Flexible heat supply pipe network system based on multi-stage pipeline pump along the way and its regulation method

Abstract

The application belongs to the technical field of urban central heating, and discloses a flexible heating pipe network system based on a multi-stage pipe pump along the way and a regulation and control method, which solves the problems of front-end overpressure, end hydraulic deficiency, regional hydraulic imbalance of a traditional heating pipe network, and many occupied areas of relay pump stations and poor coordination of distributed pumps. The system comprises a digital twin simulation analysis unit, a multi-stage pipe pump system design unit along the way, and an operation dynamic decision and regulation and control unit, which respectively realize pipe network twin modeling and district division, pump deployment design and pipe network construction, load prediction and pump dynamic regulation and control. The method realizes accurate regulation and control of pipe network pressure in different districts in three steps by using the system. The application improves the operation safety, hydraulic balance and economy of the pipe network, and is suitable for large-scale complex heating pipe network optimization design and dynamic regulation and control.

Description

Technical Field

[0001] This invention belongs to the field of urban centralized heating technology, specifically relating to a flexible heating network system based on multi-stage pipeline pumps along the pipeline and its control method. Background Technology

[0002] During the annual renovation of urban heating networks, the expansion in scale, extension of heating radius, and complexity of topology have led to a decrease in the matching degree between heating network design schemes, equipment selection parameters, and current pipeline structure and heating network demand. This results in insufficient circulation power at the end of the heating network, poor hydraulic conditions, and unsatisfactory heating effect. At the same time, there are a large number of old pipelines at the front end of the heating system, with operating pressures approaching the critical overpressure level. Improving the terminal conditions by increasing the operating pressure of the heat source poses significant safety risks.

[0003] Classic heating systems are driven by a circulating pump at the primary heating station, which continuously outputs stable pressure to propel hot water along the supply network. This network exhibits characteristics of high pressure near the primary station and low pressure, or even insufficient pressure head, at more distant points. When the heating network covers long distances or has a large heating radius, the frictional and local resistance along the flow path continuously consumes energy. By the time the hot water reaches the terminal heating station, the pressure and flow rate decrease significantly, potentially leading to insufficient circulation power at the terminal heating station and resulting in poor heating performance.

[0004] To address the issue of poor heating performance at terminal heating stations due to long heating distances or large heating radii, traditional solutions typically involve adding relay pump stations. These stations are built in dedicated ground-level buildings, requiring significant above-ground space. Due to geographical limitations, adding relay pump stations presents challenges such as large land area requirements, difficulty in site selection, and high maintenance costs. They are generally only suitable for installation on main pipelines where space is available.

[0005] Another traditional approach to address this issue is to install distributed pumps in branch heating stations or terminal heating stations with insufficient circulating power. With these distributed pumps dispersed, the start-up, shutdown, and frequency adjustment of each pump directly affect the pressure distribution and flow allocation of the entire network, creating a strong coupling relationship. Variable frequency speed control of one or more terminal branch pumps can cause pressure fluctuations in the main pipeline, leading to changes in the available head of other branches, insufficient or excessive flow to terminal users, and ultimately, "hydraulic imbalance." When the heating load dynamically changes with outdoor temperature and user behavior, the distributed pumps need to adjust their output in real time; however, the inertia of the network and the pump response delay can easily lead to pressure overshoot or undershoot. This type of arrangement suffers from poor system coordination due to the large number of managed entities and the competition for flow among different heating stations.

[0006] In general, the main problems mentioned above manifest as poor heating performance at branch lines or terminal heating stations with bottlenecks, high risk of overpressure at the front end, and inability to dynamically and flexibly adjust regional hydraulic conditions according to actual heating demand due to limitations in the pipeline network's transmission and distribution capacity. Therefore, there is a need for a heating pipeline network system and control method that can optimize pressure distribution and improve terminal hydraulic conditions. Summary of the Invention

[0007] The technical problem to be solved by this invention is to overcome the shortcomings of the prior art and provide a new configuration, selection and control method for heating systems in which multiple, multi-stage pipeline pumps are arranged on the primary water supply or return pipes of the heating system. By embedding pipeline pumps at key nodes in areas with different pressure capacities to transform the pipeline network, a heating network structure containing multi-stage pipeline pumps along the pipeline is formed, giving full play to the advantages of a distributed power system. The optimal installation location and model of pipeline pumps in different pressure areas are determined by a pipeline pump selection optimization design model. By using a heating network load prediction model and a pipeline pump collaborative optimization model, the optimal start-stop state and operating frequency of multiple pipeline pumps in different pressure areas under different operating conditions are solved, and a multi-pipeline pump collaborative operation optimization strategy is obtained. This effectively solves the problems of the pressure at the front end of the heating system approaching the overpressure critical level and the insufficient pressure difference in the core main urban area at the end of the heating network.

[0008] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: The first aspect of the present invention provides a flexible heating network system based on a multi-stage pipeline pump along the pipeline and its control method, comprising:

[0009] Digital twin simulation analysis unit: used to establish a digital twin model of the heating network, simulate the thermal and hydraulic characteristics of the network, analyze node operating parameters, quantitatively evaluate the pressure-bearing capacity of pipe sections and divide the heating network into pressure-bearing areas, and identify branches with unfavorable operation in the areas.

[0010] Design unit for multi-stage pipeline pump system along the pipeline: Based on the unfavorable operating branches, determine the number and deployment location of pipeline pumps, combine the pressure distribution of the pipeline section and the pressure bearing capacity threshold of the area to construct a set of site selection schemes, aim at minimizing installation cost and energy consumption, and constrain the hydraulic balance and transportation efficiency of the pipeline network, establish a site selection optimization model to solve for the optimal pump parameters, and form a pipeline network structure containing multi-stage pipeline pumps along the pipeline.

[0011] Dynamic decision-making and control unit for multi-stage pipeline pump operation along the pipeline: Construct a digital twin model of the pipeline network including pipeline pumps, combine load prediction of heating stations to simulate water pressure distribution under variable flow conditions, and establish a collaborative optimization operation model to control the start-up and shutdown and frequency of pumps with the goal of minimizing power consumption cost and optimizing pipeline pump pressure difference compensation, so as to realize dynamic control of pipeline network pressure in different areas.

[0012] Furthermore, when establishing the digital twin model, the digital twin simulation analysis unit uses structural mechanism modeling to construct the geometric, physical, behavioral, and rule models of the pipeline network, and combines operational data to correct the model; when dividing the pressure-bearing areas of the heating network, the first... Each region is abstracted as a directed graph. The expression is:

[0013] ;

[0014] in, For the first Directed graph model of a pressure-bearing area of ​​a heating network Let k be the set of nodes in the k-th region, containing Each heat source, heating station, and pipeline branching and merging node Let M be the pipe set of the k-th region, containing M supply and return water pipes. , It is a collection of pipeline network areas; and the areas are divided based on pipeline pressure threshold, hydraulic topology and geographical distribution.

[0015] Furthermore, when the design unit of the multi-stage pipeline pump system along the pipeline determines the key nodes for deployment, it uses the unfavorable branch in the area as a benchmark, presets the absolute pressure threshold and the drop threshold relative to the design pressure based on the initial pressure distribution parameters, and identifies the key low-pressure pipe sections with pressures below the threshold. By constructing a hydraulic path dependency graph, it analyzes the area where the low-pressure pipe section is located, its supply / return water pipeline, and the upstream and downstream connection relationships. Based on the pressure threshold drop, the number of affected stations, and the topological importance, it assesses the transmission impact of the low-pressure pipe section and determines the key nodes for pipeline pump deployment.

[0016] Furthermore, the design unit for the multi-stage pipeline pump system along the pipeline, which determines the key nodes for pipeline pump deployment, also includes: for long-distance transmission and distribution pipelines within a region, determining the pressure drop of each pipe section based on the initial pressure distribution parameters of the heating network, pre-setting the pressure drop threshold of the pipe section based on the known flow demand, and identifying high-resistance pipe sections with excessive pressure drop; setting a head margin in combination with the pipeline pump head threshold limit, and determining each key node of the long-distance transmission and distribution pipeline in a multi-stage segmentation manner using a power-distributed approach.

[0017] Furthermore, when constructing a set of pipeline pump site selection schemes for differentiated deployment by region, the design unit of the multi-stage pipeline pump system along the pipeline calculates the basic head based on the pressure drop and target pressure of the supply and return water pipe sections where the pipeline pumps are deployed, determines the safety margin by combining the pressure bearing capacity threshold of the pipe sections in the region and the fluctuation range of operating data, and determines the final head parameters; based on the hydraulic calculation results of the heating network in the region where the deployment location is located, and combined with the adjustable range on the user side, the flow benchmark value is determined, and the pipeline pump model that meets the flow requirements, has flow regulation capabilities, and whose operating point is located in the high-efficiency zone is selected; on long pipe sections with large pressure losses, multiple pipeline pump installation schemes can be combined to enrich the combination of site selection schemes.

[0018] Furthermore, when establishing a site selection optimization design model, the design unit for the multi-stage pipeline pump system along the pipeline includes the following methods:

[0019] A binary variable is used to control whether a pipeline pump is installed at each node, with the objective function being to minimize the total cost, including installation cost and operating energy consumption cost.

[0020] The constraints of the location optimization design model include:

[0021] The pressure at each node within the area shall not exceed the pressure-bearing capacity constraint of the maximum pressure-bearing capacity of the pipe section.

[0022] The flow balance constraint is that the inflow to each node equals the outflow.

[0023] Hydraulic balance constraints that describe the relationship between downstream node pressure and upstream pressure, pipeline pump head, and friction loss;

[0024] Ensure that the pressure head at each node meets the pressure head constraint within the safe operating range;

[0025] And to ensure that the efficiency and head of the installed pipeline pumps are within the design capacity range, as well as equipment performance constraints.

[0026] The specific implementation method is as follows: When establishing the site selection optimization design model, the design unit of the multi-stage pipeline pump system along the pipeline uses binary variables. The objective function for determining whether a pipeline pump is installed on the control node is:

[0027] ;

[0028] Where Z represents the total cost, including installation costs and operating energy costs. As a binary variable, when a pipeline pump is installed at node i in region k. Otherwise, it is 0; A is the set of pipeline network areas, and N is the set of pipeline network nodes. The unit cost of installing a pipeline pump at node i in region k. To convey fluid density, It is the acceleration due to gravity. Let i be the design flow rate of the pipeline pump at node i in area k. Let i be the design head of the pipeline pump at node i in area k. Let be the efficiency of the pipeline pump at node i in region k. This refers to the system's annual uptime.

