A heat pump temperature data intelligent control system and method based on big data

Abstract

The present invention discloses a heat pump temperature data intelligent control system and method based on big data, which relates to the technical field of heat pump data management, obtains the historical operation record of a target heat pump, obtains the target temperature of a water circulation heating system, divides the working cycle of the target heat pump in a day into a first working cycle and a second working cycle, obtains the inspection strategy of the heat pump in the second working cycle, fits the first target curve and the second target curve into a water temperature maintenance curve, obtains the outdoor air temperature change rate corresponding to the indoor heating area, compares the water temperature drop rate of the heat pump in a unit detection time with the temperature drop rate of the heating area, judges the working state of the target heat pump in the second working cycle, and when the temperature drop rate of the indoor heating area is greater than the outdoor air temperature drop rate, judges the abnormal working state of the circulating water heating system according to the water temperature change rate of the adjacent unit detection time.

Description

Technical Field

[0001] This invention relates to the field of heat pump data management technology, specifically to a heat pump temperature data intelligent control system and method based on big data. Background Technology

[0002] In cold weather, heat pumps are used to heat the air inside houses. A common method involves a heat pump in a heat pump station heating the water in the heating system, which then flows through pipes to the houses requiring heating and recirculates back to the heat pump station. The heat pump heating unit repeatedly heats the circulating water, transferring heat to the houses that need heating.

[0003] During actual operation, technicians found that some houses remain unoccupied for extended periods, leading to energy waste from continuous, full-power heating. Current technology addresses this by adjusting the heat pump's power output during specific time periods. However, this introduces new problems: adjusting the heat pump's power output impacts the heating system's lifespan. Due to unstable heating, the system is more susceptible to damage when using a power-adjusting heat pump management strategy. This presents challenges for maintenance personnel. Summary of the Invention

[0004] The purpose of this invention is to provide a heat pump temperature data intelligent control system and method based on big data, so as to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a method for intelligent control of heat pump temperature data based on big data.

[0006] Step S100: Set the heat pump in the water circulation heating system that provides circulating water heating for a certain indoor heating area as the target heat pump, obtain the historical operation record of the target heat pump, obtain the heating law of the target heat pump under different heating power, obtain the target temperature of the water circulation heating system, and take the water temperature change curve with the lowest heating energy consumption of the target heat pump when the water temperature reaches the target temperature as the target curve.

[0007] Step S200: Divide the working cycle of the target heat pump in a day into a first working cycle and a second working cycle. Set the water temperature at the beginning of the target curve as the first target water temperature. Set the water temperature of the circulating water at the end of the first working cycle as the second target water temperature. Obtain the inspection strategy of the heat pump in the second working cycle. Fit the first target curve and the second target curve into a water temperature maintenance curve.

[0008] Step S300: Take the time of one water circulation in the circulating water heating system as the unit detection time, obtain the rate of change of outdoor air temperature in the indoor heating area within a unit detection time, and calculate the rate of change of water temperature in the target heat pump and the rate of change of temperature in the heating area within a unit detection time.

[0009] Step S400: When the rate of temperature drop in the indoor heating area is less than or equal to the rate of temperature drop in the outdoor temperature, compare the rate of temperature drop in the heat pump water during the unit detection time with the rate of temperature drop in the heating area to determine the working status of the target heat pump in the second working cycle.

[0010] Step S500: When the rate of temperature drop in the indoor heating area is greater than the rate of temperature drop in the outdoor air, the rate of change of water temperature is obtained. Based on the rate of change of water temperature during the detection period of adjacent units, the difference between the water temperature at the endpoint of the detection interval and the target curve is determined to identify the abnormal working state of the circulating water heating system and to issue an alarm to relevant management personnel.

[0011] Furthermore, step S100 includes:

[0012] Step S101: Set the dividing point between the first working cycle and the second working cycle of the target heat pump as the first dividing point, set the dividing point between the second working cycle and the first working cycle of the target heat pump as the second dividing point, and set the water temperature of the water circulation heating system at the second dividing point as the target temperature.

