Intelligent planning method for emergency treatment of smart park based on digital twinning

Abstract

The application relates to the technical field of fire engineering, and discloses an intelligent planning method for emergency treatment of a smart park based on digital twinning, which comprises the following steps: in response to a fire alarm trigger signal, real-time acquisition of fire site water demand characteristic parameters, system water supply capacity parameters, adjustment safety boundary parameters and partition hydraulic state parameters; generation of a fire water demand intensity coefficient based on the fire site water demand characteristic parameters, generation of a water supply capacity coefficient based on the system water supply capacity parameters, generation of a pressure-response degree with pipeline leakage and actuator availability as blocking multipliers; generation of a partition hydraulic imbalance coefficient based on the partition hydraulic state parameters; generation of a target partition pressure from the fire water demand intensity coefficient and the pressure-response degree; formation of an electric valve adjustment amount through compound control of pressure deviation feedback and hydraulic imbalance feedforward compensation; and limitation of an action boundary by the pressure-response degree. The application realizes dynamic safety planning of fire-fighting pipe network pressure, and solves the problem that a traditional static constant pressure mode cannot adapt to real-time changes in fire conditions.

Description

Technical Field

[0001] This invention belongs to the field of fire protection engineering technology, and in particular relates to an intelligent planning method for emergency response in smart parks based on digital twins. Background Technology

[0002] As a modern urban functional complex, smart parks integrate high-rise buildings, dense population flow, and diversified business formats, resulting in a high concentration of fire risks and stringent requirements for emergency response timeliness. Therefore, fire-fighting water supply systems become a core line of defense for protecting life and property. Traditional fire-fighting pipe network designs generally adopt a rigid pressure zoning strategy based on the building's vertical height, dividing the pipe network into fixed high, medium, and low pressure zones. Each zone's inlet is equipped with a fixed or proportional pressure-reducing valve, and its outlet pressure is pre-set to a constant value based on the fire-fighting facilities' needs at the most unfavorable point in that zone. This leads to a disconnect between the hydraulic states of different zones, making dynamic scheduling and collaborative optimization of cross-zone resources impossible. This static pressure control mode leads to multiple structural defects: To meet the fire extinguishing pressure requirements of the most unfavorable points in high-rise areas, the pump outlet pressure is forced to remain at a high level for a long time, causing the low-rise pipe network to continuously bear static pressure loads far exceeding the design standards. This not only accelerates the aging and sealing failure of pipe joints, but also easily induces hidden leaks and even sudden pipe bursts. At the same time, different fire extinguishing facilities, such as fire hydrant systems, require instantaneous high pressure and high flow, automatic sprinkler systems rely on stable working pressure, and fire monitors require precise flow control. Fixed zoning and constant pressure settings cannot take into account the differentiated hydraulic characteristics of various facilities, resulting in low fire extinguishing efficiency and waste of resources. In addition, the severe water hammer impact generated during pump start-up and shutdown switching and manual intervention creates periodic pressure fluctuations in the pipe network. The continuously accumulated mechanical stress significantly weakens the pipe structure strength and shortens the overall service life of the system. In real fire emergency scenarios, dynamic variables such as the actual number of fire hydrants opened, the coverage area of ​​sprinkler heads, the rate of heat release, and the speed of fire spread all exhibit rapid and non-linear changes. Existing systems lack a quantitative assessment mechanism for the dynamic balance between real-time water demand at the fire scene and the system's current water supply capacity. This makes it impossible to dynamically adjust pressure distribution strategies for each zone from a global perspective, and it's also difficult to autonomously implement pressure reduction protection when safety boundaries are abnormal, such as sudden pipeline leaks or valve actuator failures. This often leads to insufficient fire-fighting pressure, delaying fire suppression, or excessive pressure, exacerbating the risk of pipeline damage. Although digital twin technology has achieved high-precision mapping of pipeline topology and hydraulic characteristics in industrial process monitoring, its application in dynamic planning of fire emergency pressure has not yet made substantial breakthroughs. Existing technologies still heavily rely on preset parameters and human experience, making it difficult to adapt to the complex and ever-changing fire emergency needs of smart parks. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent planning method for emergency response in smart parks based on digital twins, aiming to solve the above-mentioned problems.

[0004] This invention is implemented as follows: an intelligent planning method for emergency response in smart parks based on digital twins, comprising: responding to a fire alarm triggering signal; acquiring in real time fire scene water demand characteristic parameters representing the fire scene's water demand for firefighting, system water supply capacity parameters representing the current output state of the fire water supply system, adjustment safety boundary parameters representing the safety boundary of the pipeline network operation, and zone hydraulic state parameters representing the internal hydraulic conditions of each pressure zone; generating a fire water demand intensity coefficient based on the fire scene water demand characteristic parameters, wherein the fire water demand intensity coefficient monotonically maps the urgency of the current fire scene's demand for fire water supply pressure; and generating a water supply capacity coefficient based on the system water supply capacity parameters, wherein the water supply capacity coefficient monotonically maps... The remaining hydraulic energy margin of the fire-fighting water supply system that can still be dispatched; based on the fire water demand intensity coefficient and the water supply capacity coefficient, and using the pipeline leakage index and actuator availability index in the adjustment safety boundary parameters as blocking multipliers, a pressure-response degree is generated; based on the fusion of zonal hydraulic state parameters, a zonal hydraulic imbalance coefficient is generated, which monotonically maps the deviation of the current zonal hydraulic state from the design equilibrium point; the target zonal pressure is generated from the fire water demand intensity coefficient and the pressure-response degree, and the electric valve adjustment amount is formed by a composite control driven by the zonal characteristic point pressure deviation and the hydraulic imbalance coefficient feedforward compensation, and the action boundary of the electric valve adjustment amount is limited by the pressure-response degree.

[0005] A further technical solution involves calculating the fire water demand intensity coefficient as follows: First, obtain the fire scene water demand characteristic parameters, including the total flow rate of fire hydrants, the number of sprinkler heads in operation, the fire source power, and the fire spread rate. Then, compare the current total flow rate of fire hydrants, fire source power, and fire spread rate with the maximum simultaneous fire hydrant flow rate, the maximum reliable fire heat release rate, and the maximum fire spread rate in the area, respectively. After truncating the ratio to an upper limit of 1, obtain the fire hydrant total flow rate index, fire source power index, and fire spread rate index. Finally, compare the current total flow rate of fire hydrants, fire source power index, and fire spread rate index with the maximum simultaneous fire hydrant flow rate, the maximum reliable fire heat release rate, and the maximum fire spread rate in the area. The product of the number of active sprinkler heads and the rated working pressure output flow of each sprinkler head is compared with the maximum simultaneous operating flow of the sprinkler system in the area. After truncating the ratio to an upper limit of 1, the sprinkler water demand flow index is obtained. The larger of the total fire hydrant opening flow index and the sprinkler water demand flow index is used as the basic water demand term, and the fire source power index and fire spread rate index are used as enhancement terms. The weighted synthesis is performed, and the synthesis result is truncated to no more than 1 to obtain the fire water demand intensity coefficient. The higher the fire water demand intensity coefficient, the higher the urgency of the current fire scene's demand for fire water supply pressure.

[0006] A further technical solution involves calculating the water supply capacity coefficient as follows: Obtain system water supply capacity parameters, including the fire pump outlet pressure and total flow rate, as well as the water tank / reservoir level; Calculate the difference between the pump set's maximum allowable outlet pressure and the current fire pump outlet pressure, and then compare this difference with the pump set's maximum allowable outlet pressure to obtain the fire pump pressure margin index; Calculate the difference between the pump set's maximum allowable safe operating total flow rate and the current fire pump outlet total flow rate, and then compare this difference with the pump set's maximum allowable safe operating total flow rate to obtain the fire pump flow margin index; Calculate the ratio between the current water tank / reservoir level and the highest effective water tank / reservoir level to obtain the water tank / reservoir level ratio index; Finally, use the product of the fire pump pressure margin index, the fire pump flow margin index, and the water tank / reservoir level ratio index as the water supply capacity coefficient.

[0007] A further technical solution involves the following calculation process for the pressure-response ratio: First, obtain the adjustment safety boundary parameters, which include pipeline leakage and valve actuator power supply failure flags. Second, calculate the ratio of the current pipeline leakage to the maximum allowable leakage, truncating the ratio to an upper limit of 1 to obtain the pipeline leakage index. Third, determine the supply-demand matching factor based on the ratio of the water supply capacity coefficient to the fire water demand intensity coefficient, truncating this factor to no more than 1. Fourth, generate a leakage blocking factor and a power supply blocking factor from the pipeline leakage index and the valve actuator power supply failure flags, respectively. The leakage blocking factor is 1 when there is no leakage, 0 when the leakage reaches the preset maximum allowable value, 1 when the power supply is normal, and 0 when there is a power supply failure. Finally, multiply the supply-demand matching factor, leakage blocking factor, and power supply blocking factor to obtain the pressure-response ratio.

[0008] A further technical solution involves calculating the zonal hydraulic imbalance coefficient as follows: Obtain zonal hydraulic state parameters, including the zonal main flow rate and zonal inlet pressure; Ratio the absolute value of the difference between the current zonal main flow rate and the commonly used flow rate corresponding to the zonal design equilibrium point to the commonly used flow rate corresponding to the zonal design equilibrium point, truncating the ratio to an upper limit of 1 to obtain the zonal flow deviation index; Ratio the absolute value of the difference between the current zonal inlet pressure and the zonal design pressure to the zonal design pressure, truncating the ratio to an upper limit of 1 to obtain the zonal pressure deviation index; Weightedly combine the zonal flow deviation index and the zonal pressure deviation index, truncating the combined result to no more than 1 to obtain the zonal hydraulic imbalance coefficient.

[0009] A further technical solution involves calculating the target zone pressure as follows: obtaining the fire water demand intensity coefficient and pressure-response degree; using the minimum working pressure at the most unfavorable point of the fire pipeline network as the lower limit and the upper limit of the fire pipeline network pressure-bearing safety as the upper limit, determining the ideal target pressure between the lower and upper limits using the fire water demand intensity coefficient, and scaling the ideal target pressure towards the upper limit using the pressure-response degree to obtain the target zone pressure.

[0010] A further technical solution, before acquiring various parameters in real time, includes: constructing a digital twin model of the fire protection pipeline network. This digital twin model comprises a geometry and topology layer, a hydraulic characteristics layer, and a dynamic mapping layer. The geometry and topology layer includes pipe segment lengths, inner diameters, absolute roughness, node 3D coordinates, and connection relationship matrices from the park's BIM / GIS data. The hydraulic characteristics layer includes measured and calibrated pipe segment friction coefficients, electric regulating valve opening-flow coefficient curves, QH characteristic curves of the main fire pump and pressure-stabilizing pump, and the effective volume of the fire water tank and the relationship between liquid level and water storage capacity. The dynamic mapping layer registers the positions of various pressure transmitters, flow meters, level gauges, and actuators as corresponding virtual measuring points in the twin model, maintaining second-level data synchronization through an IoT platform, so that the design benchmark values ​​required for subsequent coefficient calculations can be uniformly extracted from this digital twin model.

[0011] Further technical solutions include the following design benchmark values: maximum simultaneous fire hydrant flow rate in each area, maximum credible fire heat release rate in each area, maximum fire spread rate in each area, maximum simultaneous operating flow rate of sprinkler systems in each area, maximum allowable outlet pressure of pump sets, maximum allowable total safe operating flow rate of pump sets, highest effective liquid level of water tanks / reservoirs, maximum allowable leakage, common flow rate corresponding to the design balance point of each zone, design inlet pressure of each zone, minimum working pressure at the most unfavorable point of the fire protection network, and upper limit of pressure-bearing safety of the fire protection network.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention achieves dynamic matching between the fire water demand intensity coefficient and the water supply capacity coefficient, and combines safety boundary parameters such as pipeline leakage and actuator failure as blocking multipliers to generate pressure-response degree. This enables the system to both increase pressure as needed to meet fire extinguishing requirements when a fire alarm occurs, and to actively suppress pressure increase or trigger pressure reduction protection when there are safety hazards in the pipeline network. This fundamentally balances fire extinguishing efficiency and pipeline operation safety.

[0013] 2. This invention forms a composite control strategy by combining feedforward compensation of the hydraulic imbalance coefficient of each zone with feedback control of the pressure deviation of the characteristic points of each zone. This enables the electric valve to respond quickly to changes in hydraulic conditions and has the precision to eliminate steady-state errors, effectively solving the problems of large hydraulic fluctuations and the inability to coordinate and optimize the pressure of each zone under the traditional fixed zone and constant pressure mode.

