Urban risk prediction method and system based on multivariate data fusion

Abstract

The invention discloses an urban risk prediction method and system based on multivariate data fusion, and relates to the technical field of urban tunnel risk prediction.The method comprises the steps that data such as the water flow velocity, pipeline structure parameters and real-time rainfall of a plurality of monitoring areas in an urban drainage tunnel are comprehensively monitored through the first step, the second step, the third step and the fourth step; the system can carry out comprehensive risk assessment and timely identify potential risks of blockage, sewage backflow and gas explosion. According to the comprehensive evaluation, the problem of single information island caused by a traditional single water level monitoring method is improved, a multi-level early warning mechanism is constructed, early warning signals of different levels are automatically generated when risks are recognized, and management personnel are prompted to take necessary countermeasures. The flexible early warning mechanism enables urban managers to respond quickly, improves the efficiency of emergency treatment, reduces possible loss, and ensures safe operation of the sewage discharge tunnel in various environments.

Description

Technical Field

[0001] The present invention relates to the technical field of urban tunnel risk prediction, and particularly to an urban risk prediction method and system based on multi-source data fusion. Background Art

[0002] With the acceleration of the urbanization process, the construction and maintenance of urban infrastructure have become increasingly important, especially the role of the sewage pipeline system in urban water environment management has become more prominent.

[0003] In the domestic patent publication number CN115394057A, a tunnel structure water level and water pressure monitoring and early warning method, device and system are disclosed, which relates to the technical field of urban traffic tunnel safety. It can obtain the water pressure at each monitoring point of the tunnel in real time; calculate the height from the groundwater phreatic level to each monitoring point; measure the coordinates of each water pressure monitor and the elevation of each water pressure monitor; calculate the elevation of the groundwater phreatic layer at each monitoring point; obtain the coordinates of each monitoring water level point correspondingly; connect each water level point to obtain the real-time water level line along the tunnel; compare the real-time water level line along the tunnel with the designed anti-floating water level line to obtain the water level comparison result; and conduct tunnel structure water level and water pressure early warning according to the water level comparison result and the water pressure at each monitoring point. This invention can not only accurately and real-time monitor the side structure water level of the tunnel structure, but also realize real-time water level early warning.

[0004] However, this invention mainly focuses on the real-time water level early warning problem. However, in the process of sewage tunnels, there are still potential risks such as blockage, sewage backflow and gas explosion. These risks not only affect the drainage capacity of the city, but also the single water level early warning is prone to delay the early warning of risks. In addition, the impact of rainfall changes on the sewage system is often ignored, resulting in insufficient pipeline bearing capacity during heavy rainfall, and then sewage backflow occurs. As a result, the current urban sewage river management cannot provide comprehensive and accurate risk assessment. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides an urban risk prediction method based on multi-source data fusion to solve the problems mentioned in the background art.

[0006] To achieve the above object, the present invention is realized through the following technical solutions: An urban risk prediction method based on multi-source data fusion, including the following steps: Step 1: Pre-obtain the electronic distribution map of the urban sewage tunnel, divide the sewage tunnel into several monitoring areas, and monitor the water flow velocity, fluid density viscosity and pipeline structure parameters in real time to form a first data set; and construct the average effective cross-sectional diameter of the regional pipeline according to the first data set And the pipeline flow state coefficient Bcxs, preset the blockage warning threshold P, and compare and analyze the pipeline flow state coefficient Bcxs with the blockage warning threshold P to evaluate the flow condition and potential blockage risk in the sewage tunnel, and issue corresponding blockage warning signals; Step 2. After receiving the blockage warning signal, further monitor the real-time rainfall and pipeline stress conditions in the monitoring area respectively, and establish a second data set; and preprocess, analyze and calculate the second data set to obtain the water flow ratio Lsz, water level value Swz, pipeline radial deformation degree Jbxd, pipeline axial deformation degree Zbxd and fluid impact force Ltcj, and calculate and obtain the sewage backflow index Wsdg by association; when the sewage backflow index Wsdg exceeds the risk threshold F, send out a sewage backflow risk warning signal outward; Step 3. Collect the data on the gas accumulation status in the monitoring area and establish a third data set; and extract the flammable gas characteristics, gas pressure characteristics and ventilation effect characteristics, and then analyze to obtain the flammable gas accumulation factor Gcy, ventilation effect index Vx and gas pressure difference Pc. According to the trained gas diffusion model, analyze and obtain the flammable gas accumulation factor Gcyz and gas explosion risk coefficient Bfxs, and fit the gas explosion risk coefficient Bfxs with the sewage backflow index Wsdg to obtain the safety evaluation index Spzs; Step 4. Preset the safety evaluation threshold R, compare and analyze the safety evaluation index Spzs with the safety evaluation threshold R to comprehensively evaluate the real-time safety status of the sewage tunnel, and generate corresponding strategies to prevent the occurrence of sewage backflow, pipeline blockage and gas explosion risks.

[0007] Preferably, use GIS geographic information system technology and relevant surveying and mapping data to obtain the electronic distribution map of the sewage tunnel. The electronic distribution map of the sewage tunnel includes the geometric shape, length, diameter and connection point information of the sewage tunnel; Divide the sewage tunnel into several monitoring areas according to the catchment area and drainage path, and mark the several monitoring areas on the electronic distribution map of the sewage tunnel as: Jkq1, Jkq2,..., Jkq n ; n represents the number of monitoring areas; And install monitoring points in each monitoring area to collect and obtain the first data set in real time. The first data set includes: the effective cross-sectional diameter Z of the pipeline, the thickness of the pipeline sediment , the reduction in pipeline wall thickness caused by corrosion , the original inner diameter D of the pipeline, the water flow velocity of the pipeline , the fluid density Md of the pipeline, the fluid viscosity of the pipeline and the length L of the pipeline.

[0008] Preferably, the average effective cross-sectional diameter of the regional pipeline Obtained by calculating through the following formula: In the formula, represents the effective cross-sectional diameter of the pipeline at the i-th monitoring point, represents the original inner diameter of the pipeline at the i-th monitoring point, represents the sediment thickness of the pipeline at the i-th monitoring point, represents the reduction in pipeline wall thickness caused by corrosion at the i-th monitoring point; m represents the total number of monitoring points; Based on the average effective cross-sectional diameter of the regional pipeline , the pipeline flow state coefficient Bcxs is obtained by calculating through the following formula: In the formula, represents the water flow velocity in the pipeline, represents the fluid density of the pipeline, represents the fluid viscosity of the pipeline, and L represents the pipeline length; represents the first correction constant; A clogging warning threshold P is preset, and the pipeline flow state coefficient Bcxs is compared and analyzed with the clogging warning threshold P to obtain the first evaluation result, including: When the pipeline flow state coefficient Bcxs < clogging warning threshold P, it indicates that there is no clogging risk in the monitoring area; When the clogging warning threshold P ≤ pipeline flow state coefficient Bcxs ≤ clogging warning threshold P * 120%, it indicates that there is a clogging risk in the monitoring area, and a first clogging warning signal is generated; When the pipeline flow state coefficient Bcx > clogging warning threshold P * 120%, it indicates that there is a clogging risk in the monitoring area, and a second clogging warning signal is generated, indicating that it is more serious than the first clogging warning signal.

