp. and adjacent sampling time interval The abnormal water depth coefficient JSY is calculated and compared with the abnormal water depth threshold Jth to determine whether the current urban road monitoring point is in a normal water accumulation state. If it is in an abnormal water accumulation state, the water accumulation risk enhancement assessment mechanism is activated.

[0044] The waterlogging development trend assessment module is used to activate the enhanced waterlogging risk assessment mechanism and construct a key monitoring water level change data between multiple adjacent sampling groups. Sampling time interval adjacent to the corresponding key monitoring The trend assessment dataset is used to calculate the water accumulation trend coefficient FQX and compare it with the water accumulation trend threshold Fth to determine whether the water accumulation at the current urban road monitoring points has an upward trend. If so, the equipment operation guarantee assessment mechanism is activated.

[0045] The equipment operation assurance assessment module is used to initiate the equipment operation assurance assessment mechanism. By acquiring the stable communication quality parameter Rc, the communication quality reference value Rref, the remaining energy characterization parameter Ec, and the battery energy reference value Eref, it calculates the equipment operation assurance coefficient SYB and compares it with the equipment operation assurance threshold Sth to determine whether the equipment operation status is qualified. If it is not qualified, it automatically adjusts the sampling and communication strategy, caches key data, and reports risk information to reduce power consumption, ensure continuous monitoring, and extend the equipment working cycle.

[0046] In this embodiment, by organically linking the assessment of water accumulation status, the judgment of development trend, and the protection of equipment operation, the monitoring density and equipment operation strategy are dynamically adjusted as the risk of water accumulation gradually intensifies. This not only enables early identification of abnormal water accumulation on urban roads and its evolution trend, but also ensures continuous collection and reliable reporting of key data when communication or power supply conditions are limited, thereby improving the stability, reliability, and long-term operation capability of the water accumulation monitoring system.

[0047] Example 2

[0048] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, the data acquisition module includes a water accumulation status acquisition unit, a communication link acquisition unit, and a power supply status acquisition unit;

[0049] The water accumulation status acquisition unit is used to monitor the water status and environmental conditions at water accumulation points on urban roads in real time; by installing water level probes at low-lying and easily flooded locations on urban roads, the water level is periodically collected to obtain the original water level value Hraw.

[0050] The communication link acquisition unit is used to monitor the wireless communication conditions between the water accumulation monitoring point and the remote platform in real time; through the communication component installed inside the monitoring terminal, the signal strength indicator value RSSI of the current wireless communication link is obtained in real time during the data acquisition process.

[0051] The power supply status acquisition unit is used to acquire the energy status and data time reference of the monitoring equipment under conditions without external power supply; through the power management circuit installed inside the monitoring terminal, the current battery voltage of the equipment is acquired in real time to obtain battery voltage data Vbat; through the real-time clock RTC integrated inside the terminal, the corresponding timestamp tstamp is generated and acquired when the equipment is woken up and performs sampling operation.

[0052] In this embodiment, by synchronously collecting data on water level, wireless communication link quality, and equipment power supply status, a unified perception of the water conditions, data transmission conditions, and terminal operation capabilities of urban road waterlogging monitoring points is achieved. This provides reliable and complete basic data support for subsequent waterlogging anomaly identification, trend assessment, and operational support decisions, thereby improving the accuracy of waterlogging monitoring results and the reliability of system operation.

[0053] Example 3

[0054] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, the water depth anomaly assessment module includes a water level time parameter extraction unit, a first calculation unit, and a first analysis unit;

[0055] The water level time parameter extraction unit is used to obtain the effective accumulated water level parameter Hc based on the original liquid level sequence Hraw collected by the water level probe by averaging multiple consecutive samples and zero-point offset calibration; based on two consecutive calibrated effective water levels Hc(k) and Hc(k−1) at the same monitoring point, the water level change between the two adjacent samples is calculated and obtained using differential operation. Based on the two sampling timestamps tstamp(k) and tstamp(k−1) generated by the real-time clock (RTC), the time interval between adjacent sampling is obtained by calculating the timestamp difference. Based on historical observation data synchronized from the cloud platform or on-site historical database during equipment initialization, the historical safe water level benchmark value Href of the road location is read and obtained by adopting long-term historical statistics and quantile value selection method.