[0014] 3. This invention relies on a digital twin model to uniformly extract various design benchmark values ​​and maintains second-level data synchronization through an Internet of Things platform. This ensures that the calculation of key parameters such as fire water demand intensity coefficient and water supply capacity coefficient is based on accurate and real-time pipeline status, significantly improving the credibility and response speed of emergency planning. Attached Figure Description

[0015] Figure 1 A flowchart illustrating an intelligent planning method for emergency response in smart parks based on digital twins, provided by this invention. Figure 2 The flowchart of the pressure-response degree calculation process provided by the present invention is shown. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0017] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0018] like Figure 1 As shown in the illustration, an intelligent planning method for emergency response in a smart park based on digital twins is provided in one embodiment of the present invention. The method includes: responding to a fire alarm triggering signal, whereby the fire alarm triggering signal indicates that a fire event has occurred and an emergency response needs to be initiated. This signal can be emitted by fire-fighting equipment such as smoke detectors, flame detectors, and manual alarm buttons.

[0019] Real-time acquisition of fire scene water demand characteristic parameters, system water supply capacity parameters, regulation safety boundary parameters, and zone hydraulic state parameters, which characterize the water demand for fire fighting, system water supply capacity parameters, regulation safety boundary parameters, and internal hydraulic conditions of each pressure zone. A fire water demand intensity coefficient is generated based on the fusion of the fire scene water demand characteristic parameters. This coefficient monotonically maps the urgency of the current fire scene's demand for fire-fighting water pressure. The generation of this coefficient aims to quantify the urgency of the fire scene's demand for fire-fighting water pressure. One implementation involves simply weighting and summing the acquired fire scene water demand characteristic parameters (e.g., the number of open fire hydrants, the area of ​​the sprinkler system in operation), and then normalizing the result to obtain the coefficient. For example, each open fire hydrant can be assigned a fixed weight value, and each operating sprinkler area can be assigned another fixed weight value. These weight values ​​are then summed and divided by the maximum possible weight value to obtain a coefficient between 0 and 1.

[0020] A water supply capacity coefficient is generated based on the system's water supply capacity parameters. This coefficient monotonically maps the remaining dispatchable hydraulic energy margin of the fire water supply system. The purpose of generating the water supply capacity coefficient is to assess the current dispatchable remaining hydraulic energy margin of the fire water supply system. One implementation method is to linearly combine the acquired system water supply capacity parameters (e.g., the number of operating fire pumps and the remaining water volume in the water tank). For example, a comprehensive coefficient can be calculated by simple averaging or weighted averaging based on the ratio of the currently operating number of fire pumps to the maximum number of operating pumps, and the ratio of the water tank level to the total volume.

[0021] Based on the fire water demand intensity coefficient and the water supply capacity coefficient, and using the pipeline leakage index and actuator availability index in the adjusted safety boundary parameters as blocking multipliers, a pressure-response ratio is generated. The generation of the pressure-response ratio comprehensively considers supply and demand matching and safety constraints. One implementation method is to first perform a simple ratio calculation between the fire water demand intensity coefficient and the water supply capacity coefficient to initially assess the supply and demand balance. Subsequently, the pipeline leakage index (e.g., manual judgment of whether a leak exists) and actuator availability index (e.g., whether a valve can open and close normally) in the adjusted safety boundary parameters are converted into Boolean values ​​of 0 or 1, and multiplied with the initial assessment result as multipliers. For example, if a leak or actuator failure exists, the multiplier is 0, making the final pressure-response ratio 0, indicating that the system cannot respond.

[0022] The hydraulic imbalance coefficient for each zone is generated by fusing zone hydraulic state parameters. This coefficient monotonically maps the deviation of the current zone's hydraulic state from the design equilibrium point. The generation of the zone hydraulic imbalance coefficient aims to reflect the balance of hydraulic conditions in each zone. One implementation involves comparing the acquired zone hydraulic state parameters (e.g., zone main flow rate, zone inlet pressure) with a preset static design equilibrium point and calculating their absolute deviation. Then, these deviation values ​​are simply weighted averaged or the maximum value is selected, and normalized to obtain a coefficient between 0 and 1. For example, if the flow rate or pressure in a zone deviates significantly from the design value, the imbalance coefficient for that zone will increase accordingly.

[0023] The target zone pressure is generated from the fire water demand intensity coefficient and pressure-response degree. A composite control system, driven by the pressure deviation at the zone's characteristic points and compensated by the hydraulic imbalance coefficient feedforward, forms the electric valve regulation amount. The pressure-response degree limits the action boundary of the electric valve regulation amount. Target zone pressure: The desired pressure value to be maintained, dynamically calculated based on the fire water demand intensity and system response capability for a specific pressure zone in the fire protection network. Electric valve regulation amount: The amount used to control the change in the opening of the electric regulating valve to achieve precise regulation of the network pressure.

[0024] This application's overall technical concept solves the problems of traditional fire-fighting pipe networks' inability to dynamically adjust pressure in real time, match fire scene demands with system supply, and optimize pressure distribution under safety constraints by acquiring multi-source real-time parameters, fusing multi-dimensional coefficients, and using dynamic pressure planning based on composite control. This is achieved through the acquisition of multi-source real-time parameters, the fusion and generation of multi-dimensional coefficients, and dynamic pressure planning based on composite control. This method realizes the intelligence, flexibility, and safety of fire-fighting water supply systems in emergency response, improving the efficiency and reliability of fire emergency response in smart parks.

[0025] This application further proposes the following calculation process for the fire water demand intensity coefficient: The fire scene water demand characteristics parameters are obtained, including the total flow rate of fire hydrants, the number of sprinkler heads in action, the fire source power, and the fire spread rate. These parameters are direct or indirect indicators reflecting the real-time water demand for firefighting at the fire scene and are fundamental data for assessing the severity of the fire and the urgency of water supply. They can be obtained through real-time data collection via IoT sensor networks, such as flow meters, pressure sensors, temperature sensors, and smoke detectors; or through intelligent analysis of the fire scene using technologies such as video analytics and image recognition. The total flow rate of fire hydrants refers to the total water flow from all opened fire hydrants at the fire scene. This parameter directly reflects the immediate water demand of manual firefighting forces. It can be obtained through real-time monitoring by flow sensors installed at the hydrant outlets; or through firefighter operation records or the intelligent fire hydrant's opening status feedback system. The number of sprinkler heads in action refers to the total number of sprinkler heads in the automatic sprinkler system at the fire scene that are activated (spraying water). This parameter reflects the water demand of the automatic fire extinguishing system. The heat release rate (HFR) can be obtained through feedback from the sprinkler system controller's action signals, or indirectly calculated using pressure or flow sensors installed on the sprinkler network. The heat source power refers to the rate at which the heat source releases heat during a fire, and is an important indicator for measuring the scale and intensity of a fire. It can be obtained by direct measurement using fire detectors (such as heat release rate detectors), or estimated using a fire spread model combined with data such as on-site temperature and smoke concentration. The fire spread rate refers to the speed at which a fire spreads in space, reflecting the dynamic trend of fire development. It can be obtained by real-time analysis of the flame or smoke spread area using a video surveillance system combined with image recognition technology, or calculated using the alarm time difference of multiple temperature sensors or smoke detectors combined with their spatial location.

[0026] The total flow rate of currently open fire hydrants, fire source power, and fire spread rate are compared with the maximum simultaneous fire hydrant flow rate, maximum credible fire heat release rate, and maximum fire spread rate in the area, respectively. A min function is used to truncate the ratios to a maximum of 1, yielding the total hydrant flow rate index, fire source power index, and fire spread rate index. This ratio comparison standardizes the real-time data to a relative value, reflecting the current state relative to the system's design limits. The min function's maximum truncation ensures that all index values ​​do not exceed 1; that is, when the actual value reaches or exceeds the design maximum, the index value is at most 1, avoiding excessive influence of out-of-limit data on subsequent calculations and maintaining the physical meaning and computational stability of the indices. The total hydrant flow rate index, fire source power index, and fire spread rate index are standardized dimensionless parameters that quantify the degree to which fire hydrant water demand, fire source intensity, and fire spread rate are relative to their design limits.

[0027] The ratio of the product of the current number of spray nozzles in operation and the rated working pressure output flow of each nozzle to the maximum simultaneous operating flow of the spray system in the area is processed, and the ratio is truncated to 1 using a min function to obtain the spray water demand flow index. The spray water demand flow index is a dimensionless parameter after standardization, which specifically quantifies the degree of water demand of the spray system relative to its design limit.

[0028] The fire water demand intensity coefficient is obtained by weighting the larger of the total flow rate index of fire hydrants and the water demand index of sprinklers as the basic water demand term, and by using the fire source power index and the fire spread rate index as enhancement terms. The result is then truncated to no more than 1. A higher fire water demand intensity coefficient indicates a higher urgency of the current fire scene's demand for fire-fighting water pressure. The specific calculation formula is as follows: in, This is the fire water demand intensity coefficient. , and All values ​​are fire water demand intensity weights ranging from 0 to 1. Fire water demand intensity weight , and The parameters used to quantify the relative importance of different fire scene water demand characteristics in the comprehensive assessment of fire water demand intensity are configurable. These weights can be pre-set by expert experience, such as by empirically assigning values ​​based on historical fire data and fire protection codes, or by using machine learning algorithms to train and optimize in combination with historical fire cases and fire extinguishing effect data to adaptively adjust the weight allocation. To activate the total flow rate for fire hydrants, The water flow rate index for spraying is... This refers to the fire source power index. This is the fire spread rate index. The above formula is used to comprehensively calculate the fire water demand intensity coefficient. The core mathematical model integrates the total flow rate index of fire hydrant opening through a weighted summation method. Spraying water demand index Fire source power index and fire spread rate index The final result is then limited to between 0 and 1 using the min function. The max function is used to eliminate the larger values ​​in the water demand indices for fire hydrants and sprinklers, reflecting which has a more urgent need for water pressure.

[0029] The proposed solution aims to accurately quantify the urgency of fire-fighting water supply pressure at a fire scene, overcoming the shortcomings of subjective estimation or simplification in traditional methods. This solution systematically acquires and processes multi-source fire scene water demand characteristic parameters to generate a unified fire water demand intensity coefficient, thus providing a reliable basis for subsequent dynamic pressure planning. First, the system acquires real-time fire scene water demand characteristic parameters, including total hydrant opening flow, number of sprinkler heads in operation, fire source power, and fire spread rate. These parameters comprehensively cover the fire-fighting water demand, fire scale, and development trend at the fire scene, avoiding assessment biases that may result from a single parameter. Simultaneously, the system acquires preset fire water demand intensity weights. , and These weights reflect the relative importance of different characteristic parameters in assessing the severity of a fire. Next, to unify the characteristic parameters of different dimensions and reflect their severity relative to design limits, the system standardizes these parameters. Specifically, the total flow rate of currently activated fire hydrants, fire source power, and fire spread rate are each compared to their corresponding area design maximum values, and a min function is used to truncate the upper limit of the ratio to 1, thus obtaining the fire hydrant activation total flow rate index, fire source power index, and fire spread rate index. Similarly, for sprinkler systems, the product of the current number of active sprinkler heads and the rated working pressure output flow rate of each sprinkler head is compared to the maximum simultaneous active flow rate of the sprinkler system in the area, and a min function is also used to truncate the upper limit of the ratio to 1, obtaining the sprinkler water demand flow rate index. These indices are dimensionless values ​​between 0 and 1, which can intuitively represent the severity of the current fire relative to design limits in various dimensions. Subsequently, these standardized indices and preset fire water demand intensity weights are substituted into the core calculation formula. This formula cleverly integrates multiple fire situation information: the max function ensures that when fire hydrants and sprinklers are operating simultaneously, the one with the more urgent water pressure demand is taken as the dominant factor in water demand; the weighted summation, based on preset weights, comprehensively considers the contributions of water demand (fire hydrants and sprinklers), fire intensity (fire source power), and fire development speed (fire spread rate) to the overall water demand intensity; finally, the min function again limits the calculation result to between 0 and 1, ensuring the accuracy of the fire water demand intensity coefficient. The physical meaning and interpretability of this data. Through the above process, the system can generate a fire water demand intensity coefficient between 0 and 1. .when When there is no need for firefighting water, the system only needs to maintain normal static pressure; when At this point, it indicates that the fire has developed to the extreme severity level designed for the area, requiring full-power water supply, and the target pressure should be increased to the upper limit allowed by the pipeline network. This coefficient The monotonic mapping accurately reflects the urgency of the fire scene's demand for fire-fighting water pressure, providing precise and quantitative input for the upper-level intelligent planning methods. This calculation process is closely integrated with the aforementioned intelligent planning method for smart park emergency response based on digital twins. When acquiring real-time fire scene water demand characteristic parameters, the virtual measuring points registered in the dynamic mapping layer of the digital twin model can be fully utilized, maintaining second-level data synchronization through the IoT platform to ensure the real-time nature and accuracy of the input data. Simultaneously, design benchmark values ​​required for the calculation process, such as "maximum simultaneous fire hydrant flow rate in the area," "maximum credible fire heat release rate," "maximum fire spread rate," and "maximum simultaneous operating flow rate of the sprinkler system in the area," can all be uniformly extracted from the digital twin model, ensuring the standardization and consistency of the calculation. This integration ensures that the calculation of the fire water demand intensity coefficient is based not only on real-time data but also on the design limits and actual operating characteristics of the park's fire protection system, thus more accurately reflecting the real needs of the fire scene and providing a solid foundation for subsequent pressure-response generation and target zone pressure calculation, thereby improving the reliability and responsiveness of the entire intelligent planning method.