[0013] The first working cycle refers to the continuous heating mode when people are staying in the indoor heating area. The second working cycle refers to the mode when no one is staying in the indoor heating area for a long time. In order to save energy consumption, the heating power of the heat pump is reduced, so that the temperature of the indoor heating area drops slowly. This saves energy consumption for heating and also plays a certain role in heat preservation of the indoor heating area, thereby reducing the time to reheat the indoor heating area and reducing the working pressure of the heating system.

[0014] Step S102: Obtain several historical records of the target heat pump heating the circulating water to the target temperature, and merge the water temperature change curves corresponding to p historical records into the set THW, THW: (η1, η2, η3, ..., η p ), where, eta1, eta2, eta3,... and eta p , representing the 1st, 2nd, 3rd, ... and pth water temperature change curves, respectively;

[0015] Step S103: Obtain the transition time of the target heat pump's operating state from the second working cycle to the first working cycle, and select the water temperature change curve over time from the set THW that has the same heating time and transition time and the lowest heating energy consumption of the target heat pump as the target curve.

[0016] Furthermore, step S200 includes:

[0017] Step S201: Establish a coordinate plane of circulating water temperature with respect to time, draw the target curve in the coordinate plane, set the endpoint of the target temperature in the target curve as the end position of the target curve, and set the position of the other endpoint of the target curve as the start position.

[0018] Step S202: Obtain the second target water temperature of the circulating water at the first dividing point, take the coordinate points corresponding to the first dividing point and the second target water temperature as the starting point of the water temperature maintenance curve, and set the starting position of the target curve as the ending point of the water temperature maintenance curve.

[0019] To ensure the safe use of the heat pump heating system, the system cannot be started and stopped immediately. The change in the heating power of the heat pump must comply with relevant regulations. Therefore, during the switching process from the second working cycle to the first working cycle, it is necessary to heat the water in the heating system for a certain period of time. When the water temperature reaches the target temperature, the switching of the heat pump heating system from the second working cycle to the first working cycle is completed.

[0020] Within a certain time range, the heating curve is screened based on two conditions: time and heating power, to obtain the most suitable heating method. The starting position of the target curve is then used as the endpoint of the water temperature maintenance curve.

[0021] This setup has two advantages. First, the time-based filtering criteria align with the operating habits of relevant staff. By adjusting the heat pump's operating time within the management system, the system can select the appropriate heating method based on the operating time. Second, during the heating process, due to differences in the initial water temperature or ambient temperature, as well as the heat pump's design, the heat pump has a heating power that is most energy-efficient for a given time under different conditions. Selecting this heating power achieves the effect of saving energy.

[0022] Step S203: Several sampling points are set at the starting point and the ending point of the water temperature maintenance curve. Each sampling point includes the time corresponding to the sampling point and the reference water temperature corresponding to the sampling point. The starting point, the ending point and all sampling points of the water temperature maintenance curve are collected and curve fitting is performed to obtain the water temperature maintenance curve.

[0023] Furthermore, step S300 includes:

[0024] Step S301: Obtain the unit detection duration U0, and set the unit detection cycle according to the unit detection duration;

[0025] Step S302: Obtain the temperature at the beginning of any unit detection cycle, subtract the temperature at the end of any unit detection cycle to obtain the temperature difference, and divide the temperature difference by the unit detection duration to obtain the outdoor air temperature change rate or the temperature change rate of the heating area.

[0026] Step S303: Obtain the water temperature at the target heat pump outlet at the start of any unit detection cycle, subtract the water temperature at the target heat pump inlet at the end of any unit detection cycle to obtain the water temperature difference, and divide the water temperature difference by the unit detection duration to obtain the water temperature change rate.

