A condensing unit control system

Abstract

The application provides a condensing unit control system, relates to the technical field of refrigeration control, and comprises a multi-modal sensing module, an edge computing unit, a collaborative control hub, an execution module and a human-computer interaction module. The multi-modal sensing module integrates invasive and non-invasive sensors, synchronously collects core component operation parameters, environmental parameters, load parameters and component surface temperature / vibration characteristics; the edge computing unit processes multi-source data and realizes early fault warning; the collaborative control hub generates a globally optimal control strategy through a pre-stored multi-dimensional benchmark model-based basic energy efficiency library and a dynamic correction module; and the execution module adjusts the core component according to a hierarchical response mechanism. The application solves the problems of single sensing, response lag, fixed energy efficiency model and non-priority adjustment of the traditional system, improves the reliability of the unit, the energy efficiency under all working conditions and the adaptability under complex working conditions, and reduces operation and maintenance costs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of refrigeration control, and particularly relates to a condensing unit control system. BACKGROUND

[0002] As the core equipment of the refrigeration system, the condensing unit is widely used in cold chain logistics, data centers, commercial refrigeration and other fields. Its operation energy efficiency and stability directly determine the energy consumption cost and service reliability of the industry. At present, with the promotion of the "double carbon" policy and the diversification of application scenarios, the condensing unit needs to cope with complex working condition challenges - the fluctuation range of environmental temperature and humidity increases, and the end cold load demand often presents a pulse or periodic change. The traditional control system has been difficult to adapt to the efficient operation demand.

[0003] The existing condensing unit control system has significant shortcomings: the sensing method is single, or relies on invasive sensors to cause complex installation and maintenance, or only uses non-invasive sensors to miss the core operating parameters; data processing relies on the cloud, causing response lag, and the energy efficiency model is fixed, which cannot adapt to different refrigerant types and environmental conditions; there is no priority division in execution adjustment, and when the load suddenly changes or the environment changes, local optimization may occur, resulting in a decrease in global energy efficiency, and even causing component failure, so a new type of control system that integrates multi-modal sensing, dynamic model optimization and hierarchical response is urgently needed. SUMMARY

[0004] The present application provides a condensing unit cooperative optimization control system, which aims to solve the problems of the traditional condensing unit control system, such as single sensing method leading to incomplete collection of operating parameters and component states, data processing relying on the cloud causing dynamic working condition response lag, energy efficiency model being fixed and unable to adapt to multiple refrigerant types and complex environmental load scenarios, and execution adjustment lacking priority division leading to low global energy efficiency and high risk of core component failure.

[0005] To solve the above technical problems, the present application provides a condensing unit control system, which comprises:

[0006] A multi-modal sensing module integrates invasive sensing units and non-invasive sensing units for synchronous collection of core component operating parameters, environmental parameters and load demand parameters of the condensing unit, and the non-invasive sensing unit obtains component surface temperature distribution and vibration characteristics through infrared thermal imaging and acoustic sensing;

[0007] An edge computing unit is in communication connection with the multi-modal sensing module, and is internally provided with an adaptive filtering module and a feature association engine for spatiotemporal registration, redundancy elimination and dynamic correlation feature extraction of multi-source heterogeneous parameters, and early fault warning based on feature deviation degree;

[0008] A coordination control center is in communication connection with the edge computing unit, and contains a basic energy efficiency model library and a dynamic correction module. The basic energy efficiency model library pre-stores benchmark optimization models of different refrigerant types and load scenarios. The dynamic correction module performs online iteration on the benchmark model based on real-time feature data and historical energy efficiency deviation to generate a globally optimal coordination control strategy.

[0009] An execution module is in communication connection with the coordination control center, and is configured with a hierarchical response mechanism for real-time adjustment of compressor capacity, condenser heat exchange efficiency and throttling element opening degree according to the priority of the control strategy, wherein the priority is dynamically determined according to the load mutation rate and the component safety threshold.

[0010] As preferred, the invasive sensing unit includes pressure sensors arranged at the inlet and outlet of the compressor, temperature sensors of the motor winding and flow sensors of the condenser pipeline; the non-invasive sensing unit includes infrared array sensors and sound wave collectors arranged on the unit shell, which are respectively used for acquiring component surface temperature field distribution and abnormal vibration sound wave characteristics.

