Heat pump system optimization control method and system based on big data

Abstract

The invention relates to the technical field of heat supply management, and discloses a heat pump system optimization control method and system based on big data, and the method comprises the steps: determining the area of a heat supply region, dividing the heat supply region to determine a plurality of initial heat supply regions, and determining the operation number of heat pumps; the area target temperatures of all the initial heat supply areas are collected and compared with a preset temperature threshold value to determine initial heat pump parameters; corresponding area real-time temperature gradient characteristics when the heat pumps operate based on the initial heat pump parameters are collected, a heat supply abnormal area is judged, and the initial heat pump parameters are adjusted to obtain adjusted heat pump parameters; and the area temperature change rate in the multiple initial heat supply areas is detected in real time, and according to the judgment result, heat pump parameters are corrected, or the heat pump operation number is adjusted, or the heat supply areas are adjusted. The operation parameters of the heat pump system are dynamically adjusted according to actual requirements and external environment changes, and the limitation of traditional manual adjustment is overcome.

Description

Technical Field

[0001] The present invention relates to the field of heat supply management technology, and in particular to a heat pump system optimization control method and system based on big data. Background Art

[0002] As global energy demand continues to increase, how to manage and utilize energy efficiently and intelligently has become an important research direction. As an energy-saving and environmentally friendly heating method, heat pump systems are widely used in various buildings and industrial facilities, with low energy consumption and high thermal efficiency. However, with the continuous changes in building area and usage requirements, traditional heat pump systems are often unable to be flexibly adjusted according to actual needs, resulting in energy waste or unsatisfactory heating effects.

[0003] In the prior art, the heat pump system mainly relies on manual allocation based on experience according to the heating area. The allocation process may not be able to feedback the changes in the external environment and the actual needs of users in real time, resulting in low accuracy of indoor temperature control.

[0004] Therefore, it is necessary to design a heat pump system optimization control method and system based on big data to solve the problems existing in current technology. Summary of the invention

[0005] In view of this, the present invention proposes a heat pump system optimization control method and system based on big data, aiming to solve the problem that the current heat pump-based heating cannot provide real-time feedback on changes in the external environment and changes in users' actual needs, resulting in low accuracy in indoor temperature control.

[0006] In one aspect, the present invention proposes a heat pump system optimization control method based on big data, comprising: Determine the area of ​​the heating area, divide the heating area based on the coverage of the heat pump, determine a number of initial heating areas, and determine the number of heat pumps to be operated based on the initial heating areas; Collecting the regional target temperatures of all the initial heating areas, comparing the regional target temperatures with the preset temperature thresholds respectively, and determining the initial heat pump parameters according to the comparison results; Collecting the real-time temperature gradient characteristics of the regions corresponding to the operation of several heat pumps based on the initial heat pump parameters, analyzing the real-time temperature gradient characteristics to determine the abnormal heating region, and adjusting the initial heat pump parameters according to the analysis results of the abnormal heating region to obtain adjusted heat pump parameters; Real-time detection of the regional temperature change rates in several initial heating areas, and based on the regional temperature change rate judgment result, the heat pump adjustment parameters are corrected, or the heat pump operation quantity is adjusted, or the heating area is adjusted.

[0007] Furthermore, the heating area is divided based on the coverage of the heat pump, a number of initial heating areas are determined, and the number of heat pump operations is determined according to the initial heating areas, including: The maximum value of the heat pump coverage is selected as the initial division standard; Apply the initial division standard to the heating area to obtain the area of ​​the area not covered in the traversal result; When the area of ​​the uncovered region is not zero, the initial division standard is reduced and the heating areas are traversed again until the area of ​​the uncovered region is zero, and a number of initial heating areas are determined, and the number of heat pump operations is consistent with the number of initial heating areas.

[0008] Further, the target temperature of each region is compared with a preset temperature threshold, and when the initial heat pump parameters are determined according to the comparison results, the initial heat pump parameters include an initial operating power; The target temperature of each area is compared with a preset first temperature threshold and a preset second temperature threshold, and the initial operating power of the heat pump corresponding to the heating area is determined according to the comparison result, wherein the first temperature threshold is less than the second temperature threshold; When the regional target temperature is less than or equal to the first temperature threshold, the initial operating power of the heat pump corresponding to the heating area is determined to be the first operating power; when the regional target temperature is greater than the first temperature threshold and less than or equal to the second temperature threshold, the initial operating power of the heat pump corresponding to the heating area is determined to be the second operating power; when the regional target temperature is greater than the second temperature threshold, the initial operating power of the heat pump corresponding to the heating area is determined to be the third operating power, the first operating power is less than the second operating power, and the second operating power is less than the third operating power.