[0056] In this embodiment, by performing multiple sampling averages, zero-point calibration, and time parameter extraction on the original water level data, and introducing historical safe water level benchmark values ​​for comparative analysis, the impact of single measurement errors and environmental noise on the judgment results is effectively reduced, enabling refined identification of abnormal water accumulation conditions on urban roads and improving the accuracy and reliability of water depth anomaly determination.

[0057] Example 4

[0058] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically, the first calculation unit is used to obtain the effective accumulated water level parameter Hc, the historical safe water level reference value Href, and the water level change. and adjacent sampling time interval After dimensionless processing, the anomaly coefficient JSY for water depth is calculated using the following formula:

[0059]

[0060] In the formula, w1 and w2 represent weighting coefficients.

[0061] This is obtained through statistical analysis of historical monitoring data of a large number of urban road waterlogging points under different rainfall scenarios. It is used to characterize the impact weight of the degree to which the current effective water level Hc exceeds the historical safe water level benchmark Href on the risk of road waterlogging. It has an important weight and is used to reflect the core risk factor of "whether the absolute water depth exceeds the road safety threshold". Its physical meaning is to characterize the sensitivity of road drainage capacity and traffic safety to the height of water level.

[0062] The weight of water level change rate and rapid deterioration of water accumulation events is determined by analyzing the correlation between them. It is used to characterize the impact of water level change per unit time on water accumulation risk. It has a secondary weight and reflects the contribution of water accumulation growth rate to risk assessment. Its physical meaning is to characterize the dynamic characteristics of rapid water accumulation caused by drainage failure or concentrated rainfall in a short period of time.

[0063] Through the and By performing dimensionless processing and weighted fusion, the water depth anomaly coefficient JSY comprehensively reflects two dimensions: "whether the current water level exceeds the standard" and "whether the water level is accelerating." This allows the judgment of water anomalies to consider both the static water level safety margin and the dynamic trend of change, thereby improving the accuracy and foresight of anomaly identification.

[0064] In this embodiment, a water depth anomaly coefficient is constructed by dimensionlessly weighting and fusing the deviation between the effective water level and the historical safe water level, as well as the rate of water level change per unit time. This achieves a unified quantitative assessment of the static over-limit characteristics and dynamic growth characteristics of water accumulation, thereby enabling a more comprehensive and timely reflection of the risk level of urban road water accumulation and improving the sensitivity and practicality of anomaly identification.

[0065] Example 5

[0066] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically, the first analysis unit is used to obtain a first evaluation result by comparing the water depth anomaly coefficient JSY with the water depth anomaly threshold Jth, based on a preset water depth anomaly threshold Jth:

[0067] When the abnormal water depth coefficient JSY < the abnormal water depth threshold Jth, it is determined that the water level at the current urban road monitoring point is within a controllable range and is a normal water accumulation state, and continuous monitoring is required.

[0068] When the water depth anomaly coefficient JSY is greater than or equal to the water depth anomaly threshold Jth, the water level at the current urban road monitoring point is determined to be outside the controllable range, indicating an abnormal water accumulation state. This triggers the first early warning instruction and generates the first strategy: marking the current urban road monitoring point as a key monitoring target with an abnormal water accumulation risk; initiating the enhanced water accumulation risk assessment mechanism; and shortening the sampling cycle to increase the real-time monitoring density of the current point.

[0069] The method for obtaining the abnormal water depth threshold Jth is as follows: Statistical analysis is conducted on historical water accumulation monitoring data of a large number of low-lying urban road locations under different rainfall intensities, drainage conditions, and traffic conditions. The distribution range of water levels and their variation amplitude under normal traffic conditions and abnormal water accumulation conditions is extracted. Combined with the road design drainage capacity, pavement structure safety margin, and pedestrian and vehicle traffic safety requirements, a reasonable abnormal water depth threshold is determined. Referring to urban road drainage design specifications, urban flooding risk assessment standards, and experience thresholds from management departments, this threshold is formulated to accurately reflect the boundary between controllable and risky road water accumulation, and to promptly identify the risk of excessive or abnormally increasing water accumulation.