[0030] The following is a specific example to illustrate this. As a concrete implementation method, suppose a fire occurs in a specific fire compartment of a smart park, and the system needs to calculate the fire water demand intensity coefficient for that compartment. First, the system acquires real-time water demand characteristics of the fire scene through an IoT platform. For example, it monitors the current total flow rate of the fire hydrants being open at 15 L / s, the number of sprinkler heads in operation at 5, the fire source power at 2 MW, and the fire spread rate at 0.5 m / s. Simultaneously, the system extracts the design baseline values ​​for the area from the digital twin model: the maximum simultaneous fire hydrant flow rate is 30 L / s, the maximum reliable fire heat release rate is 5 MW, the maximum fire spread rate is 1 m / s, the rated working pressure flow rate per sprinkler head is 1.5 L / s, and the maximum simultaneous operating flow rate of the sprinkler system in the area is designed to be 15 L / s. The system also includes preset fire water demand intensity weights. , and The values ​​are 0.5, 0.3, and 0.2 respectively. Next, ratio processing and index calculation are performed: Fire Hydrant Opening Total Flow Rate Index. The fire source power index is 0.5. The fire spread rate index is 0.4. The spray water demand index is 0.5. The value is 0.5. Finally, these indices are substituted into the formula for calculating the fire water demand intensity coefficient: Based on the above calculations, the fire water demand intensity coefficient for this fire scenario is obtained. The value is 0.47. This value intuitively indicates that the urgency of the current fire scene's demand for fire water supply pressure is at a moderate to low level, providing a quantitative basis for the subsequent intelligent planning system to adjust the target zone pressure.

[0031] Through the above technical solution, this application can accurately and objectively quantify the urgency of fire-fighting water supply pressure at a fire scene. By comprehensively considering multi-source fire scene water demand characteristic parameters such as total hydrant opening flow, number of sprinkler heads in action, fire source power, and fire spread rate, and combining them with preset weights for weighted fusion, the bias caused by single-parameter evaluation or subjective estimation in traditional methods is avoided. This allows the fire water demand intensity coefficient to more comprehensively and accurately reflect the true severity of the fire and the urgency of fire-fighting water. Standardization processing and min function truncation ensure the uniform comparison and calculation stability of parameters with different dimensions, making the generated fire water demand intensity coefficient... It has clear physical meaning and interpretability. As an important input for subsequent intelligent planning methods, this coefficient can effectively drive the dynamic adjustment of the target zone pressure, thereby enabling the fire water supply system to be flexibly scheduled according to the actual needs of the fire scene. This avoids the risk of pipe bursts caused by high-pressure operation, while ensuring that sufficient fire-fighting water can be provided in case of an emergency, significantly improving the intelligence level and response efficiency of emergency response in the smart park.

[0032] This application further proposes the following calculation process for the water supply capacity coefficient: The system's water supply capacity parameters are acquired, including the fire pump outlet pressure and total flow rate, as well as the water tank / reservoir level. Acquiring these parameters aims to obtain key operational status data of the fire water supply system in real time, which forms the basis for assessing the system's current water supply capacity. The fire pump outlet pressure and total flow rate directly reflect the pump set's immediate output, while the water tank / reservoir level indicates the amount of fire water reserves. These parameters can be obtained by installing pressure sensors and flow meters on the fire pump outlet pipeline and level sensors inside the fire water tank or reservoir. These sensors convert the real-time acquired analog signals into digital signals and transmit them to the data processing unit via an IoT platform. Alternatively, data synchronization can be achieved using virtual measuring points in a digital twin model. For example, by integrating with a SCADA or BMS system, these parameters can be directly obtained from existing monitoring equipment and mapped to corresponding virtual measuring points in the digital twin model, ensuring the real-time nature and accuracy of the data.

[0033] The difference between the pump set's maximum allowable outlet pressure and the current fire pump outlet pressure is compared to the maximum allowable outlet pressure of the pump set to obtain the fire pump pressure margin index; this step quantifies the remaining potential of the fire pump in terms of pressure output. By calculating the difference between the current pressure and the maximum allowable pressure and normalizing it, a dimensionless index reflecting the pump set's pressure margin can be obtained. After receiving the real-time fire pump outlet pressure, the data processing unit reads the maximum allowable outlet pressure of the pump set from the preset system parameters, performs subtraction and division operations, and obtains the pressure margin index. Alternatively, the pump set's QH characteristic curve and allowable operating range can be preset in the digital twin model. When real-time pressure data is input, the model automatically calculates its relative margin with respect to the maximum allowable pressure point and outputs the corresponding index.

[0034] The difference between the pump set's maximum safe operating flow rate and the current total outlet flow rate of the fire pump is compared with the maximum safe operating flow rate of the pump set to obtain the fire pump flow margin index; this step quantifies the remaining potential of the fire pump in terms of flow output. By calculating the difference between the current total flow rate and the maximum safe operating flow rate and normalizing it, a dimensionless index reflecting the pump set's flow margin can be obtained. After receiving the real-time total outlet flow rate of the fire pump, the data processing unit reads the maximum safe operating flow rate of the pump set from the preset system parameters, performs subtraction and division operations, and obtains the flow margin index. Alternatively, by combining the pump set's QH characteristic curve and the system pipeline resistance curve, the digital twin model can evaluate the pump set's operating point under the current operating conditions in real time and calculate its relative margin with the maximum safe operating flow rate point, generating the flow margin index.

[0035] The water tank / reservoir level is compared to its highest effective level to obtain the water tank / reservoir level ratio index; this step quantifies the reserve status of fire-fighting water resources. By comparing the current level with the highest effective level, the sufficiency of the water tank / reservoir can be intuitively reflected, providing water source security information for water supply capacity assessment. After receiving real-time water tank / reservoir level data, the data processing unit reads the highest effective level from the preset system parameters, performs a division operation, and obtains the level ratio index. Alternatively, the digital twin model presets the effective volume of the fire-fighting water tank and the relationship between level and water storage capacity. When real-time level data is input, the model can calculate the proportion of the current water storage capacity to the total effective water storage capacity and output it as the level ratio index.

[0036] The water supply capacity coefficient is the product of the fire pump pressure margin index, the fire pump flow margin index, and the water tank / reservoir level ratio index; the specific calculation method is as follows: in, This is the water supply capacity coefficient. , This indicates that the pump unit is not running at all (or is in an unloaded state) and the water tank is full. At this moment, the system has its full rated water supply potential and can respond to any pressure boosting request. This indicates that the pump unit has reached its limit and the water tank is empty, with no additional water supply capacity. The pipeline pressure must be reduced immediately to slow consumption, and the limited remaining water must be used for emergency repairs. This refers to the pressure margin index of the fire pump. The flow margin index of fire pumps. This step, using a multiplicative fusion method, integrates margin information from three key dimensions—pressure, flow rate, and water reserve—to generate a unified water supply capacity coefficient. The multiplication operation ensures that a deficiency in any one dimension will significantly reduce the overall water supply capacity coefficient, thus accurately reflecting the system's comprehensive water supply potential. The data processing unit takes the three previously calculated indices as input, performs the multiplication operation, and obtains the final water supply capacity coefficient. Alternatively, this formula can be integrated into the calculation module of the digital twin model to receive the calculation results of the three indices in real time and automatically output the water supply capacity coefficient. .

[0037] This implementation method systematically integrates real-time operating parameters of the fire water supply system to construct a comprehensive water supply capacity assessment mechanism. First, the system collects real-time data on the outlet pressure and total flow of the fire pumps, as well as the liquid level of the fire water tank or reservoir. These parameters are key indicators reflecting the system's current output status and water reserve. Then, for the pressure and flow output capacity of the fire pumps, margin indices relative to the maximum allowable values ​​are calculated, namely the fire pump pressure margin index and the fire pump flow margin index. These two indices are ratio-processed to normalize and quantify the remaining output potential of the pump set. Simultaneously, by calculating the ratio of the current water tank / reservoir level to the highest effective level, the water tank / reservoir level ratio index is obtained, directly reflecting the fire water reserve status. Finally, these three independent margin indices are merged through multiplication to generate a water supply capacity coefficient. This multiplicative fusion mechanism ensures that any deficiency in any key parameter (e.g., low water level in the tank or pumps reaching their limits) will lead to a significant decrease in the water supply capacity coefficient, thus accurately reflecting the remaining hydraulic energy margin that the fire-fighting water supply system can still dispatch. This water supply capacity coefficient... The calculation is closely integrated with other steps in the aforementioned intelligent planning method for smart park emergency response based on digital twins. In the main method, the water supply capacity coefficient... As a generator of pressure-response One of the key inputs is the fire water demand intensity coefficient. These factors collectively determine the system's responsiveness to the water pressure required at the fire scene. By accurately quantifying water supply capacity, this implementation provides a reliable basis for subsequent dynamic pressure planning, enabling the system to not only consider the needs of the fire scene when a fire alarm occurs, but also fully assess its own supply capacity. This avoids system overload or water depletion due to blindly increasing pressure, effectively solving the problem of inaccurate quantification of remaining hydraulic energy margin caused by imperfect calculation models in traditional methods, and ensuring the accuracy of dynamic pressure planning and the timeliness of system response.

[0038] As a specific implementation method, the water supply capacity coefficient can be calculated as follows: First, the fire pump outlet pressure, total fire pump outlet flow rate, and water tank level are acquired in real time using a pressure transmitter and electromagnetic flow meter deployed at the fire pump outlet, and an ultrasonic level gauge in the fire water tank. For example, at a certain moment, the measured fire pump outlet pressure is 0.8 MPa, the total flow rate is 150 L / s, and the water tank level is 3.5 m. Second, according to the system design parameters, the maximum allowable outlet pressure of the pump set is set to 1.2 MPa, the maximum allowable safe operating total flow rate of the pump set is 200 L / s, and the highest effective water tank level is 4.0 m. Next, the fire pump pressure margin index is calculated. : =(1.2-0.8) / 1.2=0.33. Calculate the fire pump flow margin index. : =(200-150) / 200=0.25. Calculate the pool / tank level ratio index. : =3.5 / 4.0=0.875. Finally, substitute these exponents into the formula. The water supply capacity coefficient is obtained. =0.072. This calculation result The value of 0.072 indicates that the current fire water supply system has a low overall water supply capacity margin, which may be close to its operating limit or that the water supply reserve is insufficient, requiring caution when carrying out pressure boosting operations.

[0039] Through the above technical solution, this application can accurately quantify the remaining hydraulic energy margin of the fire-fighting water supply system. By acquiring key parameters such as fire pump outlet pressure and total flow rate, and water tank / reservoir level in real time, and converting them into a normalized margin index, and then generating a water supply capacity coefficient through multiplication and fusion, this application overcomes the problems of inaccurate and incomplete assessment of system water supply capacity in traditional methods. This allows the system to fully consider its actual supply capacity when performing dynamic pressure planning, avoiding situations such as system overload, water depletion, or inability to meet fire scene needs due to misjudgment. Especially when used in conjunction with the fire water demand intensity coefficient, it can achieve precise matching of supply and demand, ensuring that while guaranteeing fire-fighting water pressure, it maximizes the use of existing resources and effectively avoids system operation risks, thereby improving the intelligence level and reliability of emergency response in smart parks.