[0009] Furthermore, when analyzing the real-time temperature gradient characteristics to determine the abnormal heating area, it includes: Match the real-time temperature and position of each point in the area and draw a real-time temperature gradient map of the area; Randomly determine a point in the real-time temperature gradient map as a point to be detected; Determine a judgment radius according to the maximum distance of the real-time temperature gradient map, determine a judgment area with the point to be detected as the center and the judgment radius as the radius, obtain an average temperature of the judgment area according to the judgment area, obtain a temperature difference according to the average temperature of the judgment area and the real-time temperature of the point to be detected, and determine whether the point to be detected is the abnormal area according to the temperature difference; The step of randomly selecting the points to be detected is repeated until all points in the real-time temperature gradient map are determined.

[0010] Further, judging whether the to-be-detected point is the abnormal area according to the temperature difference includes: When the temperature difference is greater than a times the target temperature of the area, the point to be detected is determined to be the abnormal area; When the temperature difference is less than or equal to a times the target temperature of the area, it is determined that the point to be detected is not the abnormal area.

[0011] Furthermore, the average temperature of the judgment area is calculated by the following formula:

[0012] in, represents the average temperature of the judgment area, r represents the judgment radius, represents the coordinates of the point to be detected, Indicates the real-time temperature gradient map of the area The real-time temperature of the point.

[0013] Furthermore, the initial heat pump parameters are adjusted according to the analysis results of the abnormal heating area to obtain the adjusted heat pump parameters, including: According to the analysis result of the abnormal heating area, the number of abnormal areas in the area and the temperature difference corresponding to each abnormal area are obtained, the number of abnormal areas and the temperature difference corresponding to each abnormal area are used as feature vectors, the feature vectors are compared with historical adjustment data, and the adjustment heat pump parameters are obtained according to the comparison results; the historical adjustment data includes a plurality of historical feature vectors and a plurality of historical adjustment coefficients, and each of the historical feature vectors corresponds to a historical adjustment coefficient; When there is data in the historical adjustment data whose similarity between the historical feature vector and the feature vector is greater than the similarity threshold, selecting the historical adjustment coefficient corresponding to the maximum similarity to adjust the initial heat pump parameter to obtain the adjusted heat pump parameter, which is the product of the initial heat pump parameter and the historical adjustment coefficient; When the similarity between the historical feature vector in the historical adjustment data and the feature vector is less than or equal to the similarity threshold, adjusting the initial heat pump parameters by determining the adjustment coefficient according to the feature vector to obtain the adjusted heat pump parameters;

[0014] Where C represents the adjustment coefficient, represents the nth temperature difference in the feature vector, N represents the number of abnormal areas in the feature vector, max(1+·) represents that the adjustment coefficient C is not less than 1, and min(1.5,·) represents that the adjustment coefficient C does not exceed 1.5.

[0015] Further, based on the determination result of the regional temperature change rate, the heat pump parameter is modified, or the number of heat pump operations is adjusted, or the heating area is adjusted, including: According to the real-time temperatures of all abnormal areas in the area and the target temperature of the area, an average temperature difference is obtained, and a standard temperature change rate is obtained according to the average temperature difference and a preset time threshold, and the temperature change rate of the area is compared with the standard temperature change rate, and it is determined according to the comparison result whether to modify the adjustment parameters of the heat pump, or adjust the number of operation of the heat pump, or adjust the heating area; When the temperature change rate of the area is greater than or equal to the standard temperature change rate, it is determined that the heat pump adjustment parameters are not corrected, the heat pump operation quantity is not adjusted, and the heating area is not adjusted; When the temperature change rate of the area is less than the standard temperature change rate, it is determined to correct the adjustment parameters of the heat pump, or to adjust the number of operation of the heat pump, or to adjust the heating area.

