Control method and system of evaporation cold magnetic suspension heat pump system

Abstract

The invention relates to the technical field of zone control, and particularly discloses a control method and system for an evaporation cold magnetic suspension heat pump system, and the method comprises the steps: obtaining an energy supply region containing segmentation information, collecting the region operation data of each sub-region in the energy supply region, and determining the demand side parameters of each sub-region; system operation data of the heat pump system are obtained in real time, and the total energy supply amount and the standby component are determined; and determining the energy supply weight of each sub-region according to the demand side parameters, distributing the total energy supply amount according to the energy supply weight, and generating a control instruction. The partition demand information of the demand side is collected through the intelligent device, the partition demand information is recognized and simplified, the load degree and the energy supply urgency degree of each sub-region are obtained, the distribution weight of the energy supply is determined according to the two parameters, the total energy supply amount is distributed, then the control instruction is generated, the manual delivery process is not needed, and the response speed is extremely high.

Description

Technical Field

[0001] This invention relates to the field of zonal control technology, specifically a control method and system for an evaporative cooling magnetic levitation heat pump system. Background Technology

[0002] Evaporative cooling magnetic levitation heat pump systems are high-efficiency and energy-saving devices that integrate magnetic levitation frequency conversion and evaporative cooling technologies. Their core function is to provide cooling or heating, and they are mainly used in large buildings and industrial plants. Existing evaporative cooling magnetic levitation heat pump systems largely rely on manual interactive control, generating control commands based on demand. These demands are actively uploaded by the demand side. This presents both a demand-uploading pressure on the demand side and a need for the energy supply side to match the control process accordingly, resulting in a certain lag and low response speed. In reality, with sufficiently advanced intelligent data acquisition technology, demand information can be collected autonomously. Therefore, the technical problem this invention aims to solve is how to identify autonomously collected demand information and generate control commands for the energy supply side to improve response speed. Summary of the Invention

[0003] The purpose of this invention is to provide a control method and system for an evaporative cooling magnetic levitation heat pump system to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A control method for an evaporative cooling magnetic levitation heat pump system, the method comprising: The energy supply area containing segmentation information is obtained, regional operation data of each sub-region within the energy supply area are collected, and the demand-side parameters of each sub-region are determined. The demand-side parameters include load level and energy supply urgency, which are used to characterize the load complexity and energy supply urgency, respectively. Real-time acquisition of system operation data of the heat pump system to determine the total energy supply and reserve capacity; The energy supply weight of each sub-region is determined based on the demand-side parameters, and the total energy supply is allocated according to the energy supply weight to generate control commands. The relevance of each sub-region is determined based on the demand-side parameters. When an active control request is received from any sub-region, the spare components are allocated based on the relevance, and control commands are generated.

[0005] As a further aspect of the present invention: the steps of obtaining the power supply area containing segmentation information, collecting regional operation data of each sub-region within the power supply area, and determining the demand-side parameters of each sub-region include: Establish a connection channel with the filing database to obtain the energy supply area and the segmentation information of the energy supply area, and determine the various sub-areas of the energy supply area; Multi-source operational data for each sub-region is collected based on preset sensors; the multi-source operational data includes at least environmental parameters, load parameters, and regional disturbance time points; Environmental and load parameters are matched in the time domain and then standardized. Based on the standardized data, a data function is fitted for each type of data, and the demand-side parameters are determined based on the data function and the time points of regional disturbances.

[0006] As a further aspect of the present invention: the step of fitting a data function for each type of data based on the standardized data, and determining the demand-side parameters based on the data function and the regional disturbance time points includes: A data function is fitted to each type of data based on the standardized data; The data are paired up in pairs, and the integral difference of the data function within a preset time period is calculated. For any given data, calculate the mean of its integral differences with all other data, and simultaneously query the maximum value of its integral differences with all other data. Select feature data based on the mean and maximum values, and determine the load level based on the values ​​of the feature data; The query function retrieves the data changes at regional disturbance time points, and the total absolute value of the cumulative data changes to determine the urgency of energy supply.

[0007] As a further aspect of the present invention: the step of acquiring real-time system operation data of the heat pump system and determining the total energy supply and reserve portion includes: The global parameters of the heat pump system are collected periodically. The global parameters include power system parameters, energy storage system parameters, and water system operating parameters. The power system parameters are used to characterize the available input power of the system and the grid status. The energy storage system parameters are used to characterize the remaining capacity and charging and discharging capability of the energy storage unit. The water system operating parameters are used to characterize the supply and return water temperature, flow rate, and heat exchange capacity. The system periodically collects equipment status parameters of preset key equipment; these status parameters include compressor operating status, magnetic levitation unit load rate, and the temperature of preset key components. By statistically analyzing global parameters and equipment status parameters, a system feature vector is obtained. The vector distance between the system feature vector and the preset standard feature vector is calculated, and the supply range is determined based on the vector distance. The total energy supply is determined based on the supply range and the preset rated total amount, and the reserve amount is determined based on the supply range and the preset rated reserve amount.