[0009] Preferably, step two includes: S21. Real-time collect the water flow data and pipeline stress conditions in the monitoring area through a flow meter, a pressure sensor, and a water level sensor, and establish a second data set; The second data set includes: the current water level value Swz, the real-time water flow , the real-time rainfall amount JyL, the fluid pressure Pa, the overall pipeline diameter WJD, the pipeline radius r, the elastic modulus E of the pipeline material, and the pipeline wall thickness Bh; the overall pipeline diameter WJD, the pipeline radius r, and the elastic modulus E of the pipeline material are obtained through pipeline design data or an ultrasonic thickness gauge; the real-time rainfall amount JyL is collected through a rain sensor; S22. Preprocess the second data set, including noise removal, smoothing, missing value processing, and normalization processing; S23. Extract the real-time water flow rate and the real-time rainfall JyL, and calculate the water flow rate ratio Lsz through the following formula: In the formula, represents the reference flow threshold; S24. Measure the fluid pressure Pa in the pipeline through a pressure sensor, and obtain the overall pipeline diameter WJD, the elastic modulus E of the pipeline material, and the pipeline wall thickness Bh. After linear normalization processing, calculate and obtain the pipeline radial deformation degree Jbxd, the pipeline axial deformation degree Zbxd, and the fluid impact force Ltcj through the following formula: In the formula, the pipeline radial deformation degree Jbxd represents the radial deformation amount of the pipeline caused by the internal pressure. Pa(r) represents the internal pressure at the position of the inner radius r in the pipeline. WJD represents the overall pipeline diameter. r represents the pipeline radius. The integration range is from the center to the outer wall; E represents the elastic modulus of the pipeline material, and Bh represents the pipeline wall thickness; the upper and lower limits of the integration are from 0 to WJD / 2, representing the radius range from the pipeline center to the outer wall; dr represents the radial integration infinitesimal increment; The pipeline axial deformation degree Zbxd represents integrating the pressure distribution within the pipeline length range from a to b. Pa(x) represents the internal pressure at the position of x in the pipeline, and A(x) represents the effective cross-sectional area at the position of x in the pipeline; L represents the pipeline length, and dx represents the axial integration infinitesimal increment; The fluid impact force Ltcj is obtained by integrating the impact force distribution within the pipeline length range from a to b to obtain the impact situation in the pipeline section interval. Among them, Cj(t) represents the impact force at the position of t in the pipeline, and Z(t) represents the water flow cross-section at the position of t in the pipeline; dt represents the impact force integration infinitesimal increment.

[0010] Preferably, step two further includes: S25. Extract the water flow rate ratio Lsz, water level value Swz, pipeline radial deformation degree Jbxd, pipeline axial deformation degree Zbxd, and fluid impact force Ltcj calculated and obtained in S21 - S24. After dimensionless processing, calculate and obtain the sewage backflow index Wsdg through the following related formulas: In the formula, represents the maximum tolerable threshold of the water flow rate ratio; represents the maximum tolerable threshold of the water level value; , and represents a weight coefficient, represents a second correction constant, and ln2 represents the natural logarithm with the natural number 2 as the base; S26. Preset a risk threshold F, and compare and evaluate the sewage backflow index Wsdg with the risk threshold F to obtain a second evaluation result, including: When the sewage backflow index Wsdg > the risk threshold F, it indicates abnormal pipeline deformation and there is a risk of sewage backflow, and a sewage backflow risk warning signal is generated; When the sewage backflow index Wsdg ≤ the risk threshold F, it indicates normal pipeline deformation and there is no risk of sewage backflow.

[0011] Preferably, step three includes: S31. Collect the methane concentration CH4, hydrogen concentration H2, ethane concentration C2H6, hydrogen sulfide concentration H2S, and ammonia concentration NH3 at different monitoring points in the tunnel within the monitoring area; and collect and obtain the gas pressure value P and the wind speed V to establish a third data set; S32. Extract the methane concentration CH4, hydrogen concentration H2, ethane concentration C2H6, hydrogen sulfide concentration H2S, and ammonia concentration NH3; after dimensionless processing, calculate and obtain the flammable gas accumulation factor Gcy through the following formula: In the formula, represents the maximum tolerable threshold of methane concentration, represents the maximum tolerable threshold of hydrogen concentration, represents the maximum tolerable threshold of ethane concentration, represents the maximum tolerable threshold of hydrogen sulfide concentration, represents the maximum tolerable threshold of ammonia concentration, , , , and represent weight coefficients.

[0012] Preferably, step three further includes: S33. Collect the gas pressure value P through the gas pressure sensor for the second time, and calculate and obtain the gas pressure difference Pc through the following formula: In the formula, represents the gas pressure value at the xth monitoring point, represents the gas pressure value at the (x + 1)th monitoring point; S34. Extract the wind speed V in the third data set, and calculate and obtain the ventilation effect index Vx through the following formula: Wherein, A represents the effective cross-sectional area of the pipeline, represents the maximum effective cross-sectional area of the pipeline, and the calculation formula according to the original inner diameter D of the pipeline is: ; S35. Obtain the flammable gas accumulation factor Gcy, ventilation effect index Vx, and gas pressure difference Pc in the monitoring area, establish a gas diffusion model through the support vector machine SVM and train it. After dimensionless processing of the flammable gas accumulation factor Gcy, ventilation effect index Vx, and gas pressure difference Pc, calculate and obtain the gas explosion risk coefficient Bfxs through the following related formulas: Wherein, and both represent weight coefficients, and ln2 represents the logarithmic function with base 2, represents the third correction constant; S36. After dimensionless processing of the gas explosion risk coefficient Bfxs and the sewage backflow index Wsdg, fit to obtain the safety assessment index Spzs through the following formula; Wherein, F1 and F2 respectively represent the weight coefficients of the gas explosion risk coefficient Bfxs and the sewage backflow index Wsdg. The meaning of the formula is: the abnormal increase in the pressure in the pipeline caused by sewage backflow poses a risk of gas accumulation or increased gas explosion.

[0013] Preferably, a safety assessment threshold R is preset to judge the gas explosion risk status of the sewage discharge tunnel. Compare and analyze the safety assessment threshold R with the safety assessment index Spzs to obtain the third assessment result: When the safety assessment index Spzs < the safety assessment threshold R, it indicates that the safety status of the monitoring area is qualified, and a "safe state" signal is generated; When the safety assessment threshold R ≤ the safety assessment index Spzs ≤ 120% of the safety assessment threshold R, it indicates that there is a gas explosion risk in the monitoring area, and a first explosion risk warning signal is generated to prompt enhanced monitoring and inspection; When the safety assessment index Spzs > 120% of the safety assessment threshold R, it indicates that there is a gas explosion risk in the monitoring area, and a second explosion risk warning signal is generated to prompt immediate measures for risk control.

[0014] Preferably, according to the first blockage warning signal, the second blockage warning signal, the sewage backflow risk prompt signal, the first explosion risk warning signal, and the second explosion risk warning signal, corresponding strategies are generated, including: Generate the first strategy based on the first blockage warning signal, including: conduct pipeline inspections at least 20% of the time per week and conduct a comprehensive cleanup once a month; focus on checking known first blockage warning signals and high-risk areas; The second strategy is generated based on the second blockage warning signal, including: immediate on-site inspection, giving priority to the blockage point, requiring it to be cleared within 24 hours, and reducing the current flow by 30%-50% to relieve pressure; The third strategy is generated according to the sewage backflow risk warning signal, including: starting the pumping equipment and increasing the current ventilation volume by more than 50%; The fourth strategy is generated based on the first explosion risk warning signal, including: increasing the current operating frequency of ventilation equipment by 20%-50% and increasing the ventilation volume to more than 80%, installing local exhaust fans in high-risk areas, and extracting flammable gases at fixed points 1-3 times per hour; The fifth strategy is generated based on the second explosion risk warning signal, including: evacuating all non-emergency personnel to a safe area, 100% sealing the monitoring area, and introducing inert gas to dilute the flammable gas concentration in the monitoring area. While introducing inert gas, exhaust equipment is used to gradually exhaust the gas in the monitoring area in the tunnel until the exhaust and dilution effects meet the standards. After safety is achieved, the monitoring areas in the tunnel will be unsealed one by one.

