Method and device for optimizing starting time of air conditioning system

Abstract

The invention discloses an air conditioner system starting time optimization method and device.The method comprises the steps that original data are obtained, and the original data are data composed of influence factors in operation of an air conditioner system; determining a target model based on the original data and a preset model; when the target model is used for reasoning, evaluating the target feature data to obtain an evaluation result; and determining the startup time based on the evaluation result and a preset condition. According to the method, static and dynamic factors of the air conditioning system are considered, evaluation and expansion are executed in the model reasoning process, the optimized starting time is more accurate, and the energy-saving effect of the air conditioning system is better.

Description

Technical Field

[0001] The present application relates to the technical field of air conditioning, and in particular to a method and device for optimizing the startup time of an air conditioning system. Background Art

[0002] In addition to operation optimization, the time management of system startup and shutdown is also particularly important for energy-saving optimization of air-conditioning systems. Usually, in shopping malls, there are clear air-conditioning cooling start time and temperature requirements. Usually, air-conditioning operation and maintenance personnel will start the air-conditioning system too early to meet this requirement, resulting in premature startup time and low indoor temperature. Therefore, how to optimize the startup time of the air-conditioning system has become an urgent problem to be solved.

[0003] At present, there are two main ways to optimize the startup time of air conditioning systems: one is the mechanism model, which is based on the analyzed influencing factors related to the target time, such as water supply temperature, room temperature difference, indoor temperature, and outdoor temperature difference, and then performs linear regression fitting on the target time. The other is the black box model, which considers the static factors that affect the target time and models the target time.

[0004] However, when the above method is used to optimize the startup time of the air-conditioning system, the accuracy of the optimized startup time is low, which leads to poor energy-saving effect of the air-conditioning system. Summary of the invention

[0005] The main purpose of the present application is to provide a method and device for optimizing the startup time of an air-conditioning system, which can improve the accuracy of the optimized startup time and thus improve the energy-saving effect of the air-conditioning system.

[0006] In order to achieve the above objectives, in a first aspect, the present application provides a method for optimizing the startup time of an air conditioning system, comprising:

[0007] Acquire original data, wherein the original data is data composed of influencing factors in the operation of the air-conditioning system;

[0008] Determine the target model based on the original data and the preset model;

[0009] When using the target model for reasoning, the target feature data is evaluated to obtain an evaluation result;

[0010] Based on the evaluation results and preset conditions, the startup time is determined.

[0011] In one embodiment, determining a target model based on the original data and a preset model includes:

[0012] Based on the original data, determine the target characteristic data;

[0013] The preset model is trained using the target feature data to obtain the target model.

[0014] In one embodiment, determining target feature data based on raw data includes:

[0015] Clean the original data to obtain the boot time index list;

[0016] The power-on time index list is screened to obtain a valid power-on index list, a target time index list, and a midnight time index list;

[0017] Based on the effective startup index list, the target time index list and the midnight time index list, determine the influencing factors of the target time, and use the influencing factors of the target time as initial feature data;

[0018] Perform feature ablation on the initial feature data to obtain the target feature data.

[0019] In one embodiment, performing feature ablation on initial feature data to obtain target feature data includes:

[0020] The preset model is trained through the initial feature data to obtain the output result;

[0021] The features exceeding the preset threshold in the output results are eliminated to obtain the target feature data.

[0022] In one embodiment, when using the target model for reasoning, the target feature data is evaluated to obtain an evaluation result, including:

[0023] When using the target model for reasoning, configure the corresponding evaluation interval for the target feature data, and generate the full feature space according to the evaluation interval;

[0024] The feature data in the entire feature space is evaluated to obtain an evaluation result.

[0025] In one embodiment, determining the startup time based on the evaluation result and the preset conditions includes:

[0026] If the evaluation result meets the preset conditions, the current time is obtained, and the startup time is determined based on the current time and the target model;

[0027] If the evaluation result does not meet the preset conditions, the startup time is determined based on the target feature data.

