A heat supply system performance analysis management system

Abstract

The present application relates to the technical field of heat supply system management, in particular to a heat supply system efficiency analysis management system, comprising: a data acquisition module, which acquires real-time operation data of the whole heat supply process; a heat source analysis module, which inputs heat source side parameters into an operation characteristic model to generate a sequence of instantaneous energy efficiency indicators; a pipe network analysis module, which constructs a hydraulic and thermal coupled simulation network based on the pressure and temperature monitoring values of key nodes of the pipe network, and calculates the distribution of dynamic hydraulic disorder and the distribution of heat loss along the pipeline; a user analysis module, which calculates a heat efficiency evaluation factor by using the indoor temperature of a representative heat user, the outdoor meteorological coupling value and the building thermal parameter, and collects the heat efficiency evaluation factor into a user end energy efficiency atlas; and a panoramic fusion module, which generates a comprehensive energy efficiency analysis panoramic view by spatiotemporally aligning and fusing the data of each module. The system improves the comprehensiveness and accuracy of heat supply system efficiency analysis through coupled simulation and multi-parameter evaluation.

Description

Technical Field

[0001] This invention relates to the field of heating system management technology, and in particular to a heating system performance analysis and management system. Background Technology

[0002] Current heating system performance analysis often employs a decentralized evaluation model. On the heat source side, static energy efficiency is typically calculated using fuel consumption and inlet / outlet water parameters. On the pipeline side, pressure or temperature is often monitored independently, and hydraulic imbalances or heat losses are estimated using empirical formulas. At the user end, energy consumption is primarily assessed based on indoor temperature monitoring. This model fails to consider the dynamic coupling between water flow and heat transfer, making it difficult to capture the correlation between flow deviations and heat loss across different time periods and pipe sections. It also fails to provide a spatial understanding of dynamic hydraulic imbalances and the spatial distribution of heat loss along the pipeline. User-side evaluation relies solely on indoor temperature, ignoring the impact of outdoor weather fluctuations on actual heat demand. Furthermore, it fails to consider the building's thermal insulation, structure, and other thermal characteristics, leading to biased assessments of the actual energy efficiency of different users and an inability to identify high-energy-consuming areas or inefficient users.

[0003] An analytical method is needed to reflect the dynamic interaction of hydraulics and heat in the pipeline network in order to obtain a more refined distribution of transmission and distribution losses. Simultaneously, a user thermal efficiency evaluation system combining environmental parameters and building characteristics needs to be constructed to achieve a precise profile of energy efficiency. This system aims to address the problems of missing pipeline network coupling and coarse user evaluation in existing analyses, and to improve the depth and relevance of performance analysis through specific technical means. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose a heating system performance analysis and management system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a heating system performance analysis and management system, comprising:

[0006] The data acquisition module acquires real-time operating data for the entire heating process. The real-time operating data includes fuel consumption, water supply temperature, water supply pressure, return water temperature, pressure and temperature monitoring values ​​of key nodes in the pipeline network, and the temperature coupling values ​​of representative heat users' indoor and outdoor meteorological conditions.

[0007] The heat source analysis module inputs the fuel consumption, water supply temperature, water supply pressure and return water temperature from the output side of the heat source into the heat source equipment operation characteristic model for calculation, and generates a sequence of instantaneous energy efficiency indicators for the heat source.

[0008] The pipeline network analysis module, based on the pressure and temperature monitoring values ​​of the key nodes of the pipeline network, constructs a hydraulic and thermal coupling simulation network for the heating transmission and distribution pipeline network, and calculates the dynamic hydraulic misalignment distribution and friction heat loss distribution of the heating transmission and distribution pipeline network.

[0009] The user analysis module uses the indoor temperature and outdoor meteorological coupling value of the representative heat user, combined with the building thermal parameters corresponding to the representative heat user, to calculate the ratio of the actual heat demand to the theoretical heat demand of the representative heat user, which serves as the thermal efficiency evaluation factor for the representative heat user. The thermal efficiency evaluation factors of all representative heat users are collected to form a user-end energy efficiency map.

[0010] The panoramic fusion module performs spatiotemporal alignment and data fusion of the instantaneous energy efficiency index sequence of the heat source, the dynamic hydraulic misalignment distribution and the heat loss distribution along the flow path, and the energy efficiency map of the user end, generating a panoramic view of comprehensive energy efficiency analysis.

[0011] As a further aspect of the present invention, the fuel consumption, water supply temperature, water supply pressure, and return water temperature on the heat source output side are input into the heat source equipment operation characteristic model for calculation, generating a heat source instantaneous energy efficiency index sequence, including:

[0012] The instantaneous energy efficiency index sequence of the heat source includes the heat output per unit of fuel, the load rate of the heat source equipment, and the degree to which the heat source equipment deviates from the design operating conditions.

[0013] The supply and return water temperature difference is calculated based on the supply water temperature and return water temperature on the output side of the heat source, and the instantaneous output heat power of the heat source is calculated based on the preset pipeline flow rate or the flow rate reference value obtained by converting the supply water pressure.

[0014] The unit fuel heat output is calculated by dividing the fuel consumption by the instantaneous output heat power of the heat source.

[0015] The rated output power of the heat source equipment is read from the design parameter database of the heat source equipment, and the instantaneous output heat power of the heat source is compared with the rated output power to obtain the load rate of the heat source equipment;

[0016] The design return water temperature and design supply and return water temperature difference of the heat source equipment under rated operating conditions are read from the design parameter database of the heat source equipment. The actual measured return water temperature and supply and return water temperature difference are compared with the corresponding design values, the deviation percentage is calculated, and the degree of deviation of the heat source equipment from the design operating conditions is calculated based on the deviation percentage.

[0017] At fixed time intervals, the calculated unit fuel heat output, heat source equipment load rate, and degree of deviation of heat source equipment from design conditions are combined into a data group to form a sequence of instantaneous energy efficiency indicators of the heat source arranged in chronological order.

[0018] As a further aspect of the present invention, based on the pressure and temperature monitoring values ​​of the key nodes of the pipeline network, a hydraulic and thermal coupling simulation network for the heating transmission and distribution pipeline network is constructed, including:

[0019] Based on the design drawings of the heating transmission and distribution pipeline network, a pipeline network topology diagram is constructed, which includes the connection relationships of pipe segments, nodes, and heat sources.

[0020] The pressure and temperature monitoring values ​​of the key nodes of the pipeline network collected on site are assigned to the corresponding nodes in the pipeline network topology diagram as boundary conditions and initial conditions of the simulation network.

[0021] Based on the pipe material, diameter, length, and insulation status, each pipe segment in the pipeline network topology diagram is assigned a hydraulic resistance coefficient and a thermal conductivity coefficient.

[0022] Based on the laws of conservation of mass, momentum, and energy, a set of simultaneous equations is established to describe the water flow and heat transfer in the heating and distribution pipeline network. The pipeline network topology diagram, the hydraulic resistance coefficient and thermal conductivity coefficient, as well as the boundary conditions and initial conditions are substituted into the set of simultaneous equations to form a computational model of the hydraulic and thermal coupling simulation network.