[0008] As a further aspect of the present invention: the step of determining the energy supply weight of each sub-region based on demand-side parameters, allocating the total energy supply according to the energy supply weight, and generating control commands includes: Read the load and energy urgency of each sub-region; The energy supply weight of each sub-region is determined based on the load level and energy supply urgency of each sub-region. The total energy supply is allocated according to the energy supply weight, and control commands are generated.

[0009] As a further aspect of the present invention: the step of determining the relevance of each sub-region based on demand-side parameters, and allocating spare components based on the relevance to generate control commands upon receiving an active control request from any sub-region, includes: For any sub-region, query the data function of the feature data to obtain the first function set; Calculate the derivative of each data function to obtain the second set of functions; Based on a preset step size, the functions in the first function set and the second function set are discretized to obtain the first array set and the second array set; Between different sub-regions, the first set of data is compared, then the second set of data is compared, and a comprehensive comparison result is obtained to determine the relevance. When an active control request is received from any sub-region, other sub-regions whose relevance to that sub-region reaches a preset relevance threshold are queried and identified as relevant sub-regions. Based on the active control request, the allocation amount is determined, and control instructions pointing to the sub-region and related sub-regions are generated.

[0010] The present invention also provides a control system for an evaporative cooling magnetic levitation heat pump system, the system comprising: The demand parameter determination module is used to obtain the energy supply area containing segmentation information, collect regional operation data of each sub-region within the energy supply area, and determine the demand-side parameters of each sub-region. The demand-side parameters include load level and energy supply urgency, which are used to characterize the load complexity and energy supply urgency, respectively. The energy supply parameter determination module is used to acquire real-time system operation data of the heat pump system and determine the total energy supply and reserve components. The energy supply allocation module is used to determine the energy supply weight of each sub-region based on the demand-side parameters, allocate the total energy supply according to the energy supply weight, and generate control commands. The reserve allocation module is used to determine the relevance of each sub-region based on the demand-side parameters. When it receives an active control request from any sub-region, it allocates the reserve components based on the relevance and generates control commands.

[0011] As a further aspect of the present invention: the requirement parameter determination module includes: The area acquisition unit is used to establish a connection channel with the filing database, obtain the energy supply area and the segmentation information of the energy supply area, and determine the various sub-areas of the energy supply area; A multi-source data acquisition unit is used to acquire multi-source operational data of each sub-region based on preset sensors; the multi-source operational data includes at least environmental parameters, load parameters, and regional disturbance time points; The data preprocessing unit is used to perform time-domain matching of environmental parameters and load parameters, and then perform standardization processing. The fitting analysis unit is used to fit a data function for each type of data based on the standardized data, and to determine the demand-side parameters based on the data function and the time points of regional disturbances.

[0012] As a further aspect of the present invention: the energy supply parameter determination module includes: A global parameter acquisition unit is used to periodically acquire global parameters of the heat pump system. The global parameters include power system parameters, energy storage system parameters, and water system operating parameters. The power system parameters are used to characterize the system's available input power and grid status, the energy storage system parameters are used to characterize the remaining capacity and charging / discharging capability of the energy storage unit, and the water system operating parameters are used to characterize the supply and return water temperature, flow rate, and heat exchange capacity. The equipment parameter acquisition unit is used to periodically acquire the equipment status parameters of preset key equipment; the operating status parameters include the compressor operating status, the load rate of the magnetic levitation unit, and the temperature of preset key components. The supply range determination unit is used to collect global parameters and equipment status parameters, obtain system feature vectors, calculate the vector distance between the system feature vectors and preset standard feature vectors, and determine the supply range based on the vector distance. The supply range application unit is used to determine the total energy supply based on the supply range and the preset rated total amount, and to determine the reserve amount based on the supply range and the preset rated reserve amount.

[0013] As a further aspect of the present invention: the energy distribution module includes: The parameter reading unit is used to read the load level and energy urgency of each sub-region; The weighting determination unit is used to determine the energy supply weight of each sub-region based on the load level and energy supply urgency of each sub-region. The allocation execution unit is used to allocate the total energy supply according to the energy supply weight and generate control commands.

