An adaptive air-cooled air conditioning system optimization control method based on an environment large model

Abstract

The present application relates to air conditioning system optimization control technical field, it is a kind of adaptive air-cooled air conditioning system optimization control method based on environment big model, specific method includes: obtaining the running state data and external environment data in the running process of air-cooled air conditioning system, risk assessment is carried out, obtains wind field disturbance risk coefficient and flocculation pollutant aggregation risk coefficient;Implement primary degradation intelligent detection, get flocculation entanglement comprehensive index and micro air flow obstruction index;Fusion assessment condenser primary performance degradation probability, get initial round adaptive control judgment result;Obtain heat backflow effect intensity and local flow field distortion index, carry out fusion assessment, output condenser secondary performance degradation probability;According to the secondary round adaptive control judgment result, execute system final regulation and control decision.The present application solves the problem that cleaning control mechanism lags behind and is insufficient in existing technology, which ultimately leads to energy efficiency decline and maintenance cost increase.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air conditioning system optimization control, and is an adaptive air-cooled air conditioning system optimization control method based on an environment large model. BACKGROUND

[0002] The operation and maintenance of the current air-cooled air conditioning system face the following technical challenges, including the following defects in dealing with suspended flocculent pollutants: unlike traditional dust particles, flocculent materials have the physical properties of fiber-like structure, strong porous adsorption, and easy formation of three-dimensional network entanglement, and the cleaning strategy based on the dust accumulation model cannot effectively cope with them; the existing system lacks the quantitative perception ability of flocculent material fiber size distribution, entanglement density and fluffy thickness, and cannot identify the sub-surface blockage structure formed between the condenser fins; at the same time, due to the stronger shielding effect of flocculent entanglement on the micro air flow channel and the easy occurrence of local vortex distortion, the traditional air field regulation method cannot alleviate the increase in heat backflow intensity and the decay of heat dissipation performance caused thereby; in addition, the current technology has not established a comprehensive evaluation model of air field disturbance risk and flocculent material specific deposition risk, resulting in a lagging and insufficient cleaning trigger mechanism, and also causing the air-cooled air conditioner to fail to realize the coordinated control of zoned precise cleaning and adaptive air field optimization, ultimately leading to energy efficiency decline and increased maintenance costs. SUMMARY

[0003] This section aims to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification to avoid obscuring the purpose of this section, abstract and title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0004] The technical problem to be solved by the present application is that in the prior art, the cleaning control mechanism is lagging and insufficient, ultimately leading to energy efficiency decline and increased maintenance costs. The present application proposes an adaptive air-cooled air conditioning system optimization control method based on an environment large model.

[0005] In order to achieve the above purpose, the adaptive air-cooled air conditioning system optimization control method based on an environment large model includes the following steps:

[0006] S1: Obtain the running state data and external environment data of the air-cooled air conditioning system during operation, and synchronously collect the surface image of the condenser in the air-cooled air conditioning system;

[0007] S2: Risk assessment is performed on the running state data and external environment data respectively to obtain the air field disturbance risk coefficient and the flocculent pollutant aggregation risk coefficient;

[0008] S3: Implementing primary degradation intelligent detection on the condenser in the air-cooled air conditioning system, and quantitatively processing the degradation intelligent detection result to obtain a flocculation entanglement comprehensive index and a micro air flow blockage index;

[0009] S4: According to steps S2 and S3, fusing the primary performance degradation probability of the condenser, and performing primary round adaptive control judgment according to the primary performance degradation probability to obtain a primary round adaptive control judgment result;

[0010] S5: Through the primary round adaptive control judgment result, performing secondary degradation intelligent detection to obtain a heat backflow effect strength and a local flow field distortion index, fusing the heat backflow effect strength and the local flow field distortion index, and outputting a secondary performance degradation probability of the condenser;

[0011] S6: According to the secondary performance degradation probability, performing secondary round adaptive control judgment, and simultaneously executing a decision of system final regulation and control according to the secondary round adaptive control judgment result.