[0015] An urban risk prediction system based on multivariate data fusion, comprising: The segmentation area module is used to obtain the electronic distribution map of the urban sewage tunnel in advance, divide the sewage tunnel into several monitoring areas according to the catchment area and drainage path, and mark the corresponding areas on the electronic distribution map of the sewage tunnel; The first prediction module is used to collect water flow velocity, fluid density and viscosity, and pipeline structural parameters in the monitoring area for real-time monitoring to form a first data set; and construct the average effective cross-sectional diameter of the regional pipeline based on the first data set. and pipeline flow state coefficient Bcxs, pre-set the blockage warning threshold P, and compare and analyze the pipeline flow state coefficient Bcxs with the blockage warning threshold P to evaluate the flow conditions and potential blockage risks in the sewage tunnel, and issue corresponding blockage warning signals; The second prediction module, after receiving the blockage warning signal, further monitors the real-time rainfall in the monitoring area and the stress condition of the pipeline to establish a second data set; preprocesses, analyzes and calculates the second data set to obtain the water flow ratio Lsz, water level value Swz, pipeline radial deformation Jbxd, pipeline axial deformation Zbxd and fluid impact force Ltcj, and associates and calculates to obtain the sewage backflow index Wsdg; when the sewage backflow index Wsdg exceeds the risk threshold F, a sewage backflow risk warning signal is issued; The third prediction module collects data on the gas accumulation status in the monitoring area, establishes a third data set; and after extracting the flammable gas characteristics, gas pressure characteristics, and ventilation effect characteristics, analyzes and obtains the flammable gas accumulation factor Gcy, ventilation effect index Vx, and gas pressure difference Pc. According to the trained gas diffusion model, analyzes and obtains the flammable gas accumulation factor Gcyz and gas explosion risk coefficient Bfxs, and fits the gas explosion risk coefficient Bfxs with the sewage backflow index Wsdg to obtain the safety assessment index Spzs; The strategy module pre-sets a safety assessment threshold R, compares and analyzes the safety assessment index Spzs with the safety assessment threshold R, and generates corresponding strategies according to the first blockage warning signal, the second blockage warning signal, the sewage backflow risk prompt signal, the first explosion risk warning signal, and the second explosion risk warning signal.

[0016] The present invention provides a method for predicting urban risks based on multi-source data fusion. It has the following beneficial effects: (1) By integrating various monitoring data, including water flow velocity, pipeline structure parameters, and real-time rainfall, the present invention comprehensively evaluates the operation status of the sewage discharge tunnel; compared with the traditional method that only relies on water level monitoring, the present invention can identify and analyze multiple risks such as potential blockages, sewage backflows, and gas accumulations in the pipeline in real time; through the dynamic analysis of different monitoring areas, a comprehensive risk assessment model is formed, thereby providing accurate data support for urban managers to assist in decision-making and resource allocation.

[0017] (2) The present invention constructs a multi-level warning system and issues corresponding warning signals according to different risk levels; when it is detected that the pipeline flow state coefficient Bcxs is higher than or equal to the set blockage warning threshold P, the system will immediately generate the first blockage warning signal to prompt relevant personnel to conduct inspections and maintenance; when the risk further intensifies, the second blockage warning signal will be generated, requiring priority treatment of the blockage point and on-site inspections; this multi-level warning mechanism greatly improves the response speed to potential risks, helps to take timely measures, and reduces losses.

[0018] (3) The present invention automatically generates and adjusts response strategies based on real-time monitoring data. The response strategies are dynamically adjusted according to real-time monitoring data. For example, after receiving the first blockage warning signal, on-site inspection and pipeline dredging can be immediately carried out, thereby reducing the probability of risk occurrence and ensuring the urban drainage capacity. When receiving the sewage backflow risk warning signal, the system automatically starts the pumping equipment and increases the ventilation volume to ensure that the water level in the pipeline drops and the gas concentration is controlled within a safe range. At the same time, for the risk of gas explosion, the system recommends increasing the operating frequency of ventilation equipment in high-risk areas and installing local exhaust fans to further reduce the explosion risk. This flexible response mechanism can effectively prevent accidents from occurring and ensure the safe and stable operation of the urban sewage system. Description of the Drawings

[0019] Figure 1 It is a schematic diagram of the steps of a method for predicting urban risks based on multi-source data fusion according to the present invention; Figure 2 It is a schematic diagram of the block diagram process of a system for predicting urban risks based on multi-source data fusion according to the present invention. Detailed Embodiments

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Embodiment 1 Please refer to Figure 1 , the present invention provides a method for predicting urban risks based on multi-source data fusion, including the following steps: Step 1: Obtain the electronic distribution map of the urban sewage tunnel in advance, divide the sewage tunnel into several monitoring areas, and monitor the water flow velocity, fluid density viscosity, and pipeline structure parameters in real time to form the first data set; and construct the average effective cross-sectional diameter of the regional pipeline and the pipeline flow state coefficient Bcxs, preset the blockage warning threshold P, and compare and analyze the pipeline flow state coefficient Bcxs with the blockage warning threshold P to evaluate the flow condition and potential blockage risk in the sewage tunnel, and issue corresponding blockage warning signals; Step 2: After receiving the blockage warning signal, further monitor the real-time rainfall in the monitoring area and the stress condition of the pipeline to establish a second data set; preprocess, analyze and calculate the second data set to obtain the water flow ratio Lsz, water level value Swz, pipeline radial deformation Jbxd, pipeline axial deformation Zbxd and fluid impact force Ltcj, and calculate and obtain the sewage backflow index Wsdg through correlation; when the sewage backflow index Wsdg exceeds the risk threshold F, send out a sewage backflow risk warning signal; Step 3: Collect the gas accumulation data in the monitoring area to establish the third data set; extract the flammable gas characteristics, gas pressure characteristics and ventilation effect characteristics, and analyze and obtain the flammable gas accumulation factor Gcy, ventilation effect index Vx and gas pressure difference Pc. According to the trained gas diffusion model, analyze and obtain the flammable gas accumulation factor Gcyz and gas explosion risk coefficient Bfxs, and fit the gas explosion risk coefficient Bfxs with the sewage backflow index Wsdg to obtain the safety assessment index Spzs; Step 4: Pre-set the safety assessment threshold R, and compare and analyze the safety assessment index Spzs with the safety assessment threshold R to comprehensively evaluate the real-time safety status of the sewage tunnel and generate corresponding strategies to prevent the occurrence of sewage backflow, pipeline blockage and gas explosion risks.