[0027] Furthermore, step S400 includes:

[0028] Step S401: Obtain the detection period Z of the j-th unit. j Obtain the unit detection cycle Z j The corresponding rate of temperature decrease R in the indoor heating area j and the rate of decrease of outdoor temperature K j ;

[0029] Step S402: When R j ≤K j At that time, obtain Z j The corresponding rate of change of water temperature Q j Obtain the unit detection cycle Z j The target water pump inlet temperature at the end time is recorded as W. 1 j Obtain the unit detection cycle Z j At the end, the water temperature W corresponding to the water temperature maintenance curve. 2 j Compare R j With Q j Relationship;

[0030] When R j ≤K j <Q j And W 1 j >W 2 j At that time, the target heat pump is in its first operating state;

[0031] In the formula, the direction of the less than sign indicates the direction of slower temperature decrease. When the rate of decrease of outdoor temperature is less than the rate of decrease of indoor temperature, it means that due to the heating protection measures, the change in indoor temperature effectively counteracts the decrease in outdoor temperature, and the temperature compensation measures play a role.

[0032] When Q j >K j This indicates that the heating compensation came at a high cost, and comparing W at this point...1 j and W 2 j The value of W 1 j >W 2 j This indicates that the water temperature has exceeded the design value of the insulation strategy, resulting in energy waste.

[0033] When Q j <R j ≤K j And W 1 j <W 2 j At that time, the target heat pump is in its second operating state;

[0034] When Q j Less than K j This indicates that the heating compensation came at a high cost, and comparing W at this point... 1 j and W 2 j The value of W 1 j <W 2 j This indicates that the water temperature is lower than the design value of the insulation strategy, and the insulation effect has not achieved the expected result.

[0035] The effectiveness of the insulation strategy is evaluated by combining the rate of temperature change with the water temperature value, and the working status of the heat pump is verified from two different perspectives to improve the accuracy of the judgment results.

[0036] The remaining states of the target heat pump are the third operating state.

[0037] Furthermore, step S500 includes:

[0038] Step S501: When R j >K j At that time, obtain the unit detection cycle Z. j The previous unit testing cycle Z j-1 The corresponding rate of temperature decrease R in the indoor heating area j-1 Water temperature change rate Q j-1 Obtain the unit detection cycle Z j-1 The target water pump inlet temperature at the end time is recorded as W. 1 j-1 Obtain the unit detection cycle Z j-1 At the end, the water temperature W corresponding to the water temperature maintenance curve. 2 j-1 ;

[0039] Step S502: Calculate the unit detection period Z respectively. j Comparison coefficient α j and unit testing cycle Z j-1 Comparison coefficient α j-1 , where α j =(R j +Q j )×(W 1 j -W 2 j ), α j-1 =(R j-1 +Q j-1 )×(W 1 j-1 -W 2 j-1 );

[0040] Step S503: When α j <α j-1 At that time, it was determined that there was an abnormality in the circulating water heating system;

[0041] When R j >K j This indicates that the rate of decrease in indoor temperature exceeds the rate of decrease in outdoor temperature, resulting in a relatively serious indoor temperature loss. The fact that the rate of decrease in indoor temperature exceeds the rate of decrease in natural temperature indicates that the heating system is not providing sufficient insulation, or even that the heating system is having a counterproductive effect, causing the indoor temperature to drop.

[0042] Comparing data from two adjacent sampling time points, the inlet temperature of the heat pump during normal operation is the lowest temperature in the circulating water of the entire heating system. The insulation strategy targets the average water temperature for indoor heating. Generally, the insulation strategy temperature is lower than the design temperature of the insulation strategy. Comparing data from two adjacent data collection points when α... j <α j-1 This indicates that the temperature loss in the heating system is worsening.

[0043] To better implement the above method, a heat pump temperature data intelligent control system based on big data is also proposed. The system includes: a data selection module, a data fitting module, a temperature detection module, a working status management module, and an anomaly management module. The data selection module is used to select the target curve, the data fitting module is used to obtain the water temperature maintenance curve, the temperature detection module is used to obtain the temperature change rate, the working status management module is used to determine the working status of the heat pump, and the anomaly management module is used to determine the abnormal working status of the circulating water heating system.