[0029] The pressure-bearing capacity constraints within the area are as follows: ;

[0030] in, For each node within the corresponding area Pressure For nodes The maximum pressure-bearing capacity of the pipe section where it is located; for each node Inflow equals outflow, as follows:

[0031] ;

[0032] in, For pipelines Traffic in E represents the collection of pipelines in the pipeline network; For nodes Inflow and outflow;

[0033] pipeline The relationship between downstream node pressure and upstream pressure, pipeline pump head, and friction loss is as follows:

[0034] ;

[0035] in, For nodes Pressure head; For nodes Elevation; For nodes Pressure head; For nodes Elevation; For nodes The head provided by the pipeline pump; For pipelines The coefficient of friction; For pipelines Length; for The absolute value of is used to characterize friction loss along the flow path, which is independent of the direction of fluid flow.

[0036] The pressure head at each node in the area must meet the safe operating range, as follows:

[0037] ;

[0038] in, For nodes The lower and upper limits of the permissible pressure head;

[0039] Each installed pipeline pump must ensure efficient delivery within the specified range, and the provided head must be within the design capacity, as follows:

[0040] ;

[0041] in, These are the lower and upper limits of the efficiency range for pipeline pumps. For the lower and upper limits of the design capacity of pipeline pumps, when hour, .

[0042] In the formula: Let 0-1 be the decision variable (representing whether the i-th pipeline pump is installed); when At that time, the pump was not installed, and the efficiency was... Yangcheng The above constraints are satisfied; when At that time, the pumps have been installed, and their efficiency and head must fall within their respective design ranges. and Inside.

[0043] Furthermore, the heating network structure containing multi-stage friction-fed pipeline pumps includes an integrated axial-flow pipeline pump with the pump body and pipeline, a pipeline pump automatic control system, and a pipeline pump edge computing system. The axial-flow pipeline pump is embedded in the optimal installation position of the heating network along the pipeline route, forming a low-flow-resistance, high-flow-capacity pipeline, transforming the original heating network into a heating network structure containing multi-stage friction-fed pipeline pumps. The pipeline pump automatic control system collects pump operating parameters, including start / stop status, head, flow rate, differential pressure, and operating frequency, monitors in real time to ensure normal pump operation, and uploads operating data to the multi-stage friction-fed pipeline pump dynamic decision-making and control unit in real time. The pipeline pump edge computing system collects vibration data and abnormal signals through vibration sensors and noise sensors arranged on the outside of the pipeline pump and pipeline, combines the data characteristics with the edge computing unit to identify possible bearing wear and impeller imbalance faults in the pump body, realizes local real-time fault diagnosis of the pump body, and reduces the delay of data upload to the cloud.

[0044] Furthermore, the method for constructing a digital twin model of a heating network including multi-stage pipeline pumps along the pipeline includes:

[0045] Based on the load prediction model of the heating station, the load prediction value of each heating station is obtained, and the flow demand of the heating network under different operating conditions is calculated accordingly.

[0046] A dynamic water pressure distribution analysis model is constructed to simulate the dynamic water pressure distribution under variable flow conditions by measuring the changes in head loss along the flow path caused by flow rate changes.

[0047] The flow rate is divided into different flow rate intervals, and water pressure simulation calculations are performed based on the head loss coefficient corresponding to each flow rate interval. The head loss coefficient is determined by the pipe characteristics and fluid state.

[0048] The specific implementation method is as follows: A digital twin model of the heating network containing multi-stage pipeline pumps along the pipeline is constructed. This model, combined with the predicted load from the heating station, simulates the dynamic distribution of water pressure under varying flow conditions in the network. The influence of the combination logic of multi-pipeline pump operating parameters on water pressure distribution under different load changes is analyzed, including:

[0049] Based on the meteorological characteristics, building and regional characteristics, historical load data, time characteristics, and system operation status characteristics of the load prediction model of the heating station, a load prediction model driven by mechanism and data is constructed to obtain the load prediction value of each heating station and calculate the flow demand of the heating network under different operating conditions.

[0050] The dynamic distribution analysis model of water pressure under variable flow conditions including pipeline pumps is as follows:

[0051] ;

[0052] in, It is a moment In position Water pressure at the location; It is the static water pressure distribution at the initial moment of the system (i.e., the initial reference water pressure that does not change with time). The pipeline pump operates at a constant time ,Location The dynamic water pressure increment generated at the location; Due to traffic Changes in head loss along the friction path caused by the change;

[0053] flow It can be divided into different intervals:

[0054] Low flow range: ;

[0055] Medium flow range: ;

[0056] High flow range: ;

[0057] The head loss coefficient varies depending on the flow rate range:

[0058] ;

[0059] in, , This is the flow rate boundary value, used to determine the range of values ​​for the head loss coefficient; The head loss coefficient for different flow ranges is determined by the pipe characteristics and fluid state.

[0060] Furthermore, when the dynamic decision-making and control unit for the operation of multi-stage pipeline pumps along the pipeline establishes a collaborative optimization operation model for multiple pipeline pumps, the method includes:

[0061] The overall optimization objective is to minimize power consumption costs and optimize the voltage difference compensation at key nodes.

[0062] The constraints of the collaborative optimization operation model include:

[0063] Ensure that the pressure in each area does not exceed the pressure constraint of the maximum pressure bearing capacity of the pipe section;

[0064] To satisfy the overall network hydraulic balance constraint that the inflow and outflow of each node are equal;

[0065] Ensure that the rate of change of pressure drop meets the hydraulic stability constraint of the preset threshold;

[0066] And the operating characteristic constraints that satisfy the relationship between pipeline pump head and frequency, frequency adjustment range, and equipment power calculation logic.

[0067] The specific implementation method is as follows: When the dynamic decision-making and control unit for multi-stage pipeline pump operation along the pipeline establishes a collaborative optimization operation model for multiple pipeline pumps, the objective function is:

[0068] ;

[0069] in, To comprehensively optimize the target value, These are weighting coefficients. ; For electricity costs; To determine the degree of differential pressure compensation;

[0070] ;

[0071] in, It refers to the number of pipeline pumps; It is the first Each pump at time The power; It's the electricity price; It is an optimization cycle;

[0072] ;

[0073] in, It refers to the number of critical nodes; It is the first The weight of each node; It is the first Each node at time... The actual differential pressure compensation value; It is the first Target differential pressure compensation value for each node;

[0074] Constraints include: area pressure ;in, For each node Pressure For nodes The maximum pressure-bearing capacity of the pipe section in which it is located;

[0075] Hydraulic balance constraints ;in, Represents nodes in the pipeline network; The set of all nodes; For inflow node A collection of pipes; outflow node A collection of pipes; For a moment Through pipes Traffic; To optimize the set of all moments within the time period;

[0076] To meet hydraulic stability requirements and ensure that the pressure drop rate meets the preset value:

[0077] ;

[0078] in, To optimize the pipeline The pressure drop; For pipelines The reference voltage drop; The maximum allowable pressure drop rate threshold is adjusted based on the pressure-bearing capacity of the pipeline network in each area;

[0079] The head-frequency relationship and frequency adjustment range constraints are as follows:

[0080] ;

[0081] in, Indicates a pipeline pump; This refers to the collection of all pipeline pumps. For a moment pump Provided head , , For pumps Rated head, frequency, flow rate; For pumps Characteristic parameters; For pumps Actual operating traffic; For pipeline pumps at all times Operating frequency below; For pumps The minimum permissible frequency; For pumps Maximum permissible frequency;

[0082] ;

[0083] in, For a moment pump The power; The density of the heat transfer medium; Gravitational acceleration; For a moment pump Actual operating traffic; For pumps In traffic The efficiency of the process.

[0084] A second aspect of the present invention provides a flexible heating network regulation method based on a multi-stage pipeline pump along the pipeline. This method employs the aforementioned system and includes the following steps:

[0085] Step 1: Digital twin simulation and zone division steps. Establish a digital twin model of the heating network, conduct multi-condition thermal and hydraulic characteristic simulation, analyze the operating parameters of each node, quantitatively evaluate the pressure bearing capacity of each pipe section, divide the heating network into several pressure-bearing zones, and identify the unfavorable branches in each zone.

[0086] Step 2: Pipeline pump system design and pipeline network construction steps. Based on the unfavorable operating branches in each area, determine the number and deployment location of multiple pipeline pumps. Combine the pressure distribution of the pipe section and the pressure bearing capacity threshold of the area to construct a set of pipeline pump site selection schemes for differentiated deployment in each area. Solve the optimal pump installation location and model parameters for each area through the site selection optimization design model. Embed multiple axial flow pipeline pumps into the optimal installation location of the heating pipeline network to form a heating pipeline network structure containing multi-stage pipeline pumps along the pipeline.

[0087] Step 3: Dynamic control step. Construct a digital twin model of the heating network including multi-stage pipeline pumps along the pipeline. Combine the predicted load of the heating station to simulate the dynamic distribution of water pressure under the variable flow conditions of the pipeline network. Analyze the impact of the combination logic of the operating parameters of the multi-pipeline pumps on the water pressure distribution under different load changes. Solve the optimal start-stop state and operating frequency of each pipeline pump under different operating conditions through the collaborative optimization operation model of the multi-pipeline pumps. Perform dynamic control of the pipeline pressure in the heating network in different areas.

[0088] The present invention, by adopting the above technical solution, has at least the following beneficial effects:

[0089] (1) This invention arranges multi-stage low-head pipeline pumps along the main and branch lines of different pressure zones in the heating pipeline network to form a low-flow-resistance and high-flow-capacity pipeline. The low-flow-resistance flow channel design of the pipeline pump equipment solves the resistance effect on the pipeline when the standby equipment is shut down, minimizes energy waste, releases the pipeline network's transmission potential, and reserves flexible space for future heating capacity expansion. The compact pump body structure of the pipeline pump enables the arrangement and installation of shorter pipe sections. Its pipeline-type shielded pump structure ensures long-term stable and efficient operation under rated conditions. It has only basic maintenance costs throughout its entire life cycle, reducing heating operation costs.

