Method and system for detecting operation state of ground source heat pump unit

Abstract

The embodiment of the present application discloses a kind of ground source heat pump unit operating state detection method and system, first operating state data is generated by first heat pump operation activity data, when second heat pump operation activity data does not exist in specified task cycle, second operating state data is derived according to first operating state data, so even in the case without new data, the current state of heat pump can be reasonably estimated and predicted.This prediction mechanism provides necessary data support for second task issuing node, allows to determine the corresponding task issuing matching data according to the best available information in the case of incomplete data.In addition, it also includes the step of optimizing second operating state data according to the new operation activity data detected after the second task issuing node, to ensure that its operation strategy can be dynamically adjusted according to the latest real-time data, to improve the response speed and operation accuracy of ground source heat pump unit.

Description

Technical Field

[0001] This invention relates to the field of intelligent equipment control technology, and more specifically, to a method and system for detecting the operating status of a ground source heat pump unit. Background Technology

[0002] Ground source heat pump (GSHP) systems are energy-saving technologies that utilize stable underground temperature resources for heating and cooling. In practical operation, ensuring the efficient and stable operation of the heat pump system is crucial for improving energy utilization and reducing operating costs. Traditional heat pump management systems typically rely on periodically collected operational data to adjust system parameters to adapt to environmental changes and user demands. However, this approach has limitations, such as the possibility of data collection interruptions due to technical or other external factors, causing the system to fail to respond promptly to changes in actual operating conditions. Furthermore, the lack of rapid prediction and adaptive adjustment mechanisms also limits the potential for system optimization.

[0003] In existing technologies, the prediction of operational status and task matching within data acquisition intervals are often not accurate enough, which affects the overall performance and efficiency of ground source heat pump systems. Furthermore, the lack of an effective data compensation mechanism means that the system's response may be slow or inaccurate in the face of missing or delayed data. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method and system for detecting the operating status of a ground source heat pump unit.

[0005] In a first aspect, embodiments of the present invention provide a method for detecting the operating status of a ground source heat pump unit, applied to an intelligent management system, comprising:

[0006] The first operating status data of the ground source heat pump unit is obtained. The first operating status data represents the state progress vector of the ground source heat pump unit in the target operating scenario before the first task issuance node. The first operating status data is determined based on the first heat pump operation activity data before the first task issuance node. The first heat pump operation activity data is adaptively accumulated and generated according to the heat pump demand.

[0007] If the second heat pump operation activity data is not available within K task cycles after the first task issuance node, the second operation status data is determined based on the first operation status data. The second heat pump operation activity data represents the heat pump operation activity data in the target operation scenario between the first task issuance node and the second task issuance node, where K is a positive integer.

[0008] Based on the second running status data, determine the task distribution matching data corresponding to the second task distribution node.

[0009] In one possible implementation of the first aspect, when the second heat pump operation activity data is absent within K task cycles after the first task issuance node, determining the second operation status data based on the first operation status data includes:

[0010] If the second heat pump operation activity data is not available within one task cycle after the first task issuance node, and the task cycle is equal to the relative node cycle between the first task issuance node and the second task issuance node, the derived predicted state data of the first operation state data will be output as the second operation state data.

[0011] In one possible implementation of the first aspect, when there is no second heat pump operation activity data within a task cycle after the first task issuance node, and the task cycle is equal to the relative node cycle between the first task issuance node and the second task issuance node, outputting the derived predicted state data of the first operation state data as the second operation state data includes:

[0012] If the second heat pump operation activity data is not available within K1 task cycles after the first task issuance node, and the task cycle is less than the relative node cycle between the first task issuance node and the second task issuance node, the derived predicted state data of the first operation state data will be output as the second operation state data.

[0013] Wherein, the period parameter of K1 task cycles is not less than the relative period of the node, and the period parameter of K1-1 task cycles is less than the relative period of the node, and K1 is a positive integer.

[0014] In one possible implementation of the first aspect, the method further includes:

[0015] If the second heat pump operation activity data is not available within a maximum of K1-1 task cycles after the first task issuance node, the derived predicted state data of the first operation state data will be output as candidate operation state data, where K1-1 is a positive integer.

[0016] Based on the candidate running status data, candidate task matching data is determined;

[0017] The step of determining the task distribution matching data corresponding to the second task distribution node based on the second operating status data includes:

[0018] Based on the second running status data, the candidate task distribution matching data is output as the task distribution matching data corresponding to the second task distribution node.

