Adaptive air-cooled air conditioning system optimization control method based on large environment model

Abstract

The invention relates to the technical field of air-conditioning system optimization control, in particular to a self-adaptive air-cooled air-conditioning system optimization control method based on an environment large model, and the method specifically comprises the steps: obtaining operation state data and external environment data of an air-cooled air-conditioning system in the operation process, and carrying out risk assessment; obtaining a wind field disturbance risk coefficient and a flocculent pollutant aggregation risk coefficient; performing primary degradation intelligent detection to obtain a floccule entanglement comprehensive index and a micro-airflow blocking index; fusing and evaluating the primary performance degradation probability of the condenser to obtain an initial wheel self-adaptive control judgment result; obtaining the heat reflux effect intensity and the local flow field distortion index, carrying out fusion evaluation, and outputting the secondary performance degradation probability of the condenser; and executing a final regulation and control decision of the system according to a secondary-round self-adaptive control judgment result. The problems that in the prior art, a cleaning control mechanism lags behind and is insufficient in pertinence, and finally the energy efficiency is reduced and the maintenance cost is increased are solved.

Description

Technical Field

[0001] This invention relates to the field of air conditioning system optimization control technology, and is an adaptive air-cooled air conditioning system optimization control method based on a large environmental model. Background Technology

[0002] Currently, the operation and maintenance of air-cooled air conditioning systems face the following technical challenges, including the following deficiencies in handling suspended flocculent pollutants: Unlike traditional dust particles, flocculents have physical characteristics such as easy entanglement of fibrous structures, strong porous adsorption, and easy formation of three-dimensional network entanglements, making cleaning strategies based on dust accumulation models difficult to effectively address; existing systems lack the ability to quantitatively perceive the fiber-scale distribution, entanglement density, and fluff thickness of flocculents, and cannot identify the subsurface blockage structure formed between condenser fins; at the same time, because the flocculent entanglements have a stronger shielding effect on micro-airflow channels and are prone to causing local eddy distortion, traditional wind field control methods cannot alleviate the resulting increase in heat recirculation intensity and decrease in heat dissipation performance; in addition, current technology has not established a comprehensive assessment model for wind field disturbance risk and flocculent-specific deposition risk, resulting in a lagging and insufficiently targeted cleaning triggering mechanism, which also prevents air-cooled air conditioning from achieving coordinated control of precise zone cleaning and adaptive wind field optimization, ultimately leading to decreased energy efficiency and increased maintenance costs. Summary of the Invention

[0003] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0004] The technical problem to be solved by the present invention is that the existing cleaning control mechanism is lagging behind and lacks specificity, which ultimately leads to decreased energy efficiency and increased maintenance costs. The present invention proposes an adaptive air-cooled air conditioning system optimization control method based on a large environmental model.

[0005] To achieve the above objectives, the present invention provides an adaptive air-cooled air conditioning system optimization control method based on a large environmental model, comprising the following steps: S1: Acquire operating status data and external environmental data of the air-cooled air conditioning system during operation, and simultaneously collect surface images of the condenser in the air-cooled air conditioning system; S2: Conduct risk assessments on operational status data and external environment data respectively to obtain wind field disturbance risk coefficient and flocculent pollutant aggregation risk coefficient; S3: Perform primary degradation intelligent detection on the condenser in the air-cooled air conditioning system, and quantify the degradation intelligent detection results to obtain the comprehensive index of flocculent entanglement and the micro-airflow blockage index; S4: Based on steps S2 and S3, the primary performance degradation probability of the condenser is evaluated and the initial adaptive control judgment is made based on the primary performance degradation probability to obtain the initial adaptive control judgment result. S5: Based on the initial adaptive control judgment result, perform secondary degradation intelligent detection, obtain the intensity of heat reflow effect and local flow field distortion index, perform fusion evaluation on the intensity of heat reflow effect and local flow field distortion index, and output the secondary performance degradation probability of the condenser. S6: Make a second-round adaptive control judgment based on the secondary performance degradation probability, and execute the final system regulation decision based on the second-round adaptive control judgment result.