[0090] (2) This invention improves the hydraulic conditions at the end of the heating network by arranging multi-stage low-lift pipeline pumps along the main and branch lines of different pressure zones, reducing the risk of overpressure in the main network. Low-lift pump groups are embedded in key nodes of each pressure zone to construct a stepped hydraulic boosting topology, which overturns the traditional pressurization mode that relies on a single high-pressure main pump. It adopts a multi-stage pressurization method to accurately control local low-pressure areas, avoid pressure fluctuations caused by concentrated pressurization, and fundamentally solve the problem of hydraulic imbalance. Without increasing the head of the main pump, it can effectively ensure the hydraulic conditions at the end, avoid the risk of pipe bursts due to near-end overpressure, reduce the risk of pipeline leakage, and simultaneously achieve a leap in energy efficiency. The pump group is based on a low-lift, high-flow, high-efficiency model, which is more energy-efficient than the traditional system. Finally, it reshapes the safety and heating quality balance of a large-scale old heating network through pressure gradient decoupling.

[0091] (3) This invention uses a digital twin model to simulate and analyze the pressure distribution of complex pipe network segments, divides the heating network into zones with different pressure-bearing capacities, and obtains the key nodes of embedded pipeline pumps in each zone based on the zone's pressure-bearing capacity and user needs. Through the pipeline pump site selection design optimization model, it ensures that the arrangement of multiple and multi-stage pipeline pumps is more in line with the zone's pressure requirements, achieving the best configuration selection. During operation, combined with the load prediction model and the dynamic distribution of water pressure under variable flow conditions, with the goal of minimizing power consumption costs and optimizing the pressure difference compensation at each key node, the operating status of multiple pipeline pumps is reasonably adjusted, and the start-up, shutdown, and operating frequency of multiple pipeline pumps are dynamically adjusted. This is to meet the pressure-bearing capacity of the pipe network, ensure the hydraulic balance of the pipe network, meet the hydraulic stability requirements, and ensure that the pipeline pumps operate within the characteristic curve range, so that the operation of the pipeline pumps matches the operating conditions of the stations, always providing reasonable pressure difference compensation for the pipe network, ensuring a high-efficiency balance between power consumption and efficiency, and achieving energy-saving and effective operation. Attached Figure Description

[0092] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0093] Figure 1 This is a schematic diagram of the flexible heating network system based on a multi-stage pipeline pump along the pipeline according to the present invention. Figure 1 ;

[0094] Figure 2 This is a schematic diagram of the flexible heating network system based on a multi-stage pipeline pump along the pipeline according to the present invention. Figure 2 ;

[0095] Figure 3 This is a schematic diagram of the flexible heating network system based on a multi-stage pipeline pump along the pipeline according to the present invention.

[0096] Figure 4 This is a water pressure diagram of the flexible heating network system based on a multi-stage pipeline pump along the pipeline, as described in this invention. Detailed Implementation

[0097] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0098] Example 1

[0099] like Figure 1 As shown, this embodiment provides a flexible heating network system based on a multi-stage pipeline pump along the pipeline and its control method, including:

[0100] Digital twin simulation analysis unit: used to establish a digital twin model of the heating network, simulate the thermal and hydraulic characteristics of the network, analyze node operating parameters, quantitatively evaluate the pressure-bearing capacity of pipe sections and divide the heating network into pressure-bearing areas, and identify branches with unfavorable operation in the areas.

[0101] Design unit for multi-stage pipeline pump system along the pipeline: Based on the unfavorable operating branches, determine the number and deployment location of pipeline pumps, combine the pressure distribution of the pipeline section and the pressure bearing capacity threshold of the area to construct a set of site selection schemes, aim at minimizing installation cost and energy consumption, and constrain the hydraulic balance and transportation efficiency of the pipeline network, establish a site selection optimization model to solve for the optimal pump parameters, and form a pipeline network structure containing multi-stage pipeline pumps along the pipeline.

[0102] Dynamic decision-making and control unit for multi-stage pipeline pump operation along the pipeline: Construct a digital twin model of the pipeline network including pipeline pumps, combine load prediction of heating stations to simulate water pressure distribution under variable flow conditions, and establish a collaborative optimization operation model to control the start-up and shutdown and frequency of pumps with the goal of minimizing power consumption cost and optimizing pipeline pump pressure difference compensation, so as to realize dynamic control of pipeline network pressure in different areas.

[0103] As one implementation method, in this embodiment, when the digital twin simulation analysis unit establishes the digital twin model, it uses structural mechanism modeling to construct the geometric, physical, behavioral, and rule models of the pipeline network, and combines operational data to correct the model; when dividing the pressure-bearing areas of the heating network, the first... Each region is abstracted as a directed graph. The expression is:

[0104] ;

[0105] in, For the first Directed graph model of a pressure-bearing area of ​​a heating network For the first The set of nodes in each region contains Each heat source, heating station, and pipeline branching and merging node For the first The pipeline system for each area comprises M supply and return water pipelines. , It is a collection of pipeline network areas; and the areas are divided based on pipeline pressure threshold, hydraulic topology and geographical distribution.

[0106] As one implementation method, when the design unit of the multi-stage pipeline pump system along the pipeline in this embodiment determines the key nodes for deployment, it uses the unfavorable branch in the area as a reference, presets the absolute pressure threshold and the drop threshold relative to the design pressure based on the initial pressure distribution parameters, and identifies the key low-pressure pipe sections with pressures below the threshold. By constructing a hydraulic path dependency graph, it analyzes the area where the low-pressure pipe section is located, its supply / return pipeline, and the upstream and downstream connection relationships. Based on the pressure threshold drop, the number of affected stations, and the topological importance, it assesses the transmission impact of the low-pressure pipe section and determines the key nodes for pipeline pump deployment.

[0107] As one implementation method, the multi-stage pipeline pump system design unit along the pipeline in this embodiment, which determines the key nodes for pipeline pump deployment, further includes: for long-distance transmission and distribution pipelines within a region, determining the pressure drop of each pipe section based on the initial pressure distribution parameters of the heating network, pre-setting the pressure drop threshold of the pipe section based on the known flow demand, and identifying high-resistance pipe sections with excessive pressure drop; setting a head margin in combination with the pipeline pump head threshold limit, and determining each key node of the long-distance transmission and distribution pipeline in a multi-stage segmentation manner using a power-distributed approach.

[0108] As one implementation method, when the multi-stage pipeline pump system design unit in this embodiment constructs a set of pipeline pump site selection schemes for differentiated deployment by region, it calculates the basic head based on the pressure drop and target pressure of the supply and return water pipe sections where the pipeline pumps are deployed, determines the safety margin by combining the pressure bearing capacity threshold of the pipe section in the region and the fluctuation range of operating data, and determines the final head parameters; based on the hydraulic calculation results of the heating network in the region where the deployment location is located, and combined with the adjustable range on the user side, it determines the flow benchmark value, selects the pipeline pump model that meets the flow requirements, has flow regulation capabilities, and whose operating point is located in the high-efficiency zone; on long pipe sections with large pressure losses, multiple pipeline pump installation schemes can be combined to enrich the combination of site selection schemes.

[0109] As one implementation method, when establishing a location optimization design model for the multi-stage pipeline pump system design unit, the method includes: using binary variables to control whether a pipeline pump is installed at each node, with the objective function being the minimum total cost, including installation cost and operating energy consumption cost; the constraints of the location optimization design model include:

[0110] The pressure at each node within the area shall not exceed the pressure-bearing capacity constraint of the maximum pressure-bearing capacity of the pipe section.

[0111] The flow balance constraint is that the inflow to each node equals the outflow.

[0112] Hydraulic balance constraints that describe the relationship between downstream node pressure and upstream pressure, pipeline pump head, and friction loss;

[0113] Ensure that the pressure head at each node meets the pressure head constraint within the safe operating range;

[0114] And to ensure that the efficiency and head of the installed pipeline pumps are within the design capacity range, as well as equipment performance constraints.

[0115] In this embodiment, when establishing the site selection optimization design model for the multi-stage pipeline pump system design unit, a binary variable is used. The objective function for determining whether a pipeline pump is installed on the control node is:

[0116] ;

[0117] in, The total cost includes installation costs and operating energy costs. As a binary variable, when a pipeline pump is installed at node i in region k. Otherwise, it is 0; A is the set of pipeline network areas, and N is the set of pipeline network nodes. The unit cost of installing a pipeline pump at node i in region k. To convey fluid density, It is the acceleration due to gravity. Let i be the design flow rate of the pipeline pump at node i in area k. Let i be the design head of the pipeline pump at node i in area k. Let be the efficiency of the pipeline pump at node i in region k. This refers to the system's annual uptime.

[0118] The pressure-bearing capacity constraints within the area are as follows:

[0119] ;

[0120] in, For each node within the corresponding area Pressure For nodes The maximum pressure-bearing capacity of the pipe section where it is located; for each node Inflow equals outflow, as follows:

[0121] ;

[0122] in, For pipelines Traffic in E represents the collection of pipelines in the pipeline network; For nodes Inflow and outflow;

[0123] pipeline The relationship between downstream node pressure and upstream pressure, pipeline pump head, and friction loss is as follows:

[0124] ;

[0125] in, For nodes Pressure head; For nodes Elevation; For nodes Pressure head; For nodes Elevation; For nodes The head provided by the pipeline pump; For pipelines The coefficient of friction; For pipelines Length; for The absolute value of is used to characterize friction loss along the flow path, which is independent of the direction of fluid flow.

[0126] The pressure head at each node in the area must meet the safe operating range, as follows:

[0127] ;

[0128] in, For nodes The lower and upper limits of the permissible pressure head;

[0129] Each installed pipeline pump must ensure efficient delivery within the specified range, and the provided head must be within the design capacity, as follows:

[0130] ;

[0131] in, These are the lower and upper limits of the efficiency range for pipeline pumps. For the lower and upper limits of the design capacity of pipeline pumps, when hour, .