[0012] Preferably, S1 comprises:

[0013] S11: Collecting running state data of the air-cooled air conditioning system in a running process;

[0014] The running state data comprises: cumulative running total time T of the air-cooled air conditioning system in the running process, single running wind speed data and single running wind direction fluctuation data.

[0015] S12: Collecting external environment data of the air-cooled air conditioning system in the running process;

[0016] The external environment data comprises: environmental temperature and humidity data, concentration and fiber size distribution information of suspended flocculation in the air.

[0017] Preferably, S2 comprises:

[0018] S21: Importing the running state data into a wind field dynamic disturbance evaluation model and analyzing to obtain a wind field disturbance risk coefficient;

[0019] S22: Based on the external environment data of the air-cooled air conditioning system during the running, generating time-varying environmental temperature curve , environmental humidity curve and flocculation deposition trend curve ;

[0020] S23: Importing the external environment data into a flocculation pollutant deposition risk evaluation model and analyzing to obtain a flocculation pollutant aggregation risk coefficient.

[0021] Preferably, S3 comprises:

[0022] S31: Implementing primary degradation intelligent detection on the condenser in the air-cooled air conditioning system, identifying the distribution area of flocculation hooking and the area of micro air flow channel obstruction;

[0023] S32: Obtaining temperature field distribution of the condenser surface and sub-surface flocculation entanglement image data, mapping image features of the sub-surface flocculation entanglement image data to a three-dimensional feature space for quantitative analysis;

[0024] S33: Locating the area with the largest cumulative contribution in the three-dimensional feature space, identifying it as the flocculation entanglement core area, and recording the total number of flocculation entanglement core areas as B;

[0025] S34: Extracting parameter attributes of each flocculation entanglement core area;

[0026] The parameter attributes of each flocculation entanglement core area include: entanglement density of each flocculation entanglement core area , fiber coverage ratio of each flocculation entanglement core area , and fluffy thickness of each flocculation entanglement core area ;

[0027] S35: According to the parameter attributes of each flocculation entanglement core area extracted in step S34, the flocculation entanglement comprehensive index of the condenser is calculated.

[0028] S36: Collecting the micro-scale air flow velocity distribution between the condenser fins, and constructing an air flow uniformity evaluation matrix;

[0029] S37: Based on the air flow uniformity evaluation matrix constructed in step S36, identifying each air flow obstruction area between the condenser fins, calculating the flow velocity non-uniformity of each air flow obstruction area between the condenser fins and the vortex generation intensity of each air flow obstruction area;

[0030] S38: Constructing a level evaluation model, importing the flow velocity non-uniformity of each air flow obstruction area and the vortex generation intensity of each air flow obstruction area into the level evaluation model and calculating, outputting the micro air flow obstruction index .

[0031] Preferably, S4 comprises:

[0032] S41: Extracting the wind field disturbance risk coefficient, the flocculation pollutant aggregation risk coefficient, the flocculation entanglement comprehensive index, and the micro air flow obstruction index, and performing fusion evaluation thereon to obtain a primary performance degradation probability .

[0033] S42: According to the primary performance degradation probability, performing the first round of adaptive control judgment, including:

[0034] When , the anti-floc entanglement self-cleaning program is started to the control back end of the air-cooled air conditioning system;

[0035] When , step S5 is executed;

[0036] Wherein, the initial wheel self-cleaning threshold of the preset air-cooled air conditioner .

[0037] Preferably, S5 includes:

[0038] S51: Obtain the condenser fin profile, and reconstruct its geometric shape by using edge detection and surface fitting algorithm to obtain the fin profile curve;

[0039] S52: Extract the fitted fin profile curve, and preset U key data collection points on the fin profile curve;

[0040] S53: Analyze the temperature gradient observation data collected at the U key data collection points to obtain the heat backflow effect strength.

[0041] S54: Collect the vortex spectrum characteristics of the condenser air outlet side, and collect the static pressure distribution data between the fins through the micro-pressure difference sensor array, wherein the total number of sensors of the micro-pressure difference sensor array is V;

[0042] S55: Construct a distortion evaluation model of the local flow field structure, input the collected vortex spectrum characteristics and static pressure distribution data into the distortion evaluation model and evaluate to obtain the local flow field distortion index;

[0043] S56: According to the heat backflow effect strength of the condenser and the local flow field distortion index, the secondary performance degradation probability of the condenser is obtained by comprehensive evaluation .