[0006] Preferably, S1 includes: S11: Collect operating status data of the air-cooled air conditioning system during operation; The operational status data includes: the total cumulative operating time T of the air-cooled air conditioning system during this operation, the wind speed data for a single operation, and the wind direction fluctuation data for a single operation; S12: Collect external environmental data during the operation of the air-cooled air conditioning system; The external environmental data includes: ambient temperature and humidity data, concentration of suspended flocculent matter in the air, and fiber-scale distribution information.

[0007] Preferably, S2 includes: S21: Import the aforementioned operating status data into the wind field dynamic disturbance assessment model and analyze it to obtain the wind field disturbance risk coefficient; S22: Generate an ambient temperature curve that changes over time based on external environmental data during the operation of the air-cooled air conditioning system. Ambient humidity curve and flocculent deposition trend curve ; S23: Import the external environmental data into the flocculent pollutant deposition risk assessment model and analyze it to obtain the flocculent pollutant aggregation risk coefficient.

[0008] Preferably, S3 includes: S31: Implement primary degradation intelligent detection for condensers in air-cooled air conditioning systems to identify areas where flocculent material is hooked and areas where micro-airflow channels are blocked; S32: Acquire image data of temperature field distribution and subsurface flocculent entanglement morphology on the condenser surface, and map the image features of the subsurface flocculent entanglement morphology image data to a three-dimensional feature space for quantitative analysis; S33: Locate the region with the largest cumulative contribution in the three-dimensional feature space, identify it as the core region of flocculent entanglement, and denote the total number of flocculent entanglement core regions as B; S34: Extract the parameter attributes of each entanglement core region of the flocculent material; The parameter attributes of each entanglement core region of the flocculent include: the entanglement density of each entanglement core region of the flocculent. Fiber coverage ratio of each entanglement core area of ​​the flocculent material and the fluffy thickness of each entangled core area of ​​the flocculent material. ; S35: Calculate the comprehensive entanglement index of the condenser flocs based on the parameter attributes of each entanglement core area of ​​the flocs extracted in step S34.

[0009] S36: Collect the microscale airflow velocity distribution between condenser fins and construct an airflow uniformity evaluation matrix; S37: Based on the airflow uniformity evaluation matrix constructed in step S36, identify each airflow blockage region between the condenser fins and calculate the velocity nonuniformity of each airflow blockage region between the condenser fins. and the intensity of vortex generation in each airflow blocking region ; S38: Construct a graded assessment model to assess the velocity nonuniformity in each airflow obstruction zone. and the intensity of vortex generation in each airflow blocking region Import the rating assessment model and calculate to output the micro-airflow blockage index. .

[0010] Preferably, S4 includes: S41: Extract the wind field disturbance risk coefficient, flocculent pollutant aggregation risk coefficient, flocculent entanglement comprehensive index, and micro-airflow blockage index, and fuse and evaluate them to obtain the primary performance degradation probability. .

[0011] S42: Initial adaptive control judgment based on the primary performance degradation probability, including: when When this happens, the feedback is sent to the control backend of the air-cooled air conditioning system, which will activate the anti-flocculent entanglement self-cleaning program; when If so, proceed to step S5; Among them, the initial self-cleaning threshold of the preset air-cooled air conditioner .

[0012] Preferably, S5 includes: S51: Obtain the condenser fin profile and reconstruct its geometric shape using edge detection and surface fitting algorithms to obtain the fin profile curve; S52: Extract the fitted fin profile curve and preset U key data collection points on the fin profile curve; S53: Analyze the temperature gradient observation data collected from U key data acquisition points to obtain the intensity of the thermal reflux effect.