[0044] Furthermore, the data selection module includes a historical data management unit and a data filtering unit, wherein the historical data management unit is used to manage historical data of heat pump heating, and the data filtering unit is used to select the target curve;

[0045] Furthermore, the data fitting module includes: an endpoint management unit, a detection strategy acquisition unit, and a data fitting unit. The endpoint management unit is used to manage the start and end points of the water temperature maintenance curve, the detection strategy acquisition unit is used to manage the sampling points in the water temperature maintenance curve, and the data fitting unit is used to acquire the water temperature maintenance curve.

[0046] Furthermore, the working status management module includes: a sampling management unit, a condition judgment unit, and a status judgment unit. The sampling management unit is used to collect data in a unit detection cycle, the condition judgment unit is used to compare and judge the rate of temperature change, and the status judgment unit is used to judge the working status of the heat pump.

[0047] Furthermore, the anomaly management module includes: a period comparison unit, a comparison coefficient calculation unit, and an anomaly judgment unit. The period comparison unit is used to acquire data from two adjacent unit detection cycles, the comparison coefficient calculation unit is used to calculate the comparison coefficient corresponding to the unit detection cycle, and the anomaly judgment unit is used to determine whether there is an anomaly in the circulating water heating system.

[0048] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By obtaining the heating curve of a heat pump with low energy consumption, the present invention reverse-engineers the temperature drop reference curve of the indoor heating area after the pump's operating power is adjusted, calculates the rate of temperature change in the circulating heating system, verifies the working status of the heat pump from two different perspectives to improve the accuracy of the judgment results, and provides an alarm for abnormal temperature drop rates, giving reference suggestions for temperature protection strategies. Relevant personnel can then make timely adjustments to the system's heating management to reduce the inspection difficulty for relevant operation and maintenance personnel. Attached Figure Description

[0049] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0050] Figure 1 This is a schematic diagram of the structure of a heat pump temperature data intelligent control system based on big data, as per this invention patent.

[0051] Figure 2 This is a flowchart illustrating an intelligent temperature control method for heat pumps based on big data, as per this invention patent. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figure 1 and Figure 2 The present invention provides the following technical solution:

[0054] Step S100: Set the heat pump in the water circulation heating system that provides circulating water heating for a certain indoor heating area as the target heat pump, obtain the historical operation record of the target heat pump, obtain the heating law of the target heat pump under different heating power, obtain the target temperature of the water circulation heating system, and take the water temperature change curve with the lowest heating energy consumption of the target heat pump when the water temperature reaches the target temperature as the target curve.

[0055] Step S100 includes:

[0056] Step S101: Set the dividing point between the first working cycle and the second working cycle of the target heat pump as the first dividing point, set the dividing point between the second working cycle and the first working cycle of the target heat pump as the second dividing point, and set the water temperature of the water circulation heating system at the second dividing point as the target temperature.

[0057] Step S102: Obtain several historical records of the target heat pump heating the circulating water to the target temperature, and merge the water temperature change curves corresponding to p historical records into the set THW, THW: (η1, η2, η3, ..., η p ), where, eta1, eta2, eta3,... and eta p , representing the 1st, 2nd, 3rd, ... and pth water temperature change curves, respectively;

[0058] Step S103: Obtain the transition time of the target heat pump's working state from the second working cycle to the first working cycle, and select the water temperature change curve over time from the set THW that has the same heating time and transition time and the lowest heating energy consumption of the target heat pump as the target curve;

[0059] Step S200: Divide the working cycle of the target heat pump in a day into a first working cycle and a second working cycle. Set the water temperature at the beginning of the target curve as the first target water temperature. Set the water temperature of the circulating water at the end of the first working cycle as the second target water temperature. Obtain the inspection strategy of the heat pump in the second working cycle. Fit the first target curve and the second target curve into a water temperature maintenance curve.

