Load distribution method, device and equipment for multiple air conditioner outdoor units and medium

Abstract

The invention discloses a load distribution method, device and equipment for multiple air conditioner outdoor units and a medium, and relates to the technical field of load distribution. The method comprises the steps that all load gears of each air conditioner outdoor unit are obtained, low-efficiency gears in the load gears are filtered, and effective gears of each air conditioner outdoor unit are obtained; the air conditioner outdoor units are traversed one by one according to the forward sequence, all effective gears of each air conditioner outdoor unit are traversed according to the reverse sequence, and the energy consumption condition of all load values which can be processed by any accidental unit combination formed by the multiple air conditioner outdoor units is determined; updating the one-dimensional state table according to the state transition equation and the traversal result; the optimal gear when each air conditioner outdoor unit processes the specified load value is recorded by selecting a recording matrix; determining an optimal matching solution from the one-dimensional state table according to the target load value; and reverse backtracking is carried out by selecting the recording matrix, the optimal gear of each air conditioner outdoor unit is determined, and a load distribution scheme is generated. According to the method, low-efficiency gears can be filtered, the calculation complexity is reduced, and the calculation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of load distribution, and in particular to a method, device, equipment and medium for distributing loads of multiple air-conditioning outdoor units. Background Art

[0002] For a system with multiple air conditioner outdoor units, the load of each air conditioner outdoor unit needs to be distributed according to indoor demand. The traditional air conditioner outdoor unit load distribution method has the following defects:

[0003] 1. Invalid state calculation: The energy consumption dominance relationship between the gears of the external unit is not considered, resulting in dynamic planning requiring traversal of a large number of invalid states, resulting in high calculation redundancy.

[0004] 2. Inefficient path storage: A two-dimensional path table is used to store historical selections, with a space complexity of O(N*M^2) (N is the number of external machines, M is the maximum load), making it difficult to deploy on embedded devices.

[0005] 3. Insufficient energy consumption optimization: The gear selection is not based on the unit energy consumption efficiency, which may result in missing gear combinations with high load and low energy consumption. Summary of the Invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method, device, equipment and medium for distributing loads among multiple air conditioner outdoor units, which can filter out inefficient gears, reduce computational complexity and improve computational efficiency.

[0007] On the one hand, a method for distributing loads to multiple air conditioner outdoor units according to an embodiment of the present invention includes the following steps:

[0008] Obtain all load gears of each air-conditioning outdoor unit, filter out inefficient gears among the load gears, and obtain an effective gear of each air-conditioning outdoor unit; each load gear has a corresponding load value and energy consumption value;

[0009] Traversing the air-conditioning outdoor units one by one in a forward order, and traversing all valid gears of each air-conditioning outdoor unit in a reverse order, determining the energy consumption of all load values that can be handled by any combination of outdoor units composed of multiple air-conditioning outdoor units, and obtaining a traversal result;

[0010] Constructing a one-dimensional state table and a state transition equation, and updating the one-dimensional state table according to the state transition equation and the traversal result; the one-dimensional state table is used to represent the minimum energy consumption value when all external units are combined to process a specified load value;

[0011] Constructing a selection record matrix, and in a traversal process, recording the optimal gear position of each air-conditioning outdoor unit when processing a specified load value through the selection record matrix;

[0012] Obtaining a target load value, and determining an optimal matching solution from the one-dimensional state table according to the target load value;

[0013] According to the best matching solution, reverse backtracking is performed through the selection record matrix to determine the optimal gear position of each air-conditioning outdoor unit, generate a load distribution plan, and output control instructions to control the load state of each air-conditioning outdoor unit.

[0014] According to some embodiments of the present invention, obtaining all load gears of each air conditioner outdoor unit, filtering inefficient gears among the load gears, and obtaining an effective gear of each air conditioner outdoor unit includes:

[0015] Obtain all load levels of each of the air-conditioning outdoor units, and sort all load levels of each of the air-conditioning outdoor units according to load values;

[0016] Traversing all load levels of each of the air conditioner outdoor units in descending order of load value;

[0017] When there are other load gears whose load value is higher than the load value of the current load gear and whose energy consumption value is not greater than the energy consumption value of the current load gear, the current load gear is regarded as the low-efficiency gear;

[0018] All inefficient gears of each of the air-conditioning outdoor units are filtered to obtain an effective gear of each of the air-conditioning outdoor units.

[0019] According to some embodiments of the present invention, constructing a selection record matrix, during a traversal process, recording the optimal gear position of each air conditioner outdoor unit when processing a specified load value through the selection record matrix, includes:

[0020] Sorting all valid gears of each of the air-conditioning outdoor units in ascending order according to the load value, and configuring a corresponding index value for each valid gear of each of the air-conditioning outdoor units according to the sorting result;

[0021] A two-dimensional array is created, and the value of the two-dimensional array is determined based on the optimal gear position of each of the air-conditioning outdoor units when processing the specified load value; the two-dimensional array is used to represent the index value of the optimal gear position of each of the air-conditioning outdoor units when processing the specified load value, and the two-dimensional array adopts a sparse storage strategy and only stores non-zero index values.

[0022] According to some embodiments of the present invention, the one-dimensional state table is represented as dp[j], which represents the minimum energy consumption value when all outdoor unit combinations process load value j; the state transfer matrix is represented as dp[j]=min(not enabling the current air-conditioning outdoor unit: dp[j], enabling the current air-conditioning outdoor unit: min(dp[j-l_k]+e_k)), wherein dp[j-l_k] represents the minimum energy consumption value when all air-conditioning outdoor units before the current air-conditioning outdoor unit process load value j-l_k, and e_k represents the energy consumption value when the current air-conditioning outdoor unit processes load value l_k.

[0023] According to some embodiments of the present invention, obtaining a target load value and determining a best matching solution from the one-dimensional state table according to the target load value includes:

[0024] When a matching state having a load value equal to the target load value exists in the one-dimensional state table, the matching state is used as the best matching solution;

[0025] When there is no matching state in the one-dimensional state table where the load value is equal to the target load value, an overload state in the one-dimensional state table where the load value is greater than the target load value is determined, and the overload state with the minimum energy consumption value is selected as the optimal matching solution.

[0026] According to some embodiments of the present invention, when there is no matching state in the one-dimensional state table in which the load value is equal to the target load value, determining an overload state in the one-dimensional state table in which the load value is greater than the target load value, and selecting the overload state with the minimum energy consumption value as the optimal matching solution further includes:

[0027] Determining an error between the load value in the overload state and the target load value;

[0028] When the error value exceeds a preset value, a low-load state in which the load value in the one-dimensional state table is lower than and closest to the target load value is determined, and the low-load state is used as the best matching solution.