[0013] S54: Collect the eddy current spectrum characteristics on the air outlet side of the condenser, and collect the static pressure distribution data between the fins through a micro differential pressure sensor array, wherein the total number of sensors in the micro differential pressure sensor array is V; S55: Construct a distortion assessment model for the local flow field structure, input the collected eddy current spectrum characteristics and static pressure distribution data into the distortion assessment model and evaluate it to obtain the local flow field distortion index; S56: The probability of secondary performance degradation of the condenser is obtained by comprehensively evaluating the intensity of the condenser's thermal reflux effect and the local flow field distortion index. .

[0014] Preferably, S6 includes: The secondary adaptive control is performed based on the probability of secondary performance degradation, and the secondary adaptive control of the air-cooled air conditioning system is executed, including: when At that time, the secondary performance degradation probability is sorted in descending order, the historical operation log of the air-cooled air conditioning system is queried simultaneously, and the system is subjected to partitioned reverse pulse cleaning based on the sorting results; when At that time, the operating environment characteristics of the air-cooled air conditioning system will be traced, adaptive wind field control parameters will be configured according to the environment type, and targeted anti-flocculent entanglement cleaning treatment will be initiated for the identified target clean area; Among them, the preset self-cleaning threshold for the second round of air-cooled air conditioning system .

[0015] Compared with the prior art, the technical effects of the present invention are as follows: 1. This invention enables early and accurate warning of condenser performance degradation. By integrating multiple parameters such as wind field disturbance risk coefficient, flocculent aggregation risk coefficient and micro-airflow blockage index for dynamic evaluation, a primary and secondary performance degradation probability model is constructed. This breaks through the limitations of traditional single threshold control, enabling the system to identify the subsurface flocculent entanglement state and airflow organization distortion trend that are invisible to the naked eye, and significantly reducing the risk of sudden performance degradation. 2. This invention improves the accuracy of cleaning operations and the efficiency of resource utilization. The air-cooled air conditioner can locate the core area of ​​entanglement of flocculents and quantify the heat return intensity and flow field distortion value, thereby triggering zoned reverse pulse cleaning and directional anti-entanglement operation, avoiding energy waste and equipment damage caused by overall cleaning, and suppressing the re-accumulation of pollutants after cleaning through adaptive wind field optimization. 3. This invention enhances the long-term stability and energy efficiency of the system under varying environments. By coupling data such as ambient temperature and humidity, the size distribution of flocculent fibers, and operating time, the anti-heat recirculation air field parameters and cleaning strategies are dynamically adjusted, effectively maintaining the unobstructed flow and heat exchange uniformity of the condenser micro-airflow channels. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating an adaptive air-cooled air conditioning system optimization control method based on a large environmental model according to the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0020] Example 1: like Figure 1 As shown in the figure, an adaptive air-cooled air conditioning system optimization control method based on a large environmental model is provided in this embodiment of the invention. Figure 1 As shown, the specific steps include the following: S1: Acquire operating status data and external environmental data of the air-cooled air conditioning system during operation, and simultaneously collect surface images of the condenser in the air-cooled air conditioning system; S1 includes: S11: Collect operating status data of the air-cooled air conditioning system during operation; The operational status data includes: the total cumulative operating time T of the air-cooled air conditioning system during this operation, the wind speed data for a single operation, and the wind direction fluctuation data for a single operation; S12: Collect external environmental data during the operation of the air-cooled air conditioning system; The external environmental data includes: ambient temperature and humidity data, concentration of suspended flocculent matter in the air, and fiber-scale distribution information.