[0044] Preferably, S6 includes:

[0045] According to the secondary performance degradation probability, the secondary wheel adaptive control is judged and the secondary wheel adaptive control of the air-cooled air conditioning system is executed, including:

[0046] When , the secondary performance degradation probability is arranged in descending order, the historical running log of the air-cooled air conditioning system is queried synchronously, and the system is executed partition reverse pulse cleaning according to the sorting result;

[0047] When , the running environment characteristics of the air-cooled air conditioning system are traced back, the adaptive wind field regulation parameters are configured according to the environment type, and the directional anti-floc entanglement cleaning process is started to the identified target cleaning area;

[0048] wherein the secondary wheel self-cleaning threshold of the preset air-cooled air conditioning system .

[0049] Compared with the prior art, the technical effects of the present application are as follows:

[0050] 1. The present application realizes early and accurate warning of condenser performance degradation. By fusing multiple parameters such as wind field disturbance risk coefficient, flocculation aggregation risk coefficient and micro-airflow obstruction index, a primary and secondary performance degradation probability model is constructed, breaking through the limitations of traditional single threshold control, enabling the system to identify the sub-surface flocculation entanglement state and airflow organization distortion trend invisible to the naked eye, and greatly reducing the risk of sudden performance degradation.

[0051] 2. The present application improves the accuracy and resource utilization efficiency of cleaning operation. The air-cooled air conditioner can locate the flocculation entanglement core area and quantify the heat backflow intensity and flow field distortion value, and then trigger the partitioned reverse pulse cleaning and directional anti-entanglement operation, avoiding energy waste and equipment loss caused by overall cleaning, and at the same time, suppressing the re-accumulation of pollutants after cleaning through adaptive wind field optimization.

[0052] 3. The present application enhances the long-term stability and energy efficiency level of the system in a variable environment. By coupling environmental temperature and humidity, flocculation fiber size distribution and running time, etc., the anti-heat backflow wind field parameters and cleaning strategy are dynamically adjusted, effectively maintaining the smoothness and heat exchange uniformity of the condenser micro-airflow channel. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating any inventive labor. Among them:

[0054] Figure 1 is a flowchart of an adaptive air-cooled air conditioning system optimization control method based on an environmental large model of the present application. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0056] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0057] Second, the "one embodiment" or "an embodiment" referred to herein means a specific feature, structure, or characteristic under discussion. Each of the various embodiments presented in this specification are not necessarily all mutually exclusive alternatives from other embodiments presented. It should be understood that the text set forth in this specification serves as a non-limiting example for teaching the general principles of the present application.

[0058] Embodiment one:

[0059] As shown in Figure 1 , an adaptive air-cooled air conditioning system optimization control method based on an environment large model according to an embodiment of the present application, as shown in Figure 1 , includes the following specific steps:

[0060] S1: obtaining running state data and external environment data of the air-cooled air conditioning system in the running process, and synchronously collecting the surface image of the condenser in the air-cooled air conditioning system;

[0061] S1 includes:

[0062] S11: collecting the running state data of the air-cooled air conditioning system in the running process;

[0063] The running state data includes: cumulative running total time T of the air-cooled air conditioning system in this running process, single running wind speed data, and single running wind direction fluctuation data.

[0064] S12: collecting the external environment data of the air-cooled air conditioning system in the running process;

[0065] The external environment data includes: environmental temperature and humidity data, concentration of suspended flocculent in the air, and fiber size distribution information.