[0022] In this embodiment, in view of the shortcomings of traditional monitoring methods, an urban risk prediction method based on multivariate data fusion is particularly important; this method first obtains the real-time status of the sewage tunnel through an electronic distribution map, monitors the water flow rate, fluid density and pipeline structure parameters, and forms a first data set; this process can construct the average effective cross-sectional diameter of the regional pipeline and pipeline flow state coefficient Bcxs, and compares it with the preset blockage warning threshold P to timely assess the potential blockage risk and issue a warning signal; After receiving the blockage warning signal, the system further monitors the real-time rainfall and pipeline stress, establishes a second data set and analyzes it; this link can calculate key parameters such as water flow ratio, water level, pipeline deformation, and calculate the sewage backflow index Wsdg; when the sewage backflow index Wsdg exceeds the risk threshold F, the system will promptly issue a sewage backflow risk warning signal to ensure that effective response measures can be taken; In addition, the method also covers the monitoring of gas accumulation, collecting gas concentration and pressure data in the monitoring area, and analyzing the risk of gas explosion; by training the gas diffusion model, the safety assessment index Spzs is obtained to achieve a comprehensive assessment of the safety status of the sewage tunnel; this multi-level monitoring and assessment method has greatly improved the timeliness and accuracy of risk identification; Finally, corresponding early warning signals and risk control plans are generated based on the evaluation results, which can effectively prevent risks such as sewage backflow, pipeline blockage, and gas explosion, ensure the safe and stable operation of the urban sewage drainage system, and improve the comprehensive ability and efficiency of urban environmental management.

[0023] Embodiment 2 This embodiment is an explanatory description based on Embodiment 1. Specifically, using GIS geographic information system technology and relevant surveying and mapping data, an electronic distribution map of the sewage drainage tunnel is obtained. The electronic distribution map of the sewage drainage tunnel includes information such as the geometric shape, length, diameter, and connection points of the sewage drainage tunnel; this high-precision data can provide a reliable basis for subsequent monitoring and analysis, reducing management errors caused by inaccurate data.

[0024] The sewage drainage tunnel is divided into several monitoring areas according to the catchment area and drainage path, and several monitoring areas are marked on the electronic distribution map of the sewage drainage tunnel as: Jkq1, Jkq2,..., Jkq n ; n represents the number of monitoring areas; dividing into regions and marking facilitates targeted monitoring and management, realizes systematization and standardization in management, and improves work efficiency.

[0025] Monitoring points are installed in each monitoring area to collect and obtain the first data set in real time. The first data set includes: the effective cross-sectional diameter Z of the pipeline, the thickness of the pipeline sediment , the reduction in the pipeline wall thickness caused by corrosion , the original inner diameter D of the pipeline, the flow velocity of the pipeline water , the fluid density Md of the pipeline, the fluid viscosity of the pipeline and the length L of the pipeline.

[0026] The average effective cross-sectional diameter of the regional pipeline is calculated and obtained through the following formula: In the formula, represents the effective cross-sectional diameter of the pipeline at the i-th monitoring point, represents the original inner diameter of the pipeline at the i-th monitoring point, represents the thickness of the pipeline sediment at the i-th monitoring point, represents the reduction in the pipeline wall thickness caused by corrosion at the i-th monitoring point; m represents the total number of monitoring points; the calculation meaning of this formula is: reflecting the changes caused by the sediment thickness and corrosion. It can more truly reflect the actual effective flow area of the pipeline, thus providing a more accurate flow assessment; Based on the average effective cross-sectional diameter of the regional pipeline for each monitoring area , the pipeline flow state coefficient Bcxs is calculated through the following formula: In the formula, represents the water flow velocity in the pipeline, represents the fluid density in the pipeline, represents the fluid viscosity in the pipeline, and L represents the pipeline length; represents the first correction constant; A clogging warning threshold P is preset, and the pipeline flow state coefficient Bcxs is compared and analyzed with the clogging warning threshold P to obtain the first evaluation result, including: When the pipeline flow state coefficient Bcxs < the clogging warning threshold P, it indicates that there is no clogging risk in the monitoring area; When the clogging warning threshold P ≤ the pipeline flow state coefficient Bcxs ≤ 120% of the clogging warning threshold P, it indicates that there is a clogging risk in the monitoring area, and a first clogging warning signal is generated; When the pipeline flow state coefficient Bcx > 120% of the clogging warning threshold P, it indicates that there is a clogging risk in the monitoring area, and a second clogging warning signal is generated, indicating that it is more serious than the first clogging warning signal.

[0027] In this embodiment, the calculation of the pipeline flow state coefficient Bcxs provides an important basis for judging the working state of the sewage tunnel; through comparison and analysis with the clogging warning threshold P, different levels of clogging risks can be quickly identified to achieve early warning; this not only improves the management efficiency, but also significantly reduces the risks of the urban drainage system, ensuring the safe and stable operation of the city; at the same time, this process makes the decision-making more scientific and reasonable, ensuring the rational allocation and efficient utilization of resources.

[0028] Embodiment 3 This embodiment is an explanatory description based on Embodiment 1. Specifically, Step 2 includes: S21. Real-time collect and obtain the water flow data and pipeline stress conditions in the monitoring area through a flow meter, a pressure sensor, and a water level sensor to establish a second data set; real-time monitoring reduces data lag, can timely reflect the current pipeline state, and is convenient for quick response.

[0029] The second data set includes: the current water level value Swz, the real-time water flow , the real-time rainfall JyL, the fluid pressure Pa, the overall pipeline diameter WJD, the pipeline radius r, the elastic modulus E of the pipeline material, and the pipeline wall thickness Bh; the overall pipeline diameter WJD, the pipeline radius r, and the elastic modulus E of the pipeline material are obtained through pipeline design data or an ultrasonic thickness gauge; the real-time rainfall JyL is collected through a rain sensor; normalization processing makes different data sources comparable and is convenient for comprehensive analysis.

[0030] S22. Preprocess the second dataset, including noise removal, smoothing, missing value handling, and normalization processing; S23. Extract the real-time water flow rate and the real-time rainfall JyL, and calculate the water flow rate ratio Lsz through the following formula: In the formula, represents the reference flow threshold; Monitoring the water flow rate ratio helps optimize the drainage strategy and ensure the efficient operation of the system.

[0031] S24. Measure the fluid pressure Pa inside the pipeline through a pressure sensor, and obtain the overall pipeline diameter WJD, the elastic modulus E of the pipeline material, and the pipeline wall thickness Bh. After linear normalization processing, calculate and obtain the pipeline radial deformation degree Jbxd, the pipeline axial deformation degree Zbxd, and the fluid impact force Ltcj through the following formula: In the formula, the pipeline radial deformation degree Jbxd represents the radial deformation amount of the pipeline caused by the internal pressure. Pa(r) represents the internal pressure at the position of the inner radius r of the pipeline. WJD represents the overall pipeline diameter. r represents the pipeline radius. The integration range is from the center to the outer wall; E represents the elastic modulus of the pipeline material, and Bh represents the pipeline wall thickness; the upper and lower limits of the integration are from 0 to WJD / 2, representing the radius range from the pipeline center to the outer wall; dr represents the radial integration infinitesimal increment; The pipeline axial deformation degree Zbxd represents the integration of the pressure distribution within the pipeline length range from a to b. Pa(x) represents the internal pressure at the position x of the pipeline. A(x) represents the effective cross-sectional area at the position x inside the pipeline; L represents the pipeline length, and dx represents the axial integration infinitesimal increment; The fluid impact force Ltcj is obtained by integrating the impact force distribution within the pipeline length range from a to b to obtain the impact situation in the pipeline section interval. Among them, Cj(t) represents the impact force at the position t inside the pipeline, Z(t) represents the cross-sectional area of the water flow at the position t inside the pipeline; dt represents the impact force integration infinitesimal increment.

