Air conditioning method and system based on ducted fan performance

Abstract

The present invention relates to the technical field of ducted fans, and specifically to an air conditioning method and system based on ducted fan performance, comprising: using an aerodynamic noise model to screen the ducted fan performance data set to obtain a suboptimal fan data set, obtaining a target adjustment parameter, starting the optimized ducted fan after initialization based on the target adjustment parameter, extracting ducted fan status data in real time, parsing the ducted fan status data, and obtaining a ducted fan electrical parameter set; if the ducted fan electrical parameter set meets the fan electrical parameter standard, until the end of a unit detection period, extracting the target data set, parsing the target data set, and obtaining an optimized ducted fan adjustment instruction, using the optimized ducted fan adjustment instruction as a start instruction to initialize the optimized ducted fan step until a shutdown instruction generated from a pre-built control end is received. The present invention can monitor and regulate the ducted fan in real time to achieve the purpose of regulating the air.

Description

Technical Field

[0001] The present invention relates to the technical field of ducted fans, and in particular to an air conditioning method and system based on ducted fan performance. Background Art

[0002] Ducted fan technology can improve propulsion efficiency and reduce noise. With the development of technology, ducted fans are widely used in air conditioning systems due to their simple structure, light weight and low noise.

[0003] Traditional air control methods include mechanical ventilation, evaporative cooling systems, and air conditioning systems. Mechanical ventilation increases indoor air flow through fans. Evaporative cooling systems reduce air temperature through water evaporation to achieve clean cooling. Air conditioning systems use a refrigerant circulation system to reduce indoor air temperature.

[0004] Although the above-mentioned traditional technologies can achieve air conditioning, they have problems such as high energy consumption and limited adjustment accuracy. Therefore, as an air conditioning technology with a simple structure, light weight and low noise, how to apply the ducted fan to actual working conditions to achieve air conditioning is an urgent problem to be solved. Summary of the invention

[0005] The present invention provides an air conditioning method based on the performance of a ducted fan. A computer can monitor and control the ducted fan in real time to achieve the purpose of regulating the air.

[0006] To achieve the above object, the present invention provides an air conditioning method based on ducted fan performance, comprising:

[0007] Acquire a ducted fan performance data set, filter the ducted fan performance data set based on a pre-built aerodynamic noise model to obtain a suboptimal fan data set, and identify an optimized ducted fan based on the suboptimal fan data set;

[0008] Obtaining target adjustment parameters corresponding to actual working conditions, generating a start instruction based on the target adjustment parameters, initializing the optimized ducted fan using the start instruction, starting the initialized optimized ducted fan and the pre-built detection unit, and obtaining real-time detection data using the started detection unit and the started optimized ducted fan, wherein the detection unit presets a detection frequency and a unit detection period, the real-time detection data includes a plurality of unit real-time data, and the unit real-time data includes terminal detection data and ducted fan status data;

[0009] Extracting ducted fan status data from the unit real-time data in real time based on the detection frequency, parsing the ducted fan status data, and obtaining a ducted fan electrical parameter set;

[0010] If each ducted fan electrical parameter in the ducted fan electrical parameter set meets the corresponding preset fan electrical parameter standard, then return to the step of extracting the ducted fan status data in the unit real-time data based on the detection frequency in real time, until it is confirmed that the unit detection period has ended, extract the multiple unit real-time data based on the real-time detection data, extract the target data set based on the multiple unit real-time data, parse the target data set, obtain the optimized ducted fan adjustment instruction, use the optimized ducted fan adjustment instruction as the start instruction, return to the step of initializing the optimized ducted fan using the start instruction, until receiving the shutdown instruction generated from the pre-built control end, and shut down the optimized ducted fan based on the shutdown instruction;

[0011] If there are ducted fan electrical parameters in the ducted fan electrical parameter set that do not meet the corresponding preset fan electrical parameter standards, a warning instruction is generated, and the optimized ducted fan is turned off based on the warning instruction;

[0012] Air conditioning based on ducted fan performance is accomplished based on the optimized ducted fan after closing.

[0013] Optionally, screening the ducted fan performance data set based on a pre-built aerodynamic noise model to obtain a suboptimal fan data set, and identifying an optimized ducted fan based on the suboptimal fan data set includes:

[0014] Using pre-constructed operating condition data to filter the ducted fan performance data set to obtain a suboptimal fan data set, combining the suboptimal fan data set with a preset grid gradient to obtain a combined data set, and constructing a simulation model set based on the combined data set;

[0015] For each simulation model in the simulation model set, perform the following operations:

[0016] Obtain an environmental model, import both the working condition data and the simulation model into the environmental model, obtain the environmental simulation model, obtain an observation point set and an air flow rate set based on the environmental simulation model, use the aerodynamic noise model, the observation point set and the air flow rate set to perform acoustic simulation operations on the simulation model in the environmental simulation model, and obtain an acoustic simulation data set, and obtain a sound pressure level spectrum set based on the acoustic simulation data set, wherein the observation point set includes multiple observation points, the air flow rate set includes air flow rate, and the sound pressure level spectrum has a one-to-one correspondence with one observation point and one air flow rate, and the acoustic simulation data and the sound pressure level spectrum Figure 1 One to one correspondence;

[0017] The sound pressure level spectrum atlas is analyzed to obtain an optimized model set, and the optimized ducted fan is confirmed based on the optimized model set.

[0018] Optionally,

[0019] The step of obtaining target adjustment parameters corresponding to the actual working condition, generating a start instruction based on the target adjustment parameters, and initializing and optimizing the ducted fan using the start instruction includes:

[0020] Determine the target adjustment parameter based on the actual working conditions, wherein the target adjustment parameter includes a plurality of unit adjustment parameters, and the plurality of unit adjustment parameters are: set temperature, required static pressure, air valve opening and temperature gradient;

[0021] Extract unit adjustment parameters from the target adjustment parameters in sequence, and perform the following operations on the extracted unit adjustment parameters:

[0022] Convert the extracted unit adjustment parameters into a preset fixed instruction format to obtain unit test parameters, and send the unit test parameters to a pre-built communication bus, wherein the fixed instruction format includes: a frame header, a second byte, a third byte, and a frame tail, wherein the second byte is an identifier corresponding to the actual working condition, the third byte is an identifier corresponding to the unit adjustment parameter, and the communication bus is a CAN bus, including: a host computer and multiple terminals;

[0023] Generate a unit start command based on the communication bus receiving the unit test parameter, send the unit start command to the terminal corresponding to the unit adjustment parameter, and if the host computer receives a reply command sent from the terminal within a preset response time, confirm that the terminal is in a preset normal access state;

[0024] After confirming that the terminals corresponding to all the unit adjustment parameters in the target adjustment parameters are in normal passage states, a start instruction is obtained based on a plurality of unit start instructions, and the optimized ducted fan is initialized based on the start instruction.

[0025] Optionally, extracting a target data set based on multiple units of real-time data, parsing the target data set, and obtaining an optimized ducted fan adjustment instruction includes:

[0026] Extract unit real-time data from multiple unit real-time data in sequence, and extract detected temperature, measured air valve opening, and measured static pressure based on the extracted unit real-time data, respectively summarize the detected temperature, measured air valve opening, and measured static pressure to obtain a detected temperature set, measured air valve opening set, and measured static pressure set corresponding to the multiple unit real-time data, summarize the detected temperature set, measured air valve opening set, and measured static pressure set to obtain a target data set, extract unit target data sets from the target data set in sequence, and perform the following operations on the extracted unit target data sets:

[0027] Acquire a sliding detection array, extract unit target data from the unit target data set in sequence, and fill the extracted unit target data into the sliding detection array until all pre-constructed blank data points in the sliding detection array are filled, thereby obtaining an initial array, wherein all unit target data in the initial array are arranged in order from earliest to latest according to the time corresponding to the unit target data;

[0028] Perform sliding segmentation on the unit target data set based on the initial array to obtain a sliding array set;

[0029] Extract sliding arrays from the sliding array set in sequence, calculate the mean of the extracted sliding arrays, obtain evaluation values, summarize the evaluation values, and obtain an evaluation value set. If the number of evaluation values ​​in the evaluation value set that do not belong to the pre-constructed evaluation value fluctuation range is greater than a preset number, generate an adjustment instruction, wherein if the unit target data set is a detected temperature set, the adjustment instruction is a temperature adjustment instruction, if the unit target data set is a measured air valve opening set, the adjustment instruction is an air valve adjustment instruction, and if the unit target data set is a measured static pressure set, the adjustment instruction is a static pressure adjustment instruction;

[0030] The adjustment instructions are summarized to obtain an adjustment instruction set based on the detected temperature set, the measured air valve opening set and the measured static pressure set, and an optimized ducted fan adjustment instruction is constructed based on the adjustment instruction set.

[0031] Optionally, performing sliding segmentation on the unit target data set based on the initial array to obtain a sliding array set includes:

[0032] Extracting the earliest unit target data and the latest unit target data from the initial array to obtain the head data and the tail data, removing the head data in the initial array, and extracting the next unit target data adjacent to the tail data from the unit target data set to obtain the adjacent data, filling the adjacent data to the initial array after the head data is removed, and arranging all the unit target data and the adjacent data in the initial array after the adjacent data is filled in the order of time from the earliest to the latest, to obtain the sliding array;

[0033] Taking the sliding array as the initial array, returning to the step of extracting the earliest unit target data and the latest unit target data from the initial array until the unit target data set is an empty set, summarizing the sliding array and the initial array to obtain a sliding array set.