[0028] In one implementation, if the evaluation result meets the preset conditions, the current time is obtained, and based on the current time and the target model, the boot time is determined, including:

[0029] When using the target model for reasoning, calculate the time to reach the target;

[0030] Calculate the theoretical target time based on the target time and current time;

[0031] If the theoretical target time is greater than the current time, output the startup time.

[0032] In one embodiment, the method further comprises:

[0033] If the theoretical target time is less than or equal to the current time, return to the step of obtaining the current time.

[0034] In one embodiment, if the evaluation result does not meet the preset condition, the startup time is determined based on the target characteristic data, including:

[0035] Based on the target characteristic data, determine the positive and negative relationship between the target time and the target time;

[0036] Based on the positive and negative relationship between the target time and the evaluation matrix, the positive sample data is counted;

[0037] Calculate the minimum target time in the positive sample data, and based on the minimum target time and the evaluation matrix, count the negative sample data;

[0038] Calculate the minimum target time in negative sample data;

[0039] Calculate the average of the minimum target time in the positive sample data and the minimum target time in the negative sample data, and use the average as the startup time.

[0040] In a second aspect, an embodiment of the present application provides a device for optimizing the startup time of an air conditioning system, comprising:

[0041] A data acquisition module is used to acquire raw data, wherein the raw data is data composed of influencing factors in the operation of the air-conditioning system;

[0042] A target model determination module is used to determine the target model based on the original data and the preset model;

[0043] An evaluation module is used to evaluate the target feature data and obtain an evaluation result when using the target model for reasoning;

[0044] The startup time determination module is used to determine the startup time based on the evaluation result and the preset conditions.

[0045] In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above methods when executing the computer program.

[0046] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0047] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above methods.

[0048] The embodiment of the present application provides a method and device for optimizing the startup time of an air-conditioning system, including: obtaining raw data, wherein the raw data is data composed of influencing factors in the operation of the air-conditioning system, and then determining a target model based on the raw data and a preset model, and then evaluating the target feature data when reasoning with the target model to obtain an evaluation result, and finally determining the startup time based on the evaluation result and preset conditions. The present application not only considers the static and dynamic factors of the air-conditioning system, but also performs evaluation and expansion in the process of model reasoning, so that the optimized startup time is more accurate and the energy-saving effect of the air-conditioning system is better. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0050] Figure 1 It is a flow chart of a method for optimizing the startup time of an air conditioning system provided in an embodiment of the present application;

[0051] Figure 2 Schematic diagram of factors affecting the target time provided in the embodiment of the present application;

[0052] Figure 3 It is a schematic diagram of a process for determining the boot time provided in an embodiment of the present application;

[0053] Figure 4 It is a schematic diagram of the flow of calculating the prediction results provided by the embodiment of the present application;

[0054] Figure 5 It is a structural schematic diagram of a device for optimizing the startup time of an air conditioning system provided in an embodiment of the present application;

[0055] Figure 6 It is a schematic diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein.

[0058] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0059] It should be understood that in the various embodiments of the present application, the size of the serial number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0060] It should be understood that in the present application, "include" and "have" 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 units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0061] It should be understood that in the present application, "plurality" refers to two or more than two. "And / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "Contains A, B and C", "Contains A, B, C" means that A, B, and C are all included, "Contains A, B or C" means that one of A, B, and C is included, and "Contains A, B and / or C" means that any one, any two, or any three of A, B, and C are included.

[0062] It should be understood that in this application, "B corresponding to A", "B corresponding to A", "A corresponds to B" or "B corresponds to A" means that B is associated with A and B can be determined based on A. Determining B based on A does not mean determining B based only on A, but B can also be determined based on A and / or other information. A and B match when the similarity between A and B is greater than or equal to a preset threshold.

[0063] Depending on the context, "if" as used herein may be interpreted as "when" or "when" or "in response to determining" or "in response to detecting."

