A method for mapping data of a heating configuration node

Abstract

The application discloses a heat supply configuration node data mapping method, and particularly relates to the technical field of configuration software data mapping, and is used for solving the problem of energy efficiency evaluation distortion caused by direct reference of transient mapping data of a process monitoring node of an existing energy efficiency accounting node; a quasi-steady state working condition is determined by calculating a thermal parameter coupling deviation degree between a physical measuring point and an associated physical measuring point; a ratio of a heat fluctuation transmission coefficient of the physical measuring point to an upstream physical measuring point and a pressure fluctuation transmission coefficient of the physical measuring point to a downstream physical measuring point is calculated as a role weight ratio, and a load heat dominant node is marked based on a comparison result of a deviation degree and a median value of the role weight ratio in a same type physical measuring point group; after time window smoothing pretreatment of transient data of the load heat dominant node, the transient data is taken as mapping input of an upper energy efficiency calculation node, and differential mapping processing of data of a same physical measuring point in configuration software in two application occasions of process monitoring and energy efficiency accounting is realized.

Description

Technical Field

[0001] This invention relates to the field of configuration software data mapping technology, and more specifically, to a heating configuration node data mapping method. Background Technology

[0002] Central heating systems are a crucial component of urban energy supply, and their operation and monitoring rely on a digital management and control platform built using configuration software. During project implementation, the first step is to digitally model physical equipment such as heat sources, heat exchange stations, and pipe networks. Corresponding monitoring nodes are then created in the configuration software, as shown in the figure, which illustrates the key equipment nodes and their attribute configuration interfaces. Subsequently, a one-to-one mapping relationship is established between the display attributes of each node and the data channels of the field data acquisition equipment. This enables real-time monitoring of thermal parameters such as temperature, pressure, and flow rate. Establishing this mapping relationship is a fundamental step for the normal operation of the configuration software and is typically configured manually by engineers based on a data point table, directing each software node to a specific physical measurement point.

[0003] However, in actual operation, when the configuration software calculates the upper-level energy efficiency index based on the above mapping relationship, since the heating medium undertakes the dual functions of heat transmission and fluid distribution, and the existing node mapping method does not distinguish the role difference of the same physical quantity in different application scenarios when establishing data relationships, the energy efficiency accounting node directly references the same transient mapping data as the process monitoring node, which makes the calculation results unable to truly reflect the physical laws of the thermal process, making it difficult to guarantee the accuracy of the system energy efficiency assessment. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a heating configuration node data mapping method to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for mapping data of heating configuration nodes, comprising:

[0007] S1: Obtain the initial mapping relationship between multiple monitoring nodes in the configuration software and physical measurement points on site, as well as the real-time thermal data collected by the physical measurement points;

[0008] S2: For each monitoring node, obtain the real-time thermal data of the physical measurement point pointed to by the initial mapping relationship and the associated physical measurement point that has a preset thermal coupling relationship with the physical measurement point pointed to. Calculate the thermal parameter coupling deviation between the physical measurement point pointed to and the associated physical measurement point. When the preset steady-state condition is met, determine that the physical measurement point pointed to is in a quasi-steady-state condition.

[0009] S3: For physical measuring points in quasi-steady-state conditions, calculate the heat fluctuation transfer coefficient between the physical measuring point in quasi-steady-state conditions and the upstream physical measuring point, as well as the pressure fluctuation transfer coefficient between the physical measuring point in quasi-steady-state conditions and the downstream physical measuring point, and use the ratio of the heat fluctuation transfer coefficient to the pressure fluctuation transfer coefficient as the role weight ratio.

[0010] S4: Obtain the distribution of the role weight ratio of the same type of physical measuring point group with the same equipment as the physical measuring point in the quasi-steady-state condition, calculate the deviation of the role weight ratio of the physical measuring point in the quasi-steady-state condition in the same type of physical measuring point group, and mark the monitoring node as a heat-carrying dominant node when the deviation exceeds the preset deviation threshold and the role weight ratio is lower than the median value of the role weight ratio of the same type of physical measuring point group.

[0011] S5: For monitoring nodes marked as heat-carrying dominant nodes, perform time-window smoothing preprocessing on their transient data before it is referenced by upper-level energy efficiency calculation nodes, and then use it as mapping input.

[0012] Furthermore, the initial mapping relationship between multiple monitoring nodes in the configuration software and on-site physical measurement points is obtained, as well as the real-time thermal data collected by the physical measurement points, including:

[0013] The system receives data point table files imported by engineers, extracts the correspondence between the equipment identifiers of the field physical measurement points and the node identifiers of the monitoring nodes in the configuration software from the data point table files as the initial mapping relationship, and establishes a communication connection with the data acquisition channel of the field physical measurement points to obtain real-time thermal data.

[0014] Furthermore, for each monitoring node, real-time thermal data of the physical measuring point pointed to by the initial mapping relationship and the associated physical measuring points with a preset thermal coupling relationship with the pointed physical measuring point are obtained. The thermal parameter coupling deviation between the pointed physical measuring point and the associated physical measuring points is calculated. When the preset steady-state conditions are met, the pointed physical measuring point is determined to be in a quasi-steady-state condition, including:

[0015] Based on the topology of the thermal pipeline network, the associated physical measuring points that have a preset thermal coupling relationship with the pointed physical measuring point are identified. The thermal parameter sequences of the pointed physical measuring point and the associated physical measuring point within a preset time period before the current moment are extracted from the real-time thermal data. The correlation coefficient between the two thermal parameter sequences is calculated as the thermal parameter coupling deviation. When the correlation coefficient is lower than the preset correlation threshold, it is determined that the pointed physical measuring point meets the preset steady-state condition. The pointed physical measuring point that meets the preset steady-state condition is marked as being in a quasi-steady-state condition.

[0016] Furthermore, the correlation coefficient between the two thermal parameter sequences is calculated as the thermal parameter coupling deviation, including: using the Pearson correlation coefficient algorithm to calculate the two thermal parameter sequences, and using the absolute value of the Pearson correlation coefficient as the thermal parameter coupling deviation.

[0017] Furthermore, for physical measuring points operating under quasi-steady-state conditions, the heat fluctuation transfer coefficient between the physical measuring point and the upstream physical measuring point, and the pressure fluctuation transfer coefficient between the physical measuring point and the downstream physical measuring point are calculated. The ratio of the heat fluctuation transfer coefficient to the pressure fluctuation transfer coefficient is used as the role weight ratio, including:

[0018] Based on the topology of the heating network, upstream and downstream physical measuring points of the physical measuring points under quasi-steady-state conditions are determined. Temperature parameter sequences of the physical measuring points under quasi-steady-state conditions and upstream physical measuring points within the same quasi-steady-state period are extracted from the historical operation dataset, and the first correlation coefficient is calculated as the heat fluctuation transfer coefficient. Pressure parameter sequences of the physical measuring points under quasi-steady-state conditions and downstream physical measuring points within the same quasi-steady-state period are extracted from the historical operation dataset, and the second correlation coefficient is calculated as the pressure fluctuation transfer coefficient. The role weight ratio is obtained by dividing the heat fluctuation transfer coefficient by the pressure fluctuation transfer coefficient.

[0019] Furthermore, based on the topology of the heating network, upstream and downstream physical measuring points of the physical measuring points in quasi-steady-state condition are determined, including: determining the adjacent physical measuring points in the heating network topology that have direct pipe connections to the physical measuring points in quasi-steady-state condition and whose medium flow direction is towards the physical measuring points in quasi-steady-state condition as upstream physical measuring points; and determining the adjacent physical measuring points that have direct pipe connections to the physical measuring points in quasi-steady-state condition and whose medium flow direction is away from the physical measuring points in quasi-steady-state condition as downstream physical measuring points.