[0032] By calculating the pipeline radial deformation degree Jbxd and the pipeline axial deformation degree Zbxd, the safety of the pipeline under the action of internal pressure can be evaluated, and potential structural problems can be detected in a timely manner. The calculation of the fluid impact force Ltcj can reveal the dynamic response of the pipeline under fluid movement, which helps formulate more effective pipeline maintenance and management strategies.

[0033] S25. Extract the water flow ratio Lsz, water level value Swz, pipeline radial deformation degree Jbxd, pipeline axial deformation degree Zbxd, and fluid impact force Ltcj obtained from the calculations in S21 - S24. After dimensionless processing, calculate and obtain the sewage backflow index Wsdg through the following related formulas: In the formula, represents the maximum tolerance threshold of the water flow ratio; represents the maximum tolerance threshold of the water level value; , and represent weight coefficients, which are adjusted and set by the user. , , , and . represents the second correction constant, and ln2 represents the natural logarithm with the natural number 2 as the base; S26. Preset a risk threshold F, and compare and evaluate the sewage backflow index Wsdg with the risk threshold F to obtain a second evaluation result, including: When the sewage backflow index Wsdg > the risk threshold F, it indicates that the pipeline deformation is abnormal and there is a risk of sewage backflow, and a sewage backflow risk warning signal is generated; When the sewage backflow index Wsdg ≤ the risk threshold F, it indicates that the pipeline deformation is normal and there is no risk of sewage backflow.

[0034] In this embodiment, by setting the weight coefficients, the influence degree of each parameter can be flexibly adjusted according to the actual situation, making the calculation of the sewage backflow index Wsdg more suitable for a specific environment. By comparing with the preset risk threshold F, the safety status of the pipeline can be intuitively understood, providing a clear basis for decision-making; when the sewage backflow index exceeds the threshold, the system can automatically generate a risk warning signal, which helps relevant personnel quickly take countermeasures to avoid environmental pollution and property losses caused by sewage backflow; the real-time monitoring of pipeline deformation can timely detect potential structural problems and provide data support for subsequent repair and maintenance.

[0035] Embodiment 4 This embodiment is an explanatory description based on Embodiment 1. Specifically, Step 3 includes: S31. Collect the methane concentration CH4, hydrogen concentration H2, ethane concentration C2H6, hydrogen sulfide concentration H2S, and ammonia concentration NH3 at different monitoring points in the tunnel within the monitoring area; and collect and obtain the gas pressure value P and the wind speed V to establish a third data set; at the same time, collect the concentration data of multiple harmful gases, making the comprehensive monitoring of the gas environment in the tunnel more comprehensive and enabling the timely discovery of potential safety hazards.

[0036] S32. Extract the methane concentration CH4, hydrogen concentration H2, ethane concentration C2H6, hydrogen sulfide concentration H2S, and ammonia concentration NH3. After dimensionless processing, calculate the flammable gas accumulation factor Gcy through the following formula: In the formula, represents the maximum tolerable threshold of methane concentration, represents the maximum tolerable threshold of hydrogen concentration, represents the maximum tolerable threshold of ethane concentration, represents the maximum tolerable threshold of hydrogen sulfide concentration, represents the maximum tolerable threshold of ammonia concentration, , , , and represent the weight coefficients. They are adjusted and set by the user. , , , , , and . Calculating the flammable gas accumulation factor Gcy can quantify the safety risks of gases, provide clear risk assessment indicators, and help decision-makers conduct effective management. Setting the maximum tolerable thresholds for each gas can issue early warning signals in a timely manner when the gas concentration approaches the dangerous level, ensuring that relevant personnel can take prompt actions to reduce accident risks.

[0037] S33. Obtain the gas pressure value P through secondary acquisition by the gas pressure sensor, and calculate the gas pressure difference Pc through the following formula: In the formula, represents the gas pressure value at the xth monitoring point, represents the gas pressure value at the (x + 1)th monitoring point; S34. Extract the wind speed V from the third dataset, and calculate the ventilation effect index Vx through the following formula: In the formula, A represents the effective cross-sectional area of the pipeline, represents the maximum effective cross-sectional area of the pipeline, and is calculated according to the original inner diameter D of the pipeline by the formula: ; Through the calculation of the effective cross-sectional area, it can provide a basis for the ventilation design of the tunnel, help optimize the configuration of the ventilation system, and improve the overall safety.

[0038] S35. Obtain the flammable gas accumulation factor Gcy, ventilation effect index Vx, and gas pressure difference Pc within the monitoring area. After establishing and training a gas diffusion model using the support vector machine (SVM), perform dimensionless processing on the flammable gas accumulation factor Gcy, ventilation effect index Vx, and gas pressure difference Pc, and calculate and obtain the gas explosion risk coefficient Bfxs through the following associated formula: In the formula, and both represent weight coefficients, which are adjusted and set by the user. , , and . ln2 represents the logarithmic function with base 2. represents the third correction constant; by establishing a gas diffusion model and comprehensively considering the flammable gas accumulation factor, ventilation effect, and pressure difference, the gas explosion risk coefficient Bfxs is obtained, which can provide quantitative data for risk management. Using the support vector machine (SVM) for training and prediction improves the accuracy and intelligence level of the model and helps to dynamically adjust safety management measures.

[0039] S36. After performing dimensionless processing on the gas explosion risk coefficient Bfxs and the sewage backflow index Wsdg, fit them through the following formula to obtain the safety assessment index Spzs; In the formula, F1 and F2 respectively represent the weight coefficients of the gas explosion risk coefficient Bfxs and the sewage backflow index Wsdg, which are adjusted and set by the user. , , and . The meaning of the formula is: sewage backflow causes an abnormal increase in the pressure inside the pipeline, which may lead to gas accumulation or increase the risk of gas explosion. Through the correlation analysis of these two risks, namely the gas explosion risk coefficient Bfxs and the sewage backflow index Wsdg, potential safety hazards can be identified in advance, and measures can be taken in a timely manner to reduce the probability of accidents.

[0040] In this embodiment, by calculating the gas pressure difference Pc between different monitoring points, the gas flow state and its changes can be further identified in a timely manner, which helps to judge the gas flow situation inside the pipeline; the abnormal change of gas pressure can be used as an indication of potential faults, and capturing the pressure difference in a timely manner helps for a quick response and reduces risks; calculating the ventilation effect index Vx can quantify the ventilation capacity of the pipeline and judge the effectiveness of air circulation, thereby improving the understanding of the tunnel ventilation condition.

[0041] Embodiment 5 This embodiment is an explanatory description carried out in Embodiment 4. Specifically, a safety assessment threshold R is preset to judge the gas explosion risk status of the sewage tunnel, and it is used as a reference value after historical data analysis; the safety assessment threshold R is set based on historical data analysis, which provides a scientific basis for risk assessment and makes the monitoring more targeted and effective. Then, the safety assessment threshold R is compared and analyzed with the safety assessment index Spzs to obtain the third assessment result: When the safety assessment index Spzs < the safety assessment threshold R, it indicates that the safety status of the monitoring area is qualified, and a "safe state" signal is generated; When the safety assessment threshold R ≤ the safety assessment index Spzs ≤ 120% of the safety assessment threshold R, it indicates that there is a gas explosion risk in the monitoring area, and a first explosion risk warning signal is generated to prompt the need to strengthen monitoring and inspection; When the safety assessment index Spzs > 120% of the safety assessment threshold R, it indicates that there is a gas explosion risk in the monitoring area, and a second explosion risk warning signal is generated to prompt the need to immediately take measures for risk control.