[0029] According to some embodiments of the present invention, obtaining a target load value and determining an optimal matching solution from the one-dimensional state table according to the target load value further includes:

[0030] Obtaining the maximum load value that can be handled by all the air-conditioning outdoor units;

[0031] When the maximum load value is lower than the target load value, the calculation is stopped and an error log is returned.

[0032] On the other hand, according to an embodiment of the present invention, a collaborative load distribution device for multiple air-conditioning outdoor units includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the load distribution method for multiple air-conditioning outdoor units as described in the above-mentioned embodiment.

[0033] On the other hand, an electronic device according to an embodiment of the present invention includes the multi-air conditioner outdoor unit collaborative load distribution device described in the above embodiment.

[0034] On the other hand, according to a computer-readable storage medium of an embodiment of the present invention, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method for distributing loads of multiple air conditioner outdoor units described in the above embodiment.

[0035] The load distribution method, device, equipment and medium for multiple air-conditioning outdoor units according to the embodiments of the present invention have at least the following beneficial effects: by screening valid gears and deleting inefficient gears dominated by high-load, low-energy-consumption gears, redundant calculations can be avoided and computing efficiency can be improved; by forward traversing the air-conditioning outdoor units and reverse traversing all valid gears of each air-conditioning outdoor unit, all situations can be covered to ensure that the optimal load distribution scheme is found; by constructing a one-dimensional state table and a state transition equation, it is easy to find the best matching solution, and by combining the selection record matrix to record the optimal gear, it is easy to backtrack according to the best matching solution to obtain the best load distribution scheme; using a one-dimensional state table to store information can reduce space complexity and facilitate the deployment of the scheme on embedded devices.

[0036] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments with reference to the following drawings, in which:

[0038] Figure 1 A flowchart of a method for distributing loads to multiple air conditioner outdoor units according to an embodiment of the present invention;

[0039] Figure 2 This is a flowchart of the specific steps of filtering inefficient gears according to an embodiment of the present invention;

[0040] Figure 3 This is a specific principle diagram of the low-efficiency filtering gear of an embodiment of the present invention;

[0041] Figure 4A flowchart of the specific steps of constructing a selection record matrix according to an embodiment of the present invention;

[0042] Figure 5 A flowchart of the steps of determining the best matching solution from a one-dimensional state table according to an embodiment of the present invention;

[0043] Figure 6 for Figure 5 FIG. 5 is a schematic diagram showing some specific steps of step S520;

[0044] Figure 7 This is a flowchart of the steps of determining whether the target load value exceeds the maximum load value according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application. The step numbers in the following embodiments are provided only for the convenience of explanation and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0046] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention.

[0047] The terms "first," "second," "third," and "fourth," etc., in the specification, claims, and accompanying drawings of the present invention are used to distinguish between different items, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0048] References to "embodiments" in this disclosure mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the disclosure. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0049] For a system with multiple air conditioner outdoor units, the load of each air conditioner outdoor unit needs to be distributed according to indoor demand. The traditional air conditioner outdoor unit load distribution method has the following defects:

[0050] 1. Invalid state calculation: The energy consumption dominance relationship between the gears of the external unit is not considered, resulting in dynamic planning requiring traversal of a large number of invalid states, resulting in high calculation redundancy.

[0051] 2. Inefficient path storage: A two-dimensional path table is used to store historical selections, with a space complexity of O(N*M^2) (N is the number of external machines, M is the maximum load), making it difficult to deploy on embedded devices.

[0052] 3. Insufficient energy consumption optimization: The gear selection is not based on the unit energy consumption efficiency, which may result in missing gear combinations with high load and low energy consumption.

[0053] In order to solve the above problems, the embodiments of the present invention provide a method, device, equipment and medium for load distribution of multiple air-conditioning outdoor units. By screening valid gears and deleting inefficient gears dominated by high-load and low-energy consumption gears, redundant calculations can be avoided and computing efficiency can be improved; by forward traversing the air-conditioning outdoor units and reverse traversing all valid gears of each air-conditioning outdoor unit, all situations can be covered to ensure that the best load distribution scheme is found; by constructing a one-dimensional state table and a state transition equation, it is easy to find the best matching solution, and combining the selection record matrix to record the optimal gear, it is easy to backtrack according to the best matching solution to obtain the best load distribution scheme; using a one-dimensional state table to store information can reduce space complexity and facilitate the deployment of the scheme on embedded devices.

[0054] On the one hand, the embodiment of the present invention proposes a method for distributing loads of multiple air conditioner outdoor units, such as Figure 1 As shown, the method includes but is not limited to steps S100 to S600:

[0055] Step S100: Obtain all load gears of each air conditioner outdoor unit, filter out inefficient gears among the load gears, and obtain the effective gear of each air conditioner outdoor unit; each load gear has a corresponding load value and energy consumption value;

[0056] Step S200: Traversing the air conditioner outdoor units one by one in a forward order, and traversing all valid gears of each air conditioner outdoor unit in a reverse order, determining the energy consumption of all load values that can be handled by any combination of outdoor units composed of multiple air conditioner outdoor units, and obtaining a traversal result;

[0057] Step S300: constructing a one-dimensional state table and a state transition equation, and updating the one-dimensional state table according to the state transition equation and the traversal result; the one-dimensional state table is used to represent the minimum energy consumption value when all external units are combined to process a specified load value;

[0058] Step S400: constructing a selection record matrix. During the traversal process, the selection record matrix is used to record the optimal gear position of each air conditioner outdoor unit when processing a specified load value.

[0059] Step S500: obtaining a target load value, and determining the best matching solution from a one-dimensional state table according to the target load value;

[0060] Step S600: According to the best matching solution, the optimal gear of each air-conditioning outdoor unit is determined by selecting the record matrix for reverse backtracking, a load distribution plan is generated, and a control instruction is output to control the load state of each air-conditioning outdoor unit.

[0061] Specifically, in the present application, each air-conditioning outdoor unit may have multiple load gears, and each load gear has a corresponding load value and energy consumption value. For example, it is assumed that outdoor unit 1 has the following load gears: {(0, 0), (10, 5), (20, 9)}, and outdoor unit 2 has the following gears: {(0, 0), (15, 7), (25, 12)}; wherein, for the load gear of outdoor unit 1: (0, 0) means that outdoor unit 1 is not started, and its load value and energy consumption value are both 0; (10, 5) means that the load value of outdoor unit 1 is 10 and the energy consumption value is 5; (20, 9) means that the load value of outdoor unit 1 is 20 and the energy consumption value is 9. For the load level of outdoor unit 2: (0, 0) indicates that outdoor unit 2 is not started, and its load value and energy consumption value are both 0; (15, 7) indicates that the load value of outdoor unit 2 is 15 and the energy consumption value is 7; (25, 12) indicates that the load value of outdoor unit 2 is 25 and the energy consumption value is 12. It should be noted that the above is only an example and not a specific limitation of this application. The specific load level of each air conditioner outdoor unit depends on the actual situation.