[0020] Furthermore, the distribution of role weight ratios for physical measuring points of the same type and equipment as those in quasi-steady-state conditions is obtained. The deviation of the role weight ratio of the physical measuring point in quasi-steady-state conditions from that of the same type of physical measuring point is calculated. When the deviation exceeds a preset deviation threshold and the role weight ratio is lower than the median value of the role weight ratio of the same type of physical measuring point, the monitoring node is marked as a heat-dominant node, including:

[0021] From the historical operation dataset, all physical measuring points with the same equipment type identifier as the physical measuring points in the quasi-steady-state condition are selected to form a group of physical measuring points of the same type. The role weight ratio of each physical measuring point in the group of physical measuring points of the same type is obtained and a role weight ratio distribution is formed. The median value of the role weight ratio distribution is calculated as the median value of the role weight ratio of the group of physical measuring points of the same type. The absolute value of the difference between the role weight ratio of the physical measuring point in the quasi-steady-state condition and the median value of the role weight ratio of the group of physical measuring points of the same type is calculated as the deviation. When the deviation exceeds the preset deviation threshold and the role weight ratio of the physical measuring point in the quasi-steady-state condition is lower than the median value of the role weight ratio of the group of physical measuring points of the same type, the monitoring node corresponding to the physical measuring point in the quasi-steady-state condition is marked as a heat-carrying dominant node.

[0022] Furthermore, from the historical operation dataset, all physical measurement points with the same equipment type identifier as the physical measurement points in the quasi-steady-state condition are selected to form a group of physical measurement points of the same type. This includes: among all physical measurement points with the same equipment type identifier, physical measurement points whose role weight ratio in the historical operation dataset has been calculated are selected to form a group of physical measurement points of the same type.

[0023] Furthermore, for monitoring nodes marked as heat-dominant nodes, time-window smoothing preprocessing is performed on their transient data before it is referenced by upper-level energy efficiency calculation nodes, and this data is then used as mapping input, including:

[0024] Acquire the transient data sequence of the monitoring node marked as the heat-carrying dominant node within a preset time period before the current moment. Perform a moving average calculation on all transient data in the transient data sequence. Use the result of the moving average calculation as the smoothed data. Use the smoothed data as the mapping input of the upper-level energy efficiency calculation node.

[0025] Furthermore, a moving average calculation is performed on all transient data in the transient data sequence, including: using an equal-weight moving average algorithm to calculate all transient data in the transient data sequence, wherein the sliding window length used in the moving average calculation is determined based on the time constant of the temperature parameter in the historical operating data of the physical measurement points under quasi-steady-state conditions.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. By identifying the differences in the media role of the same physical measuring point in different application scenarios, the mapping data of monitoring nodes in the configuration software is differentiated. After establishing the initial mapping relationship, this method uses the thermal parameter coupling deviation to determine the operating state of the physical measuring point. Only physical measuring points in quasi-steady-state conditions are calculated for role weight ratios, avoiding interference from data fluctuations under unsteady-state conditions on role identification. When calculating the role weight ratio, the heat fluctuation transfer coefficient is obtained from the upstream physical measuring point and the pressure fluctuation transfer coefficient is obtained from the downstream physical measuring point. The coupling relationship between the upstream and downstream of the heat pipeline network is used to identify the weight difference between the transport and heat-carrying attributes of the medium at the network level, rather than relying solely on the data characteristics of a single measuring point. The distribution of role weight ratios of similar physical measuring point groups is introduced as a reference benchmark. The deviation of the role weight ratio in the similar group and its relative position with the median value are calculated for classification and labeling, so that the classification results are not affected by the absolute numerical differences in role weight ratios between different equipment types.

[0028] 2. By performing time window smoothing preprocessing on monitoring nodes marked as heat-carrying dominant nodes, the smoothed data is used as the mapping input for upper-level energy efficiency calculation nodes. This allows the data obtained by the energy efficiency calculation nodes to more realistically reflect the thermal process characteristics of the medium in terms of heat-carrying properties, avoiding calculation distortion caused by the superposition of transport property fluctuations. At the same time, monitoring nodes not marked as heat-carrying dominant nodes maintain direct mapping of the original transient data, without affecting the response speed of process monitoring nodes to real-time operating condition changes. This achieves differentiated mapping processing of data from the same physical measurement point in the configuration software under different application scenarios. Attached Figure Description

[0029] Figure 1 This is a flowchart of a heating configuration node data mapping method according to the present invention;

[0030] Figure 2 This is a flowchart of the heat-carrying dominant node marking of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Example: Figure 1 This invention provides a method for mapping heating configuration node data, comprising:

[0033] S1: Obtain the initial mapping relationship between multiple monitoring nodes in the configuration software and physical measurement points on site, as well as the real-time thermal data collected by the physical measurement points;

[0034] S2: For each monitoring node, obtain the real-time thermal data of the physical measurement point pointed to by the initial mapping relationship and the associated physical measurement point that has a preset thermal coupling relationship with the physical measurement point pointed to. Calculate the thermal parameter coupling deviation between the physical measurement point pointed to and the associated physical measurement point. When the preset steady-state condition is met, determine that the physical measurement point pointed to is in a quasi-steady-state condition.

[0035] S3: For physical measuring points in quasi-steady-state conditions, calculate the heat fluctuation transfer coefficient between the physical measuring point in quasi-steady-state conditions and the upstream physical measuring point, as well as the pressure fluctuation transfer coefficient between the physical measuring point in quasi-steady-state conditions and the downstream physical measuring point, and use the ratio of the heat fluctuation transfer coefficient to the pressure fluctuation transfer coefficient as the role weight ratio.

[0036] S4: Obtain the distribution of the role weight ratio of the same type of physical measuring point group with the same equipment as the physical measuring point in the quasi-steady-state condition, calculate the deviation of the role weight ratio of the physical measuring point in the quasi-steady-state condition in the same type of physical measuring point group, and mark the monitoring node as a heat-carrying dominant node when the deviation exceeds the preset deviation threshold and the role weight ratio is lower than the median value of the role weight ratio of the same type of physical measuring point group.

[0037] S5: For monitoring nodes marked as heat-carrying dominant nodes, perform time-window smoothing preprocessing on their transient data before it is referenced by upper-level energy efficiency calculation nodes, and then use it as mapping input.

[0038] During the implementation of S1, in the construction phase of the digital management and control platform for the heating system, engineers created multiple monitoring nodes in the configuration software, corresponding to heat sources, heat exchange stations, and key locations in the network, based on the design drawings of the heating network and the results of on-site surveys. Each monitoring node has a unique node identifier in the configuration software, using character encoding, such as an abbreviation of the station name followed by a serial number. On-site physical measuring points refer to data acquisition devices installed at various key locations in the heating system, such as temperature transmitters, pressure transmitters, and flow meters. Each on-site physical measuring point has a unique device identifier in the data acquisition system. This identifier can be a hardware address code burned into the data acquisition terminal at the factory, or a logical tag number assigned to each on-site physical measuring point by engineers in the data acquisition system.

[0039] When configuring the system, engineers compile the mapping relationships between monitoring nodes in the configuration software and on-site physical measurement points into a data point table file. The data point table file is in spreadsheet format, with each row recording the binding relationship between a monitoring node and an on-site physical measurement point. Each row in the data point table file contains at least two fields: a node identifier field and a device identifier field. The node identifier field records the node identifier of the monitoring node in the configuration software, and the device identifier field records the device identifier of the on-site physical measurement point corresponding to the node identifier. Engineers import the compiled data point table file into the configuration software. After receiving the imported data point table file, the configuration software parses it line by line, extracting the node identifier and device identifier from each row. It then establishes a mapping relationship between the monitoring node represented by the extracted node identifier and the on-site physical measurement point represented by the extracted device identifier, storing this mapping relationship as the initial mapping relationship. By importing the data point table file, the configuration of mapping relationships between multiple monitoring nodes and on-site physical measurement points is completed in the configuration software at once.