[0016] Further, when determining whether to modify the heat pump parameter, or to adjust the heat pump operation quantity, or to adjust the heating area, the method includes: When the temperature change rate of the area is less than or equal to 0.5 times the standard temperature change rate, it is determined to adjust the heating area; When the temperature change rate of the area is greater than 0.5 times the standard temperature change rate and less than or equal to 0.8 times the standard temperature change rate, it is determined to adjust the heating area and adjust the operation quantity of the heat pump; When the regional temperature change rate is greater than 0.8 times the standard temperature change rate, it is determined that the adjusted heat pump parameters are to be corrected, and a correction coefficient is determined based on the ratio of the regional temperature change rate to the standard temperature change rate to correct the adjusted heat pump parameters. The correction coefficient is inversely proportional to the ratio, and the correction coefficient is (1, 1.2).

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: through intelligent adjustment based on big data and real-time temperature monitoring, the operating parameters of the heat pump system can be dynamically adjusted according to actual needs and changes in the external environment, overcoming the limitations of traditional manual adjustment. By accurately dividing the heating area and reasonably configuring it according to the coverage of the heat pump, combined with the comparison of the target temperature and the preset temperature threshold, the initial heat pump parameter setting is achieved. During operation, by collecting temperature gradient and change rate data in real time, the abnormal heating area can be quickly identified, and the heat pump operating parameters or quantity can be automatically adjusted based on the data analysis results to ensure the optimization of the heating effect. According to the real-time detection and adjustment of the regional temperature change rate, the adjustment parameters are corrected or the heating area is re-divided, which improves the system's adaptability to changes in the external environment, improves energy efficiency, reduces waste, and ensures accurate control of indoor temperature.

[0018] On the other hand, the present application also provides a heat pump system optimization control system based on big data, which is used to apply the above-mentioned heat pump system optimization control method based on big data, including: A collection unit is configured to determine the area of ​​the heating area, divide the heating area based on the coverage of the heat pump, determine a number of initial heating areas, and determine the number of heat pump operations according to the initial heating areas; a processing unit configured to collect regional target temperatures of all the initial heating areas, compare the regional target temperatures with preset temperature thresholds respectively, and determine initial heat pump parameters according to the comparison results; A judgment unit is configured to collect the real-time temperature gradient characteristics of the regions corresponding to the operation of a plurality of heat pumps based on the initial heat pump parameters, analyze the real-time temperature gradient characteristics to determine the abnormal heating region, and adjust the initial heat pump parameters according to the analysis result of the abnormal heating region to obtain adjusted heat pump parameters; The adjustment unit is configured to detect in real time the regional temperature change rate in several initial heating areas, and to modify the adjustment heat pump parameters based on the regional temperature change rate judgment result, or to adjust the number of heat pump operations, or to adjust the heating areas.

[0019] It is understandable that the above-mentioned heat pump system optimization control method and system based on big data have the same beneficial effects, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flow chart of a heat pump system optimization control method based on big data provided by an embodiment of the present invention; Figure 2 A structural block diagram of a heat pump system optimization control system based on big data provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] In some embodiments of the present application, see Figure 1 As shown, a heat pump system optimization control method based on big data includes: S100: Determine the area of ​​the heating area, divide the heating area based on the coverage of the heat pump, determine a number of initial heating areas, and determine the number of heat pumps in operation according to the initial heating areas.

[0023] S200: Collecting regional target temperatures of all initial heating areas, comparing the regional target temperatures with preset temperature thresholds, and determining initial heat pump parameters according to the comparison results.

[0024] S300: Collecting the real-time temperature gradient characteristics of the regions corresponding to a number of heat pumps when they are running based on the initial heat pump parameters, analyzing the real-time temperature gradient characteristics to determine the abnormal heating region, and adjusting the initial heat pump parameters according to the analysis results of the abnormal heating region to obtain adjusted heat pump parameters.

[0025] S400: Real-time detection of regional temperature change rates in several initial heating areas, and based on the regional temperature change rate judgment result, correction of heat pump adjustment parameters, or adjustment of the number of heat pump operations, or adjustment of the heating areas.

