A method and system for debugging and evaluating a heat pump distributed heating system

Abstract

The application provides a kind of heat pump distributed heating system debugging evaluation method and system, the method comprises: determining pipe network structure to heating pipe network, determine each monitoring node to pipe network by pipe network structure;The original data of each monitoring node is preprocessed to extract heat exchange active zone;Original temperature in the original data of monitoring node is constructed temperature field distribution map using heat exchange active zone;Based on the power data of each heat pump unit, the theoretical performance coefficient of heat pump is calculated to evaluate abnormal unit;Temperature field distribution map and abnormal unit are used to determine key significant abnormal area, and the optimal transmission path of key significant abnormal area is evaluated to construct load scheduling instruction;Heat pump distributed heating system is used to issue load scheduling instruction to carry out fluid scheduling of heating pipe network.According to the load scheduling instruction constructed and re-adjusted, the flow rate of different pipe sections can be adjusted, so that the heat transfer of the relatively weak pipe section can be better.

Description

Technical Field

[0001] This invention relates to the field of heat transfer, and more particularly to a method and system for commissioning and evaluating a distributed heat pump heating system. Background Technology

[0002] With increasing energy consumption and increasingly stringent environmental protection requirements, heat pump distributed heating systems, as a highly efficient and green heating technology, have gradually been widely used in various buildings and industrial facilities. Heat pump systems utilize ambient energy for heat exchange, using electricity to drive a compressor to transfer heat, providing indoor temperature regulation and hot water supply, exhibiting high energy efficiency. Compared to traditional gas or electric heating methods, heat pump systems not only save energy but also reduce environmental pollution, thus possessing significant social and economic value.

[0003] The operational performance of a heat pump distributed heating system is influenced by a variety of factors, including system design, pipeline layout, and the performance of the heat pump equipment. To ensure efficient system operation and to promptly identify and eliminate potential faults or energy efficiency degradation, the commissioning and evaluation of the heat pump distributed heating system are crucial. However, traditional commissioning and evaluation methods often rely on manual inspections, performance evaluation of individual devices, and simple temperature monitoring, which suffer from inaccurate control over the overall system performance and delayed diagnosis of potential faults. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for commissioning and evaluating a heat pump distributed heating system, which solves the above-mentioned technical problems pointed out in the prior art.

[0005] This invention provides a method for commissioning and evaluating a heat pump distributed heating system, comprising the following steps:

[0006] The heating network structure is determined, and each monitoring node is identified based on the network structure. Sensor arrays are arranged at each monitoring node to collect raw fluid data at each time point. Power sensors are installed in each heat pump unit in the heating network to collect power data.

[0007] The raw data of each monitoring node is preprocessed to extract the active heat exchange area; the active heat exchange area is used to construct a temperature field distribution map from the raw temperature in the raw data of the monitoring node; the theoretical performance coefficient of the heat pump is calculated based on the power data of each heat pump unit to evaluate abnormal units; the temperature field distribution map and abnormal units are used to determine key significant abnormal areas, the optimal transmission path is evaluated for the key significant abnormal areas, and load scheduling instructions are constructed.

[0008] The load scheduling command is issued by the heat pump distributed heating system to perform fluid scheduling of the heating network.

[0009] Accordingly, the present invention also proposes a commissioning and evaluation system for a heat pump distributed heating system, comprising: a raw data module; a load command construction module; and a command issuance module;

[0010] The raw data module is used to determine the structure of the heating network, identify each monitoring node in the network based on the network structure, arrange sensor arrays at each monitoring node to collect raw fluid data at each time point, and install an electric power sensor in each heat pump unit in the heating network to collect power data.

[0011] The load command module is used to preprocess the raw data of each monitoring node to extract the active heat exchange area; use the active heat exchange area to construct a temperature field distribution map from the raw temperature in the raw data of the monitoring node; calculate the theoretical performance coefficient of the heat pump based on the power data of each heat pump unit to evaluate abnormal units; use the temperature field distribution map and abnormal units to determine key significant abnormal areas, evaluate the optimal transmission path for the key significant abnormal areas, and construct load scheduling commands.

[0012] The instruction issuing module is used to issue the load scheduling instruction using the heat pump distributed heating system to perform fluid scheduling of the heating network.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following technical advantages:

[0014] Analysis of the above-mentioned commissioning and evaluation method and system for a distributed heat pump heating system provided by this invention reveals that, in specific applications, this solution monitors the temperature gradient and pressure fluctuation data of the monitoring nodes and, combined with a set threshold, judges the rate of change of the temperature gradient. This allows for the screening of active heat exchange zones, which are often the most active parts of the pipe network. Accurately identifying these zones helps optimize energy management and improve the system's thermal efficiency. For the specific needs of active heat exchange zones, the sampling frequency is adjusted to capture temperature changes more precisely, providing higher-precision data. This helps to better control the temperature distribution and avoid system instability or reduced energy efficiency due to excessive temperature differences. This solution, through local calculation of the temperature gradient and comprehensive analysis of global data, can construct a detailed temperature field distribution map, helping to analyze the heat flow within the pipe section. Furthermore, calculating the thermal efficiency between adjacent monitoring nodes allows for real-time evaluation of the system's energy efficiency status, further optimizing energy use. Simultaneously, by collecting historical and real-time data, a dynamic energy consumption model for the heat pump unit is established, enabling accurate prediction and evaluation of the heat pump unit's performance. By comparing historical theoretical coefficients of performance (COPs) with current values, abnormal heat pump units can be identified promptly, preventing equipment failures and ensuring long-term stable system operation. When a heat pump unit's theoretical COP exceeds its historical COP, the unit can be identified as abnormal and its location determined. This fault diagnosis mechanism can quickly pinpoint specific equipment and perform necessary repairs or adjustments, preventing system efficiency degradation due to equipment failure.

[0015] Furthermore, this solution utilizes infrared thermal imagers and heat balance equations to detect and optimize heat loss in pipeline systems, resulting in significant energy savings and efficiency improvements. Specifically, by performing a panoramic scan of the pipeline network and generating a thermal radiation distribution image, the solution can accurately identify areas of abnormal temperature within the system. Through pixel clustering and filtering of these abnormal areas, the solution can pinpoint overheated regions, thereby identifying potential heat loss or inefficient pipe sections. By collecting data such as the heat transfer area, flow rate, and temperature difference of the pipeline network, the solution further calculates the heat loss rate of the pipe sections. By comparing theoretical and actual heat loss rates, it can accurately assess the pipeline network performance. The system tracks heat loss in various sections, particularly in key areas with significant anomalies. When the difference between the theoretical and actual heat loss rates of a pipe segment exceeds a set threshold, the system automatically marks these segments as weak points, thus identifying vulnerable links in the pipeline network and providing a basis for subsequent optimization. A greedy algorithm is used to optimize load scheduling paths, combining the heat energy of load nodes and waste heat nodes to calculate the optimal heat transfer path, thereby achieving efficient heat distribution and optimization. Through this greedy algorithm, the system can gradually achieve the global optimal solution, improving the overall energy efficiency of the pipeline network, reducing heat waste, and ensuring that the temperature and pressure of each node remain within a reasonable range. Attached Figure Description

[0016] Figure 1 This is a flowchart of a commissioning and evaluation method for a heat pump distributed heating system, as described in Example 1.