[0040] After the initial mapping relationship is established, the configuration software needs to acquire real-time thermal data collected from each field physical measurement point in order to associate and display the real-time thermal data with the corresponding monitoring nodes and perform subsequent processing. Based on the device identifiers recorded in the initial mapping relationship, the configuration software determines the range of field physical measurement points for which communication connections need to be established, and establishes communication connections with the data acquisition channels of the field physical measurement points through the heating system data acquisition network. The heating system data acquisition network consists of data acquisition terminals deployed at each heating station and a communication backbone network covering the entire network. Each field physical measurement point's data acquisition channel corresponds to a data interface on the data acquisition terminal, and the data interface has a unique channel address. There is a pre-configured correspondence between the channel address and the device identifier of the field physical measurement point. When establishing a communication connection, the configuration software looks up the corresponding channel address based on the device identifier and sends a data request command to the channel address. After receiving the data request command, the data acquisition terminal returns the currently collected thermal data from the field physical measurement point corresponding to the channel address to the configuration software through the communication backbone network. Thermal data includes temperature, pressure, and flow rate data. Temperature is measured in degrees Celsius, pressure in megapascals (MPa), and flow rate in tons per hour (tons per hour). The configuration software periodically sends data request commands to the data acquisition terminal according to a preset acquisition cycle to continuously acquire real-time thermal data from various field physical measurement points. The preset acquisition cycle is set by engineers based on the thermal process response characteristics of the heating system and the bandwidth of the data acquisition network. For heat source outlet temperature measurement points, where temperature changes are relatively slow, the preset acquisition cycle can be set to, for example, 60 seconds. For pipeline pressure measurement points, where pressure fluctuations are more rapid, the preset acquisition cycle can be set to, for example, 5 seconds.

[0041] In the specific implementation of S2, the initial mapping relationship between multiple monitoring nodes in the configuration software and field physical measurement points has been established in step S1. The configuration software continuously acquires real-time thermal data from each field physical measurement point according to a preset acquisition cycle. Real-time thermal data includes temperature data, pressure data, and flow rate data. The unit for temperature data is degrees Celsius, the unit for pressure data is megapascals (MPa), and the unit for flow rate data is tons per hour. For each monitoring node in the configuration software, the field physical measurement point pointed to by the monitoring node can be determined through the initial mapping relationship; that is, the physical measurement point pointed to by the initial mapping relationship. For example, a monitoring node identified as the primary network water supply temperature of station A, through the initial mapping relationship, finds the corresponding temperature transmitter identified as PT100-A01. Therefore, the temperature transmitter identified as PT100-A01 is the physical measurement point pointed to by the initial mapping relationship corresponding to the monitoring node.

[0042] After determining the physical measuring points pointed to by the initial mapping relationship, it is necessary to further identify the associated physical measuring points that have a pre-defined thermal coupling relationship with the physical measuring points pointed to by the initial mapping relationship. The pre-defined thermal coupling relationship is the association relationship between physical measuring points pre-set by engineers based on the topology of the heating network. The topology of the heating network describes the spatial location and connection relationship of each field physical measuring point in the heating system. Based on the topology of the heating network, engineers pre-set other field physical measuring points that are physically connected to the physical measuring points pointed to by the initial mapping relationship and whose thermal parameters have mutual influence relationships as associated physical measuring points. For example, for a temperature transmitter identified as PT100-A01, installed on the primary water supply pipeline of heat exchange station A, according to the thermal network topology, the field physical measuring points physically connected to the temperature transmitter identified as PT100-A01 include pressure transmitters and flow meters on the same pipeline, as well as the temperature transmitter on the primary water supply pipeline of heat exchange station B downstream. Engineers pre-set these field physical measuring points as associated physical measuring points with a preset thermal coupling relationship to the temperature transmitter identified as PT100-A01. Each initial mapping relationship points to a physical measuring point that can correspond to one or more associated physical measuring points. The preset thermal coupling relationships are stored in the configuration software in the form of an association table.

[0043] After determining the physical measuring points and associated physical measuring points pointed to by the initial mapping relationship, the thermal parameter sequences of the physical measuring points pointed to by the initial mapping relationship and the associated physical measuring points within a preset time period before the current moment are extracted from the real-time thermal data. The preset time period is a time window length set in advance by engineers to extract thermal data within a recent period for analysis. The setting of the preset time period needs to consider the thermal process response characteristics of the heating system. If the preset time period is set too short, the number of data points contained in the thermal parameter sequence will be too small, which may lead to unstable results in subsequent statistical analysis. If the preset time period is set too long, historical data before the change of system operating conditions may be mixed into the thermal parameter sequence, resulting in a lag in the judgment of the current operating condition. The preset duration is set by engineers based on the thermal inertia characteristics of the heating system. For high-temperature hot water heating systems, due to the large heat capacity of water and the long pipeline transmission distance, the changes in thermal parameters are relatively slow, and the preset duration can be set to, for example, 1800 seconds. For steam heating systems, due to the rapid pressure propagation speed of steam, the changes in thermal parameters are relatively rapid, and the preset duration can be set to, for example, 600 seconds. The thermal parameter sequence extracted from the real-time thermal data is a sequence formed by arranging thermal data of the same type in chronological order within the preset duration. The thermal parameter sequences extracted from the physical measuring point pointed to by the initial mapping relationship and the associated physical measuring point are of the same type. For example, if the physical measuring point pointed to by the initial mapping relationship is a temperature transmitter, then both the physical measuring point pointed to by the initial mapping relationship and the associated physical measuring point will extract temperature parameter sequences.

[0044] After extracting the thermal parameter sequences of the physical measurement points pointed to by the initial mapping relationship and the associated physical measurement points, the correlation coefficient between the thermal parameter sequences of the physical measurement points pointed to by the initial mapping relationship and the associated physical measurement points is calculated. The calculated correlation coefficient is used as the thermal parameter coupling deviation between the physical measurement points pointed to by the initial mapping relationship and the associated physical measurement points. The correlation coefficient is used to measure the strength of the linear correlation between the two thermal parameter sequences, and the value of the correlation coefficient ranges from -1 to +1.

[0045] In this embodiment, the Pearson correlation coefficient algorithm is used to calculate the thermal parameter sequences of the physical measurement points pointed to by the initial mapping relationship and the thermal parameter sequences of the associated physical measurement points. The Pearson correlation coefficient is calculated as follows: Where r represents the Pearson correlation coefficient, X i Let Xi represent the i-th data point in the thermal parameter sequence of the physical measurement point pointed to by the initial mapping relationship, and let X̄ represent the mean of the thermal parameter sequence of the physical measurement point pointed to by the initial mapping relationship. iLet represent the i-th data point in the thermal parameter sequence of the associated physical measuring points, and let Ȳ represent the mean of the thermal parameter sequence of the associated physical measuring points. The value of i ranges from 1 to n, and n is the total number of data points in the thermal parameter sequence. The absolute value of the Pearson correlation coefficient is used as the thermal parameter coupling deviation between the physical measuring point pointed to by the initial mapping relationship and the associated physical measuring points, i.e., thermal parameter coupling deviation = |r|. The smaller the value of the thermal parameter coupling deviation, the weaker the thermal parameter coupling relationship between the physical measuring point pointed to by the initial mapping relationship and the associated physical measuring points; that is, the lower the correlation between the changes in the thermal parameters of the physical measuring point pointed to by the initial mapping relationship and the changes in the thermal parameters of the associated physical measuring points.