[0014] Compared with the prior art, the beneficial effects of the present invention are: the present invention collects the demand information of the demand side by intelligent devices, identifies and simplifies the demand information of the demand side, obtains the load degree and energy supply urgency of each sub-region, determines the allocation weight of energy supply based on these two parameters, allocates the total energy supply, and then generates control commands. There is no need for manual intervention, and the response speed is extremely fast. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0016] Figure 1 This is a flowchart of the control method for an evaporative cooling magnetic levitation heat pump system.

[0017] Figure 2 This is the first sub-flowchart of the control method for an evaporative cooling magnetic levitation heat pump system.

[0018] Figure 3 This is the second sub-flowchart of the control method for an evaporative cooling magnetic levitation heat pump system.

[0019] Figure 4 This is the third sub-flowchart of the control method for an evaporative cooling magnetic levitation heat pump system.

[0020] Figure 5 This is the fourth sub-flowchart of the control method for an evaporative cooling magnetic levitation heat pump system.

[0021] Figure 6 This is a block diagram showing the composition and structure of the control system of an evaporative cooling magnetic levitation heat pump system. Detailed Implementation

[0022] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0023] Figure 1 The flowchart illustrates a control method for an evaporative cooling magnetic levitation heat pump system. In this embodiment of the invention, a control method for an evaporative cooling magnetic levitation heat pump system includes: Step S100: Obtain the energy supply area containing the segmentation information, collect the regional operation data of each sub-region in the energy supply area, and determine the demand-side parameters of each sub-region; the demand-side parameters include load degree and energy supply urgency, which are used to characterize the load complexity and energy supply urgency, respectively. The power supply area refers to the total area requiring power supply, such as industrial parks, data centers, precision factories, and convention centers. Different parts of these areas are managed by different entities; for example, each unit within a park or each room in a building is called a sub-area. How to divide these sub-areas is management information. In this invention, the sub-area division information (how sub-areas are divided) is already provided by default and can be directly read. Generally, whenever the management entity corresponding to a sub-area changes, the division information is updated accordingly. This update is not part of this invention and will not be elaborated upon here. It should be noted that after the division information is updated, this invention reads the latest division information to determine the latest sub-area distribution. Based on existing data acquisition devices installed in the sub-areas, specific parameters of the sub-areas can be obtained, and the sub-areas can be analyzed to obtain two parameters: load level and power urgency, which are used to characterize the load complexity and power urgency, respectively.

[0024] Step S200: Obtain real-time system operation data of the heat pump system to determine the total energy supply and reserve capacity; After analyzing the demand side, the supply side is then analyzed. Analyzing the supply side is slightly easier because the rated parameters of the heat pump system are known. However, the technical solution of this invention also introduces adjustment parameters determined based on actual information, which are used to determine the energy supply situation according to the actual operating status of the heat pump system. The energy supply situation includes the total energy supply and the reserve component.

[0025] It should be noted that step S200 analyzes the supply side, while step S100 analyzes the demand side. Under the existing data collection architecture, the collected data are all multi-source data, which are mostly stored in numerical form (even some text will be converted into numerical values). Then, the numerical values ​​are standardized so that different data can be integrated and analyzed to determine the actual operating status of the supply side or the demand side.

[0026] Step S300: Determine the energy supply weight of each sub-region based on the demand-side parameters, allocate the total energy supply according to the energy supply weight, and generate control commands; After obtaining the demand-side parameters, analyzing them allows us to determine the energy supply weight for each sub-region. Simply put, the energy supply weight controls how much total energy each sub-region receives; it's essentially a ratio. Calculating the energy supply weight can also be called a normalization process. Based on these weights, the total energy supply is allocated to obtain the energy supply corresponding to each sub-region. Given the known energy supply, the process of generating control commands is an existing process, included in the system user manual. When the system is installed, the control system, as a known module, is uniformly delivered to the heat pump system's management. Generating control commands essentially controls how much energy the heat pump system supplies; this process is known.

[0027] Step S400: Determine the relevance of each sub-region based on the demand-side parameters. When an active control request is received from any sub-region, allocate the spare components based on the relevance and generate control commands. In addition to the total energy supply control process, the technical solution of this invention also includes a backup component control process. This process still involves analyzing demand-side parameters to determine the correlation between different sub-regions. The correlation represents the similarity of the changing trends of each sub-region. When an active control request is received from any sub-region, it indicates that there is some urgent demand in that sub-region. At this time, the heat pump system will supply energy to it. Simultaneously, the technical solution of this invention also queries similar sub-regions based on the correlation and supplies energy to them in advance. Of course, regarding the advance energy supply part, the demand-side parameters of the corresponding sub-region can be analyzed. If its fluctuation is still at a low level (the absolute value of the derivative is small over a period of time or other indicators indicating fluctuation), the advance energy supply process can be terminated in advance.