[0060] In the embodiments, the checking strategy is as follows:

[0061] In a certain implementation project, the circulating water temperature is set with a temperature compensation value every 5℃ from -30℃ to 15℃, for a total of 10 compensation values. The compensation temperature is calculated based on the difference between the current outdoor temperature and the temperature. For example, the compensation value is 0 when the outdoor temperature is 0℃, -0.3 when the outdoor temperature is 5℃, and -1 when the outdoor temperature is 10℃. If the current outdoor temperature is 6.5℃, then the compensation value is -0.51℃.

[0062] Every day from 0:00 to 23:00, a temperature compensation value is set every hour, for a total of 24 compensation values. The compensation temperature is calculated by interpolation based on the current time, using the same calculation method as above. To ensure rapid temperature rise in the morning, the current time compensation values ​​are: 6:00: 1℃, 7:00: 2℃, 8:00: 3℃, 9:00: 2℃, 10:00: 1℃, 11:00-14:00: -1℃, and 0℃ for the remaining times. For example, if the current time is 6:27, the compensation value is 1.45℃.

[0063] The actual controlled water supply temperature is 36℃ + outdoor temperature compensation value + time compensation value. In the example above, the controlled temperature = 36 - 0.51 + 1.45 = 36.94℃, rounded to one decimal place, which is 36.9℃. The actual control range is 36.9℃ ± 2℃.

[0064] The first work cycle is from 4:00 to 17:30 daily, and the second work cycle is from 17:30 to 4:00 the next day. Public holidays are also designated as the entire second work cycle. The daily second work cycle can be adjusted according to weather conditions and terminal conditions, and whether public holidays are designated as the second work cycle can be adjusted as needed.

[0065] Step S200 includes:

[0066] Step S201: Establish a coordinate plane of circulating water temperature with respect to time, draw the target curve in the coordinate plane, set the endpoint of the target temperature in the target curve as the end position of the target curve, and set the position of the other endpoint of the target curve as the start position.

[0067] Step S202: Obtain the second target water temperature of the circulating water at the first dividing point, take the coordinate points corresponding to the first dividing point and the second target water temperature as the starting point of the water temperature maintenance curve, and set the starting position of the target curve as the ending point of the water temperature maintenance curve.

[0068] Step S203: Several sampling points are set at the starting point and the ending point of the water temperature maintenance curve. Each sampling point includes the time corresponding to the sampling point and the reference water temperature corresponding to the sampling point. The starting point, the ending point and all sampling points of the water temperature maintenance curve are collected and curve fitting is performed to obtain the water temperature maintenance curve.

[0069] Step S300: Take the time of one water circulation in the circulating water heating system as the unit detection time, obtain the rate of change of outdoor air temperature in the indoor heating area within a unit detection time, and calculate the rate of change of water temperature in the target heat pump and the rate of change of temperature in the heating area within a unit detection time.

[0070] Step S300 includes:

[0071] Step S301: Obtain the unit detection duration U0, and set the unit detection cycle according to the unit detection duration;

[0072] Step S302: Obtain the temperature at the beginning of any unit detection cycle, subtract the temperature at the end of any unit detection cycle to obtain the temperature difference, and divide the temperature difference by the unit detection duration to obtain the outdoor air temperature change rate or the temperature change rate of the heating area.

[0073] Step S303: Obtain the water temperature at the target heat pump outlet at the start of any unit detection cycle, subtract the water temperature at the target heat pump inlet at the end of any unit detection cycle to obtain the water temperature difference, and divide the water temperature difference by the unit detection duration to obtain the water temperature change rate.

[0074] Step S400: When the rate of temperature drop in the indoor heating area is less than or equal to the rate of temperature drop in the outdoor temperature, compare the rate of temperature drop in the heat pump water during the unit detection time with the rate of temperature drop in the heating area to determine the working status of the target heat pump in the second working cycle.