[0046] After calculating the thermal parameter coupling deviation, it is compared with a preset correlation threshold. This preset threshold is a judgment threshold set by engineers to distinguish whether the physical measuring point pointed to by the initial mapping relationship is in a quasi-steady-state condition or a non-quasi-steady-state condition. In a quasi-steady-state condition, the changes in thermal parameters at the physical measuring point pointed to by the initial mapping relationship are mainly affected by the physical laws of the internal thermal processes of the system, and there is a strong coupling relationship between the thermal parameters and the changes in thermal parameters at surrounding related physical measuring points. At this time, the thermal parameter coupling deviation is low. When the heating system is subjected to external disturbances, such as adjustments to the heat source output or sudden changes in user-side load, the changes in thermal parameters at the physical measuring point pointed to by the initial mapping relationship are significantly affected by the disturbance factors, and the coupling relationship between the thermal parameters and those at related physical measuring points is disrupted. At this time, the thermal parameter coupling deviation increases. The preset relevant threshold is determined through statistical analysis of historical operating data of the heating system. Engineers select a segment of historical data from the heating system operating under known quasi-steady-state conditions, calculate the statistical distribution characteristics of the thermal parameter coupling deviation in this segment of historical data, and use the lower quantile of the statistical distribution as the preset relevant threshold. For example, the 5th percentile of the thermal parameter coupling deviation in the historical data is set as the preset relevant threshold. When the calculated thermal parameter coupling deviation is lower than the preset relevant threshold, the physical measuring point pointed to by the initial mapping relationship is determined to meet the preset steady-state conditions, and the physical measuring point pointed to by the initial mapping relationship that meets the preset steady-state conditions is marked as being in a quasi-steady-state condition. When the calculated thermal parameter coupling deviation is not lower than the preset relevant threshold, the physical measuring point pointed to by the initial mapping relationship is determined not to meet the preset steady-state conditions, and the physical measuring point pointed to by the initial mapping relationship is not marked as being in a quasi-steady-state condition. The determination is made again after the next acquisition cycle reacquires real-time thermal data.

[0047] In the specific implementation of S3, the physical measurement points pointed to by the initial mapping relationship that meets the preset steady-state conditions have been marked as being in a quasi-steady-state condition in step S2. For each physical measurement point marked as being in a quasi-steady-state condition, it is necessary to further calculate its role weight ratio in order to distinguish the weight difference between the transport properties and heat transfer properties of the medium measured by the physical measurement point in the quasi-steady-state condition.

[0048] The first step in calculating the role weight ratio is to determine the upstream and downstream physical measuring points of the physical measuring point under quasi-steady-state conditions. The determination of upstream and downstream physical measuring points is based on the topology of the heating network. The heating network topology describes the pipe connections and media flow direction between various field physical measuring points in the heating system. The media flow direction is determined by the driving direction of the circulating water pumps and the network design layout. Starting from the heat source outlet, the media is transported through the primary network to each heat exchange station, then through the secondary network to the end user, and finally returns to the heat source via the return water pipe, forming a closed loop. In the heating network topology, each field physical measuring point is assigned a specific media flow direction attribute. This attribute is pre-configured by engineers during the construction of the digital management and control platform for the heating system, based on the design drawings of the heating network and the results of on-site surveys.

[0049] The specific method for determining the upstream physical measuring point of a physical measuring point in a quasi-steady-state condition based on the heating network topology is as follows: In the heating network topology, find adjacent physical measuring points that have a direct pipeline connection to the physical measuring point in a quasi-steady-state condition and whose medium flow direction points towards the physical measuring point in a quasi-steady-state condition. These adjacent physical measuring points are determined as the upstream physical measuring points of the physical measuring point in a quasi-steady-state condition. A direct pipeline connection means that there is no intermediate transit point between the two physical measuring points; the medium flows directly from one physical measuring point to the other through a pipeline. The medium flow direction pointing towards the physical measuring point in a quasi-steady-state condition means that the direction of medium flow in the pipeline is from the adjacent physical measuring point towards the physical measuring point in a quasi-steady-state condition. For example, a physical measuring point in quasi-steady-state condition is a temperature transmitter installed on the primary water supply pipeline of heat exchange station A, with the equipment identification number PT100-A01. In the heat pipe network topology, the adjacent physical measuring points that have a direct pipe connection with the temperature transmitter with the equipment identification number PT100-A01 include a pressure transmitter and a flow meter installed in the upstream direction of the same pipeline. The medium flows from the location of the pressure transmitter and the flow meter to the location of the temperature transmitter. Therefore, the pressure transmitter and the flow meter are both identified as upstream physical measuring points of the physical measuring point in quasi-steady-state condition.

[0050] The specific method for determining the downstream physical measuring point of a physical measuring point in a quasi-steady-state condition based on the topology of the heating pipe network is as follows: In the heating pipe network topology, find adjacent physical measuring points that have a direct pipe connection to the physical measuring point in a quasi-steady-state condition and whose medium flows away from the physical measuring point in a quasi-steady-state condition. These adjacent physical measuring points are determined as the downstream physical measuring points of the physical measuring point in a quasi-steady-state condition. "The medium flows away from the physical measuring point in a quasi-steady-state condition" means that the direction of medium flow in the pipe is from the physical measuring point in a quasi-steady-state condition towards the adjacent physical measuring point. For example, if the physical measuring point in quasi-steady-state condition is a temperature transmitter with equipment identification PT100-A01, in the heat pipe network topology, the adjacent physical measuring points that have a direct pipe connection with the temperature transmitter with equipment identification PT100-A01 also include a pressure transmitter installed downstream of the same pipe. If the medium flows from the location of the temperature transmitter to the location of the pressure transmitter, then the pressure transmitter is determined to be the downstream physical measuring point of the physical measuring point in quasi-steady-state condition.

[0051] After identifying the upstream and downstream physical measuring points of the physical measuring points in quasi-steady-state condition, the temperature parameter sequences of the physical measuring points in quasi-steady-state condition and the upstream physical measuring points within the same quasi-steady-state time period are extracted from the historical operation dataset. The historical operation dataset consists of thermal data records of each field physical measuring point continuously stored during the operation of the heating system. These records include timestamps and corresponding temperature, pressure, and flow data, and are stored in the historical database of the configuration software. The same quasi-steady-state time period refers to the continuous time interval within which the physical measuring points in quasi-steady-state condition are marked as being in quasi-steady-state condition in step S2. Each quasi-steady-state time period has a clear start and end time. The start time is the moment when step S2 determines that the preset steady-state conditions are met, and the end time is the moment when subsequent determinations indicate that the preset steady-state conditions are not met, or the current moment. Temperature data of physical measuring points in quasi-steady-state conditions during the quasi-steady-state period are filtered from the historical operating dataset according to timestamps and arranged in chronological order to form a temperature parameter sequence of physical measuring points in quasi-steady-state conditions; similarly, temperature data of upstream physical measuring points during the same quasi-steady-state period are filtered from the historical operating dataset according to timestamps and arranged in chronological order to form a temperature parameter sequence of upstream physical measuring points.