[0026] Specifically, in S100, the total area of ​​the heating area is determined and the heating area is divided according to the coverage capacity of the heat pump. This includes determining the preliminary heating area division according to the area of ​​the building, the distribution range of the heat pump and the heating demand of different areas, and further determining the required number of heat pumps based on these divisions. The configuration of the heat pump ensures that each area can meet specific temperature control requirements and avoid energy waste. In S200, for each initial heating area, the target temperature of the area is collected and compared with the preset temperature threshold. The comparison result is used to adjust the initial operating parameters (such as operating power) of the heat pump. It ensures that the heat pump can be accurately set according to the needs of different areas during the initial operation to avoid temperature deviations caused by improper settings. In S300, after the heat pump starts running, the temperature gradient characteristics of each heating area, that is, the distribution changes of the temperature in the area, are monitored in real time. By analyzing the real-time temperature data, it is determined whether there is an abnormal heating area. For example, some areas have too high or too low temperatures due to improper heat pump power or changes in the external environment. According to these abnormal conditions, the operating parameters of the heat pump are automatically adjusted to optimize the heating effect. S400 detects the temperature change rate of each area in real time, that is, the temperature rise rate. It reflects the change in temperature control demand in the area and determines whether it is necessary to revise the adjustment parameters of the heat pump, increase the number of heat pumps, or readjust the division of the heating area. It ensures that the heat pump system can flexibly respond to environmental changes and achieve dynamic optimization and adjustment.

[0027] It is understandable that accurate optimization and control of the heat pump system is achieved through big data analysis and real-time feedback mechanisms. The heating area is divided based on the coverage of the heat pump, and the target temperature is preliminarily set in combination with the preset temperature threshold, which avoids the error of manual experience allocation and makes the initial configuration of the heat pump more reasonable. By real-time monitoring of temperature gradients and temperature change rates, the abnormal heating areas are accurately identified and the heat pump parameters or operating quantity are adjusted in time to ensure that the temperature control requirements of each area are met. Dynamically adjusting the heating area and the number of heat pumps according to environmental changes improves energy efficiency, reduces energy waste, and improves the overall heating effect.

[0028] In some embodiments of the present application, the heating area is divided based on the coverage of the heat pump, a number of initial heating areas are determined, and the number of heat pump operations is determined according to the initial heating areas, including: The maximum value of the heat pump coverage area is selected as the initial division criterion.

[0029] The initial division standard is traversed through the heating area to obtain the area not covered in the traversal result.

[0030] When the area of ​​the uncovered region is not zero, the initial division standard is narrowed and the heating areas are traversed again until the area of ​​the uncovered region is zero, and a number of initial heating areas are determined, and the number of heat pumps in operation is consistent with the number of initial heating areas.

[0031] It is understandable that by starting from the maximum division standard and gradually reducing the coverage until the entire heating area is fully covered, energy waste or insufficient coverage is avoided. It ensures that each heat pump can be deployed according to the actual heating demand, avoiding over-configuration or under-configuration, and improving the efficiency and flexibility of the heat pump system. Through regional division and adjustment of the number of heat pumps, refined heating management is achieved and energy utilization is optimized.

[0032] In some embodiments of the present application, the regional target temperatures are compared with preset temperature thresholds respectively, and the initial heat pump parameters are determined according to the comparison results, including: the initial heat pump parameters include initial operating power.

[0033] The target temperature of each area is compared with the preset first temperature threshold and the second temperature threshold respectively, and the initial operating power of the heat pump corresponding to the heating area is determined according to the comparison result, and the first temperature threshold is less than the second temperature threshold.

[0034] When the regional target temperature is less than or equal to the first temperature threshold, the initial operating power of the heat pump corresponding to the heating area is determined to be the first operating power. When the regional target temperature is greater than the first temperature threshold and less than or equal to the second temperature threshold, the initial operating power of the heat pump corresponding to the heating area is determined to be the second operating power. When the regional target temperature is greater than the second temperature threshold, the initial operating power of the heat pump corresponding to the heating area is determined to be the third operating power, the first operating power is less than the second operating power, and the second operating power is less than the third operating power.

[0035] It is understandable that the initial operating power of the heat pump is dynamically set by comparing the regional target temperature with the temperature threshold. The use of multiple different operating power levels (first, second, and third operating powers) makes the power regulation of the heat pump more refined and hierarchical, and better adapts to the heating needs of different regions. Through adjustment, it is possible to avoid energy waste caused by overheating while ensuring the regional temperature comfort. Intelligently adjusting the operating power according to temperature changes improves the operating efficiency of the heat pump system.

[0036] In some embodiments of the present application, when analyzing the real-time temperature gradient characteristics to determine the abnormal heating area, it includes: matching the real-time temperature and position of each point in the area, and drawing a real-time temperature gradient map of the area.

[0037] A point is randomly determined in the real-time temperature gradient map as the point to be detected.