[0075] Step S400 includes:

[0076] Step S401: Obtain the detection period Z of the j-th unit. j Obtain the unit detection cycle Z j The corresponding rate of temperature decrease R in the indoor heating area j and the rate of decrease of outdoor temperature K j ;

[0077] Step S402: When R j ≤K j At that time, obtain Z j The corresponding rate of change of water temperature Q j Obtain the unit detection cycle Z j The target water pump inlet temperature at the end time is recorded as W. 1 j Obtain the unit detection cycle Z j At the end, the water temperature W corresponding to the water temperature maintenance curve. 2 j Compare R j With Q jRelationship;

[0078] When R j ≤K j <Q j And W 1 j >W 2 j At that time, the target heat pump is in its first operating state;

[0079] When Q j <R j ≤K j And W 1 j <W 2 j At that time, the target heat pump is in its second operating state;

[0080] The remaining states of the target heat pump are the third operating state.

[0081] Step S500: When the rate of temperature drop in the indoor heating area is greater than the rate of temperature drop in the outdoor air, the rate of change of water temperature is obtained. Based on the rate of change of water temperature during the detection time of adjacent units, the difference between the water temperature at the endpoint of the detection interval and the target curve is determined to identify the abnormal working state of the circulating water heating system and to issue an alarm to the relevant management personnel.

[0082] Step S500 includes:

[0083] Step S501: When R j >K j At that time, obtain the unit detection cycle Z. j The previous unit testing cycle Z j-1 The corresponding rate of temperature decrease R in the indoor heating area j-1 Water temperature change rate Q j-1 Obtain the unit detection cycle Z j-1 The target water pump inlet temperature at the end time is recorded as W. 1 j-1 Obtain the unit detection cycle Z j-1 At the end, the water temperature W corresponding to the water temperature maintenance curve. 2 j-1 ;

[0084] Step S502: Calculate the unit detection period Z respectively. j Comparison coefficient α j and unit testing cycle Z j-1 Comparison coefficient α j-1 , where α j =(R j +Q j )×(W 1 j -W2 j ), α j-1 =(R j-1 +Q j-1 )×(W 1 j-1 -W 2 j-1 );

[0085] Step S503: When α j <α j-1 At that time, it was determined that there was an abnormality in the circulating water heating system;

[0086] For example: when R j =0.6, Q j =1.2, W 1 j -W 2 j = -3;R j-1 =0.5, Q j-1 =0.8, W 1 j-1 -W 2 j-1 = -2.5;

[0087] α j = -5.4, α j-1 = -3.25, α j <α j-1 This indicates a significant abnormal downward trend in water temperature, suggesting an anomaly in the circulating water heating system.

[0088] The system includes:

[0089] Data selection module, data fitting module, temperature detection module, working status management module, and anomaly management module;

[0090] The data selection module is used to select the target curve. The data selection module includes a historical data management unit and a data filtering unit. The historical data management unit is used to manage the historical data of heat pump heating, and the data filtering unit is used to select the target curve.

[0091] The data fitting module is used to obtain the water temperature maintenance curve. The data fitting module includes an endpoint management unit, a detection strategy acquisition unit, and a data fitting unit. The endpoint management unit is used to manage the start and end points of the water temperature maintenance curve, the detection strategy acquisition unit is used to manage the sampling points in the water temperature maintenance curve, and the data fitting unit is used to obtain the water temperature maintenance curve.

[0092] The temperature detection module is used to obtain the rate of temperature change.

[0093] The working status management module is used to determine the working status of the heat pump. The working status management module includes a sampling management unit, a condition judgment unit, and a status judgment unit. The sampling management unit is used to collect data in a unit detection cycle, the condition judgment unit is used to compare and judge the rate of temperature change, and the status judgment unit is used to judge the working status of the heat pump.

[0094] The anomaly management module is used to determine the abnormal working state of the circulating water heating system. The anomaly management module includes a cycle comparison unit, a comparison coefficient calculation unit, and an anomaly judgment unit. The cycle comparison unit is used to obtain data in two adjacent unit detection cycles, the comparison coefficient calculation unit is used to calculate the comparison coefficient corresponding to the unit detection cycle, and the anomaly judgment unit is used to determine whether there is an anomaly in the circulating water heating system.