[0066] S2: respectively performing risk assessment on the running state data and the external environment data to obtain the wind field disturbance risk coefficient and the flocculent pollutant aggregation risk coefficient;

[0067] S2 includes:

[0068] S21: importing the running state data into a wind field dynamic disturbance evaluation model and analyzing to obtain the wind field disturbance risk coefficient;

[0069] Exemplarily, in the present embodiment, a wind field disturbance risk coefficient acquisition strategy is provided, specifically: ;

[0070] Wherein, is the wind field disturbance risk coefficient, which is used to evaluate the negative influence of the external wind field on the condenser heat dissipation efficiency; is the time subscript; is the mean value of the wind speed; represents the wind speed at the t-th moment; represents the angle difference between the wind direction angle and the average angle at the t-th moment; is the angle range of the wind direction angle, and it is noted that in the embodiment, the angle range of the wind direction angle is 0-360 degrees;

[0071] It is noted that for the wind speed part of the wind field , which quantifies the wind speed instability, and reflects that when the fluctuation of the external wind field wind speed is large, the condenser heat dissipation air volume will be unstable; for the wind direction part of the wind field , which quantifies the wind direction instability, and reflects that when the external wind field wind direction changes greatly, it will cause the wind field to be turbulent, increasing the risk of heat reflux;

[0072] S22: Based on the external environment data during the operation of the air-cooled air conditioning system, generate the environment temperature curve , the environment humidity curve and the flocculent deposition trend curve varying with time;

[0073] S23: Import the external environment data into the flocculent pollutant deposition risk assessment model and analyze to obtain the flocculent pollutant aggregation risk coefficient.

[0074] Exemplarily, in the embodiment, a flocculent pollutant aggregation risk coefficient acquisition strategy is provided, specifically:

[0075] ;

[0076] wherein, is the flocculent pollutant aggregation risk coefficient; is the total duration of the current operation process; is the environment temperature data; is the standard operating temperature; is the allowable temperature fluctuation range; is the environment humidity data; is the standard operating humidity; is the allowable humidity fluctuation range; is the fiber entanglement influence factor; is the flocculent concentration monitoring value; is the flocculent critical deposition concentration threshold; is the average fiber length of the flocculent; is the reference fiber length;

[0077] It is noted that for the temperature term quantifies the influence of environmental temperature fluctuation on the overall risk of the system, reflecting that when the environmental temperature deviates from the standard operating temperature, it will lead to instability of the condenser heat dissipation efficiency, thereby increasing the risk of heat reflux;

[0078] It should be noted that for the humidity term quantifies the influence of environmental humidity instability on the overall risk, reflecting that when the environmental humidity deviates from the standard operating humidity, it will lead to stronger adhesion of the flocculation on the surface of the condenser;

[0079] It should also be noted that for the flocculation term quantifies the influence of flocculation characteristics on the risk of condenser blockage, reflecting that when the fiber concentration of flocculation is higher and the fiber is longer, the flocculation is more likely to entangle and deposit;

[0080] S3: Implementing primary degradation intelligent detection on the condenser in the air-cooled air conditioning system, and quantitatively processing the degradation intelligent detection results to obtain a flocculation entanglement comprehensive index and a micro-airflow blockage index;

[0081] S3 includes:

[0082] S31: Implementing primary degradation intelligent detection on the condenser in the air-cooled air conditioning system, identifying the flocculation hooking distribution area and the micro-airflow channel blocking area;

[0083] S32: Obtaining temperature field distribution of the condenser surface and sub-surface flocculation entanglement morphology image data, mapping image features of the sub-surface flocculation entanglement morphology image data to a three-dimensional feature space for quantitative analysis;

[0084] S33: Locating the area with the largest cumulative contribution in the three-dimensional feature space, identifying it as a flocculation entanglement core area, and recording the total number of the flocculation entanglement core areas as B;

[0085] S34: Extracting parameter attributes of each flocculation entanglement core area;

[0086] The parameter attributes of each flocculation entanglement core area include: entanglement density of each flocculation entanglement core area , fiber coverage ratio of each flocculation entanglement core area , and fluffy thickness of each flocculation entanglement core area ;

[0087] S35: According to the parameter attributes of each flocculation entanglement core area extracted in step S34, the flocculation entanglement comprehensive index of the condenser is calculated.