[0017] Figure 2 This is a flowchart of the method for obtaining abnormal units in the commissioning and evaluation of a heat pump distributed heating system according to Embodiment 1.

[0018] Figure 3 This is a schematic diagram of the heat exchange region of the temperature field in a commissioning and evaluation method for a heat pump distributed heating system according to Embodiment 1.

[0019] Figure 4 This is a flowchart illustrating the method for obtaining weak pipes in a heat pump distributed heating system commissioning and evaluation, as described in Example 1.

[0020] Figure 5 This is a schematic diagram of a significant abnormal area in the commissioning and evaluation method of a heat pump distributed heating system according to Example 1.

[0021] Figure 6 This is a flowchart illustrating the construction and load scheduling instruction adjustment of a heat pump distributed heating system commissioning and evaluation method according to Example 1.

[0022] Figure 7 This is a flowchart of a commissioning and evaluation system for a heat pump distributed heating system, as described in Example 2.

[0023] Labels: Raw data module 10; Load construction instruction module 20; Instruction issuance module 30. Detailed Implementation

[0024] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0025] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0026] Example 1

[0027] like Figure 1 As shown in the figure, this embodiment of the invention provides a commissioning and evaluation method for a heat pump distributed heating system, including the following steps:

[0028] S1: Determine the structure of the heating network and identify each monitoring node based on the network structure; arrange sensor arrays at each monitoring node to collect raw fluid data at each time point;

[0029] An electric power sensor is installed in each heat pump unit (i.e., heat pump equipment, which can be arranged on each monitoring node) in the heating network, and power data (i.e., the power data package contains active or reactive power and power factor power data) is collected using the electric power sensor of the heat pump unit.

[0030] It should be noted that, based on the pipeline network structure (i.e., the pipeline network is composed of multiple pipe segments connected to form a network structure), we can identify key locations such as the pipeline inlet and outlet. These key locations are then identified as monitoring nodes of the pipeline network (i.e., monitoring nodes represent key locations that are monitored in detail, or they can be represented as key nodes). Sensor arrays (i.e., sensor arrays include temperature sensor arrays and pressure sensor arrays) are deployed on these monitoring nodes for focused detection and data collection to obtain raw data.

[0031] S2: Preprocess the raw data of each monitoring node to extract the active heat exchange area; use the active heat exchange area to construct a temperature field distribution map from the raw temperature in the raw data of the monitoring node; calculate the theoretical performance coefficient of the heat pump based on the power data of each heat pump unit to evaluate abnormal units; use the temperature field distribution map and abnormal units to determine key significant abnormal areas, evaluate the optimal transmission path for the key significant abnormal areas, and construct load scheduling instructions.

[0032] It should be noted that the raw data collected from each monitoring node is preprocessed to extract the active heat exchange zone. The active heat exchange zone refers to the area where heat exchange is most frequent, which can allocate resources more efficiently and avoid wasting computing and energy in unnecessary areas. Based on the extracted active heat exchange zone, a temperature field distribution map is constructed using the raw temperature data of the monitoring nodes. The temperature field distribution map can clearly show the distribution and flow of heat in the pipeline network. The theoretical coefficient of performance (COP) of the heat pump is calculated using the power data of the heat pump unit to assess whether there are abnormal heat pump units. The deviation of the theoretical coefficient of performance can identify heat pump units that are not working properly or have abnormal energy. The temperature field distribution map is combined with the data of abnormal heat pump units to identify key and significantly abnormal areas. These areas may have excessive heat concentration (i.e., the heat transmission pipeline is overloaded due to excessive heat concentration, which is the heat load of the monitoring nodes at both ends of the subsequent pipe section). The optimal heat transmission path for the significantly abnormal areas needs to be further optimized and evaluated, and load scheduling instructions are constructed based on this.

[0033] S3: Use the heat pump distributed heating system to issue the load scheduling command to perform fluid scheduling of the heating network.

[0034] It should be noted that, based on the load scheduling instructions generated in the above steps, fluid scheduling instructions are issued through the heat pump distributed heating system to adjust the fluid flow state in the heating network.

[0035] Specifically, such as Figure 2 As shown, in step S2, the raw data of each monitoring node is preprocessed to extract the active heat exchange area; a temperature field distribution map is constructed using the active heat exchange area and the raw temperature in the raw data of the monitoring nodes; the theoretical performance coefficient of the heat pump is calculated based on the power data of each heat pump unit to evaluate abnormal units; key significant abnormal areas are identified using the temperature field distribution map and abnormal units, the optimal transmission path is evaluated for the key significant abnormal areas, and load scheduling instructions are constructed. The specific operation steps are as follows:

[0036] Steps S21-S22 mainly describe the process of extracting the raw temperature and pressure at each time point from the raw data of each monitoring node; calculating the temperature gradient change rate and pressure fluctuation variance between each monitoring node for the raw temperature and pressure; and using the temperature gradient change rate and a preset change threshold to determine and filter out the active heat exchange zone.

[0037] S21: Preprocess the raw data of each monitoring node to extract the raw temperature and raw pressure at each time point;

[0038] The temperature gradient of each monitoring node is calculated using the original temperature of the monitoring node, and the rate of change of temperature gradient between adjacent monitoring nodes is calculated using the temperature gradient of each monitoring node.

[0039] Calculate the variance of the original pressure at each monitoring node to obtain the pressure fluctuation variance (which is the pressure fluctuation of the pipeline network. The pressure fluctuation is affected by factors such as flow velocity, pipe length, and pipe diameter. It can be used to assess the operational stability of the pipeline network and can also be used as the pressure drop of the pipe section).

[0040] S22: Preset change threshold; Determine whether the rate of change of the temperature gradient of adjacent monitoring nodes is greater than the preset change threshold;

[0041] If so, then the adjacent monitoring nodes of the temperature gradient change rate are determined as significant points of the temperature gradient; the region between these significant points of the temperature gradient is determined as the active heat exchange zone.