[0070] In this embodiment, by comparing the abnormal coefficient of water depth with a preset threshold, the water accumulation status of urban roads can be classified and determined. When the water accumulation is within a controllable range, unnecessary warnings and resource consumption are avoided. When abnormal risks occur, warnings can be triggered in a timely manner and the monitoring frequency can be automatically increased, so that monitoring resources are dynamically tilted towards high-risk locations, thereby improving the timeliness of water accumulation risk identification and the pertinence of monitoring and management.

[0071] Example 6

[0072] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, the water accumulation development trend assessment module includes a second calculation unit and a second analysis unit;

[0073] The second calculation unit is used to activate the enhanced water accumulation risk assessment mechanism when the first early warning instruction is received; based on the enhanced current monitoring density, it obtains a continuous water level data sequence and constructs a key monitoring water level change data containing multiple sets of adjacent samples. Sampling time interval adjacent to the corresponding key monitoring The trend assessment dataset, after dimensionless processing, is used to calculate the water accumulation trend coefficient FQX, as shown in the following formula:

[0074]

[0075] In the formula, This represents the trend weighting coefficient, and N represents the number of samples within the statistical window. This represents the change in the key monitoring water level between the i-th adjacent samplings. This represents the time interval between adjacent samplings during the i-th key monitoring.

[0076] Trend weight coefficient The data is obtained by time series analysis of the historical water accumulation evolution process. It is used to characterize the amplification or inhibition effect of the rate of water level change on the overall water accumulation development trend under multiple continuous sampling conditions. The trend weight coefficient reflects the sensitivity to continuous water level rise behavior during the enhanced monitoring stage. Its value is determined by the differentiation effect between typical rise process and short-term fluctuation process within the statistical window.

[0077] The water accumulation trend coefficient FQX is obtained by analyzing multiple groups within a statistical window. Calculate the average and multiply by the trend weighting factor. It achieves smooth processing of short-term noise fluctuations and highlights the overall evolution direction of water accumulation over a period of time. Its physical essence is to quantify the trend of "average water level growth rate" to determine whether the water accumulation is in a state of continuous deepening.

[0078] In this embodiment, after triggering an abnormal water accumulation warning, the monitoring density is increased and the water level change rate of multiple adjacent samples is statistically fused to construct a water accumulation development trend coefficient. This enables the quantitative identification of the process of water accumulation changing from "instantaneous anomaly" to "continuous evolution", thereby effectively distinguishing between short-term fluctuations and continuous rises, and providing a reliable basis for predicting the risk of deepening road water accumulation in advance.

[0079] Example 7

[0080] This embodiment is an explanation based on Embodiment 6. Please refer to it. Figure 1 Specifically, the second analysis unit is used to obtain a second evaluation result by comparing the water accumulation trend coefficient FQX with the water accumulation trend threshold Fth, based on a preset water accumulation trend threshold Fth:

[0081] When the water accumulation trend coefficient FQX < the water accumulation trend threshold Fth, it is determined that the water accumulation at the current urban road monitoring point has no upward trend, and monitoring continues.

[0082] When the water accumulation trend coefficient FQX is greater than or equal to the water accumulation trend threshold Fth, it is determined that the water accumulation at the current urban road monitoring point is showing an upward trend, and there is a risk that the water accumulation will deepen further, affecting road traffic or causing safety accidents. This triggers a second warning instruction and generates a second strategy: mark the current monitoring point as a high-risk water accumulation point; and activate the equipment operation guarantee assessment mechanism to comprehensively assess the stability of the communication link and the remaining energy of the equipment.

[0083] The threshold Fth for waterlogging development trend is obtained by statistically analyzing continuous water level change sequences during historical waterlogging events. This involves extracting the distribution ranges of water level change rates and cumulative growth characteristics under short-term fluctuation and sustained rise conditions. Combined with rainfall persistence, drainage system response capacity, and waterlogging expansion speed characteristics, a critical judgment value for the waterlogging development trend is determined. This threshold is formulated with reference to urban flooding monitoring and early warning standards, drainage system dynamic response models, and long-term operational experience to effectively distinguish between occasional water level fluctuations and waterlogging development processes with a risk of continuous deepening.