[0023] As a further aspect of the present invention, the calculation of the dynamic hydraulic misalignment distribution and friction loss distribution of the heating transmission and distribution network includes:

[0024] The dynamic hydraulic misalignment distribution is used to identify areas with uneven flow distribution in the pipeline network, and the friction heat loss distribution is used to quantify the heat loss power per unit length of the pipe segment.

[0025] Using the computational model of the hydraulic-thermal coupled simulation network, the flow rate and pressure at each node in each pipe section are solved under given boundary conditions;

[0026] Extract the design flow of each user branch in the calculation model, compare the actual flow of each user branch obtained by the solution with the corresponding design flow, calculate the flow misalignment ratio of each user branch, and perform spatial interpolation rendering on the flow misalignment ratio at each location in the entire pipeline network to generate the dynamic hydraulic misalignment distribution.

[0027] Using the computational model of the hydraulic-thermal coupled simulation network, the starting and ending temperatures of each pipe segment under given boundary conditions are solved.

[0028] Based on the heat transfer coefficient, length, and temperature difference between the medium and the environment of each pipe segment, the heat loss power of each pipe segment is calculated. The heat loss power of each pipe segment is divided by its length to obtain the heat loss power per unit pipe length. The heat loss power per unit pipe length of each pipe segment in the entire pipe network is spatially interpolated and rendered to generate the friction loss distribution.

[0029] As a further aspect of the present invention, the ratio of the actual heat demand to the theoretical heat demand of the representative heat user is calculated by using the coupled values ​​of the indoor temperature and outdoor meteorological conditions of the representative heat user, combined with the building thermal parameters corresponding to the representative heat user, including:

[0030] The outdoor design temperature of the representative heat user's area during the heating season is obtained from historical operating data or meteorological departments, as well as the heat index per unit area, building usable area, room height and heat transfer coefficient of the building envelope corresponding to the representative heat user, and the heat index per unit area, building usable area, room height and heat transfer coefficient of the building envelope are used as the building thermal parameters.

[0031] Based on the building thermal parameters and the actual measured indoor temperature and outdoor meteorological coupling value of the representative heat user, the actual heat consumption required to maintain the current indoor temperature under the actual meteorological conditions represented by the outdoor meteorological coupling value is calculated as the actual heat demand.

[0032] Based on the building thermal parameters, the outdoor design temperature during the heating season, and the preset standard indoor design temperature, calculate the theoretical heat consumption required to maintain the standard indoor design temperature under standard design conditions, and use it as the theoretical heat consumption.

[0033] The actual heat demand is divided by the theoretical heat demand to calculate the thermal efficiency evaluation factor for representative heat users.

[0034] As a further aspect of the present invention, the instantaneous energy efficiency index sequence of the heat source, the dynamic hydraulic misalignment distribution and the friction loss distribution, and the user-end energy efficiency map are spatiotemporally aligned and fused, including:

[0035] A unified timestamp and spatial geographic reference system is established for the instantaneous energy efficiency index sequence of the heat source, the dynamic hydraulic misalignment distribution, the heat loss distribution along the friction path, and the energy efficiency map of the user end.

[0036] The instantaneous energy efficiency index sequence of the heat source, the dynamic hydraulic misalignment distribution, the friction heat loss distribution, and the user terminal energy efficiency map data with the same timestamp are time-sliced ​​and aligned.

[0037] After time slice alignment, the dynamic hydraulic displacement distribution, the friction heat loss distribution, and the user-end energy efficiency map data are mapped onto the same heating area geographic base map, and the instantaneous energy efficiency index sequence of the heat source is associated with the heat source location on the heating area geographic base map in the form of a chart.

[0038] Data from different data sources, aligned in time and space, are rasterized according to geographical location. Each raster cell contains information on heat source energy efficiency indicators, hydraulic runaway, heat loss along the pipeline, and user-end heat efficiency corresponding to the geographical location. This process completes data fusion and forms the data layer of the comprehensive energy efficiency analysis panoramic view.

[0039] As a further aspect of the present invention, the system further includes the step of generating energy efficiency optimization and control instructions based on the comprehensive energy efficiency analysis panoramic view:

[0040] In the data layer of the comprehensive energy efficiency analysis panoramic view, threshold ranges are set for heat source unit fuel heat supply, pipeline hydraulic misalignment, pipeline unit pipe length heat loss, and user-end thermal efficiency evaluation factors.

[0041] Scan the data of any grid cell in the data layer to identify abnormal areas where the unit fuel heat supply is lower than the set lower limit, the absolute value of hydraulic loss exceeds the set upper limit, the heat loss per unit pipe length exceeds the set upper limit, or the thermal efficiency evaluation factor is lower than the set lower limit.

[0042] Root cause analysis is performed on the identified abnormal areas to determine the related links that led to the occurrence of the abnormal areas. The related links include unreasonable heat source operating parameters, abnormal opening of pipeline valves, damage to insulation structure, or improper user adjustment behavior.

[0043] Based on the results of the root cause analysis, combined with the heat source regulation characteristics, pipeline valve control logic and user regulation strategy, specific regulation operation instructions are generated for the related links. The specific regulation operation instructions include target parameters, regulation equipment, regulation direction and regulation range.

[0044] The generated specific adjustment operation instructions are sent to the corresponding actuators of the heating system according to a preset priority order.

[0045] As a further aspect of the present invention, the root cause analysis of the identified abnormal regions includes:

[0046] When it is identified that the thermal efficiency evaluation factor of the heat user end is generally low, check whether the return water temperature of the pipe network in the abnormal area is abnormally high, and judge whether the uneven heating is caused by the hydraulic imbalance of the pipe network in combination with the dynamic hydraulic imbalance distribution.

[0047] When an abnormally high local heat loss per unit length is identified in the distribution of heat loss along the pipeline network, the corresponding pipe section is located in the panoramic view of the comprehensive energy efficiency analysis, and the historical maintenance records and insulation layer detection data of the corresponding pipe section are retrieved to determine whether the insulation structure is damaged.

[0048] When a region in the dynamic hydraulic imbalance distribution of the pipeline network is identified as having a continuous abnormal imbalance, the regulating valve upstream of the region is located in the panoramic view of the comprehensive energy efficiency analysis. The preset opening degree of the regulating valve is checked to see if it is consistent with the current actual opening degree. The heat consumption changes of other users in the branch where the regulating valve is located are also analyzed.

[0049] When it is identified that the unit fuel heat supply is consistently low in the instantaneous energy efficiency index sequence of the heat source, the load rate of the heat source equipment and the degree of deviation from the design conditions are analyzed to determine whether the decrease in heat source efficiency is due to the low load rate or the deviation of the supply and return water temperatures from the design values.

[0050] As a further aspect of the present invention, the construction of the operating characteristic model of the heat source equipment includes:

[0051] Based on the equipment model of the heat source equipment and the technical data provided by the manufacturer, establish a basic parameter database that includes the rated thermal power, rated supply water temperature, rated return water temperature, rated efficiency, and fuel characteristic parameters of the heat source equipment;

[0052] Collect historical operating data of the heat source equipment under different load rates, different outdoor temperatures and different water supply temperature setpoints. The operating data includes fuel consumption, actual water supply temperature, actual return water temperature and output heat power.

[0053] The historical operating data is cleaned to remove outliers and invalid data points. Then, the operating data under different operating conditions are classified and normalized according to the load rate range to form a standardized training sample dataset.