[0132] In the formula: Let 0-1 be the decision variable (representing whether the i-th pipeline pump is installed); when At that time, the pump was not installed, and the efficiency was... Yangcheng The above constraints are satisfied; when At that time, the pumps have been installed, and their efficiency and head must fall within their respective design ranges. and Inside.

[0133] As one implementation method, the heating network structure with multi-stage friction-fed pipeline pumps described in this embodiment includes an integrated axial-flow pipeline pump with the pump body and pipeline, a pipeline pump automatic control system, and a pipeline pump edge computing system. The axial-flow pipeline pump is embedded in the optimal installation position of the heating network along the pipeline route, forming a low-flow-resistance, high-flow-capacity pipeline, transforming the original heating network into a heating network structure with multi-stage friction-fed pipeline pumps. The pipeline pump automatic control system collects pump operating parameters, including start / stop status, head, flow rate, differential pressure, and operating frequency, monitors in real time to ensure normal pump operation, and uploads the operating data to the multi-stage friction-fed pipeline pump dynamic decision-making and control unit in real time. The pipeline pump edge computing system collects vibration data and abnormal signals through vibration sensors and noise sensors arranged on the outside of the pipeline pump and pipeline, combines the data characteristics with the edge computing unit to identify possible bearing wear and impeller imbalance faults in the pump body, realizes local real-time fault diagnosis of the pump body, and reduces the delay of data upload to the cloud.

[0134] As one implementation method, the method for constructing a digital twin model of a heating network including multi-stage pipeline pumps along the flow path includes: obtaining the load prediction values ​​of each heating station based on the heating station load prediction model, and calculating the flow demand of the heating network under different operating conditions accordingly; constructing a dynamic water pressure distribution analysis model, simulating the dynamic distribution of water pressure under variable flow conditions by the change in head loss along the flow path caused by flow changes; dividing the flow into different flow intervals, and performing water pressure simulation calculations based on the head loss coefficient corresponding to each flow interval, wherein the head loss coefficient is determined by the pipeline characteristics and fluid state.

[0135] The specific implementation method in this embodiment is as follows: The construction of a digital twin model of the heating network containing multi-stage pipeline pumps along the pipeline, combined with the predicted load of the heating station to simulate the dynamic distribution of water pressure under varying flow conditions, and the analysis of the impact of the combination logic of multi-pipeline pump operating parameters on water pressure distribution under different load changes, includes:

[0136] Based on the meteorological characteristics, building and regional characteristics, historical load data, time characteristics, and system operation status characteristics of the load prediction model of the heating station, a load prediction model driven by mechanism and data is constructed to obtain the load prediction value of each heating station and calculate the flow demand of the heating network under different operating conditions.

[0137] The dynamic distribution analysis model of water pressure under variable flow conditions including pipeline pumps is as follows:

[0138] ;

[0139] in, It is a moment In position Water pressure at the location; It is the static water pressure distribution at the initial moment of the system (i.e., the initial reference water pressure that does not change with time); The pipeline pump operates at a constant time ,Location The dynamic water pressure increment generated at the location; Due to traffic Changes in head loss along the friction path caused by the change;

[0140] flow It can be divided into different intervals:

[0141] Low flow range: ;

[0142] Medium flow range: ;

[0143] High flow range: ;

[0144] The head loss coefficient varies depending on the flow rate range:

[0145] ;

[0146] in, This is the flow rate boundary value, used to determine the range of values ​​for the head loss coefficient; The head loss coefficient for different flow ranges is determined by the pipe characteristics and fluid state.

[0147] As one implementation method, when the dynamic decision-making and control unit for multi-stage pipeline pump operation establishes a collaborative optimization operation model for multiple pipeline pumps, the method includes: taking the minimization of power consumption cost and the optimal degree of pressure difference compensation at key nodes as the comprehensive optimization objectives; the constraints of the collaborative optimization operation model include: pressure-bearing constraints to ensure that the pressure in each area does not exceed the maximum pressure-bearing capacity of the pipe section; hydraulic balance constraints to ensure that the inflow and outflow of each node are equal; hydraulic stability constraints to ensure that the pressure drop change rate meets a preset threshold; and operational characteristic constraints to satisfy the relationship between pipeline pump head and frequency, frequency adjustment range, and equipment power calculation logic.

[0148] The specific implementation method in this embodiment is as follows: When the multi-stage pipeline pump operation dynamic decision-making and control unit establishes the collaborative optimization operation model of the multi-pipeline pump, the objective function is:

[0149] ;

[0150] in, To comprehensively optimize the target value, These are weighting coefficients. ; For electricity costs; To determine the degree of differential pressure compensation;

[0151] ;

[0152] in, It refers to the number of pipeline pumps; It is the first Each pump at time The power; It's the electricity price; It is an optimization cycle;

[0153] ;

[0154] in, It refers to the number of critical nodes; It is the first The weight of each node; It is the first Each node at time... The actual differential pressure compensation value; It is the first Target differential pressure compensation value for each node;

[0155] Constraints include: area pressure ;in, For each node Pressure For nodes The maximum pressure-bearing capacity of the pipe section in which it is located;

[0156] Hydraulic balance constraints ;in, Represents nodes in the pipeline network; The set of all nodes; For inflow node A collection of pipes; outflow node A collection of pipes; For a moment Through pipes Traffic; To optimize the set of all moments within the time period;

[0157] To meet hydraulic stability requirements and ensure that the pressure drop rate meets the preset value:

[0158] ;

[0159] in, To optimize the pipeline The pressure drop; For pipelines The reference voltage drop; The maximum allowable pressure drop rate threshold is adjusted based on the pressure-bearing capacity of the pipeline network in each area;

[0160] The head-frequency relationship and frequency adjustment range constraints are as follows:

[0161] ;

[0162] ;

[0163] in, Indicates a pipeline pump; This refers to the collection of all pipeline pumps. For a moment pump Provided head, , , For pumps Rated head, frequency, flow rate; For pumps Characteristic parameters; For pumps Actual operating traffic; For pipeline pumps at all times Operating frequency below; For pumps The minimum permissible frequency; For pumps Maximum permissible frequency;

[0164] ;

[0165] in, For a moment pump The power; The density of the heat transfer medium; Gravitational acceleration; For a moment pump Actual operating traffic; For pumps In traffic The efficiency of the process.

[0166] This embodiment specifically implements the technical solution of the flexible heating network system based on multi-stage pipeline pumps along the pipeline as described in this invention. By configuring and coordinating the functions of the digital twin simulation analysis unit, the multi-stage pipeline pump system design unit, and the multi-stage pipeline pump operation dynamic decision-making and control unit, the technical process of heating network from simulation analysis, pipeline pump optimization deployment to dynamic control is fully realized, providing a basic implementation basis for the implementation of the invention's technical solution.

[0167] Specifically, in the implementation of the digital twin simulation analysis unit, a structural mechanism modeling method is used to construct the geometric, physical, behavioral, and rule models of the pipeline network. The mechanism model is then identified and corrected based on the actual operating data of the heating pipeline network to ensure the consistency between the digital twin model and the real pipeline network. By abstracting each pressure-bearing area of ​​the heating network into a directed graph containing a set of nodes (heat sources, heating stations, pipeline branching and merging nodes) and a set of pipes (supply and return water pipes), the area is divided based on the pipe pressure threshold, hydraulic topology, and geographical distribution. At the same time, unfavorable branches within the area are identified by combining operating parameters, providing accurate operating condition basis for the subsequent deployment of pipeline pumps.

[0168] In the implementation of the multi-stage pipeline pump system design unit, based on the unfavorable branch lines in the area, low-pressure pipe sections are identified by pre-setting absolute pressure thresholds and relative design pressure drop thresholds. The hydraulic path dependency diagram is used to analyze the area where the pipe section is located, its associated pipeline, and its upstream and downstream connections. The transmission impact is assessed based on pressure drop, the number of affected stations, and topological importance to determine deployment nodes. For long-distance distribution pipelines, the initial pressure distribution of the pipeline network is used as a benchmark to determine the pressure drop of the pipe section. Pre-set pressure drop thresholds are used to identify high-resistance pipe sections. A margin is set based on the pipeline pump head threshold, and key nodes are determined through multi-stage segmentation using a distributed power approach. A site selection method is constructed. During the case study, the foundation head of the pipeline pump is calculated based on the pressure drop of the pipeline section and the target pressure (with a safety margin reserved in combination with the pressure-bearing capacity threshold of the area and the fluctuation range of operating data). The flow benchmark value is determined by combining the hydraulic calculation results of the heating network and the adjustable range on the user side. Pump types that meet the flow requirements, have adjustment capabilities, and whose operating points are located in the high-efficiency zone are selected. For long pipeline sections, multiple pumps can be installed in combination to enrich the scheme. A site selection optimization model is established with the goal of minimizing installation costs and operating energy consumption costs, and with the hydraulic balance of the pipeline network and the pressure-bearing capacity of the pipeline section as constraints. The optimal pump installation location and model parameters for each area are obtained by solving the model, forming a pipeline network structure that adapts to the pressure-bearing requirements of the area.

[0169] In the implementation of the dynamic decision-making and control unit for multi-stage pipeline pumps along the pipeline, a digital twin model of the heating network containing multi-stage pipeline pumps along the pipeline is constructed. Based on meteorological characteristics, building and regional characteristics, historical load data, time characteristics, and system operation status characteristics, a mechanism-based and data-driven load prediction model for the heating station is constructed to calculate the pipeline flow demand under different operating conditions. The flow demand is divided into low, medium, and high flow ranges, and the head loss coefficients corresponding to each range (determined by pipeline characteristics and fluid state) are matched to simulate the dynamic distribution of water pressure under variable flow conditions. A collaborative optimization operation model is established with the goal of "minimizing power consumption cost and optimizing pressure difference compensation at key nodes". Constraints such as ensuring that the pressure at the regional nodes does not exceed the maximum pressure bearing capacity of the pipe section, balancing the inflow and outflow of the pipeline nodes, and ensuring that the operation of the pipeline pumps conforms to the characteristic curve and frequency adjustment range are superimposed to obtain the optimal start-stop state and operating frequency of the pipeline pumps under different operating conditions, thereby realizing the dynamic control of pipeline pressure in different regions.