[0052] After extracting the temperature parameter sequences of the physical measuring points under quasi-steady-state conditions and the upstream physical measuring points, the first correlation coefficient between these two sequences is calculated and used as the heat fluctuation transfer coefficient. The first correlation coefficient measures the degree of correlation between the temperature fluctuations at the physical measuring points under quasi-steady-state conditions and those at the upstream physical measuring points. The heat fluctuation transfer coefficient reflects the intensity of the heat property fluctuations at the upstream physical measuring points being transferred to the physical measuring points under quasi-steady-state conditions. A larger heat fluctuation transfer coefficient indicates a more significant influence of the temperature changes at the upstream physical measuring points on the temperature changes at the quasi-steady-state conditions, meaning that the measured medium at the quasi-steady-state physical measuring points has a higher role in terms of heat transfer properties. The first correlation coefficient can be calculated using the Pearson correlation coefficient algorithm, with the calculation process being the same as that in step S2, except that the calculation objects are replaced with the temperature parameter sequences of the physical measuring points under quasi-steady-state conditions and the upstream physical measuring points. The heat fluctuation transfer coefficient ranges from -1 to +1. Engineers can filter the heat fluctuation transfer coefficient based on their actual operating experience with the heating system. For example, when the heat fluctuation transfer coefficient is negative, it indicates that the temperature change direction of the physical measuring point in quasi-steady-state condition is opposite to that of the upstream physical measuring point. In actual heating systems, this usually means that there is a measurement anomaly or an error in the configuration of the pipeline topology. Engineers can exclude cases with negative heat fluctuation transfer coefficients from the calculation range of the role weight ratio.

[0053] Pressure parameter sequences of physical measuring points under quasi-steady-state conditions and downstream physical measuring points within the same quasi-steady-state period are extracted from historical operational datasets. Pressure data of physical measuring points under quasi-steady-state conditions within the quasi-steady-state period are selected from the historical operational datasets by timestamp and arranged chronologically to form the pressure parameter sequence of the physical measuring points under quasi-steady-state conditions. Similarly, pressure data of downstream physical measuring points within the same quasi-steady-state period are selected from the historical operational datasets by timestamp and arranged chronologically to form the pressure parameter sequence of the downstream physical measuring points.

[0054] After extracting the pressure parameter sequences of the physical measuring points under quasi-steady-state conditions and the downstream physical measuring points, a second correlation coefficient is calculated between these two sequences. This second correlation coefficient is used as the pressure fluctuation transmission coefficient. The second correlation coefficient measures the degree of correlation between the pressure fluctuations at the physical measuring points under quasi-steady-state conditions and those at the downstream physical measuring points. The pressure fluctuation transmission coefficient reflects the degree of correlation between the fluctuations in transport properties at the downstream physical measuring points and those at the physical measuring points under quasi-steady-state conditions. A larger pressure fluctuation transmission coefficient indicates a more significant influence of the pressure changes at the downstream physical measuring points on the pressure changes at the physical measuring points under quasi-steady-state conditions. In the subsequent calculation of the role weight ratio, the pressure fluctuation transmission coefficient is used as the denominator. Therefore, the role weight of the measured medium in transport properties at the physical measuring points under quasi-steady-state conditions is inversely related to the pressure fluctuation transmission coefficient. The second correlation coefficient can also be calculated using the Pearson correlation coefficient algorithm. The calculation process is the same as that of the first correlation coefficient, except that the calculation objects are replaced with the pressure parameter sequences of physical measuring points under quasi-steady-state conditions and the pressure parameter sequences of downstream physical measuring points. The pressure fluctuation transmission coefficient also ranges from -1 to +1.

[0055] After calculating the heat fluctuation transfer coefficient and the pressure fluctuation transfer coefficient, the heat fluctuation transfer coefficient is divided by the pressure fluctuation transfer coefficient to obtain the role weight ratio. The formula for calculating the role weight ratio is: W = H / P; where W represents the role weight ratio, H represents the heat fluctuation transfer coefficient, and P represents the pressure fluctuation transfer coefficient. The role weight ratio reflects the relative proportion between the role weight of the measured medium in the heat-carrying attribute and the role weight in the transport attribute at the physical measuring point under quasi-steady-state conditions. When the value of W is large, it indicates that the role weight of the measured medium in the heat-carrying attribute is greater than its role weight in the transport attribute at the physical measuring point under quasi-steady-state conditions, meaning that the measured medium at the physical measuring point under quasi-steady-state conditions is more inclined to undertake the function of heat transport; when the value of W is small, it indicates that the role weight of the measured medium in the transport attribute is greater than its role weight in the heat-carrying attribute at the physical measuring point under quasi-steady-state conditions, meaning that the measured medium at the physical measuring point under quasi-steady-state conditions is more inclined to undertake the function of fluid distribution. If the pressure fluctuation transmission coefficient P is zero, meaning there is no linear correlation between the pressure parameter sequence of the downstream physical measuring point and the pressure parameter sequence of the physical measuring point in quasi-steady-state condition, then the role weight ratio W cannot be directly calculated by division. In this case, the role weight ratio W can be set to a preset maximum value. The preset maximum value is set by engineers based on experience, for example, set to +∞, which indicates that the role weight of the medium measured by the physical measuring point in quasi-steady-state condition in terms of transport properties is extremely low.

[0056] Figure 2 A flowchart of the heat-carrying-dominant node marking method of the present invention is provided. In specific implementation S4, the role weight ratio of the physical measuring point in the quasi-steady-state condition has been calculated in step S3. The role weight ratio reflects the relative proportional relationship between the role weight of the measured medium in the heat-carrying attribute and the role weight in the transport attribute of the physical measuring point in the quasi-steady-state condition. In order to accurately classify and mark the physical measuring points in the quasi-steady-state condition, it is not possible to rely solely on the absolute value of the role weight ratio of a single physical measuring point for judgment. This is because the absolute values ​​of the role weight ratios of different types of thermal equipment are not comparable across equipment types due to differences in their design parameters, operating conditions, and functional positioning in the heating system. For example, the typical range of the role weight ratio of a plate heat exchanger may differ significantly from the typical range of the role weight ratio of a circulating water pump. If a uniform fixed threshold is used to classify and judge the role weight ratio, it will lead to systematic deviations in the classification results between different equipment types. Therefore, it is necessary to introduce the distribution of the role weight ratio of similar physical measuring points as a reference benchmark, and to classify and label the relative position of the role weight ratio of physical measuring points in quasi-steady-state conditions within the same group of physical measuring points.

[0057] The first step in forming a group of similar physical measuring points is to select physical measuring points with the same equipment type identifier as those in quasi-steady-state conditions from historical operational data. The equipment type identifier is an attribute label assigned to each field physical measuring point by engineers when building a digital management and control platform for the heating system. It identifies the equipment category to which the field physical measuring point belongs. The classification of equipment type identifiers is based on the type of thermal equipment at the installation location of the field physical measuring point. For example, temperature transmitters, pressure transmitters, and flow meters installed on the primary side supply water pipeline of a plate heat exchanger can all be identified as plate heat exchangers; pressure transmitters installed on the outlet pipeline of a circulating water pump can be identified as circulating water pumps; and temperature transmitters installed on the outlet pipeline of a heat source boiler can be identified as heat source boilers. The granularity of equipment type identification is set by engineers based on the actual equipment configuration and classification requirements of the heating system. The finer the granularity, the fewer the number of physical measuring points in the same physical measuring point group, but the higher the homogeneity within the same physical measuring point group; the coarser the granularity, the more the number of physical measuring points in the same physical measuring point group, but there may be different subtypes of equipment within the same physical measuring point group, resulting in increased dispersion of role weight ratio distribution.

[0058] During the selection of physical measuring points, not all physical measuring points with the same equipment type identifier are included in the same type of physical measuring point group. Instead, physical measuring points with the same equipment type identifier whose role weight ratios in the historical operation dataset have been calculated are selected to form the same type of physical measuring point group. "Role weight ratios have been calculated" means that after a physical measuring point is determined to be in a quasi-steady-state condition in step S2, its corresponding role weight ratio has been calculated in step S3. Physical measuring points that have not yet been determined to be in a quasi-steady-state condition in step S2, or those that have been determined to be in a quasi-steady-state condition but have not yet completed the role weight ratio calculation in step S3, are temporarily not included in the same type of physical measuring point group. As the heating system continues to operate and steps S2 and S3 are executed periodically, more and more physical measuring points will complete the calculation of their role weight ratios, gradually increasing the number of physical measuring points included in the same type of physical measuring point group, and gradually improving the statistical stability of the role weight ratio distribution.