[0028] Figure 2 This is a first sub-flowchart of the control method for an evaporative cooling magnetic levitation heat pump system. The steps of obtaining the energy supply area containing segmentation information, collecting regional operation data of each sub-region within the energy supply area, and determining the demand-side parameters of each sub-region include: Step S101: Establish a connection channel with the filing database, obtain the energy supply area and the segmentation information of the energy supply area, and determine the sub-areas of the energy supply area; Step S102: Collect multi-source operational data for each sub-region based on preset sensors; the multi-source operational data includes at least environmental parameters, load parameters, and regional disturbance time points; Step S103: Perform time-domain matching on environmental parameters and load parameters, and then perform standardization processing; Step S104: Fit a data function for each type of data based on the standardized data, and determine the demand-side parameters based on the data function and the time points of regional disturbances.

[0029] In one example of the technical solution of this invention, the process of determining demand-side parameters is described. This involves establishing a connection channel with the registration database, acquiring the energy supply area and its segmentation information, and determining the various sub-areas of the energy supply area. This process is a simple data reading process and is not complex. Multi-source operational data from each sub-area is collected based on preset sensors. This multi-source operational data includes at least environmental parameters, load parameters, and regional disturbance time points. Environmental parameters include the sub-area's ambient temperature, humidity, wind speed, or other indicators. Load parameters are the operational parameters of each energy source in the sub-area. The regional disturbance time point is a unique feature of this invention; it describes events such as someone entering a room, a door or window opening, or a heat source entering. These events are difficult to statistically analyze in traditional data acquisition architectures. Although existing technologies have sufficient ability to identify these events, the results are mostly descriptive information such as text or images, which are difficult to integrate with environmental and load parameters. Applying algorithms for mathematical analysis requires introducing at least other transcoding models for subsequent processing, making the data processing process very complex. However, in the technical solution of this invention, these events are directly limited to the regional disturbance time point. That is, after the corresponding event is detected (the detection process is existing), the detection time is recorded. At this time, this time can be used to query the corresponding change in environmental parameters and load parameters. The change in different parameters is the value of the quantified event. This itself can be connected with environmental parameters and load parameters without the need to introduce other transcoding models or a series of additional operations. The data structure is extremely clear. When collecting environmental parameters and load parameters, the collection time needs to be recorded. Based on the collection time, the environmental parameters and load parameters are time-domain matched (parameters with sufficiently small time differences are used as parameters at the same time). Then, standardization processing is performed. The standardization process maps each type of data to a preset interval, such as 0 to 1. At this time, the value range of each type of data is the same. Function fitting is performed on each type of standardized data to obtain the data function of each type of data. Based on the data function and the regional disturbance time point, the demand-side parameters are uniformly determined. It is worth mentioning that environmental parameters and load parameters are not a single type of data, but multiple types of data. Their types are predetermined, and the corresponding collection subjects are also corresponding sensors.

[0030] As a preferred embodiment of the technical solution of the present invention, the step of fitting a data function for each type of data based on the standardized data, and determining the demand-side parameters based on the data function and the regional disturbance time points includes: A data function is fitted to each type of data based on the standardized data; The data are paired up in pairs, and the integral difference of the data function within a preset time period is calculated. For any given data, calculate the mean of its integral differences with all other data, and simultaneously query the maximum value of its integral differences with all other data. Select feature data based on the mean and maximum values, and determine the load level based on the values ​​of the feature data; The query function retrieves the data changes at regional disturbance time points, and the total absolute value of the cumulative data changes to determine the urgency of energy supply.

[0031] In one example of the technical solution of this invention, the process of fitting and applying data functions is described. Based on standardized data, a data function is fitted for each type of data. The fitting process determines the relationship between numerical values ​​and time, which is a conventional data processing procedure. Different types of data are paired, and the integral difference of the data functions within a preset time period is calculated to describe the distance between the two data functions. For any type of data, its integral difference with other data is queried, the mean of the integral differences is calculated, and the maximum value of the integral differences is simultaneously queried. Using the mean and maximum value as references, some data are selected as feature data. This process is actually a data simplification process. Because there are a large number and variety of existing sensors, the data types in environmental and load parameters are numerous. If all of them were analyzed, the computational load would be enormous, and the analysis of many types of data would be ineffective. Therefore, it is necessary to first simplify and filter, extracting some representative data types, i.e., feature data, before proceeding with subsequent processing. The subsequent processing involves determining the load level and the urgency of energy supply.