[0062] In this application, in order to avoid the problem of redundant calculation and improve the calculation efficiency, it is necessary to consider the energy consumption dominance relationship between the outdoor unit gears, delete the inefficient gears dominated by the high-load and low-energy-consumption gears, and only retain the effective gears of each air-conditioning outdoor unit. For example, it is assumed that the outdoor unit 3 has the following load gears: {(0, 0), (20, 8), (25, 8), (30, 14)}. Since the load value of the load gear (25, 8) is higher than that of the load gear (20, 8), and the energy consumption value is not higher than that of the load gear (20, 8), it means that (20, 8) is an inefficient gear and needs to be filtered and deleted. By filtering the inefficient gears and only retaining the effective gears of the air-conditioning outdoor unit, the calculation redundancy can be reduced and the calculation efficiency can be improved.

[0063] After obtaining the valid gear position for each outdoor air conditioner, the system traverses each outdoor air conditioner in forward order, i.e., in the order of outdoor air conditioner 1, outdoor air conditioner 2, outdoor air conditioner 3, etc. Furthermore, since each outdoor air conditioner has multiple valid gear positions, each valid gear position of each outdoor air conditioner must be traversed when traversing each outdoor air conditioner. In this example, all valid gear positions of the current outdoor air conditioner are traversed in reverse order to avoid state overlap (reverse order means traversing each valid gear position in descending order of load value). By traversing all air-conditioning outdoor units, the energy consumption of all load values that can be handled by any combination of outdoor units composed of all air-conditioning outdoor units can be determined; for example, assuming that outdoor unit 1 has the following load gears: {(0, 0), (10, 5), (20, 9)}, and outdoor unit 2 has the following gears: {(0, 0), (15, 7), (25, 12)}, when traversing outdoor unit 1, it is found that the load values that can be handled are 0, 10, and 20, and the corresponding energy consumption conditions are 0, 5, and 9 respectively; when traversing outdoor unit 2, in addition to determining the load states 15 and 25 that outdoor unit 2 itself can handle, it is also necessary to determine the load values that can be handled by the outdoor unit combination composed of outdoor unit 2 + outdoor unit 1, including 25 (external unit 1 load 10, outdoor unit 2 load 15), 35 (external unit 1 load 10, outdoor unit 2 load 25, or outdoor unit 1 load 20, outdoor unit 2 load 15), 45 (external unit 1 load 20, outdoor unit 2 load 25), etc. It is necessary to determine the energy consumption of the system under each load condition.

[0064] Construct a one-dimensional state table and state transition equation. In this example, the one-dimensional state table can be expressed as dp[j], which represents the minimum energy consumption value when all external units are combined to process load value j. Still taking the example of external unit 1 having the following load levels: {(0, 0), (10, 5), (20, 9)} and external unit 2 having the following load levels: {(0, 0), (15, 7), (25, 12)}, when traversing external unit 1, dp[0] = 0, dp

[10] = 5, dp

[20] = 9; when traversing to external unit 2, dp

[15] = 7, dp

[25] = min(5+7, 0+12) = 12 (5+7 represents the energy consumption value when external unit 1 has a load of 10 and external unit 2 has a load of 15, 0+12 represents the energy consumption value when external unit 1 has a load of 0 and external unit 2 has a load of 25), dp

[35] = min(9+7, 5+12) = 16, dp

[45] = 9+12 = 21. And so on, the one-dimensional state table can be updated based on all the traversal results. In this example, the state transition equation can be expressed as: dp[j] = min(not enabling the current air-conditioning outdoor unit: dp[j], enabling the current air-conditioning outdoor unit: min(dp[j-l_k]+e_k)), where dp[j-l_k] represents the minimum energy consumption value of all air-conditioning outdoor units before the current air-conditioning outdoor unit when processing the load value j-l_k, and e_k represents the energy consumption value when the current air-conditioning outdoor unit processes the load value l_k. Specifically, for each air-conditioning outdoor unit, there are options of starting and not starting. Not starting indicates that the load gear of the current air-conditioning outdoor unit is (0, 0). Assuming that the total load value to be processed by i air conditioners is j, since the i-th air-conditioning outdoor unit is not started, the load value to be processed by the first i-1 air-conditioning outdoor units is j. Since dp[j] has been obtained when traversing the first i-1 air-conditioning outdoor units, dp[j] = dp[j] when traversing the i-th air-conditioning outdoor unit; if you choose to start the i-th air-conditioning outdoor unit, since each air-conditioning outdoor unit may have multiple valid gears, when traversing the i-th air-conditioning outdoor unit, its processing There are multiple options for the load value l_k (for example, l_k for outdoor unit 1 can be 10 or 20). For each value of l_k, dp[j] needs to be calculated. Assuming the load value of the i-th air conditioner is l_k, then the load values of the first i-1 air conditioners are j-l_k. In this case, dp[j] = dp[j-l_k] + e_k, where e_k is the energy consumption of the i-th air conditioner to handle the load value l_k. Since there are multiple options for the load value l_k, it is necessary to find the l_k value that minimizes dp[j] among the multiple l_k values, that is, min(dp[j-l_k]+e_k). Therefore, we can obtain dp[j] = min(dp[j], min(dp[j-l_k]+e_k)). Based on the state transition equation and the traversal results, we can update the one-dimensional state table to obtain a one-dimensional state table corresponding to different load values.

[0065] In addition, in this example, a selection record matrix is also constructed. During the traversal process, the selection record matrix records the optimal gear of each air-conditioning outdoor unit when processing a specified load value, which facilitates the subsequent use of the selection record matrix to find the required load gear of the air-conditioning outdoor unit.

[0066] Finally, according to the required target load value, the best matching solution is determined from the one-dimensional state table, and based on the best matching solution, the optimal gear of each air-conditioning outdoor unit is determined by selecting the record matrix for reverse backtracking, generating a load distribution plan, and outputting control instructions to control the load state of each of the air-conditioning outdoor units.

[0067] According to the load distribution method for multiple air-conditioning outdoor units in an embodiment of the present invention, by screening valid gears and deleting inefficient gears dominated by high-load, low-energy-consumption gears, redundant calculations can be avoided and computing efficiency can be improved; by forward traversing the air-conditioning outdoor units and reverse traversing all valid gears of each air-conditioning outdoor unit, all situations can be covered to ensure that the optimal load plan is found; by constructing a one-dimensional state table and a state transition equation, it is easy to find the best matching solution, and by combining the selection record matrix to record the optimal gear, it is easy to perform a backtracking path based on the best matching solution to obtain the optimal load plan.

[0068] Furthermore, if Figure 2 As shown, in some embodiments of the present application, the above-mentioned step S100: obtains all load gears of each air-conditioning outdoor unit, filters out inefficient gears in the load gears, and obtains the effective gear of each air-conditioning outdoor unit, including but not limited to steps S110-step S140.