[0170] This embodiment, through the aforementioned technical means, can effectively alleviate the problems of overpressure at the front end of the traditional heating network, insufficient hydraulic conditions at the end, and regional hydraulic imbalance, thereby improving the safety, hydraulic balance, and economy of the network operation. At the same time, it lays the technical framework and practical foundation for the detailed implementation of embodiment two for specific heating scenarios (such as complex multi-area networks and long-distance transmission and distribution networks).

[0171] Example 2

[0172] like Figure 2As shown, this embodiment proposes a flexible heating network system based on friction-multi-stage pipeline pumps and its control method, including: a digital twin simulation analysis unit, a friction-multi-stage pipeline pump system design unit, and a friction-multi-stage pipeline pump operation dynamic decision-making and control unit.

[0173] Digital Twin Simulation Analysis Unit: Used to establish a digital twin model of the heating network, perform simulation calculations of the thermal and hydraulic characteristics of the network under various operating conditions, analyze the operating parameters of each node in the network, quantitatively evaluate the pressure-bearing capacity of each pipe section and divide the heating network into several pressure-bearing zones, and analyze the pressure-bearing capacity and unfavorable operating branches in each zone.

[0174] Design unit for multi-stage pipeline pump system along the pipeline: Based on the unfavorable operating branches in each area, determine the number and deployment location of multiple pipeline pumps. Combine the pressure distribution of the pipeline section and the pressure-bearing capacity threshold of the area, construct a set of pipeline pump site selection schemes for differentiated deployment according to the area. With the goal of minimizing installation cost and energy consumption, and with the constraints of meeting the pressure-bearing capacity of each area, the hydraulic balance of the pipeline network, and the transmission efficiency, establish a site selection optimization design model for multiple pipeline pumps to solve for the optimal pump installation location and model parameters in each area. By using the pipeline pump configuration method that matches the pressure-bearing capacity of the area, a heating network structure containing multi-stage pipeline pumps along the pipeline is formed.

[0175] Dynamic Decision-Making and Control Unit for Multi-Stage Pipeline Pumps Along the Heating Network: This unit is used to construct a digital twin model of a heating network containing multi-stage pipeline pumps along the heating network. It combines the predicted load of the heating station to simulate the dynamic distribution of water pressure under varying flow conditions in the network. It analyzes the impact of the combination logic of multi-stage pipeline pump operating parameters on water pressure distribution under different load changes. With the objective function of minimizing power consumption cost and optimizing pipeline pump differential pressure compensation, and under the constraints of ensuring that the pressure in each area does not exceed the threshold and meets the pressure bearing capacity requirements, hydraulic balance and hydraulic stability requirements of the network, and the operating range of the pipeline pump characteristic curves, a collaborative optimization operation model for multi-stage pipeline pumps is established. The optimal start-stop state and operating frequency of each pipeline pump under different operating conditions are obtained, and the network pressure in the heating network is dynamically controlled by area.

[0176] like Figure 3 The diagram shows a flexible heating network system based on a multi-stage pipeline pump, which includes:

[0177] The heating network consists of four different pressure zones (Zone 1, Zone 2, Zone 3, and Zone 4), comprising heat sources, pipeline pumps, stations, and connecting pipe networks. The heat sources provide the basic energy source for heat transmission within the network. The pipeline pumps, as power components, meet the hydraulic requirements of each zone with varying pressure capacities. Multiple pipeline pumps are deployed in stages along the heating pipelines of each zone (e.g., five pumps, 1-1, 1-2, 1-3, 1-4, and 1-5, are deployed along the pipeline in Zone 1). These pumps work in relay and coordinated operation to form a distributed overall power transmission system, compensating for pressure losses caused by friction and local resistance as the heating medium flows through the network. Stations receive the heating medium from the network and supply heat to surrounding buildings and areas, meeting actual heating demands. The connecting pipe network serves as the channel for the flow of the heating medium, linking the heat sources, pipeline pumps, and stations into a complete circulation system, completing the process from heat source output to station distribution.

[0178] The pump body and pipeline are integrated into a single flow type, low head pipeline pump. The external dimensions are similar to the pipeline to which it is installed. It is arranged along the pipeline route and can be placed in a small room or buried directly underground. The typical head of a single pipeline pump is 5-15m. Since the installation is not limited by geographical location and the head is small, the number of installations is not limited. The pressure before and after the pump is monitored by a vacuum tube pressure gauge. It is driven by electricity to meet the hydraulic conditions of various heating network areas with different pressure bearing capacities and reduce the operating pressure of the pipeline network.

[0179] It should be noted that the axial flow inline pump features a compact structure, maintenance-free operation, high reliability, and low flow resistance. The inlet and outlet flanges are coaxially connected to the pipeline, eliminating the need for additional elbows or reducing fittings, thus saving pipeline layout space. Wear-resistant and corrosion-resistant materials, along with a leak-free sealing system, allow for up to 10 years of maintenance-free operation. It can operate reliably and stably in heating systems. The inlet and outlet pipe diameters are consistent with the pipeline, reducing local resistance caused by diameter changes. The low flow resistance design reduces friction along the pipeline, and combined with frequency conversion control, it effectively reduces system energy consumption.

[0180] In this embodiment, the typical design configuration of the multi-stage pipeline pump system is to arrange one pipeline pump every 1-3 kilometers along the pipeline route. In a 10 million square meter urban heating system, typically dozens of pipeline pumps are arranged along the primary heating network to form a lower pressure distribution throughout the network and to regulate the network operating pressure to match the network's pressure-bearing capacity.

[0181] It should be noted that the multi-stage pipeline pump system along the pipeline is different from the previous design concept of configuring 1-2 large relay pump stations, and also different from the previous scheme of configuring distributed booster pumps in multiple heating stations with poor circulation. For example, in the invention patent CN201821656951.4 "A pump control system for multi-stage pumping stations in a heating network", relay energy stations and three large relay pumping stations are configured in long-distance supply and return water pipelines to regulate the network pressure and ensure operation. However, the overall investment is large and the land area is large, and it is generally only suitable for installation on main pipelines with space. In CN202210679988.3 "An operation method for hydraulic balance of heating network", distributed booster pumps are configured inside the heating station to improve the hydraulic conditions of the heating network. In CN202221944597.1 "A distributed three-stage pump heating distribution system", relay pumps are added, combined with heat source circulation pumps and distributed booster pumps at the end of the heating station to meet the heating pressure requirements. This type of arrangement has poor system coordination because there are many management objects and flow competition between heating stations.

[0182] In this embodiment, the establishment of a digital twin model of the heating network, simulation calculations of the network's thermal and hydraulic characteristics under various operating conditions, analysis of the operating parameters of each node in the network, quantitative evaluation of the pressure-bearing capacity of each pipe section, and division of the heating network into several pressure-bearing zones, along with analysis of the pressure-bearing capacity and unfavorable branches within each zone, includes:

[0183] The geometric model, physical model, behavioral model and rule model of the physical entity of the real heating network are established by adopting the structural mechanism modeling method. After mapping and model fusion in the virtual space, a virtual entity mechanism model of the real heating network is formed.

[0184] The operation data of the heating network is obtained, the mechanism model is driven to perform simulation calculations, and the mechanism model is identified and corrected based on the deviation between the simulation results and the actual data, and a digital twin model of the real heating network is established.

[0185] Using a digital twin model of a real heating network, hydraulic simulation conditions are set up to analyze the pressure distribution of each pipe section, forming a water pressure map of the entire network. Based on the pressure-bearing capacity threshold of each pipe, hydraulic topology, and geographical distribution, multiple pressure-bearing zones of the heating network are divided.

[0186] No. The structure and heat transport process of a heating network's pressure-bearing zone can be abstracted as a directed graph. The expression specifically consists of a set containing N heat sources, heating stations, and branching and merging nodes of the pipeline network. and the set of M supply and return water pipes connecting the nodes. Composition, in which ( (This refers to a collection of pipeline network areas), as follows:

[0187] ;

[0188] Based on operational data, it is determined whether the stations in each area meet the expected hydraulic operating conditions, whether the differential pressure meets the threshold, whether the circulating flow is insufficient, and whether there is a backup heat source nearby, the lag between heat source regulation and station response, and the characteristics of station user load. Pressure deviation index, flow shortage rate index, load fluctuation index, backup heat source distance index, regulation lag time index, and load importance index are set to comprehensively analyze the unfavorable branches in each area.

[0189] It should be noted that the hydraulic topology is based on the branch nodes of the heating network, dividing the pipe sections under the same branch into the same area to avoid cross-branch regulation affecting the hydraulic balance. The geographical distribution is divided according to administrative regions and street boundaries to facilitate project implementation (such as using roads and rivers as area boundaries), while also taking into account topographic elevation differences (such as dividing into different sub-areas every 5 meters of elevation difference).

[0190] The pressure deviation index is composed of the ratio of the absolute difference between the actual pressure and the design pressure to the design pressure; the flow deficit rate index is composed of the ratio of the difference between the design flow and the actual flow to the design flow; the load fluctuation index is composed of the ratio of the load standard deviation to the average load; the standby heat source distance index is composed of the ratio of the distance from the station to the standby heat source to the ideal distance; the adjustment lag time index is composed of the difference between the station pressure stabilization time after heat source adjustment and the design stabilization time; the load importance index is weighted according to user type (e.g., hospitals and schools have a weight of 1.0, and residential buildings have a weight of 0.5). According to the comprehensive scoring model, different scoring weights are set for different indicators. Based on the comprehensive score, areas with a score ≥ 0.6 are defined as unfavorable areas of the heating network.

[0191] In this embodiment, the analysis of the operating parameters of each node in the pipeline network, the quantitative evaluation of the pressure-bearing capacity of each pipe section, and the division of several pressure-bearing zones of the heating network include: obtaining the temperature, pressure, and flow rate of the heat source outlet, and the historical time series data of the temperature, pressure, flow rate, pressure and temperature of key nodes, and valve opening at the inlet and outlet of the heating station.

[0192] In this embodiment, the digital twin model of the real heating network is used to set hydraulic simulation conditions, including: basic parameters such as design flow rate, design supply and return water temperature, pump head, and valve opening; boundary conditions such as heat source outlet pressure and flow rate, end-user resistance characteristics, and water supply pressure; and working scenarios such as design load conditions, partial load conditions, extreme weather conditions (such as cold waves), and heat source failure conditions. The pressure distribution of each pipe section of the network is then calculated to form a network-wide water pressure map.