[0019] In one possible implementation of the first aspect, when there is no second heat pump operation activity data within a task cycle after the first task issuance node, and the task cycle is equal to the relative node cycle between the first task issuance node and the second task issuance node, outputting the derived predicted state data of the first operation state data as the second operation state data includes:

[0020] If the second heat pump operation activity data is not available within K2 task cycles after the first task issuance node, and the task cycle is less than the relative node cycle between the first task issuance node and the second task issuance node, the derived predicted state data of the first operation state data will be output as the second operation state data.

[0021] Wherein, the period parameters of K2 task cycles are not greater than the relative period of the node, and the period parameters of K2+1 task cycles are greater than the relative period of the node, and K2 is a positive integer;

[0022] The step of determining the task distribution matching data corresponding to the second task distribution node based on the second operating status data includes:

[0023] Based on the second running status data, candidate task matching data is determined;

[0024] If the second heat pump operation activity data does not exist before the second task issuance node, the candidate task issuance matching data is output as the task issuance matching data corresponding to the second task issuance node.

[0025] In one possible implementation of the first aspect, the method further includes:

[0026] When the second heat pump operation activity data is detected after the second task issuance node, the second operation status data is optimized based on the second heat pump operation activity data, specifically including:

[0027] When the second heat pump operation activity data is detected after the second task issuance node, the operation status feature data corresponding to the second heat pump operation activity data is extracted.

[0028] The second operating status data is optimized based on the operating status characteristic data, which is the state variable status-related data of the target operating scenario carried in the second heat pump operating activity data.

[0029] In one possible implementation of the first aspect, the method further includes:

[0030] Based on prior operational status data, the relative period between the first task issuing node and the second task issuing node is determined;

[0031] The weighted value of the relative period of the node and the preset weight coefficient is output as the task period, where the preset weight coefficient is a value between 0 and 1.

[0032] In one possible implementation of the first aspect, the determination of the relative node period between the first task issuing node and the second task issuing node based on prior operational status data, wherein the relative node period includes at least one of the following:

[0033] Based on the operating cluster parameters of the ground source heat pump unit characterized by the prior operating state data, the relative period of the node is determined, and the relative period of the node is negatively correlated with the operating cluster parameters of the ground source heat pump unit.

[0034] Based on the rate at which the heat pump unit achieves its operating efficiency as characterized by the prior operating state data, the relative period of the node is determined, and the relative period of the node is negatively correlated with the rate at which the heat pump unit achieves its operating efficiency.

[0035] Based on the emergency demand command generation information represented by the prior operational status data, the relative period of the node is generated by period reduction according to the set period coefficient.

[0036] In a second aspect, embodiments of the present invention provide an intelligent management system, including:

[0037] processor;

[0038] A memory containing a computer program, which, when executed, implements the ground source heat pump unit operation status detection method described in the first aspect.

[0039] As described above, in this embodiment of the invention, the operating status of a heat pump can be predicted and optimized even in the event of missing data, thereby improving the adaptability and operating efficiency of the ground source heat pump unit. Specifically, by acquiring first heat pump operating activity data and generating first operating status data based on this data, the method can reflect the state progression vector of heat pump operating events, providing basic information for subsequent control decisions. When second heat pump operating activity data is not available within a specified task cycle, the second operating status data is derived from the first operating status data by introducing an algorithm model. This allows for a reasonable estimation and prediction of the current state of the heat pump even without new data. This prediction mechanism provides necessary data support for the second task issuance node, allowing the determination of the corresponding task issuance matching data based on the best available information even when data is incomplete. Furthermore, it includes a step of optimizing the second operating status data based on new operating activity data detected after the second task issuance node. This step ensures that the operating strategy can be dynamically adjusted based on the latest real-time data to improve the response speed and operating accuracy of the ground source heat pump unit. In other words, the embodiments of this application significantly improve the operating efficiency and stability of the ground source heat pump system. Attached Figure Description

[0040] Figure 1 This is a schematic flowchart of a method for detecting the operating status of a ground source heat pump unit according to an embodiment of the present invention;

[0041] Figure 2 The embodiments of the present invention are provided for execution Figure 1 A schematic diagram of the structure of an intelligent management system for detecting the operating status of ground source heat pump units.