[0095] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0096] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A heat pump temperature data intelligent control method based on big data, characterized in that: The method comprises the following steps: Step S100: setting a heat pump in a water circulation heating system for circulating water heating in a certain indoor heating area as a target heat pump, obtaining the historical operation record of the target heat pump, obtaining the heating law of the target heat pump under different heating powers, obtaining the target temperature of the water circulation heating system, and taking the water temperature change curve with the lowest heating energy consumption of the target heat pump when the water temperature reaches the target temperature as the target curve; Step S200: dividing the working cycle of the target heat pump in a day into a first working cycle and a second working cycle, setting the water temperature at the starting position of the target curve as the first target water temperature, setting the water temperature of the circulating water at the end of the first working cycle to the second target water temperature, obtaining the inspection strategy of the heat pump in the second working cycle, and fitting the first target curve and the second target curve into a water temperature maintenance curve; Step S300: taking the duration of one water circulation in the circulating water heating as the unit detection duration, obtaining the outdoor air temperature change rate corresponding to the indoor heating area in one unit detection duration, and calculating the water temperature change rate in the target heat pump and the temperature change rate of the heating area in one unit detection duration; Step S400: When the temperature drop rate of the indoor heating area is less than or equal to the outdoor temperature drop rate, the heat pump water temperature drop rate in the unit detection time is compared with the temperature drop rate of the heating area to determine the working state of the target heat pump in the second working cycle; Step S500: When the temperature drop rate of the indoor heating area is greater than the outdoor temperature drop rate, the water temperature change rate is obtained, and the abnormal working state of the circulating water heating system is judged according to the water temperature change rate of the adjacent unit detection time and the difference between the water temperature at the end point of the detection interval and the target curve, and an alarm is issued to the relevant management personnel; Wherein, step S300 includes: Step S301: Obtain the unit detection duration U0, and set the unit detection period according to the unit detection duration; Step S302: Obtain the temperature at the start of any unit detection cycle, subtract the temperature at the end of any unit detection cycle to obtain a temperature difference, and divide the temperature difference by the unit detection time to obtain the outdoor temperature change rate or the temperature change rate of the heating area; Step S303: Obtain the water temperature at the target heat pump outlet at the start time of any unit detection cycle, subtract the water temperature at the target heat pump inlet at the end time of any unit detection cycle, obtain the water temperature difference, and divide the water temperature difference by the unit detection time to obtain the water temperature change rate; Wherein, step S400 includes: Step S401: Obtain the jth unit detection cycle Z j , get the unit detection cycle Z j The corresponding temperature drop rate R of the indoor heating area j and the outdoor temperature drop rate K j ; Step S402: When R j ≤K j When Z j The corresponding water temperature change rate Q j , get the unit detection cycle Z j The target water temperature at the water pump inlet at the end time is recorded as W 1 j , get the unit detection cycle Z j At the end of the water temperature, the water temperature corresponding to the water temperature curve is maintained at W 2 j , compare R j With Q j relationship; When R j ≤K j <Q j , and W 1 j >W 2 j When , the target heat pump is in the first working state; When Q j <R j ≤K j , and W 1 j <W 2 j When , the target heat pump is in the second working state; The remaining state of the target heat pump is the third working state; Wherein, step S500 includes: Step S501: When R j >K j When the unit detection period Z is obtained j The previous unit detection cycle Z j-1 The corresponding temperature drop rate R of the indoor heating area j-1 , water temperature change rate Q j-1 , get the unit detection cycle Z j-1 The target water temperature at the water pump inlet at the end time is recorded as W 1 j-1 , get the unit detection cycle Z j-1 At the end of the water temperature, the water temperature corresponding to the water temperature curve is maintained at W 2 j-1 ; Step S502: Calculate the unit detection period Z respectively j The comparison coefficient α j and unit detection period Z j-1 The comparison coefficient α j-1 , where α j =(R j +Q j )×(W 1 j -W 2 j ), α j-1 =(R j-1 +Q j-1 )×(W 1 j-1 -W 2 j-1 ); Step S503: When α j <α j-1 When the circulating water heating system is abnormal, it is judged that there is an abnormality.