[0059] After forming a group of physical measuring points of the same type, the role weight ratio of each physical measuring point in the group is obtained. All role weight ratios are then arranged in ascending order to form a role weight ratio distribution. This distribution describes the overall distribution of role weight ratios among different physical measuring points in the group. The shape and central tendency of the distribution reflect the overall characteristics of the equipment category represented by the equipment type identifier in terms of heat transfer and transport attributes. The median of the role weight ratio distribution is calculated as the median of the role weight ratios in the group of physical measuring points of the same type. The median is calculated as follows: if the number of role weight ratios in the group is odd, the median is the value of the role weight ratio in the middle position after sorting; if the number of role weight ratios is even, the median is the average of the values ​​of the two middle positions after sorting. The median of the role weight ratios in the group represents the typical role weight level of the equipment category corresponding to the equipment type identifier and serves as a statistically significant reference benchmark.

[0060] After calculating the median value of the role weight ratio of a group of physical measuring points of the same type, the absolute value of the difference between the role weight ratio of a physical measuring point under quasi-steady-state conditions and the median value of the role weight ratio of the group of physical measuring points of the same type is calculated as the deviation. The formula for calculating the deviation is: D = |WM|; where D represents the deviation, W represents the role weight ratio of a physical measuring point under quasi-steady-state conditions, M represents the median value of the role weight ratio of the group of physical measuring points of the same type, || represents the absolute value operation, and - represents the subtraction operation. The deviation measures the degree of deviation between the role weight ratio of a physical measuring point under quasi-steady-state conditions and the typical role weight level of the same type of equipment. The larger the value of the deviation, the more significant the difference between the role characteristics of the physical measuring point under quasi-steady-state conditions and the typical role characteristics of the same type of equipment.

[0061] After calculating the deviation, it is compared with a preset deviation threshold. Simultaneously, the role weight ratio of the physical measuring points in quasi-steady-state conditions is compared with the median of the role weight ratios of similar physical measuring point groups. The preset deviation threshold is a judgment threshold pre-set by engineers to define whether the deviation has reached a significant level. The preset deviation threshold can be determined through statistical analysis of the role weight ratio distribution of similar physical measuring point groups. Engineers can calculate the standard deviation or interquartile range of the role weight ratio distribution of similar physical measuring point groups and set a multiple of the standard deviation or interquartile range as the preset deviation threshold, for example, setting twice the standard deviation of the role weight ratio distribution as the preset deviation threshold. When the deviation exceeds the preset deviation threshold, it indicates that there is a significant difference between the role weight ratio of the physical measuring points in quasi-steady-state conditions and the typical role weight level of similar equipment types. The physical measuring points in quasi-steady-state conditions have role attributes that differ from the typical characteristics of similar equipment types.

[0062] When the deviation exceeds a preset deviation threshold and the role weight ratio of a physical measuring point in quasi-steady-state condition is lower than the median role weight ratio of a group of similar physical measuring points, the monitoring node corresponding to the physical measuring point in quasi-steady-state condition is marked as a heat-carrying dominant node. A role weight ratio lower than the median role weight ratio of a group of similar physical measuring points indicates that the measured medium of the physical measuring point in quasi-steady-state condition has a lower role weight in heat-carrying properties compared to the typical level of similar equipment types, but a higher role weight in transport properties compared to the typical level of similar equipment types. Since the role weight ratio is defined as the heat fluctuation transfer coefficient divided by the pressure fluctuation transfer coefficient, a lower role weight ratio means that the measured medium of the physical measuring point in quasi-steady-state condition is more inclined to undertake fluid transport and distribution functions. The logic for marking heat-carrying dominant nodes is as follows: when the role weight ratio of a physical measuring point in a quasi-steady-state condition is significantly lower than the typical level of similar equipment, it indicates that the medium measured by the physical measuring point in a quasi-steady-state condition has an abnormally prominent role in transport properties, while its role in heat-carrying properties is relatively weak. For cases where the role of the medium in transport properties deviates significantly from the normal level, the corresponding monitoring node needs to be marked as a heat-carrying dominant node so that the transient data of the heat-carrying dominant node can be enhanced and smoothed in step S5 to compensate for the interference of transient data fluctuations caused by the weakening of the heat-carrying attribute role on the upper-level energy efficiency calculation.

[0063] When the deviation does not exceed the preset deviation threshold, or when the deviation exceeds the preset deviation threshold but the role weight ratio of the physical measuring point in the quasi-steady-state condition is not lower than the median value of the role weight ratio of the same type of physical measuring point group, the monitoring node corresponding to the physical measuring point in the quasi-steady-state condition will not be marked as a heat-carrying dominant node. For monitoring nodes that are not marked as heat-carrying dominant nodes, the transient data of the monitoring nodes are directly provided to the upper-level energy efficiency calculation node according to the initial mapping relationship, and the time window smoothing preprocessing in step S5 is not performed.

[0064] In the specific implementation of S5, the monitoring nodes corresponding to the physical measurement points in the quasi-steady-state condition have already been marked as heat-carrying dominant nodes in step S4. For monitoring nodes marked as heat-carrying dominant nodes, their transient data needs to be smoothed and preprocessed before being used as mapping inputs for the upper-level energy efficiency calculation nodes. The original transient data cannot be directly used as mapping inputs. As pointed out in the background section, in existing node mapping methods, the energy efficiency calculation nodes directly reference the same transient mapping data as the process monitoring nodes, resulting in calculation results that cannot truly reflect the physical laws of the thermal process, making it difficult to guarantee the accuracy of system energy efficiency assessment. The purpose of smoothing preprocessing is precisely to overcome this deficiency. By suppressing the noise components generated by the superposition of transport attribute fluctuations in the transient data of heat-carrying dominant nodes, the smoothed data more accurately reflects the thermal process characteristics of the medium in terms of heat-carrying properties.

[0065] The first step in performing smoothing preprocessing is to acquire the transient data sequence of the monitoring node marked as the heat-carrying dominant node within a preset time period before the current moment. The preset time period is a time window length pre-set by engineers to extract transient data from the most recent period for analysis and smoothing. The setting of the preset time period needs to comprehensively consider the thermal process response characteristics of the heating system and the computational efficiency of smoothing. If the preset time period is set too short, the number of data points in the transient data sequence will be too small, the effect of moving average calculation will be insignificant, and noise components cannot be effectively suppressed. If the preset time period is set too long, the result of moving average calculation will be over-smoothed, causing the smoothed data to lag significantly in response to changes in real thermal parameters, affecting the timely perception of changes in system operating status by upper-level energy efficiency calculation nodes. The preset duration is set by engineers based on the thermal inertia characteristics of the physical measuring points under quasi-steady-state conditions. For measuring points with large thermal inertia, such as temperature transmitters installed on the primary side water supply pipeline of large heat exchange stations, the preset duration can be set to, for example, 900 seconds; for measuring points with small thermal inertia, such as pressure transmitters installed at the user inlet of the secondary network, the preset duration can be set to, for example, 300 seconds.