[0054] Using the fuel consumption, water supply temperature, and return water temperature under different operating conditions in the standardized training sample dataset as input features, and the actual operating efficiency calculated by the heat balance equation under the corresponding operating conditions as the output label, a predictive model describing the nonlinear mapping relationship between the actual operating efficiency of the heat source equipment and key operating parameters is trained by a supervised learning algorithm.

[0055] The prediction model is connected to the real-time data interface of the heat source equipment control system, and the basic parameter database is integrated to form the heat source equipment operation characteristic model that can receive real-time operating parameters and output predicted values ​​of energy efficiency indicators.

[0056] As a further aspect of the present invention, the step of extracting the design flow of each user branch in the calculation model, comparing the actual flow of each user branch obtained by the solution with the corresponding design flow, calculating the flow misalignment ratio of each user branch, and performing spatial interpolation rendering of the flow misalignment ratio at each location in the entire pipeline network includes:

[0057] Read the flow rate value under the design conditions corresponding to each user branch from the design database or preset configuration file associated with the calculation model of the hydraulic and thermal coupling simulation network, and use it as the design flow rate of each user branch;

[0058] The solution results of the computational model of the hydraulic and thermal coupling simulation network are called to obtain the instantaneous flow rate value of each user branch obtained by numerical calculation under the given boundary conditions, which is used as the actual flow rate of each user branch;

[0059] The actual traffic of each user branch is compared with the corresponding design traffic item by item. The traffic imbalance ratio of each user branch is calculated by subtracting the actual traffic from the design traffic and then dividing by the design traffic. The traffic imbalance ratio represents the degree of deviation of the actual traffic from the design traffic.

[0060] Using the geographic node coordinates corresponding to each user branch in the heating transmission and distribution pipeline network topology diagram as spatial location points, the calculated flow imbalance ratio of each user branch is used as the attribute value of the spatial location point.

[0061] Using a spatial interpolation algorithm, based on the attribute values ​​of each location point, the estimated flow imbalance ratio of pipe sections without directly set monitoring points is calculated within the geographical area of ​​the heating region, generating a continuous spatial field covering the entire heating region;

[0062] The numerical results of the continuous spatial field are mapped to corresponding colors or gray levels, and then superimposed onto the geographic information map of the heating network according to spatial coordinates to complete spatial interpolation rendering, forming a dynamic hydraulic imbalance distribution map that intuitively displays the uneven flow distribution area.

[0063] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0064] Based on pressure and temperature monitoring values ​​at key nodes in the pipeline network, a hydraulic and thermal coupled simulation network is constructed to simulate the interaction between water flow and heat transfer, dynamically calculating the distribution of dynamic hydraulic imbalance and friction-related heat loss. This technology overcomes the limitations of conventional single-dimensional analysis by capturing the correlation between hydraulic imbalance and heat loss during pipeline operation through a coupled model. This allows the actual losses in the transmission and distribution process to be quantified in terms of time and space, providing a more realistic data foundation for identifying high-imbalance pipeline sections and optimizing transmission and distribution paths, thereby improving the accuracy of pipeline network energy efficiency assessment.

[0065] By combining indoor temperature and outdoor meteorological data from representative heat users with building thermal parameters, the ratio of actual heat demand to theoretical heat demand is calculated as a thermal efficiency evaluation factor. All factors are then aggregated to form a user-side energy efficiency map. This technology quantifies the actual energy consumption status of users through multi-parameter correlation. Compared to the crude method of only monitoring indoor temperature, it can distinguish the impact of meteorological fluctuations and building performance on energy consumption, clearly demonstrating the efficiency differences among different users or regions. This provides a more accurate basis for identifying inefficient users and developing differentiated energy-saving strategies, enhancing the targeted nature of user-side energy efficiency management. Attached Figure Description

[0066] Figure 1 This is a timing diagram of a heating system performance analysis and management system according to the present invention;

[0067] Figure 2 A flowchart for constructing a hydraulic-thermal coupling simulation network;

[0068] Figure 3 This is a time-series analysis diagram of the instantaneous energy efficiency index of the heat source;

[0069] Figure 4 A flowchart for fusion of panoramic view data for comprehensive energy efficiency analysis;

[0070] Figure 5 Heatmap of thermal efficiency evaluation factors for user end. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0072] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0073] See Figure 1The data acquisition module is responsible for collecting real-time operational data throughout the heating process. This data includes fuel consumption, supply water temperature, supply water pressure, and return water temperature at the heat source output side; pressure and temperature monitoring values ​​at key nodes in the pipeline network; and the temperature of pre-selected representative heat users' indoor spaces coupled with their corresponding outdoor meteorological values. The heat source analysis module receives fuel consumption, supply water temperature, supply water pressure, and return water temperature from the heat source side and inputs them into a pre-set heat source equipment operation characteristic model for calculation. This model outputs a sequence of instantaneous energy efficiency indicators for the heat source arranged in chronological order. Based on the acquired pressure and temperature monitoring values ​​of key nodes in the pipeline network, the pipeline network analysis module constructs a simulation network of the heating transmission and distribution network capable of simulating the coupling state of hydraulics and thermodynamics. Through calculations using this simulation network, the dynamic hydraulic misalignment distribution reflecting the uniformity of flow distribution across the entire network and the friction loss distribution characterizing the heat dissipation intensity of the pipeline are obtained. The user analysis module processes the coupled values ​​of indoor temperature and outdoor meteorological data for representative heat users. Combining this with the building thermal parameters corresponding to each representative heat user, it calculates the ratio of the actual heat demand under actual meteorological conditions to the theoretical heat demand under standard design conditions for each representative heat user. This ratio is defined as the user's thermal efficiency evaluation factor. The calculation results for all representative heat users are aggregated to form a user-end energy efficiency map reflecting the overall energy consumption status at the user end. The panoramic fusion module receives the output results from the heat source analysis module, pipeline network analysis module, and user analysis module. It aligns and integrates these data in terms of time and spatial coordinates, fusing them into a unified view. Finally, it generates a comprehensive energy efficiency analysis panoramic view that simultaneously displays the energy efficiency status of the heat source, pipeline network, and user ends.

[0074] In one embodiment of the present invention, the construction of the heat source equipment operation characteristic model and its application in generating the instantaneous energy efficiency index sequence of the heat source are illustrated using a gas-fired hot water boiler with a rated thermal power of 58 MW as an example. The construction process of the heat source equipment operation characteristic model begins with the establishment of a basic parameter database. The basic parameter database is derived from the technical manual provided by the equipment manufacturer, which records the rated thermal power of the gas-fired hot water boiler as 58 MW, the rated supply water temperature as 115°C, the rated return water temperature as 70°C, the rated efficiency as 92%, and the lower heating value of the natural gas used as 35 MJ / standard cubic meter. Historical operating condition data of the gas-fired hot water boiler under different load rates, different outdoor temperatures, and different supply water temperature setpoints over the past three heating seasons are collected. The historical operating condition data is recorded every 15 minutes, and each record point includes gas consumption, actual supply water temperature, actual return water temperature, and output thermal power calculated based on the heat formula. Historical operating data is cleaned to remove outliers that significantly exceed physical limits, such as records where the water supply temperature is above 130 degrees Celsius or below 40 degrees Celsius. The cleaned data is then divided into load rate ranges: 0-30%, 30-60%, and 60-100%. The operating data within each range is normalized to form a standardized training sample dataset. Using the gas consumption, water supply temperature, and return water temperature of each record in the standardized training sample dataset as input features, and the actual operating efficiency calculated using the heat balance equation under the corresponding operating conditions as the output label, a gradient boosting regression tree (GRRP) supervised learning algorithm is used for training. This yields a predictive model describing the nonlinear mapping relationship between the actual operating efficiency of the gas-fired hot water boiler and key operating parameters. In some embodiments, the GRRP predictive model is connected to the real-time data interface of the gas-fired hot water boiler's distributed control system and integrated with the aforementioned basic parameter database to form a heat source equipment operating characteristic model capable of receiving real-time operating parameters and outputting predicted energy efficiency indicators.