[0042] Specific execution steps

[0043] 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art through the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0044] See Figure 1 As shown:

[0045] Step S110: Obtain the first operating status data of the ground source heat pump unit. The first operating status data represents the state progress vector of the ground source heat pump unit in the target operating scenario before the first task issuance node. The first operating status data is determined based on the first heat pump operation activity data before the first task issuance node. The first heat pump operation activity data is adaptively accumulated and generated according to the heat pump demand.

[0046] For example, suppose there is a large office building equipped with a ground source heat pump (GSHP) system to regulate indoor temperature. This system heats or cools the building by extracting heat from or releasing heat into the ground. The building's intelligent management system controls the operation of the ground source heat pump units based on different seasons, weather conditions, and indoor temperature requirements.

[0047] For example, at 8:00 AM, the intelligent management system is scheduled to execute a task aimed at optimizing the operating efficiency of the heat pump. Before issuing this task (referred to as the first task issuance node), the system needs to understand the current operating status of the ground source heat pump. Therefore, it collects heat pump operating status data at 7:59 AM, including but not limited to the following parameters: heat pump power consumption, soil temperature, water circulation flow rate, and indoor demand temperature. This data reflects the state progress vector of the ground source heat pump in the predetermined target operating scenario and is generated based on accumulated operating activity data over a period of time (e.g., data from midnight to 7:59 AM).

[0048] Step S120: If the second heat pump operation activity data is not available within K task cycles after the first task issuance node, determine the second operation status data based on the first operation status data. The second heat pump operation activity data represents the heat pump operation activity data in the target operation scenario between the first task issuance node and the second task issuance node, where K is a positive integer.

[0049] For example, assuming K=3, this means that if no new heat pump operation data is recorded in the next three task cycles (possibly due to sensor failure or communication interruption), the intelligent management system needs to infer the subsequent heat pump status based on the first operating status data at 8:00 AM. To do this, the system might use historical data patterns, known daily temperature variation trends, and previous operating efficiencies to predict the heat pump status between 8:00 AM and the second task issuance node (assumed to be 11:00 AM).

[0050] Step S130: Based on the second running status data, determine the task distribution matching data corresponding to the second task distribution node.

[0051] For example, at 11:00 AM, even without new operational data, the intelligent management system still needs to schedule a new round of tasks based on the predicted second-stage operational status data. The system considers the predicted heat pump status (including expected energy consumption, efficiency, and performance) and formulates a matching task plan. For instance, if the prediction indicates that the ground source heat pump will enter an inefficient operating phase, the system may issue a task to reduce the heat pump load or adjust the water pump flow rate to improve heat exchange efficiency. This task allocation data will be used to guide the subsequent operation of the heat pump to ensure the overall performance of the ground source heat pump unit.

[0052] Based on the above steps, this application embodiment can predict and optimize the operating status of the heat pump even when data is missing, thereby improving the adaptability and operating efficiency of the ground source heat pump unit. Specifically, by acquiring first heat pump operating activity data and generating first operating status data based on it, this method can reflect the state progress vector of heat pump operating events, providing basic information for subsequent control decisions. When second heat pump operating activity data is not available within a specified task cycle, the second operating status data is derived from the first operating status data by introducing an algorithm model. This allows for a reasonable estimation and prediction of the current state of the heat pump even without new data. This prediction mechanism provides necessary data support for the second task issuance node, allowing the determination of the corresponding task issuance matching data based on the best available information when data is incomplete. Furthermore, it includes a step of optimizing the second operating status data based on new operating activity data detected after the second task issuance node. This step ensures that the operating strategy can be dynamically adjusted based on the latest real-time data to improve the response speed and operating accuracy of the ground source heat pump unit. In other words, this application embodiment significantly improves the operating efficiency and stability of the ground source heat pump system.

[0053] In one possible implementation, step S120 may include: when there is no second heat pump operation activity data within a task cycle after the first task issuing node, and the task cycle is equal to the node relative cycle between the first task issuing node and the second task issuing node, outputting the derived predicted state data of the first operation state data as the second operation state data.

[0054] For example, in the hypothetical large office building described above, an intelligent management system controls a ground source heat pump system to regulate indoor temperature. Assume that the intelligent management system has already acquired the first operational status data at 8:00 AM.