[0084] In this embodiment, by comparing and analyzing the water accumulation development trend coefficient with the preset threshold, the risk of water accumulation evolution can be determined in stages. When the water accumulation does not show a continuous upward trend, excessive warnings can be avoided. When a deepening trend is confirmed, high-risk locations can be identified in a timely manner and equipment operation and maintenance assessment can be initiated in conjunction with the risk warning and equipment reliability management, thereby improving the foresight and system stability of urban road water accumulation risk prevention and control.

[0085] Example 8

[0086] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, the equipment operation assurance assessment module includes a communication quality parameter extraction unit, a third calculation unit, and a third analysis unit;

[0087] The communication quality parameter extraction unit is used to extract and obtain stable communication quality parameter Rc based on the wireless signal strength sequence RSSI periodically collected by the communication module, using sliding window outlier elimination and interval normalization mapping; based on the signal strength of historical statistics from the cloud at the same monitoring point, a long-term median comparison method is used to obtain the communication quality reference value Rref; based on the battery terminal voltage sequence Vbat periodically collected by the power management circuit, a sampling smoothing and segmented mapping method is used to obtain the remaining energy characterization parameter Ec; and through the battery's factory nominal value, a baseline calculation method combining nominal capacity and temperature correction is used to obtain the battery energy reference value Eref.

[0088] In this embodiment, by extracting multi-dimensional parameters of wireless communication quality and remaining energy status of the equipment and comparing them with a reference benchmark, an objective quantitative assessment of the operating conditions of the monitoring equipment can be achieved. This enables the early identification of potential operational risks due to communication link fluctuations or power attenuation, providing a reliable basis for subsequent strategy adjustments, thereby improving the stability and continuity of the urban road waterlogging monitoring system under complex environments and long-term operating conditions.

[0089] Example 9

[0090] This embodiment is an explanation based on Embodiment 8. Please refer to it. Figure 1 Specifically, the third calculation unit is used to calculate the equipment operation guarantee coefficient SYB after dimensionless processing of the obtained stable communication quality parameter Rc, communication quality reference value Rref, remaining energy characterization parameter Ec, and battery energy reference value Eref, as follows:

[0091]

[0092] In the formula, a1 and a2 represent weighting coefficients.

[0093] The RSSI index is determined by statistical analysis of the relationship between the data reporting success rate, latency, and RSSI index of monitoring equipment under different communication environments. It is used to characterize the influence weight of the communication quality parameter Rc relative to the reference value Rref on the continuous monitoring capability of the equipment. It has a major weight and reflects the importance of communication link stability in ensuring remote real-time monitoring.

[0094] The weight of the remaining energy parameter Ec relative to the battery energy reference value Eref is determined by analyzing the device's sustainable operating time, sampling reliability, and risk of disconnection in different remaining power ranges. It accounts for a secondary weight, reflecting the fundamental role of power supply status in the reliable operation of long-term unattended monitoring equipment.

[0095] The Equipment Operation Assurance Factor (SYB) is a comprehensive measure of the overall operational assurance level of equipment by dimensionlessly normalizing and weighting the two key operating conditions of communication capability and power supply capability. Its physical meaning is to characterize the comprehensive availability of equipment in terms of "whether it can communicate stably" and "whether it has the ability to continuously supply power", providing a quantitative basis for subsequent adaptive adjustment of sampling and communication strategies.

[0096] In this embodiment, by performing dimensionless fusion calculation of communication quality status and equipment remaining energy status, a unified equipment operation guarantee coefficient is formed, realizing a comprehensive quantitative judgment of the overall operating capability of the monitoring equipment, avoiding misjudgment caused by the distortion of a single indicator, thereby improving the accuracy of equipment operation status assessment and the reliability of decision-making basis.

