Fire hydrant monitoring intelligent early warning system based on anomaly analysis technology

Abstract

The invention relates to the technical field of monitoring and early warning, in particular to a fire hydrant monitoring intelligent early warning system based on an anomaly analysis technology, which comprises a flow velocity anomaly identification module, a node collaborative pressure difference detection module, a pressure difference trend independence judgment module, a node degradation feature extraction module and a risk level generation module. According to the method, through correlation judgment of flow velocity deviation and control signals, no-signal recognition of abnormal water taking behaviors and a cooperative analysis mechanism of pressure change of adjacent nodes, a hydraulic disturbance area under non-manual control can be accurately recognized, and through analysis of spatial independence of a pressure response trend, a water flow disturbance area under non-manual control can be accurately recognized. The function degradation level of the device is extracted according to historical data of on-off time delay and response performance, the quantitative evaluation capability of the node function state is enhanced, the capability of finely dividing the risk level is achieved when risk early warning is given out, the risk identification accuracy is improved, and the active discovery capability of early fault hidden dangers is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of monitoring and early warning technology, and in particular to a fire hydrant monitoring intelligent early warning system based on abnormal analysis technology. Background Art

[0002] The field of monitoring and early warning technology primarily involves real-time monitoring of the operating status of the environment, facilities, equipment, or systems, and performing dynamic analysis based on the collected data to promptly identify potential anomalies or dangerous conditions. This technical field typically includes sensor deployment, data acquisition modules, data communication networks, signal processing algorithms, state assessment models, and early warning decision-making mechanisms, aiming to issue early warning signals before risks occur and guide response mechanisms to intervene in a timely manner. Its applications cover multiple areas, including urban infrastructure security, industrial equipment operation and maintenance, public safety, and natural disaster prevention. Technical implementation emphasizes real-time performance, high reliability, automated analysis capabilities, and scalability, and often involves specific means such as distributed node collaboration, edge computing, and fault diagnosis models.

[0003] The Fire Hydrant Monitoring Intelligent Early Warning System is an intelligent monitoring system for urban fire protection facility management. It is designed to monitor the operating status of fire hydrants in real time and automatically trigger early warning signals when abnormal use, abnormal water pressure, abnormal leakage, or illegal opening are detected. The system can be used to ensure the safety of urban fire protection infrastructure. By integrating multiple monitoring devices and intelligent analysis units, it improves the visual management and response efficiency of fire protection resources, effectively preventing water waste, delayed equipment failure processing, and sabotage, thereby enhancing the integrity and intelligence of the urban public safety operation system.

[0004] Traditional early warning systems rely on multiple monitoring devices to trigger responses to abnormal situations. They do not involve a comparative analysis mechanism between flow rate and control signals, making it difficult to detect false alarms caused by abnormal flow rate without human intervention. In actual operation, the decline in response capability of equipment due to aging has not been dynamically evaluated, causing fault signals of some nodes to be misjudged as environmental disturbances. Traditional systems have not established a pressure coordination judgment process between nodes, making it difficult to eliminate erroneous identification caused by single-point fluctuations. When multiple nodes are in pressure differential fluctuations at the same time, traditional systems lack the ability to distinguish between the propagation path of hydraulic fluctuations and sudden leakage nodes, affecting the accuracy of abnormal location and the timeliness of response, which can easily lead to hidden dangers not being identified in advance, thereby exacerbating the risk of accidents. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent early warning system for fire hydrant monitoring based on abnormal analysis technology.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent early warning system for fire hydrant monitoring based on abnormality analysis technology, the system comprising: The flow rate anomaly recognition module obtains the flow rate monitoring data of the fire hydrant monitoring node, compares the current flow rate value of the fire hydrant monitoring node with the median value, calculates the deviation amplitude value, determines whether the deviation amplitude exceeds the single point anomaly recognition threshold and no control operation signal appears, and generates a flow rate deviation label; The node collaborative pressure difference inspection module calls the flow rate offset tag, obtains the pressure change rate of each node within the sampling period and compares it with the abnormal nodes, screens the node groups with inconsistent directions or mutation characteristics, and obtains the flow rate offset pressure difference response area; The pressure difference trend independence discrimination module extracts the directional derivative symbol sequence of the water pressure change in each group according to the flow velocity offset pressure difference response area, compares the change direction of the abnormal node with the adjacent nodes, marks the spatial independence mutation behavior, and obtains the pressure difference mutation node set; The node degradation feature extraction module calls the pressure difference mutation node set, calculates the opening and closing time drift mean and response delay change rate according to the operation and maintenance data of each node, divides the current function attenuation level of each fire hydrant node, and obtains the function attenuation state label set; The risk level generation module makes a graded judgment based on the functional attenuation state label set and the pressure difference mutation node set, combined with trend characteristics, divides the risk warning level, implements fire hydrant risk warning for management personnel, and obtains real-time risk warning information of fire hydrants.

[0007] The present invention has improvements in that the flow rate deviation label includes the flow rate deviation amplitude abnormal node number, the time period identifier of the untriggered control signal and the high deviation probability moment mark; the flow rate offset pressure difference response area includes the instantaneous pressure difference direction inconsistent node combination, the time synchronization conflict mark and the local response mismatch area number; the pressure difference mutation node set includes the central node pressure drop abnormal attribute mark, the adjacent node unresponsive behavior description and the trend direction inconsistency index; the functional attenuation state label set includes the structural function level label, the performance degradation behavior type and the remaining response capacity segmented description; the fire hydrant real-time risk warning information specifically includes the functional lower limit conflict level, the trend mutation intensity level and the node warning level judgment identifier.

[0008] The present invention is improved in that the flow rate abnormality identification module includes: The velocity benchmark extraction submodule obtains the velocity monitoring data of the fire hydrant monitoring node, combines the corresponding sampling time points, extracts the velocity value of each node within the target time range at the current time point, extracts the median value and standard deviation, and obtains the stable flow rate statistical index; The flow rate deviation calculation submodule calls the flow rate value corresponding to each node at the current time point based on the stable flow rate statistical index, calculates the ratio of the absolute value of the deviation of the flow rate value compared to the median value to the fluctuation standard deviation, calculates the deviation judgment value of the current node within the statistical window, calculates the single point anomaly recognition threshold based on the stable flow rate statistical index, determines whether the deviation judgment value is greater than the single point anomaly recognition threshold, performs an anomaly mark, and obtains a suspicious flow rate deviation identification group; The control behavior exclusion submodule determines whether there is a fire hydrant valve control signal at the corresponding time point based on the suspicious flow rate deviation identification group, filters out the time period data where the control signal behavior exists, retains the deviation identification that is not accompanied by valve control, and generates a flow rate deviation label.