[0042] It should be noted that, by pre-setting a threshold for change, adjacent monitoring nodes with significant temperature gradient change rates are identified as significant temperature gradient points. These significant temperature gradient points reflect points or pipe sections in the pipeline network with excessive temperature changes. Since the monitoring nodes are key locations such as the inlet and outlet of the pipeline network, the pipe sections between adjacent monitoring nodes (i.e., significant temperature gradient points) represent areas with relatively significant temperature changes, i.e., active heat exchange zones. Active heat exchange zones indicate abnormally high or low rates of temperature change displayed at the monitoring nodes (i.e., temperature activity). These active heat exchange zones, which meet the criteria, are often the most active or abnormal parts of the pipeline network in terms of heat transfer. By identifying these active heat exchange zones, it is possible to determine which nodes or areas in the pipe section actually experience drastic temperature changes.

[0043] Steps S23-S25 mainly describe the following steps: adjusting the acquisition frequency of the corresponding monitoring nodes in the active heat exchange zone; re-acquiring the original temperature of the corresponding monitoring nodes to calculate the local temperature gradient; constructing a temperature field distribution map using the local temperature gradient and the temperature gradients of other monitoring nodes; acquiring the inlet and outlet fluid temperatures and heat pump flow rates for each heat pump unit and calculating the instantaneous heat generation power; obtaining the theoretical performance coefficient based on the instantaneous heat generation power and power data; and determining whether the heat pump unit corresponding to the theoretical performance coefficient is an abnormal unit by comparing the theoretical performance coefficient with the acquired historical theoretical performance coefficient.

[0044] S23: Adjust the frequency transmission of the communication path for the monitoring node in the active heat exchange zone, and re-collect the original temperature of the monitoring node in the active heat exchange zone;

[0045] The local temperature gradient is calculated from the original temperature of the active heat exchange region; the local temperature gradient and the temperature gradients of other monitoring nodes are used to construct the temperature field distribution map at the current time point;

[0046] Collect the fluid properties and flow rate of the pipeline network; extract the original temperature of the inlet and outlet of each monitoring node based on the original temperature of each monitoring node.

[0047] Calculate the original temperatures of the inlet and outlet of the monitoring node to obtain the temperature difference between the inlet and outlet of the monitoring node; use the temperature difference between the inlet and outlet of the monitoring node, the fluid flow rate, and the specific heat capacity of the fluid in the fluid properties to calculate the thermal efficiency of the pipe section between adjacent monitoring nodes.

[0048] It should be noted that adjusting the sampling frequency of the communication path for monitoring nodes in areas with active heat exchange means adjusting the acquisition frequency of monitoring nodes in areas with more active temperature differences (i.e., the sampling frequency can be increased from the original 1Hz to at least 5Hz to capture subtle differences) to transmit temperature, and the temperature is transmitted through the communication channel.

[0049] The local temperature gradient is calculated using the newly acquired original temperature from the heat exchange active region mentioned above. "Local" refers to the heat exchange active region, and only the temperature gradient of the newly acquired original temperature in this region is calculated. Simultaneously, the temperature gradients of the remaining monitoring nodes (i.e., the temperature gradients of the remaining monitoring nodes, excluding the newly acquired original temperatures from the monitoring nodes in S22 in the heat exchange active region) are used together with the temperature gradients of the remaining monitoring nodes and the newly acquired original temperatures from the monitoring nodes in the heat exchange active region to construct a temperature field distribution map, as shown below. Figure 3 (As shown) and construct a temperature field distribution map using the local temperature gradient;

[0050] The above steps construct a temperature field distribution map. Linear interpolation or other suitable interpolation algorithms (such as spline interpolation and Kriging interpolation) can be used to estimate the temperature distribution within the pipe section and form a continuous temperature field. Visualization tools or built-in algorithms can be used to display the processed gradient temperature in two-dimensional or three-dimensional graphics (such as heat maps, isotherm maps, or three-dimensional surface maps).

[0051] The fluid properties in the above steps include specific heat capacity, density, thermal conductivity, and fluid density. Then, by using the inlet and outlet temperature difference of the monitoring node (i.e., the inlet and outlet of the monitoring node are monitoring nodes connecting pipe segments, where fluid flows from one pipe segment through the monitoring node to the adjacent pipe segment; this differs from the inlet and outlet of the weak pipe segment in subsequent step S2641), as well as the fluid flow rate and specific heat capacity, the ratio of heat energy entering (i.e., monitoring node) and leaving (i.e. monitoring node) the fluid can be calculated. This heat energy ratio is used as the thermal efficiency between each monitoring node (i.e., thermal efficiency reflects the effectiveness of heat energy transfer within the pipe segment; ideally, higher thermal efficiency indicates that heat energy is transferred more fully within the system; conversely, lower thermal efficiency indicates significant heat loss or uneven transfer, providing data for subsequent (i.e., step S2646) adjustments to the fluid flow rate and its impact on pipe segment transmission).

[0052] S24: Install ultrasonic calorimeters at the inlet and outlet of each heat pump unit in the heating network; use the ultrasonic calorimeters and power sensors to synchronously collect the fluid temperature and heat pump flow rate at the inlet and outlet of the heat pump unit.

[0053] Calculate the temperature difference between the inlet and outlet fluid temperatures of the heat pump unit; calculate the instantaneous heat generation power using the temperature difference between the inlet and outlet fluid temperatures of the heat pump unit, the heat pump flow rate, and the fluid specific heat capacity in the fluid properties.

[0054] It should be noted that the above steps can collect the fluid temperature and flow rate of the heat pump using an ultrasonic calorimeter; then, the instantaneous heat generation power is calculated by using the temperature difference between the fluid temperature at the inlet and outlet of the heat pump unit (i.e., the inlet and outlet of the heat pump unit represent the inlet and outlet of the pump equipment), the heat pump flow rate, and the specific heat capacity of the fluid in the fluid properties. This instantaneous heat generation power is the same as the thermal efficiency calculation in step S24 above, both of which are calculated using the inlet and outlet temperatures, flow rate, and the specific heat capacity of the fluid in the fluid properties. The specific heat capacity of the fluid in the fluid properties remains unchanged because the fluid properties remain unchanged regardless of whether it is in the monitoring node or the flow rate in the heat pump.

[0055] S25: Collect historical power consumption data (i.e., the cumulative average power consumption data of the heat pump unit in the same historical period) and historical instantaneous heat generation power, first load degree and current ambient temperature data to construct a dynamic energy consumption model of the heat pump unit.

[0056] The first load of the current heat pump unit and the current ambient temperature are obtained, and the obtained first load of the current heat pump unit and the current ambient temperature, along with the power data and the instantaneous heat generation power, are input into the dynamic energy consumption model of the heat pump unit to obtain the theoretical performance coefficient (that is, reflecting the ideal working state of the heat pump equipment under the current operating conditions, that is, the first load of the heat pump equipment and the ambient temperature are in the ideal working state).