[0066] Transient data from monitoring nodes refers to the raw thermal data acquired by the configuration software through the data acquisition channel at the current moment, before any filtering or smoothing processing. The timestamp of the transient data is the current moment, and the value of the transient data reflects the actual measurement value of the physical measuring point at the current moment. The transient data sequence is a sequence formed by arranging multiple historical transient data within a preset time period before the current moment in ascending order of timestamp. The time interval between two adjacent transient data in the transient data sequence is equal to the preset acquisition period.

[0067] After acquiring the transient data sequence, a moving average is calculated for all transient data points in the sequence, and the result of the moving average is used as the smoothed data. The basic process of the moving average calculation is as follows: a fixed-length sliding window is slid across the transient data sequence. At each position, the arithmetic mean of all transient data points within the sliding window is calculated, and the calculated arithmetic mean is used as the data value at the corresponding time position in the smoothed data.

[0068] In this embodiment, an equal-weighted moving average algorithm is used to calculate the arithmetic mean for all transient data in the transient data sequence. The equal-weighted moving average algorithm means that each transient data point within the sliding window has the same weight when calculating the arithmetic mean. The calculation process of the equal-weighted moving average algorithm is as follows: A sliding window length is determined, representing the number of transient data points contained within the sliding window; starting from the first transient data point in the transient data sequence, a window data group is formed by sequentially taking a number of transient data points of the sliding window length; the arithmetic mean of all transient data points within the window data group is calculated, i.e., the values ​​of all transient data points within the window data group are first added together, and then the sum is divided by the sliding window length. The quotient is the smoothed data value corresponding to the center position of the window data group in the smoothed data; the sliding window is moved forward one transient data point along the time sequence of the transient data sequence to form a new window data group, and the arithmetic mean is calculated repeatedly until the sliding window covers the very end of the transient data sequence. For the boundary position at the beginning of the transient data sequence, when the remaining amount of transient data is insufficient to fill the sliding window, the actual amount of remaining transient data is used as the shortened sliding window length for calculation.

[0069] The sliding window length used in the moving average calculation is determined based on the time constant of the temperature parameter in the historical operating dataset of physical measuring points under quasi-steady-state conditions. The time constant of the temperature parameter is a characteristic parameter in thermal engineering used to describe the speed of temperature response to input changes. Physically, the time constant represents the time required for the temperature to change from its initial value to 63.2% of its steady-state value. In heating systems, the time constant reflects the speed of temperature response to changes in heat source output or load; a larger time constant indicates a slower temperature response, and a smaller time constant indicates a faster temperature response. The time constant of the temperature parameter can be determined by analyzing the temperature data in the historical operating dataset of physical measuring points under quasi-steady-state conditions. Specifically, the method involves selecting the temperature response curve of a physical measuring point under quasi-steady-state conditions from the historical operating dataset at the moment of condition change (the moment of step change in heat source output). After the moment of condition change, the time taken for the temperature value in the temperature response curve to rise from its initial value to 63.2% of its steady-state value is recorded. This time length is used as the time constant of the temperature parameter of the physical measuring point under quasi-steady-state conditions.

[0070] After determining the time constant of the temperature parameter, the time constant is divided by the preset acquisition period, and the quotient is used as the sliding window length. The formula for calculating the sliding window length is: L = T / Δt; where L represents the sliding window length, T represents the time constant of the temperature parameter at the physical measuring point under quasi-steady-state conditions, and Δt represents the preset acquisition period. The unit of the sliding window length is seconds, the unit of the temperature parameter time constant is seconds, and the unit of the preset acquisition period is seconds. For example, if the time constant of the temperature parameter at the physical measuring point under quasi-steady-state conditions is 120 seconds, and the preset acquisition period is 10 seconds, then the sliding window length is 120 / 10 = 12. The setting of the sliding window length is related to the time constant of the temperature parameter and has a clear physical basis: the larger the time constant of the temperature parameter, the greater the inertia of temperature changes, requiring a longer sliding window length to fully smooth the random fluctuations in transient data; the smaller the time constant of the temperature parameter, the faster the response to temperature changes, allowing for the use of a shorter sliding window length to avoid excessive lag in the smoothed data.

[0071] After obtaining the smoothed data, it is used as the mapping input for the upper-level energy efficiency calculation node. The upper-level energy efficiency calculation node is a software node in the configuration software that calculates energy efficiency indicators based on the thermal data of multiple monitoring nodes. These energy efficiency indicators include the heat exchange efficiency of heat exchange stations, the transmission efficiency of pipeline networks, and the heating efficiency of heat sources. The upper-level energy efficiency calculation node no longer directly references the original transient data from the monitoring nodes, but instead uses the smoothed data, making the energy efficiency calculation results more stable and accurate, avoiding calculation distortion caused by the superposition of transport attribute fluctuations in the transient data. For monitoring nodes not marked as heat-carrying dominant nodes in step S4, the transient data of the monitoring nodes is not pre-processed with smoothing and is directly provided to the upper-level energy efficiency calculation node according to the initial mapping relationship to maintain the rapid response capability of the process monitoring node to real-time operating condition changes.

[0072] All calculations involved in the embodiments are performed using dimensionless numerical values, and the preset parameters and thresholds in the calculations can be set by those skilled in the art according to actual conditions.

[0073] This technical solution can be flexibly deployed, for example, as embedded software running on device hardware, or installed on personal computers or other smart terminals with user interfaces, thus adapting to various hardware environments and usage requirements.

[0074] The above solutions can be implemented in software, hardware, firmware, or a combination thereof. When implemented in software, they are presented, in whole or in part, as a computer program product, containing one or more computer instructions or programs. When these instructions or programs are loaded and executed on a computer, results are produced corresponding to the processes or functions of the embodiments of this application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one medium to another via wired or wireless means, such as from a website, server, or data center via wired means such as fiber optic cables, twisted-pair cables, or coaxial cables, or wireless means such as infrared or microwaves to another site. A computer-readable storage medium refers to any usable medium that a computer can access or a data storage device such as a server or data center that contains one or more usable media, including magnetic media such as floppy disks, hard disks, and magnetic tapes, optical media such as DVDs, and semiconductor media such as solid-state drives.

[0075] The specific working process of the system, device and module can be found in the method embodiment, and will not be repeated here.

[0076] The disclosed systems, devices, and methods can be implemented in other ways. The device embodiments are for illustrative purposes only, and the module division is only a logical division. In practice, different divisions can be implemented, such as merging or integrating multiple modules or components, or omitting some features. Coupling, direct coupling, or communication connections between the components can be achieved through interfaces, while indirect coupling or communication connections can take electrical, mechanical, or other forms.

[0077] The modules described as separate components may or may not be physically separated. The components shown as modules can be physical hardware or software, and can be deployed centrally or distributed across multiple network nodes. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0078] The functional modules in each embodiment can be integrated into one processing module, or they can exist independently, or two or more modules can be integrated into one.

[0079] If the functionality is implemented as a software module and used as an independent product, it can be stored in a computer-readable storage medium. Under this understanding, the substantial contribution of the technical solution of this application can be embodied in the form of a software product. This computer software product is stored in a storage medium and contains instructions to cause a computer device, such as a personal computer, server, or network device, to execute all or part of the steps of the methods in the embodiments of this application. The storage medium includes any medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory, random access memory, magnetic disk, or optical disk.

[0080] The above are merely specific embodiments of this application, and the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should fall within the scope of protection of this application.