[0042] In this embodiment, multiple warning signals can be generated for the third assessment result, realizing hierarchical warning. This mechanism can take different response measures according to the risk level, enhancing the flexibility of management.

[0043] Embodiment 6 This embodiment is an explanatory description carried out in Embodiment 1. Specifically, corresponding strategies are generated according to the first blockage warning signal, the second blockage warning signal, the sewage backflow risk prompt signal, the first explosion risk warning signal, and the second explosion risk warning signal, including: Generate the first strategy according to the first blockage warning signal, including: conduct at least 20% of the pipeline inspection frequency per week and a comprehensive cleaning once a month; focus on checking the known first blockage warning signal, which belongs to a high-risk area; Generate the second strategy according to the second blockage warning signal, including: immediately conduct on-site inspection, prioritize dealing with the blockage point, and require it to be dredged within 24 hours, reducing the current flow by 30% - 50% to relieve the pressure; Generate the third strategy according to the sewage backflow risk prompt signal, including: start the pumping equipment and increase the current ventilation volume by more than 50%; Generate the fourth strategy according to the first explosion risk warning signal, including: increase the current operating frequency of the ventilation equipment by 20% - 50% and raise the ventilation volume to more than 80%, install local exhaust fans in high-risk areas, and extract flammable gases at fixed points 1 - 3 times per hour; Generate the fifth strategy based on the second explosion risk warning signal, including: evacuating all non-emergency personnel to a safe area, sealing the monitoring area by 100%, introducing inert gas to dilute the concentration of flammable gas in the monitoring area, while introducing inert gas, using exhaust equipment to gradually evacuate the gas in the monitored area of the tunnel until the evacuation and dilution effects meet the standards, and unsealing the monitored area in the tunnel one by one after safety.

[0044] In this embodiment, corresponding strategies are generated according to different risk signals to ensure that the countermeasures can accurately target the current risks and effectively reduce potential hazards; the implementation of the first strategy will increase the pipeline inspection frequency and cleaning intensity, which helps to detect and solve problems in a timely manner, thereby reducing the occurrence of blockage risks; the implementation of the second strategy for the rapid on-site inspection and treatment measures of the second blockage warning signal can effectively reduce the negative impact brought by blockage and ensure the normal operation of the sewage system; the third strategy of starting the pumping equipment and increasing the ventilation volume can quickly respond to the risk of sewage backflow and protect the surrounding environment and the safety of residents; the implementation of the fourth strategy for the enhanced ventilation equipment measures under the first explosion risk warning signal can reduce the concentration of flammable gas and reduce the explosion risk, creating favorable conditions for safety; the implementation of the fifth strategy for the comprehensive sealing and personnel evacuation measures under the second explosion risk warning signal ensures the safety of non-emergency personnel and avoids possible injuries; by introducing inert gas and using a dedicated exhaust system for gas dilution and evacuation, the concentration of flammable gas can be effectively reduced, providing guarantee for subsequent safe unsealing.

[0045] Embodiment 7 Please refer to Figure 2 , a city risk prediction system based on multi-source data fusion, including: A segmentation area module, used to pre-obtain the electronic distribution map of the urban sewage tunnel, divide the sewage tunnel into several monitoring areas according to the catchment area and drainage path, and make corresponding marks on the electronic distribution map of the sewage tunnel; A first prediction module, used to collect the water flow velocity, fluid density viscosity, and pipeline structure parameters in the monitoring area for real-time monitoring to form a first data set; and construct the average effective cross-sectional diameter of the regional pipeline and the pipeline flow state coefficient Bcxs based on the first data set, preset the blockage warning threshold P, and compare and analyze the pipeline flow state coefficient Bcxs with the blockage warning threshold P to evaluate the flow condition and potential blockage risk in the sewage tunnel, and issue corresponding blockage warning signals; The second prediction module, after receiving the blockage warning signal, further monitors the real-time rainfall and the pipeline stress condition in the monitoring area respectively, and establishes a second data set; and preprocesses, analyzes and calculates the second data set to obtain the water flow ratio Lsz, the water level value Swz, the pipeline radial deformation degree Jbxd, the pipeline axial deformation degree Zbxd and the fluid impact force Ltcj, and calculates by association to obtain the sewage backflow index Wsdg; when the sewage backflow index Wsdg exceeds the risk threshold F, it sends out a sewage backflow risk warning signal outward. The third prediction module collects the data on the gas accumulation condition in the monitoring area and establishes a third data set; and extracts the flammable gas characteristics, the gas pressure characteristics and the ventilation effect characteristics, and then analyzes to obtain the flammable gas accumulation factor Gcy, the ventilation effect index Vx and the gas pressure difference Pc. According to the trained gas diffusion model, it analyzes and obtains the flammable gas accumulation factor Gcyz and the gas explosion risk coefficient Bfxs, and fits the gas explosion risk coefficient Bfxs with the sewage backflow index Wsdg to obtain the safety assessment index Spzs. The strategy module pre-sets a safety assessment threshold R, compares and analyzes the safety assessment index Spzs with the safety assessment threshold R, and generates corresponding strategies according to the first blockage warning signal, the second blockage warning signal, the sewage backflow risk warning signal, the first explosion risk warning signal and the second explosion risk warning signal.

[0046] In this embodiment, the urban risk prediction system based on multi-source data fusion realizes the comprehensive monitoring and risk warning of the sewage tunnel through the collaborative action of multiple modules; firstly, the segmentation area module provides a detailed electronic distribution map and divides the tunnel into monitoring areas, laying a foundation for subsequent data collection and analysis; the first prediction module monitors the water flow and pipeline parameters in real time, calculates the pipeline flow state coefficient Bcxs and issues a blockage warning, thus improving the response speed to potential blockage risks. The second prediction module further analyzes the real-time rainfall and pipeline stress, generates the sewage backflow index Wsdg, and issues a warning when the risk exceeds the threshold to ensure that measures are taken in a timely manner; the third prediction module focuses on the gas accumulation condition, calculates the gas explosion risk coefficient Bfxs through the gas diffusion model, and combines it with the sewage backflow index Wsdg to provide a comprehensive safety assessment. The strategy module formulates corresponding countermeasures according to the evaluation results and warning signals, covering specific measures such as inspection, pumping and gas discharge, thus effectively reducing the blockage and explosion risks; overall, this system improves the safety and management efficiency of the urban sewage tunnel, ensures public safety and environmental protection, and provides a scientific basis for urban water environment management. The setting of the threshold value is for the convenience of comparison. Regarding the size of the threshold value, it depends on the amount of sample data and the base quantity set by those skilled in the art for each group of sample data; as long as the proportional relationship between the parameter and the quantized value is not affected.

[0047] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation. As mentioned above, the above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered within the protection scope of the present invention.