[0034] Optionally, constructing an optimized ducted fan adjustment instruction based on the adjustment instruction set includes:

[0035] Extracting a detection temperature set from multiple units of real-time data, and constructing a temperature fitting curve based on the detection temperature set, extracting a local temperature extreme value set from the temperature fitting curve, and arranging the local temperature extreme value set in chronological order to obtain a local temperature extreme value sequence;

[0036] The local temperature extreme values ​​are extracted from the local temperature extreme value sequence in sequence, and the following operations are performed on the extracted local temperature extreme values:

[0037] Extracting the next local temperature extreme value adjacent to the extracted local temperature extreme value to obtain an adjacent local temperature extreme value, calculating the absolute value of the difference between the adjacent local temperature extreme value and the extracted local temperature extreme value to obtain a unit temperature change amplitude;

[0038] Summarize the unit temperature change amplitudes to obtain a unit temperature change amplitude set;

[0039] If the number of unit temperature change amplitudes that are greater than the unit temperature change amplitude of the temperature gradient is greater than the preset number of temperature controls, a gradient adjustment instruction is generated, and the gradient adjustment instruction and the adjustment instruction set are integrated to obtain an optimized ducted fan adjustment instruction.

[0040] Optionally, the using pre-constructed operating condition data to filter the ducted fan performance data set to obtain a suboptimal fan data set includes:

[0041] Based on the working condition data, a working condition interval set is determined, wherein the working condition interval set includes: a voltage interval, a power interval, a current interval, a bypass ratio interval, a wind speed interval, a temperature interval, and a speed interval, and the ducted fan performance data includes a plurality of performance data;

[0042] The following operations are performed on the operating intervals in the operating interval set:

[0043] The following operations are performed on the ducted fan performance data in the ducted fan performance data set:

[0044] If the performance data corresponding to the operating condition interval does not exist in the ducted fan performance data, the fan model corresponding to the ducted fan performance data is obtained, and the standard electrical data of the fan model is retrieved using a pre-built retrieval method, wherein the standard electrical data includes a plurality of standard data;

[0045] Determine whether there is standard data corresponding to the operating condition interval in the standard electrical data; if there is standard data in the standard electrical data, update the ducted fan performance data using the standard data, retain the updated ducted fan performance data, and confirm the updated ducted fan performance data as the ducted fan performance data; otherwise, discard the ducted fan performance data;

[0046] If the performance data exists in the ducted fan performance data, extracting an available interval based on the performance data, and if the operating condition interval is a sub-interval of the available interval, retaining the ducted fan performance data, otherwise, discarding the ducted fan performance data;

[0047] The retained ducted fan performance data are aggregated to obtain the suboptimal fan dataset.

[0048] Optionally, the aerodynamic noise model is as follows:

[0049] ,

[0050] in, represents the far-field sound speed, Indicates that the observation point is The change in sound pressure at the observation point at time, Indicates time, represents the air density in an undisturbed environment. It represents the velocity component of the air velocity in the normal direction of the integration surface, represents the Dirac function, represents the compressive stress tensor, and Both represent the directional components of the compressive stress tensor, The unit normal vector represents the normal direction of the integration surface, represents the change in fluid density of air, Indicates the air velocity The direction of the component, represents the component of air velocity in the normal direction of the control surface, and All represent the spatial position coordinates of the observation point with the ducted fan as the origin. represents the Lighthill stress tensor, represents the Heaviside function.

[0051] Optionally, the analyzing the sound pressure level spectrum atlas to obtain the optimized model set includes:

[0052] The sound pressure level spectrograms are extracted from the sound pressure level spectrum set in sequence, and the following operations are performed on the extracted sound pressure level spectrograms:

[0053] Acquiring a sound pressure threshold of an observation point under actual working conditions based on a sound pressure level spectrum graph, and extracting a sound pressure data set greater than the sound pressure threshold from the sound pressure level spectrum graph, wherein the horizontal axis of the sound pressure level spectrum graph is frequency, the vertical axis is sound pressure level, and the sound pressure data sets are sorted according to the order of the horizontal axis in the sound pressure level spectrum graph;

[0054] If the sound pressure data set is an empty set, the simulation model corresponding to the sound pressure data set is confirmed as an available fan;

[0055] If the sound pressure data set is not an empty set, sound source feature recognition is performed on the sound pressure data set to obtain a feature recognition result. If the feature recognition result is fitting noise, the simulation model corresponding to the sound pressure data set is confirmed as an available fan. If the feature recognition result is sound source noise, the simulation model corresponding to the sound pressure data set is confirmed as an unavailable fan.

[0056] The available fans are aggregated, and all the aggregated available fans are screened and recursively operated to obtain an optimized model set.

[0057] To achieve the above object, the present invention further provides an air conditioning system based on ducted fan performance, comprising:

[0058] A suboptimal fan data set acquisition module, used to acquire a ducted fan performance data set, screen the ducted fan performance data set based on a pre-built aerodynamic noise model to obtain a suboptimal fan data set, and identify an optimized ducted fan based on the suboptimal fan data set;

[0059] A real-time detection data acquisition module, used to obtain target adjustment parameters corresponding to actual working conditions, generate a startup instruction based on the target adjustment parameters, initialize the optimized ducted fan using the startup instruction, start the initialized optimized ducted fan and the pre-built detection unit, and obtain real-time detection data using the started detection unit and the started optimized ducted fan, wherein the detection unit presets a detection frequency and a unit detection period, the real-time detection data includes a plurality of unit real-time data, and the unit real-time data includes terminal detection data and ducted fan status data;

[0060] An optimized ducted fan adjustment instruction generation module is used to extract the ducted fan status data in the unit real-time data based on the detection frequency in real time, parse the ducted fan status data, and obtain the ducted fan electrical parameter set. If each ducted fan electrical parameter in the ducted fan electrical parameter set meets the corresponding preset fan electrical parameter standard, then return to the step of extracting the ducted fan status data in the unit real-time data based on the detection frequency in real time until it is confirmed that the unit detection period has ended, extract the multiple unit real-time data based on the real-time detection data, extract the target data set based on the multiple unit real-time data, parse the target data set, and obtain the optimized ducted fan adjustment instruction, using the optimized ducted fan adjustment instruction as the start instruction, and return to the step of initializing the optimized ducted fan using the start instruction until receiving the shutdown instruction generated from the pre-built control end, and shutting down the optimized ducted fan based on the shutdown instruction;

[0061] The early warning instruction module is used to generate an early warning instruction if there are ducted fan electrical parameters in the ducted fan electrical parameter set that do not meet the corresponding preset fan electrical parameter standards, shut down the optimized ducted fan based on the early warning instruction, and complete air conditioning based on the ducted fan performance based on the shut down optimized ducted fan.

[0062] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0063] A memory storing at least one instruction;

[0064] The processor executes the instructions stored in the memory to implement the air conditioning method based on the ducted fan performance described above.

[0065] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned air conditioning method based on ducted fan performance.

[0066] In order to solve the problems described in the background technology, the present invention first obtains a ducted fan performance data set, filters the ducted fan performance data set based on a pre-built aerodynamic noise model to obtain a suboptimal fan data set, and confirms an optimized ducted fan based on the suboptimal fan data set. The present invention filters the ducted fan performance data set by utilizing operating condition data, and performs acoustic simulation on the ducted fans corresponding to the filtered optimized data set, and considers multiple characteristics to filter out the optimized ducted fans that meet the usage conditions. The present invention obtains target adjustment parameters corresponding to actual working conditions, generates a start instruction based on the target adjustment parameters, initializes the optimized ducted fan using the start instruction, starts the initialized optimized ducted fan and a pre-built detection unit, and uses the started detection unit and the started optimized ducted fan to obtain real-time detection data, wherein the detection unit presets a detection frequency and a unit detection period, the real-time detection data includes a plurality of unit real-time data, and the unit real-time data includes terminal detection data and ducted fan status data, extracts the ducted fan status data from the unit real-time data in real time based on the detection frequency, parses the ducted fan status data, and obtains a ducted fan electrical parameter set, if each ducted fan electrical parameter in the ducted fan electrical parameter set meets the corresponding preset fan electrical parameter standard, then returns to the step of extracting the ducted fan status data from the unit real-time data in real time based on the detection frequency until it is confirmed that the unit detection period has ended, then the ducted fan status data is output based on the real-time detection data. According to the extraction of the multiple unit real-time data, the target data set is extracted based on the multiple unit real-time data, the target data set is parsed, and the optimized ducted fan adjustment instruction is obtained. The optimized ducted fan adjustment instruction is used as the start instruction, and the step of initializing the optimized ducted fan using the start instruction is returned until a shutdown instruction generated from the pre-built control end is received, and the optimized ducted fan is closed based on the shutdown instruction. In order to ensure the safety of the optimized ducted fan during use, the present invention judges the real-time ducted fan electrical parameter set. If the use state is met and it can run safely, an analysis is performed every time a unit detection period is passed to judge the use effect of the optimized ducted fan. If there are ducted fan electrical parameters that do not meet the corresponding preset fan electrical parameter standards in the ducted fan electrical parameter set, an early warning instruction is generated, and the optimized ducted fan is closed based on the early warning instruction. Based on the closed optimized ducted fan, air conditioning based on the ducted fan performance is completed. The present invention proposes an air conditioning method based on ducted fan performance, so that it can be applied to cooling, heating, and air cooling, so as to achieve the purpose of air conditioning under different actual working conditions. Therefore, the present invention can realize real-time monitoring and regulation of the ducted fan to achieve the purpose of regulating the air. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic flow chart of an air conditioning method based on ducted fan performance provided by an embodiment of the present invention;

[0068] Figure 2 A functional module diagram of an air conditioning system based on ducted fan performance provided by an embodiment of the present invention;

[0069] Figure 3 A schematic structural diagram of an electronic device for implementing the ducted fan performance-based air conditioning method provided in one embodiment of the present invention.