[0064] The data involved in this application may be data authorized by the tester or fully authorized by all parties. The collection, dissemination, and use of the data shall comply with the relevant laws, regulations, and standards of the relevant countries and regions. The implementation methods / examples of this application may be combined with each other.

[0065] The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0066] Next, the present application scheme will be described through specific embodiments in conjunction with the accompanying drawings.

[0067] See also Figure 1 , Figure 1 The flowchart of a method for optimizing the startup time of an air conditioning system provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the following steps are included:

[0068] Step S101: Acquire original data.

[0069] The original data is the data composed of the influencing factors in the operation of the air conditioning system, including but not limited to indoor temperature, water supply temperature, return water temperature, startup time, target reaching time, etc.

[0070] Step S102: Determine a target model based on the original data and the preset model.

[0071] Among them, the preset model is an AI model, including but not limited to a neural network model, a deep learning model, etc.

[0072] To determine the target model based on the original data and the preset model, it is necessary to first determine the target feature data based on the original data, and then use the target feature data to train the preset model to obtain the target model.

[0073] Among them, based on the original data, the target feature data is determined, including: cleaning the original data to obtain a power-on time index list; screening the power-on time index list to obtain a valid power-on index list, a standard time index list and a midnight time index list; based on the valid power-on index list, the standard time index list and the midnight time index list, determining the influencing factors of the standard time, and using the influencing factors of the standard time as the initial feature data; performing feature ablation on the initial feature data to obtain the target feature data.

[0074] Since each influencing factor in the original data has its corresponding boundary, such as the upper and lower limits of the indoor temperature in Table 1.

[0075] Table 1 Boundaries of various impact factors

[0076]

[0077]

[0078] Therefore, the present application mainly uses a threshold method to remove abnormal data in the original data for cleaning the original data. After the abnormal data is removed, the abnormal data is filled with a NAN value.

[0079] After the above cleaning, the startup time index list start_up_index_List is obtained. Then, the startup time index list start_up_index_List is filtered according to the target temperature target_t_indoor, and the indexes that fail to reach the target temperature on the day after startup or the room temperature has reached the target temperature when the machine is started are removed, and finally the valid startup index list, the target time index list and the early morning time index list are obtained.

[0080] After obtaining the valid power-on index list, the target time index list and the early morning time index list, the influencing factors of the target time can be determined based on the valid power-on index list, the target time index list and the early morning time index list, and the influencing factors of the target time can be used as the initial feature data.

[0081] The target time refers to the time required to reach the target temperature after startup.

[0082] like Figure 2 As shown in the figure, the factors affecting the time to reach the standard include circulating water factor, building energy storage factor, internal disturbance factor and external disturbance factor. Among them, the circulating water factor includes the flow rate after startup, the water temperature at startup, the number of main units running after startup, and the water temperature setting value after startup. The building energy storage factor includes the indoor temperature before startup and the outdoor temperature before startup. The internal disturbance factor includes the room temperature at startup. The external disturbance factor includes the outdoor temperature after startup.

[0083] Specifically, as shown in Table 2 below, the average indoor temperature is calculated from the early morning to the power-on time, and the average outdoor temperature is calculated from the early morning to the power-on time.

[0084] Table 2 Calculation table of factors affecting time to achieve target

[0085]

[0086]

[0087] Afterwards, the initial feature data is subjected to feature ablation to obtain target feature data, including: training a preset model with the initial feature data to obtain an output result; and removing features exceeding a preset threshold in the output result to obtain target feature data.

[0088] Since redundant features not only affect the modeling accuracy but also increase the difficulty of subsequent application of the model, feature ablation is used to remove redundant features. Specifically, the preset model is trained according to the current initial feature data. Taking feature R2 in the initial feature data as an example, the corresponding model evaluation result S is calculated. Then, the features in the initial feature data are subjected to a feature removal experiment with replacement. The model is trained according to the remaining features. Taking feature R2 as an example, the model evaluation result Si (i = 1, 2, 3..n) of each removal experiment is calculated and the maximum value Smax = max(Si) is calculated.