[0069] Step S110: Obtain all load levels of each air-conditioning outdoor unit, and sort all load levels of each air-conditioning outdoor unit according to load values;

[0070] Step S120: traverse all load levels of each air conditioner outdoor unit in descending order of load value;

[0071] Step S130: when there is another load gear whose load value is higher than the load value of the current load gear and whose energy consumption value is not greater than the energy consumption value of the current load gear, the current load gear is regarded as an inefficient gear;

[0072] Step S140: Filter all inefficient gears of each air-conditioning outdoor unit to obtain an effective gear of each air-conditioning outdoor unit.

[0073] Specifically, assuming that outdoor unit 1 has the following load levels: {(0, 0), (20, 9), (10, 5)}, outdoor unit 2 has the following levels: {(0, 0), (15, 7), (25, 12)}; and outdoor unit 3 has the following levels: {(0, 0), (20, 8), (30, 14), (25, 7)}, then after sorting all load levels of each air conditioner outdoor unit in ascending (or descending) order according to the load value, the following results are obtained:

[0074] External unit 1: (0, 0), (10, 5), (20, 9);

[0075] External unit 2: (0, 0), (15, 7), (25, 12);

[0076] Outdoor unit 3: (0, 0), (20, 8), (25, 7), (30, 14).

[0077] Then, in descending order of load value, traverse all load levels of each air conditioner outdoor unit to check whether there are other load levels with higher load values and no greater energy consumption than the current load level. For example, when traversing all valid levels of outdoor unit 1, we first see load level (20, 9). Since there is no subsequent higher load level, we retain this level. Then, we see load level (10, 5). We find that its energy consumption is lower than that of level (20, 9), so we retain this level. We do not need to traverse the (0, 0) level of all air conditioner outdoor units. When traversing all valid gears of outdoor unit 3, we first see (30, 14). Since there is no subsequent higher load gear, we keep this gear; for load gear (25, 7), since its energy consumption value is lower than (30, 14), we keep this gear; for load gear (20, 8), since its load value is lower than (25, 7), but its energy consumption value is higher than (25, 7), this gear is an inefficient gear and needs to be removed. For specific filtering methods, please refer to Figure 3 , first initialize: the total number of gears n = 3 (there are 3 valid gears after sorting in the example). After excluding the zero-load gear, let i = n-1 (i is the last load gear), j = i-1 (j is the comparison gear), and compare the energy consumption. If e_j ≥ e_i (the energy consumption of the previous gear ≥ the energy consumption of the current gear), delete the j-th gear. After deleting the gear, reset i = i-1, j = i-1, and skip the invalid comparison. If you choose to keep the j-th gear after the energy consumption comparison, then j-- continue traversing. Termination condition: when j < 0, reset the pointer; when i < 0 (all gears have been processed), end the process.

[0078] This application traverses all load gears of each air-conditioning outdoor unit in the order of load value from large to small. Compared with traversing all load gears of each air-conditioning outdoor unit in the order of load value from small to large, it has the following advantages: the inefficient gears can be filtered out by one traversal, and the time complexity is reduced from O(n 2 )O(n 2 ) is reduced to O(n)O(n) (n is the number of gears). The forward traversal must compare each gear with all subsequent gears one by one, even if some low-load gears are clearly better. However, the reverse traversal can directly eliminate the processed high-load gear when a low-load gear is found to be better, without the need for subsequent repeated comparisons.

[0079] Furthermore, if Figure 4 As shown, in some embodiments of the present application, step S400: construct a selection record matrix, and during the traversal process, the optimal gear of each air-conditioning outdoor unit when processing a specified load value is recorded through the selection record matrix, including but not limited to steps S410-step S420.

[0080] Step S410: sorting all valid gears of each air conditioner outdoor unit in ascending order according to the load value, and assigning a corresponding index value to each valid gear of each air conditioner outdoor unit according to the sorting result;

[0081] Step S420: Creating a two-dimensional array and determining the value of the two-dimensional array based on the optimal gear position of each air conditioner outdoor unit when processing the specified load value; the two-dimensional array is used to represent the index value of the optimal gear position of each air conditioner outdoor unit when processing the specified load value. The two-dimensional array adopts a sparse storage strategy and only stores non-zero index values;

[0082] Specifically, in this example, all valid gears of each air-conditioning outdoor unit are sorted in ascending order according to the size of the load value, and after sorting, a corresponding index value is configured for each valid gear of each air-conditioning outdoor unit. For example, for the first valid gear, the corresponding index value is 0; for the second valid gear, the corresponding index value is 1; for the third valid gear, the corresponding index value is 2. The two-dimensional array is represented as choice[i][j], which is used to record the index value of the optimal gear of the i+1th air-conditioning outdoor unit when the load value is j. Assume that after sorting, outdoor unit 1 has the following load gears: {(0, 0), (10, 5), (20, 9)}, and outdoor unit 2 has the following gears: {(0, 0), (15, 7), (25, 12)}. For outdoor unit 1, there are:

[0083] When j=20: dp

[20] =9, then choice[0]

[20] =2 (index offset).

[0084] When j=10: dp

[10] =5, then choice[0]

[10] =1.

[0085] When j=0: dp[0]=0, then choice[0][0]=0.

[0086] The updated one-dimensional state table is: dp[0]=0, dp

[10] =5, dp

[20] =9, and the remaining j values are INF (indicating no solution).

[0087] For external unit 2, there are:

[0088] When j=25: select load gear (25, 12)→dp

[25] =dp[0]+12=12, choice[1]

[25] =2.

[0089] When j=15: select load gear (15, 7)→dp

[15] =dp[0]+7=7, choice[1]

[15] =1.

[0090] When j = 35: the load of outdoor unit 1 is 10, the load of outdoor unit 2 is 25 → dp

[35] = dp

[25] + dp

[10] = 12 + 5 = 17; or, the load of outdoor unit 1 is 20, the load of outdoor unit 2 is 15 → dp

[35] = dp

[15] + dp

[20] = 7 + 9 = 16 < 17. Therefore, dp

[35] = 16, choice[1]

[35] = 1, and choice[0]

[35] = 2.

[0091] When j=45: load of outdoor unit 1 is 20, load of outdoor unit 2 is 25 → dp

[45] =dp

[25] +dp

[20] =12+9=21, choice[1]

[45] =2, choice[0]

[45] =2.

[0092] The updated one-dimensional state table is: dp

[15] =7, dp

[25] =12, dp

[35] =16, dp

[45] =21.

[0093] It should be noted that in this example, the two-dimensional array uses a sparse storage strategy, storing only non-zero index values to reduce memory usage. If an air conditioner outdoor unit is not started, that is, the load value is 0, there is no need to record its index value in the two-dimensional array.

[0094] Furthermore, in some embodiments of the present application, Figure 5 As shown, the above step S500: obtaining a target load value, and determining the best matching solution from a one-dimensional state table according to the target load value, includes but is not limited to step S510 and step S520.