[0088] Exemplarily, in the present embodiment, a calculation strategy of the flocculation entanglement comprehensive index is provided, specifically:

[0089] ;

[0090] wherein, is the flocculation entanglement comprehensive index; is the entanglement density of the bth flocculation entanglement core region; is the fiber coverage ratio of the bth flocculation entanglement core region; is the bulk thickness of the bth flocculation entanglement core region; is the average value of the entanglement density of the flocculation entanglement core region; is the average value of the fiber coverage of the flocculation entanglement core region; is the average value of the bulk thickness of the flocculation entanglement core region;

[0091] S36: Collect the micro-scale air flow velocity distribution between the condenser fins, and construct an air flow uniformity evaluation matrix;

[0092] Exemplarily, in the present embodiment, a construction strategy of an air flow uniformity evaluation matrix is provided, specifically:

[0093] ;

[0094] wherein, denotes the joint occurrence probability of the velocity level i and the velocity level j in the direction k; is the conditional indicator function; denotes the wind speed value at the position , and ; is the flow channel region between the condenser fins; are all spatial offsets; denotes the specific discrete wind speed value corresponding to the velocity level i; denotes the specific discrete wind speed value corresponding to the velocity level j;

[0095] S37: Based on the air flow uniformity evaluation matrix constructed in step S36, identify each air flow blockage region between the condenser fins, and calculate the flow velocity non-uniformity and the vortex generation intensity of each air flow blockage region between the condenser fins;

[0096] Exemplarily, in the present embodiment, the flow velocity non-uniformity is calculated by the standard deviation of the velocity distribution within the blockage region, specifically as follows:

[0097] ; wherein, is the flow velocity non-uniformity, and it is to be noted that in the present embodiment, it reflects the non-uniformity of the flow velocity in space; the number of velocity sampling points in the blocking area; the velocity sampling value in the blocking area; the average velocity in the blocking area;

[0098] the vortex generation intensity calculated by the average vorticity modulus value in the blocking area, specifically as follows:

[0099] ; wherein, the vortex generation intensity; it is noted that in the embodiment, it quantifies the intensity of the vortex between the condenser fins; represents the vorticity at position m;

[0100] S38: constructing a level evaluation model, and calculating the flow velocity non-uniformity of each airflow blocking area and the vortex generation intensity of each airflow blocking area importing the level evaluation model and calculating, and outputting the micro-airflow blocking index .

[0101] Exemplarily, in the embodiment, a micro-airflow blocking index acquisition strategy is provided, specifically as follows:

[0102] ;

[0103] wherein, the micro-airflow blocking index; the total number of airflow blocking areas; represents the flow velocity non-uniformity of the lth airflow blocking area; the reference flow velocity non-uniformity of each airflow blocking area; represents the vortex generation intensity of the lth airflow blocking area; the reference vortex generation intensity of each airflow blocking area; the standard deviation of the flow velocity non-uniformity of each airflow blocking area; the standard deviation of the vortex generation intensity of each airflow blocking area;

[0104] S4: according to step S2 and step S3, fusing to evaluate the primary performance degradation probability of the condenser, and performing the first round of adaptive control judgment according to the primary performance degradation probability, to obtain the first round of adaptive control judgment result;

[0105] S4 includes:

[0106] S41: extracting the wind field disturbance risk coefficient, the flocculent pollutant aggregation risk coefficient, the flocculent entanglement comprehensive index, and the micro-airflow blocking index, and fusing to evaluate them to obtain the primary performance degradation probability .

[0107] Exemplarily, in the embodiment, an evaluation strategy of primary performance degradation probability is provided, specifically:

[0108] ;

[0109] wherein, is the primary performance degradation probability; is a pre-labeled weight coefficient; is a wind field disturbance risk coefficient; is a pre-labeled weight coefficient; is a flocculent pollutant aggregation risk coefficient; is a pre-labeled weight coefficient; is a flocculent entanglement comprehensive index; is a weight coefficient of micro-airflow obstruction index; is a micro-airflow obstruction index;

[0110] S42: Perform primary adaptive control judgment according to the primary performance degradation probability, including:

[0111] When , feedback to the control back end of the air-cooled air conditioning system, and start the anti-flocculent entanglement self-cleaning program;

[0112] When , execute step S5;

[0113] wherein, the preset air-cooled air conditioner primary self-cleaning threshold .