[0193] In this embodiment, determining the key deployment nodes for each pipeline pump based on the unfavorable branch lines in each area includes:

[0194] Based on the unfavorable branch lines in the area, the absolute pressure threshold and the drop threshold relative to the design pressure are preset based on the initial pressure distribution parameters. The key low-pressure pipe sections with pressure below the threshold are identified, and the key deployment nodes are determined.

[0195] The identification of key low-pressure pipe sections with pressure below the threshold includes: constructing a hydraulic path dependency graph, identifying the region where the low-pressure pipe section is located and its supply / return water pipeline, analyzing the upstream and downstream connection relationship of the low-pressure pipe section based on the topological location of the pipe section and the connectivity of the network topology, assessing the transmission impact of the low-pressure pipe section based on the pressure threshold drop, the number of affected stations, and topological importance, and determining key nodes;

[0196] It should be noted that the topology connectivity analysis method is based on graph theory, specifically breadth-first search (BFS) or depth-first search (DFS), to identify the upstream and downstream paths of low-pressure pipe sections. A three-dimensional evaluation index system is established, including pressure threshold reduction, number of affected stations, and topological importance. By setting weights, a comprehensive impact index is calculated. If the comprehensive index is ≥0.7, immediate rectification is required, indicating a critical node in the heating network.

[0197] The topological importance quantification uses betweenness centrality to measure the importance of a pipe segment as a "bridge" in the entire renovation process, with the betweenness centrality value of the main pipe usually being higher than that of the branch pipe segments.

[0198] In this embodiment, determining the key nodes for pipeline pump deployment further includes:

[0199] For long-distance transmission and distribution pipelines, the pressure drop of each section of the pipeline is determined based on the initial pressure distribution parameters of the heating network. The pressure drop threshold of each section is preset based on the known flow demand. High-resistance sections with excessive pressure drop are identified. Based on the identification results, the head margin is set considering the pipeline pump head threshold limit. The key nodes of the long-distance transmission and distribution pipeline are determined by multi-stage segmentation along the pipeline in a power-distributed manner.

[0200] It should be noted that, considering the head threshold limitation of pipeline pumps, each pump can provide a head of approximately 5 to 15 meters.

[0201] According to the "Design Code for Urban Heating Pipeline Networks" (CJJ34-2010), the pressure requirements for hot water pipeline networks vary. The specific principles for setting pressure drop thresholds based on these regulations are as follows:

[0202] Common low-pressure range: 0.3-1.6MPa, mostly used in residential buildings and general public buildings, the pressure needs to meet the building height requirements (e.g., ≥0.2MPa for a 6-story building);

[0203] Common medium pressure range: 1.6-2.5MPa, suitable for large heating areas or high-rise buildings (such as more than 10 floors), which need to be depressurized through a heat exchange station before being connected to users.

[0204] The primary pipeline pressure from the heat source to the heat exchange station is typically 0.6-2.5 MPa, depending on the output capacity and heating radius of the heat source (such as a power plant or boiler). For example, long-distance heating (over 10 kilometers) may require a pressure of over 1.0 MPa to overcome frictional resistance. The rated pressure of the heat source equipment determines the maximum pressure of the primary pipeline (e.g., if the boiler's rated pressure is 1.6 MPa, then the primary network pressure should be ≤1.4 MPa, leaving a 10%-15% safety margin). During normal operation, the primary network pressure fluctuation should be ≤±0.05 MPa to avoid sudden pressure increases or decreases that could lead to pipeline rupture or unstable heating at the user end.

[0205] In this embodiment, the method of constructing a set of pipeline pump site selection schemes based on the pressure distribution of pipeline segments and the pressure bearing capacity threshold of the area includes:

[0206] Based on the pressure drop and target pressure of the supply and return water pipe sections where the pipeline pump is deployed, calculate the foundation head of the pipeline pump, determine the safety margin by combining the pressure bearing capacity threshold of the pipe section in the area and the fluctuation range of the operating data, and determine the final head parameters.

[0207] Based on the hydraulic calculation results of the heating network in the area where the pipeline pump is deployed, and combined with the adjustable range on the user side, the flow reference value is determined, and the pipeline pump model that meets the requirements and has a certain flow regulation capability is selected. On long pipe sections with large pressure loss, multiple pipeline pump installation schemes can be combined to enrich the scheme combination.

[0208] Based on the characteristic curves of different models of pipeline pumps, compare their efficiency performance under the required pressure boost and flow delivery requirements, and select the model whose operating point is in the high-efficiency zone.

[0209] In this embodiment, with the goal of minimizing installation cost and energy consumption, and constrained by the pressure-bearing capacity of each area, the hydraulic balance of the pipeline network, and the transportation efficiency, a pipeline pump site selection optimization design model is established to solve for the optimal pump installation location and model parameters for each area, including:

[0210] The objective function considers the installation cost and energy consumption of pipeline pumps at all possible nodes within the area, using a binary variable... Whether to include it is determined as follows:

[0211] ;

[0212] in, If it is a binary variable, if it is in the region nodes Install a pipeline pump, then ,otherwise (N is the set of pipeline nodes); ( (for collection of pipeline area) For the area nodes The unit cost of installing a pipeline pump; For conveying fluid density; It is the acceleration due to gravity; For the area nodes The design flow rate of the installed pump; For the area nodes The design head of the installed pump; For the area nodes The efficiency of the installed pump (determined by the pump model); This refers to the system's annual uptime.

[0213] The area's pressure-bearing capacity constraints are as follows:

[0214] ;

[0215] in, For each node Pressure For nodes The maximum pressure-bearing capacity of the pipe section in which it is located;

[0216] For each node Inflow equals outflow, as follows:

[0217] ;

[0218] in, For pipelines Traffic in (E represents the collection of pipelines in the network); For nodes Inflow and outflow;

[0219] pipeline The relationship between downstream node pressure and upstream pressure, pipeline pump head, and friction loss is as follows:

[0220] ;

[0221] in, For nodes Pressure head; For nodes Elevation; For nodes Pressure head; For nodes Elevation; For nodes The head provided by the pipeline pump; For pipelines The coefficient of friction; For pipelines Length; for The absolute value of is used to characterize friction loss along the flow path, which is independent of the direction of fluid flow.

[0222] The pressure head at each node in the area must meet the safe operating range, as follows:

[0223] ;

[0224] in, For nodes The lower and upper limits of the permissible pressure head;

[0225] Each installed pipeline pump must ensure efficient delivery within the specified range, and the provided head must be within the design capacity, as follows:

[0226] ;

[0227] in, These are the lower and upper limits of the efficiency range for pipeline pumps. The formula represents the lower and upper limits of the design capacity of the pipeline pump, where: Let 0-1 be the decision variable (representing whether the i-th pipeline pump is installed); when At that time, the pump was not installed, and the efficiency was... Yangcheng The above constraints are satisfied; when At that time, the pumps have been installed, and their efficiency and head must fall within their respective design ranges. and Inside.

[0228] It should be noted that the NSGA-II multi-objective genetic algorithm is used to obtain the optimal installation pipeline location and model of a multi-stage pipeline pump along the pipeline. The solution includes installation location, pump rated head, flow rate, power model parameters, power consumption data, and equipment investment cost to assist user decision-making. The NSGA-II multi-objective genetic algorithm simultaneously optimizes multiple conflicting objective functions. The core principles and steps are as follows:

[0229] 1) Initialize the population and randomly generate N individuals (N individuals represent the set of site selection schemes);

[0230] 2) Perform non-dominated sorting on the population and calculate the crowding degree at each level;

[0231] 3) Generate offspring populations through selection, crossover, and mutation. ;

[0232] 4) Merging and get ,right Sort and calculate crowding;

[0233] 5) Select N individuals based on non-dominant hierarchy and crowding level to form a new parent population. ;

[0234] 6) Repeat steps 3-5 until the termination condition (such as number of iterations, convergence) is met.

[0235] In this embodiment, the recommended range for the number of iterations is 100 to 500; the convergence criterion is: the non-dominated solution set of the population no longer changes significantly for 20 to 50 consecutive generations, or the rate of change of the objective function value is less than a preset threshold (e.g., ...). to By setting the above range, we can ensure the accuracy of the solution while taking into account the computational efficiency, and avoid the algorithm getting stuck in local optima or overcomputing.

[0236] This embodiment uses a large-scale heating network in a town as the application object, whose heating area covers various types of buildings, including residential buildings and public buildings. Due to its long construction period, the upstream pipeline has pressure aging problems, while the downstream heating stations suffer from insufficient pressure head and lack of circulation power due to excessive friction loss along the pipeline.

[0237] System construction: Using digital twin simulation analysis units, the entire network is divided into 5 interrelated pressure zones based on the actual design pressure thresholds and physical topology connections of each pipeline segment.

[0238] For branch lines with poor operation in each area, the NSGA-II algorithm described in Example 2 is used for location optimization. In this example, the number of algorithm iterations is set to 300, and the convergence criterion is that the Pareto front has no significant displacement within 50 consecutive generations and the fluctuation of the objective function is less than 1%. .

[0239] Based on the optimized model calculations, 12 low-head axial flow pipeline pumps were deployed on 3 key low-pressure operating paths to form a stepped hydraulic boost topology.

[0240] After the renovation, the actual pressure difference of the previously insufficient terminal heating stations all rose above the design threshold, completely eliminating the phenomenon of "insufficient heat at the terminal" and achieving dynamic balance of hydraulic conditions across the entire network. This significantly reduced the initial operating pressure at the heat source outlet. The real-time pressure distribution curve of the entire pipeline network remained below the safe pressure bearing line (SS) of each pipe section, avoiding the risk of overpressure and pipe bursts in the aging upstream pipeline network caused by blindly increasing the head of the first station. Because the axial-flow pipeline pumps are directly embedded in the existing pipeline routes, there is no need to build independent relay pumping stations, effectively solving the technical contradictions of difficult site selection and large land occupation for relay power facilities in old urban areas. Combined with the coordinated control of the dynamic decision-making unit, the output power of the pipeline pumps can be compensated in real time according to changes in the predicted load of the heating stations, ensuring precise pressure control in each area under variable flow conditions.