[0038] The judgment radius is determined according to the maximum distance of the real-time temperature gradient map, and the judgment area is determined with the point to be detected as the center and the judgment radius as the radius. The average temperature of the judgment area is obtained according to the judgment area. The temperature difference is obtained according to the average temperature of the judgment area and the real-time temperature of the point to be detected. Whether the point to be detected is an abnormal area is determined according to the temperature difference.

[0039] Repeat the step of randomly selecting the points to be tested until all points in the real-time temperature gradient map are determined.

[0040] In some embodiments of the present application, when judging whether the point to be detected is an abnormal area according to the temperature difference, it includes: when the temperature difference is greater than a times the target temperature of the area, the point to be detected is determined to be an abnormal area. When the temperature difference is less than or equal to a times the target temperature of the area, the point to be detected is determined not to be an abnormal area.

[0041] In some embodiments of the present application, the average temperature of the judgment area is calculated by the following formula:

[0042] in, represents the average temperature of the judgment area, r represents the judgment radius, represents the coordinates of the point to be detected, Indicates the real-time temperature gradient map of the area The real-time temperature of the point.

[0043] It is understandable that by combining real-time temperature data with location, using temperature gradient maps and judgment radius, the heating area can be accurately detected for abnormalities. By dynamically selecting the points to be detected and analyzing the temperature differences in the surrounding areas one by one, heating areas with uneven or abnormal temperatures can be effectively identified. This method is smarter and more flexible than traditional static monitoring, and can adjust the judgment area in real time to ensure that heating anomalies can be discovered and corrected in a timely manner, thereby improving the operating accuracy and response speed of the heat pump system. In addition, by setting the temperature difference judgment threshold, the detection sensitivity can be flexibly adjusted according to actual needs, optimizing the overall heating effect, and further improving energy efficiency and user comfort.

[0044] In some embodiments of the present application, the initial heat pump parameters are adjusted according to the analysis results of the abnormal heating area, and when the adjusted heat pump parameters are obtained, it includes: obtaining the number of abnormal areas in the area and the temperature difference corresponding to each abnormal area according to the analysis results of the abnormal heating area, taking the number of abnormal areas and the temperature difference corresponding to each abnormal area as a feature vector, comparing the feature vector with the historical adjustment data, and obtaining the adjusted heat pump parameters according to the comparison results. The historical adjustment data includes a number of historical feature vectors and a number of historical adjustment coefficients, and each historical feature vector corresponds to a historical adjustment coefficient.

[0045] Specifically, when there is data in the historical adjustment data whose similarity between the historical feature vector and the feature vector is greater than the similarity threshold, the historical adjustment coefficient corresponding to the maximum similarity is selected to adjust the initial heat pump parameters to obtain the adjusted heat pump parameters, and the adjusted heat pump parameters are the product of the initial heat pump parameters and the historical adjustment coefficient.

[0046] When the similarities between the historical feature vector and the feature vector in the historical adjustment data are both less than or equal to the similarity threshold, the initial heat pump parameters are adjusted by determining the adjustment coefficient according to the feature vector to obtain the adjusted heat pump parameters.

[0047]

[0048] Where C represents the adjustment coefficient, represents the nth temperature difference in the feature vector, N represents the number of abnormal areas in the feature vector, max(1+·) represents that the adjustment coefficient C is not less than 1, and min(1.5,·) represents that the adjustment coefficient C does not exceed 1.5.

[0049] Specifically, by dynamically analyzing the temperature difference in the abnormal heating area, combined with historical data and similarity comparison, the operating parameters of the heat pump are adjusted intelligently. The adjustment accuracy of the heat pump is improved, energy waste is reduced, and the heat pump system can be flexibly adjusted according to actual needs. By limiting the adjustment coefficient, it is ensured that the adjustment process will not excessively change the operating state of the heat pump, thereby avoiding the impact on normal areas as much as possible. At the same time, by comparing historical data, we can learn from past experience, improve adjustment efficiency, and ensure the response speed and accuracy of the system when facing different heating modes.

[0050] In some embodiments of the present application, when the heat pump parameters are corrected based on the regional temperature change rate judgment result, or the number of heat pump operations is adjusted, or the heating area is adjusted, it includes: obtaining the average temperature difference based on the real-time temperature of all abnormal areas in the area and the regional target temperature, obtaining the standard temperature change rate based on the average temperature difference and a preset time threshold, comparing the regional temperature change rate with the standard temperature change rate, and judging whether to correct the heat pump parameters, adjust the number of heat pump operations, or adjust the heating area based on the comparison result.