2. According to the big data-based heat pump temperature data intelligent control method of claim 1, it is characterized by: Step S100 includes: Step S101: setting the boundary time point of switching from the first working cycle to the second working cycle of the target heat pump as the first boundary point, setting the boundary time point of switching from the second working cycle to the first working cycle of the target heat pump as the second boundary point, and setting the water temperature of the water circulation heating system at the second boundary point to the target temperature; Step S102: Obtain several historical records of the target heat pump heating the circulating water to the target temperature, and collect the water temperature change curves corresponding to the p historical records into a set THW, THW: (η1, η2, η3, ..., η p ), where, eta1, eta2, eta3,... and eta p , respectively represent the first, second, third, ... and pth water temperature variation curves; Step S103: Obtain the conversion time of the working state of the target heat pump from the second working cycle to the first working cycle, and select from the set THW the water temperature variation curve with time whose heating time is consistent with the conversion time and whose heating energy consumption of the target heat pump is the lowest as the target curve.

3. According to claim 2, a heat pump temperature data intelligent control method based on big data is characterized in that: Step S200 includes: Step S201: establishing a coordinate plane of circulating water temperature with respect to time, drawing a target curve in the coordinate plane, setting the endpoint corresponding to the target temperature in the target curve as the end position of the target curve, and setting the position where the other endpoint of the target curve is located as the starting position; Step S202: obtaining the second target water temperature of the circulating water at the first dividing point, taking the coordinate point corresponding to the first dividing point and the second target water temperature as the starting point of the water temperature keeping curve, and setting the starting position of the target curve as the end point of the water temperature keeping curve; Step S203: A number of sampling points are set at the starting point and the end point of the water temperature maintenance curve, each sampling point includes the time corresponding to the sampling point and the reference water temperature corresponding to the sampling point, and the starting point, the end point and all sampling points of the water temperature maintenance curve are collected to perform curve fitting to obtain the water temperature maintenance curve.

4. A heat pump temperature data intelligent control system using the heat pump temperature data intelligent control method based on big data as described in any one of claims 1 to 3, characterized in that: The system includes the following modules: a data selection module, a data fitting module, a temperature detection module, a working status management module and an abnormality management module, wherein the data selection module is used to select a target curve, the data fitting module is used to obtain a water temperature maintenance curve, the temperature detection module is used to obtain a temperature change rate, the working status management module is used to determine the working status of a heat pump, and the abnormality management module is used to determine an abnormal working status of a circulating water heating system.

5. The heat pump temperature data intelligent control system according to claim 4 is characterized in that: The data selection module includes: a historical data management unit and a data screening unit, wherein the historical data management unit is used to manage the historical data of heat pump heating, and the data screening unit is used to select the target curve; The data fitting module includes: an endpoint management unit, a detection strategy acquisition unit and a data fitting unit, wherein the endpoint management unit is used to manage the starting point and end point of the water temperature maintenance curve, the detection strategy acquisition unit is used to manage the sampling points in the water temperature maintenance curve, and the data fitting unit is used to obtain the water temperature maintenance curve.

6. The heat pump temperature data intelligent control system according to claim 4, characterized in that: The working status management module includes: a sampling management unit, a condition judgment unit and a status judgment unit, wherein the sampling management unit is used to collect data in a unit detection cycle, the condition judgment unit is used to compare and judge the temperature change rate, and the status judgment unit is used to judge the working status of the heat pump.

7. The heat pump temperature data intelligent control system according to claim 4 is characterized in that: The abnormality management module includes: a cycle comparison unit, a comparison coefficient calculation unit and an abnormality judgment unit, wherein the cycle comparison unit is used to obtain data in two adjacent unit detection cycles, the comparison coefficient calculation unit is used to calculate the comparison coefficient corresponding to the unit detection cycle, and the abnormality judgment unit is used to judge whether there is an abnormality in the circulating water heating system.

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