[0021] S2: Conduct risk assessments on operational status data and external environment data respectively to obtain wind field disturbance risk coefficient and flocculent pollutant aggregation risk coefficient; S2 includes: S21: Import the aforementioned operating status data into the wind field dynamic disturbance assessment model and analyze it to obtain the wind field disturbance risk coefficient; For example, in this embodiment, a strategy for obtaining the wind field disturbance risk coefficient is provided, specifically as follows: ; in, This is the wind field disturbance risk coefficient, used to assess the negative impact of the external wind field on the condenser's heat dissipation efficiency; Use time subscripts; This represents the average wind speed. Let represent the wind speed at time t; The angle difference between the wind direction angle and the average angle at time t; The wind direction angle is within a certain range. It should be noted that in this embodiment, the wind direction angle ranges from 0 degrees to 360 degrees. It should be noted that the wind speed part of the wind field... It quantifies wind speed instability, reflecting that when the external wind speed fluctuates greatly, the condenser's heat dissipation airflow becomes unstable; regarding the wind direction part of the wind field... It quantifies wind direction instability, reflecting that when the external wind direction changes significantly, it will lead to wind field turbulence and increase the risk of heat backflow. S22: Generate an ambient temperature curve that changes over time based on external environmental data during the operation of the air-cooled air conditioning system. Ambient humidity curve and flocculent deposition trend curve ; S23: Import the external environmental data into the flocculent pollutant deposition risk assessment model and analyze it to obtain the flocculent pollutant aggregation risk coefficient.

[0022] For example, in this embodiment, a strategy for obtaining the aggregation risk coefficient of flocculent pollutants is provided, specifically as follows: ; in, The risk coefficient for the aggregation of flocculent pollutants; This is the total duration of this operation. This refers to ambient temperature data. Standard operating temperature; The allowable temperature fluctuation range; This refers to ambient humidity data. Standard operating humidity; This refers to the allowable range of humidity fluctuations. The influencing factor of fiber entanglement; This refers to the monitoring value of flocculent concentration; This is the concentration threshold at which flocculent matter becomes critically deposited; The average fiber length of the flocculent material; For reference fiber length; It should be noted that, for the temperature item... It quantifies the impact of ambient temperature fluctuations on the overall system risk, reflecting that when the ambient temperature deviates from the standard operating temperature, it will lead to instability in the condenser's heat dissipation efficiency, thereby increasing the risk of heat backflow. It should be noted that, regarding the humidity item... It quantifies the impact of environmental humidity instability on overall risk, reflecting that when the environmental humidity deviates from the standard operating humidity, the adhesion of flocculent matter on the condenser surface becomes stronger. It should also be noted that for flocculent items... It quantifies the impact of flocculent properties on the risk of condenser blockage, reflecting that the higher the concentration of flocculent fibers and the longer the fibers, the easier it is for the flocculent to entangle and deposit. S3: Perform primary degradation intelligent detection on the condenser in the air-cooled air conditioning system, and quantify the degradation intelligent detection results to obtain the comprehensive index of flocculent entanglement and the micro-airflow blockage index; S3 includes: S31: Implement primary degradation intelligent detection for condensers in air-cooled air conditioning systems to identify areas where flocculent material is hooked and areas where micro-airflow channels are blocked; S32: Acquire image data of temperature field distribution and subsurface flocculent entanglement morphology on the condenser surface, and map the image features of the subsurface flocculent entanglement morphology image data to a three-dimensional feature space for quantitative analysis; S33: Locate the region with the largest cumulative contribution in the three-dimensional feature space, identify it as the core region of flocculent entanglement, and denote the total number of flocculent entanglement core regions as B; S34: Extract the parameter attributes of each entanglement core region of the flocculent material; The parameter attributes of each entanglement core region of the flocculent include: the entanglement density of each entanglement core region of the flocculent. Fiber coverage ratio of each entanglement core area of ​​the flocculent material and the fluffy thickness of each entangled core area of ​​the flocculent material. ; S35: Calculate the comprehensive entanglement index of the condenser flocs based on the parameter attributes of each entanglement core area of ​​the flocs extracted in step S34.