[0051] Specifically, when the regional temperature change rate is greater than or equal to the standard temperature change rate, it is determined that the heat pump parameters are not corrected, the number of heat pump operations is not adjusted, and the heating area is not adjusted. When the regional temperature change rate is less than the standard temperature change rate, it is determined that the heat pump parameters are corrected, or the number of heat pump operations is adjusted, or the heating area is adjusted.

[0052] In some embodiments of the present application, when determining to correct the adjustment of heat pump parameters, or adjusting the number of heat pump operations, or adjusting the heating area, it includes: when the regional temperature change rate is less than or equal to 0.5 times the standard temperature change rate, determining to adjust the heating area, such as reducing the heating range of each heat pump. When the regional temperature change rate is greater than 0.5 times the standard temperature change rate and less than or equal to 0.8 times the standard temperature change rate, determining to adjust the number of heat pump operations in the heating area, such as increasing the number of heat pump operations, to improve the heating efficiency. When the regional temperature change rate is greater than 0.8 times the standard temperature change rate, determining to correct the adjustment of the heat pump parameters, and determining the correction coefficient based on the ratio of the regional temperature change rate to the standard temperature change rate to correct the adjustment of the heat pump parameters, the correction coefficient is inversely proportional to the ratio, and the correction coefficient is (1, 1.2).

[0053] It is understandable that by introducing the comparison between the temperature change rate and the standard rate, the operating effect of the heat pump system can be evaluated in real time, and the operating parameters of the system can be adjusted intelligently. It can dynamically optimize the heating area and the number of heat pumps in operation when demand changes, ensuring heating efficiency while avoiding energy waste. The introduction of the correction coefficient provides a fine adjustment mechanism that can accurately control the heat pump parameters according to the real-time temperature changes, thereby improving the system's responsiveness and energy-saving effects.

[0054] In the above-mentioned embodiments, intelligent adjustment based on big data and real-time temperature monitoring is used to dynamically adjust the operating parameters of the heat pump system according to actual needs and changes in the external environment, thus overcoming the limitations of traditional manual adjustment. The initial heat pump parameter setting is achieved by accurately dividing the heating area and reasonably configuring it according to the coverage of the heat pump, combined with the comparison of the target temperature and the preset temperature threshold. During operation, the abnormal heating area is quickly identified by real-time collection of temperature gradient and change rate data, and the heat pump operating parameters or quantity are automatically adjusted based on the data analysis results to ensure the optimization of the heating effect. According to the real-time detection and adjustment of the regional temperature change rate, the adjustment parameters are corrected or the heating area is re-divided, which improves the system's adaptability to changes in the external environment, improves energy efficiency, reduces waste, and ensures accurate control of indoor temperature.

[0055] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a heat pump system optimization control system based on big data, which is used to apply the above-mentioned heat pump system optimization control method based on big data, including: The collection unit is configured to determine the area of ​​the heating area, divide the heating area based on the coverage of the heat pump, determine a number of initial heating areas, and determine the number of heat pump operations based on the initial heating areas.

[0056] The processing unit is configured to collect the regional target temperatures of all initial heating areas, compare the regional target temperatures with preset temperature thresholds respectively, and determine the initial heat pump parameters according to the comparison results.

[0057] The judgment unit is configured to collect the real-time temperature gradient characteristics of the corresponding areas when several heat pumps are running based on the initial heat pump parameters, analyze the real-time temperature gradient characteristics to determine the abnormal heating area, and adjust the initial heat pump parameters according to the analysis results of the abnormal heating area to obtain adjusted heat pump parameters.

[0058] The adjustment unit is configured to detect the regional temperature change rate in several initial heating areas in real time, and modify the heat pump parameters based on the regional temperature change rate judgment result, or adjust the number of heat pump operations or the heating area.

[0059] It is understandable that through intelligent adjustment based on big data and real-time temperature monitoring, the operating parameters of the heat pump system can be dynamically adjusted according to actual needs and changes in the external environment, overcoming the limitations of traditional manual adjustment. By accurately dividing the heating area and reasonably configuring it according to the coverage of the heat pump, combined with the comparison of the target temperature and the preset temperature threshold, the initial heat pump parameter setting is achieved. During operation, by collecting temperature gradient and change rate data in real time, the abnormal heating area can be quickly identified, and the heat pump operating parameters or quantity can be automatically adjusted based on the data analysis results to ensure the optimization of the heating effect. According to the real-time detection and adjustment of the regional temperature change rate, the adjustment parameters are corrected or the heating area is re-divided, which improves the system's adaptability to changes in the external environment, improves energy efficiency, reduces waste, and ensures accurate control of indoor temperature.