[0057] ;

[0058] In the formula, This represents the theoretical coefficient of performance (COP) of the current heat pump unit.

[0059] P represents the power data of the current heat pump unit;

[0060] It is expressed as the instantaneous heat generation power within the current preset time period (unit: kW);

[0061] This represents the cumulative average electricity consumption data for the same historical period (unit: This reflects the historical energy consumption level of the equipment;

[0062] This is represented as the first load of the current heat pump unit (i.e., the first load of the heat pump unit is the load obtained from the heat pump, which is the load obtained from the heat pump unit deployed at the monitoring node. It is dimensionless and between 0 and 1 for the monitoring node and the heat pump equipment).

[0063] This is represented as the ideal preset first load level (as a normalization benchmark).

[0064] This is represented as the current ambient temperature;

[0065] This is expressed as the reference ambient temperature, which is the preset temperature under ideal operating conditions for the equipment;

[0066] It is expressed as the temperature deviation sensitivity coefficient, which is determined based on historical operating data and experimental results, and controls the effect of temperature deviation on performance degradation;

[0067] Collect historical theoretical performance coefficients (i.e., the most ideal working state); determine whether the theoretical performance coefficients are greater than the historical theoretical performance coefficients;

[0068] If so, the heat pump unit corresponding to the theoretical performance coefficient is determined to be an abnormal unit, and the location coordinates of the abnormal unit are determined.

[0069] It should be noted that a mathematical model is established using the principle of heat balance and the relationship between electrical energy and heat energy conversion. The first load degree and ambient temperature are introduced into the model as correction coefficients. This model reflects the energy consumption status of the heat pump under different load and environmental conditions. The construction process is common knowledge and will not be described in detail.

[0070] The above steps of the dynamic energy consumption model can reflect the energy consumption performance of the heat pump unit under different operating conditions based on historical and real-time data, and thus evaluate the efficiency of the heat pump unit. By comparing the historical and current theoretical coefficients of performance through the above steps, abnormal heat pump units can be quickly identified, and timely adjustments or repairs can be made to avoid system inefficiency due to unit failure.

[0071] S26: Obtain the thermal radiation distribution image from the panoramic scan of the infrared thermal imager based on the temperature field distribution map; filter key significant abnormal areas in the thermal radiation distribution image based on the position coordinates of the abnormal units; calculate the heat loss rate of the key significant abnormal areas; use the heat loss rate of the key significant abnormal areas to identify weak pipe sections; evaluate the thermal energy of each weak pipe section to determine the optimal transmission path (i.e., the transmission path is the pipe section); and construct a load scheduling instruction.

[0072] It should be noted that thermal imaging provides a non-contact, real-time, and comprehensive way to monitor the thermal state of a system. It can quickly locate areas where there may be heat loss or anomalies, effectively avoiding energy waste caused by the inability to identify problem points. By locating abnormal units and filtering out abnormal areas, the most important problem areas can be effectively focused on, which can save computing resources and accelerate the solution of problems.

[0073] The heat loss rate of the screened significant anomaly areas is calculated. The heat loss rate is typically related to factors such as temperature difference, heat transfer coefficient, and pipe material. Calculation quantifies the heat loss in each area. Calculating the heat loss rate allows for more accurate identification of weak pipe sections with high heat loss, enabling targeted scheduling or improvement. Through the aforementioned thermal energy assessment, the system can more accurately determine which pipe sections require heat transfer optimization. Calculating the optimal transmission path maximizes heat transfer. Based on the aforementioned thermal energy assessment and optimal transmission path, load scheduling instructions are constructed to ensure that each weak pipe section can transfer heat energy in the most optimized manner.

[0074] Specifically, such as Figure 4 As shown, in step S26, a panoramic scan of the thermal radiation distribution image by an infrared thermal imager is obtained based on the temperature field distribution map. Key significant abnormal areas are screened from the thermal radiation distribution image based on the position coordinates of the abnormal units. The heat loss rate of these key significant abnormal areas is calculated, and weak pipe sections are identified using the heat loss rate. The optimal transmission path (i.e., the transmission path is the pipe section) is evaluated for the thermal energy of each weak pipe section, and a load scheduling instruction is constructed. The specific operation steps are as follows:

[0075] Steps S261-S263 mainly describe how to acquire thermal radiation distribution images using an infrared thermal imager based on a temperature field distribution map; how to perform pixel clustering on the thermal radiation distribution images to filter out abnormal deviation areas; how to determine whether the position coordinates of the abnormal unit are within the position coordinates of the abnormal deviation area to filter out key and significantly abnormal areas;

[0076] The actual heat loss rate is obtained by calculating the original temperature difference between the inlet and outlet fluid temperatures, the obtained flow rate and velocity, the fluid specific heat capacity, and the original temperature of each monitoring node using the heat balance equation.

[0077] Weak pipe sections are screened by the heat loss rate of each pipe section in key areas of significant anomalies.

[0078] S261: Based on the temperature field distribution map, use an infrared thermal imager to perform a panoramic scan of the pipeline network and the ground to obtain thermal radiation distribution images;

[0079] The thermal radiation distribution image is converted to grayscale to obtain a grayscale thermal radiation distribution image; a grayscale deviation threshold is preset; it is then determined whether the grayscale value of a pixel in the grayscale thermal radiation distribution image is greater than the grayscale deviation threshold.

[0080] If so, the pixels that deviate from the grayscale threshold are clustered to obtain an abnormal deviation cluster, which is then used as the abnormal deviation region.

[0081] The location coordinates of the abnormal deviation area are determined, and it is determined whether the location coordinates of the abnormal unit are within the location coordinates of the abnormal deviation area (that is, by matching the expected location of the abnormal unit with the abnormal deviation area identified in the thermal radiation distribution image, the problem area can be located more accurately; if the two locations coincide, it indicates that the abnormal unit predicted by the system is consistent with the abnormal deviation area actually captured by thermal radiation changes, which helps to identify which pipe sections in the problem area (i.e., the key and significant abnormal area) have problems; by comparing two different data sources (monitoring node data and infrared thermal images), more comprehensive pipeline network status information can be obtained).

[0082] If so, the abnormal deviation area is determined to be a key significant abnormal area (i.e., the key area to be detected).

[0083] It should be noted that the temperature field distribution map reflects the temperature distribution areas in the pipeline network. Therefore, based on the temperature field distribution map, the infrared thermal imager's temperature measurement range, resolution, and imaging frequency are adjusted to run the scanning program and continuously image target areas such as the ground surface and pipeline network to obtain thermal radiation distribution images. The above steps use threshold filtering to identify pixels with significantly abnormal grayscale values ​​as areas of drastic temperature changes and cluster pixels with local overheating as abnormal deviation areas.