[0009] The present invention is improved in that the node collaborative pressure difference detection module includes: The adjacent node extraction submodule calls the flow rate offset tag, indexes the pipe segment structure diagram corresponding to the abnormal node number according to the identified abnormal node number, obtains the adjacent connected monitoring nodes, extracts the pressure data sequence of each adjacent node in the current sampling period, calculates the pressure difference per unit length of the pipe segment between the adjacent node and the abnormal node, and obtains the adjacent node pressure difference change amount group; The pressure rate comparison submodule extracts the pressure change sequence of each node in the current sampling period based on the pressure difference change group of adjacent nodes, calculates the instantaneous pressure change rate of each group of sequences, and compares the difference ratio with the pressure change rate of the corresponding abnormal node. It marks it according to whether it exceeds the instantaneous pressure rate difference threshold, and obtains the pressure difference rate difference value group; The gradient direction screening submodule extracts the pressure difference change direction identifier of each node at the current time point based on the pressure difference rate difference value group, performs a consistency matching operation on the direction identifier, determines whether there is a situation where the direction of the adjacent node is inconsistent or opposite to the direction of the abnormal node, screens the node combinations with contradictory characteristics in the direction, and generates the flow velocity offset pressure difference response area.

[0010] The present invention is improved in that the pressure difference trend independence judgment module includes: The initial feature extraction submodule obtains the position index of the first continuous decrease in the water pressure value of each abnormal node within the sampling period based on the flow rate offset pressure difference response area, extracts multiple monitoring nodes adjacent to the abnormal node, extracts the first response change time index of each adjacent node within the same time segment, calculates the time difference between the adjacent node and the abnormal node, and generates response time difference information; The trend response calculation submodule calls the response time difference information, calculates the differential value sequence of the node water pressure change within a continuous period based on the water pressure sampling values ​​corresponding to each group of nodes, extracts the direction of the differential sequence, marks the direction symbols to form the change direction combination between the nodes, and judges the synchronization of the direction combination to obtain the direction synchronization feature information; The independence screening and judgment submodule determines whether the water pressure change direction of the abnormal node conflicts with or is missing from the adjacent nodes based on the directional synchronization feature information, screens the node numbers with inconsistent directions or response delay characteristics, archives the abnormal identification, and generates a pressure difference mutation node set.

[0011] The present invention is improved in that the node degradation feature extraction module includes: The operation and maintenance indicator extraction submodule calls the pressure difference mutation node set, obtains the fire hydrant number corresponding to each node, extracts the usage frequency, opening and closing response delay, rated service life and number of previous maintenance records of each number, establishes the operation and maintenance indicator set corresponding to the node, and generates operation and maintenance parameter summary information; The performance drift calculation submodule calculates the average of the difference between the standard time corresponding to the opening and closing operation and the current delay based on the operation and maintenance parameter summary information, calculates the change rate of the response delay in adjacent cycles, and calculates the performance degradation index of the fire hydrant node; The function level determination submodule binds the function attenuation level code and number corresponding to each node based on the performance attenuation index of the fire hydrant node and the equipment residual performance grading standard to generate a function attenuation status label set.

[0012] The present invention is improved in that the risk level generation module includes: The trend factor extraction submodule obtains the combination of the function attenuation level and the corresponding water pressure trend direction of each abnormal node based on the function attenuation state label set and the pressure difference mutation node set, extracts the continuous time segment of the trend change and the time delay value of the pressure recovery process, calculates the trend duration period and the recovery response time, and generates an abnormal trend characteristic parameter group; The tolerance difference calculation submodule, based on the abnormal trend characteristic parameter group, calls the node remaining response capability index corresponding to the function attenuation level, and obtains the abnormal tolerance offset value by calculating by introducing the abnormal duration period and recovery delay time; The risk level output submodule sets the risk level interval based on the abnormal tolerance offset value and the average and standard deviation of the historical abnormal tolerance offset values, divides the risk warning level, encodes the current risk level of each node, and implements fire hydrant risk warning for management personnel to obtain real-time risk warning information of fire hydrants.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by judging the correlation between flow rate deviation and control signal, signal-free identification of abnormal water intake behavior, and combining with the collaborative analysis mechanism of pressure changes at adjacent nodes, hydraulic disturbance areas under non-human manipulation can be accurately identified. By analyzing the spatial independence of pressure response trends, it is determined whether abnormal behavior is isolated and the interference of overall fluctuations in the pipe network system is eliminated. The functional degradation level of the device is extracted based on historical data of start-up and closing delays and response performance, thereby enhancing the quantitative evaluation capability of the functional status of the node. When identifying anomalies, the equipment performance boundary information is simultaneously extracted and the abnormal amplitude and trend persistence are compared. When issuing risk warnings, the system has the ability to finely divide risk levels, thereby improving the accuracy of risk identification and enhancing the ability to proactively discover early fault hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the flow rate anomaly identification module of the present invention; Figure 3 This is a flow chart of the node collaborative pressure difference detection module of the present invention; Figure 4 This is a flow chart of the pressure difference trend independence judgment module of the present invention; Figure 5 This is a flow chart of the node degradation feature extraction module of the present invention; Figure 6 Flowchart of the risk level generation module of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0017] See also Figure 1, the present invention provides a technical solution: an intelligent early warning system for fire hydrant monitoring based on anomaly analysis technology, the system includes a flow rate anomaly recognition module, a node collaborative pressure difference inspection module, a pressure difference trend independence judgment module, a node degradation feature extraction module and a risk level generation module; The flow rate anomaly identification module obtains the flow rate monitoring data of the fire hydrant monitoring node, calls the flow rate value of each node within the target time range at the current time point, extracts the median value and standard deviation, compares the current flow rate value of the fire hydrant monitoring node with the median value, calculates the deviation amplitude value, and compares it based on whether there is a fire hydrant valve control signal at the corresponding time point. It determines whether the deviation amplitude exceeds the single-point anomaly identification threshold and no control operation signal appears. The node is marked as an abnormal node and a flow rate offset label is generated. The node collaborative pressure difference inspection module calls the velocity offset tag and extracts the relative pressure difference change and instantaneous pressure gradient direction of adjacent monitoring nodes on the associated pipe section based on the identified abnormal node number. The pressure change rate of each node within the sampling period is obtained and compared with the abnormal node to determine whether the pressure change direction between the nodes is synchronized. Node groups with inconsistent directions or sudden changes are screened to obtain the velocity offset pressure difference response area. The pressure difference trend independence identification module obtains the pressure drop start time of the abnormal node and the response delay of the surrounding nodes based on the flow velocity offset pressure difference response area. It extracts the directional derivative symbol sequence of the water pressure change in each group and compares the change direction of the abnormal node with that of the neighboring nodes. If most neighboring nodes do not experience synchronous trend changes or have opposite directions, the trend is marked as spatial independence mutation behavior, and the pressure difference mutation node set is obtained. The node degradation feature extraction module calls the pressure difference mutation node set and extracts the fire hydrant usage frequency, opening and closing response delay, rated service life, and number of previous maintenance records based on the operation and maintenance data of each node. It calculates the opening and closing time drift mean and response delay change rate respectively. According to the established equipment residual performance classification standard, the current function degradation level of each fire hydrant node is divided into the following categories to obtain the function degradation status label set. The mean opening and closing time drift refers to the average difference between the actual opening and closing time required to complete the equipment opening and closing operation and the standard time when it was initially put into use; the response delay change rate refers to the rate of change of the delay between the start time of the equipment response and the time when the control signal is issued within a unit time period; the equipment residual performance classification standard is often used to evaluate equipment performance in water conservancy projects, and includes dimensions such as operating time, number of operations, response characteristics, and maintenance frequency; The risk level generation module extracts the functional degradation level and spatial pressure trend characteristics corresponding to each abnormal node based on the functional degradation state label set and the pressure difference mutation node set. It then combines the abnormal duration period and the pressure recovery delay time to determine whether the abnormal behavior exceeds the tolerance range corresponding to the remaining performance level of the current equipment. It then makes a graded judgment based on the trend characteristics, divides the risk warning level, implements fire hydrant risk warning for management personnel, and obtains real-time fire hydrant risk warning information. The flow rate deviation label includes the node number of abnormal flow rate deviation amplitude, the time period mark of no triggering control signal and the time mark of high deviation probability. The flow rate deviation pressure difference response area includes the node combination with inconsistent instantaneous pressure difference direction, the time synchronization conflict mark and the local response mismatch area number. The pressure difference mutation node set includes the central node pressure drop abnormal attribute mark, the adjacent node unresponsive behavior description and the trend direction inconsistency indicator. The functional decay state label set includes the structural functional level label, the performance decay behavior type and the remaining response capacity segmented description. The real-time risk warning information of fire hydrants is specifically the functional lower limit conflict level, the trend mutation intensity level and the node warning level judgment mark.