[0089] If Smax is greater than S, remove the feature corresponding to Smax from the current feature set, assign S a value: S=Smax, and return to execute the step of training the preset model according to the current initial feature data until all features are traversed; if Smax is less than or equal to S, end.

[0090] Step S103: When using the target model for reasoning, the target feature data is evaluated to obtain an evaluation result.

[0091] When using the target model for reasoning, the target feature data is evaluated to obtain an evaluation result, including: when using the target model for reasoning, a corresponding evaluation interval is configured for the target feature data, and a full feature space is generated according to the evaluation interval; the feature data in the full feature space is evaluated to obtain an evaluation result.

[0092] In order to evaluate the matching degree of samples in the trained model during inference, the training samples (i.e., target feature data) need to be evaluated. First, the evaluation interval shown in Table 3 below needs to be set for each sample.

[0093] Table 3 Feature evaluation interval table

[0094]

[0095] Then, the full feature space is generated according to the evaluation interval, and finally the coverage of the samples is evaluated in the feature space (i.e., the evaluation results).

[0096] Step S104: Determine the startup time based on the evaluation result and the preset conditions.

[0097] Based on the evaluation results and preset conditions, the startup time is determined, including: if the evaluation results meet the preset conditions, the current time is obtained, and the startup time is determined based on the current time and the target model; if the evaluation results do not meet the preset conditions, the startup time is determined based on the target feature data.

[0098] Among them, the preset conditions can be set according to the specific situation, and the credibility of the optional model in this application can be set.

[0099] like Figure 3 As shown, if the evaluation result meets the preset conditions, the current time is obtained, and based on the current time and the target model, the power-on time is determined, including: when using the target model for reasoning, the target time is calculated; based on the target time and the current time, the theoretical target time is calculated; if the theoretical target time is greater than the current time, the power-on time is output; if the theoretical target time is less than or equal to the current time, the step of obtaining the current time is returned to execute.

[0100] like Figure 4 As shown, if the evaluation result does not meet the preset conditions, the power-on time is determined based on the target feature data, including: determining the positive and negative relationship between the target time and the target feature data; counting the positive sample data based on the positive and negative relationship between the target time and the evaluation matrix; calculating the minimum target time in the positive sample data, and counting the negative sample data based on the minimum target time and the evaluation matrix; calculating the minimum target time in the negative sample data; calculating the mean of the minimum target time in the positive sample data and the minimum target time in the negative sample data, and taking the mean as the power-on time.

[0101] Among them, the positive / negative relationship of the time to reach the standard is shown in Table 4. For example, the positive relationship of the indoor temperature before starting up is 1.

[0102] Table 4 Positive / negative relationship table of reaching target time

[0103]

[0104] The embodiment of the present application provides a method for optimizing the startup time of an air-conditioning system, comprising: obtaining raw data, wherein the raw data is data composed of influencing factors in the operation of the air-conditioning system, and then determining a target model based on the raw data and a preset model, and then evaluating the target feature data when reasoning with the target model to obtain an evaluation result, and finally determining the startup time based on the evaluation result and preset conditions. The present application not only considers the static and dynamic factors of the air-conditioning system, but also performs evaluation and expansion in the process of model reasoning, so that the optimized startup time is more accurate and the energy-saving effect of the air-conditioning system is better.

[0105] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0106] The following is an embodiment of the device of the present application. For details not described in detail, please refer to the corresponding method embodiment described above.

[0107] Figure 5 The schematic diagram of the structure of an air-conditioning system startup time optimization device provided by an embodiment of the present application is shown. For the convenience of explanation, only the part related to the embodiment of the present application is shown. The air-conditioning system startup time optimization device includes a data acquisition module 501, a target model determination module 502, an evaluation module 503 and a startup time determination module 504, which are as follows:

[0108] The data acquisition module 501 is used to acquire original data, wherein the original data is data composed of influencing factors in the operation of the air-conditioning system;

[0109] A target model determination module 502 is used to determine a target model based on the original data and a preset model;

[0110] An evaluation module 503 is used to evaluate the target feature data and obtain an evaluation result when using the target model for reasoning;

[0111] The power-on time determination module 504 is used to determine the power-on time based on the evaluation result and the preset conditions.