[0060] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0061] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A heat pump system optimization control method based on big data, characterized in that: include: Determine the area of ​​the heating area, divide the heating area based on the coverage of the heat pump, determine a number of initial heating areas, and determine the number of heat pumps to be operated based on the initial heating areas; Collecting the regional target temperatures of all the initial heating areas, comparing the regional target temperatures with the preset temperature thresholds respectively, and determining the initial heat pump parameters according to the comparison results; Collecting the real-time temperature gradient characteristics of the regions corresponding to the operation of several heat pumps based on the initial heat pump parameters, analyzing the real-time temperature gradient characteristics to determine the abnormal heating region, and adjusting the initial heat pump parameters according to the analysis results of the abnormal heating region to obtain adjusted heat pump parameters; Real-time detection of the regional temperature change rates in several initial heating areas, and based on the regional temperature change rate judgment result, the heat pump adjustment parameters are corrected, or the heat pump operation quantity is adjusted, or the heating area is adjusted.

2. The heat pump system optimization control method based on big data according to claim 1 is characterized in that: The heating area is divided based on the coverage of the heat pump, a number of initial heating areas are determined, and the number of heat pump operations is determined according to the initial heating areas, including: The maximum value of the heat pump coverage is selected as the initial division standard; Apply the initial division standard to the heating area to obtain the area of ​​the area not covered in the traversal result; When the area of ​​the uncovered region is not zero, the initial division standard is reduced and the heating areas are traversed again until the area of ​​the uncovered region is zero, and a number of initial heating areas are determined, and the number of heat pump operations is consistent with the number of initial heating areas.

3. The heat pump system optimization control method based on big data according to claim 2 is characterized in that: The target temperature of each region is compared with the preset temperature threshold, and when the initial heat pump parameters are determined according to the comparison results, the initial heat pump parameters include initial operating power; The target temperature of each area is compared with a preset first temperature threshold and a preset second temperature threshold, and the initial operating power of the heat pump corresponding to the heating area is determined according to the comparison result, wherein the first temperature threshold is less than the second temperature threshold; When the regional target temperature is less than or equal to the first temperature threshold, the initial operating power of the heat pump corresponding to the heating area is determined to be the first operating power; when the regional target temperature is greater than the first temperature threshold and less than or equal to the second temperature threshold, the initial operating power of the heat pump corresponding to the heating area is determined to be the second operating power; when the regional target temperature is greater than the second temperature threshold, the initial operating power of the heat pump corresponding to the heating area is determined to be the third operating power, the first operating power is less than the second operating power, and the second operating power is less than the third operating power.

4. The heat pump system optimization control method based on big data according to claim 3 is characterized in that: When analyzing the real-time temperature gradient characteristics to determine the abnormal heating area, it includes: Match the real-time temperature and position of each point in the area and draw a real-time temperature gradient map of the area; Randomly determine a point in the real-time temperature gradient map as a point to be detected; Determine a judgment radius according to the maximum distance of the real-time temperature gradient map, determine a judgment area with the point to be detected as the center and the judgment radius as the radius, obtain an average temperature of the judgment area according to the judgment area, obtain a temperature difference according to the average temperature of the judgment area and the real-time temperature of the point to be detected, and determine whether the point to be detected is the abnormal area according to the temperature difference; The step of randomly selecting the points to be detected is repeated until all points in the real-time temperature gradient map are determined.

5. The heat pump system optimization control method based on big data according to claim 4 is characterized in that: When judging whether the to-be-detected point is the abnormal area according to the temperature difference, it includes: When the temperature difference is greater than a times the target temperature of the area, the point to be detected is determined to be the abnormal area; When the temperature difference is less than or equal to a times the target temperature of the area, it is determined that the point to be detected is not the abnormal area.

6. The heat pump system optimization control method based on big data according to claim 5 is characterized in that: The average temperature of the judgment area is calculated by the following formula: ; in, represents the average temperature of the judgment area, r represents the judgment radius, represents the coordinates of the point to be detected, Indicates the real-time temperature gradient map of the area The real-time temperature of the point.