[0070] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0071] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0072] The embodiment of the present application provides an air conditioning method based on ducted fan performance. The execution subject of the air conditioning method based on ducted fan performance includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the air conditioning method based on ducted fan performance can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0073] Reference Figure 1 FIG. 1 is a flow chart of an air conditioning method based on ducted fan performance according to an embodiment of the present invention. In this embodiment, the air conditioning method based on ducted fan performance includes:

[0074] S1. Acquire a ducted fan performance data set, filter the ducted fan performance data set based on a pre-built aerodynamic noise model to obtain a suboptimal fan data set, and identify an optimized ducted fan based on the suboptimal fan data set.

[0075] It is understandable that a ducted fan is a fan that completely or partially encloses the fan blades in a duct. In industrial applications, it can be used for ventilation, exhaust, providing directional air flow, etc. It can also be used to control air distribution and temperature. The purpose of the embodiment of the present invention is to use a ducted fan to regulate the air, so as to achieve the purpose of temperature control. It should be noted that in industrial applications, there are many directions for temperature control, such as acting as an air conditioner for heating and cooling, or air cooling in a cooling system. Although the purposes are different, they are all achieved by regulating the air through a ducted fan. Therefore, the embodiment of the present invention proposes an air conditioning method based on the performance of a ducted fan, so that it can be used for cooling, heating, and air cooling, so as to achieve the purpose of air conditioning under different actual working conditions.

[0076] It is understandable that the ducted fan performance data set is a data set composed of the performances corresponding to different ducted fans. In addition to the necessary structural parameters (such as the number of fins, rib thickness, rib height, outer diameter, wall thickness, etc.), the ducted fan performance data set can also include the power range, speed range, adaptive torque range, different operating modes (such as pulse cleaning mode, air supply mode), etc. The ducted fan performance data set can be obtained through various database queries or through manufacturer testing, and the specific acquisition method is not limited here.

[0077] Specifically, the screening of the ducted fan performance data set based on the pre-built aerodynamic noise model to obtain a suboptimal fan data set, and identifying an optimized ducted fan based on the suboptimal fan data set includes:

[0078] Using pre-constructed operating condition data to filter the ducted fan performance data set to obtain a suboptimal fan data set, combining the suboptimal fan data set with a preset grid gradient to obtain a combined data set, and constructing a simulation model set based on the combined data set;

[0079] For each simulation model in the simulation model set, perform the following operations:

[0080] Obtain an environmental model, import both the working condition data and the simulation model into the environmental model, obtain the environmental simulation model, obtain an observation point set and an air flow rate set based on the environmental simulation model, use the aerodynamic noise model, the observation point set and the air flow rate set to perform acoustic simulation operations on the simulation model in the environmental simulation model, and obtain an acoustic simulation data set, and obtain a sound pressure level spectrum set based on the acoustic simulation data set, wherein the observation point set includes multiple observation points, the air flow rate set includes air flow rate, and the sound pressure level spectrum has a one-to-one correspondence with one observation point and one air flow rate, and the acoustic simulation data and the sound pressure level spectrum Figure 1 One to one correspondence;

[0081] The sound pressure level spectrum atlas is analyzed to obtain an optimized model set, and the optimized ducted fan is confirmed based on the optimized model set.

[0082] It is understandable that the operating condition data is data composed of rated data used by the ducted fan in the embodiment of the present invention under actual use. For example, there is a factory that needs to use a ducted fan. After calculation by engineers, the maximum wind speed, pulse wind speed, power usage, current usage, bypass ratio, duct size, etc. required by the ducted fan are confirmed. The engineer confirms the maximum wind speed, pulse wind speed, power usage, current usage, bypass ratio, duct size, etc. required by the ducted fan as the operating condition data, and based on the operating condition data, searches for ducted fan performance data that can meet the operating condition data in the ducted fan performance data set.

[0083] Furthermore, the step of combining the suboptimal fan data set with the preset grid gradient is: extracting suboptimal fan data from the suboptimal fan data set in turn, obtaining the model of the suboptimal fan data, and extracting the number of grids from the grid gradient in turn, and combining the model of the suboptimal fan data and the number of grids in pairs, that is, the combined data set includes multiple combined data, and one combined data includes a model of suboptimal fan data and a number of grids. Therefore, the simulation model set is a set of multiple simulation models constructed according to the combined data set, and the simulation model corresponds to the combined data one by one.

[0084] It should be noted that the more grids there are, the more accurate the simulation will be, but the greater the computing energy consumption will be. The purpose of this step is to find a suitable grid for each simulation model to simulate the model, while reducing the computing energy consumption as much as possible and still obtaining sufficient simulation accuracy.

[0085] Exemplarily, the existing suboptimal fan data sets correspond to models An and Am, and the grid gradients are Wa and Wb. An is extracted and Wa is combined to obtain a combined data: An-Wa. Am is extracted and Wa is combined to obtain a combined data: Am-Wa, and so on. No further details are given here.

[0086] It is understandable that the environmental model is a model constructed according to the actual environment required for the ducted fan. The observation point set is a set of artificially set locations for observation, for example, locations 1m and 2m away from the ducted fan. The air velocity set is a set of air velocity extracted from the operating condition data, and the air velocity in the air velocity set needs to include the maximum wind speed and the minimum wind speed in the wind speed range, that is, the air velocity set needs to include not only the wind speed conventionally used in the operating condition data, but also the air velocity corresponding to the extreme case, so that the acoustic simulation can cover all situations in the actual operating condition. In an embodiment of the present invention, one observation point and one air velocity correspond to one acoustic simulation data and one sound pressure level spectrum. After the observation point set and the air velocity set are combined and imported into the aerodynamic noise model, an acoustic simulation data set is obtained. The method of combining the observation point set and the air velocity set is the same as the step of combining the suboptimal fan data set with the preset grid gradient, which will not be repeated here.

[0087] Furthermore, the acoustic simulation operation is an operation of simulating the simulation model using the aerodynamic noise model, and the acoustic simulation data is the data obtained after the aerodynamic noise model is simulated. The sound pressure level spectrum diagram is a spectrum diagram related to the sound pressure level obtained after time-frequency processing of the acoustic simulation data. The purpose of parsing the sound pressure level spectrum diagram set is to determine whether the corresponding ducted fan meets the use requirements based on the sound pressure level spectrum set, and confirm the most suitable ducted fan as the optimized ducted fan. For the specific implementation method, please refer to the subsequent embodiments.

[0088] Specifically, the using of the pre-constructed operating condition data to screen the ducted fan performance data set to obtain a suboptimal fan data set includes:

[0089] Based on the working condition data, a working condition interval set is determined, wherein the working condition interval set includes: a voltage interval, a power interval, a current interval, a bypass ratio interval, a wind speed interval, a temperature interval, and a speed interval, and the ducted fan performance data includes a plurality of performance data;

[0090] The following operations are performed on the operating intervals in the operating interval set:

[0091] The following operations are performed on the ducted fan performance data in the ducted fan performance data set:

[0092] If the performance data corresponding to the operating condition interval does not exist in the ducted fan performance data, the fan model corresponding to the ducted fan performance data is obtained, and the standard electrical data of the fan model is retrieved using a pre-built retrieval method, wherein the standard electrical data includes a plurality of standard data;

[0093] Determine whether there is standard data corresponding to the operating condition interval in the standard electrical data; if there is standard data in the standard electrical data, update the ducted fan performance data using the standard data, retain the updated ducted fan performance data, and confirm the updated ducted fan performance data as the ducted fan performance data; otherwise, discard the ducted fan performance data;

[0094] If the performance data exists in the ducted fan performance data, extracting an available interval based on the performance data, and if the operating condition interval is a sub-interval of the available interval, retaining the ducted fan performance data, otherwise, discarding the ducted fan performance data;

[0095] The retained ducted fan performance data are aggregated to obtain the suboptimal fan dataset.

[0096] It is understandable that the operating condition data includes a variety of unit operating condition data, and the operating condition intervals correspond to the unit operating condition data one by one. For example, the various unit operating condition data in the operating condition data include: operating voltage, operating power, operating current, bypass ratio, wind speed, operating temperature and speed, which correspond to the fixed voltage interval, power interval, current interval, bypass ratio interval, wind speed interval, operating temperature interval and speed interval in the operating condition interval set. The operating voltage interval is the range interval corresponding to the operating voltage applicable to the ducted fan after installation, the power interval is the range interval corresponding to the power applicable to the ducted fan after installation, and the current interval, bypass ratio interval, wind speed interval, operating temperature interval and speed interval have similar interpretations, which are the range intervals corresponding to the operating current, bypass ratio, wind speed, operating temperature and speed, respectively. The operating condition interval is an interval extracted from the operating condition interval set.

[0097] Furthermore, the performance data is the data included in the ducted fan performance data. Exemplarily, if the operating condition interval is a voltage interval, and there is performance data corresponding to the operating condition interval in the ducted fan performance data, then the performance data should include the voltage range in which the corresponding ducted fan can be used normally. The fan model is the model of the ducted fan corresponding to the ducted fan performance data. The retrieval method is: through database retrieval, and the source of the database includes but is not limited to a network database, a local database, a manufacturer's self-test database, etc. The standard electrical data is the parameters that the ducted fan can use normally and meet the specifications after being tested and leaving the factory. The standard electrical data includes voltage, current, etc. The standard data is the data included in the standard electrical data, which is similar to the interpretation of the performance data and will not be repeated here.

[0098] It is understandable that if standard data exists in the standard electrical data, it is described that by retrieving the standard data, the performance data that does not exist in the ducted fan performance data can be supplemented. If standard data does not exist in the standard electrical data, and performance data does not exist in the ducted fan performance data, directly using the corresponding ducted fan to work under the operating data will be dangerous, so it needs to be eliminated.

[0099] Furthermore, even if the ducted fan performance data is completed, it is still necessary to determine whether the performance data can meet the usage conditions. Therefore, the embodiment of the present invention extracts the available interval based on the performance data. The available interval is the numerical range in which the corresponding ducted fan can be used normally. For example, if the operating condition interval is voltage, the available interval is the interval composed of all voltages in which the ducted fan corresponding to the performance data can work normally.