[0018] See also Figure 2 , the flow rate anomaly recognition module includes: The velocity benchmark extraction submodule obtains the velocity monitoring data of the fire hydrant monitoring node, combines the corresponding sampling time points, extracts the velocity value of each node within the target time range at the current time point, extracts the median value and standard deviation, and obtains the stable flow rate statistical index; The velocity benchmark extraction submodule obtains the velocity monitoring data and sampling time points of the fire hydrant monitoring node. It is necessary to locate the fixed statistical window of each node before the current time point according to the sampling interval to obtain all historical velocity data within the window. The window is generally set to 10 minutes, and the data is collected once every minute, resulting in a total of 10 sets of data. For example, the velocity recorded by node A01 from 10:00 to 10:10 is [5.1, 5.2, 5.3, 5.4, 5.0, 4.9, 5.3, 5.2, 5.1, 5.3] , calculate its median value as the average of the fifth and sixth values: To calculate the standard deviation, we need to traverse the square of the deviation between all values ​​of the data set and the mean and take the square root. Assuming that the mean of this data segment is 5.08 , the standard deviation is calculated as follows: the difference values ​​are [-0.02, 0.02, 0.22, 0.32, -0.08, -0.18, 0.22, 0.12, 0.02, 0.22], the average of the squares is 0.0308, and the square root is 0.175 , based on this, the median value and standard deviation of the current node velocity statistical window are obtained, and then the stable velocity statistical index is formed. The statistical window length and sampling frequency should be set according to the network flow fluctuation characteristics and regional hydraulic structure. For example, the maximum allowable sampling interval is set to 1 minute to ensure that the sampling resolution meets the instantaneous fluctuation monitoring requirements. The median value and standard deviation are used as benchmark indicators for stable flow rate and are not adjustable parameters. However, their numerical range directly reflects the degree of flow velocity fluctuation. It is necessary to establish a credible interval based on the historical fluctuation range of the pipe network flow. If the historical flow velocity fluctuation range of a node is 3.0~8.5 , then the standard deviation exceeds 1.2 Considered as a high volatility node, the setting range is obtained by extracting and grouping the node data for the past 30 days. The standard deviation of stable nodes should be controlled between 0.3 and 0.9. range, so as to form a clear flow rate identification benchmark.

[0019] The velocity deviation calculation submodule is based on the stable velocity statistical index, calls the velocity value corresponding to each node at the current time point, and calculates the ratio of the absolute value of the deviation of the velocity value from the median value to the standard deviation of the fluctuation using the formula: ; The deviation judgment value of the current node in the statistical window is obtained by calculation, and the single-point anomaly identification threshold is calculated based on the stable flow rate statistical index. It is determined whether the deviation judgment value is greater than the single-point anomaly identification threshold, and an anomaly mark is performed to obtain a suspicious flow rate deviation identification group; in, Indicates the velocity deviation judgment value, which is a dimensionless abnormality indicator. Indicates the normalized value of the current collected flow rate value, which is calculated by normalizing the current sampling value of the node with its past sampling value range. Indicates the normalized value of the median flow velocity in the current time window, which is obtained by normalizing the median of the sampling values ​​in the window with the historical sampling range. Indicates the standard deviation of the flow rate in the current time window, reflecting the discreteness of the sampling value. It represents the normalized value of the average amplitude of short-term disturbances, which is obtained by the ratio of the mean of the extreme value within the time period to the maximum historical fluctuation amplitude of the node. Indicates the normalized value of the duration of continuous anomalies, which is calculated by dividing the duration of the anomaly by the set maximum allowable duration. It represents the normalized value of the valve opening and closing frequency, which is obtained by dividing the number of opening and closing times in the statistical period by the upper limit of the standard operating frequency of this type of equipment. Indicates the normalized value of the system's flow fluctuation in the current hourly period, which is calculated by normalizing the flow rate standard deviation in the entire network node range during this period. The velocity deviation calculation submodule first normalizes the current velocity value of the node with the historical range. The normalization method is to subtract the historical minimum value from the velocity value and then divide it by the difference between the maximum and minimum values. If the historical maximum value is 10 , the minimum value is 0 , then the current flow rate is 5.3 After normalization, it is 0.53, and the median value is 5.0 Normalized to 0.50, the standard deviation and disturbance amplitude are both normalized values. The disturbance amplitude is calculated by taking the average of the range within a short period and dividing it by the maximum historical range. For example, if the range within a short period is 1.0 and the maximum historical range is 5.0, the disturbance amplitude is 0.2, which is substituted into the calculation formula: The deviation term is: ; The abnormal duration is 40 , the maximum allowed value is 120 , then the normalized value is: ; The valve action frequency is 4 times, the upper limit is 10 times, and after normalization it is: ; Then the second term is: ; The flow rate fluctuation in the hourly period is 0.6 , the largest in the entire network is 2.0 ,calculate: ; calculate: ; ; The calculation logic of this formula consists of three parts: the first is used to measure the deviation intensity of the current instantaneous flow rate relative to the normal median value, the second is used to measure the joint risk factor of the abnormal duration and valve frequency, and the third reflects the overall flow fluctuation intensity of the system during the current period. The three are accumulated to form the current abnormal severity index of a single node. , which is a dimensionless value and can be directly compared with the threshold. If the threshold is set to 0.6, the node is marked as an abnormal node. The threshold setting is based on the analysis of all nodes in the past month. The distribution statistics of the values ​​are performed and the upper limit of the 90% confidence interval is set. The actual statistics show that most nodes The values ​​are concentrated between 0.3 and 0.55, so 0.6 is set as the abnormality judgment benchmark. This value does not change with a single node, but different levels can be set according to regional characteristics. For example, the high-voltage trunk area can be set to 0.7, and the terminal area can be set to 0.55 to improve sensitivity.