[0112] In one embodiment, the target model determination module 502 is further used to determine target feature data based on the original data;

[0113] The preset model is trained using the target feature data to obtain the target model.

[0114] In one embodiment, the target model determination module 502 is further used to clean the original data to obtain a power-on time index list;

[0115] The power-on time index list is screened to obtain a valid power-on index list, a target time index list, and a midnight time index list;

[0116] Based on the effective startup index list, the target time index list and the midnight time index list, determine the influencing factors of the target time, and use the influencing factors of the target time as initial feature data;

[0117] Perform feature ablation on the initial feature data to obtain the target feature data.

[0118] In one embodiment, the target model determination module 502 is further used to train the preset model through the initial feature data to obtain an output result;

[0119] The features exceeding the preset threshold in the output results are eliminated to obtain the target feature data.

[0120] In one embodiment, the evaluation module 503 is further used to configure a corresponding evaluation interval for the target feature data when using the target model for reasoning, and generate a full feature space according to the evaluation interval;

[0121] The feature data in the entire feature space is evaluated to obtain an evaluation result.

[0122] In one embodiment, the startup time determination module 504 is further configured to obtain the current time if the evaluation result satisfies a preset condition, and determine the startup time based on the current time and the target model;

[0123] If the evaluation result does not meet the preset conditions, the startup time is determined based on the target feature data.

[0124] In one embodiment, the boot time determination module 504 is further used to calculate the target time when using the target model for reasoning;

[0125] Calculate the theoretical target time based on the target time and current time;

[0126] If the theoretical target time is greater than the current time, output the startup time.

[0127] In one embodiment, the device further includes a loop module, and the loop module is used to return to the step of obtaining the current time if the theoretical target-reaching time is less than or equal to the current time.

[0128] In one embodiment, the startup time determination module 504 is further used to determine the positive and negative relationship of the target reaching time based on the target characteristic data;

[0129] Based on the positive and negative relationship between the target time and the evaluation matrix, the positive sample data is counted;

[0130] Calculate the minimum target time in the positive sample data, and based on the minimum target time and the evaluation matrix, count the negative sample data;

[0131] Calculate the minimum target time in negative sample data;

[0132] Calculate the average of the minimum target time in the positive sample data and the minimum target time in the negative sample data, and use the average as the startup time.

[0133] The embodiment of the present application provides an air conditioning system startup time optimization device, which is specifically used to: obtain raw data, wherein the raw data is data composed of influencing factors in the operation of the air conditioning system, and then determine the target model based on the raw data and the preset model, and then evaluate the target feature data when using the target model for reasoning to obtain the evaluation result, and finally determine the startup time based on the evaluation result and the preset conditions. The present application not only considers the static and dynamic factors of the air conditioning system, but also performs evaluation and expansion in the model reasoning process, so that the optimized startup time is more accurate and the energy-saving effect of the air conditioning system is better.

[0134] This application Figure 6 A schematic diagram of a computer device is provided. Figure 6 As shown, the computer device 6 of this embodiment includes: a processor 601, a memory 602, and a computer program 603 stored in the memory 602 and executable on the processor 601. When the processor 601 executes the computer program 603, the steps in the above-mentioned embodiments of the method for optimizing the startup time of the air conditioning system are implemented, for example Figure 1 Alternatively, when the processor 601 executes the computer program 603, the functions of each module / unit in the above-mentioned embodiments of the air conditioning system startup time optimization device are realized, for example Figure 5 Functionality of modules / units 501 to 504 is shown.

[0135] The present application also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the air conditioning system startup time optimization method provided by the various embodiments described above.

[0136] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general or special-purpose computer. For example, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist in a communication device as discrete components. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0137] The present application also provides a program product, which includes an execution instruction, which is stored in a readable storage medium. At least one processor of the device can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction so that the device implements the air conditioning system startup time optimization method provided by the various embodiments described above.