[0100] Specifically, the operating condition interval being a sub-interval of the available interval means that the numerical value of the operating condition interval is mathematically included in the numerical value corresponding to the available interval. For example, the operating condition interval is [1, 3] and the available interval is [0, 100]. At this time, [1, 3] is included in [0, 100], which is called the operating condition interval being a sub-interval of the available interval.

[0101] It is understandable that if the operating condition interval is not a sub-interval of the available interval, it means that the usage requirements corresponding to the operating condition interval exceed the normal usage range of the ducted fan corresponding to the ducted fan performance data. At this time, if the ducted fan corresponding to the ducted fan performance data is used under actual working conditions, it will be dangerous, so it needs to be eliminated.

[0102] Specifically, the aerodynamic noise model is as follows:

[0103] ,

[0104] in, represents the far-field sound speed, Indicates that the observation point is The change in sound pressure at the observation point at time, Indicates time, represents the air density in an undisturbed environment. It represents the velocity component of the air velocity in the normal direction of the integration surface, represents the Dirac function, represents the compressive stress tensor, and Both represent the directional components of the compressive stress tensor, The unit normal vector represents the normal direction of the integration surface, represents the change in fluid density of air, Indicates the air velocity The direction of the component, represents the component of air velocity in the normal direction of the control surface, and All represent the spatial position coordinates of the observation point with the ducted fan as the origin. represents the Lighthill stress tensor, represents the Heaviside function.

[0105] It is understandable that the far-field sound speed is the propagation speed of sound waves under far-field conditions, reflecting the speed of sound waves when they propagate at an infinite distance or in a sufficiently large space. This speed is related to the properties of the medium. The change in sound pressure represents the instantaneous change in pressure caused by sound waves during air propagation relative to the equilibrium state, where the equilibrium state is the state when there is no sound wave propagation in the environment. The integral surface is a preset control volume, which is set to the inner surface of the duct of the ducted fan in the embodiment of the present invention. Compressive stress tensor Unit normal vector to the integration surface The product of It represents the projection of the compressive stress tensor on the normal direction of the integral surface, reflecting the distribution of the compressive stress in the normal direction. The spatial position coordinates are coordinates in a three-dimensional coordinate system with the center of mass of the ducted fan as the origin. The three-dimensional coordinate system is constructed with the center of mass of the ducted fan as the origin, the z-axis is aligned with the axis of rotation, the x-axis points to the fan radial direction of the ducted fan, and the y-axis direction is a Cartesian coordinate system that is perpendicular to both the z-axis and the x-axis. The Lighthill stress tensor is a stress tensor constructed based on the Lighthill aeroacoustics analogy theory. The Lighthill stress tensor, Dirac function and Heaviside function are all prior art and will not be described in detail here.

[0106] Specifically, the analytical sound pressure level spectrum atlas obtains an optimized model set, including:

[0107] The sound pressure level spectrograms are extracted from the sound pressure level spectrum set in sequence, and the following operations are performed on the extracted sound pressure level spectrograms:

[0108] Acquiring a sound pressure threshold of an observation point under actual working conditions based on a sound pressure level spectrum graph, and extracting a sound pressure data set greater than the sound pressure threshold from the sound pressure level spectrum graph, wherein the horizontal axis of the sound pressure level spectrum graph is frequency, the vertical axis is sound pressure level, and the sound pressure data sets are sorted according to the order of the horizontal axis in the sound pressure level spectrum graph;

[0109] If the sound pressure data set is an empty set, the simulation model corresponding to the sound pressure data set is confirmed as an available fan;

[0110] If the sound pressure data set is not an empty set, sound source feature recognition is performed on the sound pressure data set to obtain a feature recognition result. If the feature recognition result is fitting noise, the simulation model corresponding to the sound pressure data set is confirmed as an available fan. If the feature recognition result is sound source noise, the simulation model corresponding to the sound pressure data set is confirmed as an unavailable fan.

[0111] The available fans are aggregated, and all the aggregated available fans are screened and recursively operated to obtain an optimized model set.

[0112] It can be understood that the sound pressure threshold is the maximum tolerable sound pressure level artificially set by the sound pressure level spectrum at the corresponding observation point and the corresponding air flow rate, and the sound pressure data set is a set of sound pressure levels and frequencies greater than or equal to the sound pressure threshold. If the sound pressure data set is an empty set, it means that the simulation model meets the sound requirements at the corresponding observation point and the corresponding air flow rate, so it is retained and confirmed as an available fan.

[0113] Furthermore, the sound source feature recognition of the sound pressure data set can be realized by a variety of existing technologies, such as deep learning technology, acoustic holography technology, etc. This is because the sound data features of sounds generated by different reasons are different, such as sound pressure level, frequency, time series features, etc., so the sound source can be identified through the sound pressure data. The embodiment of the present invention can determine the source of the sound data through sound source feature recognition, and thus determine whether the ducted fan meets the use requirements based on the source.

[0114] Furthermore, fitting noise is the noise generated by the structure of the non-ducted fan itself, such as errors caused by the equipment when collecting sound and errors caused by data processing. If the feature recognition result is fitting noise, it means that the ducted fan still has the potential to be used, so the simulation model corresponding to the sound pressure data set is confirmed as a usable fan. The sound source noise is the noise generated by the structure of the ducted fan itself, which means that when the corresponding ducted fan is used, a large noise will be generated, so the simulation model corresponding to the sound pressure data set is confirmed as an unusable fan and needs to be eliminated.

[0115] It is understandable that the sound pressure level spectrum graph in the sound pressure level spectrum graph set corresponds to one observation point and one air flow rate. At the same time, different grids are used to model the same simulation model. Therefore, the same ducted fan may appear in the available fans, and under different grids, the same observation point and the same air flow rate may be an available fan and an unavailable fan respectively. It is also possible that the use conditions are met at observation point A, but not at observation point B. Therefore, it is necessary to screen and recursively operate all the aggregated available fans so that each optimization model in the retained optimization model set meets the following conditions: they are all available fans at different observation points, different air flow rates and different numbers of grids, and there is one and only one ducted fan of the same model in the optimization model set.

[0116] It should be noted that the determination of the optimized ducted fan based on the optimization model set is: selecting an optimal ducted fan from the optimization model as the optimized ducted fan, which can be achieved by a variety of existing technologies. For example, manually selecting the one with the lowest cost, using the entropy weight method to score the optimization model set, and manually selecting the ducted fan corresponding to the optimization model with the best score to optimize the ducted fan last night, which is not limited in the embodiment of the present invention.

[0117] S2. Obtain target adjustment parameters corresponding to actual working conditions, generate a start instruction based on the target adjustment parameters, use the start instruction to initialize the optimized ducted fan, start the initialized optimized ducted fan and the pre-built detection unit, and use the started detection unit and the started optimized ducted fan to obtain real-time detection data.

[0118] Among them, the detection unit presets the detection frequency and the unit detection period, the real-time detection data includes a plurality of unit real-time data, and the unit real-time data includes terminal detection data and ducted fan status data.

[0119] It is understandable that the target adjustment parameter is the parameter that needs to be set for the optimized ducted fan under actual working conditions. The start instruction is an instruction for starting the optimized ducted fan containing the target adjustment parameter. It is understandable that after confirming the optimized ducted fan, the optimized ducted fan can be installed at the place where it needs to be installed for air conditioning. The unit detection period is the time period for adaptive adjustment of the optimized ducted fan, which can be set manually or by big data detection. For example, for the actual working conditions that require air supply and ventilation, if the unit detection period of the optimized ducted fan is set to 5min, the air supply effect is adaptively detected every 5min. If the air supply effect does not meet the standard or there is a problem with the ducted fan, timely feedback processing can be performed. The terminal detection data is a data set obtained by detection by multiple different sensors, such as temperature, wind speed, etc. The fan status data detects whether the optimized ducted fan is operating normally during operation, for example, whether the voltage is in the preset normal operating voltage range, whether the wind speed is in the normal operating wind speed range, etc.

[0120] Specifically, the step of obtaining a target adjustment parameter corresponding to the actual working condition, generating a start instruction based on the target adjustment parameter, and initializing and optimizing the ducted fan using the start instruction includes:

[0121] Determine the target adjustment parameter based on the actual working conditions, wherein the target adjustment parameter includes a plurality of unit adjustment parameters, and the plurality of unit adjustment parameters are: set temperature, required static pressure, air valve opening and temperature gradient;

[0122] Extract unit adjustment parameters from the target adjustment parameters in sequence, and perform the following operations on the extracted unit adjustment parameters:

[0123] Convert the extracted unit adjustment parameters into a preset fixed instruction format to obtain unit test parameters, and send the unit test parameters to a pre-built communication bus, wherein the fixed instruction format includes: a frame header, a second byte, a third byte, and a frame tail, wherein the second byte is an identifier corresponding to the actual working condition, the third byte is an identifier corresponding to the unit adjustment parameter, and the communication bus is a CAN bus, including: a host computer and multiple terminals;

[0124] Generate a unit start command based on the communication bus receiving the unit test parameter, send the unit start command to the terminal corresponding to the unit adjustment parameter, and if the host computer receives a reply command sent from the terminal within a preset response time, confirm that the terminal is in a preset normal access state;

[0125] After confirming that the terminals corresponding to all the unit adjustment parameters in the target adjustment parameters are in normal passage states, a start instruction is obtained based on a plurality of unit start instructions, and the optimized ducted fan is initialized based on the start instruction.