[0011] As preferred, the feature correlation engine of the edge computing unit mines parameter correlations in the following ways:

[0012] A multi-dimensional correlation matrix of operating parameters, environmental parameters and energy efficiency indicators is established;

[0013] The phase difference and amplitude coupling degree of parameter changes are analyzed based on a sliding time window;

[0014] The parameter response lag feature at the time of load mutation is extracted.

[0015] As preferred, the early fault warning includes:

[0016] Comparing the real-time vibration sound wave characteristics with the sound wave spectrum template under normal working conditions to identify abnormal frequency components;

[0017] Analyzing the gradient anomaly of the temperature field distribution to judge the local scaling or blocking trend of the heat exchange component;

[0018] Based on the fluctuation variance of the pressure parameter, the risk of throttling element jamming is predicted.

[0019] As preferred, the basic energy efficiency model library is classified according to the following dimensions:

[0020] According to the refrigerant type, the optimization models are divided into freon, natural working medium and mixed working medium;

[0021] According to the load characteristics, the control models are divided into steady-state load, pulse load and periodic load;

[0022] According to the environmental conditions, the adaptation model corresponding to the high-temperature environment, the high-humidity environment and the sand-dust environment is divided.

[0023] As preferred, the online iteration of the dynamic correction module comprises:

[0024] Based on the environmental deviation of the current environmental parameters and the reference model, the environmental influence coefficient in the model is corrected.

[0025] According to the deviation of the recent energy efficiency measured value and the model predicted value, the parameter weight factor is dynamically adjusted.

[0026] The component aging coefficient is introduced, and the aging coefficient is calculated based on the running time and the performance attenuation trend.

[0027] As preferred, the hierarchical response mechanism of the execution module comprises:

[0028] Primary response: when the load mutation rate exceeds the preset threshold, the capacity adjustment component of the compressor and the opening of the throttling element are preferentially adjusted, and the response delay control is within the preset range;

[0029] Secondary response: when the environmental parameter change amplitude exceeds the preset value, the fan speed of the condenser and the condensing medium flow are mainly adjusted.

[0030] Tertiary response: when the system energy efficiency ratio is lower than the reference value, the running parameter combination of all core components is simultaneously optimized.

[0031] As preferred, the present application further comprises a man-machine interaction module for displaying real-time running parameters, energy efficiency curves and fault warning information, and supporting manual adjustment of the weight parameters of the collaborative control strategy.

[0032] Compared with the related art, the condensing unit control system provided by the present application has the following beneficial effects:

[0033] 1. The scheme significantly improves the operation reliability and fault warning capability of the condensing unit through the deep integration of multi-modal perception and edge computing; the invasive sensing unit collects core parameters such as pressure, temperature and flow in real time, the non-invasive infrared array and the sound wave collector synchronously capture the surface temperature field distribution and vibration characteristics, the edge computing unit realizes real-time correlation mining of multi-source data through algorithms such as covariance matrix analysis and phase difference coupling degree calculation, and through the global perception and local real-time analysis architecture, it can accurately identify early fault characteristics such as abnormal frequency components, temperature gradient abnormalities and pressure fluctuations, and can early warn potential risks such as fouling of heat exchange components and jamming of throttling elements, thereby greatly reducing the probability of sudden shutdown and prolonging the fault-free operation cycle of the equipment.

[0034] 2. The system is given strong working condition adaptability and energy saving benefit by dynamic energy efficiency optimization system. The basic energy efficiency model library is classified in multiple dimensions according to refrigerant type, load characteristics and environmental conditions. The dynamic correction module realizes self-iteration of energy efficiency in the whole life cycle: the influence coefficient is corrected in real time through environmental parameter deviation, the parameter weight is dynamically adjusted according to the deviation between the measured value and the predicted value of energy efficiency, the component aging coefficient based on the running time is introduced, and through the introduction of the classification model combined with the online correction mechanism, it can accurately adapt to different refrigerant characteristics such as freon / natural working medium, load changes such as steady state / pulse and environmental differences such as high temperature / high humidity, so as to ensure that the system always maintains the optimal energy efficiency ratio under complex working conditions and reduces invalid energy loss.

[0035] 3. The precision and economy of system regulation are balanced through the hierarchical response mechanism. The first level response controls the compressor and throttling element in priority for load mutation, and the mutation rate is calculated by time difference method and the response delay is controlled. The second level response focuses on adjusting the condenser fan and medium flow according to environmental parameter change, and dynamically matches the heat dissipation capacity based on environmental deviation. The third level response synchronously optimizes all component parameters relying on the corrected weight factor when the energy efficiency is low. The three-level regulation logic of the application not only ensures the rapid response under key working conditions, but also avoids the component loss caused by excessive regulation. Through reasonable allocation of regulation resources, the service life of core components is prolonged and the long-term operation and maintenance cost is reduced while ensuring the stability of refrigeration.