[0023] For example, in this embodiment, a calculation strategy for the comprehensive index of flocculent entanglement is provided, specifically as follows: ; in, The comprehensive index of entanglement of flocculent matter; Let be the entanglement density of the b-th flocculent entanglement core region; The fiber coverage ratio of the b-th flocculent entanglement core region; The fluffy thickness of the b-th flocculent entanglement core region; This represents the average entanglement density of the core region of the flocculent material. This represents the average fiber coverage in the core area of ​​the flocculent entanglement. The average fluffy thickness of the core area of ​​the flocculent material entanglement; S36: Collect the microscale airflow velocity distribution between condenser fins and construct an airflow uniformity evaluation matrix; For example, in this embodiment, a strategy for constructing an airflow uniformity evaluation matrix is ​​provided, specifically as follows: ; in, This represents the joint probability of velocity level i and velocity level j occurring in direction k; For conditional indicator functions; Indicates position , as well as Wind speed value at the location; This refers to the flow channel area between the condenser fins. All are spatial offsets; This represents the specific discrete wind speed value corresponding to speed level i; This represents the specific discrete wind speed value corresponding to speed level j; S37: Based on the airflow uniformity evaluation matrix constructed in step S36, identify each airflow blockage region between the condenser fins and calculate the velocity nonuniformity of each airflow blockage region between the condenser fins. and the intensity of vortex generation in each airflow blocking region ; For example, in this embodiment, the flow rate nonuniformity The calculation is based on the standard deviation of the velocity distribution within the blockage region, as follows: ;in, For flow velocity non-uniformity, it should be noted that in this embodiment, it reflects the spatial non-uniformity of flow velocity. This represents the number of velocity sampling points within the blocked area. The velocity sample value is within the blocked area; The average velocity within the blocked area; The intensity of eddy current generation The calculation is based on the average vorticity modulus within the blocked region, as follows: ;in, The intensity of eddy current generation is used; it should be noted that in this embodiment, it quantifies the intensity of the eddy current between the condenser fins. This represents the vorticity at position m; S38: Construct a graded assessment model to assess the velocity nonuniformity in each airflow obstruction zone. and the intensity of vortex generation in each airflow blocking region Import the rating assessment model and calculate to output the micro-airflow blockage index. .

[0024] For example, in this embodiment, a strategy for obtaining the micro-airflow blockage index is provided, specifically as follows: ; in, This refers to the micro-airflow obstruction index; This represents the total number of airflow obstruction areas. This indicates the velocity nonuniformity in the l-th airflow blockage region; The reference velocity nonuniformity for each airflow blockage region; This represents the vortex generation intensity in the l-th airflow obstruction region; The reference vortex generation intensity for each airflow obstruction region; is the standard deviation of the velocity nonuniformity in each airflow blockage region; Standard deviation of vortex generation intensity in each airflow obstruction region; S4: Based on steps S2 and S3, the primary performance degradation probability of the condenser is evaluated and the initial adaptive control judgment is made based on the primary performance degradation probability to obtain the initial adaptive control judgment result. S4 includes: S41: Extract the wind field disturbance risk coefficient, flocculent pollutant aggregation risk coefficient, flocculent entanglement comprehensive index, and micro-airflow blockage index, and fuse and evaluate them to obtain the primary performance degradation probability. .

[0025] For example, in this embodiment, a strategy for evaluating the probability of primary performance degradation is provided, specifically as follows: ; in, This represents the initial performance degradation probability. These are pre-calibrated weighting coefficients; This refers to the wind field disturbance risk coefficient. These are pre-calibrated weighting coefficients; The risk coefficient for the aggregation of flocculent pollutants; These are pre-calibrated weighting coefficients; The comprehensive index of entanglement of flocculent matter; This is the weighting coefficient for the micro-airflow blockage index; This refers to the micro-airflow obstruction index; S42: Initial adaptive control judgment based on the primary performance degradation probability, including: when When this happens, the feedback is sent to the control backend of the air-cooled air conditioning system, which will activate the anti-flocculent entanglement self-cleaning program; when If so, proceed to step S5; Among them, the initial self-cleaning threshold of the preset air-cooled air conditioner .