[0097] Example 10

[0098] This embodiment is an explanation based on Embodiment 8. Please refer to it. Figure 1 Specifically, the third analysis unit is used to compare and analyze the equipment operation guarantee coefficient SYB with the equipment operation guarantee threshold Sth by setting a preset equipment operation guarantee threshold, and obtain the third evaluation result, including:

[0099] When the equipment operation guarantee coefficient SYB ≥ the equipment operation guarantee threshold Sth, it indicates that the equipment operation status is qualified and should be continuously monitored.

[0100] When the equipment operation guarantee coefficient SYB < the equipment operation guarantee threshold Sth, it indicates that the equipment is in an unqualified operating state. The current urban road waterlogging monitoring equipment is experiencing a decline in communication quality or power supply capacity. The equipment is at risk of reduced monitoring capabilities, delayed data reporting, or equipment offline due to communication link degradation or insufficient remaining energy. This triggers a third early warning instruction and generates a third strategy: automatically reducing the sampling frequency or extending the sampling interval to reduce the energy consumption of high-frequency sampling; limiting the number of retries for communication failures to avoid repeated reporting attempts under poor signal quality conditions, which would cause additional power consumption; retaining key waterlogging status data and caching it locally, and uploading it centrally after communication conditions improve; sending an equipment operation risk indicator to the cloud to prompt maintenance personnel to conduct inspections or arrange maintenance plans for the current equipment location; after the adjustment is completed, the equipment re-enters a low-power sleep mode, waiting for the next sampling wake-up, extending the equipment's available working cycle.

[0101] The method for obtaining the equipment operation assurance threshold Sth is as follows: Statistical analysis is performed on long-term operational data from a large number of monitoring terminals under different communication environments and power supply conditions. Typical distribution ranges of communication quality and remaining energy indicators under stable operation, performance degradation, and pre-failure states are extracted. Combined with terminal power consumption characteristics, communication link reliability requirements, and minimum continuous monitoring needs, the critical value for equipment operation assurance capability is determined. This threshold is established by referencing wireless communication reliability evaluation standards, low-power device operation specifications, and performance parameters provided by equipment manufacturers, and by incorporating operational experience. It accurately reflects the lower limit conditions for continuous reliable operation of the equipment and identifies operational risks caused by insufficient communication or power supply capabilities in advance.

[0102] In this embodiment, by comparing and analyzing the equipment operation guarantee coefficient with the preset threshold, the qualification of the monitoring equipment operation status is determined. When a decline in communication or power supply is detected, measures such as adaptive adjustment of sampling and communication strategies, data caching, and risk reporting are automatically implemented to effectively reduce energy consumption, avoid the risk of equipment offline, ensure the continuous acquisition of water accumulation monitoring data, thereby extending the equipment working cycle and improving the overall reliability of the system.

[0103] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0104] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A real-time monitoring and management system for urban road waterlogging based on wireless communication technology, characterized in that, include: The data acquisition module is used to monitor the water status of low-lying and easily flooded points on urban roads in real time and periodically collect the raw value of the water level Hraw. The system collects and monitors the wireless link quality between the terminal and the remote platform, and obtains the signal strength indicator value RSSI; it also collects the terminal power supply and time reference status, and obtains the battery voltage Vbat. Collect the sampling timestamp (tstamp); The water depth anomaly assessment module is used to obtain effective water level parameters Hc, historical safe water level benchmark Href, and water level change based on the original water level value Hraw and the sampling timestamp tstamp. and adjacent sampling time interval The abnormal water depth coefficient JSY is calculated and compared with the abnormal water depth threshold Jth to determine whether the current urban road monitoring point is in a normal water accumulation state. If it is in an abnormal water accumulation state, the water accumulation risk enhancement assessment mechanism is activated. The waterlogging development trend assessment module is used to activate the enhanced waterlogging risk assessment mechanism and construct a key monitoring water level change data between multiple adjacent sampling groups. Sampling time interval adjacent to the corresponding key monitoring The trend assessment dataset is used to calculate the water accumulation trend coefficient FQX and compare it with the water accumulation trend threshold Fth to determine whether the water accumulation at the current urban road monitoring points has an upward trend. If so, the equipment operation guarantee assessment mechanism is activated. The equipment operation assurance assessment module is used to initiate the equipment operation assurance assessment mechanism. By acquiring the stable communication quality parameter Rc, the communication quality reference value Rref, the remaining energy characterization parameter Ec, and the battery energy reference value Eref, it calculates the equipment operation assurance coefficient SYB and compares it with the equipment operation assurance threshold Sth to determine whether the equipment operation status is qualified. If it is not qualified, it automatically adjusts the sampling and communication strategy, caches key data, and reports risk information to reduce power consumption, ensure continuous monitoring, and extend the equipment working cycle.