[0036] In summary, through the collaborative architecture of global perception, intelligent optimization and precise execution, the reliability, energy efficiency and adaptability of the condensing unit are comprehensively improved. The multi-modal perception and edge computing build a three-dimensional fault defense system. The dynamic energy efficiency model library and correction mechanism ensure the energy saving advantage under all working conditions. The hierarchical response mechanism balances the regulation speed and equipment protection demand. The three work together to make the system stable in complex environments, avoid faults in advance, dynamically adapt to working condition changes and continuously optimize energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 A principle block diagram of a condensing unit control system provided by the application is shown in the figure. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0039] The terminology used in the disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or," as used herein, refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0040] It will be understood that, although the terms first, second, third, etc. can be used herein to describe various information, these

[0041] Reference will now be made to the drawings Figure 1 A condensing unit control system, comprising:

[0042] A multi-modal sensing module integrates invasive sensing units and non-invasive sensing units to synchronously collect core component operating parameters, environmental parameters, and load demand parameters of the condensing unit. The non-invasive sensing units acquire component surface temperature distribution and vibration characteristics through infrared thermal imaging and acoustic wave sensing.

[0043] An edge computing unit is in communication connection with the multi-modal sensing module, and is internally provided with an adaptive filtering module and a feature association engine. The edge computing unit is used to perform time-space registration, redundancy elimination, and dynamic associated feature extraction on multi-source heterogeneous parameters, and realizes early fault warning based on feature deviation degree.

[0044] A collaborative control hub is in communication connection with the edge computing unit, and contains a basic energy efficiency model library and a dynamic correction module. The basic energy efficiency model library pre-stores benchmark optimization models of different refrigerant types and load scenarios. The dynamic correction module performs online iteration on the benchmark models based on real-time feature data and historical energy efficiency deviation, and generates a globally optimal collaborative control strategy.

[0045] An execution module is in communication connection with the collaborative control hub, and is configured with a hierarchical response mechanism. The execution module is used to adjust compressor capacity, condenser heat exchange efficiency, and throttling element opening degree in real time according to the priority of the control strategy, wherein the priority is dynamically determined according to load mutation rate and component safety threshold.

[0046] Specifically, the invasive sensing units include pressure sensors arranged at the inlet and outlet of the compressor, temperature sensors of the motor winding, and flow sensors of the condenser pipeline.

[0047] The pressure sensor collects the compressor discharge pressure with the suction pressure for calculating the compression ratio , combined with the winding temperature collected by the motor winding temperature sensor to evaluate the compressor operating state; the flow sensor of the condenser pipeline collects the heat exchange medium flow Q, for monitoring the medium flow efficiency;

[0048] The non-invasive sensing unit includes an infrared array sensor and a sound wave collector deployed on the unit shell, respectively for acquiring the component surface temperature field distribution and abnormal vibration sound wave characteristics;

[0049] The infrared array sensor reconstructs the component surface temperature field through the original temperature of N1 sensing points , i is the sensing point serial number; the bicubic interpolation algorithm is used to reconstruct the component surface temperature field, that is, through the formula , wherein is the interpolation coefficient obtained by least squares fitting, and the fitting process minimizes the error function , s1, s2 are the degrees of the interpolation polynomial, taking values in the range of 0, 1, 2, 3, obtained by least squares fitting, the reconstructed temperature value at the component surface coordinates (x, y), (x, y) represents the two-dimensional coordinates of the component surface, realizing the acquisition of the continuous temperature field distribution of the component surface;

[0050] The sound wave collector collects the vibration sound wave signal s(t), and converts the time domain signal into a frequency domain signal through fast Fourier transform, and the formula is , wherein N is the number of sampling points, f is the frequency, represents the fast Fourier transform operator, u represents the sampling point serial number of the time domain signal, and j is the imaginary unit, and then the frequency spectrum deviation degree is calculated , wherein represents the reference sound wave spectrum under normal working condition, represents the spectrum amplitude of the actual sound wave signal at the frequency point , M is the number of sampling points in the frequency range of interest, is the kth frequency point, used to identify abnormal vibration sound wave characteristics.