[0055] A specific scenario needs to be addressed: after the first task is issued at 8:00 AM, the second heat pump operation data becomes unavailable for some reason during one or more subsequent task cycles. To continue effectively managing and regulating the heat pump system, the intelligent management system must utilize the latest available data (i.e., the first operating status data) to infer the future heat pump status.

[0056] First, it's important to define the task cycle. In this scenario, the task cycle can be defined as the fixed time interval at which the system checks and updates the heat pump status. For example, assuming a task cycle of 3 hours, the system will check and update the heat pump status every 3 hours.

[0057] The calculation started at 8:00 AM (the first task issuance node) and ended at 11:00 AM (the second task issuance node), which was exactly the end of a task cycle. However, no new data on the second heat pump operation was obtained during this period, possibly due to sensor malfunction, maintenance, or communication problems.

[0058] In the absence of new data, the intelligent management system will use the initial operating status data collected from 7:59 AM (including heat pump power consumption, soil temperature, water circulation flow rate, and indoor demand temperature, etc.) as a basis, combined with information such as historical operating patterns, seasonality, and daily trends, to estimate the heat pump status at 11:00 AM through a predictive algorithm. This is the so-called derived predictive status data.

[0059] The intelligent management system uses the estimated predicted status data as the second operating status data at 11:00 AM, and uses this data to formulate subsequent task instructions. For example, if the prediction results indicate that the heat pump efficiency may decrease, the system may issue a task to reduce the load or adjust parameters to optimize performance.

[0060] Through the above steps, even when the equipment status cannot be monitored in real time, the intelligent management system can maintain effective control over the ground source heat pump system to ensure that the temperature regulation of the office building is not affected, while also maximizing the system's energy efficiency.

[0061] In one possible implementation, when there is no second heat pump operation activity data within a task cycle following the first task issuance node, and the task cycle is equal to the relative node cycle between the first task issuance node and the second task issuance node, outputting the derived predicted state data of the first operation state data as the second operation state data may include:

[0062] If the second heat pump operation activity data is absent within K1 task cycles after the first task issuance node, and the task cycle is less than the relative node cycle between the first task issuance node and the second task issuance node, the derived predicted state data of the first operation state data will be output as the second operation state data.

[0063] Wherein, the period parameter of K1 task cycles is not less than the relative period of the node, and the period parameter of K1-1 task cycles is less than the relative period of the node, and K1 is a positive integer.

[0064] For example, in the previous example, we considered a scenario where an intelligent management system operates a ground source heat pump. Now, we will elaborate on how to predict the second operating status data based on the first operating status data when the second heat pump operating activity data is unavailable.

[0065] Let's continue with the example of a smart management system for a large office building. Suppose that this system periodically (e.g., hourly) collects and analyzes the operating data of the ground source heat pump to optimize temperature control throughout the building.

[0066] For example, at 8:00 AM, the intelligent management system plans to issue a task to adjust heat pump settings. However, within a subsequent task cycle (say, 9:00 AM), the system cannot acquire new second heat pump operation activity data. In this case, the task cycle equals the time between the first task issuance node and the second task issuance node (i.e., one hour). The system will use the first operating status data collected at 8:00 AM and generate the second operating status data for 9:00 AM using a predictive model.

[0067] Assume K1 is defined as 4, representing four consecutive task cycles. If the system does not obtain new heat pump operation data between 8:00 AM and 12:00 PM (covering K1 task cycles), then the system needs to continue using the first operating status data at 8:00 AM to predict the heat pump status for each subsequent hour. In this case, each task cycle (i.e., each hour) is shorter than the total duration from the first task dispatch node to the second task dispatch node.

[0068] Since K1 is 4, this means that if there is still no new data before 11:00 AM 3 hours later (K1-1), the system needs to ensure that it can continuously produce the predicted second operating status data. Therefore, the intelligent management system will use the predictive model to continuously output the operating status data at 9:00 AM, 10:00 AM, and 11:00 AM.

[0069] In this way, the intelligent management system can guide the operation of the ground source heat pump by relying on the derived predicted status data of the first operating status data for up to 3 task cycles (i.e., until 11:00 AM), even if the actual second heat pump operating activity data does not exist.

[0070] In this way, the intelligent management system can cope with data collection interruptions and ensure that the heat pump system continues to operate under optimal estimates, thereby maintaining high energy efficiency and meeting indoor temperature control requirements. This also demonstrates that even under conditions of high uncertainty, the system possesses a certain degree of adaptability and can continuously make effective operational decisions.