[0075] It is understandable that the process involves inputting real-time data from the heat source output side into the heat source equipment operating characteristic model for calculation and generation of a heat source instantaneous energy efficiency index sequence. This sequence includes unit fuel heat output, heat source equipment load rate, and the degree of deviation of the heat source equipment from its design operating conditions. In specific implementation, based on the real-time supply water temperature of 98 degrees Celsius and the real-time return water temperature of 65 degrees Celsius at the output side of the gas-fired hot water boiler, the real-time supply and return water temperature difference is calculated to be 33 degrees Celsius. Based on the actual circulating water flow rate of 800 cubic meters per hour measured by the ultrasonic flow meter installed on the boiler outlet main pipe, and combined with the specific heat capacity of water, the instantaneous output heat power of the gas-fired hot water boiler is calculated to be 30.7 megawatts. The unit fuel heat output at that moment is calculated by dividing the gas consumption of 850 standard cubic meters per hour collected at the same time by the calculated instantaneous output heat power of 30.7 megawatts. The calculation of unit fuel heat output can be expressed as:

[0076]

[0077] in: Indicates the instantaneous heat output per unit of fuel. This indicates the instantaneous output heat power of the heat source. This represents the instantaneous fuel consumption. The rated output power of the gas-fired hot water boiler (58 MW) is retrieved from the basic parameter database. The calculated instantaneous output heat power (30.7 MW) is compared with the rated output power of 58 MW, yielding a current heat source equipment load rate of 52.9%. The design return water temperature (70°C) and design supply-return water temperature difference (45°C) under rated operating conditions are retrieved from the basic parameter database. The actual measured return water temperature (65°C) and the calculated actual supply-return water temperature difference (33°C) are compared with their corresponding design values, resulting in a return water temperature deviation percentage of -7.1% and a supply-return water temperature difference deviation percentage of -26.7%. These two percentage deviations are combined to calculate the degree of deviation of the gas-fired hot water boiler from its design operating conditions. Following a fixed time interval of 5 minutes per data group, the calculated unit fuel heat output, heat source equipment load rate, and degree of deviation from design operating conditions are combined into a single data group. All data groups arranged in chronological order constitute a sequence of instantaneous energy efficiency indicators reflecting changes in the operating efficiency of the gas-fired hot water boiler.

[0078] See Figure 2In one embodiment of the present invention, a hydraulic and thermal coupling simulation network for a heating distribution network is constructed based on the pressure and temperature monitoring values ​​of key nodes in the pipeline network. Taking a centralized heating system comprising one heating station, thirty building heating inlets, and corresponding connecting pipes as an example, the process of constructing the hydraulic and thermal coupling simulation network for the heating distribution network begins with the design drawings of the heating distribution network. The design drawings are computer-aided design drawings of the network plan and system diagram. A digital network topology diagram is constructed in the simulation software. The network topology diagram includes the location and connection relationships of all main pipes, branch pipes, pipe connection points, user inlets, and heating stations. The pressure and temperature monitoring values ​​of key nodes in the pipeline network, collected in real time by the data acquisition module through sensors deployed on the pipeline network, are assigned to the corresponding node positions in the network topology diagram. For example, the 0.85 MPa pressure value read by the pressure sensor at the outlet of the heating station and the 90°C temperature value read by the temperature sensor are assigned to the node representing the outlet of the heating station in the network topology diagram as boundary conditions and initial conditions for the simulation network calculation. Based on the pipe segment material, diameter, length, and insulation information recorded in the heating distribution pipeline network engineering archives, each pipe segment in the network topology diagram is assigned a corresponding hydraulic resistance coefficient and thermal conductivity coefficient. For example, a 500-meter-long, 200-millimeter-diameter prefabricated direct-buried insulated steel pipe has a hydraulic resistance coefficient of 6.5 calculated based on its inner wall roughness and fluid velocity, and a thermal conductivity coefficient of 0.8 W / m Kelvin calculated based on the thickness and thermal conductivity of its polyurethane insulation material. Based on the laws of conservation of mass, momentum, and energy, a set of simultaneous equations describing water flow and heat transfer in the heating distribution pipeline network is established. The constructed network topology diagram, the assigned hydraulic resistance coefficient and thermal conductivity coefficient for each pipe segment, and the boundary and initial conditions derived from real-time data are substituted into this set of simultaneous equations to form a computable model of the hydraulic and thermal coupling simulation network.

[0079] In some embodiments, optionally, the process of assigning a hydraulic resistance coefficient to the pipe involves calculating the friction factor using the Colbrook formula and determining the local resistance loss using a table of local resistance coefficients for pipe fittings. It is understood that, in specific implementations, when establishing the equations describing heat transfer, axial and radial heat transfer within the pipe needs to be considered, and the energy conservation equation can be expressed as:

[0080]

[0081] in: This indicates the power loss due to heat dissipation in the pipe section. Indicates the total thermal conductivity of the pipe section. Indicates the length of the pipe section. This represents the difference between the average temperature of the fluid inside the pipe section and the average temperature of the surrounding soil. The differential equations describing mass, momentum, and energy are discretized and numerically solved using the finite difference method or the finite element method.

[0082] In one embodiment of the present invention, the dynamic hydraulic misalignment distribution and friction loss distribution of the heating distribution network are calculated. Taking a branched heating network containing heating inlets of 35 buildings as an example, the process of calculating the dynamic hydraulic misalignment distribution and friction loss distribution begins with the use of a computational model of a hydraulic-thermal coupled simulation network. The dynamic hydraulic misalignment distribution is used to identify areas of uneven flow distribution in the network, and the friction loss distribution is used to quantify the heat dissipation power loss per unit length of the pipe segment. In a specific implementation, the solver of the computational model of the hydraulic-thermal coupled simulation network is invoked to perform numerical solutions under given boundary conditions. The boundary conditions include a heat source outlet pressure of 0.82 MPa, a temperature of 88 degrees Celsius, and a preset pressure of 0.25 MPa at the farthest user inlet. The flow rate of each pipe segment and the pressure of each node are obtained by solving. For example, the flow rate of a certain pipe segment is found to be 42.5 cubic meters per hour, and the pressure of a certain node is found to be 0.51 MPa. From the design database file associated with the computational model of the hydraulic-thermal coupled simulation network, read the design flow rate value of each user branch under the design conditions. For example, the design flow rate of user branch B-07 is read as 4.8 cubic meters per hour. Using the solution results of the computational model of the hydraulic-thermal coupled simulation network, obtain the instantaneous flow rate value of each user branch obtained through numerical calculation under given boundary conditions. For example, the actual flow rate of user branch B-07 at a specific moment is obtained as 3.2 cubic meters per hour. Compare the actual flow rate of each user branch with the corresponding design flow rate item by item. Calculate the flow misalignment ratio of each user branch by subtracting the actual flow rate from the design flow rate and then dividing by the design flow rate. The calculation of the flow misalignment ratio can be expressed as:

[0083]

[0084] in: This indicates the traffic imbalance ratio of user branches. This represents the actual traffic of the user branch obtained through the computational model. This represents the design flow rate of the user branch read from the design database. The geographical node coordinates corresponding to each user branch in the heating distribution network topology diagram are used as spatial location points. For example, the coordinates (X=125.7, Y=308.4) of user branch number B-07 are used as the spatial location point, and the calculated flow misalignment ratio of this user branch, -0.33, is used as the attribute value of this spatial location point. Using the inverse distance weighting method in spatial interpolation algorithms, based on the attribute values ​​of all spatial location points with calculated flow misalignment ratios, the estimated flow misalignment ratio is calculated for pipe sections without directly set monitoring points within the geographical area of ​​the heating region. This generates a continuous spatial field covering the entire heating region, reflecting the continuous change in the degree of flow misalignment. The numerical results of the continuous spatial field, namely the estimated flow imbalance ratio corresponding to each geographic coordinate point, are mapped to the corresponding color level. For example, the range of imbalance ratio from -0.5 to +0.5 is mapped to a continuous color spectrum from blue to red. The spatial coordinates are then superimposed on the geographic information map of the heating network to complete spatial interpolation rendering and form a dynamic hydraulic imbalance distribution map that intuitively displays the uneven flow distribution area.

[0085] Optionally, the computational model of the hydraulic-thermal coupled simulation network can be used to solve for the starting and ending temperatures of each pipe segment. The solution yields a starting temperature of 85.2 degrees Celsius and an ending temperature of 84.1 degrees Celsius for a specific insulated direct-buried pipe segment. Based on the thermal conductivity coefficient, length, and temperature difference between the medium and the environment within the pipe segment, the heat dissipation power loss of each segment is calculated. For example, for a 200-meter-long pipe segment with a thermal conductivity coefficient of 0.9 W / m Kelvin, an average medium temperature of 84.7 degrees Celsius, and a surrounding soil temperature of 10 degrees Celsius, the calculated heat dissipation power loss is 13446 W. Dividing the heat dissipation power loss of each segment by its corresponding length yields the heat dissipation power loss per unit length. For instance, the heat dissipation power loss per unit length for the aforementioned pipe segment is 67.23 W / m. It is understandable that the heat loss per unit length of all pipe segments in the entire pipeline network is spatially interpolated and rendered. The rendering process is similar to generating a dynamic hydraulic misalignment distribution map. Based on the coordinates of the midpoint of the pipe segment and the heat loss per unit length attribute value, a continuous spatial field is generated through spatial interpolation and mapped to color or grayscale levels, ultimately generating a friction loss distribution map. It is also understood that both the dynamic hydraulic misalignment distribution map and the friction loss distribution map are outputs of the system analysis module for subsequent panoramic fusion and energy efficiency analysis. In some embodiments, when calculating the flow misalignment ratio, if the design flow rate is zero or extremely small, a preset extremely small positive number can be used instead to avoid calculation overflow.

[0086] See Figure 3This is a time-series analysis chart of the instantaneous energy efficiency indicators of a heat source, showing the dynamic changes of three key energy efficiency indicators over 24 hours. It is used to assess the operating status of the heat source and the degree of deviation from design conditions. The heat output per unit of fuel fluctuates between 830-1200 MJ / kg throughout the day, with significant peaks at 0:00, 3:00, 11:00, and 16:00, and troughs at 7:00, 14:00, and 22:00. Peaks typically correspond to the heat source's efficient operating range, while troughs may be related to low-load operation or deviation from design conditions. The heat source load rate fluctuates between 610-970 throughout the day, showing a certain positive correlation with the heat output per unit of fuel. When the load rate is too low, the heat output per unit of fuel also decreases, indicating that the heat source's efficiency decreases under low load. The degree of deviation from design conditions fluctuates drastically between 0-18 throughout the day, reaching peaks at 12:00 and 19:00, and approaching 0 at 5:00, 14:00, and 21:00. During periods of high deviation, the heat output per unit of fuel is often low, confirming the rule that deviations in operating conditions lead to decreased efficiency.

[0087] See Figure 4 In one embodiment of the present invention, the thermal efficiency evaluation factor of representative heat users is calculated and a user-end energy efficiency map is formed. Multi-source data is then spatiotemporally aligned and fused. Taking a heating area containing ten buildings as representative heat users as an example, the process of calculating the ratio of actual heat demand to theoretical heat demand for representative heat users begins by obtaining the outdoor design temperature for the heating season of the area where the representative heat users are located from the building energy consumption management historical database (the outdoor design temperature for the heating season is -7.6 degrees Celsius), and obtaining the building thermal parameters corresponding to each representative heat user from the building completion drawings and energy-saving assessment reports of each building. Refer to Table 1 for an example of the building thermal parameters of representative heat users used to calculate the thermal efficiency evaluation factor.

[0088] Table 1: Thermal Parameters of Representative Heat Users' Buildings

[0089] User ID Thermal index per unit area (W / m²) Usable building area (m²) Heat transfer coefficient of external envelope (W / m²·K) User Building A 35 8500 0.45 User Building B 40 6200 0.55 User Building C 38 10500 0.50

[0090] In practical implementation, based on building thermal parameters and the actual measured indoor temperature and outdoor meteorological coupling value of representative heat users, the actual heat consumption required to maintain the current indoor temperature under actual meteorological conditions is calculated. For example, for user building A, the measured average indoor temperature at a certain moment is 22 degrees Celsius, and the outdoor meteorological coupling value is -3 degrees Celsius. Combining the building area of ​​user building A of 8500 square meters and the heat index per unit area of ​​35 watts per square meter, the actual heat demand of user building A at that moment is calculated to be 743.75 kilowatts using a steady-state heat transfer model. Based on the same building thermal parameters, and the outdoor design temperature of -7.6 degrees Celsius and the preset standard indoor design temperature of 18 degrees Celsius obtained from local meteorological specifications, the theoretical heat consumption required to maintain the standard indoor design temperature under standard design conditions is calculated, resulting in a theoretical heat demand of 760.75 kilowatts for user building A. Dividing the calculated actual heat demand by the theoretical heat demand yields the thermal efficiency evaluation factor for representative heat users. The calculation of the thermal efficiency evaluation factor can be expressed as:

[0091]

[0092] in: This represents the thermal efficiency evaluation factor. This indicates the actual heat demand calculated based on actual indoor and outdoor conditions. This represents the theoretical heat demand calculated according to design specifications. Taking the aforementioned user building A data as an example, its thermal efficiency evaluation factor is calculated to be 0.978. The thermal efficiency evaluation factors of all ten representative heat users are collected and plotted in a geographic information system layer, using the geographical location of each user building as a point, with the calculated... Values ​​are labeled and rendered as attributes to form a user terminal energy efficiency map that reflects the spatial distribution of user terminal energy consumption.