[0051] Specifically, the feature association engine of the edge computing unit mines parameter associations in the following ways:

[0052] A multi-dimensional association matrix of operating parameters, environmental parameters and energy efficiency indicators is established:

[0053] Let the operating parameter set be , the environmental parameter set be , and the energy efficiency indicator be ; construct a joint vector containing all parameters , denotes a transpose operation, quantifies linear correlation between parameters by calculating a covariance matrix whose elements are: where is the mean of the p-th parameter, denotes mathematical expectation; the larger the absolute value of the matrix element, the stronger the linear correlation between the corresponding parameter pair;

[0054] Phase difference and amplitude coupling degree of parameter variation are analyzed based on a sliding time window:

[0055] A sliding window with a length of and a sliding step of is used to collect discrete sampling sequences for any two parameter sequences A(t) (such as compressor power) and B(t) (such as condenser fan speed) within the window, where k is the number of sampling points within the window.

[0056] Fast Fourier transform is performed on the sampling sequences to obtain frequency domain sequences , and cross power spectrum is calculated, where denotes a complex conjugate), and the phase difference at frequency f is: ;

[0057] The amplitude correlation degree is measured by a coherence coefficient: where is the auto-power spectrum, and the closer the value is to 1, the stronger the coupling is;

[0058] The parameter response lag feature at load mutation is extracted:

[0059] Let the load parameter be , the unit to be analyzed be , and the load mutation time be ; calculate the cross-correlation function: ;

[0060] where are the means of the load and the unit parameters, respectively, and T is the analysis duration; the maximum correlation value is determined by optimization to determine the corresponding lag:

[0061] which is the response lag time of the parameter pair to the load mutation.

[0062] Specifically, early fault warning includes:

[0063] Compare the real-time vibration sound wave characteristics with the sound wave spectrum template under normal working conditions to identify abnormal frequency components:

[0064] Based on the real-time frequency domain acoustic wave signal S(f) acquired by the acoustic wave collector, and the pre-stored reference acoustic wave spectrum under normal working condition , the spectral deviation rate at the kth frequency point is calculated ; the abnormal frequency determination threshold is set , if there is at least one frequency point that meets , and the duration of the frequency point exceeds the pre-set time length , it is determined that the frequency point is an abnormal frequency component, triggering the vibration abnormality warning;

[0065] Analyze the gradient anomaly of the temperature field distribution to determine the local fouling or blocking trend of the heat exchange component:

[0066] Based on the component surface temperature field T(x, y) reconstructed by the infrared array sensor, the gradient vector of the temperature field at two-dimensional coordinates (x, y) is calculated , where is the partial derivative of temperature along the x direction, is the partial derivative of temperature along the y direction, and the central difference method is used to calculate the partial derivative: , where is a small increment of coordinates;

[0067] The modulus of the gradient vector is calculated , and the maximum value of the temperature field gradient modulus under normal working condition is pre-stored ;

[0068] If there is a local area that meets , where K is the gradient anomaly amplification coefficient, K>1, it is determined that the temperature field gradient of the area is abnormal, and there is a local fouling or blocking trend of the heat exchange component, triggering the heat exchange abnormality warning;

[0069] Based on the fluctuation variance of the pressure parameter, the risk of choking of the throttling element is predicted:

[0070] Based on the compressor discharge pressure and the suction pressure acquired by the invasive sensing unit, a sliding time window with a time length of is selected, and the pressure sampling sequence in the window is collected, q is the number of sampling points in the window;

[0071] The fluctuation variance of the pressure sequence is calculated , where is the mean value of the pressure in the window; the upper threshold of the pressure fluctuation variance under normal working condition is pre-stored , if the continuous n1 sliding windows all meet , n1 is the number of continuous abnormal determinations, it is determined that the throttling element has a risk of jamming, triggering a throttling abnormality warning.