[0114] S5: Perform secondary degradation intelligent detection through the primary adaptive control judgment result, obtain the heat backflow effect strength and the local flow field distortion index, perform fusion evaluation on the heat backflow effect strength and the local flow field distortion index, and output the secondary performance degradation probability of the condenser;

[0115] S5 includes:

[0116] S51: Obtain the condenser fin profile, and reconstruct the geometric shape thereof by using edge detection and surface fitting algorithm to obtain a fin profile curve;

[0117] S52: Extract the fin profile curve after fitting, and preset U key data collection points on the fin profile curve;

[0118] Exemplarily, in the embodiment, the preset method of the key data collection points includes: dividing an air inlet area, a fin core area and an air outlet area according to the condenser structure, and uniformly distributing points in the temperature gradient sensitive area based on the computational fluid dynamics simulation result, wherein the distribution density of the air outlet area is higher than that of other areas;

[0119] S53: For U key data collection points, analyze the collected temperature gradient observation data to obtain the thermal reflux effect strength.

[0120] Exemplarily, in the present embodiment, a strategy for obtaining the thermal reflux effect strength is provided, specifically:

[0121] ;

[0122] Among them, represents the thermal reflux effect strength; represents the real-time temperature gradient measurement value at the u-th data collection point; represents the temperature gradient measurement value of the point under the initial state of the system;

[0123] S54: Collect the vortex spectrum characteristics of the condenser outflow side, and collect the static pressure distribution data between the fins through a micro pressure difference sensor array, wherein the total number of sensors of the micro pressure difference sensor array is V;

[0124] S55: Construct a distortion evaluation model of the local flow field structure, input the collected vortex spectrum characteristics and static pressure distribution data into the distortion evaluation model and evaluate, to obtain a local flow field distortion index;

[0125] Exemplarily, in the present embodiment, a strategy for obtaining the local flow field distortion index is provided, specifically:

[0126] ;

[0127] Among them, is the local flow field distortion index; represents the vortex spectrum characteristics collected by the v-th sensor; is the vortex spectrum characteristics under the initial state; is the allowable fluctuation range of the vortex spectrum characteristics; represents the static pressure value collected by the v-th sensor; is the static pressure value under the initial state; is the allowable fluctuation range of the static pressure value;

[0128] Among them, for the vortex spectrum item , it aims to monitor the change degree of vortex in the condenser, which reflects that in the flow field, the greater the change of the spectrum, the vortex in the flow field has been generated; for the static pressure item , it aims to monitor the change degree of static pressure in the condenser, which reflects that in the flow field, when the static pressure changes become larger, then the flocculation has blocked the local flow field between the condenser fins;

[0129] S56: According to the thermal reflux effect strength and the local flow field distortion index of the condenser, the secondary performance degradation probability of the condenser is comprehensively evaluated .

[0130] Exemplarily, in the embodiment, an evaluation strategy of secondary performance degradation probability is provided, specifically:

[0131] ;

[0132] wherein, is the secondary performance degradation probability; is the primary performance degradation probability; is the weight coefficient of the thermal backflow effect intensity; is the thermal backflow effect intensity; is the weight coefficient of the local flow field distortion index; is the local flow field distortion index;

[0133] S6: According to the secondary performance degradation probability, a secondary round adaptive control judgment is made, and a decision of system final regulation and control is executed according to the result of the secondary round adaptive control judgment.

[0134] S6 includes:

[0135] According to the secondary performance degradation probability, a secondary round adaptive control judgment is made and a secondary round adaptive control of the air-cooled air conditioning system is executed, including:

[0136] When , the secondary performance degradation probability is arranged in descending order, the historical operation log of the air-cooled air conditioning system is queried synchronously, and partition reverse pulse cleaning is executed on the system according to the sorting result;

[0137] When , the operation environment characteristics of the air-cooled air conditioning system are traced back, adaptive wind field regulation and control parameters are configured according to the environment type, and directional anti-floc entanglement cleaning processing is started on the identified target cleaning area;

[0138] wherein, the secondary round self-cleaning threshold of the air-cooled air conditioning system is preset .