[0095] Step S510: when there is a matching state in the one-dimensional state table whose load value is equal to the target load value, the matching state is taken as the best matching solution;

[0096] Step S520: When there is no matching state in the one-dimensional state table where the load value is equal to the target load value, an overload state in the one-dimensional state table where the load value is greater than the target load value is determined, and the overload state with the minimum energy consumption is selected as the best matching solution.

[0097] Specifically, assuming the target load value is 25, and there is a matching state in the one-dimensional state table with a load value equal to the target load value, for example, dp

[25] = 12, then this state can be determined as the best matching solution; if there is no matching state in the one-dimensional state table with a load value equal to the target load value, then it is necessary to select an overload state in the one-dimensional state table with a load value greater than the target load value, and select the overload state with the lowest energy consumption as the best matching solution. Assuming the target load value is 30, and there is no dp

[30] in the one-dimensional state table, it is necessary to select an overload state in the one-dimensional state table with a load value greater than 30, such as dp

[35] , dp

[45] , etc., to meet the load demand, and select the state with the lowest energy consumption value among these overload states. Since dp

[35] = 16 < dp

[45] = 21, dp

[35] is selected as the best matching solution. Based on the best matching solution, the load distribution state of each air conditioner outdoor unit can be reversed. This solution can achieve an accurate match with the target load value as much as possible. If an accurate match cannot be achieved, the over-configuration with the lowest energy consumption is selected, thereby reducing energy consumption as much as possible while meeting the load demand.

[0098] Furthermore, in some embodiments of the present application, Figure 6 As shown, the above step S520: when there is no matching state in the two-dimensional state table where the load value is equal to the target load value, determining an overload state in the two-dimensional state table where the load value is greater than the target load value, and selecting the overload state with the lowest energy consumption as the best matching solution, further includes:

[0099] Step S521: determining the error between the load value in the overload state and the target load value;

[0100] Step S522: When the error value exceeds the preset value, a low-load state in the one-dimensional state table whose load value is lower than and closest to the target load value is determined, and the low-load state is used as the best matching solution.

[0101] It should be noted that in this example, when selecting an overload state, it is also necessary to verify whether the error between the load value of the overload state and the target load value exceeds the error value to avoid excessive overload states and waste of resources. For example, if the target load value is 41KW, but the smallest overload state found is 75KW, then the overload is too large. In this case, it is necessary to find a low-load state in the one-dimensional state table that has a load value lower than the target load value and is closest to the target load value, such as 40KW.

[0102] Furthermore, in some embodiments of the present application, Figure 7 As shown, the above step S500: obtaining the target load value, and determining the best matching solution from the one-dimensional state table according to the target load value, further includes:

[0103] Step S530: Obtain the maximum load value of all air-conditioning outdoor units;

[0104] Step S540: When the maximum load value is lower than the target load value, the calculation is stopped and an error log is returned.

[0105] Specifically, assuming that the maximum load that outdoor unit 1 can handle is 20KW, the maximum load that outdoor unit 2 can handle is 25KW, and the maximum load that outdoor unit 3 can handle is 30KW, then the maximum load value that outdoor unit 1 + outdoor unit 2 + outdoor unit 3 can handle is 20+25+30=75KW. If the target load value exceeds 75KW, it means that it exceeds the level that the system can handle. At this time, the system stops calculating and directly returns the error log to avoid invalid calculations.

[0106] In some embodiments of the present application, after obtaining the best matching solution, it is determined whether each air-conditioning outdoor unit has a non-zero gear enabled according to the two-dimensional array choice[i][j]. If the non-zero gear is not enabled, it means that the air-conditioning outdoor unit does not need to be started; if the non-zero gear is enabled, it is necessary to determine the load gear selected by each air-conditioning outdoor unit according to the index value of the two-dimensional array [i][j], and operate according to the load gear.

[0107] The following describes in detail the load distribution method for multiple air-conditioning outdoor units in an embodiment of the present application with several examples. It should be noted that the following examples are only for illustrative purposes and are not specific limitations to the present application.

[0108] Example 1 (target load value 40KW):

[0109] (1) Pre-processing the outdoor unit gear

[0110] 1. Original gear data:

[0111] External unit 1: [(0, 0), (20, 9), (10, 5)];

[0112] External unit 2: [(0, 0), (15, 7), (25, 12)];

[0113] External unit 3: [(0, 0), (30, 14), (15, 8), (20, 8)];

[0114] 2. Reverse dominance filtering:

[0115] Outdoor unit 1: Arrange the non-zero gears in ascending order of load value to obtain [(10, 5), (20, 9)], check the energy consumption values in reverse order, and retain the valid gears that are not dominated by high load and low energy consumption:

[0116] (20, 9) → No subsequent higher load gear, reserved.

[0117] (10, 5) → The subsequent gear (20, 9) has a higher energy consumption value and is retained.

[0118] External unit 2: Non-zero gear [(15, 7), (25, 12)], filtering logic is the same as above, retaining all gears.

[0119] Outdoor unit 3: After ascending sorting, the non-zero gears are [(15, 8), (20, 8), (30, 14)]. Check the energy consumption values in reverse order and retain the valid gears that are not dominated by high load and low energy consumption:

[0120] (30, 14) → No subsequent higher load gear, reserved;

[0121] (20, 8) → The subsequent gear (30, 14) has a higher energy consumption value and is retained;

[0122] (15, 8) → The energy consumption value is equal to the subsequent gear (20, 8), indicating that this gear is dominated by the high-load, low-energy gear (20, 8). It is an inefficient gear and needs to be filtered.

[0123] The non-zero gear positions of the outdoor unit 3 after filtering are [(15, 8), (30, 14)].

[0124] (2) Dynamic Programming Reverse Update

[0125] 1. Initialize the dynamic planning table:

[0126] Maximum system capacity: 20+25+30=75KW, target load value 40KW<75KW, enter calculation.

[0127] Create a one-dimensional state table dp[0], dp[1], ..., dp

[75] , where, except for the initial value of dp[0] which is zero (the energy consumption value is 0 at zero load), the other initial values are set to infinity (INF), indicating that there is no feasible solution. Create a two-dimensional array choice[2]

[76] to record the index value of the optimal gear of each air-conditioning outdoor unit.

[0128] 2. External machine 1 processing (the first machine in the forward traversal order)

[0129] Candidate gear positions: (0, 0), (10, 5), (20, 9).

[0130] Reverse traversal to update the load status (from 75 to 0):

[0131] j=20: select 20kW→dp

[20] =dp[0]+9=9, choice[0]

[20] =2 (index offset).

[0132] j=10: select 10kW→dp

[10] =dp[0]+5=5, choice[0]

[10] =1.

[0133] j=0: select 0kW→dp[0] remains unchanged, choice[0][0]=0.