[0040] like Figure 2 As shown, this application further proposes the following calculation process for pressure-response degree: Acquire adjustment safety boundary parameters, including pipeline leakage and valve actuator power supply failure indicators. These parameters are key indicators for assessing the operational safety of fire protection pipe network systems, reflecting the physical limitations and equipment status of the network under pressure and flow variations. Their role is to provide a safety baseline for intelligent planning, ensuring that secondary disasters are not caused by system defects during emergency response. For example, these parameters may include fatigue life data of pipe network materials, sealing performance indicators of connectors, or structural stress data monitored in real time by sensors. Pipeline leakage refers to the actual water loss in the fire protection pipe network. In fire emergency scenarios, pipeline leakage not only wastes water resources but, more importantly, leads to a drop in network pressure, affecting firefighting effectiveness, and may even exacerbate leakage or cause pipe bursts due to excessive pressure fluctuations. This parameter can be obtained in various ways, such as calculating the inflow-outflow difference using flow meters installed on the pipe section, or using acoustic sensors, pressure sensor arrays, and algorithms to locate and quantify leak points. The valve actuator power supply failure indicator indicates whether the actuator of the electric regulating valve is in normal working condition. Electric regulating valves are key devices for dynamically regulating pipeline pressure, and their normal power supply (such as electricity or gas) is a prerequisite for the valve to operate accurately according to instructions. This indicator is usually generated by the self-diagnostic module inside the actuator. For example, when a power interruption, insufficient gas pressure, or abnormality in the internal control circuit is detected, a fault signal will be output.

[0041] The pipeline leakage index is obtained by comparing the current pipeline leakage rate with the maximum allowable leakage rate and then truncating the ratio to 1 using a min function. The maximum allowable leakage rate refers to the upper limit of pipeline leakage that the system design or management specifications can tolerate during normal operation or emergency response of the fire protection pipeline network. Exceeding this limit indicates a serious safety hazard, requiring pressure reduction or shutdown measures. This value can be derived from statistical analysis of historical operating data or determined according to engineering specifications such as pipeline materials, design pressure rating, and safety margin. Ratio processing is a data normalization method that compares the current measurement value with a benchmark value to obtain a dimensionless relative value. Its purpose is to unify physical quantities with different dimensions and ranges onto a comparable scale, facilitating subsequent mathematical calculations and model fusion. For example, the current leakage rate can be compared with the maximum allowable leakage rate to assess the severity of the leakage. Truncation of the ratio to 1 using a min function is a data processing mechanism to ensure that the result after ratio processing does not exceed 1. In some cases, if the current measurement value exceeds the benchmark value, the ratio may be greater than 1. By truncating it to 1 using the min function, we can avoid the unreasonable impact of abnormally high values ​​on subsequent calculations, while preserving its physical meaning as an exponent. Pipeline leakage index. This is a quantitative indicator of the severity of current pipeline leakage, typically ranging from 0 to 1. 0 indicates no leakage or minimal leakage, while 1 indicates that the leakage has reached or exceeded the maximum permissible leakage amount. This index is obtained through the aforementioned ratio processing and min function truncation, and is used as a safety constraint factor in pressure-response calculations.

[0042] The supply-demand matching factor is determined by the ratio of the water supply capacity coefficient to the fire water demand intensity coefficient, and this factor is truncated to no more than 1. A leakage interruption factor and a power supply interruption factor are generated from the pipeline leakage index and the valve actuator power supply fault flag, respectively. The leakage interruption factor is 1 when there is no leakage, 0 when the leakage reaches a preset maximum allowable value, 1 when the power supply is normal, and 0 when there is a power supply fault. The pressure-response ratio is obtained by multiplying the supply-demand matching factor, the leakage interruption factor, and the power supply interruption factor. The specific calculation method is as follows: in, For pressure-response, This is the water supply capacity coefficient. This is the fire water demand intensity coefficient. It is a very small positive number. This is an index of pipeline leakage. A power supply failure indicator for the valve actuator.

[0043] Pressure-Response This is a comprehensive indicator that reflects the actual responsiveness of the fire water supply system to the required water pressure for a given fire, after considering fire scene demands, system supply capacity, and pipeline safety constraints. Its value ranges from 0 to 1, where 0 indicates the system cannot respond or must reduce pressure, and 1 indicates the system can fully respond. This indicator is a core basis for intelligent planning decisions and guides the adjustment of electric valves. (Formula follows) It is to calculate the pressure-response ratio. The core mathematical model. It determines the degree of supply and demand matching (by...) and Decision) and safety constraints (by and (Decision) organically combined. Among them, The study partially assessed the matching degree between the system's water supply capacity and the fire's water demand intensity under ideal safety conditions; while and This acts as a "blocking multiplier," correcting the supply-demand matching result and forcibly introducing security considerations. Minimal positive number. It is a very small positive value, such as 0.001 or less. Its function is to prevent water intensity coefficients from being exceeded during fires. When the value is 0, the denominator in the formula becomes 0, thus avoiding mathematical division by zero errors. This ensures that calculations can be performed stably even in the absence of fire or in the presence of a very minor fire. This is a self-diagnostic signal from the actuator, indicating a power supply failure in the valve actuator. Instead of inferring information indirectly through external sensors, the fault is detected and reported directly by the actuator's own intelligent module. This self-diagnostic mechanism provides highly reliable and real-time fault information because the actuator is most aware of its own operating status. 0 indicates normal power supply, while 1 indicates a power outage / air shortage causing the valve to become uncontrollable; this is a power supply fault indicator for the valve actuator. The specific definition of . When When the value is 0, it indicates that the actuator is powered or supplied with air normally, and the valve can operate according to the command; when A value of 1 indicates that the actuator has lost its control capability due to a power supply failure, and the valve cannot be adjusted normally. This binary representation simplifies the processing of fault information, allowing it to be directly used as a multiplier in calculations.

[0044] This solution introduces crucial safety considerations into the pressure-response calculation by acquiring and adjusting safety boundary parameters, including pipeline leakage and valve actuator power supply failure indicators. First, the current pipeline leakage is compared to the maximum permissible leakage, and a min function is used to truncate the ratio to 1, generating a pipeline leakage index. This process quantifies the real-time monitored leakage as an index between 0 and 1, intuitively reflecting the severity of the leakage. Simultaneously, the valve actuator power supply failure indicator directly indicates the availability of critical actuators. Subsequently, these safety indicators are compared with existing water supply capacity coefficients. and fire water demand intensity coefficient Combine and substitute into the formula Thus obtaining pressure-response ratio The ingenuity of this solution lies in its application of the core logic of supply and demand matching. With safety constraint factors and The fusion is achieved through a multiplicative relationship. Among these, the water supply capacity coefficient... and fire water demand intensity coefficient These factors collectively determine the system's response potential to fire pressure under ideal conditions. However, when the pipeline leakage index... When the multiplier increases, This will decrease, thereby reducing pressure-response. When the valve actuator power supply failure flag is displayed. When the value is 1 (indicating a fault), the multiplier To change it to 0, directly reduce the pressure-response ratio. The system is forced to reduce pressure to zero. This multiplicative relationship gives the safety constraint a "blocking" effect; that is, once a serious leak or actuator failure is detected, regardless of the supply-demand match, the system will be forced to switch to a safety response mode, such as reverting to the minimum pressure holding state. In this way, this solution, based on a dynamic assessment of fire scene demand and system supply capacity, superimposes real-time monitoring and quantification of the pipeline network's operational safety boundaries, ensuring that during emergency response, the system not only meets firefighting needs but also avoids secondary risks caused by its own inherent safety hazards.

[0045] As a specific implementation method, assuming a fire alarm occurs in a smart park, the system has already calculated the fire water demand intensity coefficient based on the fire's water demand characteristic parameters. The value is 0.8, and the water supply capacity coefficient is calculated based on the system's water supply capacity parameters. The value is 0.9. Ideally, this represents the degree of supply-demand matching. A value close to 1 indicates that the system is capable of meeting the pressure requirements of the fire scene. However, when acquiring and adjusting the safety boundary parameters in real time, the system detected a current pipeline leakage rate of 5 cubic meters per hour through flow meter differential readings, while the maximum allowable leakage rate for this area is set at 10 cubic meters per hour. At this point, the pipeline leakage index... This will be calculated as 5 / 10 = 0.5. Simultaneously, through the self-diagnostic signal of the electric regulating valve, the system detected that all valve actuators were powered normally; therefore, the valve actuator power supply fault flag was removed. The value is 0. Substitute these parameters into the pressure-response ratio. The calculation formula is as follows: The calculated pressure-response degree The pressure-response ratio is approximately 0.5. This means that although the water supply capacity is well-matched to the fire scene's needs, the system's responsiveness to the required water pressure is significantly reduced by the safety constraint factor due to a moderate degree of pipe leakage. The system will plan the target zone pressure and electric valve adjustment based on this reduced pressure-response ratio, thereby avoiding blindly increasing the pressure in the event of a leak and effectively reducing the risk of pipe bursts. For example, in another scenario, the fire water demand intensity coefficient... The water supply capacity coefficient is 0.6. The value is 0.7. However, at this time, the actuator of a critical electric control valve reports a fault due to power loss, causing the valve actuator power supply fault indicator to be displayed. The value is 1, and there is no obvious leakage in the pipeline; the pipeline leakage index is 1. If it is 0, then the pressure-response ratio is 0. The calculation is as follows: The calculated pressure-response degree Approximately 0. In this scenario, even with a reasonable supply-demand match, the pressure-response ratio is low due to the failure of a critical actuator. The pressure was forcibly reduced to 0. This indicates that the system must immediately revert to the minimum pressure holding state and rely on the limited remaining water to extinguish the fire, while simultaneously initiating the fault handling procedure to avoid greater damage caused by valve malfunction.

[0046] Through the above technical solution, this application effectively solves the problem of potential risks caused by neglecting safety constraints in emergency response using traditional methods. By acquiring and quantifying safety boundary parameters such as pipeline leakage and valve actuator power supply failure indicators in real time, and incorporating them as blocking multipliers into the pressure-response calculation, the system can dynamically and safely assess its response capability to fire pressure. When a pipeline leak or actuator failure is detected, the pressure-response is forcibly reduced, thereby guiding the system to adopt a conservative pressure regulation strategy and avoiding secondary disasters such as pipe bursts, exacerbated leaks, or valve malfunctions caused by blindly increasing pressure. This not only improves the operational safety and reliability of the fire water supply system, but also effectively protects the pipeline infrastructure while ensuring fire extinguishing efficiency, achieving an organic combination of intelligent emergency response and risk avoidance.

[0047] This application further proposes the following calculation process for the zoned hydraulic imbalance coefficient: The process involves acquiring zonal hydraulic state parameters, including the zonal main flow rate and zonal inlet pressure. These parameters are key real-time measurement data describing the hydraulic conditions within a specific pressure zone of the fire protection network, forming the basis for assessing the hydraulic balance of that zone. These parameters can be acquired in real-time by installing flow sensors on the zonal main pipe and pressure sensors at the zonal inlet; for example, flow meters based on electromagnetic induction and piezoresistive pressure transmitters can be used. Alternatively, data can be acquired from physical sensors through virtual measurement points in a digital twin model, combined with an IoT platform, and then cleaned and preprocessed to ensure accuracy and real-time performance. The zonal main flow rate refers to the real-time water flow through the main water supply pipeline of a specific pressure zone in the fire protection network; it is a key indicator for measuring the hydraulic load and water supply capacity of that zone. This flow rate is typically measured in real-time using flow meters (such as ultrasonic or electromagnetic flow meters) installed on the zonal main pipe. In a digital twin system, the zonal main flow rate can also be estimated and verified through hydraulic model simulation, combined with data such as upstream pump station output and downstream valve opening. Zone inlet pressure refers to the real-time water pressure at the inlet of a specific pressure zone in a fire protection network. It directly reflects the hydraulic conditions of that zone and affects the water supply pressure of all fire extinguishing facilities within the zone. This pressure is typically measured in real time using a pressure transmitter (such as a diffused silicon pressure transmitter) installed at the zone inlet. In a digital twin model, the zone inlet pressure can be accurately simulated and predicted using a hydraulic calculation module, combined with network topology, pipe segment characteristics, and flow data.