[0072] Specifically, the basic energy efficiency model library is classified according to the following dimensions:

[0073] According to the type of refrigerant, it is divided into Freon, natural working medium and mixed working medium corresponding to the optimization model:

[0074] Define the refrigerant type identification parameter RefType, the value is Freon, natural working medium, mixed working medium, and pre-store the thermodynamic characteristic coefficients corresponding to different types of refrigerants , corresponding to Freon, corresponding to natural working medium, corresponding to mixed working medium;

[0075] Based on the refrigerant type parameter RefType preset by the unit, the corresponding type of reference energy efficiency model is called , which satisfies , wherein is the general reference energy efficiency value, is the associated function of the operating parameter set and the environmental parameter set Env;

[0076] According to the load characteristics, it is divided into steady-state load, pulse load and periodic load corresponding to the control model:

[0077] Based on the load parameter , the load fluctuation coefficient is calculated, wherein is the average value of the load within a preset time, and the autocorrelation function is used to detect the periodicity of the load;

[0078] If , is the steady-state load determination threshold, the steady-state load control model is called ; if and there is no significant periodicity (if , it means that there is no significant periodicity, is the periodicity determination threshold), the pulse load control model is called ; if there is a significant periodicity (if , it means that there is a significant periodicity), the periodic load control model is called ;

[0079] According to the environmental conditions, it is divided into high temperature environment, high humidity environment and dust environment corresponding to the adaptive model:

[0080] extracting the ambient temperature in the ambient parameter , the ambient humidity , the sand concentration , setting the ambient classification threshold: ( as the high temperature determination threshold) is determined as a high temperature environment, ( as the high humidity determination threshold) is determined as a high humidity environment, ( as the sand determination threshold) is determined as a sand environment;

[0081] corresponding to the call of different environment adaptation models , the introduction of environmental impact factors in the model ( corresponding to the high temperature environment, corresponding to the high humidity environment, corresponding to the sand environment), the adapted model meets .

[0082] It should be noted that the thermal characteristic coefficients of the refrigerant type optimization model in the basic energy efficiency model library are taken from thermodynamic public data, and the correlation function is obtained by regression of multi-condition energy efficiency experiments of corresponding refrigerant units; The load characteristics of the load characteristic control model are identified by mature signal processing technology, and the control strategy is adjusted by referring to existing refrigeration variable load control algorithm and combining unit operation data; The environmental impact law of the environmental condition adaptation model is derived from existing research on refrigeration and environmental engineering, and the environmental impact factor is obtained by fitting the energy efficiency test experiment of the environmental simulation cabin. The whole is a classification integration and adaptation of existing technology, industry experimental data and theoretical methods.

[0083] Specifically, the online iteration of the dynamic correction module includes:

[0084] Based on the environmental deviation of the current environmental parameter and the baseline model, the environmental impact coefficient in the model is corrected:

[0085] extracting the ambient temperature in the current environmental parameter , the ambient humidity , calling the baseline ambient temperature , the baseline ambient humidity (preserved in the basic energy efficiency model library) corresponding to the baseline model; Calculate the environmental deviation: ;

[0086] correct the environmental impact coefficient (taken from the environmental adaptation model of the basic energy efficiency model library), the correction formula is , wherein , Temperature deviation, humidity deviation correction coefficient (pre-stored in dynamic correction module);

[0087] According to the deviation between the recent energy efficiency measured value and the model predicted value, the parameter weight factor is dynamically adjusted:

[0088] Obtain the energy efficiency measured value in the recent preset time length And the model predicted energy efficiency value of the corresponding period ; Calculate the energy efficiency deviation: ;

[0089] For the set of operating parameters The corresponding weight factor (Preserved in the baseline model), the adjustment formula is , wherein is the weight adjustment coefficient (pre-stored in the dynamic correction module), is the normalized value of the i-th operating parameter, wherein , are the minimum and maximum rated values of the parameter, respectively;

[0090] Introduce the component aging coefficient, which is calculated based on the running time and performance attenuation trend:

[0091] Obtain the cumulative running time of the core components (compressor, condenser, throttling element) of the condensing unit (Recorded by the system timing module); Pre-store the performance attenuation coefficient of each core component (Determined based on the life attenuation curve provided by the component manufacturer);

[0092] Calculate the component aging coefficient (When , is the rated life of the component), if , then , wherein is the minimum performance corresponding to the aging coefficient threshold of the component); Incorporate the aging coefficient into the corrected model, so that the final energy efficiency model satisfies , wherein is the model energy efficiency value corrected by the environmental impact coefficient.