[0134] The updated one-dimensional state table: dp[0]=0, dp

[10] =5, dp

[20] =9, and the remaining j values are INF.

[0135] 3. External machine 2 processing (the second machine in the forward traversal order)

[0136] Candidate gear positions: (0, 0), (15, 7), (25, 12).

[0137] Reverse traversal updates the load status, some of which are as follows:

[0138] j=25: select 25kW→dp

[25] =dp[0]+12=12, choice[1]

[25] =2.

[0139] j=15: select 15kW→dp

[15] =dp[0]+7=7, choice[1]

[15] =1.

[0140] Updated one-dimensional state table: some newly added states are dp

[15] =7, dp

[25] =12.

[0141] 4. External machine 3 processing (the third machine in the forward traversal order)

[0142] Candidate gear positions: (0, 0), (20, 8), (30, 14).

[0143] Reverse order update example:

[0144] j=40: Select 20kW, outdoor unit 1 and outdoor unit 2 need to process 20kW → dp

[40] =dp

[20] +8=9+8=17, choice[2]

[40] =1.

[0145] j = 30: Select 30kW. Outdoor units 1 and 2 need to process 10kW → dp

[30] = dp

[10] + 14 = 19. choice[2]

[30] = 2.

[0146] j=20: select 0 kW or 20 kW, dp

[20] =min(dp

[20] , dp[0]+8)=8. choice[2]

[20] =1.

[0147] Finally, dp

[40] =17, which corresponds to the lowest total energy consumption.

[0148] (3) Reverse backtracking analysis

[0149] 1. Starting from the target load of 40kW:

[0150] Outdoor unit 3 (the third unit): choice[2]

[40] = 1 → selects the first non-zero gear 20kW, and the energy consumption value is 8. Remaining load: 40-20 = 20kW.

[0151] Outdoor unit 2 (the second unit): choice[1]

[20] =0→select 0kW, and the energy consumption value is 0.

[0152] Residual load: 20-0=20kW.

[0153] Outdoor unit 1 (the first unit): choice[0]

[20] =2 → select the second non-zero gear 20kW, and the energy consumption value is 9.

[0154] 2. Allocation plan:

[0155] Outdoor unit 1: 20kW (energy consumption value is 9);

[0156] Outdoor unit 2: 0kW (energy consumption value is 0);

[0157] Outdoor unit 3: 20kW (energy consumption value is 8);

[0158] Total load: 20+0+20=40kW

[0159] Total energy consumption value: 9+0+8=17.

[0160] (4) Double verification and exception handling

[0161] 1. Load verification: 20+0+20=40kW, consistent with the target.

[0162] 2. Energy consumption value verification: 9+0+8=17, consistent with dp

[40] =17.

[0163] 3. Abnormal fuse: No abnormality, output valid solution.

[0164] Example 2 (target load 41KW):

[0165] (1) Pre-processing the outdoor unit gear

[0166] The preprocessing method is the same as that in Example 1 and will not be described here.

[0167] (2) Dynamic Programming Reverse Update

[0168] 1. Initialize the dynamic planning table:

[0169] Maximum system capacity: 20+25+30=75KW, target load 40KW<75KW, enter calculation.

[0170] Create a one-dimensional state table dp[0], dp[1], ..., dp

[75] , where, except for the initial value of dp[0] which is zero (the energy consumption value is 0 at zero load), the initial values of the remaining values are set to infinity (INF). Create a two-dimensional array choice[2]

[76] to record the index value of the optimal gear of each air-conditioning outdoor unit.

[0171] 2. External machine 1 processing (the first machine in the forward traversal order)

[0172] Candidate gear positions: (0, 0), (10, 5), (20, 9).

[0173] Reverse traversal to update the load status (from 75 to 0):

[0174] j=20: select 20kW→dp

[20] =dp[0]+9=9, choice[0]

[20] =2 (index offset).

[0175] j=10: select 10kW→dp

[10] =dp[0]+5=5, choice[0]

[10] =1.

[0176] j=0: select 0kW→dp[0] remains unchanged, choice[0][0]=0.

[0177] The updated one-dimensional state table: dp[0]=0, dp

[10] =5, dp

[20] =9, and the remaining j values are INF.

[0178] 3. External machine 2 processing (the second machine in the forward traversal order)

[0179] Candidate gear positions: (0, 0), (15, 7), (25, 12).

[0180] Reverse traversal updates the load status, some of which are as follows:

[0181] j=25: select 25kW→dp

[25] =dp[0]+12=12, choice[1]

[25] =2.

[0182] j=15: select 15kW→dp

[15] =dp[0]+7=7, choice[1]

[15] =1.

[0183] Updated one-dimensional state table: some newly added states are dp

[15] =7, dp

[25] =12.

[0184] 4. External machine 3 processing (the third machine in the forward traversal order)

[0185] Candidate gear positions: (0, 0), (20, 8), (30, 14).

[0186] Reverse update example: Start from the maximum load (outdoor unit 1 + outdoor unit 2 + outdoor unit 3 = 20 + 25 + 30 = 75kW) and update in reverse order.

[0187] The target load value is 41kW. After traversal, it is found that there is no feasible solution of dp

[41] , so it is necessary to find the minimum excess solution (≥41kW and the lowest energy consumption value):

[0188] Select the 45kW solution: dp

[45] = dp

[20] (outdoor unit 1) + dp

[25] (outdoor unit 2) + dp[0](outdoor unit 3) → total energy consumption 21;

[0189] Select the 50kW solution: dp

[50] =dp

[20] (outdoor unit 1)+dp

[15] (outdoor unit 2)+dp

[15] (outdoor unit 3) → Since outdoor unit 3 does not have a 15KW gear, this solution is invalid.

[0190] 55kW solution: dp

[30] (outdoor unit 3)+dp

[25] (outdoor unit 2)→total energy consumption 26 (overload).

[0191] (3) Reverse backtracking analysis

[0192] Overload decision-making & allocation plan:

[0193] The system's maximum capacity is 75kW, and 41kW is within the limit. However, during actual allocation, no exact matching solution was found (due to discrete pre-processing gears). The minimum feasible excess solution of 45kW (total load 45kW, total energy consumption 21) was selected, and reverse analysis was performed using the dynamic programming table:

[0194] dp

[45] =21→Outdoor unit 3 selects 0kW, outdoor unit 2 selects 25kW(12), and outdoor unit 1 selects 20kW(9).

[0195] (4) Double verification and exception handling

[0196] 1. Load verification: 20+25+0=45kW≥41KW and the error value is 2.5%<5%, which is consistent with the target.

[0197] 2. Energy consumption verification: 9+12+0=21, consistent with dp

[45] =21.

[0198] 3. Abnormal fuse: No abnormality, output valid solution.