[0138] In the embodiments of the above-mentioned devices, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The steps of the method disclosed in the present application may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0139] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the startup time of an air conditioning system, It is characterized in that include: Acquiring original data, wherein the original data is data composed of influencing factors in the operation of the air-conditioning system; Determine a target model based on the original data and the preset model; When using the target model for reasoning, evaluating the target feature data to obtain an evaluation result; Based on the evaluation result and the preset conditions, a startup time is determined.

2. The method for optimizing the startup time of the air conditioning system according to claim 1, It is characterized in that The step of determining a target model based on the original data and the preset model includes: Based on the original data, determining target characteristic data; The preset model is trained using the target feature data to obtain a target model.

3. The method for optimizing the startup time of the air conditioning system according to claim 2, It is characterized in that The step of determining target characteristic data based on the original data includes: Cleaning the raw data to obtain a boot time index list; The power-on time index list is screened to obtain a valid power-on index list, a target time index list, and a midnight time index list; Determine the influencing factor of the target time based on the valid power-on index list, the target time index list and the midnight time index list, and use the influencing factor of the target time as initial feature data; Perform feature ablation on the initial feature data to obtain the target feature data.

4. The method for optimizing the startup time of the air conditioning system according to claim 3, It is characterized in that The performing feature ablation on the initial feature data to obtain the target feature data includes: The preset model is trained using the initial feature data to obtain an output result; The features exceeding a preset threshold in the output result are eliminated to obtain the target feature data.

5. The method for optimizing the startup time of the air conditioning system according to claim 1, It is characterized in that When the target model is used for reasoning, the target feature data is evaluated to obtain an evaluation result, including: When using the target model for reasoning, configuring a corresponding evaluation interval for the target feature data, and generating a full feature space according to the evaluation interval; The feature data in the full feature space is evaluated to obtain the evaluation result.

6. The method for optimizing the startup time of the air conditioning system according to claim 1, It is characterized in that The step of determining the startup time based on the evaluation result and the preset condition includes: If the evaluation result meets the preset conditions, the current time is obtained, and the startup time is determined based on the current time and the target model; If the evaluation result does not meet the preset conditions, the startup time is determined based on the target feature data.

7. The method for optimizing the startup time of the air conditioning system according to claim 6, It is characterized in that If the evaluation result meets the preset condition, obtaining the current time, and determining the boot time based on the current time and the target model, including: When using the target model for reasoning, calculating the target reaching time; Calculate a theoretical target-reaching time based on the target-reaching time and the current time; If the theoretical target-reaching time is greater than the current time, the power-on time is output.

8. The method for optimizing the startup time of the air conditioning system according to claim 7, It is characterized in that The method further comprises: If the theoretical target time is less than or equal to the current time, return to the step of obtaining the current time.

9. The method for optimizing the startup time of an air conditioning system according to claim 6, It is characterized in that If the evaluation result does not meet the preset condition, determining the startup time based on the target feature data includes: Based on the target characteristic data, determining a positive and negative relationship between the target time and the target time; Based on the positive and negative relationship of the target-reaching time and the evaluation matrix, statistically analyzing positive sample data; Calculating the minimum target-reaching time in the positive sample data, and counting the negative sample data based on the minimum target-reaching time and the evaluation matrix; Calculate the minimum target time in the negative sample data; The average of the minimum target-reaching time in the positive sample data and the minimum target-reaching time in the negative sample data is calculated, and the average is used as the power-on time.

10. An air conditioning system startup time optimization device, It is characterized in that include: A data acquisition module, used to acquire raw data, wherein the raw data is data composed of influencing factors in the operation of the air-conditioning system; A target model determination module, used to determine a target model based on the original data and a preset model; An evaluation module, used to evaluate the target feature data and obtain an evaluation result when using the target model for reasoning; The power-on time determination module is used to determine the power-on time based on the evaluation result and preset conditions.

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