[0071] In one possible implementation, the method further includes:

[0072] Step A110: If the second heat pump operation activity data does not exist within a maximum of K1-1 task cycles after the first task issuance node, the derived predicted state data of the first operation state data is output as candidate operation state data, where K1-1 is a positive integer.

[0073] Step A120: Based on the candidate running status data, determine the candidate task distribution matching data.

[0074] For example, these technical steps can be explained in detail by referring to the ground source heat pump system of the large office building mentioned earlier.

[0075] The intelligent management system periodically adjusts the ground source heat pump to respond to different environmental and usage conditions. This system relies on collected heat pump operating status data to formulate and issue operational tasks.

[0076] Assuming that no new second heat pump operation data is recorded within a maximum of K1-1 task cycles after 8:00 AM (the first task issuance node), where K1-1 is a positive integer, say 3, then if no new operation data is obtained within the three hours from 8:00 AM to 11:00 AM, the intelligent management system needs to rely on the first operation status data at 8:00 AM to generate candidate operation status data for 9:00 AM, 10:00 AM, and 11:00 AM through a predictive model.

[0077] Next, the intelligent management system will use this candidate operating status data to determine the corresponding candidate tasks and issue matching data. For example, if the predictive model indicates that the external temperature will rise, causing changes in soil temperature, then candidate tasks may include reducing the heat pump load in advance or adjusting the circulating water flow to adapt to the expected changes.

[0078] Step S130 may include:

[0079] Based on the second running status data, the candidate task distribution matching data is output as the task distribution matching data corresponding to the second task distribution node.

[0080] For example, when the second task distribution node is reached at 11:00 AM, since there is no real-time data, the intelligent management system will treat the previously determined candidate task distribution matching data as the final task distribution matching data. Therefore, despite the lack of new real-time operational activity data, the system can still make decisions based on the best available information—candidate operational status data. For example, it might reduce the output of the heat pump based on the morning forecast, thereby avoiding overheating or overcooling the space.

[0081] Through these steps, the intelligent management system can continuously optimize the performance of ground source heat pumps even when data access is limited. This approach allows the system to remain flexible and efficient in the face of challenges such as sensor failures or communication interruptions, while ensuring maximum comfort and energy efficiency within the office building.

[0082] In one possible implementation, when there is no second heat pump operation activity data within a task cycle after the first task issuance node, and the task cycle is equal to the relative node cycle between the first task issuance node and the second task issuance node, outputting the derived predicted state data of the first operation state data as the second operation state data may further include:

[0083] If the second heat pump operation activity data is absent within K2 task cycles after the first task issuance node, and the task cycle is less than the relative node cycle between the first task issuance node and the second task issuance node, the derived predicted state data of the first operation state data will be output as the second operation state data.

[0084] Wherein, the period parameter of K2 task cycles is not greater than the relative period of the node, and the period parameter of K2+1 task cycles is greater than the relative period of the node, and K2 is a positive integer.

[0085] The step of determining the task distribution matching data corresponding to the second task distribution node based on the second operating status data includes:

[0086] Based on the second running status data, candidate tasks are determined and matching data is distributed.

[0087] If the second heat pump operation activity data does not exist before the second task issuance node, the candidate task issuance matching data is output as the task issuance matching data corresponding to the second task issuance node.

[0088] For example, the following steps can be used to illustrate how to implement the above technical content in a specific scenario:

[0089] Let's return to the large office building and its intelligent management system discussed earlier. This system is responsible for controlling the ground source heat pump (GSHP) to regulate indoor temperature and periodically acquiring the heat pump's operating status data to optimize system performance.

[0090] At 8:00 AM, the intelligent management system plans to issue a task to optimize heat pump efficiency (this is the first task issuance node). However, in a subsequent task cycle (let's say 9:00 AM), no new data on the second heat pump operation activity is collected. At this point, the task cycle (1 hour) equals the time between the first and second task issuance nodes.

[0091] Now, assuming K2 is defined as 2, this means that the intelligent management system may not collect new second heat pump operation activity data for the next two task cycles (i.e., until 10:00 AM). Since the total duration of these two task cycles is no greater than the relative node cycle from the first task issuance node to the second task issuance node (i.e., 2 hours), the intelligent management system will use the first operating status data obtained from 8:00 AM to output the second operating status data at 9:00 AM and 10:00 AM through the prediction model.