[0032] Furthermore, feature data are selected based on the mean and maximum value, and the load is determined based on the value of the feature data. This is generally a threshold-based selection process. When the mean is less than a preset mean threshold and the maximum value reaches a preset maximum value threshold, the data can be selected as feature data. A mean less than the preset mean threshold indicates that the integral difference between this data and other data is generally small. In this case, it can represent most of the data and can be selected as feature data. A maximum value reaching the preset maximum value threshold indicates that it represents the data type in most of the data. It differs significantly from a certain data point, and compared to data with a relatively small maximum value, it is more reflective of the problem. In fact, in addition to the mean and maximum value, the mean and standard deviation can also be analyzed. In short, the mean is used to reflect whether the data can represent other data, and the standard deviation reflects whether the data has high analytical value.

[0033] Specifically, the load level is determined based on the numerical value of the characteristic data, and it is determined whether the characteristic data belongs to the maximum or minimum value index based on the type of characteristic data. Then, they are merged to obtain the load level. The data function of the characteristic data is queried for the amount of data change at the regional disturbance time point. This process can quantify the degree of impact of the event. The total absolute value of the cumulative data change determines the energy urgency. The more regional disturbance time points, the more events there are. The larger the absolute value of the data change, the greater the impact of a single event. The total absolute value of the cumulative data change can reflect the impact of the event. Multiplying it by a preset coefficient gives the energy urgency.

[0034] Figure 3 This is the second sub-flowchart of the control method for an evaporative cooling magnetic levitation heat pump system. The step of acquiring real-time system operating data of the heat pump system and determining the total energy supply and reserve components includes: Step S201: Periodically collect global parameters of the heat pump system; the global parameters include power system parameters, energy storage system parameters and water system operating parameters, wherein the power system parameters are used to characterize the system's available input power and grid status, the energy storage system parameters are used to characterize the remaining capacity and charging / discharging capability of the energy storage unit, and the water system operating parameters are used to characterize the supply and return water temperature, flow rate and heat exchange capacity. Step S202: Periodically collect the equipment status parameters of preset key equipment; the operating status parameters include the compressor operating status, the load rate of the magnetic levitation unit, and the temperature of preset key components; Step S203: Statistically analyze global parameters and equipment status parameters to obtain system feature vectors, calculate the vector distance between the system feature vectors and preset standard feature vectors, and determine the supply range based on the vector distance; Step S204: Determine the total energy supply based on the supply range and the preset rated total amount, and determine the reserve amount based on the supply range and the preset rated reserve amount.

[0035] In one example of the technical solution of this invention, the working process on the energy supply side is described, which involves periodically collecting global parameters of the heat pump system. These global parameters include power system parameters, energy storage system parameters, and water system operating parameters. The power system parameters characterize the system's available input power and grid status; the energy storage system parameters characterize the remaining capacity and charging / discharging capability of the energy storage units; and the water system operating parameters characterize the supply and return water temperatures, flow rates, and heat exchange capacity. These data all belong to the actual data of the heat pump system. Then, the equipment status parameters of preset key devices are periodically collected. These data are collected from the equipment components, including the compressor's operating status, etc. The load rate of the magnetic levitation unit and the preset temperatures of key components are used as parameters. These parameters are standardized and then statistically analyzed into an array called the system feature vector. The vector distance between the system feature vector and the preset standard feature vector is calculated. The vector distance represents the difference between the heat pump system and the conventional state (the standard feature vector is the value of global parameters and equipment status parameters under the standard state). The supply amplitude is determined based on the vector distance. The supply amplitude is inversely proportional to the vector distance, indicating that the more non-standard the heat pump system is, the larger the supply amplitude. After determining the supply amplitude, the total energy supply and reserve can be determined by combining the preset rated total and rated reserve.

[0036] Figure 4 The third sub-flowchart of the control method for an evaporative cooling magnetic levitation heat pump system includes the following steps: determining the energy supply weight of each sub-region based on demand-side parameters, allocating the total energy supply according to the energy supply weight, and generating control commands. Step S301: Read the load level and energy urgency of each sub-region; Step S302: Determine the energy supply weight of each sub-region based on the load level and energy supply urgency of each sub-region; Step S303: Allocate the total energy supply according to the energy supply weight and generate control commands.