[0026] S5: Based on the initial adaptive control judgment result, perform secondary degradation intelligent detection, obtain the intensity of heat reflow effect and local flow field distortion index, perform fusion evaluation on the intensity of heat reflow effect and local flow field distortion index, and output the secondary performance degradation probability of the condenser. S5 includes: S51: Obtain the condenser fin profile and reconstruct its geometric shape using edge detection and surface fitting algorithms to obtain the fin profile curve; S52: Extract the fitted fin profile curve and preset U key data collection points on the fin profile curve; For example, in this embodiment, the method for presetting the key data acquisition points includes: dividing the air inlet area, fin core area and air outlet area according to the condenser structure, and uniformly distributing the points in the temperature gradient sensitive area based on the computational fluid dynamics simulation results, wherein the distribution density of the points in the air outlet area is higher than that in other areas. S53: Analyze the temperature gradient observation data collected from U key data acquisition points to obtain the intensity of the thermal reflux effect.

[0027] For example, in this embodiment, a strategy for obtaining the intensity of the heat reflow effect is provided, specifically as follows: ; in, Indicates the intensity of the thermal reflow effect; This represents the real-time temperature gradient measurement value at the u-th data acquisition point; This represents the measured temperature gradient at this point in the initial state of the system. S54: Collect the eddy current spectrum characteristics on the air outlet side of the condenser, and collect the static pressure distribution data between the fins through a micro differential pressure sensor array, wherein the total number of sensors in the micro differential pressure sensor array is V; S55: Construct a distortion assessment model for the local flow field structure, input the collected eddy current spectrum characteristics and static pressure distribution data into the distortion assessment model and evaluate it to obtain the local flow field distortion index; For example, in this embodiment, a strategy for obtaining local flow field distortion indices is provided, specifically as follows: ; in, This is an indicator of local flow field distortion. This represents the eddy current spectrum characteristics acquired by the v-th sensor; The eddy current spectrum characteristics in the initial state; This refers to the allowable fluctuation range of the eddy current spectrum characteristics. This represents the static pressure value collected by the v-th sensor; This is the static pressure value under the initial conditions; This refers to the allowable fluctuation range of the static pressure value. Among them, for the eddy current spectrum term Its purpose is to monitor the degree of change in eddies in the condenser, reflecting that in the flow field, the greater the change in the spectrum, the more eddies have been generated in the flow field; for the static pressure term... Its purpose is to monitor the degree of change in static pressure in the condenser, reflecting that when the static pressure change increases, it indicates that flocculent material has blocked the local flow field between the condenser fins. S56: The probability of secondary performance degradation of the condenser is obtained by comprehensively evaluating the intensity of the condenser's thermal reflux effect and the local flow field distortion index. .

[0028] For example, in this embodiment, a strategy for evaluating the probability of secondary performance degradation is provided, specifically as follows: ; in, This represents the probability of secondary performance degradation. This represents the initial performance degradation probability. This is a weighting coefficient for the intensity of the thermal reflow effect; The intensity of the thermal reflow effect; The weighting coefficients for local flow field distortion indices; This is an indicator of local flow field distortion. S6: Make a second-round adaptive control judgment based on the secondary performance degradation probability, and execute the final system regulation decision based on the second-round adaptive control judgment result.

[0029] S6 includes: The secondary adaptive control is performed based on the probability of secondary performance degradation, and the secondary adaptive control of the air-cooled air conditioning system is executed, including: when At that time, the secondary performance degradation probability is sorted in descending order, the historical operation log of the air-cooled air conditioning system is queried simultaneously, and the system is subjected to partitioned reverse pulse cleaning based on the sorting results; when At that time, the operating environment characteristics of the air-cooled air conditioning system will be traced, adaptive wind field control parameters will be configured according to the environment type, and targeted anti-flocculent entanglement cleaning treatment will be initiated for the identified target clean area; Among them, the preset self-cleaning threshold for the second round of air-cooled air conditioning system .