[0093] It is understandable that the spatiotemporal alignment and data fusion of the instantaneous energy efficiency index sequence of heat sources, the dynamic hydraulic misalignment distribution and the heat loss distribution along the pipeline, and the user-end energy efficiency map includes the following steps: Establish unified timestamps and spatial geographic reference systems for the instantaneous energy efficiency index sequence of heat sources, the dynamic hydraulic misalignment distribution, the heat loss distribution along the pipeline, and the user-end energy efficiency map. The timestamps use Coordinated Universal Time (UTC), and the spatial geographic reference system uses the national geodetic coordinate system. Align different source data with the same timestamp by performing time slice alignment. For example, align the instantaneous energy efficiency index sequence of heat sources at 14:00:00 on January 15, 2026, the dynamic hydraulic misalignment distribution and the heat loss distribution along the pipeline calculated based on monitoring data at the same time, and the user-end energy efficiency map calculated based on representative indoor temperature data of heat users at that time. After time slice alignment, the dynamic hydraulic runaway distribution, friction loss distribution, and user-end energy efficiency map data are mapped onto the same geographic base map of the heating area. The geographic base map includes road and building outlines and pipeline routes. The instantaneous energy efficiency index sequence of heat sources is linked to the coordinates of heat sources on the geographic base map of the heating area in the form of a dynamic chart. In some embodiments, optionally, data from different data sources that are aligned in time and space are rasterized according to geographic location. The raster cell size is set to 10 meters by 10 meters. Through spatial interpolation or attribute assignment, each raster cell contains the heat source energy efficiency index, hydraulic runaway distribution, friction loss, and user-end heat efficiency information corresponding to its geographic location, completing data fusion and forming a data layer for a comprehensive energy efficiency analysis panoramic view.

[0094] In one embodiment of the present invention, energy efficiency optimization and control instructions are generated based on a comprehensive energy efficiency analysis panoramic view. Taking a system covering fifteen heating communities as an example, the steps for generating energy efficiency optimization and control instructions are as follows: In the data layer of the comprehensive energy efficiency analysis panoramic view, threshold ranges for various energy efficiency indicators are set. The threshold ranges include a lower limit of 0.032 MW per standard cubic meter per hour for heat source unit fuel heat supply, an upper limit of 0.3 for absolute value of hydraulic misalignment in the pipeline network, an upper limit of 80 watts per meter of heat loss per unit pipe length in the pipeline network, and a lower limit of 0.85 for the user-end thermal efficiency evaluation factor. The data of all grid cells in the comprehensive energy efficiency analysis panoramic view data layer are scanned to identify abnormal areas where the unit fuel heat supply is lower than the set lower limit, the absolute value of hydraulic misalignment exceeds the set upper limit, the heat loss per unit pipe length exceeds the set upper limit, or the thermal efficiency evaluation factor is lower than the set lower limit. For example, the scan found that the average user-end thermal efficiency evaluation factor of the area with geographical coordinates (X:100-150, Y:200-250) is 0.82, which is lower than the set lower threshold of 0.85. Root cause analysis was conducted on the identified abnormal areas to determine the contributing factors. These factors included unreasonable heat source operating parameters, abnormal valve openings in the pipeline network, damaged insulation structures, or improper user adjustments. When the thermal efficiency evaluation factor at the user end was generally low, the return water temperature in the pipeline network of the abnormal area was checked for abnormal increases. For example, the average return water temperature in the eastern area was found to be 52 degrees Celsius, higher than the system average return water temperature of 48 degrees Celsius. Combined with the dynamic hydraulic imbalance distribution, it was determined whether uneven heating was caused by hydraulic imbalance in the pipeline network. The dynamic hydraulic imbalance distribution map showed that the flow imbalance ratio of the eastern area pipeline network branches was generally below -0.25. When an abnormally high heat loss per unit length is identified in the distribution of heat loss along the pipeline network, the corresponding pipe section is located in the panoramic view of the comprehensive energy efficiency analysis. For example, the heat loss per unit length of the pipeline on Central Avenue is located to be 120 watts per meter. Historical maintenance records and insulation layer inspection data of the Central Avenue pipeline are retrieved. The historical maintenance records show that the pipe section underwent partial repair three years ago, and the insulation layer inspection data indicates that its current thermal resistance is lower than the design value. When a persistent abnormality in the dynamic hydraulic imbalance distribution of the pipeline network is identified, the regulating valve upstream of the area is located in the panoramic view of the comprehensive energy efficiency analysis. For example, the electric regulating valve V-205 controlling the flow in the East Zone is located. The preset opening degree of regulating valve V-205 is checked to see if it matches the current actual opening degree. The preset opening degree is 60%, while the actual opening degree is 45%. The heat consumption changes of other users on the branch where regulating valve V-205 is located are analyzed, and it is found that the heat consumption of two users upstream of this branch has increased recently.When the unit fuel heat supply is consistently low in the instantaneous energy efficiency index sequence of the heat source, the heat source equipment load rate and the degree of deviation from the design conditions are analyzed to determine whether the heat source efficiency has decreased due to the low load rate or the supply and return water temperatures deviating from the design values. For example, the instantaneous energy efficiency index sequence of the heat source shows that the unit fuel heat supply is 0.029 MW per standard cubic meter per hour, while the heat source equipment load rate is 38% and the supply and return water temperature difference is 28 degrees Celsius, which is lower than the design value of 45 degrees Celsius.

[0095] In some embodiments, based on the results of root cause analysis, combined with the characteristics of heat source regulation, pipeline valve control logic, and user regulation strategies, specific regulation operation instructions for related links are generated. The specific regulation operation instructions include target parameters, regulating equipment, regulation direction, and regulation amplitude. For example, the instruction generated for the aforementioned abnormal opening of the electric regulating valve V-205 is: target parameter is hydraulic imbalance in the eastern pipeline network, regulating equipment is electric regulating valve V-205, regulation direction is to open the valve wider, and regulation amplitude is to increase the opening to 65%. Optionally, the regulation amplitude of the specific regulation operation instruction can be calculated using a feedback model based on historical data. The calculation of the regulation amplitude can be expressed as:

[0096]

[0097] in: Indicates the adjustment range. This represents a preset proportional coefficient for a specific regulating device and operating condition. This indicates the set value of the target parameter. This represents the actual value of the target parameter. It can be understood that the generated specific adjustment operation commands are sent to the corresponding actuators in the heating system according to a preset priority order. This priority order is typically set to ensure heating safety, restore hydraulic balance, improve heat source efficiency, and reduce pipeline losses. For example, the command to open valve V-205 is sent to the electric actuator installed on the pipeline via an industrial communication network. Optionally, if the root cause analysis determines that insulation structure damage is the cause of the anomaly, the generated command is a maintenance work order containing pipeline location information sent to the operation and maintenance management system, rather than a direct control command. It can be understood that for multiple simultaneously occurring anomaly areas, the system sorts the generated adjustment operation commands according to the anomaly level and impact range, and sends them sequentially according to the sorting order.