[0048] The absolute value of the difference between the current partition's main flow and the commonly used flow corresponding to the partition's design equilibrium point is compared with the commonly used flow corresponding to the partition's design equilibrium point. The ratio is then truncated to an upper limit of 1 using a min function to obtain the partition flow deviation index. This step aims to quantify the degree of deviation of the current partition's main flow from its design equilibrium point's commonly used flow and normalize it into an index between 0 and 1. By taking the absolute value, it ensures that both excessively large and small flows are considered deviations; the ratio processing achieves dimensionlessness; and the min function truncation prevents extreme flow fluctuations from causing the index to become too large, thus ensuring the index's rationality and stability. This step can be implemented in the data processing module through programming, i.e., first calculating the absolute value of the difference, then dividing by the design commonly used flow, and finally applying the min(value,1) function; or it can be approximated using a lookup table or piecewise linear function, especially in embedded systems, to improve computational efficiency.

[0049] The absolute value of the difference between the current partition inlet pressure and the partition's design pressure is compared to the partition's design pressure. A minus function is then used to truncate the ratio to a maximum of 1, yielding the partition pressure deviation index. This step quantifies the deviation of the current partition inlet pressure from its design pressure and normalizes it to an index between 0 and 1. Similar to the flow deviation index, the use of absolute value, ratio processing, and minus function truncation ensures reasonable quantification of pressure deviation and index stability, avoiding interference from extreme pressure values ​​in the assessment. This step can be performed in the data processing unit using software algorithms, including absolute value operations, division operations, and minus function truncation; alternatively, a dedicated signal processing chip or FPGA can be used for high-speed real-time calculation to meet the response speed requirements of emergency response.

[0050] The zone flow deviation index and zone pressure deviation index are weighted and synthesized, and the synthesized result is truncated to no more than 1 to obtain the zone hydraulic imbalance coefficient; the specific calculation formula is as follows: in, This represents the hydraulic imbalance coefficient for the zone. , Indicates stable hydraulic pressure. The imbalance is severe and requires strong feedforward compensation. This refers to the zone flow deviation index. This refers to the zoned pressure deviation index. The weights for zonal hydraulic imbalance range from 0 to 1. This is a value between 0 and 1, used to balance the contributions of the zonal flow deviation index and the zonal pressure deviation index to the final result when calculating the zonal hydraulic imbalance coefficient. It reflects the relative importance of flow deviation and pressure deviation to the severity of hydraulic imbalance in a specific application scenario. This weight can be calibrated offline based on historical operating data and expert experience; for example, a higher weight can be set in areas more sensitive to flow changes. The value can be dynamically optimized or adaptively adjusted using machine learning algorithms, combined with pipeline response data under different fire scenarios, to better reflect actual operating conditions. This formula is the core fusion calculation; it integrates flow and pressure deviations into a single zone hydraulic imbalance coefficient through a weighted average. This coefficient intuitively reflects the overall deviation of the current hydraulic state of the zone from the design equilibrium point, and is normalized to the range of 0 to 1 for direct use by subsequent control systems. Through weighting... This allows for flexible adjustment of the relative importance of flow and pressure in imbalance assessment. The calculation of the formula, including multiplication, addition, and min function operations, can be performed by software programs in a central controller or edge computing device; alternatively, dedicated mathematical coprocessors or DSP chips can be used to accelerate complex floating-point operations, ensuring that coefficients can be generated quickly and accurately in scenarios with high real-time requirements.

[0051] The solution proposed in this application involves accurately calculating the hydraulic imbalance coefficient of each zone. This solution addresses the problem of inaccurate hydraulic state quantification in dynamic pressure control of traditional fire-fighting pipe networks. The scheme first acquires the hydraulic state parameters for each zone, including the main flow rate and inlet pressure of each zone, as well as preset hydraulic imbalance weights for each zone. These parameters are the fundamental data for assessing the hydraulic balance state of a zone. Real-time monitoring and data synchronization with the digital twin model ensure the accuracy and timeliness of the data. Subsequently, the system compares the current main flow rate of the zone with the commonly used flow rate corresponding to the zone's design equilibrium point, calculates the absolute value of the difference, and then ratios it to the commonly used design flow rate. Finally, a min function is used to truncate the ratio to an upper limit of 1, thereby generating the zone flow deviation index. This process effectively makes the deviation of flow dimensionless and limits the index value under extreme conditions, enabling it to stably and reasonably reflect the flow imbalance. Simultaneously, the system processes the zone inlet pressure in a similar manner, comparing it with the zone's design pressure, calculating the absolute value of the difference, and then performing a ratio calculation with the design pressure. Similarly, a min function is used to truncate the ratio to an upper limit of 1, generating the zone pressure deviation index. This ensures that the quantification of pressure deviation is equally accurate and stable. Finally, these two deviation indices are... and Combined with the weight of hydraulic imbalance in the region Substitute into the formula A weighted average is calculated, and the upper limit is truncated to 1 using a min function to finally obtain the hydraulic imbalance coefficient for each zone. This coefficient combines information from both flow rate and pressure, providing a continuous value between 0 and 1, intuitively representing the degree of deviation of the current hydraulic state of the zone from the design equilibrium point. Among these, the weights... The introduction of this factor allows the system to flexibly adjust the relative importance of flow rate and pressure in assessing hydraulic imbalance based on the characteristics of different zones or the needs of emergency scenarios. Through the above calculation process, the scheme of this application can provide an accurate and reliable hydraulic imbalance feedforward compensation signal for electric valve regulation. In the above scheme, the electric valve regulation is driven by the pressure deviation at the characteristic point of the zone, while this scheme introduces a zone hydraulic imbalance coefficient. As a feedforward compensation component, this allows the electric valve to anticipate the degree of hydraulic imbalance in the current zone while responding to pressure deviations. For example, when the zone's hydraulic imbalance coefficient... At higher pressure levels, even with small pressure deviations, the system can proactively adjust the electric valve opening through feedforward compensation to correct potential hydraulic instability. This avoids the lag or overshoot that might result from relying solely on hysteresis feedback control, significantly improving the stability and adaptability of dynamic pressure control. This composite control strategy combines the precision of feedback control with the predictability of feedforward compensation, enabling fire-fighting pipelines to reach target pressure more quickly and smoothly during emergency response, effectively addressing the rapidly changing hydraulic demands of a fire.

[0052] As a specific implementation method, the calculation of the zonal hydraulic imbalance coefficient can be performed as follows: Assume a specific pressure zone in a smart park's fire protection network, with a design equilibrium point corresponding to a common flow rate of 100 L / s and a design inlet pressure of 0.6 MPa. During a fire emergency response, real-time monitoring shows that the main flow rate of this zone is 120 L / s, and the zone inlet pressure is 0.55 MPa. Simultaneously, the zonal hydraulic imbalance weight... It is set to 0.7 (indicating a greater emphasis on flow deviation). First, the zone flow deviation index is calculated. The current main flow rate of the partition is 120 L / s, and the designed operating flow rate is 100 L / s. The absolute value of the flow rate difference is |120-100| = 20 L / s. The ratio is calculated as 20 / 100 = 0.2. The min function is used to truncate the ratio to an upper limit of 1, resulting in the partition flow rate deviation index. =min(0.2,1)=0.2. Next, calculate the zone pressure deviation index. The current inlet pressure of the zone is 0.55 MPa, and the design pressure is 0.6 MPa. The absolute value of the pressure difference is |0.55 - 0.6| = 0.05 MPa. The ratio is calculated as 0.05 / 0.6 = 0.0833. Using the min function to truncate the ratio to its upper limit of 1, the zone pressure deviation index is obtained. =min(0.0833,1)=0.0833. Finally, substitute these two deviation indices into the formula to calculate the hydraulic imbalance coefficient of the zone. : The hydraulic imbalance coefficient of this zone is obtained through the above calculations. Approximately 0.165. This value will be used as part of the feedforward compensation signal to adjust the opening of the electric valve, thereby more precisely controlling the pressure in that zone and enabling it to quickly and stably reach the target pressure in emergency situations. For example, in the calculation of the electric valve adjustment amount, this... The value will be multiplied by a feedforward gain. The opening of the electric valve is adjusted according to the direction of the pressure deviation sgn(e), thereby increasing the ability to predict and compensate for hydraulic imbalance based on feedback control.

[0053] Through the above technical solution, this application provides an accurate and reliable method for calculating the hydraulic imbalance coefficient of a zone, effectively solving the problem of inaccurate quantification of hydraulic imbalance state in dynamic pressure control. This solution comprehensively considers real-time data of the zone's main flow rate and inlet pressure, comparing them with the design equilibrium point to generate physically meaningful flow deviation and pressure deviation indices. These indices are weighted and fused to form a normalized and intuitive zone hydraulic imbalance coefficient, which accurately reflects the degree of deviation of the current zone's hydraulic state from the design equilibrium point. When combined with the aforementioned composite control strategy, this zone hydraulic imbalance coefficient, as a feedforward compensation component, can significantly improve the response speed and stability of the electric valve regulation. When the system detects hydraulic imbalance, even if the pressure deviation has not yet fully manifested, the feedforward compensation mechanism can intervene in advance, guiding the electric valve to make predictive adjustments, thereby avoiding the lag that may exist in traditional feedback control. This enables the fire protection network to adjust the pressure of each zone to the target value more quickly and smoothly when facing the rapidly changing hydraulic demand at the fire scene, effectively suppressing hydraulic fluctuations, reducing pipeline fatigue damage, and ensuring a timely and stable supply of fire-fighting water, thereby improving the overall efficiency and safety of emergency response in the smart park.

[0054] This application further proposes the following calculation process for the dynamic pressure of the target fire protection pipe network area: Obtain the zone characteristic point pressure, pressure-response ratio, and zone hydraulic imbalance coefficient. Zone characteristic point pressure refers to the pressure value at key monitoring points within a specific area of ​​the fire protection network that represents the overall hydraulic state of that area. These characteristic points are typically selected near zone inlets, the most unfavorable points, or important fire extinguishing facilities; their pressure data forms the basis for assessing the zone's hydraulic conditions and making control decisions. The pressure-response ratio and zone hydraulic imbalance coefficient are obtained through the above calculations.

[0055] Using the minimum working pressure at the most unfavorable point of the fire protection pipe network as the lower limit and the upper limit of the fire protection pipe network's pressure-bearing safety as the upper limit, the ideal target pressure is determined between the lower and upper limits by the fire water demand intensity coefficient. The target zone pressure is then obtained by scaling this ideal target pressure towards the upper limit using the pressure-response ratio. The specific calculation formula is as follows: in, For target partition pressure, To minimize the working pressure at the most unfavorable point of the fire protection pipe network, This represents the upper limit of the pressure resistance for fire protection pipe networks. For pressure-response, The fire water demand intensity coefficient is used to dynamically determine the ideal water supply pressure for each fire compartment based on the actual fire demand and the system's response capability, thus avoiding the drawbacks of the traditional static pressure setting mode.

[0056] Real-time pressure of feature points in the data collection area This leads to deviations. ,deviation The formation of this is the foundation of feedback control. By comparing the target pressure with the real-time pressure, the difference between the current system state and the desired state is quantified, providing a basis for subsequent adjustments; the increment of the electric valve opening is calculated: in, For increments, For proportional gain, For integral gain, The sampling period of the discrete control system. For feedforward gain, This represents the hydraulic imbalance coefficient for the zone. Used to determine the sign of the deviation.

[0057] proportional gain Integral gain is a control parameter used to measure the instantaneous response of the controller to the current deviation. Its value directly affects the system's response speed and stability, and it is usually tuned through system identification, rules of thumb, or methods such as the Ziegler-Nichols method. This is another control parameter used to accumulate historical deviations to eliminate long-standing small steady-state errors, ensuring the system can ultimately and accurately reach the set target value. Its tuning method is similar to that of the proportional gain, aiming to balance eliminating steady-state errors and avoiding integral saturation. Sampling period. Feedforward gain refers to the time interval between data acquisition and control calculations in a discrete control system. It determines the system's response frequency to dynamic changes and is typically set based on the dynamic characteristics of the controlled object and the required control accuracy; for example, it can be set to the second or millisecond level. This is a control parameter used to map the hydraulic imbalance coefficient of a zone to the valve opening compensation amount, in order to proactively address potential hydraulic imbalances. Its value reflects the strength of the feedforward compensation and can be adjusted through simulation optimization or actual operating data. (Electric valve opening increment) The calculation combines PI feedback control components and hydraulic imbalance feedforward compensation components, where the PI feedback control components... The proportional term provides a rapid response to the current deviation, while the integral term eliminates long-term accumulated steady-state error, ensuring the accuracy and stability of control; hydraulic imbalance feedforward compensation component. Compensation will be made in advance based on the hydraulic imbalance in each zone, and through... The function ensures that the compensation direction is correct, thereby improving the system's dynamic response speed and anti-interference capability.