[0092] Using the predicted second operating status data, the intelligent management system can determine the candidate tasks and issue matching data, that is, adjust the operating parameters of the heat pump, such as power consumption and water circulation flow rate, according to the predicted status, in order to maintain efficient operation and meet the temperature control requirements of the building.

[0093] If no new data on the second heat pump operation activity is collected before 10:00 AM (the second task dispatch node), the intelligent management system will use the previously determined candidate task dispatch matching data as the final task dispatch matching data. In this way, even without the latest real-time data, the system can still ensure that the heat pump operates according to the best estimated state.

[0094] In this example, the intelligent management system demonstrated its ability to adapt to data gaps, ensuring the stable and efficient operation of the heat pump system through prediction and the generation of alternative solutions. The core of this approach lies in its ability to ensure that office building temperature regulation remains unaffected while minimizing energy waste, even when real-time monitoring data is unavailable, through intelligent prediction and strategy adjustment.

[0095] In one possible implementation, the method further includes:

[0096] Step B110: When the second heat pump operation activity data is detected after the second task issuance node, the second operation status data is optimized based on the second heat pump operation activity data, specifically including:

[0097] Step B120: When the second heat pump operation activity data is detected after the second task issuance node, extract the operation status feature data corresponding to the second heat pump operation activity data.

[0098] Step B130: Optimize the second operating status data based on the operating status feature data, wherein the operating status feature data is the state variable status-related data of the target operating scenario carried in the second heat pump operating activity data.

[0099] For example, we will continue to use previous examples of intelligent management systems for large office buildings and the operation of ground source heat pumps to illustrate these technical steps.

[0100] Due to data loss after 8:00 AM (the first task issuance node), the intelligent management system used derived predicted status data to replace the actual second heat pump operation data, and generated candidate task issuance matching data based on this. Then, at 11:00 AM (the second task issuance node), it is assumed that the system has now recovered and detected new second heat pump operation data.

[0101] After 11:00 AM, the operational data of the second heat pump was reacquired. It immediately extracted key information from this new data, such as operational status characteristics like temperature, pressure, and flow rate. This data carries status-related information about the target operating scenario's state variables, reflecting the actual performance of the heat pump and environmental conditions.

[0102] Using the collected real-time operational status data, the intelligent management system begins to optimize the previously predicted second operational status data. The optimization process may involve adjusting model parameters or recalculating the predicted values ​​using new input data to ensure that the predictions better match the actual situation. For example, if new data shows that the heat pump output is lower than expected, the system may adjust its control strategy to increase the heat pump output to meet the building's heat load requirements.

[0103] In this way, the intelligent management system can not only maintain the continuous operation of the heat pump during periods of data loss, but also use newly acquired data to correct and optimize previous forecasts and decisions. This ensures that the system responds promptly and accurately, further improving the operating efficiency of the heat pump system and the building's energy management effectiveness. Furthermore, this method enhances the system's adaptability to unforeseen circumstances, enabling it to make optimal operational decisions even when faced with incomplete or inaccurate data.

[0104] In one possible implementation, the method further includes:

[0105] Step C110: Based on prior operational status data, determine the relative node period between the first task issuing node and the second task issuing node.

[0106] Step C120: Output the weighted value of the relative period of the node and the preset weight coefficient as the task period, where the preset weight coefficient is a value between 0 and 1.

[0107] In one possible implementation, in step C110, the node relative period includes at least one of the following:

[0108] 1. Based on the operating aggregation parameters of the ground source heat pump unit characterized by the prior operating state data, determine the relative period of the node, wherein the relative period of the node and the operating aggregation parameters of the ground source heat pump unit are negatively correlated.

[0109] 2. Based on the rate at which the heat pump unit achieves its operating efficiency as represented by the prior operating state data, the relative period of the node is determined, and the relative period of the node is negatively correlated with the rate at which the heat pump unit achieves its operating efficiency.

[0110] 3. Based on the emergency demand command generation information represented by the prior operating status data, the relative period of the node is generated by period reduction according to the set period coefficient.

[0111] For example, we will continue to explore the scenario of operating a ground source heat pump (GSHP) in a smart management system for a large office building, and specifically explain how to determine the task cycle based on prior operating status data.