[0037] In one example of the technical solution of this invention, the process of allocating the total energy supply is described. The load level and energy urgency of each sub-region are read, and the energy supply weight of each sub-region is determined based on these values. This process can involve first normalizing the load level and energy urgency using preset coefficients to obtain a comprehensive value reflecting the state of the sub-region; then calculating the sum of the comprehensive values ​​of all sub-regions; and finally calculating the ratio of each comprehensive value to the sum of the comprehensive values ​​as the energy supply weight. The total energy supply is then allocated according to the energy supply weights, and after obtaining the allocated amount, control commands are generated based on the allocated amount.

[0038] Figure 5The fourth sub-flowchart of the control method for an evaporative cooling magnetic levitation heat pump system includes the following steps: determining the correlation of each sub-region based on demand-side parameters; allocating spare components based on correlation when receiving an active control request from any sub-region; and generating control commands. Step S401: For any sub-region, query the data function of the feature data to obtain the first function set; Step S402: Calculate the derivative of each data function to obtain the second function set; Step S403: Discretize the functions in the first function set and the second function set based on a preset step size to obtain the first array set and the second array set; Step S404: Compare the first set of data with the second set of data between different sub-regions to obtain a comprehensive comparison result and determine the relevance. Step S405: When receiving an active control request from any sub-region, query other sub-regions whose relevance to that sub-region reaches a preset relevance threshold, and designate them as relevant sub-regions; Step S406: Determine the allocation amount based on the active control request, and generate control instructions pointing to the sub-region and related sub-regions.

[0039] In one example of the technical solution of this invention, the allocation process of the spare components is described. For any sub-region, the feature data serves as the representative of the sub-region. The data function of the feature data is queried to obtain a first function set. The derivative function of each data function is calculated to obtain a second function set. The elements in the second function set correspond one-to-one with the elements in the first function set. Then, based on a preset step size, the functions in the first and second function sets are discretized to obtain a first array set and a second array set. This process involves selecting values ​​in each function using the same step size and discretizing the functions into arrays. At this point, the first function set is integrated into the first array set, and the second function set is integrated into the second array set. The first array set is compared between different sub-regions, and then the second array set is compared to obtain... Based on the comprehensive comparison results, the relevance is determined. The comparison process for the array sets needs explanation. Since the feature data of different sub-regions may differ, direct comparison between arrays is impossible. Therefore, the comparison process for the array sets is actually divided into two steps: First, the data types of the feature data are compared, and the intersection-union ratio (IU) is calculated. If the IU is low, there is no need to calculate the relevance, as the two sub-regions can be directly determined to be different. If the IU is high, the corresponding array is queried for the intersection data type, and the array relevance is calculated. The same operation is performed on the second array set as on the first array set to obtain the array relevance. Finally, the mean of all array relevances is calculated (different weights can be set for the first and second array sets to change the process of determining the mean), which is used as the final relevance.

[0040] For the execution entity of this method, it will receive the active reporting information from each sub-region in real time. When it receives an active control request from any sub-region, it will query other sub-regions whose relevance to the sub-region reaches the preset relevance threshold and regard them as related sub-regions. Based on the active control request, it will determine the allocation amount and generate control instructions pointing to the sub-region and related sub-regions.

[0041] Figure 6 This is a block diagram illustrating the structural composition of a control system for an evaporative cooling magnetic levitation heat pump system. In this embodiment of the invention, a control system 10 for an evaporative cooling magnetic levitation heat pump system includes: The demand parameter determination module 11 is used to obtain the energy supply area containing segmentation information, collect the regional operation data of each sub-region in the energy supply area, and determine the demand-side parameters of each sub-region; the demand-side parameters include load degree and energy supply urgency, which are used to characterize the load complexity and energy supply urgency, respectively. The energy supply parameter determination module 12 is used to acquire the system operation data of the heat pump system in real time and determine the total energy supply and the reserve component. The energy distribution module 13 is used to determine the energy supply weight of each sub-region according to the demand side parameters, distribute the total energy supply according to the energy supply weight, and generate control commands. The reserve allocation module 14 is used to determine the relevance of each sub-region based on the demand-side parameters. When it receives an active control request sent by any sub-region, it allocates the reserve components based on the relevance and generates control commands.

[0042] Furthermore, the requirement parameter determination module 11 includes: The area acquisition unit is used to establish a connection channel with the filing database, obtain the energy supply area and the segmentation information of the energy supply area, and determine the various sub-areas of the energy supply area; A multi-source data acquisition unit is used to acquire multi-source operational data of each sub-region based on preset sensors; the multi-source operational data includes at least environmental parameters, load parameters, and regional disturbance time points; The data preprocessing unit is used to perform time-domain matching of environmental parameters and load parameters, and then perform standardization processing. The fitting analysis unit is used to fit a data function for each type of data based on the standardized data, and to determine the demand-side parameters based on the data function and the time points of regional disturbances.