Claims

1. A method for predicting urban risks based on multi-source data fusion, characterized in that: It includes the following steps: Step 1: Pre-acquire the electronic distribution map of the urban sewage tunnel, divide the sewage tunnel into several monitoring areas, and monitor the water flow velocity, fluid density viscosity, and pipeline structure parameters in real time to form the first data set; and construct the average effective cross-sectional diameter of the regional pipeline based on the first data set and the pipeline flow state coefficient Bcxs, preset the blockage warning threshold P, and compare and analyze the pipeline flow state coefficient Bcxs with the blockage warning threshold P to evaluate the flow condition and potential blockage risk in the sewage tunnel, and issue corresponding blockage warning signals; Step 2: After receiving the blockage warning signal, further monitor the real-time rainfall and the pipeline stress situation in the monitoring area respectively, and establish a second data set; and preprocess, analyze and calculate the second data set to obtain the water flow ratio Lsz, water level value Swz, pipeline radial deformation degree Jbxd, pipeline axial deformation degree Zbxd and fluid impact force Ltcj, and calculate by association to obtain the sewage backflow index Wsdg; when the sewage backflow index Wsdg exceeds the risk threshold F, send out a sewage backflow risk warning signal outward; Step 3: Collect the data on the gas accumulation status in the monitoring area and establish a third data set; and extract the flammable gas characteristics, gas pressure characteristics and ventilation effect characteristics, and then analyze to obtain the flammable gas accumulation factor Gcy, ventilation effect index Vx and gas pressure difference Pc. According to the trained gas diffusion model, analyze and obtain the flammable gas accumulation factor Gcyz and gas explosion risk coefficient Bfxs, and fit the gas explosion risk coefficient Bfxs and the sewage backflow index Wsdg to obtain the safety assessment index Spzs; Step 4: Preset a safety assessment threshold R, compare and analyze the safety assessment index Spzs with the safety assessment threshold R to comprehensively evaluate the real-time safety status of the sewage discharge tunnel, and generate corresponding strategies to prevent the occurrence of sewage backflow, pipeline blockage and gas explosion risks.

2. The urban risk prediction method based on multi-source data fusion according to claim 1, wherein: Use GIS geographic information system technology and relevant surveying and mapping data to obtain the electronic distribution map of the sewage discharge tunnel. The electronic distribution map of the sewage discharge tunnel includes the geometric shape, length, diameter and connection point information of the sewage discharge tunnel; Divide the sewage tunnel into several monitoring areas according to the catchment area and drainage path, and mark the several monitoring areas on the electronic distribution map of the sewage tunnel as: Jkq1, Jkq2,..., Jkq n ; n represents the number of monitoring areas; Install monitoring points in each monitoring area to collect and obtain the first data set in real time. The first data set includes: the effective cross-sectional diameter Z of the pipeline, the thickness of the pipeline sediment , the reduction in pipeline wall thickness caused by corrosion , the original inner diameter D of the pipeline, the water flow velocity of the pipeline , the fluid density Md of the pipeline, the fluid viscosity of the pipeline and the pipeline length L.

3. A method for predicting urban risks based on multi-source data fusion according to claim 2, characterized in that: The average effective cross-sectional diameter of the regional pipeline is obtained by calculating with the following formula: Wherein, represents the effective cross-sectional diameter of the pipeline at the i-th monitoring point, represents the original inner diameter of the pipeline at the i-th monitoring point, represents the sediment thickness of the pipeline at the i-th monitoring point, represents the reduction in the pipeline wall thickness caused by corrosion at the i-th monitoring point; m represents the total number of monitoring points; Based on the average effective cross-sectional diameter of the regional pipeline , the pipeline flow state coefficient Bcxs is calculated and obtained through the following formula: In the formula, represents the flow velocity of the water in the pipeline, represents the density of the fluid in the pipeline, represents the viscosity of the fluid in the pipeline, and L represents the length of the pipeline; represents the first correction constant; Preset a blockage warning threshold P, and compare and analyze the pipeline flow state coefficient Bcxs with the blockage warning threshold P to obtain a first evaluation result, including: When the pipeline flow state coefficient Bcxs < the blockage warning threshold P, it means that there is no blockage risk in the monitoring area; When the blockage warning threshold P ≤ the pipeline flow state coefficient Bcxs ≤ 120% of the blockage warning threshold P, it means that there is a blockage risk in the monitoring area, and a first blockage warning signal is generated; When the pipeline flow state coefficient Bcx > 120% of the blockage warning threshold P, it means that there is a blockage risk in the monitoring area, and a second blockage warning signal is generated, indicating that it is more serious than the first blockage warning signal.

4. A method for predicting urban risks based on multi-source data fusion according to claim 3, characterized in that: Step 2 includes: S21: Real-time collect the water flow data and pipeline stress situation in the monitoring area through a flow meter, a pressure sensor and a water level sensor, and establish a second data set; The second data set includes: the current water level value Swz, the real-time water flow rate , the real-time rainfall amount JyL, the fluid pressure Pa, the overall pipeline diameter WJD, the pipeline radius r, the elastic modulus E of the pipeline material, and the pipeline wall thickness Bh; the overall pipeline diameter WJD, the pipeline radius r, and the elastic modulus E of the pipeline material are obtained by measuring through pipeline design data or an ultrasonic thickness gauge; the real-time rainfall amount JyL is collected through a rain gauge sensor; S22: Preprocess the second data set, including noise removal, smoothing, missing value processing and normalization processing; S23. Extract the real-time water flow rate and the real-time rainfall JyL, and calculate the water flow rate ratio Lsz through the following formula: In the formula, represents the reference flow threshold; S24. Measure the fluid pressure Pa inside the pipeline through a pressure sensor, obtain the overall pipeline diameter WJD, the elastic modulus E of the pipeline material, and the pipeline wall thickness Bh. After linear normalization processing, calculate and obtain the pipeline radial deformation degree Jbxd, the pipeline axial deformation degree Zbxd, and the fluid impact force Ltcj through the following formula: In the formula, the pipeline radial deformation degree Jbxd represents the radial deformation amount of the pipeline caused by internal pressure. Pa(r) represents the internal pressure at the position of radius r inside the pipeline. WJD represents the overall pipeline diameter. r represents the pipeline radius. The integration range is from the center to the outer wall. E represents the elastic modulus of the pipeline material. Bh represents the pipeline wall thickness. The upper and lower limits of the integration range from 0 to WJD / 2 represent the radius range from the pipeline center to the outer wall. dr represents the infinitesimal increment of radial integration; The pipeline axial deformation degree Zbxd represents integrating the pressure distribution within the pipeline length range from a to b. Pa(x) represents the internal pressure at the position of x inside the pipeline. A(x) represents the effective cross-sectional area at the position of x inside the pipeline. L represents the pipeline length. dx represents the infinitesimal increment of axial integration; The fluid impact force Ltcj is obtained by integrating the impact force distribution within the pipeline length range from a to b to obtain the impact situation in the pipeline section interval. Among them, Cj(t) represents the impact force at the position of t inside the pipeline. Zs(t) represents the cross-sectional area of the water flow at the position of t inside the pipeline. dt represents the infinitesimal increment of impact force integration.

5. The urban risk prediction method based on multi-source data fusion according to claim 4, characterized in that: Step two also includes: S25. Extract the water flow ratio Lsz, water level value Swz, pipeline radial deformation degree Jbxd, pipeline axial deformation degree Zbxd, and fluid impact force Ltcj calculated in S21 - S24. After dimensionless processing, calculate and obtain the sewage backflow index Wsdg through the following related formula: In the formula, represents the maximum tolerable threshold of the water flow ratio; represents the maximum tolerable threshold of the water level value; , and represent the weight coefficients, represents the second correction constant, and ln2 represents the natural logarithm with the natural number 2 as the base; S26. Preset a risk threshold F, and compare and evaluate the sewage backflow index Wsdg with the risk threshold F to obtain a second evaluation result, including: When the sewage backflow index Wsdg > risk threshold F, it indicates that the pipeline deformation is abnormal and there is a risk of sewage backflow, and a sewage backflow risk warning signal is generated; When the sewage backflow index Wsdg ≤ risk threshold F, it indicates that the pipeline deformation is normal and there is no risk of sewage backflow.