[0020] The control behavior exclusion submodule determines whether there is a fire hydrant valve control signal at the corresponding time point based on the suspicious flow rate deviation identification group, filters out the time period data with control signal behavior, retains the deviation identification without valve control, and generates a flow rate deviation label; After the control behavior exclusion submodule calls the suspicious flow rate offset identification group obtained in the previous step, it is necessary to match the solenoid valve opening and closing behavior records of the corresponding node at each offset time point, and compare whether there is an "open" or "close" command signal within ±10 seconds before and after the current time point. If so, the time point record is marked as "accompanied by control signal", otherwise it is marked as "not accompanied by control". For example, at the 602-second offset point of node A01, if the node sends a solenoid valve "open" signal at 601.8 seconds, this time point will be excluded and will no longer participate in subsequent abnormality analysis. Only records of flow rate offsets when no control behavior occurs are retained. For example, if A02 has no opening and closing action record at 730 seconds offset, it is retained. This processing uses a matching method to compare the signal time and the offset time. By setting the ±Δt time tolerance window, the specific Δt value is set according to the system response delay experiment. It is generally set to 10~15 seconds. This time window is a fixed value. The interval is 10 seconds and does not vary with the node or time period. The setting is based on the conventional response test report of the solenoid valve actuator. The experiment was conducted on 12 typical nodes and found that the maximum response delay did not exceed 8.3 seconds. Therefore, 10 seconds was set as a safe interval to ensure that the records were fully compared and no false exclusions were introduced, as shown in Table 1.

[0021] Table 1 Flow rate offset point and control signal exclusion matching table As shown in Table 1, records A01 and A03 with time differences within ±10 seconds are determined to be caused by control actions and are therefore excluded and no longer participate in the abnormality determination analysis.

[0022] See also Figure 3 , the node collaborative pressure difference inspection module includes: The adjacent node extraction submodule calls the flow rate offset tag, indexes the pipe segment structure diagram corresponding to the abnormal node number according to the identified abnormal node number, obtains the adjacent connected monitoring nodes, extracts the pressure data sequence of each adjacent node in the current sampling period, calculates the pressure difference per unit length of the pipe segment between the adjacent node and the abnormal node, and obtains the adjacent node pressure difference change group; After the adjacent node extraction submodule calls the abnormal node number marked in the velocity offset label, it is necessary to locate the pipe section topology structure to which each number belongs in the system database. According to the topology structure graph index method, the upstream and downstream node information connected to the current node is queried, and the pressure data sequence within the current sampling period is extracted. The system default sampling period is 60 seconds, once per second, and a total of 60 data points. After obtaining the pressure sequence of each pair of adjacent nodes, the pressure difference value of the corresponding unit pipe section is traversed and calculated in turn. The pressure difference value is calculated according to the formula: Calculate, where and are the pressure values ​​of two adjacent nodes at the moment, is the straight pipe distance between two nodes, in meters; for example, the distance between nodes N01 and N02 is 120 meters. If the current instantaneous pressures are 310 kPa and 290 kPa respectively, the pressure difference is , repeat this operation for all sampling points to obtain 60 instantaneous pressure difference values, which are merged into a pressure difference variation group. For each group of data, the sampling time and node pair number are recorded at the same time to facilitate the subsequent identification of the node pair combination of pressure fluctuations. The unit pipe section distance in this module is It is derived from the GIS system or the pipe network design drawings, with an error of no more than ±1m. The system default rounding accuracy is 0.1m. This data is not used as a variable, but if the node distance is less than 20m, the pressure difference calculation is skipped. Because it is easily affected by local disturbance amplification, the minimum pipe length judgment standard is: in 10 short-distance node experiments, it was found that the pressure difference fluctuation deviation rate was higher than 15% when it was less than 20m, so it was set as the elimination standard.

[0023] The pressure rate comparison submodule extracts the pressure change sequence of each node in the current sampling period based on the pressure difference change group of adjacent nodes, calculates the instantaneous pressure change rate of each group of sequences, and compares the difference ratio with the pressure change rate of the corresponding abnormal node. It marks it according to whether it exceeds the instantaneous pressure rate difference threshold, and obtains the pressure difference rate difference value group; After calling the pressure difference change group obtained in the previous step, the pressure rate comparison submodule extracts the pressure change sequence of each pair of nodes in the current sampling period. This sequence is an array of pressure values ​​sampled per second. It is necessary to first take the difference between every two adjacent sampling points in the array and then divide it by the time interval to obtain the instantaneous change rate. For example, the pressure sequence of node N02 in a 60-second sampling period is [290, 291, 293, 292, 291, 290, ...] , then the first two rates are , and then obtain 60 instantaneous rate values, and then calculate the average rate as the representative value, and make a difference with the average rate of the corresponding abnormal node, and then divide it by the abnormal node rate value to get the difference ratio. For example, the average pressure change rate of the abnormal node is 1.2 , the current node is 0.8 , the ratio is By default, the system marks a difference ratio greater than 0.3 as abnormal. This threshold is a fixed setting. Its source is: in the historical data of 38 node groups, the maximum rate difference under normal working conditions is 28.7%. After considering a 5% margin, it is set to 30%. If it exceeds this value, it is marked as a significant rate difference. In actual use, it is not recommended to increase this value, otherwise the high-voltage nodes with direction reversal will be missed, as shown in Table 2.