[0084] The above steps compare the abnormal deviation area with the location of the abnormal unit to determine if there is any overlap. If so, it is identified as a key and significant abnormal area. Figure 5 As shown, this helps to identify areas of uneven heat transfer and overheating in the pipeline network for focused inspection;

[0085] S262: Collect the heat transfer area and flow rate of the pipeline network; calculate the heat transfer coefficient based on the heat transfer area and the inlet and outlet temperature difference of the monitoring node;

[0086] The theoretical heat loss rate of each pipe segment is obtained by calculating the heat transfer coefficient and the inlet and outlet temperature difference of the monitoring node.

[0087] Calculate the original temperature difference between each monitoring node.

[0088] The actual heat loss rate is obtained by calculating the inlet and outlet temperature difference, flow rate, fluid specific heat capacity, and original temperature difference of the monitoring node using the heat balance equation.

[0089] It should be noted that the above-mentioned steps involve collecting the heat transfer area of ​​the pipeline network, determining the surface area for heat transfer in the pipe section, calculating the heat transfer coefficient using the heat transfer area and the inlet and outlet temperature difference of the monitoring node, calculating the theoretical heat loss rate of the pipe section using the heat transfer coefficient and the temperature difference, and calculating the original temperature difference between the monitoring nodes. Through the calculation of these parameters in the above steps, the degree of heat loss in different parts of the pipeline network can be accurately assessed. Especially in critical pipe sections or abnormal areas, by comparing the actual heat loss rate and the theoretical heat loss rate, unreasonable heat transfer can be identified, and energy waste or potential thermal efficiency problems in the operation of the pipeline network can be predicted.

[0090] S263: Calculate the difference between the theoretical heat loss rate and the actual heat loss rate for each pipe segment in the key significantly abnormal area; preset a heat loss difference threshold; determine whether the difference in heat loss rate is greater than the heat loss difference threshold;

[0091] If so, the pipe segment with a heat loss difference exceeding the threshold is marked as a weak segment, and its location is determined (i.e., indicating a significant discrepancy between the actual heat loss rate and the theoretical calculation value; this significant discrepancy may be due to decreased pipe insulation performance (i.e., decreased pipe insulation leading to excessively cold environment and rapid heat dissipation) or abnormal flow velocity of the transmission medium (i.e., slow fluid flow velocity can also lead to rapid heat loss and dissipation), thus classifying this pipe segment as a weak segment); see [link to relevant documentation]. Figure 5 It can be seen that the pipe sections in the key areas of significant abnormality may be normal pipe sections or weak pipe sections.

[0092] It should be noted that the above method compares the theoretical heat loss rate with the actual heat loss rate of pipe sections in key areas of significant anomalies, calculating the difference between them. When the heat loss difference exceeds a threshold, the pipe section is identified as a weak section, and its location is marked (i.e., a weak section is one where the actual heat transfer effect is significantly lower than the design requirements due to factors such as aging insulation, insufficient flow velocity, or other operating conditions, resulting in a large heat loss, reflecting deficiencies in heat transfer and fluid transport). By comparing the theoretical and actual heat loss rates, it is possible to identify which pipe sections have significant heat loss. By setting a difference threshold, weak sections can be further screened, improving the energy efficiency of the pipeline network. This process identifies and optimizes weak areas in the pipeline network, ensuring the stability and efficiency of the pipeline system, and enabling scheduling decisions for pipeline network operation and energy management.

[0093] S264: The corresponding monitoring node of the weak pipe section is designated as a heat storage node (i.e., the heat storage node is a newly built heat storage tank installed on the monitoring node to store fluid as a heat storage node). Load nodes and waste heat nodes are selected from the heat storage nodes. The optimal transmission path is evaluated by calculating the heat energy of the load nodes and the heat energy of the waste heat nodes using a greedy algorithm, and a load scheduling instruction is constructed. The fluid temperature (i.e., the actual node temperature) and actual pressure of each monitoring node are collected using a sensor array to adjust the fluid flow rate of the load scheduling instruction, and the final load scheduling instruction is obtained.

[0094] It should be noted that by classifying the heat storage nodes, the system can clearly identify which areas require more heat (load nodes) and which areas can recover heat (waste heat nodes). A greedy algorithm is applied, based on the thermal energy of the load nodes and waste heat nodes, to calculate and evaluate the greedy selection value to choose the optimal heat transfer path. In each step, the greedy algorithm prioritizes the currently optimal path, thus gradually approaching the global optimum.

[0095] Specifically, such as Figure 6 As shown, in step S264, the corresponding monitoring node of the weak pipe section is designated as a heat storage node, and load nodes and waste heat nodes are selected from the heat storage nodes. A greedy algorithm is used to calculate the optimal transmission path based on the heat energy of the load nodes and the heat energy of the waste heat nodes, and a load scheduling command is constructed. A sensor array is used to collect fluid temperature and actual pressure data from each monitoring node, and the fluid flow rate is adjusted according to the load scheduling command to obtain the final load scheduling command. The specific operation steps are as follows:

[0096] S2641: The corresponding monitoring node of the weak pipe section is used as a heat storage node (that is, the heat storage node is a newly built heat storage tank installed on the monitoring node to store fluid as a heat storage node; at the same time, the weak pipe section is a long pipe section with two ends, namely the inlet end and the outlet end, which are the monitoring ends. The two monitoring nodes corresponding to the weak pipe section with heat and transmission problems are used as heat storage nodes, that is, the heat of one end heat storage node (that is, the subsequent load node) is transferred to the other end heat storage node (that is, the subsequent waste heat node) for processing), and a heat storage node distribution map is constructed.

[0097] The thermal energy at the current time point and the maximum thermal storage capacity of the thermal storage node are obtained for the thermal storage node.

[0098] The second load factor is calculated using the original temperature of the heat storage node (i.e., the monitoring node), the thermal energy of the heat storage node, and the maximum heat storage capacity.

[0099] Obtain the preset load threshold of the thermal storage node; determine whether the second load degree is greater than or less than the load threshold of the thermal storage node;

[0100] If the load exceeds the load threshold of the heat storage node, the heat storage node is determined to be a load node (that is, the heat storage capacity is already fully loaded and cannot store more heat for use as a load medium).

[0101] If the load is less than the load threshold of the heat storage node, the heat storage node is determined to be a waste heat node (that is, the heat storage capacity can still store more heat for use as an unloaded medium).