[0058] And apply pressure-response limit to the increment. The final output is sent to the valve for execution, whereby... This represents the maximum permissible opening increment in a single operation. Pressure-response limit. Safety limits were imposed on the calculated electric valve opening increments to ensure that valve action did not exceed the system's maximum permissible range, especially in systems with limited responsiveness. When the value is low or there is a safety hazard, it can effectively reduce the valve's actuation range and prevent over-adjustment or water hammer impact. Maximum permissible opening increment per operation. This refers to the maximum allowable change in valve opening within a single control cycle. Its function is to limit the intensity of valve action and prevent water hammer or mechanical wear caused by rapid opening and closing. This parameter is usually set according to the valve's mechanical characteristics, the pressure-bearing capacity of the pipeline network, and the system stability requirements.

[0059] The proposed solution achieves intelligent control of regional pressure in fire protection pipe networks through a refined dynamic pressure planning process. First, the system acquires a series of key parameters, including the pressure at characteristic points in each zone, control gain, and pressure-response ratio calculated from upstream parameters. and the hydraulic imbalance coefficient of the zone These parameters provide comprehensive real-time information and control basis for subsequent dynamic pressure calculations and valve regulation. Next, the system utilizes the fire water demand intensity coefficient. and pressure-response Through formula Dynamically generate target partition pressure This formula cleverly captures the urgency of the fire's pressure requirements. With respect to the actual responsiveness of the system This combination ensures that the target pressure meets firefighting needs without exceeding the safe pressure tolerance of the pipeline network. arrive This dynamic target pressure setting overcomes the limitation of traditional static pressure control methods, which cannot adapt to real-time changes in fire conditions. Subsequently, the system collects pressure data at key points within the designated zones in real time. and the calculated target partition pressure Comparison to form pressure deviation Based on this deviation, the system employs a composite control strategy to calculate the increment of the electric valve opening. This composite control consists of two parts: one is a PI feedback control component, which is based on the current deviation. The system employs two main methods: first, it uses historical deviation accumulation (through an integral term) to adjust the valve, ensuring that the pressure accurately tracks the target value and eliminates steady-state errors; second, it uses a hydraulic imbalance feedforward compensation component, which utilizes the zonal hydraulic imbalance coefficient. Compensate the valve in advance, and through The function ensures that the compensation direction is consistent with the pressure deviation direction, thereby effectively responding to rapid changes in the hydraulic conditions within the pipeline network and reducing control lag. Finally, the calculated electric valve opening increment is... Apply pressure - response limit This limiting mechanism is crucial, as it is based on the system's actual response capability. and the maximum allowable opening increment per transaction This limits the valve's actuation range to prevent water hammer or system instability caused by over-adjustment, especially in cases of pipeline leaks or actuator malfunctions. When the pressure drops, the system can be proactively guided to safely reduce pressure, ensuring the safe operation of the pipeline network. Through the above synergistic effect, the solution proposed in this application achieves precise, stable, and safe dynamic control of the fire protection pipeline network pressure, significantly improving the intelligence level of emergency response in smart parks.

[0060] As a specific implementation method, the minimum working pressure at the most unfavorable point of the fire protection pipe network can be set. The upper limit of the pressure bearing capacity of the fire protection pipeline network is 0.15 MPa. The pressure is 1.6 MPa. Assuming a specific fire scenario, the fire water demand intensity coefficient is calculated upstream. The pressure-response ratio is 0.7. It is 0.8. At this point, the target partition pressure is... The calculated pressure is 0.962 MPa. This means that under the current fire demand and system response capabilities, the ideal water supply pressure for this zone should be 0.962 MPa. This assumes that the pressure at the zone's characteristic points is collected in real time. If the pressure deviation is 0.9 MPa, then the pressure deviation is... =0.962 - 0.9 = 0.062 MPa. Meanwhile, assume a zone hydraulic imbalance coefficient. The proportional gain is 0.3. The integral gain is 0.5. The value is 0.01, and the sampling period is... For 1 second, feedforward gain The maximum allowable opening increment is 0.2. The value is 0.05 (for example, if the valve opening range is 0-1, 0.05 represents a 5% change in opening). At this time, the electric valve opening increment... The calculation will include a PI feedback component and a feedforward compensation component. Assuming the historical integral term is 0, the PI feedback component is: =0.031. The feedforward compensation component for hydraulic imbalance is =0.06. Therefore, the electric valve opening increment is... =0.031 + 0.06 = 0.091. Finally, apply a pressure-response limit to this increment: =0.04. Because the calculated... =0.091 exceeds the limit of 0.04, therefore the actual adjustment amount output to the electric valve will be limited to 0.04. This indicates that while responding to fire emergencies, the system strictly adheres to safe operating boundaries, avoiding excessive valve movement.

[0061] Through the above technical solutions, this application provides a precise and reliable dynamic pressure calculation and regulation method, effectively solving the problems of uneven pressure distribution in the pipeline network, inability to adapt to real-time fire situation changes, and safe pressure reduction in case of actuator failure caused by the traditional static pressure control mode. This method dynamically generates target zone pressures, enabling the fire water supply pressure to adaptively adjust according to the actual needs of the fire scene and the system's response capability, avoiding long-term damage to the low-zone pipeline network caused by high pressure. Simultaneously, a composite control strategy using zone characteristic point pressure deviation drive and hydraulic imbalance coefficient feedforward compensation, combined with the accuracy of PI feedback control and the rapid response of feedforward compensation, ensures the timeliness and accuracy of electric valve regulation, effectively suppressing hydraulic fluctuations. Furthermore, the introduction of a pressure-response limiting mechanism provides a safety boundary for the electric valve's operation. In cases of severe supply-demand imbalance, pipeline leakage, or actuator failure, it can actively limit the regulation amount and even guide the system to safely reduce pressure, thereby significantly improving the stability and safety of the fire pipeline network operation and reducing the risk of pipe bursts and water hammer.

[0062] Traditional smart park fire water supply systems suffer from inaccurate emergency response due to a lack of real-time, accurate perception and unified management of the pipeline network status, potentially leading to delayed or inconsistent parameter acquisition and difficulty in uniformly extracting design baseline values.

[0063] To this end, this application further proposes that, before acquiring various parameters in real time, the process includes: constructing a digital twin model of the fire protection pipe network, wherein the digital twin model includes a geometric and topological layer, a hydraulic characteristic layer, and a dynamic mapping layer; constructing a digital twin model of the fire protection pipe network refers to creating a virtual mapping of the physical fire protection pipe network, and realizing the monitoring, analysis, prediction, and optimization of the physical pipe network through real-time data connection. This model can be based on CAD drawings, BIM models, or GIS data of the physical pipe network for 3D modeling and integrate sensor data interfaces; or it can use a professional digital twin platform to interact with existing SCADA systems and sensor networks through API interfaces.

[0064] The geometry and topology layer includes pipe segment lengths, inner diameters, absolute roughness, node 3D coordinates, and connection relationship matrices from the park's BIM / GIS data. This layer describes the physical layout and connectivity of the fire protection pipe network. It can be a 3D geometric model accurately representing the spatial location and shape of pipe segments, or a topology map representing the network's connectivity with nodes and edges, along with geometric attributes. Park BIM / GIS data is a digital tool for managing building and geospatial information. Geometric information and attributes of the pipe network can be directly extracted from existing BIM models within the park, or digitized using geospatial data from a GIS system combined with the network layout map. Pipe segment lengths, inner diameters, absolute roughness, node 3D coordinates, and connection relationship matrices are fundamental parameters describing the physical characteristics and connectivity of the pipe network. These parameters can be directly imported from BIM / GIS data, or manually entered and verified through on-site measurements and design drawings. The connection relationship matrix can be an adjacency matrix or an association matrix, representing the connections between nodes and between nodes and pipe segments within the network.

[0065] The hydraulic characteristic layer includes measured and calibrated pipe segment friction coefficients, electric regulating valve opening-flow coefficient curves, QH characteristic curves of fire main pumps and pressure-stabilizing pumps, and the relationship between effective volume and level-storage capacity of fire water tanks. This layer describes the physical laws governing fluid movement and the hydraulic performance of equipment in the fire protection network. It can be a hydraulic calculation model, such as a set of hydraulic balance equations for the network based on Darcy-Weisbach or Hazen-Williams formulas; or a database containing equipment performance curves to simulate the hydraulic response of pumps, valves, and other equipment under different operating conditions. The measured and calibrated pipe segment friction coefficient reflects the energy loss caused by friction when fluid flows in the pipe. This coefficient can be derived by installing pressure sensors and flow meters at both ends of the pipe segment, measuring pressure drop at different flow rates, and then inversely calculating the friction coefficient; or by optimizing it using professional network hydraulic model calibration software combined with historical operating data. The opening-flow coefficient curve of an electric regulating valve describes the relationship between its fluid throughput capacity (flow coefficient) at different opening degrees. This curve can be plotted by conducting bench tests on the valve in the laboratory, measuring the flow rate and pressure drop at different opening degrees, or by fitting the curve to the performance parameter table provided by the manufacturer. The QH characteristic curve of a fire pump and a pressure-stabilizing pump describes the head (H) that the pump can provide at different flow rates (Q), and is a core indicator of pump performance. This curve can be plotted by conducting performance tests at the pump station, measuring the outlet pressure and suction pressure at different flow rates, calculating the head, or by directly using the performance curve provided by the pump manufacturer. The effective volume of a fire water tank and the level-storage capacity relationship describes the water storage capacity of the fire water tank and its correspondence with the level. The volume can be calculated using the tank's geometric dimensions, establishing a mathematical relationship between level and storage capacity; or by installing a level sensor in the tank and calibrating it according to the actual shape of the tank.

[0066] The dynamic mapping layer registers the position feedback of each pressure transmitter, flow meter, level gauge, and actuator as corresponding virtual measuring points in the digital twin model. It maintains second-level data synchronization through an IoT platform, ensuring that the design baseline values ​​required for subsequent coefficient calculations are uniformly extracted from this digital twin model. The dynamic mapping layer is responsible for mapping real-time data from the physical world to the digital twin model, achieving virtual-real synchronization. It can be a data integration platform, acquiring data from sensors and actuators via protocols such as OPCUA and MQTT; or a real-time database, storing and managing this dynamic data. The pressure transmitter, flow meter, level gauge, and actuator position feedback are sensors and feedback devices used for real-time monitoring of pipeline network status and equipment operation. Pressure transmitters can be diffused silicon pressure sensors; flow meters can be electromagnetic or ultrasonic flow meters; level gauges can be ultrasonic or radar level gauges; and actuator position feedback can be achieved through encoders or limit switches. Registering the position feedback of these sensors and actuators as corresponding virtual measurement points in the digital twin model means associating the data of physical sensors with virtual points in the digital twin model. This can be done through the configuration interface of the digital twin platform, where a virtual measurement point can be created for each physical sensor, specifying its location and data type in the model; or through a programming interface, the sensor ID can be bound to the node ID in the model. The IoT platform maintains second-level data synchronization. The IoT platform is the infrastructure connecting physical devices and digital systems, enabling data acquisition, transmission, and processing. Second-level synchronization ensures data real-time performance. This can be achieved using an IoT cloud platform based on the MQTT protocol, uploading sensor data to the cloud via an edge gateway and pushing it to the digital twin model in real time; or using a locally deployed SCADA system, transmitting data via high-speed industrial Ethernet. The design baseline values ​​required for subsequent coefficient calculations are uniformly extracted from this digital twin model, ensuring that all reference values ​​for calculations originate from the same authoritative, real-time digital twin model. The digital twin model can provide a unified data interface or API for upper-layer applications (such as the coefficient calculation module) to call; or, through the model's internal data management module, these baseline values ​​can be stored and provided in the form of structured data.