[0241] In this embodiment, the heating network structure including a multi-stage pipeline pump along the pipeline includes:

[0242] Construct an integrated cross-flow pipeline pump that couples the pump body and the pipeline. Embed multiple cross-flow pipeline pumps into the optimal installation position of the heating network along the pipeline route to form a low flow resistance and high flow capacity pipeline, and transform the original heating network into a heating network structure containing multi-stage pipeline pumps along the pipeline.

[0243] Construct an automatic control system for pipeline pumps, and collect pump operating parameters through the system, including start-stop status, head, flow rate, differential pressure, and operating frequency. Real-time data monitoring ensures normal pump operation, and real-time uploads the operating data to the dynamic decision-making and control unit for multi-stage pipeline pumps along the pipeline.

[0244] An edge computing system for pipeline pumps is constructed, with vibration and noise sensors installed on the outside of the pumps and pipelines. Vibration data and anomalies are collected by the sensors, and the data characteristics are analyzed by the edge computing unit to identify potential pump failures such as bearing wear and impeller imbalance, enabling local real-time fault diagnosis and reducing the latency of data upload to the cloud.

[0245] In this embodiment, the construction of a digital twin model of a heating network containing multi-stage pipeline pumps along the pipeline, combined with the dynamic distribution of water pressure under varying flow conditions simulated by the predicted load of the heating station, and the analysis of the impact of the combination logic of multi-pipeline pump operating parameters on water pressure distribution under different load changes, includes:

[0246] Based on the meteorological characteristics, building and regional characteristics, historical load data, time characteristics, and system operation status characteristics of the load forecasting model of the heating station, a load forecasting model driven by physical mechanism and data is constructed to obtain the load forecast values ​​of each heating station and calculate the flow demand of the heating network under different operating conditions.

[0247] The dynamic distribution analysis model of water pressure under variable flow conditions including pipeline pumps is as follows:

[0248] ;

[0249] in, It is a moment In position Water pressure at the location; It is the static water pressure distribution at the initial moment of the system (i.e., the initial reference water pressure that does not change with time); The pipeline pump operates at a constant time ,Location The dynamic water pressure increment generated at the location; Due to traffic Changes in head loss along the friction path caused by the change;

[0250] flow It can be divided into different intervals:

[0251] Low flow range: ;

[0252] Medium flow range: ;

[0253] High flow range: ;

[0254] The head loss coefficient varies depending on the flow rate range:

[0255] ;

[0256] in, This is the flow rate boundary value, used to determine the range of values ​​for the head loss coefficient; The head loss coefficient for different flow ranges is determined by the pipe characteristics and fluid state.

[0257] In this embodiment, with the objective function of minimizing power consumption cost and optimizing pipeline pump differential pressure compensation, and under the constraints of ensuring that the pressure in each area does not exceed the threshold and meets the pressure-bearing capacity requirements, pipeline hydraulic balance, hydraulic stability requirements, and the operating range of the pipeline pump characteristic curve, a collaborative optimization operation model for multiple pipeline pumps is established. This model solves for the optimal start-stop state and operating frequency of each pipeline pump under different operating conditions, enabling dynamic regional control of the pipeline pressure in the heating network, including:

[0258] The objective function is to minimize power consumption cost and optimize voltage difference compensation at key nodes, as follows:

[0259] ;

[0260] in, These are weighting coefficients. ; For electricity costs; To determine the degree of differential pressure compensation;

[0261] The electricity cost is detailed below:

[0262] ;

[0263] in, It refers to the number of pipeline pumps; It is the first Each pump at time The power; It's the electricity price; It is an optimization cycle;

[0264] The degree of differential pressure compensation is represented by assessing whether the difference between the updated pressure distribution at key nodes of the heating network containing pipeline pumps and the initial pressure distribution meets the differential pressure target, as detailed below:

[0265] ;

[0266] in, It refers to the number of critical nodes; It is the first The weight of each node; It is the first Each node at time... The actual differential pressure compensation value; It is the first Target differential pressure compensation value for each node;

[0267] The area's pressure-bearing capacity constraints are as follows:

[0268] ;

[0269] in, For each node Pressure For nodes The maximum pressure-bearing capacity of the pipe section in which it is located;

[0270] Hydraulic balance constraints are as follows:

[0271] ;

[0272] in, Represents nodes in the pipeline network; The set of all nodes; For inflow node A collection of pipes; outflow node A collection of pipes; For a moment Through pipes Traffic; To optimize the set of all moments within the time period;

[0273] To meet hydraulic stability requirements and ensure that the pressure drop rate meets preset values, as follows:

[0274] ;

[0275] in, To optimize the pipeline The pressure drop; For pipelines The reference voltage drop; The maximum allowable pressure drop rate threshold is adjusted based on the pressure-bearing capacity of the pipeline network in each area;

[0276] The pipeline pump conforms to the operating characteristic curve, and the head-frequency relationship and frequency adjustment range constraints are as follows:

[0277] ;

[0278] in, Indicates a pipeline pump; This refers to the collection of all pipeline pumps. For a moment pump Provided head, , , For pumps Rated head, frequency, flow rate; For pumps Characteristic parameters; For pumps Actual operating traffic; For pipeline pumps at all times Operating frequency below; For pumps The minimum permissible frequency; For pumps Maximum permissible frequency;

[0279] ;

[0280] in, For a moment pump The power; The density of the heat transfer medium; Gravitational acceleration; For a moment pump Actual operating traffic; For pumps In traffic The efficiency of the process.

[0281] It should be noted that the dynamic decision-making and control unit for the operation of multi-stage pipeline pumps along the pipeline adopts the model predictive control method to solve for the optimal start-stop state and operating frequency of each pipeline pump under different operating conditions.

[0282] Model predictive control (MMC) predicts the future behavior of a system based on a model and achieves dynamic control through rolling optimization. It divides the control process into multiple finite time intervals. Within each interval, based on the current system state, the set objective, and the prediction of the future, an optimization problem is solved to obtain the optimal control sequence for that interval. However, only the first control variable of this sequence is applied to the actual system. In the next time interval, the process is repeated based on the new system state, continuously optimizing to adapt to the dynamic changes and uncertainties of the system.

[0283] In the dynamic decision-making and control unit for multi-stage pipeline pumps along the heating network, model predictive control uses the predicted load of each heating station as an external input. Combined with a digital twin model of the heating network containing multi-stage pipeline pumps, it predicts the dynamic distribution of water pressure in the network under different operating conditions. By continuously optimizing the pipeline pump collaborative operation model through rolling optimization, the optimal start-stop state and operating frequency of each pipeline pump at different times are obtained. During operation, real-time monitoring data is used for feedback correction, continuously optimizing the control strategy to achieve precise dynamic control of the pipeline pumps in the heating network. This ensures that energy conservation, consumption reduction, and stable system operation are achieved while meeting heating demand.

[0284] To facilitate understanding of the impact of the multi-stage low-head pipeline pump of the present invention on the water pressure distribution of the heating network system, the following will use... Figure 4 The diagram illustrates the impact on water pressure distribution.

[0285] Figure 4 This diagram illustrates the water pressure of heating systems with and without pumps, with relay pumps, and with multi-stage pipeline pumps along the pipeline. The heat source is located at point O. The vertical axis represents water pressure H (mH2O), reflecting the pressure of the pipeline network; the higher the axis, the greater the pressure. The horizontal axis represents the length of the pipeline network L (m), showing the pressure changes along the pipeline. The safe operating overpressure line (ss) is the maximum safe pressure the pipeline network can withstand; exceeding this line may result in pipe bursts and equipment damage. The static pressure line (jj) is the static pressure benchmark of the pipeline network, representing the natural water pressure when there is no flow, determined by factors such as terrain and water source elevation.

[0286] The water pressure diagram without pumps, where no pumps are installed on the supply and return water pipelines, results in high overall pressure. The heat source circulating water pump has a large head, and while ensuring the usable head at the end, some areas exceed the safe operating pressure limit, making it difficult to simultaneously meet the pressure requirements of all users on the supply and return water pipelines. The water pressure diagram with relay pump stations, where a single relay pump station is added to both the supply and return water pipelines for centralized pressurization, significantly increases the pressure after the pump. Pressure regulation is based on overall demand, but when local heating demand changes within a region, it is difficult to quickly and accurately adjust the water pressure in that area, easily leading to excessively high or low water pressure in some areas. The water pressure diagram with multi-stage pipeline pumps along the pipeline, based on the region's pressure-bearing capacity, deploys multiple low-head pipeline pumps along the pipeline. Each pump provides a small increase in pressure to a local section, flexibly covering different sections and forming multi-stage stepped water pressure. This ensures safe pressure operation, accurately regulates local low-pressure areas, avoids pressure fluctuations caused by centralized pressurization, and neither exceeds the safety limit nor fails to meet the needs of end users.

[0287] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0288] This embodiment also provides a flexible heating network regulation method based on a multi-stage pipeline pump along the pipeline. This method uses the system described in the above embodiment, and the regulation method includes the following steps:

[0289] Step 1: Digital twin simulation and zone division steps. Establish a digital twin model of the heating network, conduct multi-condition thermal and hydraulic characteristic simulation, analyze the operating parameters of each node, quantitatively evaluate the pressure bearing capacity of each pipe section, divide the heating network into several pressure-bearing zones, and identify the unfavorable branches in each zone.

[0290] Step 2: Pipeline pump system design and pipeline network construction steps. Based on the unfavorable operating branches in each area, determine the number and deployment location of multiple pipeline pumps. Combine the pressure distribution of the pipe section and the pressure bearing capacity threshold of the area to construct a set of pipeline pump site selection schemes for differentiated deployment in each area. Solve the optimal pump installation location and model parameters for each area through the site selection optimization design model. Embed multiple axial flow pipeline pumps into the optimal installation location of the heating pipeline network to form a heating pipeline network structure containing multi-stage pipeline pumps along the pipeline.