[0112] In this office building, an intelligent management system regularly adjusts the heat pump settings to maintain efficient energy use and a comfortable indoor environment. The system sets the cycle between task issuance nodes based on various parameters, such as heat pump operating efficiency, historical data, and emergency demand commands.

[0113] By analyzing historically collected heat pump operating data, key aggregated parameters affecting heat pump performance, such as average power consumption and start-stop frequency, can be identified. Trends and changes in these parameters may indicate that the heat pump requires more frequent monitoring or adjustments. For example, if data shows an increased heat pump start-up frequency, this may mean that external conditions (such as temperature changes) are more volatile, and therefore the node relative cycle should be shortened so that the system can respond to these changes more quickly.

[0114] On the other hand, if it is noted that the time required for the heat pump to reach the predetermined operating efficiency has shortened, this may mean that the system has adapted to the current operating conditions, and therefore the relative cycle of the nodes can be extended to reduce frequent adjustments.

[0115] If a sudden event or special need causes the system to generate an emergency demand command, such as a sudden change in weather or heating / cooling demand during unusual working hours, the intelligent management system may need to reduce the relative cycle of nodes according to the set cycle coefficient to ensure timely response to these emergencies.

[0116] Finally, a relative cycle for each node will be given based on the actual situation (considering factors such as running aggregation parameters, speed of achieving running efficiency, and emergency needs). Then, the final task cycle will be calculated using a preset weighting coefficient (a value between 0 and 1). This weighting coefficient can be adjusted according to management strategies or other considerations to reflect the importance of different factors in determining the task cycle.

[0117] For example, suppose at 8:00 AM, the intelligent management system calculates based on historical data that the relative cycle of nodes should theoretically be two hours. However, because a large meeting is scheduled for 10:00 AM, requiring additional cooling capacity, the system decides to adjust the task cycle to be shorter than two hours by setting a higher weighting coefficient (e.g., 0.8). The final task cycle will be the product of the relative cycle of nodes and the weighting coefficient, ensuring that the necessary cooling can be provided for the meeting in a timely manner.

[0118] In this way, the intelligent management system can flexibly optimize the task cycle based on historical and real-time data, as well as other relevant factors, to ensure that the heat pump system operates efficiently and meets ever-changing needs.

[0119] Based on the above description, in another embodiment, the present invention also provides an intelligent management system, see below. Figure 2 , Figure 2 The diagram illustrates the structure of an intelligent management system 100 provided in this embodiment of the invention. The intelligent management system 100 can vary significantly due to different configurations or performance characteristics, and may include one or more central processing units (CPUs) 112 (e.g., one or more processors) and a memory 111. The memory 111 may be temporary or persistent storage. The program stored in the memory 111 may include one or more modules, each module comprising a series of instruction operations within the intelligent management system 100. Furthermore, the central processing unit 112 may be configured to communicate with the memory 111 and execute the series of instruction operations stored in the memory 111 on the intelligent management system 100.

[0120] The intelligent management system 100 may also include one or more power supplies, one or more communication units 113, one or more output interfaces, and / or one or more operating systems, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0121] The steps performed by the intelligent management system in the above embodiments can be combined Figure 2 The structure of the intelligent management system is shown.

[0122] In addition, embodiments of the present invention also provide a storage medium for storing a computer program for executing the methods provided in the above embodiments.

[0123] This invention also provides a computer program product including instructions that, when run on a computer, cause the computer to perform the methods provided in the above embodiments.

[0124] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium can be at least one of the following media: read-only memory (ROM), RAM, magnetic disk, or optical disk, etc., and other media capable of storing program code.

[0125] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. The focus of each embodiment is to describe the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiments. The device and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment based on actual needs. Those skilled in the art can understand and implement this without creative effort.

[0126] The above description is merely one specific implementation step of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for detecting the operating status of a ground source heat pump unit, characterized in that, include: The first operating status data of the ground source heat pump unit is obtained. The first operating status data represents the state progress vector of the ground source heat pump unit in the target operating scenario before the first task issuance node. The first operating status data is determined based on the first heat pump operation activity data before the first task issuance node. The first heat pump operation activity data is adaptively accumulated and generated according to the heat pump demand. If the second heat pump operation activity data is not available within K task cycles after the first task issuance node, the second operation status data is determined based on the first operation status data. The second heat pump operation activity data represents the heat pump operation activity data in the target operation scenario between the first task issuance node and the second task issuance node, where K is a positive integer. Based on the second running status data, determine the task distribution matching data corresponding to the second task distribution node; The method further includes: Based on prior operational status data, the relative period between the first task issuing node and the second task issuing node is determined; The weighted value of the relative period of the node and the preset weight coefficient is output as the task period, where the preset weight coefficient is a value between 0 and 1.