[0030] Example 2: This 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; The processor executes the aforementioned adaptive air-cooled air conditioning system optimization control method based on a large environmental model by calling the computer program stored in memory.

[0031] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the adaptive air-cooled air conditioning system optimization control method based on a large environmental model provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Further details are omitted in this embodiment.

[0032] Example 3: This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored. When a computer program runs on a computer device, it causes the computer device to execute the aforementioned adaptive air-cooled air conditioning system optimization control method based on a large environmental model.

[0033] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0034] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0035] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0036] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A 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, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0037] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0038] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0039] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0040] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0041] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0042] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0043] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. An adaptive air-cooled air conditioning system optimization control method based on a large environmental model, characterized in that, The method includes: S1: Acquire operating status data and external environmental data of the air-cooled air conditioning system during operation, and simultaneously collect surface images of the condenser in the air-cooled air conditioning system; S2: Conduct risk assessments on operational status data and external environment data respectively to obtain wind field disturbance risk coefficient and flocculent pollutant aggregation risk coefficient; S3: Perform primary degradation intelligent detection on the condenser in the air-cooled air conditioning system, and quantify the degradation intelligent detection results to obtain the comprehensive index of flocculent entanglement and the micro-airflow blockage index; S4: Based on steps S2 and S3, the primary performance degradation probability of the condenser is evaluated and the initial adaptive control judgment is made based on the primary performance degradation probability to obtain the initial adaptive control judgment result. S5: Based on the initial adaptive control judgment result, perform secondary degradation intelligent detection, obtain the intensity of heat reflow effect and local flow field distortion index, perform fusion evaluation on the intensity of heat reflow effect and local flow field distortion index, and output the secondary performance degradation probability of the condenser. S6: Make a second-round adaptive control judgment based on the secondary performance degradation probability, and execute the final system regulation decision based on the second-round adaptive control judgment result.

2. The adaptive air-cooled air conditioning system optimization control method based on a large environmental model according to claim 1, characterized in that, S1 includes: S11: Collect operating status data of the air-cooled air conditioning system during operation; The operational status data includes: the total cumulative operating time T of the air-cooled air conditioning system during this operation, the wind speed data for a single operation, and the wind direction fluctuation data for a single operation; S12: Collect external environmental data during the operation of the air-cooled air conditioning system; The external environmental data includes: ambient temperature and humidity data, concentration of suspended flocculent matter in the air, and fiber-scale distribution information.

3. The adaptive air-cooled air conditioning system optimization control method based on a large environmental model according to claim 2, characterized in that, S2 include: S21: Import the aforementioned operating status data into the wind field dynamic disturbance assessment model and analyze it to obtain the wind field disturbance risk coefficient; S22: Generate an ambient temperature curve that changes over time based on external environmental data during the operation of the air-cooled air conditioning system. Ambient humidity curve and flocculent deposition trend curve ; S23: Import the external environmental data into the flocculent pollutant deposition risk assessment model and analyze it to obtain the flocculent pollutant aggregation risk coefficient.

4. The adaptive air-cooled air conditioning system optimization control method based on a large environmental model according to claim 3, characterized in that, S3 include: S31: Implement primary degradation intelligent detection for condensers in air-cooled air conditioning systems to identify areas where flocculent material is hooked and areas where micro-airflow channels are blocked; S32: Acquire image data of temperature field distribution and subsurface flocculent entanglement morphology on the condenser surface, and map the image features of the subsurface flocculent entanglement morphology image data to a three-dimensional feature space for quantitative analysis; S33: Locate the region with the largest cumulative contribution in the three-dimensional feature space, identify it as the core region of flocculent entanglement, and denote the total number of flocculent entanglement core regions as B; S34: Extract the parameter attributes of each entanglement core region of the flocculent material; The parameter attributes of each entanglement core region of the flocculent include: the entanglement density of each entanglement core region of the flocculent. Fiber coverage ratio of each entanglement core area of ​​the flocculent material and the fluffy thickness of each entangled core area of ​​the flocculent material. ; S35: Based on the parameter attributes of each entanglement core area of ​​the flocculent material extracted in step S34, calculate the comprehensive entanglement index of the flocculent material in the condenser.