[0291] Step 3: Dynamic control step. Construct a digital twin model of the heating network including multi-stage pipeline pumps along the pipeline. Combine the predicted load of the heating station to simulate the dynamic distribution of water pressure under the variable flow conditions of the pipeline network. Analyze the impact of the combination logic of the operating parameters of the multi-pipeline pumps on the water pressure distribution under different load changes. Solve the optimal start-stop state and operating frequency of each pipeline pump under different operating conditions through the collaborative optimization operation model of the multi-pipeline pumps. Perform dynamic control of the pipeline pressure in the heating network in different areas.

[0292] This embodiment of the flexible heating network regulation method based on multi-stage pipeline pumps along the pipeline mainly includes three steps: First, by establishing a digital twin model of the heating network, multi-condition simulation is performed to divide the pressure-bearing areas of the heating network and identify unfavorable operating branches; second, based on the unfavorable branches in the area, the number and deployment location of pipeline pumps are determined, a set of site selection schemes is constructed, and the optimal installation location and model are determined through optimization model to form a network structure containing multi-stage pipeline pumps along the pipeline; third, a digital twin model of the network containing pumps is constructed, and combined with the load prediction of the heating station to simulate the dynamic distribution of water pressure, the optimal start-stop state and frequency of the pipeline pumps are solved through a collaborative optimization model to realize the dynamic regulation of network pressure by area.

[0293] Furthermore, the functional modules in the various embodiments of this invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part. If the function is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0294] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A flexible heating network system based on a multi-stage pipeline pump along the pipeline, characterized in that: include: Digital twin simulation analysis unit: used to establish a digital twin model of the heating network, simulate the thermal and hydraulic characteristics of the network, analyze node operating parameters, quantitatively evaluate the pressure-bearing capacity of pipe sections and divide the heating network into pressure-bearing areas, and identify branches with unfavorable operation in the areas. Design unit for multi-stage pipeline pump system along the pipeline: Based on the unfavorable operating branches, determine the number and deployment location of pipeline pumps, combine the pressure distribution of the pipeline section and the pressure bearing capacity threshold of the area to construct a set of site selection schemes, aim at minimizing installation cost and energy consumption, and constrain the hydraulic balance and transportation efficiency of the pipeline network, establish a site selection optimization model to solve for the optimal pump parameters, and form a pipeline network structure containing multi-stage pipeline pumps along the pipeline. Dynamic decision-making and control unit for multi-stage pipeline pump operation along the pipeline: Construct a digital twin model of the pipeline network including pipeline pumps, combine load prediction of heating stations to simulate water pressure distribution under variable flow conditions, and establish a collaborative optimization operation model to control the start-up and shutdown and frequency of pumps with the goal of minimizing power consumption cost and optimizing pipeline pump pressure difference compensation, so as to realize dynamic control of pipeline network pressure in different areas.

2. The system according to claim 1, characterized in that: When establishing the digital twin model, the digital twin simulation analysis unit uses structural mechanism modeling to construct the geometric, physical, behavioral, and rule models of the pipeline network, and then corrects the model using operational data; when dividing the pressure-bearing areas of the heating network, the first... Each region is abstracted as a directed graph. The expression is: ； in, For the first Directed graph model of a pressure-bearing area of ​​a heating network For the first The set of nodes in each region contains Each heat source, heating station, and pipeline branching and merging node For the first The pipeline system for each area comprises M supply and return water pipelines. , It is a collection of pipeline network areas; and the areas are divided based on pipeline pressure threshold, hydraulic topology and geographical distribution.

3. The system according to claim 2, characterized in that: When the design unit of the multi-stage pipeline pump system along the pipeline determines the key nodes for deployment, it uses the unfavorable branch lines in the area as a benchmark, and presets the absolute pressure threshold and the drop threshold relative to the design pressure based on the initial pressure distribution parameters to identify the key low-pressure pipe sections with pressure below the threshold. By constructing a hydraulic path dependency graph, we analyze the region where the low-pressure pipe section is located, its supply / return water pipeline, and its upstream and downstream connections. Based on the pressure threshold reduction, the number of affected stations, and topological importance, we assess the transmission impact of the low-pressure pipe section and determine the key nodes for pipeline pump deployment.

4. The system according to claim 3, characterized in that: The design unit for the multi-stage pipeline pump system along the pipeline also includes determining key nodes for pipeline pump deployment, such as: for long-distance transmission and distribution pipelines within a region, determining the pressure drop of each pipe section based on the initial pressure distribution parameters of the heating network, setting a pressure drop threshold for each pipe section based on known flow demand, and identifying high-resistance pipe sections with excessive pressure drop; setting a head margin in combination with the pipeline pump head threshold limit, and determining each key node of the long-distance transmission and distribution pipeline in a multi-stage segmentation manner using a power-distributed approach.

5. The system according to claim 1, characterized in that: When constructing a set of pipeline pump site selection schemes for differentiated deployment in different areas, the design unit of the multi-stage pipeline pump system along the pipeline pump calculates the basic head based on the pressure drop and target pressure of the supply and return water pipe sections where the pipeline pump is deployed, determines the safety margin by combining the pressure bearing capacity threshold of the pipe section in the area and the fluctuation range of operating data, and determines the final head parameters. Based on the hydraulic calculation results of the heating network in the area where the deployment location is located, and combined with the adjustable range on the user side, the flow reference value is determined, and the pipeline pump model that meets the flow requirements, has flow regulation capability, and whose operating point is located in the high-efficiency zone is selected; on long pipe sections with large pressure loss, multiple pipeline pump installation schemes can be combined to enrich the combination of site selection schemes.

6. The system according to claim 1, characterized in that: When establishing a site selection optimization design model for the aforementioned multi-stage pipeline pump system design unit, the method includes: A binary variable is used to control whether a pipeline pump is installed at each node, with the objective function being to minimize the total cost, including installation cost and operating energy consumption cost. The constraints of the location optimization design model include: The pressure at each node within the area shall not exceed the pressure-bearing capacity constraint of the maximum pressure-bearing capacity of the pipe section. The flow balance constraint is that the inflow to each node equals the outflow. Hydraulic balance constraints that describe the relationship between downstream node pressure and upstream pressure, pipeline pump head, and friction loss; Ensure that the pressure head at each node meets the pressure head constraint within the safe operating range; And to ensure that the efficiency and head of the installed pipeline pumps are within the design capacity range, as well as equipment performance constraints.

7. The system according to claim 1, characterized in that: The heating network structure including a multi-stage friction-feed pipeline pump comprises an integrated axial-flow pipeline pump with the pump body and pipeline, a pipeline pump automatic control system, and a pipeline pump edge computing system. The axial-flow pipeline pump is embedded in the optimal installation position of the heating network along the pipeline route, forming a low-flow-resistance, high-flow-capacity pipeline, transforming the original heating network into a heating network structure containing a multi-stage friction-feed pipeline pump. The pipeline pump automatic control system collects pump operating parameters, including start / stop status, head, flow rate, differential pressure, and operating frequency, monitors in real time to ensure normal pump operation, and uploads operating data to the multi-stage friction-feed pipeline pump dynamic decision-making and control unit in real time. The pipeline pump edge computing system collects vibration data and abnormal signals through vibration sensors and noise sensors arranged on the outside of the pipeline pump and pipeline, combines the data characteristics with the edge computing unit to identify possible bearing wear and impeller imbalance faults in the pump body, realizes local real-time fault diagnosis of the pump body, and reduces the delay of data upload to the cloud.

8. The system according to claim 1, characterized in that: The methods for constructing a digital twin model of a heating network including multi-stage pipeline pumps along the pipeline include: Based on the load prediction model of the heating station, the load prediction value of each heating station is obtained, and the flow demand of the heating network under different operating conditions is calculated accordingly. A dynamic water pressure distribution analysis model is constructed to simulate the dynamic water pressure distribution under variable flow conditions by measuring the changes in head loss along the flow path caused by flow rate changes. The flow rate is divided into different flow rate intervals, and water pressure simulation calculations are performed based on the head loss coefficient corresponding to each flow rate interval. The head loss coefficient is determined by the pipe characteristics and fluid state.

9. The system according to claim 1, characterized in that: When establishing a collaborative optimization operation model for multiple pipeline pumps using the dynamic decision-making and control unit along the pipeline, the method includes: The overall optimization objective is to minimize power consumption costs and optimize the voltage difference compensation at key nodes. The constraints of the collaborative optimization operation model include: Ensure that the pressure in each area does not exceed the pressure constraint of the maximum pressure bearing capacity of the pipe section; To satisfy the overall network hydraulic balance constraint that the inflow and outflow of each node are equal; Ensure that the rate of change of pressure drop meets the hydraulic stability constraint of the preset threshold; And the operating characteristic constraints that satisfy the relationship between pipeline pump head and frequency, frequency adjustment range, and equipment power calculation logic.

10. A flexible heating network regulation method based on multi-stage pipeline pumps along the pipeline, characterized in that... The method employs the system described in any one of claims 1 to 9, and the control method includes the following steps: Step 1: Digital twin simulation and zone division steps. Establish a digital twin model of the heating network, conduct multi-condition thermal and hydraulic characteristic simulation, analyze the operating parameters of each node, quantitatively evaluate the pressure bearing capacity of each pipe section, divide the heating network into several pressure-bearing zones, and identify the unfavorable branches in each zone. Step 2: Pipeline pump system design and pipeline network construction steps. Based on the unfavorable operating branches in each area, determine the number and deployment location of multiple pipeline pumps. Combine the pressure distribution of the pipe section and the pressure bearing capacity threshold of the area to construct a set of pipeline pump site selection schemes for differentiated deployment in each area. Solve the optimal pump installation location and model parameters for each area through the site selection optimization design model. Embed multiple axial flow pipeline pumps into the optimal installation location of the heating pipeline network to form a heating pipeline network structure containing multi-stage pipeline pumps along the pipeline. Step 3: Dynamic control step. Construct a digital twin model of the heating network including multi-stage pipeline pumps along the pipeline. Combine the predicted load of the heating station to simulate the dynamic distribution of water pressure under the variable flow conditions of the pipeline network. Analyze the impact of the combination logic of the operating parameters of the multi-pipeline pumps on the water pressure distribution under different load changes. Solve the optimal start-stop state and operating frequency of each pipeline pump under different operating conditions through the collaborative optimization operation model of the multi-pipeline pumps. Perform dynamic control of the pipeline pressure in the heating network in different areas.

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