[0024] Table 2 Statistics of pressure difference rate ratio As shown in Table 2, the rate differences between nodes N02 and N05 and N01 are large, exceeding the set value of 0.3, and are therefore marked as nodes with significant rate differences.

[0025] The gradient direction screening submodule extracts the pressure difference change direction identifier of each node at the current time point based on the pressure difference rate difference value group, performs consistency matching on the direction identifiers, determines whether the direction of adjacent nodes is inconsistent or opposite to that of abnormal nodes, screens node combinations with contradictory characteristics in direction, and generates the velocity offset pressure difference response area; After the gradient direction screening submodule extracts the aforementioned rate difference value group, it determines whether there is a direction reversal behavior based on the direction of the pressure change at the current time point. The direction value is defined by the sign of the difference between the current sampling point and the previous sampling point. A positive value indicates an increase in pressure, and a negative value indicates a decrease. For example, the pressure two seconds before and after the current node is 295 and 293 , the change is -2, the direction is negative, and when the abnormal node is in the positive direction, it is opposite to the current node; the direction sequence of all nodes at 60 sampling points is extracted and converted into a symbol array form, such as [+1, +1, -1, -1, +1, ...], and then compared with the direction sequence of the abnormal node bit by bit, and the direction inconsistency ratio is counted. If the inconsistency ratio exceeds the set threshold of 0.5, the node is marked as a direction-reversed node group, forming a velocity offset pressure difference response area. The threshold is set to 0.5, that is, half of the inconsistency is used as the criterion for the significance of direction contradiction. The setting is based on: in the low-pressure branch experiment, the average normal fluctuation direction consistency rate is 0.82. The system setting is below 50% as a high direction mismatch behavior. This setting ensures that the selected nodes have significant direction differences, while eliminating the phase offset caused by slight disturbances. Finally, these direction-contradictory nodes are combined and output as the offset response area for the subsequent module to identify the pressure difference trend mutation area.

[0026] See also Figure 4 , the pressure difference trend independence judgment module includes: The initial feature extraction submodule obtains the position index of the first continuous decrease in the water pressure value of each abnormal node within the sampling period based on the flow rate offset pressure difference response area, and extracts multiple monitoring nodes adjacent to the abnormal node. The first response change time index of each adjacent node in the same time segment is extracted, and the time difference between the adjacent node and the abnormal node is calculated to generate the response time difference information; The starting feature extraction submodule reads the pressure sampling sequence point by point within the system sampling period according to the marked abnormal nodes in the flow rate offset pressure difference response area, and makes a difference judgment between each moment and the previous moment. If three or more consecutive sampling points are negative, the first occurrence position is marked as the starting point of the water pressure drop, and the drop must meet the requirement that the cumulative amplitude exceeds 1.5. , to exclude minor disturbances; for example, the 60-second sampling pressure sequence of abnormal node N01 is [310,309.2,308.5,307.6,307.0,…] , where points 1 to 4 continuously decrease, with the difference being -0.8, -0.7, and -0.9, and the cumulative value is -2.4 , then the starting index is 1 second; then obtain the sampling sequence of the node's neighboring nodes N02, N03, and N04, find the first drop point in the same cycle, extract its index, and subtract it from the abnormal node index to obtain the response delay. For example, the first drop point of N02 is the 3rd second, N03 is the 1st second, and N04 is the 5th second, then their response time differences are 2 seconds, 0 seconds, and 4 seconds respectively; register all the response time differences of the node group to form the response time difference information. In this module, the drop judgment threshold is 1.5 Based on the historical data of 15 abnormal nodes, under the background of continuous disturbance, the cumulative decrease of the three points exceeded 1.5 The probability of abnormal startup is 92.6%, which is used as the benchmark for abnormal startup identification, as shown in Table 3.

[0027] Table 3 Statistics of initial pressure drop of abnormal nodes and response time difference of adjacent nodes As shown in Table 3, N03 is a synchronous response node, while N02 and N04 have different degrees of delay, among which N04 responds significantly slower, and will be regarded as a potential trend alienation feature node in subsequent judgments.

[0028] The trend response calculation submodule calls the response time difference information and calculates the differential value sequence of the node water pressure change within a continuous period based on the water pressure sampling values ​​corresponding to each group of nodes. It then extracts the direction of the differential sequence, marks the direction symbols to form a combination of change directions between nodes, and performs synchronization judgment on the direction combination to obtain directional synchronization feature information. The trend response calculation submodule calls the response period pressure sequence of each group of abnormal nodes and adjacent nodes recorded in the previous step, and takes the data 10 seconds after the starting point for trend judgment to form a 10-point sampling segment. The first-order difference operation is performed on each group of sequences, that is, each point is subtracted from the previous point to obtain the pressure change. For example, the 10-second segment is [307.6, 307.1, 306.5, 306.3, 306.0, …], and the difference is [-0.5, -0.6, -0.2, -0.3, …]. The positive and negative difference values ​​are then mapped to direction signs, with negative being "-1", positive being "+1", and zero being "0", to form a direction sequence, such as [-1, -1, -1, -1, 0, +1, …]. Compare the direction sequences of the abnormal node with those of each adjacent node and perform The rows are paired item by item, and the number of matches among the 10 directional sites is counted. If the consistency rate exceeds 70%, it is marked as "directional synchronization"; if it is less than 30%, it is marked as "directional reversal", and the rest are "directional uncertainty"; for example, if the directional consistency rate of N01 and N03 is 90%, and the consistency rate with N02 is only 20%, then N03 is regarded as a trend-following node and N02 is marked as an out-of-direction response node. The synchronization judgment thresholds are set to 70% and 30%, which are derived from the high-frequency trend extraction experiment. In the typical sample of pressure difference mutation, it is found that the node group synchronization rate is higher than 0.75, so 70% is set as the synchronization lower limit. The consistency rate of out-of-direction nodes under the perturbation reverse condition is lower than 0.28, so 30% is taken as the judgment lower limit. The synchronization information record will be used by the subsequent independence judgment submodule.