[0139] Embodiment two:

[0140] The embodiment provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0141] The processor executes the above-mentioned adaptive air-cooled air conditioning system optimization control method based on the environment large model by calling the computer program stored in the memory.

[0142] The electronic device can have a large difference due to configuration or performance, and can include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memory stores at least one computer program, the computer program is loaded and executed by the processor to implement the adaptive air-cooled air conditioning system optimization control method based on the environment large model provided by the above method embodiment. The electronic device can also include other components for implementing device functions, for example, the electronic device can also have a wired or wireless network interface and an input and output interface, and the like, so as to perform data input and output. This embodiment will not be described here.

[0143] Embodiment three:

[0144] The embodiment provides a computer readable storage medium, which stores an erasable computer program.

[0145] When the computer program runs on the computer device, the computer device executes the adaptive air-cooled air conditioning system optimization control method based on the environment large model.

[0146] For example, the computer readable storage medium can be a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a read-only compact disc (Compact Disc Read-Only Memory, CD-ROM), a magnetic tape, a floppy disk and an optical data storage device, etc.

[0147] It should be understood that in various embodiments of the present application, the size of the sequence number of each process does not mean the order of execution, the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0148] It should be understood that according to A, B is determined, which means that B is determined only according to A, but also B can be determined according to A and / or other information.

[0149] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0150] Those skilled in the art can clearly understand that the units and algorithm steps of the examples described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device, and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0152] In several embodiments provided by the present application, it should be understood that the disclosed system, device, and method can be implemented in other ways. For example, the above-described device embodiments are merely schematic, for example, the division of units is only one, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices, or units, which can be electrical, mechanical, or other forms.

[0153] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e. may be located in one place, or may be distributed to multiple network units. Part or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.

[0154] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0155] In the description of the present specification, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0156] The basic principles and main features of the present application and the advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only illustrative of the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. An adaptive wind-cooled air conditioning system optimization control method based on an environmental large model, characterized by, The method comprises: S1: acquiring running state data and external environment data of the air-cooled air conditioning system in the running process, and synchronously collecting surface images of the condenser in the air-cooled air conditioning system; S2: respectively performing risk assessment on the running state data and the external environment data to obtain a wind field disturbance risk coefficient and a flocculent pollutant aggregation risk coefficient; S3: performing primary degradation intelligent detection on the condenser in the air-cooled air conditioning system, and simultaneously performing quantitative processing on the degradation intelligent detection result to obtain a flocculent entanglement comprehensive index and a micro air flow blockage index; S31: performing primary degradation intelligent detection on the condenser in the air-cooled air conditioning system, and identifying a flocculent hooking distribution area and a micro air flow passage obstruction area; S32: acquiring temperature field distribution of the condenser surface and sub-surface flocculent entanglement morphology image data, mapping image features of the sub-surface flocculent entanglement morphology image data to a three-dimensional feature space for quantitative analysis; S33: locating a region with the largest cumulative contribution in the three-dimensional feature space, identifying the region as a flocculent entanglement core area, and recording a total number of the flocculent entanglement core areas as B; S34: extracting parameter attributes of each flocculent entanglement core area; the parameter properties of each entangled core region of the batt include: entangled density of each entangled core region of the batt , fiber coverage ratio of each entangled core region of the batt , and loft thickness of each entangled core region of the batt ; S35: calculating the flocculent entanglement comprehensive index of the condenser according to the parameter attributes of each flocculent entanglement core area extracted in step S34; S36: collecting micro-scale air flow velocity distribution between condenser fins, and constructing an air flow uniformity evaluation matrix; S37: Based on the air flow uniformity evaluation matrix constructed in step S36, identify each air flow blockage area between the condenser fins, and calculate the flow velocity non-uniformity of each air flow blockage area between the condenser fins and the vortex generation intensity of each air flow blockage area ; S38: Construct a level evaluation model, and calculate the flow rate non-uniformity of each airflow obstruction area and the vortex generation intensity of each airflow obstruction area Import the level evaluation model and calculate, and output the micro-airflow obstruction index ; S4: according to steps S2 and S3, fusing to evaluate a primary performance degradation probability of the condenser, and performing primary adaptive control judgment according to the primary performance degradation probability to obtain a primary adaptive control judgment result; S41: Extract the wind field disturbance risk coefficient, the flocculent pollutant aggregation risk coefficient, the flocculent entanglement comprehensive index and the micro-airflow blockage index, and fuse and evaluate them to obtain a primary performance degradation probability ; S42: performing primary adaptive control judgment according to the primary performance degradation probability, comprising: When time, the anti-flocculation entanglement self-cleaning program will be started by feeding back to the control back end of the air-cooled air conditioning system. When step S5 is performed. In the formula, the initial self-cleaning threshold of the preset air-cooled air conditioner ; S5: performing secondary degradation intelligent detection through the primary adaptive control judgment result, acquiring a heat reflux effect strength and a local flow field distortion index, fusing to evaluate the heat reflux effect strength and the local flow field distortion index, and outputting a secondary performance degradation probability of the condenser; S6: performing secondary adaptive control judgment according to the secondary performance degradation probability, and simultaneously executing a decision of system final regulation and control according to the secondary adaptive control judgment result.