[0098] See Figure 5This is a heatmap of user-side thermal efficiency evaluation factors. The heatmap visually displays the distribution of thermal efficiency evaluation factors across different areas and building types, helping to identify weaknesses in energy efficiency. The eastern area has relatively high overall thermal efficiency, especially high-rise residential buildings, but multi-story residential buildings are less efficient. The central area generally has low thermal efficiency, with office buildings and schools being significant weaknesses. The northern area has extremely high efficiency in multi-story residential buildings, but extremely low efficiency in schools, showing a polarization. The southern area has relatively high efficiency in commercial buildings and multi-story residential buildings, but low efficiency in office buildings. The western area has extremely high efficiency in office buildings and high-rise residential buildings, but low efficiency in schools. School buildings have low thermal efficiency in all areas and should be prioritized for optimization, with a focus on issues such as external envelope insulation and heating regulation strategies. The central area has overall low thermal efficiency and requires systematic optimization in areas such as network hydraulic balance and heat source distribution.

[0099] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A heating system performance analysis and management system, characterized in that, The system includes: The data acquisition module acquires real-time operating data for the entire heating process. The real-time operating data includes fuel consumption, water supply temperature, water supply pressure, return water temperature, pressure and temperature monitoring values ​​of key nodes in the pipeline network, and the temperature coupling values ​​of representative heat users' indoor and outdoor meteorological conditions. The heat source analysis module inputs the fuel consumption, water supply temperature, water supply pressure and return water temperature from the output side of the heat source into the heat source equipment operation characteristic model for calculation, and generates a sequence of instantaneous energy efficiency indicators for the heat source. The pipeline network analysis module, based on the pressure and temperature monitoring values ​​of the key nodes of the pipeline network, constructs a hydraulic and thermal coupling simulation network for the heating transmission and distribution pipeline network, and calculates the dynamic hydraulic misalignment distribution and friction heat loss distribution of the heating transmission and distribution pipeline network. The user analysis module uses the indoor temperature and outdoor meteorological coupling value of the representative heat user, combined with the building thermal parameters corresponding to the representative heat user, to calculate the ratio of the actual heat demand to the theoretical heat demand of the representative heat user, which serves as the thermal efficiency evaluation factor for the representative heat user. The thermal efficiency evaluation factors of all representative heat users are collected to form a user-end energy efficiency map. The panoramic fusion module performs spatiotemporal alignment and data fusion of the instantaneous energy efficiency index sequence of the heat source, the dynamic hydraulic misalignment distribution and the heat loss distribution along the friction path, and the energy efficiency map of the user end to generate a comprehensive energy efficiency analysis panoramic view. Based on the pressure and temperature monitoring values ​​of the key nodes in the pipeline network, a hydraulic and thermal coupling simulation network for the heating transmission and distribution pipeline network is constructed, including: Based on the design drawings of the heating transmission and distribution pipeline network, a pipeline network topology diagram is constructed, which includes the connection relationships of pipe segments, nodes, and heat sources. The pressure and temperature monitoring values ​​of the key nodes of the pipeline network collected on site are assigned to the corresponding nodes in the pipeline network topology diagram as boundary conditions and initial conditions of the simulation network. Based on the pipe material, diameter, length, and insulation status, each pipe segment in the pipeline network topology diagram is assigned a hydraulic resistance coefficient and a thermal conductivity coefficient. Based on the laws of conservation of mass, momentum, and energy, a set of simultaneous equations is established to describe the water flow and heat transfer in the heating and distribution pipeline network. The pipeline network topology diagram, the hydraulic resistance coefficient and thermal conductivity coefficient, as well as the boundary conditions and initial conditions are substituted into the set of simultaneous equations to form a computational model of the hydraulic and thermal coupling simulation network.

2. The heating system performance analysis and management system according to claim 1, characterized in that, The fuel consumption, water supply temperature, water supply pressure, and return water temperature on the output side of the heat source are input into the heat source equipment operation characteristic model for calculation, generating a sequence of instantaneous energy efficiency indicators for the heat source, including: The instantaneous energy efficiency index sequence of the heat source includes the heat output per unit of fuel, the load rate of the heat source equipment, and the degree to which the heat source equipment deviates from the design operating conditions. The supply and return water temperature difference is calculated based on the supply water temperature and return water temperature on the output side of the heat source, and the instantaneous output heat power of the heat source is calculated based on the preset pipeline flow rate or the flow rate reference value obtained by converting the supply water pressure. The unit fuel heat output is calculated by dividing the fuel consumption by the instantaneous output heat power of the heat source. The rated output power of the heat source equipment is read from the design parameter database of the heat source equipment, and the instantaneous output heat power of the heat source is compared with the rated output power to obtain the load rate of the heat source equipment; The design return water temperature and design supply and return water temperature difference of the heat source equipment under rated operating conditions are read from the design parameter database of the heat source equipment. The actual measured return water temperature and supply and return water temperature difference are compared with the corresponding design values, the deviation percentage is calculated, and the degree of deviation of the heat source equipment from the design operating conditions is calculated based on the deviation percentage. At fixed time intervals, the calculated unit fuel heat output, heat source equipment load rate, and degree of deviation of heat source equipment from design conditions are combined into a data group to form a sequence of instantaneous energy efficiency indicators of the heat source arranged in chronological order.

3. The heating system performance analysis and management system according to claim 2, characterized in that, The calculations yield the dynamic hydraulic misalignment distribution and friction loss distribution of the heating transmission and distribution network, including: The dynamic hydraulic misalignment distribution is used to identify areas with uneven flow distribution in the pipeline network, and the friction heat loss distribution is used to quantify the heat loss power per unit length of the pipe segment. Using the computational model of the hydraulic-thermal coupled simulation network, the flow rate and pressure at each node in each pipe section are solved under given boundary conditions; Extract the design flow of each user branch in the calculation model, compare the actual flow of each user branch obtained by the solution with the corresponding design flow, calculate the flow misalignment ratio of each user branch, and perform spatial interpolation rendering on the flow misalignment ratio at each location in the entire pipeline network to generate the dynamic hydraulic misalignment distribution. Using the computational model of the hydraulic-thermal coupled simulation network, the starting and ending temperatures of each pipe segment under given boundary conditions are solved. Based on the heat transfer coefficient, length, and temperature difference between the medium and the environment of each pipe segment, the heat loss power of each pipe segment is calculated. The heat loss power of each pipe segment is divided by its length to obtain the heat loss power per unit pipe length. The heat loss power per unit pipe length of each pipe segment in the entire pipe network is spatially interpolated and rendered to generate the friction loss distribution.

4. The heating system performance analysis and management system according to claim 3, characterized in that, Using the coupled indoor temperature and outdoor meteorological values ​​of the representative heat users, and combining them with the building thermal parameters corresponding to the representative heat users, the ratio of the actual heat demand to the theoretical heat demand of the representative heat users is calculated, including: The outdoor design temperature of the representative heat user's area during the heating season is obtained from historical operating data or meteorological departments, as well as the heat index per unit area, building usable area, room height and heat transfer coefficient of the building envelope corresponding to the representative heat user, and the heat index per unit area, building usable area, room height and heat transfer coefficient of the building envelope are used as the building thermal parameters. Based on the building thermal parameters and the actual measured indoor temperature and outdoor meteorological coupling value of the representative heat user, the actual heat consumption required to maintain the current indoor temperature under the actual meteorological conditions represented by the outdoor meteorological coupling value is calculated as the actual heat demand. Based on the building thermal parameters, the outdoor design temperature during the heating season, and the preset standard indoor design temperature, calculate the theoretical heat consumption required to maintain the standard indoor design temperature under standard design conditions, and use it as the theoretical heat consumption. The actual heat demand is divided by the theoretical heat demand to calculate the thermal efficiency evaluation factor for representative heat users.