2. The urban road waterlogging real-time monitoring and management system based on wireless communication technology according to claim 1, characterized in that, The data acquisition module includes a water accumulation status acquisition unit, a communication link acquisition unit, and a power supply status acquisition unit; The water accumulation status acquisition unit is used to monitor the water status and environmental conditions at water accumulation points on urban roads in real time; by installing water level probes at low-lying and easily flooded locations on urban roads, the water level is periodically collected to obtain the original water level value Hraw. The communication link acquisition unit is used to monitor the wireless communication conditions between the water accumulation monitoring point and the remote platform in real time; through the communication component installed inside the monitoring terminal, the signal strength indicator value RSSI of the current wireless communication link is obtained in real time during the data acquisition process. The power supply status acquisition unit is used to acquire the energy status and data time reference of the monitoring equipment under conditions without external power supply; through the power management circuit installed inside the monitoring terminal, the current battery voltage of the equipment is acquired in real time to obtain battery voltage data Vbat; through the real-time clock RTC integrated inside the terminal, the corresponding timestamp tstamp is generated and acquired when the equipment is woken up and performs sampling operation.

3. The urban road waterlogging real-time monitoring and management system based on wireless communication technology according to claim 1, characterized in that, The water depth anomaly assessment module includes a water level time parameter extraction unit, a first calculation unit, and a first analysis unit; The water level time parameter extraction unit is used to obtain the effective accumulated water level parameter Hc based on the original liquid level sequence Hraw collected by the water level probe by averaging multiple consecutive samples and zero-point offset calibration; based on two consecutive calibrated effective water levels Hc(k) and Hc(k−1) at the same monitoring point, the water level change between the two adjacent samples is calculated and obtained using differential operation. ; Based on the two sampling timestamps tstamp(k) and tstamp(k−1) generated by the real-time clock (RTC), the time interval between adjacent sampling is obtained by calculating the difference between the timestamps. Based on historical observation data synchronized from the cloud platform or on-site historical database during equipment initialization, the historical safe water level benchmark value Href of the road location is read and obtained by adopting long-term historical statistics and quantile value selection method.

4. The urban road waterlogging real-time monitoring and management system based on wireless communication technology according to claim 3, characterized in that, The first calculation unit is used to calculate the effective accumulated water level parameter Hc, the historical safe water level reference value Href, and the water level change. and adjacent sampling time interval After dimensionless processing, the anomaly coefficient JSY for water depth is calculated and obtained.

5. The urban road waterlogging real-time monitoring and management system based on wireless communication technology according to claim 3, characterized in that, The first analysis unit is used to obtain a first evaluation result by comparing and analyzing the water depth anomaly coefficient JSY with the water depth anomaly threshold Jth, based on a preset water depth anomaly threshold Jth: When the abnormal water depth coefficient JSY < the abnormal water depth threshold Jth, it is determined that the water level at the current urban road monitoring point is within a controllable range and is a normal water accumulation state, and continuous monitoring is required. When the water depth anomaly coefficient JSY is greater than or equal to the water depth anomaly threshold Jth, the water level at the current urban road monitoring point is determined to be outside the controllable range, indicating an abnormal water accumulation state. This triggers the first early warning instruction and generates the first strategy: marking the current urban road monitoring point as a key monitoring target with an abnormal water accumulation risk; initiating the enhanced water accumulation risk assessment mechanism; and shortening the sampling cycle to increase the real-time monitoring density of the current point.