5. The adaptive air-cooled air conditioning system optimization control method based on a large environmental model according to claim 4, characterized in that, S3 also includes: S36: Collect the microscale airflow velocity distribution between condenser fins and construct an airflow uniformity evaluation matrix; S37: Based on the airflow uniformity evaluation matrix constructed in step S36, identify each airflow blockage region between the condenser fins and calculate the velocity nonuniformity of each airflow blockage region between the condenser fins. and the intensity of vortex generation in each airflow blocking region ; S38: Construct a graded assessment model to assess the velocity nonuniformity in each airflow obstruction zone. and the intensity of vortex generation in each airflow blocking region Import the rating assessment model and calculate to output the micro-airflow blockage index. .

6. The adaptive air-cooled air conditioning system optimization control method based on a large environmental model according to claim 5, characterized in that, S4 include: S41: Extract the wind field disturbance risk coefficient, flocculent pollutant aggregation risk coefficient, flocculent entanglement comprehensive index, and micro-airflow blockage index, and fuse them for evaluation to obtain the primary performance degradation probability. .

7. The adaptive air-cooled air conditioning system optimization control method based on a large environmental model according to claim 6, characterized in that, S4 also includes: S42: Initial adaptive control judgment based on the initial performance degradation probability, including: when When this happens, the feedback is sent to the control backend of the air-cooled air conditioning system, which will activate the anti-flocculent entanglement self-cleaning program; when If so, proceed to step S5; Among them, the initial self-cleaning threshold of the preset air-cooled air conditioner .

8. The adaptive air-cooled air conditioning system optimization control method based on a large environmental model according to claim 7, characterized in that, S5 include: S51: Obtain the condenser fin profile and reconstruct its geometric shape using edge detection and surface fitting algorithms to obtain the fin profile curve; S52: Extract the fitted fin profile curve and preset U key data collection points on the fin profile curve; S53: Analyze the temperature gradient observation data collected from U key data acquisition points to obtain the intensity of the thermal reflux effect.

9. The adaptive air-cooled air conditioning system optimization control method based on a large environmental model according to claim 8, characterized in that, S5 also includes: S54: Collect the eddy current spectrum characteristics on the air outlet side of the condenser, and collect the static pressure distribution data between the fins through a micro differential pressure sensor array, wherein the total number of sensors in the micro differential pressure sensor array is V; S55: Construct a distortion assessment model for the local flow field structure, input the collected eddy current spectrum characteristics and static pressure distribution data into the distortion assessment model and evaluate it to obtain the local flow field distortion index; S56: The probability of secondary performance degradation of the condenser is obtained by comprehensively evaluating the intensity of the condenser's thermal reflux effect and the local flow field distortion index. .

10. The adaptive air-cooled air conditioning system optimization control method based on a large environmental model according to claim 9, characterized in that, S6 include: The secondary adaptive control is performed based on the probability of secondary performance degradation, and the secondary adaptive control of the air-cooled air conditioning system is executed, including: when At that time, the secondary performance degradation probability is sorted in descending order, the historical operation log of the air-cooled air conditioning system is queried simultaneously, and the system is subjected to partitioned reverse pulse cleaning based on the sorting results; when At that time, the operating environment characteristics of the air-cooled air conditioning system will be traced, adaptive wind field control parameters will be configured according to the environment type, and targeted anti-flocculent entanglement cleaning treatment will be initiated for the identified target clean area; Among them, the preset self-cleaning threshold for the second round of air-cooled air conditioning system .

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