5. The heating system performance analysis and management system according to claim 4, characterized in that, The instantaneous energy efficiency index sequence of the heat source, the dynamic hydraulic misalignment distribution and the friction loss distribution, and the user-end energy efficiency map are spatiotemporally aligned and fused, including: A unified timestamp and spatial geographic reference system is established for the instantaneous energy efficiency index sequence of the heat source, the dynamic hydraulic misalignment distribution, the heat loss distribution along the friction path, and the energy efficiency map of the user end. The instantaneous energy efficiency index sequence of the heat source, the dynamic hydraulic misalignment distribution, the friction heat loss distribution, and the user terminal energy efficiency map data with the same timestamp are time-sliced ​​and aligned. After time slice alignment, the dynamic hydraulic displacement distribution, the friction heat loss distribution, and the user-end energy efficiency map data are mapped onto the same heating area geographic base map, and the instantaneous energy efficiency index sequence of the heat source is associated with the heat source location on the heating area geographic base map in the form of a chart. Data from different data sources, aligned in time and space, are rasterized according to geographical location. Each raster cell contains information on heat source energy efficiency indicators, hydraulic runaway, heat loss along the pipeline, and user-end heat efficiency corresponding to the geographical location. This process completes data fusion and forms the data layer of the comprehensive energy efficiency analysis panoramic view.

6. The heating system performance analysis and management system according to claim 5, characterized in that, The system also includes the step of generating energy efficiency optimization and control instructions based on the comprehensive energy efficiency analysis panoramic view: In the data layer of the comprehensive energy efficiency analysis panoramic view, the threshold ranges for the heat supply per unit of fuel from the heat source, the hydraulic misalignment of the pipeline network, the heat loss per unit length of the pipeline network, and the user-end thermal efficiency evaluation factors are set. Scan the data of any grid cell in the data layer to identify abnormal areas where the unit fuel heat supply is lower than the set lower limit, the absolute value of hydraulic misalignment exceeds the set upper limit, the heat loss per unit pipe length exceeds the set upper limit, or the thermal efficiency evaluation factor is lower than the set lower limit. The identified abnormal areas are analyzed to trace the root causes and determine the related links that led to the occurrence of the abnormal areas. The related links include unreasonable heat source operating parameters, abnormal opening of pipeline valves, damage to insulation structure, or improper user adjustment behavior. Based on the results of the root cause analysis, combined with the heat source regulation characteristics, pipeline valve control logic and user regulation strategy, specific regulation operation instructions are generated for the related links. The specific regulation operation instructions include target parameters, regulation equipment, regulation direction and regulation range. The generated specific adjustment operation instructions are sent to the corresponding actuators of the heating system according to a preset priority order.

7. The heating system performance analysis and management system according to claim 6, characterized in that, The root cause analysis of the identified abnormal regions includes: When it is identified that the thermal efficiency evaluation factor of the heat user end is generally low, check whether the return water temperature of the pipe network in the abnormal area is abnormally high, and determine whether the uneven heating is caused by the hydraulic imbalance of the pipe network in combination with the dynamic hydraulic imbalance distribution. When an abnormally high local heat loss per unit length is identified in the distribution of heat loss along the pipeline network, the corresponding pipe section is located in the panoramic view of the comprehensive energy efficiency analysis, and the historical maintenance records and insulation layer detection data of the corresponding pipe section are retrieved to determine whether the insulation structure is damaged. When a region in the dynamic hydraulic imbalance distribution of the pipeline network is identified as having a continuous abnormal imbalance, the regulating valve upstream of the region is located in the panoramic view of the comprehensive energy efficiency analysis. The preset opening degree of the regulating valve is checked to see if it is consistent with the current actual opening degree. The heat consumption changes of other users in the branch where the regulating valve is located are also analyzed. When it is identified that the unit fuel heat supply is consistently low in the instantaneous energy efficiency index sequence of the heat source, the load rate of the heat source equipment and the degree of deviation from the design conditions are analyzed to determine whether the decrease in heat source efficiency is due to the low load rate or the deviation of the supply and return water temperatures from the design values.

8. The heating system performance analysis and management system according to claim 7, characterized in that, The construction of the operating characteristic model of the heat source equipment includes: Based on the equipment model of the heat source equipment and the technical data provided by the manufacturer, establish a basic parameter database that includes the rated thermal power, rated supply water temperature, rated return water temperature, rated efficiency, and fuel characteristic parameters of the heat source equipment; Collect historical operating data of the heat source equipment under different load rates, different outdoor temperatures and different water supply temperature setpoints. The operating data includes fuel consumption, actual water supply temperature, actual return water temperature and output heat power. The historical operating data is cleaned to remove outliers and invalid data points. Then, the operating data under different operating conditions are classified and normalized according to the load rate range to form a standardized training sample dataset. Using the fuel consumption, water supply temperature, and return water temperature under different operating conditions in the standardized training sample dataset as input features, and the actual operating efficiency calculated by the heat balance equation under the corresponding operating conditions as the output label, a predictive model describing the nonlinear mapping relationship between the actual operating efficiency of the heat source equipment and key operating parameters is trained by a supervised learning algorithm. The prediction model is connected to the real-time data interface of the heat source equipment control system, and the basic parameter database is integrated to form the heat source equipment operation characteristic model that can receive real-time operating parameters and output predicted values ​​of energy efficiency indicators.

9. A heating system performance analysis and management system according to claim 8, characterized in that, The process involves extracting the design flow of each user branch from the calculation model, comparing the actual flow of each user branch obtained from the solution with the corresponding design flow, calculating the flow misalignment ratio of each user branch, and performing spatial interpolation rendering of the flow misalignment ratio at each location in the entire pipeline network, including: Read the flow rate value under the design conditions corresponding to each user branch from the design database or preset configuration file associated with the calculation model of the hydraulic and thermal coupling simulation network, and use it as the design flow rate of each user branch; The solution results of the computational model of the hydraulic and thermal coupling simulation network are called to obtain the instantaneous flow rate value of each user branch obtained by numerical calculation under the given boundary conditions, which is used as the actual flow rate of each user branch; The actual traffic of each user branch is compared with the corresponding design traffic item by item. The traffic imbalance ratio of each user branch is calculated by subtracting the actual traffic from the design traffic and then dividing by the design traffic. The traffic imbalance ratio represents the degree of deviation of the actual traffic from the design traffic. Using the geographic node coordinates corresponding to each user branch in the heating transmission and distribution pipeline network topology diagram as spatial location points, the calculated flow imbalance ratio of each user branch is used as the attribute value of the spatial location point. Using a spatial interpolation algorithm, based on the attribute values ​​of each location point, the estimated flow imbalance ratio of pipe sections without directly set monitoring points is calculated within the geographical area of ​​the heating region, generating a continuous spatial field covering the entire heating region; The numerical results of the continuous spatial field are mapped to corresponding colors or gray levels, and then superimposed onto the geographic information map of the heating network according to spatial coordinates to complete spatial interpolation rendering, forming a dynamic hydraulic imbalance distribution map that intuitively displays the uneven flow distribution area.

Images