[0102] It should be noted that, based on the monitoring nodes of the weak pipe sections as heat storage nodes, the heat storage nodes are evaluated for thermal energy and maximum heat storage capacity. Simultaneously, a second load factor is calculated (i.e., the second load factor is calculated for the heat storage nodes at both ends of the weak pipe section; the heat storage node represents the load on the heat transferred through the weak pipe section, filtering between load nodes and waste heat nodes; therefore, this second load factor is calculated for the weak pipe section, which differs from the first load factor calculated for the heat pump equipment). This determines whether a node is a load node or a waste heat node, assessing the heat storage capacity of the monitoring nodes and effectively identifying which nodes have reached their load limit (load nodes) and which can continue to store heat (waste heat nodes). This helps the system rationally allocate heat storage capacity, avoiding overload or resource waste; it ensures that the system rationally utilizes the heat storage capacity of each heat storage node during scheduling, avoiding efficiency reduction or equipment damage due to overload, and improving the system's safety and stability.

[0103] Steps S2642-S2643 mainly describe how a greedy algorithm is used to calculate the heat transfer driving force of the thermal energy between the load node and the waste heat node; the maximum heat transfer driving force and the minimum pipe section pressure drop determined by the pressure fluctuation variance are selected to calculate the greedy selection evaluation value of the weak pipe section; and the optimal transmission path is selected by the greedy selection evaluation value.

[0104] Calculate the heat energy to be transferred between the load node and the waste heat node of the optimal transmission path and the node temperature difference; calculate based on the heat energy to be transferred, the transmission time of the heat energy to be transferred, the node temperature difference, and the specific heat capacity of the fluid.

[0105] A load scheduling instruction is constructed based on the optimal transmission path and the quality flow.

[0106] S2642: Use a greedy algorithm to calculate the heat transfer driving force (i.e., the driving force is the fluid from the load node to the waste heat node, reflecting the heat flow driven by the temperature difference between the two nodes) based on the heat energy of the load node, the heat energy of the waste heat node, the heat transfer coefficient, and the heat transfer area.

[0107] For the weak pipe section, select the largest heat transfer driving force (i.e., the connection between the load node and the waste heat node is the weak pipe section). Based on the pressure fluctuation variance, determine the minimum pipe section pressure drop for the weak pipe section (i.e., the pressure fluctuation variance can reflect the pipe section pressure drop, which has been explained in step S21 and will not be repeated here).

[0108] The greedy selection evaluation value is obtained by weighting the maximum heat transfer driving force and the minimum pipe section pressure drop.

[0109] Based on the distribution map of heat storage nodes, the greedy selection evaluation value of weak pipe sections is used to sort the heat transfer between load nodes and waste heat nodes, which is then used as the optimal transmission path (i.e. the optimal transmission pipe section).

[0110] It should be noted that the above steps use a greedy algorithm to evaluate the thermal energy, heat transfer coefficient, and heat transfer area of ​​the load node and the waste heat node, calculate the heat transfer driving force, and select the optimal transmission path based on the heat transfer driving force and pressure fluctuation variance. The greedy algorithm selects the optimal transmission path based on the heat transfer driving force (i.e., the heat flow driven by the temperature difference) to ensure that the heat energy transfer efficiency between weak pipe sections is maximized. By maximizing the heat transfer efficiency, the system can reduce heat energy loss in the transmission process and ensure that heat can be transferred more effectively from the load node to the waste heat node, thereby improving the efficiency of the entire heating system.

[0111] S2643: Calculate the thermal energy required to be transmitted by combining the thermal energy of the load node of the optimal transmission path with the maximum heat storage capacity of the waste heat node, and record the transmission time;

[0112] Calculate the node temperature difference between the original temperature of the load node and the original temperature of the waste heat node in the optimal transmission path.

[0113] The mass flow rate (i.e., the mass of the fluid) is calculated using the heat energy to be transferred, the transfer time, the node temperature difference, and the specific heat capacity of the fluid.

[0114] The optimal transmission path and the quality flow are used to construct a load scheduling instruction;

[0115] It should be noted that the calculation of the heat energy required for the transfer between the load node and the waste heat node, the recording of the transfer time, the calculation of the temperature difference between the nodes, the calculation of the mass flow rate, the construction of load scheduling instructions, the calculation of the actual heat energy required for the transfer with the flow rate and temperature difference, and the generation of load scheduling instructions ensure that the fluid can be accurately scheduled according to actual needs.

[0116] The above steps ensure that each part of the fluid transmission reaches the required temperature by accurately calculating the heat energy and flow rate, without causing heat waste or insufficiency, and optimize the distribution and transmission efficiency of heat energy.

[0117] Steps S2644-S2646 mainly describe how to use a sensor array to collect fluid temperature and actual pressure at each monitoring node, calculate the average pressure gradient and average temperature drop rate respectively, and use the average pressure gradient and average temperature drop rate to determine whether the heat pump distributed heating system should start the load scheduling command.

[0118] If a load scheduling command is initiated, the pressure gradient difference and temperature drop rate difference are calculated using the average pressure gradient and average temperature drop rate, respectively. The pressure gradient difference and temperature drop rate difference are calculated using the incremental PID calculation formula to obtain the flow rate adjustment amount. The flow rate adjustment amount is used to modify the flow rate of the current pipe segment according to the load scheduling command to obtain the adjusted fluid flow rate.

[0119] The heat loss compensation coefficient is calculated by obtaining the fluid flow rate before and after the adjustment; the theoretical heat loss rate is calculated using the heat loss compensation coefficient; the difference between the theoretical heat loss rate and the actual heat loss rate is calculated to re-screen weak pipe sections; and the above steps are repeated to construct a new load scheduling instruction based on the re-screened weak pipe sections.

[0120] S2644: Uses a sensor array to collect fluid temperature (i.e., actual node temperature) and actual pressure at each monitoring node;

[0121] Calculate the pressure difference between the inlet and outlet of the monitoring node and the temperature difference between the inlet and outlet fluids of the monitoring node.

[0122] Calculate the distance between adjacent monitoring nodes as the pipe segment length (i.e., transmission distance).

[0123] The average pressure gradient is calculated using the pipe section length and the pressure difference between the inlet and outlet.

[0124] The average temperature drop rate is calculated using the length of the pipe section and the temperature difference between the inlet and outlet fluids.

[0125] It should be noted that by using a sensor array to collect fluid temperature and pressure data, key parameters such as pressure difference, temperature difference, and pipe length are calculated for each pipe section, thereby analyzing the basic fluid flow conditions. The collected sensor data provides actual temperature, pressure, and other parameters for subsequent analysis, helping the system to understand the fluid conditions in real time and make corresponding adjustments. The acquisition of actual data ensures that the system maintains a good operating state during dynamic changes, and promptly detects and corrects potential problems (such as excessive pressure or excessive temperature difference).