[0043] Specifically, the power supply parameter determination module 12 includes: A global parameter acquisition unit is used to periodically acquire global parameters of the heat pump system. The global parameters include power system parameters, energy storage system parameters, and water system operating parameters. The power system parameters are used to characterize the system's available input power and grid status, the energy storage system parameters are used to characterize the remaining capacity and charging / discharging capability of the energy storage unit, and the water system operating parameters are used to characterize the supply and return water temperature, flow rate, and heat exchange capacity. The equipment parameter acquisition unit is used to periodically acquire the equipment status parameters of preset key equipment; the operating status parameters include the compressor operating status, the load rate of the magnetic levitation unit, and the temperature of preset key components. The supply range determination unit is used to collect global parameters and equipment status parameters, obtain system feature vectors, calculate the vector distance between the system feature vectors and preset standard feature vectors, and determine the supply range based on the vector distance. The supply range application unit is used to determine the total energy supply based on the supply range and the preset rated total amount, and to determine the reserve amount based on the supply range and the preset rated reserve amount.

[0044] Furthermore, the energy distribution module 13 includes: The parameter reading unit is used to read the load level and energy urgency of each sub-region; The weighting determination unit is used to determine the energy supply weight of each sub-region based on the load level and energy supply urgency of each sub-region. The allocation execution unit is used to allocate the total energy supply according to the energy supply weight and generate control commands.

[0045] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A control method for an evaporative cooling magnetic levitation heat pump system, characterized in that, The method includes: The energy supply area containing segmentation information is obtained, regional operation data of each sub-region within the energy supply area are collected, and the demand-side parameters of each sub-region are determined. The demand-side parameters include load level and energy supply urgency, which are used to characterize the load complexity and energy supply urgency, respectively. Real-time acquisition of system operation data of the heat pump system to determine the total energy supply and reserve capacity; The energy supply weight of each sub-region is determined based on the demand-side parameters, and the total energy supply is allocated according to the energy supply weight to generate control commands. The relevance of each sub-region is determined based on the demand-side parameters. When an active control request is received from any sub-region, the spare components are allocated based on the relevance, and control commands are generated.

2. The control method for the evaporative cooling magnetic levitation heat pump system according to claim 1, characterized in that, The steps of obtaining the energy supply area containing segmentation information, collecting regional operation data of each sub-region within the energy supply area, and determining the demand-side parameters of each sub-region include: Establish a connection channel with the filing database to obtain the energy supply area and the segmentation information of the energy supply area, and determine the various sub-areas of the energy supply area; Multi-source operational data for each sub-region is collected based on preset sensors; the multi-source operational data includes at least environmental parameters, load parameters, and regional disturbance time points; Environmental and load parameters are matched in the time domain and then standardized. Based on the standardized data, a data function is fitted for each type of data, and the demand-side parameters are determined based on the data function and the time points of regional disturbances.

3. The control method for the evaporative cooling magnetic levitation heat pump system according to claim 2, characterized in that, The steps of fitting a data function for each type of data based on the standardized data, and determining the demand-side parameters based on the data function and the regional disturbance time points include: A data function is fitted to each type of data based on the standardized data; The data are paired up in pairs, and the integral difference of the data function within a preset time period is calculated. For any given data, calculate the mean of its integral differences with all other data, and simultaneously query the maximum value of its integral differences with all other data. Select feature data based on the mean and maximum values, and determine the load level based on the values ​​of the feature data; The query function retrieves the data changes at regional disturbance time points, and the total absolute value of the cumulative data changes to determine the urgency of energy supply.

4. The control method for the evaporative cooling magnetic levitation heat pump system according to claim 1, characterized in that, The steps of acquiring real-time system operation data of the heat pump system and determining the total energy supply and reserve include: The global parameters of the heat pump system are collected periodically. The global parameters include power system parameters, energy storage system parameters, and water system operating parameters. The power system parameters are used to characterize the available input power of the system and the grid status. The energy storage system parameters are used to characterize the remaining capacity and charging and discharging capability of the energy storage unit. The water system operating parameters are used to characterize the supply and return water temperature, flow rate, and heat exchange capacity. The system periodically collects equipment status parameters of preset key equipment; these status parameters include compressor operating status, magnetic levitation unit load rate, and the temperature of preset key components. By statistically analyzing global parameters and equipment status parameters, a system feature vector is obtained. The vector distance between the system feature vector and the preset standard feature vector is calculated, and the supply range is determined based on the vector distance. The total energy supply is determined based on the supply range and the preset rated total amount, and the reserve amount is determined based on the supply range and the preset rated reserve amount.