6. The urban risk prediction method based on multi-source data fusion according to claim 1, characterized in that: Step three includes: S31. Collect the methane concentration CH4, hydrogen concentration H2, ethane concentration C2H6, hydrogen sulfide concentration H2S, and ammonia concentration NH3 at different monitoring points in the tunnel within the monitoring area; and collect and obtain the gas pressure value P and the wind speed V, and establish a third data set; S32. Extract the methane concentration CH4, hydrogen concentration H2, ethane concentration C2H6, hydrogen sulfide concentration H2S, and ammonia concentration NH3; after dimensionless processing, calculate and obtain the flammable gas accumulation factor Gcy through the following formula: In the formula, represents the maximum tolerable threshold of methane concentration, represents the maximum tolerable threshold of hydrogen concentration, represents the maximum tolerable threshold of ethane concentration, represents the maximum tolerable threshold of hydrogen sulfide concentration, represents the maximum tolerable threshold of ammonia concentration, , , , and represent the weight coefficients.

7. A method for predicting urban risks based on multi-source data fusion according to claim 6, characterized in that: Step three also includes: S33. Collect and obtain the gas pressure value P through the gas pressure sensor, and calculate and obtain the gas pressure difference Pc through the following formula: In the formula, represents the gas pressure value at the x-th monitoring point, represents the gas pressure value at the (x + 1)-th monitoring point; S34. Extract the wind speed V in the third data set, and calculate and obtain the ventilation effect index Vx through the following formula: Wherein, A represents the effective cross-sectional area of the pipeline, represents the maximum effective cross-sectional area of the pipeline, and the calculation formula according to the original inner diameter D of the pipeline is: ; S35, obtain the flammable gas accumulation factor Gcy, ventilation effect index Vx and gas pressure difference Pc in the monitoring area, and establish a gas diffusion model through support vector machine SVM and train it, then process the flammable gas accumulation factor Gcy, ventilation effect index Vx and gas pressure difference Pc dimensionlessly, and calculate the gas explosion risk factor Bfxs through the following correlation formula: In the formula, and both represent weight coefficients, and ln2 represents the logarithmic function with base 2. represents the third correction constant; S36, after dimensionless processing of the gas explosion risk coefficient Bfxs and the sewage backflow index Wsdg, a safety assessment index Spzs is obtained by fitting the following formula; In the formula, F1 and F2 represent the weight coefficients of the gas explosion risk coefficient Bfxs and the sewage backflow index Wsdg respectively. The meaning of the formula is: sewage backflow causes abnormal increase in pressure in the pipeline, which may cause gas accumulation or increase the risk of gas explosion.

8. A method for predicting urban risks based on multi-source data fusion according to claim 7, characterized in that: A safety assessment threshold R is preset to determine the gas explosion risk status of the sewage tunnel, and the safety assessment threshold R is compared and analyzed with the safety assessment index Spzs to obtain a third assessment result: When the safety assessment index Spzs is less than the safety assessment threshold R, it means that the safety status of the monitoring area is qualified and a "safe status" signal is generated; When the safety assessment threshold R≤safety assessment index Spzs≤safety assessment threshold R*120%, it indicates that there is a risk of gas explosion in the monitoring area, and the first explosion risk warning signal is generated; When the safety assessment index Spzs>safety assessment threshold R*120%, it indicates that there is a risk of gas explosion in the monitoring area, and a second explosion risk warning signal is generated.

9. A method for predicting urban risks based on multi-source data fusion according to claim 8, characterized in that: According to the first blockage warning signal, the second blockage warning signal, the sewage backflow risk warning signal, the first explosion risk warning signal and the second explosion risk warning signal, a corresponding strategy is generated, including: Generate the first strategy based on the first blockage warning signal, including: conduct pipeline inspections at least 20% of the time per week and conduct a comprehensive cleanup once a month; focus on checking known first blockage warning signals and high-risk areas; The second strategy is generated based on the second blockage warning signal, including: immediate on-site inspection, giving priority to the blockage point, requiring it to be cleared within 24 hours, and reducing the current flow by 30%-50% to relieve pressure; The third strategy is generated according to the sewage backflow risk warning signal, including: starting the pumping equipment and increasing the current ventilation volume by more than 50%; The fourth strategy is generated based on the first explosion risk warning signal, including: increasing the current operating frequency of ventilation equipment by 20%-50% and increasing the ventilation volume to more than 80%, installing local exhaust fans in high-risk areas, and extracting flammable gases at fixed points 1-3 times per hour; The fifth strategy is generated based on the second explosion risk warning signal, including: evacuating all non-emergency personnel to a safe area, 100% sealing the monitoring area, and introducing inert gas to dilute the flammable gas concentration in the monitoring area. While introducing inert gas, exhaust equipment is used to gradually exhaust the gas in the monitoring area in the tunnel until the exhaust and dilution effects meet the standards. After safety is achieved, the monitoring areas in the tunnel will be unsealed one by one.

10. A city risk prediction system based on multi-source data fusion, which is applied to a city risk prediction method based on multi-source data fusion according to any one of claims 1-9, and is characterized in that: include: The splitting area module is used to pre-acquire the electronic distribution map of the urban sewage tunnel, divide the sewage tunnel into several monitoring areas according to the catchment area and drainage path, and make corresponding marks on the electronic distribution map of the sewage tunnel; The first prediction module is used to collect the water flow velocity, fluid density viscosity, and pipeline structure parameters in the monitoring area for real-time monitoring to form a first data set; and construct the average effective cross-sectional diameter of the regional pipeline based on the first data set and the pipeline flow state coefficient Bcxs, preset the blockage warning threshold P, and compare and analyze the pipeline flow state coefficient Bcxs with the blockage warning threshold P to evaluate the flow condition and potential blockage risk in the sewage tunnel, and issue a corresponding blockage warning signal; The second prediction module, after receiving the blockage warning signal, further monitors the real-time rainfall and the pipe stress condition in the monitoring area respectively, and establishes a second data set; and preprocesses, analyzes and calculates the second data set to obtain the water flow ratio Lsz, the water level value Swz, the pipe radial deformation degree Jbxd, the pipe axial deformation degree Zbxd and the fluid impact force Ltcj, and calculates by association to obtain the sewage backflow index Wsdg; when the sewage backflow index Wsdg exceeds the risk threshold F, a sewage backflow risk warning signal is sent outwards; The third prediction module collects the data of the gas accumulation status in the monitoring area and establishes a third data set; and after extracting the flammable gas characteristics, gas pressure characteristics and ventilation effect characteristics, analyzes to obtain the flammable gas accumulation factor Gcy, the ventilation effect index Vx and the gas pressure difference Pc, and according to the trained gas diffusion model, analyzes and obtains the flammable gas accumulation factor Gcyz and the gas explosion risk coefficient Bfxs, and fits the gas explosion risk coefficient Bfxs with the sewage backflow index Wsdg to obtain the safety assessment index Spzs; The strategy module pre-sets a safety assessment threshold R, compares and analyzes the safety assessment index Spzs with the assessment threshold R, and generates corresponding strategies according to the first blockage warning signal, the second blockage warning signal, the sewage backflow risk warning signal, the first explosion risk warning signal and the second explosion risk warning signal.

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