[0126] It is understandable that the set temperature is the temperature that needs to be set for the actual working condition. For example, if the actual working condition is air cooling, the set temperature can be the temperature detected by the temperature sensor after the target air-cooled object is air-cooled. The required static pressure is the static pressure at the air outlet valve of the optimized ducted fan. If the corresponding static pressure at the air outlet valve is too large, the system efficiency will be reduced, energy consumption and noise will be increased, and the environmental comfort may also be affected. Therefore, the required static pressure needs to be set. The same is true for the air valve opening, which will not be repeated here. The temperature gradient is a physical quantity that describes the rate of change of temperature. The limited temperature gradient can be used to maintain the use effect of the optimized ducted fan. The unit adjustment parameter is a parameter extracted from the target adjustment parameter. If the set temperature is extracted, the unit adjustment parameter is the set temperature. The unit test parameter is the extracted unit adjustment parameter in a preset fixed instruction format. The frame header and frame tail are used to identify the start and end of the data. The second byte and the third byte carry the information required to be controlled in the unit adjustment parameter. The second byte is the identifier corresponding to the actual working condition. For example, if the actual working condition is air supply, the second byte can be set to: Sf, and the third byte is the identifier corresponding to the unit adjustment parameter. Converting the extracted unit adjustment parameter into a preset fixed instruction format is conducive to ensuring the correct transmission and processing of data. The response time is the time set by the host computer to receive the return command, which is used to determine whether the terminal is in a normal channel state. The frame header, second byte, third byte and frame tail are all existing technologies and basic theories in data communication, which will not be repeated here.

[0127] Furthermore, the unit start instruction is a directional instruction generated after the communication bus identifies the object that needs to be controlled by the unit test parameter. Therefore, the purpose of generating the unit start instruction based on the communication bus receiving the unit test parameter and sending the unit start instruction to the terminal corresponding to the unit adjustment parameter is to send the unit test parameter to the corresponding terminal and determine whether the corresponding terminal can receive the instruction normally and whether it can control according to the received instruction. It is understandable that in the embodiment of the present invention, if the unit adjustment parameter is the air valve opening, the terminal corresponding to the unit adjustment parameter should be a device that can detect and control the degree of opening of the air valve.

[0128] It is understandable that the return command is the command returned by the terminal to the host computer when the terminal can normally receive the command and can control according to the received command. The normal access state is the state in which the terminal corresponding to the optimized ducted fan can be used normally. When it is confirmed that the terminals corresponding to all unit adjustment parameters in the target adjustment parameters are in the normal access state, it indicates that the optimized ducted fan can be used normally and there is no damage.

[0129] It is understandable that the start-up instruction not only includes multiple unit start-up instructions, but also includes other control instructions that can set the optimized ducted fan to meet the corresponding state of the target adjustment parameters, such as wind speed.

[0130] S3. Extracting ducted fan status data from the unit real-time data in real time based on the detection frequency, parsing the ducted fan status data, and obtaining a ducted fan electrical parameter set.

[0131] It is understandable that, in the embodiment of the present invention, after the optimized ducted fan is started, the target adjustment parameter is adjusted and tested once every unit detection period, and the ducted fan status is detected in real time according to the detection frequency. This is to ensure that the optimized ducted fan can be stopped in time when an abnormality is detected during operation to avoid danger. The electrical parameter set of the ducted fan is: a set of parameters obtained by parsing the real-time detected ducted fan status data, including real-time voltage, real-time current, real-time wind speed, real-time sound pressure level, lubricating oil status, vibration signal, etc., which will not be repeated here.

[0132] S4. If each ducted fan electrical parameter in the ducted fan electrical parameter set meets the corresponding preset fan electrical parameter standard, then return to the step of extracting the ducted fan status data in the unit real-time data based on the detection frequency until it is confirmed that the unit detection period has ended, extract the multiple unit real-time data based on the real-time detection data, extract the target data set based on the multiple unit real-time data, parse the target data set, and obtain the optimized ducted fan adjustment instruction.

[0133] It is understandable that one detection frequency corresponds to one ducted fan status data, and one ducted fan status data corresponds to one ducted fan electrical parameter set. If each ducted fan electrical parameter in the ducted fan electrical parameter set meets the corresponding preset fan electrical parameter standard, it means that the optimized ducted fan is operating normally at the time point corresponding to the ducted fan status data. The preset fan electrical parameter standard is the standard state corresponding to each ducted fan electrical parameter in the ducted fan electrical parameter set. For example, the ducted fan electrical parameter is voltage, and the preset fan electrical parameter standard should include the range corresponding to the voltage during normal use. If the voltage is within the range corresponding to the voltage during normal use, the ducted fan electrical parameter meets the corresponding preset fan electrical parameter standard.

[0134] Furthermore, if the optimized ducted fan operates normally at the time point corresponding to the ducted fan status data, the step of extracting the ducted fan status data in the unit real-time data based on the detection frequency is returned until the unit detection period ends, and the operating effect of the unit detection period needs to be detected.

[0135] Specifically, extracting a target data set based on multiple units of real-time data, parsing the target data set, and obtaining an optimized ducted fan adjustment instruction include:

[0136] Extract unit real-time data from multiple unit real-time data in sequence, and extract detected temperature, measured air valve opening, and measured static pressure based on the extracted unit real-time data, respectively summarize the detected temperature, measured air valve opening, and measured static pressure to obtain a detected temperature set, measured air valve opening set, and measured static pressure set corresponding to the multiple unit real-time data, summarize the detected temperature set, measured air valve opening set, and measured static pressure set to obtain a target data set, extract unit target data sets from the target data set in sequence, and perform the following operations on the extracted unit target data sets:

[0137] Acquire a sliding detection array, extract unit target data from the unit target data set in sequence, and fill the extracted unit target data into the sliding detection array until all pre-constructed blank data points in the sliding detection array are filled, thereby obtaining an initial array, wherein all unit target data in the initial array are arranged in order from earliest to latest according to the time corresponding to the unit target data;

[0138] Perform sliding segmentation on the unit target data set based on the initial array to obtain a sliding array set;

[0139] Extract sliding arrays from the sliding array set in sequence, calculate the mean of the extracted sliding arrays, obtain evaluation values, summarize the evaluation values, and obtain an evaluation value set. If the number of evaluation values ​​in the evaluation value set that do not belong to the pre-constructed evaluation value fluctuation range is greater than a preset number, generate an adjustment instruction, wherein if the unit target data set is a detected temperature set, the adjustment instruction is a temperature adjustment instruction, if the unit target data set is a measured air valve opening set, the adjustment instruction is an air valve adjustment instruction, and if the unit target data set is a measured static pressure set, the adjustment instruction is a static pressure adjustment instruction;

[0140] The adjustment instructions are summarized to obtain an adjustment instruction set based on the detected temperature set, the measured air valve opening set and the measured static pressure set, and an optimized ducted fan adjustment instruction is constructed based on the adjustment instruction set.

[0141] It is understandable that the detected temperature, the measured air valve opening and the measured static pressure are respectively the temperature of the location to be detected detected by the detection unit, the opening of the air valve of the optimized ducted fan under actual working conditions, and the pressure at the air valve outlet. The unit target data set is a data set extracted from the target data set, and all unit target data in the unit target data set have been arranged in chronological order. For example, if the data extracted from the target data set is a detected temperature set, then the unit target data set is a detected temperature set. In the embodiment of the present invention, the same operation is performed on all unit target data sets in the target data set.

[0142] Furthermore, the sliding detection array is a set of blank arrays. The initial array is an array obtained by filling the sliding detection array with unit target data in the order of time from first to last. The sliding array set is a set of sliding arrays after sliding segmentation. For the specific implementation of the sliding segmentation, please refer to the subsequent embodiments. The blank data point is a site in the sliding detection array where unit target data can be imported. The number of blank data points in the sliding detection array is consistent with the number of unit target data that can be imported.

[0143] It is understandable that the evaluation value set includes multiple evaluation values, and the evaluation values ​​correspond one-to-one to the sliding array. If the number of evaluation values ​​in the evaluation value set that do not belong to the pre-constructed evaluation value fluctuation range is greater than the preset number, it means that under the actual use conditions, the operating state corresponding to the unit detection period of the optimized ducted fan cannot meet the use requirements, and therefore it is necessary to adjust the operating data of the optimized ducted fan in the next unit detection period.

[0144] Exemplarily, the unit target data set is a detection temperature set, there are 5 evaluation value sets in total, the preset number is 2, the evaluation value fluctuation range is greater than or equal to 100°C and less than or equal to 110°C, if there are 3 evaluation values ​​in the evaluation value set that are 111°C, 115°C and 120°C respectively, it means that the operating state corresponding to the unit detection time period of the optimized ducted fan under actual use conditions cannot meet the use requirements, so an adjustment instruction is generated, and the adjustment instruction is a temperature adjustment instruction.

[0145] Specifically, the temperature adjustment instruction is an instruction generated because the ducted fan cannot meet the usage requirements under the current situation. For example, under air cooling conditions, in the sliding array set corresponding to the object cooled by the optimized ducted fan, the number of evaluation values ​​that do not belong to the pre-constructed evaluation value fluctuation range is greater than the preset number, indicating that the cooling effect of the current optimized ducted fan is not good and the object cooled by the optimized ducted fan cannot achieve sufficient cooling effect, so a temperature adjustment instruction is generated. The air valve adjustment instruction is the same as the static pressure adjustment instruction, which will not be repeated here.

[0146] Specifically, the sliding segmentation of the unit target data set is performed based on the initial array to obtain a sliding array set, including:

[0147] Extracting the earliest unit target data and the latest unit target data from the initial array to obtain the head data and the tail data, removing the head data in the initial array, and extracting the next unit target data adjacent to the tail data from the unit target data set to obtain the adjacent data, filling the adjacent data to the initial array after the head data is removed, and arranging all the unit target data and the adjacent data in the initial array after the adjacent data is filled in the order of time from the earliest to the latest, to obtain the sliding array;

[0148] Taking the sliding array as the initial array, returning to the step of extracting the earliest unit target data and the latest unit target data from the initial array until the unit target data set is an empty set, summarizing the sliding array and the initial array to obtain a sliding array set.