[0199] The computational efficiency of the load distribution method for multiple air conditioner outdoor units in the embodiment of the present application is verified below:

[0200] 1. Time complexity = O(N·M·L), where:

[0201] N: number of air conditioner outdoor units;

[0202] M: Maximum load of the system (the sum of all air-conditioning outdoor units running at full load);

[0203] L: number of gears (including zero load);

[0204] Dimension decomposition description:

[0205] 1. Air conditioner outdoor unit traversal dimension: Each air conditioner outdoor unit needs to independently process state transitions, with a complexity of O(N). For example, three outdoor units require three rounds of state transition calculations.

[0206] 2. Load traversal dimension: The state transition of each air conditioner outdoor unit must traverse all possible load values, with a complexity of O(M). Assuming the maximum system load is 75KW (outdoor unit 1:20 + outdoor unit 2:25 + outdoor unit 3:30), then M = 75.

[0207] 3. Option traversal dimension: Each load option of each external unit must be calculated independently, with a complexity of O(L). For example, if external unit 1 has three options (0, 10, 20), then L = 3.

[0208] Theoretical calculation example:

[0209] 1. Original gear data:

[0210] External unit 1: [(0, 0), (20, 9), (20, 10), (10, 5)];

[0211] External unit 2: [(0, 0), (15, 7), (25, 12), (25, 13)];

[0212] External unit 3: [(0, 0), (30, 14), (15, 8), (20, 8)];

[0213] 2. Preprocess and filter the gear data:

[0214] External unit 1: [(0, 0), (20, 9), (10, 5)];

[0215] External unit 2: [(0, 0), (15, 7), (25, 12)];

[0216] External unit 3: [(0, 0), (30, 14), (20, 8)];

[0217] Comparison table of time complexity before and after preprocessing:

[0218]

[0219]

[0220] 2. Space Complexity

[0221] 1. Theoretical formula:

[0222] The space complexity of the traditional dynamic programming two-dimensional table is O(N·M2);

[0223] The space complexity of a one-dimensional array + sparse matrix is O(N·M)+O(M), (when the number of external machines is ≥ 1, O(N·M) is the dominant term);

[0224] in:

[0225] N: number of air conditioner outdoor units;

[0226] M: Maximum load of the system (the sum of all air-conditioning outdoor units running at full load);

[0227] 2. Case comparison:

[0228] Scenario 1 (N=3, M=75):

[0229] The total storage capacity of this solution is: (3×75+75)×4Bytes=1.2KB;

[0230] Storage capacity of traditional solution: 3×752×4Bytes=675KB;

[0231] Optimization ratio: [1-(1.2KB / 675KB)]×100%≈99.82%;

[0232] Scenario 2 (N=10, M=2000):

[0233] The total storage capacity of this solution is: (10×2000+2000)×4Bytes=0.086MB;

[0234] Traditional solution storage capacity: 10×2000 2 ×4Bytes=1.6GB;

[0235] Optimization ratio: [1-(0.086MB / 1600MB)]×100%≈99.99%.

[0236] 3. Response time simulation verification:

[0237] Scenario 1: There are three external units, the maximum system load is 75 kW, the number of load options for each unit is three, and the target loads are 40 kW and 41 kW. Using Python code as an example, the code runs in approximately 0.07-0.09 ms on a computer equipped with a 12th Gen Intel(R) Core(TM) i7-12700 2.10 GHz processor. Assuming the planning algorithm is used for industrial-grade applications, using C++ code, the response time can be reduced by 10-100 times with the same configuration.

[0238] Scenario 2: There are 10 external units, the maximum system load is 2000 kW, the number of load options for each unit is 10, and the target loads are 1000 kW and 1001 kW. Using Python code as an example, the code runs in approximately 13.33ms to 13.42ms on a computer equipped with a 12th Gen Intel(R) Core(TM) i7-12700 2.10GHz processor. Assuming this planning algorithm is used at the industrial level, using C++ code, the response time can be reduced by 10 to 100 times with the same configuration.

[0239] According to the load distribution method for multiple air-conditioning outdoor units of the embodiment of the present application, by filtering inefficient gears, the computational complexity is reduced and redundant computation is avoided; by constructing a selection record matrix, it is convenient to reversely trace the gear selection of each air-conditioning outdoor unit, and a sparse storage strategy is adopted to reduce the memory usage from O(NM) to NM. 2 ) is reduced to O(NM); load status is updated in reverse order, from maximum load to zero load, to avoid overwriting unprocessed status; overload prediction and double-checking mechanisms are used to prevent logical errors. This multi-air conditioner outdoor unit load distribution method is suitable for scenarios such as central air conditioning and multi-split systems that require coordinated operation of multiple outdoor units.

[0240] In addition, the load distribution method for multiple air-conditioning outdoor units in the embodiment of the present application can be implemented in an embedded control system, and the dynamic planning table adopts "ring buffer storage" to dynamically allocate memory space according to the number of outdoor units.

[0241] On the other hand, an embodiment of the present application further provides a load distribution device for multiple air conditioner outdoor units, comprising:

[0242] The processor may be implemented as a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0243] The memory can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory and is called by the processor to execute the load distribution method for multiple air conditioner outdoor units in the embodiments of this application.

[0244] Input / output interface, used to realize information input and output;

[0245] Communication interface, used to realize communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, Wi-Fi, Bluetooth, etc.);

[0246] The bus transmits information between the various components of the device (such as the processor, memory, input / output interfaces, and communication interfaces);

[0247] The processor, memory, input / output interface and communication interface are connected to each other through a bus within the device.

[0248] On the other hand, an embodiment of the present invention further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned multi-air conditioner outdoor unit load distribution device is implemented.

[0249] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory optionally includes a memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of the above-mentioned networks include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0250] Although specific embodiments are described herein, those skilled in the art will recognize that many other modifications or alternative embodiments are also within the scope of this disclosure. For example, any of the functions and / or processing capabilities described in conjunction with a particular device or component may be performed by any other device or component. In addition, although various exemplary implementations and architectures have been described in accordance with embodiments of the present disclosure, those skilled in the art will recognize that many other modifications to the exemplary implementations and architectures described herein are also within the scope of this disclosure.

[0251] Some aspects of the present disclosure have been described above with reference to the block diagrams and flow charts of the systems, methods, systems and / or computer program products according to the exemplary embodiments. It should be understood that the combination of one or more blocks in the block diagram and the flow chart and the blocks in the block diagram and the flow chart can be realized by executing computer executable program instructions respectively. Equally, according to some embodiments, some blocks in the block diagram and the flow chart may not need to be executed in the order shown, or may not need to be executed in full. In addition, additional components and / or operations beyond those components and / or operations shown in the blocks in the block diagram and the flow chart may be present in certain embodiments.