[0126] S2645: Pre-set the maximum allowable temperature drop threshold and the maximum allowable pressure gradient threshold;

[0127] Determine whether the average pressure gradient is greater than the maximum permissible pressure gradient threshold and whether the average temperature drop rate is greater than the maximum permissible temperature drop rate threshold.

[0128] If any of the above criteria is greater than the maximum allowable temperature drop rate threshold or the maximum allowable pressure gradient threshold, then the heat pump distributed heating system is determined to start a load scheduling command (that is, the heat storage node must consider not only the load of fluid temperature and fluid pressure, but also the risk of pressure drop in the pipe section and the reduction of heat loss due to the fluid flow rate, so the load scheduling command is started to reduce the fluid flow rate).

[0129] It should be noted that the maximum allowable values ​​for temperature drop rate and pressure gradient are set to determine whether the pressure difference and temperature difference in the pipe section exceed the threshold. If they do, a load scheduling command is initiated to reduce the flow rate. The above-mentioned real-time monitoring of the temperature drop and pressure gradient of the system ensures that the system will not suffer heat loss or equipment damage due to exceeding the normal range. If the threshold is exceeded, the system will automatically adjust the flow rate to avoid excessive losses. By monitoring the pressure and temperature of the pipe section in real time, abnormal situations can be effectively prevented, ensuring the stability and safety of the entire system operation.

[0130] S2646: When the load scheduling command is initiated, calculate the pressure gradient difference between the average pressure gradient and the maximum allowable pressure gradient threshold, and calculate the temperature drop rate difference between the average temperature drop rate and the maximum allowable temperature drop rate threshold.

[0131] The flow rate adjustment is obtained by calculating the pressure gradient difference and the temperature drop rate difference using the incremental PID calculation formula.

[0132] The flow rate of the current pipe segment is modified by the flow rate adjustment amount to obtain the adjusted fluid flow rate;

[0133] Obtain the fluid velocity before adjustment; calculate the heat loss compensation coefficient using the thermal efficiency, the adjusted fluid velocity, and the fluid velocity before adjustment.

[0134] It should be noted that after initiating the load scheduling command, the flow rate adjustment is calculated, and the flow rate is adjusted according to the incremental PID calculation formula to ensure system efficiency. The formula is as follows: ;in: Indicates at time The increment of the flow rate adjustment; Indicates at time The pressure gradient difference and the temperature drop rate difference; and These are the pressure gradient difference and the temperature drop rate difference between the two time points preceding time k, respectively. , and These are the proportional, integral, and derivative control coefficients, respectively. The above steps use a PID control algorithm to precisely adjust the flow rate, avoiding excessive pressure gradients and temperature drop rates, and reducing heat loss. Adjusting the flow rate can effectively prevent pipe losses caused by excessive pressure and temperature differences, while optimizing the energy transfer process and improving the overall efficiency of the system.

[0135] S2647: Recalculate the theoretical heat loss rate using the aforementioned heat loss compensation coefficient;

[0136] The difference in heat loss rate is recalculated by comparing the recalculated theoretical heat loss rate with the actual heat loss rate.

[0137] Determine whether the difference in the recalculated loss rate is greater than the threshold for the difference in heat loss;

[0138] If so, the pipe segment with a heat loss difference exceeding the threshold should be re-marked as a weak pipe segment;

[0139] By using the remarked weak pipe segment, the above steps S2641-S2645 are performed to obtain a new load scheduling instruction.

[0140] It should be noted that in step S262 above, the theoretical heat loss rate is calculated using the heat transfer coefficient. However, after multiple steps, the fluid flow rate is adjusted, which affects the heat loss. Therefore, it is necessary to recalculate the theoretical heat loss rate. This is achieved in step S2646 above by calculating the heat loss compensation coefficient using the adjusted fluid flow rate, the original fluid flow rate, and the thermal efficiency. This compensates for the heat loss caused by the adjusted flow rate, and the theoretical heat loss rate is recalculated (the specific calculation is the same as in step S262 above, and will not be repeated here). The thermal efficiency is calculated using the previous cycle of the system, i.e., when the system is operating at normal flow rate. In step S23, this is used to help evaluate the current operating status of the system in real time and select the optimal adjustment control parameters during scheduling processes such as greedy algorithms to achieve efficient operation of the overall system.

[0141] Example 1

[0142] like Figure 7 As shown, the present invention also provides a commissioning and evaluation system for a heat pump distributed heating system, comprising: a raw data module 10; a load construction instruction module 20; and an instruction issuance module 30.

[0143] The raw data module 10 is used to determine the structure of the heating network, identify each monitoring node in the network based on the network structure, arrange sensor arrays at each monitoring node to collect raw fluid data at each time point, and install an electric power sensor in each heat pump unit in the heating network to collect power data using the electric power sensor of the heat pump unit.

[0144] The load command module 20 is used to preprocess the raw data of each monitoring node to extract the active heat exchange area; use the active heat exchange area to construct a temperature field distribution map from the raw temperature in the raw data of the monitoring node; calculate the theoretical performance coefficient of the heat pump based on the power data of each heat pump unit to evaluate abnormal units; use the temperature field distribution map and abnormal units to determine key significant abnormal areas, evaluate the optimal transmission path for the key significant abnormal areas, and construct a load scheduling command.

[0145] The instruction module 30 is used to issue the load scheduling instruction using the heat pump distributed heating system to perform fluid scheduling of the heating network.

[0146] Example 3

[0147] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described heat pump distributed heating system commissioning and evaluation methods.

[0148] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; those skilled in the art can modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for commissioning and evaluating a distributed heat pump heating system, characterized in that, The following steps are included: The heating network structure is determined, and each monitoring node is identified based on the network structure. Sensor arrays are arranged at each monitoring node to collect raw fluid data at each time point. Power sensors are installed in each heat pump unit in the heating network to collect power data. The raw data from each monitoring node is preprocessed to extract the active heat exchange zone; the acquisition frequency of the monitoring node corresponding to the active heat exchange zone is adjusted, and the raw temperature of the corresponding monitoring node is re-acquired to calculate the local temperature gradient; a temperature field distribution map is constructed using the local temperature gradient and the temperature gradients of the other monitoring nodes; the inlet and outlet fluid temperatures and heat pump flow rates are acquired for each heat pump unit, and the instantaneous heat generation power is calculated; the theoretical performance coefficient is obtained based on the instantaneous heat generation power and power data; the theoretical performance coefficient is compared with the acquired historical theoretical performance coefficients to determine that the heat pump unit corresponding to the theoretical performance coefficient is an abnormal unit; a panoramic scan of the thermal radiation distribution image is acquired based on the temperature field distribution map, and key significantly abnormal areas are screened from the thermal radiation distribution image based on the location coordinates of the abnormal units; The heat loss rate of the key and significantly abnormal areas is calculated, and the weak pipe sections are identified using the heat loss rate of the key and significantly abnormal areas; the optimal heat transmission path is evaluated for each weak pipe section, and load scheduling instructions are constructed. The load scheduling command is issued by the heat pump distributed heating system to perform fluid scheduling of the heating network.