5. The control method for the evaporative cooling magnetic levitation heat pump system according to claim 1, characterized in that, The steps of determining the energy supply weight of each sub-region based on demand-side parameters, allocating the total energy supply according to the energy supply weight, and generating control commands include: Read the load and energy urgency of each sub-region; The energy supply weight of each sub-region is determined based on the load level and energy supply urgency of each sub-region. The total energy supply is allocated according to the energy supply weight, and control commands are generated.

6. The control method for the evaporative cooling magnetic levitation heat pump system according to claim 3, characterized in that, The steps of determining the relevance of each sub-region based on demand-side parameters, allocating spare components based on relevance, and generating control commands upon receiving an active control request from any sub-region include: For any sub-region, query the data function of the feature data to obtain the first function set; Calculate the derivative of each data function to obtain the second set of functions; Based on a preset step size, the functions in the first function set and the second function set are discretized to obtain the first array set and the second array set; Between different sub-regions, the first set of data is compared, then the second set of data is compared, and a comprehensive comparison result is obtained to determine the relevance. When an active control request is received from any sub-region, other sub-regions whose relevance to that sub-region reaches a preset relevance threshold are queried and identified as relevant sub-regions. Based on the active control request, the allocation amount is determined, and control instructions pointing to the sub-region and related sub-regions are generated.

7. A control system for an evaporative cooling magnetic levitation heat pump system, characterized in that, The system includes: The demand parameter determination module is used to obtain the energy supply area containing segmentation information, collect regional operation data of each sub-region within the energy supply area, and determine the demand-side parameters of each sub-region. The demand-side parameters include load level and energy supply urgency, which are used to characterize the load complexity and energy supply urgency, respectively. The energy supply parameter determination module is used to acquire real-time system operation data of the heat pump system and determine the total energy supply and reserve components. The energy supply allocation module is used to determine the energy supply weight of each sub-region based on the demand-side parameters, allocate the total energy supply according to the energy supply weight, and generate control commands. The reserve allocation module is used to determine the relevance of each sub-region based on the demand-side parameters. When it receives an active control request from any sub-region, it allocates the reserve components based on the relevance and generates control commands.

8. The control system of the evaporative cooling magnetic levitation heat pump system according to claim 7, characterized in that, The requirement parameter determination module includes: The area acquisition unit is used to establish a connection channel with the filing database, obtain the energy supply area and the segmentation information of the energy supply area, and determine the various sub-areas of the energy supply area; A multi-source data acquisition unit is used to acquire multi-source operational data of each sub-region based on preset sensors; the multi-source operational data includes at least environmental parameters, load parameters, and regional disturbance time points; The data preprocessing unit is used to perform time-domain matching of environmental parameters and load parameters, and then perform standardization processing. The fitting analysis unit is used to fit a data function for each type of data based on the standardized data, and to determine the demand-side parameters based on the data function and the time points of regional disturbances.

9. The control system of the evaporative cooling magnetic levitation heat pump system according to claim 7, characterized in that, The power supply parameter determination module includes: A global parameter acquisition unit is used to periodically acquire global parameters of the heat pump system. The global parameters include power system parameters, energy storage system parameters, and water system operating parameters. The power system parameters are used to characterize the system's available input power and grid status, the energy storage system parameters are used to characterize the remaining capacity and charging / discharging capability of the energy storage unit, and the water system operating parameters are used to characterize the supply and return water temperature, flow rate, and heat exchange capacity. The equipment parameter acquisition unit is used to periodically acquire the equipment status parameters of preset key equipment; the operating status parameters include the compressor operating status, the load rate of the magnetic levitation unit, and the temperature of preset key components. The supply range determination unit is used to collect global parameters and equipment status parameters, obtain system feature vectors, calculate the vector distance between the system feature vectors and preset standard feature vectors, and determine the supply range based on the vector distance. The supply range application unit is used to determine the total energy supply based on the supply range and the preset rated total amount, and to determine the reserve amount based on the supply range and the preset rated reserve amount.

10. The control system of the evaporative cooling magnetic levitation heat pump system according to claim 7, characterized in that, The energy distribution module includes: The parameter reading unit is used to read the load level and energy urgency of each sub-region; The weighting determination unit is used to determine the energy supply weight of each sub-region based on the load level and energy supply urgency of each sub-region. The allocation execution unit is used to allocate the total energy supply according to the energy supply weight and generate control commands.

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