[0149] For example, let the sliding detection array be [null, null, null] and the unit target data set be [ ], the first one is The latest time is , then there are 3 blank data points in the sliding detection array, and the unit target data are extracted from the unit target data set in sequence until the 3 blank data points in the sliding detection array are filled. Then the initial array is [ ]. The earliest unit target data extracted from the initial array is , the latest unit target data is , then the header data is , the tail data is , remove the header data in the initial array, then the initial array is [ ], and extract the next unit target data adjacent to the tail data from the unit target data set, that is, extract The next adjacent unit target data, that is , the adjacent data is ,Will Fill the initial array to [ ],get[ ], and [ ] Sort in chronological order to get a sliding array[ ] and so on, I will not go into details here.

[0150] Specifically, constructing an optimized ducted fan adjustment instruction based on an adjustment instruction set includes:

[0151] Extracting a detection temperature set from multiple units of real-time data, and constructing a temperature fitting curve based on the detection temperature set, extracting a local temperature extreme value set from the temperature fitting curve, and arranging the local temperature extreme value set in chronological order to obtain a local temperature extreme value sequence;

[0152] The local temperature extreme values ​​are extracted from the local temperature extreme value sequence in sequence, and the following operations are performed on the extracted local temperature extreme values:

[0153] Extracting the next local temperature extreme value adjacent to the extracted local temperature extreme value to obtain an adjacent local temperature extreme value, calculating the absolute value of the difference between the adjacent local temperature extreme value and the extracted local temperature extreme value to obtain a unit temperature change amplitude;

[0154] Summarize the unit temperature change amplitudes to obtain a unit temperature change amplitude set;

[0155] If the number of unit temperature change amplitudes that are greater than the unit temperature change amplitude of the temperature gradient is greater than the preset number of temperature controls, a gradient adjustment instruction is generated, and the gradient adjustment instruction and the adjustment instruction set are integrated to obtain an optimized ducted fan adjustment instruction.

[0156] It is understandable that the temperature fitting curve is a curve obtained by fitting the detected temperature set using a polynomial fitting method. The horizontal axis of the temperature fitting curve is time, and the vertical axis is the detected temperature. The local temperature extreme value set is a set consisting of all maxima and all minima in the temperature fitting curve. It should be noted that since the temperature fitting curve is a continuous curve, the absolute value of the difference between two adjacent extreme values ​​can represent the temperature change amplitude. When the temperature change amplitude is smaller, the temperature change is more stable, which means that the effect of optimizing the ducted fan is better. Therefore, after considering the directly detected temperature value, it is also necessary to consider whether the unit temperature change amplitude meets the demand. If the unit temperature change amplitude is greater than the preset number of temperature controls and is greater than the temperature gradient, it means that the temperature change is unstable, and the temperature control of the optimized ducted fan needs to be adjusted.

[0157] It is understandable that the method for extracting the next local temperature extreme value adjacent to the extracted local temperature extreme value is consistent with the method for extracting the next unit target data adjacent to the tail data from the unit target data set. The method for determining that the number of unit temperature change amplitudes greater than the temperature gradient in the unit temperature change amplitude concentration is greater than the preset number of temperature controls is similar to the method for determining that the number of evaluation values ​​that do not belong to the pre-constructed evaluation value fluctuation range in the evaluation value concentration is greater than the preset number, so the embodiments of the present invention will not be repeated here.

[0158] Furthermore, in the process of integrating the gradient adjustment instructions and the adjustment instruction set to obtain the optimized ducted fan adjustment instructions, since the adjustment instruction set may include one or more of the air valve adjustment instructions, the static pressure adjustment instructions, and the temperature adjustment instructions, when combined with the gradient adjustment instructions, it is necessary to make comprehensive adjustments according to the items that need to be adjusted, such as the opening of the air valve, the speed of the ducted fan, the air outlet temperature of the ducted fan, the inlet and outlet gauge pressures, and other different items. After the adjusted parameters are imported into the optimized ducted fan adjustment instructions, the optimized ducted fan is adjusted using the optimized ducted fan adjustment instructions.

[0159] S5. Using the optimized ducted fan adjustment instruction as the start instruction, returning to the step of initializing the optimized ducted fan using the start instruction, until receiving a shutdown instruction generated from a pre-built control end, and shutting down the optimized ducted fan based on the shutdown instruction.

[0160] It is understandable that in the embodiment of the present invention, an analysis is performed once after each unit detection period to obtain the optimized ducted fan adjustment instruction. It should also be noted that the foregoing embodiment of the present invention assumes that the optimized ducted fan cannot meet the use requirements, and how to generate the optimized ducted fan adjustment instruction. If the use requirements can be met after the detection in the foregoing embodiment of the present invention, the optimized ducted fan adjustment instruction should be the optimized ducted fan adjustment instruction of the previous unit detection period adjacent to the current optimized ducted fan adjustment instruction. In other words, if the use requirements can be met, the optimized ducted fan does not need to be adjusted.

[0161] It is understandable that the pre-built control end is a master control switch of the optimized ducted fan that can be controlled manually, which can directly control the optimized ducted fan to be turned on or off, and the closing instruction is an instruction to turn off the optimized ducted fan.

[0162] S6. If there are ducted fan electrical parameters in the ducted fan electrical parameter set that do not meet the corresponding preset fan electrical parameter standards, a warning instruction is generated, and the optimized ducted fan is turned off based on the warning instruction. Air conditioning based on the ducted fan performance is completed based on the turned-off optimized ducted fan.

[0163] It is understandable that if there are ducted fan electrical parameters in the ducted fan electrical parameter set that do not meet the corresponding preset fan electrical parameter standards, it means that the optimized ducted fan has an abnormality at the current time and it is very dangerous to continue using it. Therefore, an early warning instruction is generated to notify the control end that the abnormality has occurred and shut down the optimized ducted fan.

[0164] In order to solve the problems described in the background technology, the present invention first obtains a ducted fan performance data set, filters the ducted fan performance data set based on a pre-built aerodynamic noise model, obtains a suboptimal fan data set, and confirms an optimized ducted fan based on the suboptimal fan data set. The embodiment of the present invention filters the ducted fan performance data set by using operating condition data, and performs acoustic simulation on the ducted fans corresponding to the filtered optimized data set, and considers multiple characteristics to filter out the optimized ducted fans that meet the usage conditions. The present invention obtains target adjustment parameters corresponding to actual working conditions, generates a start instruction based on the target adjustment parameters, initializes the optimized ducted fan using the start instruction, starts the initialized optimized ducted fan and a pre-built detection unit, and uses the started detection unit and the started optimized ducted fan to obtain real-time detection data, wherein the detection unit presets a detection frequency and a unit detection period, the real-time detection data includes a plurality of unit real-time data, and the unit real-time data includes terminal detection data and ducted fan status data, extracts the ducted fan status data from the unit real-time data in real time based on the detection frequency, parses the ducted fan status data, and obtains a ducted fan electrical parameter set, if each ducted fan electrical parameter in the ducted fan electrical parameter set meets the corresponding preset fan electrical parameter standard, then returns to the step of extracting the ducted fan status data from the unit real-time data in real time based on the detection frequency until it is confirmed that the unit detection period has ended, then the ducted fan status data is output based on the real-time detection data. According to the extraction of the multiple unit real-time data, the target data set is extracted based on the multiple unit real-time data, the target data set is parsed, and the optimized ducted fan adjustment instruction is obtained. The optimized ducted fan adjustment instruction is used as the start instruction, and the step of initializing the optimized ducted fan using the start instruction is returned until a shutdown instruction generated from the pre-built control end is received, and the optimized ducted fan is closed based on the shutdown instruction. In order to ensure the safety of the optimized ducted fan during use, the present invention judges the real-time ducted fan electrical parameter set. If the use state is met and it can run safely, an analysis is performed every time a unit detection period is passed to judge the use effect of the optimized ducted fan. If there are ducted fan electrical parameters that do not meet the corresponding preset fan electrical parameter standards in the ducted fan electrical parameter set, an early warning instruction is generated, and the optimized ducted fan is closed based on the early warning instruction. Based on the closed optimized ducted fan, air conditioning based on the ducted fan performance is completed. The present invention proposes an air conditioning method based on ducted fan performance, so that it can be applied to cooling, heating, and air cooling, so as to achieve the purpose of air conditioning under different actual working conditions. Therefore, the present invention can realize real-time monitoring and regulation of the ducted fan to achieve the purpose of regulating the air.

[0165] like Figure 2 1 is a functional module diagram of an air conditioning system based on ducted fan performance provided by an embodiment of the present invention.

[0166] The air conditioning system 100 based on ducted fan performance of the present invention can be installed in an electronic device. According to the functions to be implemented, the air conditioning system 100 based on ducted fan performance can include a suboptimal fan data set acquisition module 101, a real-time detection data acquisition module 102, an optimized ducted fan adjustment instruction generation module 103 and an early warning instruction module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0167] The suboptimal fan data set acquisition module 101 is used to acquire a ducted fan performance data set, filter the ducted fan performance data set based on a pre-built aerodynamic noise model to obtain a suboptimal fan data set, and identify an optimized ducted fan based on the suboptimal fan data set;

[0168] The real-time detection data acquisition module 102 is used to obtain the target adjustment parameters corresponding to the actual working conditions, generate a startup instruction based on the target adjustment parameters, initialize the optimized ducted fan using the startup instruction, start the initialized optimized ducted fan and the pre-built detection unit, and obtain real-time detection data using the started detection unit and the started optimized ducted fan, wherein the detection unit presets the detection frequency and the unit detection period, the real-time detection data includes a plurality of unit real-time data, and the unit real-time data includes terminal detection data and ducted fan status data;

[0169] The optimized ducted fan adjustment instruction generating module 103 is used to extract the ducted fan status data in the unit real-time data based on the detection frequency in real time, parse the ducted fan status data, and obtain the ducted fan electrical parameter set. If each ducted fan electrical parameter in the ducted fan electrical parameter set meets the corresponding preset fan electrical parameter standard, then return to the step of extracting the ducted fan status data in the unit real-time data based on the detection frequency in real time until it is confirmed that the unit detection period has ended, extract the multiple unit real-time data based on the real-time detection data, extract the target data set based on the multiple unit real-time data, parse the target data set, obtain the optimized ducted fan adjustment instruction, use the optimized ducted fan adjustment instruction as the start instruction, return to the step of initializing the optimized ducted fan using the start instruction, until receiving the shutdown instruction generated from the pre-built control end, and shutting down the optimized ducted fan based on the shutdown instruction;

[0170] The warning instruction module 104 is used to generate a warning instruction if there are ducted fan electrical parameters in the ducted fan electrical parameter set that do not meet the corresponding preset fan electrical parameter standards, shut down the optimized ducted fan based on the warning instruction, and complete air conditioning based on the ducted fan performance based on the shut-down optimized ducted fan.