[0067] This application's solution constructs a digital twin model of the fire protection pipe network as the foundation of the entire intelligent planning method, achieving a comprehensive, accurate, and real-time virtual mapping of the physical fire protection pipe network. The geometry and topology layer provides the static physical skeleton of the pipe network, including the size, location, and connection method of pipe segments. This is a prerequisite for any hydraulic calculations and state analysis, ensuring the consistency of the virtual model with the actual pipe network in spatial structure. The hydraulic characteristics layer, based on the geometric skeleton, incorporates the dynamic characteristics of fluid movement in the pipe network, such as friction, valve and pump performance. These measured and calibrated parameters enable the digital twin model to accurately simulate the hydraulic behavior of the real pipe network, providing reliable physical support for subsequent pressure and flow calculations. The dynamic mapping layer acts as a bridge between the virtual and real worlds. It synchronizes real-time data from various sensors (pressure transmitters, flow meters, level gauges) and actuators (electric control valves) deployed in the physical pipe network to virtual measurement points in the digital twin model via an IoT platform at a rate of seconds. This ensures that the state of the digital twin model remains highly consistent with the real-time operating state of the physical pipe network. This multi-layered digital twin model enables the integration and management of previously scattered and heterogeneous pipeline network information (design parameters, equipment performance, and real-time operational data) on a unified platform. By providing a precise physical structure through a geometric and topological layer, realistic operational patterns through a hydraulic characteristic layer, and real-time status updates through a dynamic mapping layer, the model provides unified, accurate, and real-time design benchmarks and operational data for calculating key parameters such as fire-time water demand intensity coefficient, water supply capacity coefficient, pressure-response ratio, and zonal hydraulic imbalance coefficient. This allows intelligent planning methods to make decisions based on the most accurate information, avoiding misjudgments and inefficient scheduling caused by data lag or inaccuracy.

[0068] The following is a concrete example illustrating this. Suppose a smart park's fire protection pipe network; its digital twin model can be built based on the park's existing Revit BIM model. The geometry and topology layer can directly derive the length, inner diameter, and material (for calculating absolute roughness) of all fire protection pipe sections from the Revit model, as well as the 3D coordinates of each node (such as tees, elbows, valves, and fire hydrant interfaces) and their interconnections, forming a topology network suitable for hydraulic analysis. In the hydraulic characteristics layer, the pipe section friction coefficient can be obtained by installing ultrasonic flow meters and differential pressure transmitters on typical pipe sections, collecting data during routine inspections or tests, and then using hydraulic calculation software to calibrate the model. Electric control valves can be Siemens or Honeywell brands; their opening-flow coefficient curves can be obtained from the manufacturer's technical manuals and digitally stored. The main fire pump can be Grundfos or Dongfang Pump Industry fire pumps; their QH characteristic curves can also be obtained from the manufacturer's documentation. The effective volume and level-storage relationship of the fire water tank can be calculated based on the tank's CAD design drawings and stored in the model database. In the dynamic mapping layer, SICK or Rosemount pressure transmitters, electromagnetic flow meters, and ultrasonic level gauges are installed at key locations in the piping network, such as zone inlets, fire pump outlets, and inside the tank. Electric regulating valves can integrate Hall effect sensors or potentiometers to provide real-time valve opening feedback. These sensors and actuators are connected to an edge computing gateway via industrial Ethernet, and then the data is uploaded to an IoT platform on Alibaba Cloud or Huawei Cloud via the MQTT protocol. This IoT platform is configured to synchronize data to the corresponding virtual measuring points in the digital twin model every second. When calculating the fire water demand intensity coefficient, the model can uniformly extract design baseline values ​​such as the maximum simultaneous fire hydrant flow rate and the maximum reliable fire heat release rate for the area from its internal database. Similarly, when calculating the water supply capacity coefficient, parameters such as the maximum allowable outlet pressure of the pump set and the highest effective level of the tank / reservoir are also uniformly obtained from this digital twin model.

[0069] Through the above technical solutions, this application provides a comprehensive, accurate, and real-time virtual pipeline network environment. The geometry and topology layer ensures an accurate grasp of the pipeline network's physical structure, laying a solid foundation for hydraulic analysis. The hydraulic characteristics layer, through measured and calibrated parameters, enables the model to realistically reflect the hydraulic behavior of the pipeline network, improving the accuracy of the simulation. The dynamic mapping layer achieves real-time synchronization between the physical and digital worlds, solving the problems of data lag and inconsistency in traditional systems. This integrated model allows all design benchmark values ​​and real-time operating parameters required for subsequent coefficient calculations to be extracted from a unified digital twin model, avoiding the uncertainty and management complexity brought about by multi-source data. Therefore, this application ensures that during emergency response, all decisions are based on the most accurate and real-time pipeline network status and performance data, significantly improving the accuracy and response efficiency of intelligent planning methods, effectively avoiding emergency response deviations caused by inaccurate or untimely data, thereby improving the overall reliability and safety of the smart park fire water supply system.

[0070] In some embodiments described above in this application, a unified extraction of design baseline values ​​from a digital twin model is proposed to support the calculation of key parameters such as fire water demand intensity coefficient and water supply capacity coefficient. However, in its implementation, if the specific design baseline values ​​to be extracted are not clearly defined, the extraction process may be incomplete or inaccurate, thereby affecting the reliability and accuracy of subsequent dynamic pressure planning and failing to effectively address the rapidly changing water demand intensity and water supply capacity matching requirements during fire alarms. Therefore, this application further proposes that the design baseline values ​​include: the maximum simultaneous fire hydrant flow rate in each area, the maximum credible fire heat release rate in each area, the maximum fire spread rate in each area, the maximum simultaneous operating flow rate of the sprinkler system in each area, the maximum allowable outlet pressure of the pump set, the maximum allowable total safe operating flow rate of the pump set, the highest effective liquid level of the water tank / reservoir, the maximum allowable leakage, the commonly used flow rate corresponding to the design balance point of each zone, the design inlet pressure of each zone, the minimum working pressure at the most unfavorable point of the fire protection network, and the upper limit of the pressure-bearing safety of the fire protection network.

[0071] The maximum simultaneous fire hydrant flow rate in each area refers to the maximum total flow rate that all fire hydrants designed to be able to open simultaneously can provide within a specific fire compartment. This parameter is an important basis for assessing the water demand for fire fighting at a fire scene and can be determined based on fire protection design codes, building fire protection facility configuration standards, or historical fire drill data. Another method is to calculate this based on the fire pipeline design drawings and fire hydrant parameters stored in a digital twin model. The maximum credible fire heat release rate in each area refers to the maximum fire heat release rate that can be estimated within a specific fire compartment based on building function, combustible material load, and fire scenario simulation. This parameter is used to quantify the severity of the fire and the potential difficulty of fire fighting, and can be set based on fire dynamics models, building fire protection design codes, or historical fire case data. In addition, it can also be dynamically assessed by combining the risk levels of different areas within the park through an expert system. The maximum fire spread rate in each area refers to the maximum speed at which a fire may spread under the most unfavorable conditions within a specific fire compartment. This parameter reflects the urgency of the fire development and is a key indicator for assessing fire response time. It can be determined based on fire spread models, building material combustion characteristics, or fire safety assessment reports. Another approach is to comprehensively consider the fire compartments and flame-retardant measures in different areas of the park. The maximum simultaneous operating flow rate of the sprinkler system in each area refers to the maximum total flow rate that all sprinkler heads that can operate simultaneously within a specific fire compartment under design conditions. This parameter is an important basis for evaluating the fire extinguishing capacity and water demand of the sprinkler system and can be determined based on sprinkler system design specifications, hydraulic calculation results, or system commissioning reports. It can also be calculated using sprinkler system configuration information and sprinkler head parameters stored in a digital twin model. The maximum allowable outlet pressure of the pump set refers to the maximum outlet pressure that the fire pump set can provide under safe operating conditions. This parameter is a key indicator for measuring the potential of the fire water supply system and can be determined based on the pump set nameplate, performance curves, or technical parameters provided by the manufacturer. Another approach is to obtain this parameter through regular performance testing and calibration of the pump set. The maximum allowable safe total operating flow rate of the pump set refers to the maximum total flow rate that the fire pump set can provide under safe operating conditions. This parameter reflects the overall water supply capacity of the fire water supply system and can be determined based on the pump set nameplate, performance curves, or system design flow rate. In addition, verification can also be performed by monitoring the operating status and historical data of the pump set. The highest effective liquid level of the water tank / reservoir refers to the highest liquid level at which the fire water tank or reservoir can provide fire-fighting water under normal operating conditions. This parameter is used to assess the reserve of fire-fighting water resources and can be determined based on the design drawings, volume calculations, or calibration results of the water tank / reservoir. Another approach is to consider both the water tank / reservoir's water replenishment capacity and the location of the overflow outlet. The maximum permissible leakage rate refers to the maximum amount of pipeline leakage that the fire-fighting pipeline network can tolerate under normal operating or emergency conditions.This parameter is a crucial indicator for maintaining the safe operation of the pipeline network. It can be set based on pipeline design standards, material characteristics, or historical leak detection data. Alternatively, it can be determined through pressure testing and leak detection of the pipeline network. The typical flow rate corresponding to the design equilibrium point of each zone refers to the typical flow rate required to maintain hydraulic balance within a specific fire compartment under normal, non-fire alarm conditions. This parameter is used to assess the degree of deviation of the zone's hydraulic state and can be determined based on the pipeline network hydraulic calculation model, historical operating data, or design conditions. Another method is to obtain it through long-term statistical analysis of flow sensor data within the zone. The design inlet pressure of each zone refers to the pressure value that the pipeline network should reach at the inlet of a specific fire compartment under design conditions. This parameter serves as a benchmark for assessing the degree of pressure imbalance within a zone and can be determined based on the pipeline network hydraulic calculation model, fire protection design specifications, or the setting value of the zone's pressure reducing valve. Furthermore, it can be verified through calibrating the zone inlet pressure sensor data. The minimum working pressure at the most unfavorable point of the fire pipeline network refers to the minimum working pressure that must be met under fire alarm conditions at the furthest, highest, or hydraulically worst fire extinguishing facility in the fire pipeline network. This parameter is a key indicator for ensuring fire extinguishing effectiveness and can be determined based on fire protection design codes, hydraulic calculation results, or simulation exercise data. Another approach is to assess it in conjunction with the actual fire extinguishing needs at the most unfavorable point. The upper limit of the pressure bearing capacity of the fire protection pipeline network refers to the maximum pressure value that the network can withstand during operation; exceeding this value may lead to pipe rupture or equipment damage. This parameter is an important indicator for ensuring the structural safety of the pipeline network and can be determined based on the strength of the pipe materials, connection methods, safety factors, or design codes. It can also be obtained through pressure testing and safety assessment of the pipeline network.

[0072] This application's solution clarifies and uniformly extracts a series of key design benchmark values ​​from the constructed digital twin model of the fire protection pipe network, thereby providing accurate and comprehensive data support for subsequent intelligent planning methods. These design benchmark values ​​collectively constitute the quantitative basis for assessing the fire scene's water demand intensity, system water supply capacity, adjustment safety boundaries, and zonal hydraulic states. Specifically, fire scene water demand characteristic parameters (such as total fire hydrant opening flow rate, fire source power, fire spread rate, and number of sprinkler heads in operation) can be accurately converted into dimensionless exponents after being compared with these design benchmark values, and then integrated to generate a fire water demand intensity coefficient. For example, the ratio of the current total fire hydrant opening flow rate to the maximum simultaneous fire hydrant outflow rate in each area directly reflects the urgency of the current fire scene's water demand for fire hydrants. Similarly, system water supply capacity parameters (such as fire pump outlet pressure, total flow rate, and water tank / reservoir level) are compared with benchmark values ​​such as the maximum allowable outlet pressure of the pump set, the maximum allowable safe operating total flow rate of the pump set, and the highest effective water tank / reservoir level. This allows for precise quantification of the remaining hydraulic energy margin that the fire water supply system can still dispatch, generating a water supply capacity coefficient. Adjusting the ratio of pipeline leakage to the maximum allowable leakage in the safety boundary parameters generates a pipeline leakage index, which serves as a blocking multiplier in the pressure-response calculation, ensuring timely strategy adjustments when safety hazards exist in the pipe network. Furthermore, comparing zonal hydraulic state parameters (such as zonal main flow rate and zonal inlet pressure) with the commonly used flow rate and design inlet pressure corresponding to the design equilibrium point of each zonal accurately reflects the deviation of the current zonal hydraulic state from the design equilibrium point, generating a zonal hydraulic imbalance coefficient. Finally, the minimum working pressure at the most unfavorable point of the fire pipe network and the upper limit of the fire pipe network's pressure bearing capacity are directly used to calculate the target zonal pressure, ensuring that the planned pressure meets fire extinguishing requirements without exceeding the safe pressure bearing capacity of the pipe network. By uniformly registering these design benchmark values ​​in the dynamic mapping layer of the digital twin model and ensuring their synchronization with real-time data, the proposed solution enables a comprehensive, real-time, and accurate assessment of fire-related water demand, system water supply, pipeline safety, and hydraulic balance. This method of quantitative calculation based on precise benchmark values ​​provided by the digital twin model allows intelligent planning methods to more accurately understand the current operating status of the fire protection pipeline network and fire alarm requirements, thereby generating more reasonable and safer electric valve adjustment values. This effectively solves the problems of inaccurate assessment and unreliable planning caused by the lack of unified and precise benchmarks in traditional methods.