2. The adaptive wind-cooling air conditioning system optimization control method based on an environmental large model according to claim 1, characterized in that, S1 comprises: S11: collecting running state data of the air-cooled air conditioning system in the running process; wherein the running state data comprises: cumulative running total time T of the air-cooled air conditioning system in this running process, single running wind speed data and single running wind direction fluctuation data; S12: collecting external environment data of the air-cooled air conditioning system in the running process; the external environment data comprises: environmental temperature and humidity data, concentration and fiber size distribution information of suspended flocculent in the air.

3. The adaptive wind-cooling air conditioning system optimization control method based on an environmental large model according to claim 2, characterized in that S2 comprises: S21: importing the running state data into a wind field dynamic disturbance evaluation model and analyzing to obtain a wind field disturbance risk coefficient; S22: generating an ambient temperature curve varying with time based on the external environment data during operation of the air-cooled air conditioning system , an ambient humidity curve , and a flocculence deposition tendency curve ; S23: importing the external environment data into a flocculent pollutant deposition risk evaluation model and analyzing to obtain a flocculent pollutant aggregation risk coefficient.

4. The adaptive wind-cooling air conditioning system optimization control method based on an environmental large model according to claim 3, characterized in that S5 comprises: S51: acquiring a condenser fin profile, and reconstructing a geometric shape thereof by using an edge detection and curved surface fitting algorithm to obtain a fin profile curve; S52: extract the completed fin profile curve of the fitting, and preset U key data acquisition points on the fin profile curve; S53: analyze the temperature gradient observation data collected at the U key data acquisition points to obtain the strength of the thermal reflux effect.

5. The adaptive wind-cooling air conditioning system optimization control method based on an environmental large model according to claim 4, characterized in that, S5 further includes: S54: collect the vortex spectrum characteristics of the condenser outflow side and collect the static pressure distribution data between the fins through a micro pressure difference sensor array, wherein the total number of sensors of the micro pressure difference sensor array is V; S55: construct a distortion evaluation model of the local flow field structure, input the collected vortex spectrum characteristics and static pressure distribution data into the distortion evaluation model and evaluate to obtain a local flow field distortion index; S56: According to the heat reflux effect intensity of the condenser and the local flow field distortion index, the secondary performance degradation probability of the condenser is comprehensively evaluated .

6. The adaptive wind-cooling air conditioning system optimization control method based on an environmental large model according to claim 5, characterized in that S6 including: perform the secondary round adaptive control judgment and the secondary round adaptive control on the air-cooled air conditioning system according to the secondary performance degradation probability, including: When the secondary performance degradation probability is arranged in descending order, the historical operation log of the air-cooled air conditioning system is inquired synchronously, and partition reverse pulse cleaning is performed on the system according to the sorting result. When the operating environment characteristics of the air-cooled air conditioning system are traced back, adaptive wind field regulation parameters are configured according to the environment type, and the identified target cleaning area is started for directional anti-floc entanglement cleaning treatment. In the formula, the preset threshold value of the secondary wheel self-cleaning of the air-cooled air conditioning system .

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