[0252] Therefore, the blocks in the block diagrams and flow charts support combinations of means for performing the specified functions, combinations of elements or steps for performing the specified functions, and program instruction means for performing the specified functions. It should also be understood that each block in the block diagrams and flow charts, and combinations of blocks in the block diagrams and flow charts, can be implemented by a dedicated hardware computer system that performs the specific functions, elements, or steps, or a combination of dedicated hardware and computer instructions.

[0253] The program modules, applications, etc. described herein may include one or more software components, including, for example, software objects, methods, data structures, etc. Each such software component may include computer-executable instructions that, in response to execution, cause at least a portion of the functionality described herein (e.g., one or more operations of the illustrative methods described herein) to be performed.

[0254] Software component can be encoded with any one in various programming languages.A kind of exemplary programming language can be low-level programming language, such as the assembly language associated with specific hardware architecture and / or operating system platform.Comprise that the software component of assembly language instruction may need to be converted to executable machine code by assembler before being executed by hardware architecture and / or platform.Another exemplary programming language can be a more advanced programming language, and it can be transplanted across multiple architectures.Comprise that the software component of more advanced programming language may need to be converted to intermediate representation by interpreter or compiler before execution.Other examples of programming language include but are not limited to macro language, shell or command language, job control language, script language, database query or search language or report writing language.In one or more exemplary embodiments, the software component that comprises the instruction of one in the above-mentioned programming language example can be directly executed by operating system or other software component, without first being converted into another form.

[0255] Software components can be stored as files or other data storage structures. Software components of similar types or related functions can be stored together, such as in a specific directory, folder, or library. Software components can be static (e.g., preset or fixed) or dynamic (e.g., created or modified at execution time).

[0256] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in the relevant technical field without departing from the scope of the present invention.

Claims

1. A method for distributing loads among multiple air conditioner outdoor units, characterized in that: The following steps are involved: Obtain all load gears of each air-conditioning outdoor unit, filter out inefficient gears among the load gears, and obtain an effective gear of each air-conditioning outdoor unit; each load gear has a corresponding load value and energy consumption value; Traversing the air-conditioning outdoor units one by one in a forward order, and traversing all valid gears of each air-conditioning outdoor unit in a reverse order, determining the energy consumption of all load values that can be handled by any combination of outdoor units composed of multiple air-conditioning outdoor units, and obtaining a traversal result; Constructing a one-dimensional state table and a state transfer equation, and updating the one-dimensional state table according to the state transfer equation and the traversal result; The one-dimensional state table is used to represent the minimum energy consumption value when all external units are combined to process a specified load value; Constructing a selection record matrix, and in a traversal process, recording the optimal gear position of each air-conditioning outdoor unit when processing a specified load value through the selection record matrix; Obtaining a target load value, and determining an optimal matching solution from the one-dimensional state table according to the target load value; According to the best matching solution, reverse backtracking is performed through the selection record matrix to determine the optimal gear position of each air-conditioning outdoor unit, generate a load distribution plan, and output control instructions to control the load state of each air-conditioning outdoor unit.

2. The method for distributing loads of multiple air conditioner outdoor units according to claim 1, characterized in that: The step of obtaining all load gears of each air-conditioning outdoor unit, filtering out inefficient gears among the load gears, and obtaining an effective gear of each air-conditioning outdoor unit includes: Obtain all load levels of each of the air-conditioning outdoor units, and sort all load levels of each of the air-conditioning outdoor units according to load values; Traversing all load levels of each of the air conditioner outdoor units in descending order of load value; When there are other load gears whose load value is higher than the load value of the current load gear and whose energy consumption value is not greater than the energy consumption value of the current load gear, the current load gear is regarded as the low-efficiency gear; All inefficient gears of each of the air-conditioning outdoor units are filtered to obtain an effective gear of each of the air-conditioning outdoor units.

3. The method for distributing loads of multiple air conditioner outdoor units according to claim 1, characterized in that: The construction of the selection record matrix, during the traversal process, records the optimal gear position of each air conditioner outdoor unit when processing a specified load value through the selection record matrix, including: Sorting all valid gears of each of the air-conditioning outdoor units in ascending order according to the load value, and configuring a corresponding index value for each valid gear of each of the air-conditioning outdoor units according to the sorting result; A two-dimensional array is created, and the value of the two-dimensional array is determined based on the optimal gear position of each of the air-conditioning outdoor units when processing the specified load value; the two-dimensional array is used to represent the index value of the optimal gear position of each of the air-conditioning outdoor units when processing the specified load value, and the two-dimensional array adopts a sparse storage strategy and only stores non-zero index values.

4. The method for distributing loads of multiple air conditioner outdoor units according to claim 1, characterized in that: The one-dimensional state table is represented as dp[j], which represents the minimum energy consumption value when all outdoor units are combined to process the load value j; the state transfer matrix is represented as dp[j]=min(not enabling the current air-conditioning outdoor unit: dp[j], enabling the current air-conditioning outdoor unit: min(dp[j-l_k]+e_k)), wherein dp[j-l_k] represents the minimum energy consumption value when all air-conditioning outdoor units before the current air-conditioning outdoor unit process the load value j-l_k, and e_k represents the energy consumption value when the current air-conditioning outdoor unit processes the load value l_k.

5. The method for distributing loads of multiple air conditioner outdoor units according to claim 1, characterized in that: The obtaining of the target load value and determining the best matching solution from the one-dimensional state table according to the target load value include: When a matching state having a load value equal to the target load value exists in the one-dimensional state table, the matching state is used as the best matching solution; When there is no matching state in the one-dimensional state table where the load value is equal to the target load value, an overload state in the one-dimensional state table where the load value is greater than the target load value is determined, and the overload state with the minimum energy consumption value is selected as the optimal matching solution.

6. The method for distributing loads of multiple air conditioner outdoor units according to claim 5, characterized in that: When there is no matching state in the one-dimensional state table in which the load value is equal to the target load value, determining an overload state in the one-dimensional state table in which the load value is greater than the target load value, and selecting the overload state with the minimum energy consumption value as the best matching solution, further comprising: Determining an error between the load value in the overload state and the target load value; When the error value exceeds a preset value, a low-load state in which the load value in the one-dimensional state table is lower than and closest to the target load value is determined, and the low-load state is used as the best matching solution.

7. The method for distributing loads of multiple air conditioner outdoor units according to claim 1, characterized in that: The acquiring of the target load value and determining the best matching solution from the one-dimensional state table according to the target load value further includes: Obtaining the maximum load value that can be handled by all the air-conditioning outdoor units; When the maximum load value is lower than the target load value, the calculation is stopped and an error log is returned.

8. A load distribution device for multiple air conditioner outdoor units, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute the multi-air conditioner outdoor unit load distribution method according to any one of claims 1 to 7.

9. An electronic device, characterized in that: It includes the multi-air conditioner outdoor unit load distribution device as described in claim 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method for distributing loads to outdoor units of multiple air conditioners according to any one of claims 1 to 7.

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