[0073] As a specific implementation method, in the digital twin model of the fire protection network in a smart park, these design baseline values ​​can be pre-configured and stored in the model's attribute database. For example, for a high-rise building area within the park, the maximum simultaneous fire hydrant flow rate in each area can be labeled and summarized in the geometry and topology layer of the digital twin model based on the building's fire protection design specifications, combined with the number of fire hydrants and the design flow rate per hydrant. The maximum credible fire heat release rate in each area can be calculated using a fire risk assessment model based on the high-rise building's purpose, the type and quantity of combustibles inside, and stored as an attribute of that area in the hydraulic characteristics layer of the digital twin model. The maximum allowable outlet pressure and the maximum allowable safe operating total flow rate of the pump unit can be directly extracted from the digital nameplate or performance curve data of the fire pump equipment and registered in the dynamic mapping layer of the digital twin model as fixed attributes of the pump unit's virtual measuring points. The highest effective liquid level of the water tank / reservoir can be determined based on the design drawings of the fire water tank, specifying the highest liquid level corresponding to its overflow height or effective volume, and used as an attribute of the water tank's virtual measuring point. The maximum permissible leakage rate can be set as an empirical value or calibrated using historical test data, based on the construction standards and material properties of the park's fire protection pipe network, and stored in the model's adjustable safety boundary parameters. The minimum working pressure at the most unfavorable point of the fire protection pipe network and the upper limit of the fire protection pipe network's pressure bearing capacity are stored as global safety constraint parameters in the top-level configuration of the digital twin model, based on the overall fire protection design plan and hydraulic calculation results of the entire park. When a fire alarm is triggered, the system obtains parameters such as the current total flow rate of fire hydrants, fire source power, and fire pump outlet pressure in real time, compares and calculates these parameters with the preset design benchmark values ​​in the digital twin model, thereby generating fire water demand intensity coefficients, water supply capacity coefficients, etc., which in turn guide the adjustment of electric valves to achieve dynamic pressure planning.

[0074] The above technical solutions clarify the design benchmark values ​​uniformly extracted from the digital twin model. These benchmark values ​​serve as precise references for quantifying fire water demand intensity, system water supply capacity, adjustment safety boundaries, and zonal hydraulic states, solving the assessment bias problem caused by unclear or incomplete benchmark values ​​in traditional methods. Specifically, by providing fire water demand-related benchmarks such as the maximum simultaneous fire hydrant flow rate and the maximum credible fire heat release rate in each area, the calculation of the fire water demand intensity coefficient is closer to the actual fire extinguishing needs; by providing water supply-related benchmarks such as the maximum allowable outlet pressure of the pump set, the maximum allowable safe total flow rate of the pump set, and the highest effective liquid level of the water tank / reservoir, the assessment of the water supply capacity coefficient more accurately reflects the system potential; by providing safety-related benchmarks such as the maximum allowable leakage, the pressure-response ratio can more reliably consider the pipeline network safety boundaries; and by providing hydraulic-related benchmarks such as the common flow rate corresponding to the design balance point of each zone and the design inlet pressure of each zone, the zonal hydraulic imbalance coefficient can more accurately reflect the deviation of the hydraulic state. Furthermore, the clear definition of the minimum operating pressure at the most unfavorable point of the fire protection pipeline network and the upper limit of the safe pressure bearing capacity of the fire protection pipeline network provides a solid safety guarantee for setting the pressure of the target zones. Therefore, the solution proposed in this application ensures that the data provided by the digital twin model fully covers all key calculation stages, greatly improving the accuracy and reliability of intelligent planning for fire emergency response. This precise benchmark data support enables the system to more effectively match supply and demand when facing a fire alarm, dynamically adjust the pressure distribution of each zone, thereby optimizing fire extinguishing efficiency, ensuring the safe operation of the pipeline network, and effectively responding to the rapidly changing water demand intensity and water supply capacity matching requirements during a fire alarm.

[0075] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart planning method for emergency response in smart parks based on digital twins, characterized in that, include: In response to a fire alarm trigger signal; Real-time acquisition of fire scene water demand characteristic parameters, system water supply capacity parameters, regulation safety boundary parameters, and zone hydraulic state parameters, which characterize the water demand for fire fighting, system water supply capacity parameters, regulation safety boundary parameters, and internal hydraulic conditions of each pressure zone. Based on the fusion of the fire water demand characteristic parameters, a fire water demand intensity coefficient is generated. The fire water demand intensity coefficient monotonically maps the urgency of the current fire scene's demand for fire water supply pressure. The water supply capacity coefficient is generated by fusing the system's water supply capacity parameters. The water supply capacity coefficient monotonically maps the remaining hydraulic energy margin that the fire water supply system can still dispatch. Based on the fire water demand intensity coefficient and the water supply capacity coefficient, and using the pipeline leakage index and actuator availability index in the safety boundary parameters as blocking multipliers, pressure-response degree is generated. The hydraulic imbalance coefficient of a zone is generated by fusing the hydraulic state parameters of the zone. The hydraulic imbalance coefficient of a zone monotonically maps the degree of deviation of the current hydraulic state of the zone from the design equilibrium point. The target zone pressure is generated by the fire water demand intensity coefficient and the pressure-response degree. The electric valve adjustment amount is formed by a composite control driven by the pressure deviation of the zone characteristic point and the hydraulic imbalance coefficient feedforward compensation. The action boundary of the electric valve adjustment amount is limited by the pressure-response degree.

2. The intelligent planning method for emergency response in smart parks based on digital twins according to claim 1, characterized in that, The calculation process for the fire water demand intensity coefficient is as follows: The water demand characteristics of the fire scene are obtained, including the total flow rate of fire hydrants, the number of sprinkler heads in action, the power of the fire source, and the fire spread rate. The total flow rate, fire source power, and fire spread rate of the current fire hydrant opening are compared with the maximum simultaneous fire hydrant flow rate, maximum credible fire heat release rate, and maximum fire spread rate in the area, respectively. After truncating the upper limit of the ratio to 1, the total flow rate index, fire source power index, and fire spread rate index of the fire hydrant opening are obtained. The ratio of the product of the current number of spray nozzles in operation and the rated working pressure output flow rate of each nozzle to the maximum simultaneous operating flow rate of the spray system in this area is processed, and the ratio is truncated to an upper limit of 1 to obtain the spray water demand flow rate index. The fire water demand intensity coefficient is obtained by taking the larger of the total flow rate index of fire hydrant opening and the water demand index of sprinkler as the basic water demand term, and taking the fire source power index and fire spread rate index as the enhancement terms. The result is then truncated to no more than 1. The higher the fire water demand intensity coefficient, the higher the urgency of the current fire scene's demand for fire water supply pressure.

3. The intelligent planning method for emergency response in smart parks based on digital twins according to claim 1, characterized in that, The calculation process for the water supply capacity coefficient is as follows: Obtain system water supply capacity parameters, including fire pump outlet pressure and total flow rate, as well as water tank / reservoir level; The difference between the maximum allowable outlet pressure of the pump set and the current outlet pressure of the fire pump is compared with the maximum allowable outlet pressure of the pump set to obtain the fire pump pressure margin index. The difference between the maximum safe operating flow of the pump set and the current total outlet flow of the fire pump is compared with the maximum safe operating flow of the pump set to obtain the fire pump flow margin index. The current water level in the pool / tank is compared with the highest effective water level in the pool / tank to obtain the water level ratio index. The product of the fire pump pressure margin index, the fire pump flow margin index, and the water tank / tank level ratio index is used as the water supply capacity coefficient.

4. The intelligent planning method for emergency response in smart parks based on digital twins according to claim 2 or 3, characterized in that, The calculation process for the pressure-response degree is as follows: Acquire adjustment safety boundary parameters, including pipeline leakage and valve actuator power supply fault indicators; The pipeline leakage index is obtained by comparing the current pipeline leakage with the maximum allowable leakage and truncating the ratio to a maximum of 1. The supply and demand matching factor is determined by the ratio of the water supply capacity coefficient to the fire water demand intensity coefficient, and this supply and demand matching factor is truncated to no more than 1. Leakage interruption factor and power supply interruption factor are generated respectively from pipeline leakage index and valve actuator power supply failure flag; Among them, the leakage blocking factor is 1 when there is no leakage, the leakage blocking factor is 0 when the leakage reaches the preset maximum allowable value, the power supply blocking factor is 1 when the power supply is normal, and the power supply blocking factor is 0 when the power supply fails. The pressure-response degree is obtained by multiplying the supply-demand matching factor, leakage prevention factor, and energy supply prevention factor together.

5. The intelligent planning method for emergency response in smart parks based on digital twins according to claim 1, characterized in that, The calculation process for the hydraulic imbalance coefficient of the zone is as follows: Obtain the hydraulic state parameters of the zone, which include the main flow rate of the zone and the inlet pressure of the zone; The absolute value of the difference between the current main traffic of the partition and the common traffic corresponding to the design balance point of the partition is compared with the common traffic corresponding to the design balance point of the partition. After truncating the ratio to the upper limit of 1, the partition traffic deviation index is obtained. The absolute value of the difference between the current partition inlet pressure and the partition design pressure is compared with the partition design pressure. After truncating the ratio to the upper limit of 1, the partition pressure deviation index is obtained. The zone flow deviation index and zone pressure deviation index are weighted and synthesized, and the synthesis result is truncated to no more than 1 to obtain the zone hydraulic imbalance coefficient.

6. The intelligent planning method for emergency response in smart parks based on digital twins according to claim 1, characterized in that, The calculation process for the target partition pressure is as follows: Obtain the fire water demand intensity coefficient and pressure-response ratio; Using the minimum working pressure at the most unfavorable point of the fire protection pipeline as the lower limit and the upper limit of the pressure-bearing safety of the fire protection pipeline as the upper limit, the ideal target pressure is determined between the lower and upper limits by the fire water demand intensity coefficient. The target zone pressure is obtained by scaling the ideal target pressure towards the upper limit using the pressure-response ratio.

7. The intelligent planning method for emergency response in smart parks based on digital twins according to claim 1, characterized in that, Before acquiring each parameter in real time, the process also includes: constructing a digital twin model of the fire protection network, wherein the digital twin model includes a geometry and topology layer, a hydraulic characteristics layer, and a dynamic mapping layer; The geometry and topology layer includes pipe segment length, inner diameter, absolute roughness, node three-dimensional coordinates, and connection relationship matrix from the park's BIM / GIS data. The hydraulic characteristic layer includes the measured and calibrated pipe section friction coefficient, electric regulating valve opening-flow coefficient curve, QH characteristic curve of fire main pump and pressure stabilizing pump, and the relationship between effective volume of fire water tank and liquid level-storage capacity. The dynamic mapping layer registers the position feedback of each pressure transmitter, flow meter, level gauge, and actuator as corresponding virtual measuring points in the digital twin model, and maintains second-level data synchronization through the Internet of Things platform, so that the design benchmark values ​​required for subsequent coefficient calculations can be uniformly extracted from the digital twin model.

8. The intelligent planning method for emergency response in smart parks based on digital twins according to claim 7, characterized in that, The design benchmark values ​​include: the maximum simultaneous fire hydrant flow rate in each area, the maximum credible fire heat release rate in each area, the maximum fire spread rate in each area, the maximum simultaneous operating flow rate of the sprinkler system in each area, the maximum allowable outlet pressure of the pump set, the maximum allowable total safe operating flow rate of the pump set, the highest effective liquid level of the water tank / reservoir, the maximum allowable leakage, the common flow rate corresponding to the design balance point of each zone, the design inlet pressure of each zone, the minimum working pressure at the most unfavorable point of the fire protection network, and the upper limit of the pressure-bearing safety of the fire protection network.

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