6. The urban road waterlogging real-time monitoring and management system based on wireless communication technology according to claim 1, characterized in that, The water accumulation development trend assessment module includes a second calculation unit and a second analysis unit; The second calculation unit is used to activate the enhanced water accumulation risk assessment mechanism when the first early warning instruction is received; based on the enhanced current monitoring density, it obtains a continuous water level data sequence and constructs a key monitoring water level change data containing multiple sets of adjacent samples. Sampling time interval adjacent to the corresponding key monitoring The trend assessment dataset was processed to be dimensionless, and the water accumulation trend coefficient FQX was calculated and obtained.

7. The urban road waterlogging real-time monitoring and management system based on wireless communication technology according to claim 6, characterized in that, The second analysis unit is used to obtain a second evaluation result by comparing the water accumulation trend coefficient FQX with the water accumulation trend threshold Fth, based on a preset water accumulation trend threshold Fth: When the water accumulation trend coefficient FQX < the water accumulation trend threshold Fth, it is determined that the water accumulation at the current urban road monitoring point has no upward trend, and monitoring continues. When the water accumulation trend coefficient FQX is greater than or equal to the water accumulation trend threshold Fth, it is determined that the water accumulation at the current urban road monitoring point is showing an upward trend, and there is a risk that the water accumulation will deepen further, affecting road traffic or causing safety accidents. This triggers a second warning instruction and generates a second strategy: mark the current monitoring point as a high-risk water accumulation point; and activate the equipment operation guarantee assessment mechanism to comprehensively assess the stability of the communication link and the remaining energy of the equipment.

8. The urban road waterlogging real-time monitoring and management system based on wireless communication technology according to claim 1, characterized in that, The equipment operation assurance assessment module includes a communication quality parameter extraction unit, a third calculation unit, and a third analysis unit; The communication quality parameter extraction unit is used to extract and obtain the stable communication quality parameter Rc based on the wireless signal strength sequence RSSI periodically collected by the communication module, using sliding window outlier elimination and interval normalization mapping; based on the signal strength of the same monitoring point in the cloud historical statistics, the communication quality reference value Rref is obtained by using the long-term median comparison method; and based on the battery terminal voltage sequence Vbat periodically collected by the power management circuit, the remaining energy characterization parameter Ec is obtained by using sampling smoothing and segmented mapping methods. The battery energy reference value Eref is obtained by using a baseline calculation method that combines nominal capacity and temperature correction based on the battery's factory nominal value.

9. The urban road waterlogging real-time monitoring and management system based on wireless communication technology according to claim 8, characterized in that, The third calculation unit is used to calculate and obtain the equipment operation guarantee coefficient SYB by dimensionlessly processing the obtained stable communication quality parameter Rc, communication quality reference value Rref, remaining energy characterization parameter Ec, and battery energy reference value Eref.

10. The urban road waterlogging real-time monitoring and management system based on wireless communication technology according to claim 8, characterized in that, The third analysis unit is used to compare and analyze the equipment operation guarantee coefficient SYB with the equipment operation guarantee threshold Sth by setting a preset equipment operation guarantee threshold, and obtain the third evaluation result, including: When the equipment operation guarantee coefficient SYB ≥ the equipment operation guarantee threshold Sth, it indicates that the equipment operation status is qualified and should be continuously monitored. When the equipment operation guarantee coefficient SYB < the equipment operation guarantee threshold Sth, it indicates that the equipment is in an unqualified operating state. The current urban road waterlogging monitoring equipment is experiencing a decline in communication quality or power supply capacity. The equipment is at risk of reduced monitoring capabilities, delayed data reporting, or equipment offline due to communication link degradation or insufficient remaining energy. This triggers a third early warning instruction and generates a third strategy: automatically reducing the sampling frequency or extending the sampling interval to reduce the energy consumption of high-frequency sampling; limiting the number of retries for communication failures to avoid repeated reporting attempts under poor signal quality conditions, which would cause additional power consumption; retaining key waterlogging status data and caching it locally, and uploading it centrally after communication conditions improve; sending an equipment operation risk indicator to the cloud to prompt maintenance personnel to conduct inspections or arrange maintenance plans for the current equipment location; after the adjustment is completed, the equipment re-enters a low-power sleep mode, waiting for the next sampling wake-up, extending the equipment's available working cycle.

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