[0171] In detail, each module in the ducted fan performance-based air conditioning system 100 described in the embodiment of the present invention is used in the same manner as described above. Figure 1 The air conditioning method based on ducted fan performance described in the invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0172] like Figure 3 , is a schematic diagram of the structure of an electronic device for implementing an air conditioning method based on ducted fan performance provided by an embodiment of the present invention.

[0173] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as an air conditioning method program based on ducted fan performance.

[0174] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the air conditioning method program based on the performance of the ducted fan, etc., but also can be used to temporarily store data that has been output or is to be output.

[0175] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules (such as air conditioning method programs based on ducted fan performance, etc.) stored in the memory 11, and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0176] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0177] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0178] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0179] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0180] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0181] The air conditioning method program based on ducted fan performance stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, the following can be achieved:

[0182] Acquire a ducted fan performance data set, filter the ducted fan performance data set based on a pre-built aerodynamic noise model to obtain a suboptimal fan data set, and identify an optimized ducted fan based on the suboptimal fan data set;

[0183] Obtaining target adjustment parameters corresponding to actual working conditions, generating a start instruction based on the target adjustment parameters, initializing the optimized ducted fan using the start instruction, starting the initialized optimized ducted fan and the pre-built detection unit, and obtaining real-time detection data using the started detection unit and the started optimized ducted fan, wherein the detection unit presets a detection frequency and a unit detection period, the real-time detection data includes a plurality of unit real-time data, and the unit real-time data includes terminal detection data and ducted fan status data;

[0184] Extracting ducted fan status data from the unit real-time data in real time based on the detection frequency, parsing the ducted fan status data, and obtaining a ducted fan electrical parameter set;

[0185] If each ducted fan electrical parameter in the ducted fan electrical parameter set meets the corresponding preset fan electrical parameter standard, then return to the step of extracting the ducted fan status data in the unit real-time data based on the detection frequency in real time, until it is confirmed that the unit detection period has ended, extract the multiple unit real-time data based on the real-time detection data, extract the target data set based on the multiple unit real-time data, parse the target data set, obtain the optimized ducted fan adjustment instruction, use the optimized ducted fan adjustment instruction as the start instruction, return to the step of initializing the optimized ducted fan using the start instruction, until receiving the shutdown instruction generated from the pre-built control end, and shut down the optimized ducted fan based on the shutdown instruction;

[0186] If there are ducted fan electrical parameters in the ducted fan electrical parameter set that do not meet the corresponding preset fan electrical parameter standards, a warning instruction is generated, and the optimized ducted fan is turned off based on the warning instruction;

[0187] Air conditioning based on ducted fan performance is accomplished based on the optimized ducted fan after closing.

[0188] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0189] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0190] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0191] Acquire a ducted fan performance data set, filter the ducted fan performance data set based on a pre-built aerodynamic noise model to obtain a suboptimal fan data set, and identify an optimized ducted fan based on the suboptimal fan data set;

[0192] Obtaining target adjustment parameters corresponding to actual working conditions, generating a start instruction based on the target adjustment parameters, initializing the optimized ducted fan using the start instruction, starting the initialized optimized ducted fan and the pre-built detection unit, and obtaining real-time detection data using the started detection unit and the started optimized ducted fan, wherein the detection unit presets a detection frequency and a unit detection period, the real-time detection data includes a plurality of unit real-time data, and the unit real-time data includes terminal detection data and ducted fan status data;

[0193] Extracting ducted fan status data from the unit real-time data in real time based on the detection frequency, parsing the ducted fan status data, and obtaining a ducted fan electrical parameter set;

[0194] If each ducted fan electrical parameter in the ducted fan electrical parameter set meets the corresponding preset fan electrical parameter standard, then return to the step of extracting the ducted fan status data in the unit real-time data based on the detection frequency in real time, until it is confirmed that the unit detection period has ended, extract the multiple unit real-time data based on the real-time detection data, extract the target data set based on the multiple unit real-time data, parse the target data set, obtain the optimized ducted fan adjustment instruction, use the optimized ducted fan adjustment instruction as the start instruction, return to the step of initializing the optimized ducted fan using the start instruction, until receiving the shutdown instruction generated from the pre-built control end, and shut down the optimized ducted fan based on the shutdown instruction;

[0195] If there are ducted fan electrical parameters in the ducted fan electrical parameter set that do not meet the corresponding preset fan electrical parameter standards, a warning instruction is generated, and the optimized ducted fan is turned off based on the warning instruction;

[0196] Air conditioning based on ducted fan performance is accomplished based on the optimized ducted fan after closing.

[0197] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0198] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0199] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0200] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0201] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An air conditioning method based on ducted fan performance, characterized in that: The method comprises: Acquire a ducted fan performance data set, filter the ducted fan performance data set based on a pre-built aerodynamic noise model to obtain a suboptimal fan data set, and identify an optimized ducted fan based on the suboptimal fan data set; Obtaining target adjustment parameters corresponding to actual working conditions, generating a start instruction based on the target adjustment parameters, initializing the optimized ducted fan using the start instruction, starting the initialized optimized ducted fan and the pre-built detection unit, and obtaining real-time detection data using the started detection unit and the started optimized ducted fan, wherein the detection unit presets a detection frequency and a unit detection period, the real-time detection data includes a plurality of unit real-time data, and the unit real-time data includes terminal detection data and ducted fan status data; Extracting ducted fan status data from the unit real-time data in real time based on the detection frequency, parsing the ducted fan status data, and obtaining a ducted fan electrical parameter set; If each ducted fan electrical parameter in the ducted fan electrical parameter set meets the corresponding preset fan electrical parameter standard, then return to the step of extracting the ducted fan status data in the unit real-time data based on the detection frequency in real time, until it is confirmed that the unit detection period has ended, extract the multiple unit real-time data based on the real-time detection data, extract the target data set based on the multiple unit real-time data, parse the target data set, obtain the optimized ducted fan adjustment instruction, use the optimized ducted fan adjustment instruction as the start instruction, return to the step of initializing the optimized ducted fan using the start instruction, until receiving the shutdown instruction generated from the pre-built control end, and shut down the optimized ducted fan based on the shutdown instruction; If there are ducted fan electrical parameters in the ducted fan electrical parameter set that do not meet the corresponding preset fan electrical parameter standards, a warning instruction is generated, and the optimized ducted fan is turned off based on the warning instruction; Air conditioning based on ducted fan performance is accomplished based on the optimized ducted fan after closing; The method of screening the ducted fan performance data set based on the pre-built aerodynamic noise model to obtain a suboptimal fan data set, and confirming the optimized ducted fan based on the suboptimal fan data set includes: Using pre-constructed operating condition data to filter the ducted fan performance data set to obtain a suboptimal fan data set, combining the suboptimal fan data set with a preset grid gradient to obtain a combined data set, and constructing a simulation model set based on the combined data set; For each simulation model in the simulation model set, perform the following operations: Acquire an environmental model, import both the working condition data and the simulation model into the environmental model to obtain the environmental simulation model, acquire an observation point set and an air flow rate set based on the environmental simulation model, perform acoustic simulation operations on the simulation model in the environmental simulation model using the aerodynamic noise model, the observation point set and the air flow rate set to obtain an acoustic simulation data set, and acquire a sound pressure level spectrum graph set based on the acoustic simulation data set, wherein the observation point set includes multiple observation points, the air flow rate set includes air flow rate, and the sound pressure level spectrum graph corresponds one-to-one to one observation point and one air flow rate, and the acoustic simulation data corresponds one-to-one to the sound pressure level spectrum graph; The sound pressure level spectrum atlas is analyzed to obtain an optimized model set, and the optimized ducted fan is confirmed based on the optimized model set.

2. The air conditioning method based on ducted fan performance according to claim 1, characterized in that: The step of obtaining target adjustment parameters corresponding to the actual working condition, generating a start instruction based on the target adjustment parameters, and initializing and optimizing the ducted fan using the start instruction includes: Determine the target adjustment parameter based on the actual working conditions, wherein the target adjustment parameter includes a plurality of unit adjustment parameters, and the plurality of unit adjustment parameters are: set temperature, required static pressure, air valve opening and temperature gradient; Extract unit adjustment parameters from the target adjustment parameters in sequence, and perform the following operations on the extracted unit adjustment parameters: Convert the extracted unit adjustment parameters into a preset fixed instruction format to obtain unit test parameters, and send the unit test parameters to a pre-built communication bus, wherein the fixed instruction format includes: a frame header, a second byte, a third byte, and a frame tail, wherein the second byte is an identifier corresponding to the actual working condition, the third byte is an identifier corresponding to the unit adjustment parameter, and the communication bus is a CAN bus, including: a host computer and multiple terminals; Generate a unit start command based on the communication bus receiving the unit test parameter, send the unit start command to the terminal corresponding to the unit adjustment parameter, and if the host computer receives a reply command sent from the terminal within a preset response time, confirm that the terminal is in a preset normal access state; After confirming that the terminals corresponding to all the unit adjustment parameters in the target adjustment parameters are in normal passage states, a start instruction is obtained based on a plurality of unit start instructions, and the optimized ducted fan is initialized based on the start instruction.