7. The heat pump system optimization control method based on big data according to claim 6 is characterized in that: The initial heat pump parameters are adjusted according to the analysis results of the abnormal heating area to obtain the adjusted heat pump parameters, including: According to the analysis result of the abnormal heating area, the number of abnormal areas in the area and the temperature difference corresponding to each abnormal area are obtained, the number of abnormal areas and the temperature difference corresponding to each abnormal area are used as feature vectors, the feature vectors are compared with historical adjustment data, and the adjustment heat pump parameters are obtained according to the comparison results; the historical adjustment data includes a plurality of historical feature vectors and a plurality of historical adjustment coefficients, and each of the historical feature vectors corresponds to a historical adjustment coefficient; When there is data in the historical adjustment data whose similarity between the historical feature vector and the feature vector is greater than the similarity threshold, selecting the historical adjustment coefficient corresponding to the maximum similarity to adjust the initial heat pump parameter to obtain the adjusted heat pump parameter, which is the product of the initial heat pump parameter and the historical adjustment coefficient; When the similarity between the historical feature vector in the historical adjustment data and the feature vector is less than or equal to the similarity threshold, adjusting the initial heat pump parameters by determining the adjustment coefficient according to the feature vector to obtain the adjusted heat pump parameters; ; Where C is the adjustment coefficient, represents the nth temperature difference in the feature vector, N represents the number of abnormal areas in the feature vector, max(1+·) represents that the adjustment coefficient C is not less than 1, and min(1.5,·) represents that the adjustment coefficient C does not exceed 1.

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8. The heat pump system optimization control method based on big data according to claim 7 is characterized in that: When the heat pump parameters are modified based on the regional temperature change rate judgment result, or the number of heat pump operations is adjusted, or the heating area is adjusted, the method includes: According to the real-time temperatures of all abnormal areas in the area and the target temperature of the area, an average temperature difference is obtained, and a standard temperature change rate is obtained according to the average temperature difference and a preset time threshold, and the temperature change rate of the area is compared with the standard temperature change rate, and it is determined according to the comparison result whether to modify the adjustment parameters of the heat pump, or adjust the number of operation of the heat pump, or adjust the heating area; When the temperature change rate of the area is greater than or equal to the standard temperature change rate, it is determined that the heat pump adjustment parameters are not corrected, the heat pump operation quantity is not adjusted, and the heating area is not adjusted; When the temperature change rate of the area is less than the standard temperature change rate, it is determined to correct the adjustment parameters of the heat pump, or to adjust the number of operation of the heat pump, or to adjust the heating area.

9. The heat pump system optimization control method based on big data according to claim 8, characterized in that: When determining whether to modify the heat pump parameter, or to adjust the heat pump operation quantity, or to adjust the heating area, the method includes: When the temperature change rate of the area is less than or equal to 0.5 times the standard temperature change rate, it is determined to adjust the heating area; When the temperature change rate of the area is greater than 0.5 times the standard temperature change rate and less than or equal to 0.8 times the standard temperature change rate, it is determined to adjust the heating area and adjust the operation quantity of the heat pump; When the regional temperature change rate is greater than 0.8 times the standard temperature change rate, it is determined that the adjusted heat pump parameters are to be corrected, and a correction coefficient is determined based on the ratio of the regional temperature change rate to the standard temperature change rate to correct the adjusted heat pump parameters. The correction coefficient is inversely proportional to the ratio, and the correction coefficient is (1, 1.2).

10. A heat pump system optimization control system based on big data, used for applying the heat pump system optimization control method based on big data according to any one of claims 1 to 9, characterized in that: include: A collection unit is configured to determine the area of ​​the heating area, divide the heating area based on the coverage of the heat pump, determine a number of initial heating areas, and determine the number of heat pump operations according to the initial heating areas; a processing unit configured to collect regional target temperatures of all the initial heating areas, compare the regional target temperatures with preset temperature thresholds respectively, and determine initial heat pump parameters according to the comparison results; A judgment unit is configured to collect the real-time temperature gradient characteristics of the regions corresponding to the operation of a plurality of heat pumps based on the initial heat pump parameters, analyze the real-time temperature gradient characteristics to determine the abnormal heating region, and adjust the initial heat pump parameters according to the analysis result of the abnormal heating region to obtain adjusted heat pump parameters; The adjustment unit is configured to detect in real time the regional temperature change rate in several initial heating areas, and to modify the adjustment heat pump parameters based on the regional temperature change rate judgment result, or to adjust the number of heat pump operations, or to adjust the heating areas.

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