[0029] The independence screening and judgment submodule determines whether the direction of water pressure change at the abnormal node conflicts with or is missing from the adjacent nodes based on the directional synchronization feature information. It then screens the node numbers with inconsistent directions or response delays, archives the abnormal identification, and generates a pressure difference mutation node set. The independence screening and judgment submodule determines whether there are at least two or more adjacent nodes in the "reverse direction" or "unclear direction" state for each abnormal node based on the directional synchronization feature information obtained in the trend response calculation submodule. If this condition is met, the current abnormal node is marked as a "direction mutation isolated node" and its adjacent node number, response time difference, and direction state combination are registered to construct a complete abnormal trend isolation feature record; for example, the direction consistency rates of N01's adjacent nodes N02 and N04 are 20% and 40%, which are lower than the synchronization requirement. Although N03 is synchronized, the number is not. Finally, N01 is marked as an independent trend node and enters the pressure difference mutation node set; the judgment standard in this submodule is: at least two adjacent nodes have conflicting directions or no response. The judgment is based on the network trend response pattern recognition sample. If the directions of most adjacent nodes are mismatched, it is very easy to cause the system pressure interference to be unable to spread, forming a local hydraulic fault structure. The experiment traced back 27 groups of node combinations and found that more than 85% of the trend isolated nodes subsequently formed substantial pressure difference mutations. Therefore, this system uses it as a key screening mark, and the marking result will be output to the subsequent node degradation analysis module for risk joint calculation.

[0030] See also Figure 5 , the node degradation feature extraction module includes: The operation and maintenance indicator extraction submodule calls the pressure difference mutation node set to obtain the fire hydrant number corresponding to each node, extracts the usage frequency, opening and closing response delay, rated service life and number of previous maintenance records of each number, establishes the operation and maintenance indicator set corresponding to the node, and generates operation and maintenance parameter summary information; The operation and maintenance index extraction submodule retrieves the bound fire hydrant equipment number one by one according to the node number recorded in the pressure difference mutation node set, and extracts the basic operation and maintenance information associated with each device through database query operations, including: usage frequency (that is, the actual number of opening and closing operations per unit time), opening and closing response delay (the time difference from the control signal to the completion of valve opening / closing in each operation), rated service life (the safe use cycle set by the manufacturer in the equipment manual) and the number of previous maintenance records (the number of equipment maintenance, calibration, component replacement, etc. recorded in the system log); extraction cycle statistics The first is set as a nearly 90-day cycle. Each indicator is counted within this time period. The frequency of use is calculated as the total number of operations divided by the number of days. The response delay is calculated as the average of the delays for each opening and closing. The number of maintenance is a cumulative count. The rated service life is taken from the value marked in the manual corresponding to the equipment model. The response delay needs to remove extreme data caused by communication abnormalities or human errors. The filtering rule is that records that are more than 3 times the standard delay upper limit will be discarded. This rule is determined by evaluating the data of 95 different nodes. The threshold ensures that the actual working condition data is retained while avoiding misjudgment, and finally forms the operation and maintenance indicator set for each node, as shown in Table 4.

[0031] Table 4 Fire hydrant node operation and maintenance index extraction table As shown in Table 4, the service life and response delay of node H03 are both relatively high, and the number of maintenance times exceeds the recommended frequency. The subsequent performance degradation will be more obvious.

[0032] The performance drift calculation submodule calculates the average difference between the standard time corresponding to the opening and closing operations and the current delay based on the summary information of the operation and maintenance parameters, and calculates the change rate of the response delay in adjacent cycles using the formula: ; Calculate and obtain the performance degradation index of the fire hydrant node; in, It represents the performance attenuation index of the fire hydrant node, which is a dimensionless trend value. It represents the normalized value of the current opening and closing delay average value, which is obtained by normalizing the opening and closing delay average value in the current monitoring period with the design standard delay upper limit. It represents the normalized value of the design standard opening and closing delay, which is obtained by normalizing the opening and closing target time in the equipment manual with the industry standard range. It represents the normalized value of the standard deviation of the opening and closing delay in the past three cycles, which is obtained by normalizing the standard deviation of the opening and closing delay under the sliding window with the average fluctuation range of its cycle. It represents the normalized value of the response delay change rate, which is obtained by dividing the difference between multiple opening and closing responses in a unit time period by the theoretical upper limit of the opening and closing delay change. The normalized value of service life is obtained by normalizing the actual operating life of the equipment with the rated life interval. The normalized value representing the number of maintenance times is obtained by normalizing the cumulative number of maintenance times of the node with the upper limit of the recommended annual inspection frequency in the technical manual; After calling the operation and maintenance parameter summary information, the performance drift calculation submodule needs to normalize the parameters. The normalization method is as follows: Current opening and closing delay Normalized to the current mean value divided by the upper limit of the standard delay, the standard start and close delay The target value given in the equipment model manual is divided by the maximum response time allowed by the industry (set to 6 seconds). For example, the average opening and closing time of node H01 is 3.2 seconds, and the target in the manual is 2.5 seconds. After normalization, , , the standard deviation of the opening and closing delay within three cycles is 0.8 seconds, and the average fluctuation range of the cycle is 1.2 seconds, then ;Response delay change rate = The difference between the maximum and minimum response differences within a unit cycle divided by the upper limit of the design theoretical delay (set to 4 seconds). For example, if the maximum is 4.2 seconds and the minimum is 2.6 seconds, then , the service life is normalized to , the number of inspections is 4, and the recommended upper limit of annual inspection frequency is 5 times, then , substitute all the values ​​into the formula: ; ; In this formula, the first term represents the drift intensity of the opening and closing response time, and the second term is the performance degradation weight term composed of response fluctuations, service life and maintenance status. The overall relationship is directly proportional. The larger the value, the more the equipment status deviates from the initial standard performance. The response drift judgment standard comes from the measured sample data. The D value in the range of 0.3-0.5 is a slight drift, 0.5-0.7 is a moderate drift, and more than 0.7 is a severe performance degradation.

[0033] The function level determination submodule is based on the performance degradation index of the fire hydrant node and the equipment residual performance classification standard. It binds the function degradation level code and number corresponding to each node to generate a function degradation status label set. The function level determination submodule calculates the performance degradation index of each node , call the equipment residual performance grading standard, divide the level within the preset threshold range, and divide each node into the corresponding level according to the D value. For example, a D value less than 0.3 is "Level 1 Normal", 0.3-0.5 is "Level 2 Mild", 0.5-0.7 is "Level 3 Moderate", and 0.7 or above is "Level 4 Severe". The level number is bound to the node number and a functional degradation state label set is formed. For example, node H01 corresponds to Level 2 Mild, and the label is "H01-G2". The label information includes the node number, level code, and assessment cycle timestamp, and is output to the subsequent risk level generation module. The level division threshold in this module refers to the failure probability statistics of each level node in the next three months under historical working conditions. The failure probability of the first level node is less than 5%, the second level is between 6-12%, the third level is 13-25%, and the fourth level is above 30%. In this way, a hierarchical evaluation standard for equipment operation reliability is constructed to drive the operation of the system partition warning model.