3. The air conditioning method based on ducted fan performance according to claim 2, characterized in that: The step of extracting a target data set based on the real-time data of multiple units, parsing the target data set, and obtaining an optimized ducted fan adjustment instruction includes: Extract unit real-time data from multiple unit real-time data in sequence, and extract detected temperature, measured air valve opening, and measured static pressure based on the extracted unit real-time data, respectively summarize the detected temperature, measured air valve opening, and measured static pressure to obtain a detected temperature set, measured air valve opening set, and measured static pressure set corresponding to the multiple unit real-time data, summarize the detected temperature set, measured air valve opening set, and measured static pressure set to obtain a target data set, extract unit target data sets from the target data set in sequence, and perform the following operations on the extracted unit target data sets: Acquire a sliding detection array, extract unit target data from the unit target data set in sequence, and fill the extracted unit target data into the sliding detection array until all pre-constructed blank data points in the sliding detection array are filled, thereby obtaining an initial array, wherein all unit target data in the initial array are arranged in order from earliest to latest according to the time corresponding to the unit target data; Perform sliding segmentation on the unit target data set based on the initial array to obtain a sliding array set; Extract sliding arrays from the sliding array set in sequence, calculate the mean of the extracted sliding arrays, obtain evaluation values, summarize the evaluation values, and obtain an evaluation value set. If the number of evaluation values ​​in the evaluation value set that do not belong to the pre-constructed evaluation value fluctuation range is greater than a preset number, generate an adjustment instruction, wherein if the unit target data set is a detected temperature set, the adjustment instruction is a temperature adjustment instruction, if the unit target data set is a measured air valve opening set, the adjustment instruction is an air valve adjustment instruction, and if the unit target data set is a measured static pressure set, the adjustment instruction is a static pressure adjustment instruction; The adjustment instructions are summarized to obtain an adjustment instruction set based on the detected temperature set, the measured air valve opening set and the measured static pressure set, and an optimized ducted fan adjustment instruction is constructed based on the adjustment instruction set.

4. The air conditioning method based on ducted fan performance according to claim 3, characterized in that: The sliding segmentation of the unit target data set is performed based on the initial array to obtain a sliding array set, including: Extracting the earliest unit target data and the latest unit target data from the initial array to obtain the head data and the tail data, removing the head data in the initial array, and extracting the next unit target data adjacent to the tail data from the unit target data set to obtain the adjacent data, filling the adjacent data to the initial array after the head data is removed, and arranging all the unit target data and the adjacent data in the initial array after the adjacent data is filled in the order of time from the earliest to the latest, to obtain the sliding array; Taking the sliding array as the initial array, returning to the step of extracting the earliest unit target data and the latest unit target data from the initial array until the unit target data set is an empty set, summarizing the sliding array and the initial array to obtain a sliding array set.

5. The air conditioning method based on ducted fan performance according to claim 4, characterized in that: The step of constructing an optimized ducted fan adjustment instruction based on an adjustment instruction set includes: Extracting a detection temperature set from multiple units of real-time data, and constructing a temperature fitting curve based on the detection temperature set, extracting a local temperature extreme value set from the temperature fitting curve, and arranging the local temperature extreme value set in chronological order to obtain a local temperature extreme value sequence; The local temperature extreme values ​​are extracted from the local temperature extreme value sequence in sequence, and the following operations are performed on the extracted local temperature extreme values: Extracting the next local temperature extreme value adjacent to the extracted local temperature extreme value to obtain an adjacent local temperature extreme value, calculating the absolute value of the difference between the adjacent local temperature extreme value and the extracted local temperature extreme value to obtain a unit temperature change amplitude; Summarize the unit temperature change amplitudes to obtain a unit temperature change amplitude set; If the number of unit temperature change amplitudes that are greater than the unit temperature change amplitude of the temperature gradient is greater than the preset number of temperature controls, a gradient adjustment instruction is generated, and the gradient adjustment instruction and the adjustment instruction set are integrated to obtain an optimized ducted fan adjustment instruction.

6. The air conditioning method based on ducted fan performance according to claim 5, characterized in that: The using the pre-constructed operating condition data to screen the ducted fan performance data set to obtain a suboptimal fan data set includes: Based on the working condition data, a working condition interval set is determined, wherein the working condition interval set includes: a voltage interval, a power interval, a current interval, a bypass ratio interval, a wind speed interval, a temperature interval, and a speed interval, and the ducted fan performance data includes a plurality of performance data; The following operations are performed on the operating condition intervals in the operating condition interval set and the ducted fan performance data in the ducted fan performance data set: If the performance data corresponding to the operating condition interval does not exist in the ducted fan performance data, the fan model corresponding to the ducted fan performance data is obtained, and the standard electrical data of the fan model is retrieved using a pre-built retrieval method, wherein the standard electrical data includes a plurality of standard data; Determine whether there is standard data corresponding to the operating condition interval in the standard electrical data; if there is standard data in the standard electrical data, update the ducted fan performance data using the standard data, retain the updated ducted fan performance data, and confirm the updated ducted fan performance data as the ducted fan performance data; otherwise, discard the ducted fan performance data; If the performance data exists in the ducted fan performance data, extracting an available interval based on the performance data, and if the operating condition interval is a sub-interval of the available interval, retaining the ducted fan performance data, otherwise, discarding the ducted fan performance data; The retained ducted fan performance data are aggregated to obtain the suboptimal fan dataset.

7. The air conditioning method based on ducted fan performance according to claim 6, characterized in that: The aerodynamic noise model is as follows: ， in, represents the far-field sound speed, Indicates that the observation point is The change in sound pressure at the observation point at time, Indicates time, It represents the air density in the undisturbed environment, and it represents the velocity component of the air flow velocity in the normal direction of the integration surface. represents the Dirac function, represents the compressive stress tensor, and Both represent the directional components of the compressive stress tensor, The unit normal vector represents the normal direction of the integration surface, represents the change in fluid density of air, Indicates the air velocity The direction of the component, represents the component of air velocity in the normal direction of the control surface, and All represent the spatial position coordinates of the observation point with the ducted fan as the origin. represents the Lighthill stress tensor, represents the Heaviside function.

8. The air conditioning method based on ducted fan performance according to claim 7, characterized in that: The analytical sound pressure level spectrum atlas obtains an optimized model set, including: The sound pressure level spectrograms are extracted from the sound pressure level spectrum set in sequence, and the following operations are performed on the extracted sound pressure level spectrograms: Acquiring a sound pressure threshold of an observation point under actual working conditions based on a sound pressure level spectrum graph, and extracting a sound pressure data set greater than the sound pressure threshold from the sound pressure level spectrum graph, wherein the horizontal axis of the sound pressure level spectrum graph is frequency, the vertical axis is sound pressure level, and the sound pressure data sets are sorted according to the order of the horizontal axis in the sound pressure level spectrum graph; If the sound pressure data set is an empty set, the simulation model corresponding to the sound pressure data set is confirmed as an available fan; If the sound pressure data set is not an empty set, sound source feature recognition is performed on the sound pressure data set to obtain a feature recognition result. If the feature recognition result is fitting noise, the simulation model corresponding to the sound pressure data set is confirmed as an available fan. If the feature recognition result is sound source noise, the simulation model corresponding to the sound pressure data set is confirmed as an unavailable fan. The available fans are aggregated, and all the aggregated available fans are screened and recursively operated to obtain an optimized model set.

9. An air conditioning system based on ducted fan performance, using the air conditioning method based on ducted fan performance according to claim 1, characterized in that: The system comprises: A suboptimal fan data set acquisition module, used to acquire a ducted fan performance data set, screen the ducted fan performance data set based on a pre-built aerodynamic noise model to obtain a suboptimal fan data set, and identify an optimized ducted fan based on the suboptimal fan data set; A real-time detection data acquisition module, used to obtain target adjustment parameters corresponding to actual working conditions, generate a startup instruction based on the target adjustment parameters, initialize the optimized ducted fan using the startup instruction, start the initialized optimized ducted fan and the pre-built detection unit, and obtain real-time detection data using the started detection unit and the started optimized ducted fan, wherein the detection unit presets a detection frequency and a unit detection period, the real-time detection data includes a plurality of unit real-time data, and the unit real-time data includes terminal detection data and ducted fan status data; An optimized ducted fan adjustment instruction generation module is used to extract the ducted fan status data in the unit real-time data based on the detection frequency in real time, parse the ducted fan status data, and obtain the ducted fan electrical parameter set. If each ducted fan electrical parameter in the ducted fan electrical parameter set meets the corresponding preset fan electrical parameter standard, then return to the step of extracting the ducted fan status data in the unit real-time data based on the detection frequency in real time until it is confirmed that the unit detection period has ended, extract the multiple unit real-time data based on the real-time detection data, extract the target data set based on the multiple unit real-time data, parse the target data set, and obtain the optimized ducted fan adjustment instruction, using the optimized ducted fan adjustment instruction as the start instruction, and return to the step of initializing the optimized ducted fan using the start instruction until receiving the shutdown instruction generated from the pre-built control end, and shutting down the optimized ducted fan based on the shutdown instruction; The early warning instruction module is used to generate an early warning instruction if there are ducted fan electrical parameters in the ducted fan electrical parameter set that do not meet the corresponding preset fan electrical parameter standards, shut down the optimized ducted fan based on the early warning instruction, and complete air conditioning based on the ducted fan performance based on the shut down optimized ducted fan.

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