Claims

1. A method for mapping data of heating configuration nodes, characterized in that, include: S1: Obtain the initial mapping relationship between multiple monitoring nodes in the configuration software and physical measurement points on site, as well as the real-time thermal data collected by the physical measurement points; S2: For each monitoring node, obtain the real-time thermal data of the physical measurement point pointed to by the initial mapping relationship and the associated physical measurement point that has a preset thermal coupling relationship with the physical measurement point pointed to. Calculate the thermal parameter coupling deviation between the physical measurement point pointed to and the associated physical measurement point. When the preset steady-state condition is met, determine that the physical measurement point pointed to is in a quasi-steady-state condition. S3: For physical measuring points in quasi-steady-state conditions, calculate the heat fluctuation transfer coefficient between the physical measuring point in quasi-steady-state conditions and the upstream physical measuring point, as well as the pressure fluctuation transfer coefficient between the physical measuring point in quasi-steady-state conditions and the downstream physical measuring point, and use the ratio of the heat fluctuation transfer coefficient to the pressure fluctuation transfer coefficient as the role weight ratio. S4: Obtain the distribution of the role weight ratio of the same type of physical measuring point group with the same equipment as the physical measuring point in the quasi-steady-state condition, calculate the deviation of the role weight ratio of the physical measuring point in the quasi-steady-state condition in the same type of physical measuring point group, and mark the monitoring node as a heat-carrying dominant node when the deviation exceeds the preset deviation threshold and the role weight ratio is lower than the median value of the role weight ratio of the same type of physical measuring point group. S5: For monitoring nodes marked as heat-carrying dominant nodes, perform time-window smoothing preprocessing on their transient data before it is referenced by upper-level energy efficiency calculation nodes, and then use it as mapping input.

2. The heating configuration node data mapping method according to claim 1, characterized in that, Obtain the initial mapping relationship between multiple monitoring nodes in the configuration software and on-site physical measurement points, as well as the real-time thermal data collected by the physical measurement points, including: The system receives data point table files imported by engineers, extracts the correspondence between the equipment identifiers of the field physical measurement points and the node identifiers of the monitoring nodes in the configuration software from the data point table files as the initial mapping relationship, and establishes a communication connection with the data acquisition channel of the field physical measurement points to obtain real-time thermal data.

3. The heating configuration node data mapping method according to claim 1, characterized in that, For each monitoring node, real-time thermal data of the physical measuring point pointed to by the initial mapping relationship and the associated physical measuring points with a preset thermal coupling relationship with the pointed physical measuring point are obtained. The thermal parameter coupling deviation between the pointed physical measuring point and the associated physical measuring points is calculated. When the preset steady-state conditions are met, the pointed physical measuring point is determined to be in a quasi-steady-state condition, including: Based on the topology of the thermal pipeline network, the associated physical measuring points that have a preset thermal coupling relationship with the pointed physical measuring point are identified. The thermal parameter sequences of the pointed physical measuring point and the associated physical measuring point within a preset time period before the current moment are extracted from the real-time thermal data. The correlation coefficient between the two thermal parameter sequences is calculated as the thermal parameter coupling deviation. When the correlation coefficient is lower than the preset correlation threshold, it is determined that the pointed physical measuring point meets the preset steady-state condition. The pointed physical measuring point that meets the preset steady-state condition is marked as being in a quasi-steady-state condition.

4. The heating configuration node data mapping method according to claim 3, characterized in that, The correlation coefficient between two thermal parameter sequences is calculated as the thermal parameter coupling deviation. This includes: using the Pearson correlation coefficient algorithm to calculate the two thermal parameter sequences, and using the absolute value of the Pearson correlation coefficient as the thermal parameter coupling deviation.

5. The heating configuration node data mapping method according to claim 1, characterized in that, For physical measuring points operating in a quasi-steady-state condition, calculate the heat fluctuation transfer coefficient between the physical measuring point and the upstream physical measuring point, and the pressure fluctuation transfer coefficient between the physical measuring point and the downstream physical measuring point. Use the ratio of the heat fluctuation transfer coefficient to the pressure fluctuation transfer coefficient as the role weight ratio, including: Based on the topology of the heating network, upstream and downstream physical measuring points of the physical measuring points under quasi-steady-state conditions are determined. Temperature parameter sequences of the physical measuring points under quasi-steady-state conditions and upstream physical measuring points within the same quasi-steady-state period are extracted from the historical operation dataset, and the first correlation coefficient is calculated as the heat fluctuation transfer coefficient. Pressure parameter sequences of the physical measuring points under quasi-steady-state conditions and downstream physical measuring points within the same quasi-steady-state period are extracted from the historical operation dataset, and the second correlation coefficient is calculated as the pressure fluctuation transfer coefficient. The role weight ratio is obtained by dividing the heat fluctuation transfer coefficient by the pressure fluctuation transfer coefficient.

6. The heating configuration node data mapping method according to claim 5, characterized in that, Based on the topology of the heating network, the upstream and downstream physical measuring points of the physical measuring points in the quasi-steady-state condition are determined, including: determining the adjacent physical measuring points in the heating network topology that have direct pipe connections to the physical measuring points in the quasi-steady-state condition and whose medium flow direction is towards the physical measuring points in the quasi-steady-state condition as upstream physical measuring points; and determining the adjacent physical measuring points that have direct pipe connections to the physical measuring points in the quasi-steady-state condition and whose medium flow direction is away from the physical measuring points in the quasi-steady-state condition as downstream physical measuring points.

7. The heating configuration node data mapping method according to claim 1, characterized in that, Obtain the distribution of role weight ratios for physical measuring points of the same type and equipment as those in quasi-steady-state conditions. Calculate the deviation of the role weight ratio of the physical measuring point in quasi-steady-state conditions from that of the same type of physical measuring point. When the deviation exceeds a preset deviation threshold and the role weight ratio is lower than the median of the role weight ratios of the same type of physical measuring point, mark the monitoring node as a heat-dominant node, including: From the historical operation dataset, all physical measuring points with the same equipment type identifier as the physical measuring points in the quasi-steady-state condition are selected to form a group of physical measuring points of the same type. The role weight ratio of each physical measuring point in the group of physical measuring points of the same type is obtained and a role weight ratio distribution is formed. The median value of the role weight ratio distribution is calculated as the median value of the role weight ratio of the group of physical measuring points of the same type. The absolute value of the difference between the role weight ratio of the physical measuring point in the quasi-steady-state condition and the median value of the role weight ratio of the group of physical measuring points of the same type is calculated as the deviation. When the deviation exceeds the preset deviation threshold and the role weight ratio of the physical measuring point in the quasi-steady-state condition is lower than the median value of the role weight ratio of the group of physical measuring points of the same type, the monitoring node corresponding to the physical measuring point in the quasi-steady-state condition is marked as a heat-carrying dominant node.

8. The heating configuration node data mapping method according to claim 7, characterized in that, Select all physical measurement points with the same equipment type identifier as physical measurement points in quasi-steady-state conditions from the historical operation dataset to form a group of physical measurement points of the same type. This includes selecting physical measurement points with the same equipment type identifier from all physical measurement points with the same equipment type identifier whose role weight ratio in the historical operation dataset has been calculated to form a group of physical measurement points of the same type.

9. The heating configuration node data mapping method according to claim 1, characterized in that, For monitoring nodes marked as heat-carrying dominant nodes, time-window smoothing preprocessing is performed on their transient data before it is referenced by upper-level energy efficiency calculation nodes, and this data is then used as mapping input, including: Acquire the transient data sequence of the monitoring node marked as the heat-carrying dominant node within a preset time period before the current moment. Perform a moving average calculation on all transient data in the transient data sequence. Use the result of the moving average calculation as the smoothed data. Use the smoothed data as the mapping input of the upper-level energy efficiency calculation node.

10. A heating configuration node data mapping method according to claim 9, characterized in that, The moving average calculation is performed on all transient data in the transient data sequence, including: using an equal-weight moving average algorithm to calculate all transient data in the transient data sequence. The sliding window length used for the moving average calculation is determined based on the time constant of the temperature parameter in the historical operating data of the physical measurement points under quasi-steady-state conditions.

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