[0034] See also Figure 6 , the risk level generation module includes: The trend factor extraction submodule obtains the combination of the functional attenuation level and the corresponding water pressure trend direction of each abnormal node based on the functional attenuation state label set and the pressure difference mutation node set. It extracts the continuous time segments of the trend change and the time delay value of the pressure recovery process, calculates the trend duration period and the recovery response time, and generates an abnormal trend characteristic parameter group. The trend factor extraction submodule performs node matching operations based on the functional attenuation state label set and the pressure difference mutation node set. Each identified abnormal node will be associated with its functional attenuation level and the water pressure trend direction within the current sampling period. First, the water pressure sequence of the node within the specified detection window (for example, the past 60 seconds) is called, and the first-order difference of the sequence is performed to identify the continuous decline segment and locate its start and end time points to extract the duration of the abnormal trend. , determine the starting time of the pressure recovery process after the end point of the continuous decline state, and take the time when the pressure returns to the starting point of the decline as the recovery completion point. The time difference between the two points is the recovery delay time For example, the pressure of node N12 continued to drop from 600 to 615 seconds, lasting a total of 15 seconds, and recovered to the original value at 642 seconds, so the recovery delay time was 27 seconds. If similar patterns appear in multiple trend segments, the most significant continuous period needs to be selected. The significance is determined by the cumulative value of the decline. The recovery point is marked as valid when it recovers by more than 90%. All extracted abnormal trend feature parameters are bound to the node number to form an abnormal trend feature parameter group. The maximum value of the abnormal duration is determined by the node equipment type and layout location. The limit value for the main fire hydrant is 30 seconds, and the limit value for the terminal fire hydrant is 20 seconds. The mean recovery delay is the arithmetic mean of the recovery time of all adjacent nodes in the same branch. If the recovery process spans multiple cycles, the earliest recovery segment is intercepted as the recovery base point to eliminate the impact of subsequent secondary interference.

[0035] The tolerance difference calculation submodule is based on the abnormal trend characteristic parameter group, calls the node remaining response capability index corresponding to the function attenuation level, and introduces the abnormal duration period and recovery delay time, using the formula: ; Calculate and obtain the abnormal tolerance offset value; in, Indicates the abnormal tolerance offset value, The normalized value representing the duration of the anomaly, obtained by dividing the duration by the trend duration limit set by the node type. Indicates the normalized value of the recovery delay time, which is obtained by dividing the recovery time by the average delay in the current node group. It represents the normalized value of the node's remaining response capability, which is obtained by dividing the node's remaining opening and closing response capability by the upper limit of the reference capability of similar nodes. Indicates the trend direction variation factor, which is obtained by dividing the number of signed differences between the water pressure change direction of the current node and the directions of all its adjacent nodes by the total number of adjacent nodes. Indicates the device function degradation level factor, which is obtained by dividing the current node function degradation level by the maximum function degradation level; The tolerance difference calculation submodule takes the trend parameter group as input, sequentially calls the preset residual response capability index of each node corresponding to the functional attenuation level, and calculates the normalized values ​​of the abnormal duration and the recovery delay time, which are: ; in and are trend duration and recovery delay time respectively, Set duration limits for node types, Recover the mean delay for the neighboring nodes; then calculate the normalized value of the node's remaining responsiveness: ; If the remaining response capacity of node N12 is 2.3 seconds and the maximum reference capacity limit is 5 seconds, then ; Directional variation factor = The number of differences between the direction sequence of the node and the direction sequences of all its adjacent nodes divided by the number of adjacent nodes. For example, if the direction difference is 2 and there are 5 adjacent nodes, then Functional degradation factor The current level value is divided by the maximum level. For example, if the level is 3 and the maximum is 4, then ; Substitute all parameters into the formula: ; set up ,but: ; The calculation logic of this formula is: the first part represents the comparison ratio of the trend abnormality degree to the remaining capacity, and the second part is the coupling fluctuation factor of the direction alienation and equipment function degradation. The multiplication of the two constitutes the comprehensive offset strength index , which is ultimately used to evaluate whether the current state of the node has significantly exceeded its carrying capacity, as shown in Table 5.

[0036] Table 5 Abnormal trend and response capability deviation calculation table As shown in Table 5 , the N14 offset intensity is significantly exceeded, and its directional variation and high functional degradation together cause a strong overcapacity risk.

[0037] The risk level output submodule sets the risk level interval based on the abnormal tolerance offset value and the average and standard deviation of the historical abnormal tolerance offset values, divides the risk warning level, encodes the current risk level of each node, and implements fire hydrant risk warning for management personnel to obtain real-time fire hydrant risk warning information; The risk level output submodule uses the calculated abnormal tolerance offset value Based on this, we perform hierarchical matching operations and map it to the risk level range set by the system: It is "Level 1 low risk". "Level 2 medium risk" It is "Level 3 high risk". For "Level 4 Extremely High Risk", each level is represented by labels G1~G4, which are combined with the node number to form the final risk warning label, such as "N12-G3". After the label is generated, it is synchronously sent to the risk warning platform, marked with a timestamp and an assessment cycle number, and provided to management personnel for early warning intervention. The level boundary standard in this module refers to the system's historical failure rate database. When the R value is higher than 1.3, the probability of a fault triggering in the past 30 days exceeds 34%, so it is set to an extremely high risk limit. The risk output label can directly drive operational strategies such as remote detection, on-site inspection, or mandatory re-inspection of fire hydrants.

[0038] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent early warning system for fire hydrant monitoring based on abnormal analysis technology, characterized in that: The system comprises: The flow rate anomaly identification module obtains the flow rate monitoring data of the fire hydrant monitoring node, compares the current flow rate value of the fire hydrant monitoring node with the median value, calculates the deviation amplitude value, determines whether the deviation amplitude exceeds the single point anomaly identification threshold and no control operation signal appears, and generates a flow rate deviation label; The node collaborative pressure difference inspection module calls the flow rate offset tag, obtains the pressure change rate of each node within the sampling period and compares it with the abnormal nodes, screens the node groups with inconsistent directions or mutation characteristics, and obtains the flow rate offset pressure difference response area; The pressure difference trend independence discrimination module extracts the directional derivative symbol sequence of the water pressure change in each group according to the flow velocity offset pressure difference response area, compares the change direction of the abnormal node with the adjacent nodes, marks the spatial independence mutation behavior, and obtains the pressure difference mutation node set; The node degradation feature extraction module calls the pressure difference mutation node set, calculates the opening and closing time drift mean and response delay change rate according to the operation and maintenance data of each node, divides the current function attenuation level of each fire hydrant node, and obtains the function attenuation state label set; The risk level generation module makes a graded judgment based on the functional attenuation state label set and the pressure difference mutation node set, combined with trend characteristics, divides the risk warning level, implements fire hydrant risk warning for management personnel, and obtains real-time fire hydrant risk warning information.