2. The method for detecting the operating status of a ground source heat pump unit according to claim 1, characterized in that, When the second heat pump operation activity data is not available within K task cycles after the first task issuance node, determining the second operation status data based on the first operation status data includes: If the second heat pump operation activity data is not available within one task cycle after the first task issuance node, and the task cycle is equal to the relative node cycle between the first task issuance node and the second task issuance node, the derived predicted state data of the first operation state data will be output as the second operation state data.

3. The method for detecting the operating status of a ground source heat pump unit according to claim 1, characterized in that, include: If the second heat pump operation activity data is not available within K1 task cycles after the first task issuance node, and the task cycle is less than the relative node cycle between the first task issuance node and the second task issuance node, the derived predicted state data of the first operation state data will be output as the second operation state data. Wherein, the period parameter of K1 task cycles is not less than the relative period of the node, and the period parameter of K1-1 task cycles is less than the relative period of the node, and K1 is a positive integer.

4. The method for detecting the operating status of a ground source heat pump unit according to claim 3, characterized in that, The method further includes: If the second heat pump operation activity data is not available within a maximum of K1-1 task cycles after the first task issuance node, the derived predicted state data of the first operation state data will be output as candidate operation state data, where K1-1 is a positive integer. Based on the candidate running status data, candidate task matching data is determined; The step of determining the task distribution matching data corresponding to the second task distribution node based on the second operating status data includes: Based on the second running status data, the candidate task distribution matching data is output as the task distribution matching data corresponding to the second task distribution node.

5. The method for detecting the operating status of a ground source heat pump unit according to claim 1, characterized in that, include: If the second heat pump operation activity data is not available within K2 task cycles after the first task issuance node, and the task cycle is less than the relative node cycle between the first task issuance node and the second task issuance node, the derived predicted state data of the first operation state data will be output as the second operation state data. Wherein, the period parameters of K2 task cycles are not greater than the relative period of the node, and the period parameters of K2+1 task cycles are greater than the relative period of the node, and K2 is a positive integer; The step of determining the task distribution matching data corresponding to the second task distribution node based on the second operating status data includes: Based on the second running status data, candidate task matching data is determined; If the second heat pump operation activity data does not exist before the second task issuance node, the candidate task issuance matching data is output as the task issuance matching data corresponding to the second task issuance node.

6. The method for detecting the operating status of a ground source heat pump unit according to any one of claims 1-5, characterized in that, The method further includes: When the second heat pump operation activity data is detected after the second task issuance node, the second operation status data is optimized based on the second heat pump operation activity data, specifically including: When the second heat pump operation activity data is detected after the second task issuance node, the operation status feature data corresponding to the second heat pump operation activity data is extracted. The second operating status data is optimized based on the operating status characteristic data, which is the state variable status-related data of the target operating scenario carried in the second heat pump operating activity data.

7. The method for detecting the operating status of a ground source heat pump unit according to claim 1, characterized in that, Based on prior operational status data, the relative period between the first task issuing node and the second task issuing node is determined, and the relative period includes at least one of the following: Based on the operating cluster parameters of the ground source heat pump unit characterized by the prior operating state data, the relative period of the node is determined. The relative period of the node and the operating cluster parameters of the ground source heat pump unit are negatively correlated. The operating cluster parameters include average power consumption and start-stop frequency. Based on the rate at which the heat pump unit achieves its operating efficiency as characterized by the prior operating state data, the relative period of the node is determined, and the relative period of the node is negatively correlated with the rate at which the heat pump unit achieves its operating efficiency. Based on the emergency demand command generation information represented by the prior operational status data, the relative period of the node is generated by period reduction according to the set period coefficient.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the ground source heat pump unit operation status detection method according to any one of claims 1-7.

9. An intelligent management system, characterized in that, include: processor; A memory containing a computer program, which, when executed, implements the ground source heat pump unit operation status detection method according to any one of claims 1-7.

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