2. The commissioning and evaluation method for a distributed heat pump heating system according to claim 1, characterized in that, The raw data from each monitoring node is preprocessed to extract the active hot-swapping area. The specific steps are as follows: The raw temperature and pressure at each time point are extracted from the raw data of each monitoring node; the temperature gradient change rate and pressure fluctuation variance between each monitoring node are calculated based on the raw temperature and pressure; and the active heat exchange zone is selected by using the temperature gradient change rate and a preset change threshold.

3. The method for commissioning and evaluating a distributed heat pump heating system according to claim 2, characterized in that, A panoramic scan of the thermal radiation distribution image obtained by an infrared thermal imager is used to acquire a temperature field distribution map. Based on the location coordinates of the abnormal units, key and significantly abnormal areas are selected from the thermal radiation distribution image. The heat loss rate of these key and significantly abnormal areas is calculated, and the weak pipe sections are identified using the heat loss rate of these areas. The specific operation steps are as follows: Based on the temperature field distribution map, an infrared thermal imager is used to acquire a thermal radiation distribution image; the thermal radiation distribution image is clustered into pixels to filter out abnormal deviation areas; it is determined whether the position coordinates of the abnormal unit are in the position coordinates of the abnormal deviation area, and key significantly abnormal areas are filtered out. The actual heat loss rate is obtained by calculating the original temperature difference between the inlet and outlet fluid temperatures, the obtained flow rate and velocity, the fluid specific heat capacity, and the original temperature of each monitoring node using the heat balance equation. Weak pipe sections are screened by the heat loss rate of each pipe section in key areas of significant anomalies.

4. The commissioning and evaluation method for a distributed heat pump heating system according to claim 3, characterized in that, The optimal heat transfer path is assessed for each weak pipe section, and load scheduling instructions are constructed. The specific operation steps are as follows: The corresponding monitoring nodes of the weak pipe sections are designated as heat storage nodes. Load nodes and waste heat nodes are selected from the heat storage nodes. A greedy algorithm is used to calculate the optimal transmission path by evaluating the heat energy of the load nodes and the heat energy of the waste heat nodes, and a load scheduling command is constructed. The fluid temperature and actual pressure of each monitoring node are collected by a sensor array, and the fluid flow rate of the load scheduling command is adjusted to obtain the final load scheduling command.

5. The commissioning and evaluation method for a distributed heat pump heating system according to claim 4, characterized in that, The corresponding monitoring nodes of the weak pipe sections are designated as heat storage nodes. Load nodes and waste heat nodes are then selected from these heat storage nodes. The specific operation steps are as follows: The corresponding monitoring nodes of the weak pipe sections are designated as heat storage nodes, and the maximum heat storage capacity of the heat storage nodes is calculated. The load of each heat storage node is calculated based on the maximum heat storage capacity of the heat storage nodes. Based on the load of each heat storage node and the preset heat storage node load threshold, load nodes and waste heat nodes are distinguished.

6. The method for commissioning and evaluating a distributed heat pump heating system according to claim 5, characterized in that, A greedy algorithm is used to calculate the optimal transmission path by evaluating the thermal energy of the load node and the thermal energy of the waste heat node, and a load scheduling instruction is constructed. The specific operation steps are as follows: A greedy algorithm is used to calculate the heat transfer driving force between the load node and the waste heat node; the maximum heat transfer driving force and the minimum pipe section pressure drop determined by the pressure fluctuation variance are selected to calculate the evaluation value of the weak pipe section using a greedy selection algorithm. The optimal transmission path is selected by greedily choosing the evaluation value; Calculate the heat energy to be transferred between the load node and the waste heat node of the optimal transmission path and the node temperature difference; The calculation is based on the required heat energy, the required heat energy transmission time, the node temperature difference, and the fluid specific heat capacity. Load scheduling instructions are constructed based on the optimal transmission path and quality flow.

7. The method for commissioning and evaluating a distributed heat pump heating system according to claim 6, characterized in that, The fluid temperature and actual pressure are collected at each monitoring node using a sensor array. The fluid flow rate is then adjusted based on the load scheduling command to obtain the final load scheduling command. The specific operation steps are as follows: The sensor array is used to collect fluid temperature and actual pressure at each monitoring node, and the average pressure gradient and average temperature drop rate are calculated respectively. The average pressure gradient and average temperature drop rate are used to determine whether the heat pump distributed heating system should start the load scheduling command. If a load scheduling command is initiated, the pressure gradient difference and temperature drop rate difference are calculated using the average pressure gradient and average temperature drop rate, respectively. The pressure gradient difference and temperature drop rate difference are calculated using the incremental PID calculation formula to obtain the flow rate adjustment amount. The flow rate adjustment amount is used to modify the flow rate of the current pipe segment according to the load scheduling command to obtain the adjusted fluid flow rate. The heat loss compensation coefficient is calculated by obtaining the fluid flow rate before and after adjustment; the theoretical heat loss rate is calculated using the heat loss compensation coefficient; the difference between the theoretical heat loss rate and the actual heat loss rate is used to re-screen weak pipe sections; and the above steps are repeated to construct a new load scheduling instruction based on the re-screened weak pipe sections.

8. A commissioning and evaluation system for a heat pump distributed heating system, characterized in that, The evaluation is performed using the commissioning and evaluation method for a heat pump distributed heating system as described in any one of claims 1-7, including: a raw data module; a load command construction module; and a command issuance module. The raw data module is used to determine the structure of the heating network, identify each monitoring node in the network based on the network structure, arrange sensor arrays at each monitoring node to collect raw fluid data at each time point, and install an electric power sensor in each heat pump unit in the heating network to collect power data. The load command module is used to preprocess the raw data of each monitoring node to extract the active heat exchange area; use the active heat exchange area to construct a temperature field distribution map from the raw temperature in the raw data of the monitoring node; calculate the theoretical performance coefficient of the heat pump based on the power data of each heat pump unit to evaluate abnormal units; use the temperature field distribution map and abnormal units to determine key significant abnormal areas, evaluate the optimal transmission path for the key significant abnormal areas, and construct load scheduling commands. The instruction issuing module is used to issue the load scheduling instruction using the heat pump distributed heating system to perform fluid scheduling of the heating network.

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