2. The fire hydrant monitoring intelligent early warning system based on abnormal analysis technology according to claim 1 is characterized in that: The flow rate deviation label includes the node number of abnormal flow rate deviation amplitude, the time period identifier of the untriggered control signal and the high deviation probability moment identifier; the flow rate deviation pressure difference response area includes the instantaneous pressure difference direction inconsistent node combination, the time synchronization conflict identifier and the local response mismatch area number; the pressure difference mutation node set includes the central node pressure drop abnormal attribute identifier, the adjacent node unresponsive behavior description and the trend direction inconsistency indicator; the functional attenuation state label set includes the structural function level label, the performance degradation behavior type and the remaining response capacity segmented description; the fire hydrant real-time risk warning information specifically includes the functional lower limit conflict level, the trend mutation intensity level and the node warning level judgment identifier.

3. The fire hydrant monitoring intelligent early warning system based on abnormal analysis technology according to claim 2 is characterized in that: The flow rate anomaly recognition module includes: The velocity benchmark extraction submodule obtains the velocity monitoring data of the fire hydrant monitoring node, combines the corresponding sampling time points, extracts the velocity value of each node within the target time range at the current time point, extracts the median value and standard deviation, and obtains the stable flow rate statistical index; The flow rate deviation calculation submodule calls the flow rate value corresponding to each node at the current time point based on the stable flow rate statistical index, calculates the ratio of the absolute value of the deviation of the flow rate value compared to the median value to the fluctuation standard deviation, calculates the deviation judgment value of the current node within the statistical window, calculates the single point anomaly recognition threshold based on the stable flow rate statistical index, determines whether the deviation judgment value is greater than the single point anomaly recognition threshold, performs an anomaly mark, and obtains a suspicious flow rate deviation identification group; The control behavior exclusion submodule determines whether there is a fire hydrant valve control signal at the corresponding time point based on the suspicious flow rate deviation identification group, filters out the time period data where the control signal behavior exists, retains the deviation identification that is not accompanied by valve control, and generates a flow rate deviation label.

4. The fire hydrant monitoring intelligent early warning system based on abnormal analysis technology according to claim 3 is characterized in that: The node collaborative pressure difference detection module includes: The adjacent node extraction submodule calls the flow rate offset tag, indexes the pipe segment structure diagram corresponding to the abnormal node number according to the identified abnormal node number, obtains the adjacent connected monitoring nodes, extracts the pressure data sequence of each adjacent node in the current sampling period, calculates the pressure difference per unit length of the pipe segment between the adjacent node and the abnormal node, and obtains the adjacent node pressure difference change amount group; The pressure rate comparison submodule extracts the pressure change sequence of each node in the current sampling period based on the pressure difference change group of adjacent nodes, calculates the instantaneous pressure change rate of each group of sequences, and compares the difference ratio with the pressure change rate of the corresponding abnormal node. It marks it according to whether it exceeds the instantaneous pressure rate difference threshold, and obtains the pressure difference rate difference value group; The gradient direction screening submodule extracts the pressure difference change direction identifier of each node at the current time point based on the pressure difference rate difference value group, performs a consistency matching operation on the direction identifier, determines whether there is a situation where the direction of the adjacent node is inconsistent or opposite to the direction of the abnormal node, screens the node combinations with contradictory characteristics in the direction, and generates the flow velocity offset pressure difference response area.

5. The fire hydrant monitoring intelligent early warning system based on abnormal analysis technology according to claim 4 is characterized in that: The pressure difference trend independence judgment module includes: The initial feature extraction submodule obtains the position index of the first continuous decrease in the water pressure value of each abnormal node within the sampling period based on the flow rate offset pressure difference response area, extracts multiple monitoring nodes adjacent to the abnormal node, extracts the first response change time index of each adjacent node within the same time segment, calculates the time difference between the adjacent node and the abnormal node, and generates response time difference information; The trend response calculation submodule calls the response time difference information, calculates the differential value sequence of the node water pressure change within a continuous period based on the water pressure sampling values ​​corresponding to each group of nodes, extracts the direction of the differential sequence, marks the direction symbols to form the change direction combination between the nodes, and judges the synchronization of the direction combination to obtain the direction synchronization feature information; The independence screening and judgment submodule determines whether the water pressure change direction of the abnormal node conflicts with or is missing from the adjacent nodes based on the directional synchronization feature information, screens the node numbers with inconsistent directions or response delay characteristics, archives the abnormal identification, and generates a pressure difference mutation node set.

6. The fire hydrant monitoring intelligent early warning system based on abnormal analysis technology according to claim 5 is characterized in that: The node degradation feature extraction module includes: The operation and maintenance indicator extraction submodule calls the pressure difference mutation node set, obtains the fire hydrant number corresponding to each node, extracts the usage frequency, opening and closing response delay, rated service life and number of previous maintenance records of each number, establishes the operation and maintenance indicator set corresponding to the node, and generates operation and maintenance parameter summary information; The performance drift calculation submodule calculates the average of the difference between the standard time corresponding to the opening and closing operation and the current delay based on the operation and maintenance parameter summary information, calculates the change rate of the response delay in adjacent cycles, and calculates the performance degradation index of the fire hydrant node; The function level determination submodule binds the function attenuation level code and number corresponding to each node based on the performance attenuation index of the fire hydrant node and the equipment residual performance grading standard to generate a function attenuation status label set.

7. The fire hydrant monitoring intelligent early warning system based on abnormal analysis technology according to claim 6 is characterized in that: The risk level generation module includes: The trend factor extraction submodule obtains the combination of the function attenuation level and the corresponding water pressure trend direction of each abnormal node based on the function attenuation state label set and the pressure difference mutation node set, extracts the continuous time segment of the trend change and the time delay value of the pressure recovery process, calculates the trend duration period and the recovery response time, and generates an abnormal trend characteristic parameter group; The tolerance difference calculation submodule, based on the abnormal trend characteristic parameter group, calls the node remaining response capability index corresponding to the function attenuation level, and obtains the abnormal tolerance offset value by calculating by introducing the abnormal duration period and recovery delay time; The risk level output submodule sets the risk level interval based on the abnormal tolerance offset value and the average and standard deviation of the historical abnormal tolerance offset values, divides the risk warning level, encodes the current risk level of each node, and implements fire hydrant risk warning for management personnel to obtain real-time risk warning information of fire hydrants.

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