[0093] Specifically, the hierarchical response mechanism of the execution module includes:

[0094] Primary response: when the load mutation rate exceeds the preset threshold, the capacity adjustment component of the compressor and the opening of the throttling element are preferentially adjusted, and the response delay is controlled within the preset range:

[0095] Based on the real-time load parameter Load}(t) collected by the multi-modal perception module, the time difference method is used to calculate the load mutation rate , set load mutation threshold , trigger primary response;

[0096] , priority adjust compressor capacity adjustment component, capacity adjustment amount , wherein is the compressor capacity proportional adjustment coefficient, pre-stored in the execution module; simultaneously adjust the opening of the throttling element, the opening adjustment amount , wherein is the coupling coefficient of the throttling element and the capacity, which is set based on historical adjustment data;

[0097] , ensure the response delay time from triggering to executing the adjustment instruction through local processing by the edge computing unit , wherein is the preset delay threshold of the primary response;

[0098] Secondary response: when the change amplitude of the environmental parameters exceeds the preset value, focus on adjusting the fan speed of the condenser and the flow rate of the condensing medium:

[0099] Extract the current environmental parameters collected by the multi-modal perception module, including at least the environmental temperature , environmental humidity , calculate the change amplitude of the reference environmental parameters in the basic energy efficiency model library , , set the environmental change threshold , when or , trigger the secondary response;

[0100] Focus on adjusting the fan speed of the condenser, the speed adjustment amount , is the fan speed adjustment coefficient, is the humidity influence weight; simultaneously adjust the flow rate of the condensing medium, the flow rate adjustment amount , wherein is the matching coefficient of the flow rate and the speed;

[0101] Tertiary response: when the system energy efficiency ratio is lower than the reference value, simultaneously optimize the operating parameter combination of all core components:

[0102] Obtain the real-time energy efficiency measured value of the system collected by the multi-modal perception module , compare it with the reference energy efficiency ratio in the basic energy efficiency model library , calculate the energy efficiency deviation ; set the energy efficiency deviation threshold , when , trigger the tertiary response;

[0103] ​Parameter weight factor output based on dynamic correction module synchronously optimize all core component operating parameters: compressor frequency adjustment amount , condenser fan speed adjustment amount , throttling element opening adjustment amount ; wherein is the energy efficiency adjustment coefficient of each component, is the corrected weight factor corresponding to the parameter.

[0104] Specifically, the present application also includes a human-computer interaction module for displaying real-time operating parameters, energy efficiency curves and fault warning information, and supporting manual adjustment of the weight parameters of the collaborative control strategy.

[0105] All formulas in the present scheme are calculated using dimensionless values. Dimensionless can be achieved by standardization, etc., which is not described here. The formulas are obtained by collecting a large amount of condensing unit operating data and simulating by software, which can be close to the actual working condition. The preset parameters in the formula are set by the person skilled in the art in combination with the actual application scene of the unit.

[0106] The above embodiments can be realized by software, hardware, firmware or any combination thereof. If realized by software, it can be embodied as a computer program product, which includes computer instructions that can be stored in a computer readable storage medium or transmitted between media, and loaded and executed after loading to realize the control process and function described in the present scheme.

[0107] It should be noted that each procedure number does not represent the execution order, and the execution order is determined by the function logic; the function implementation method (hardware or combination of hardware and software) depends on the specific application and design constraints; the unit division in the device embodiment can be logical function division, which can be adjusted as needed, and the function units can be integrated or exist separately; if the function is realized in the form of a software unit and is used independently, it can be stored in a computer readable storage medium containing program code, and the corresponding control steps are executed by driving the device through instructions.

[0108] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art. The specification and examples are to be regarded as exemplary only, and the true scope and spirit of the application are indicated by the following claims.

[0109] It should be understood that the present application is not limited to the precise construction which has been described above and illustrated in the accompanying drawings and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application is indicated by the appended claims rather than by the description.

Claims

1. A condensing unit control system, characterized in that, include: The multimodal sensing module integrates invasive and non-invasive sensing units to synchronously collect operating parameters, environmental parameters, and load demand parameters of the core components of the condensing unit. The non-invasive sensing unit obtains the surface temperature distribution and vibration characteristics of the components through infrared thermal imaging and acoustic sensing. The edge computing unit is communicatively connected to the multimodal perception module. It has a built-in adaptive filtering module and feature association engine, which are used to perform spatiotemporal registration, redundancy removal and dynamic association feature extraction on multi-source heterogeneous parameters, and realize early fault warning based on feature deviation. The collaborative control center, which is communicatively connected to the edge computing unit, includes a basic energy efficiency model library and a dynamic correction module. The basic energy efficiency model library pre-stores benchmark optimization models for different refrigerant types and load scenarios. The dynamic correction module iterates the benchmark models online based on real-time feature data and historical energy efficiency deviations to generate a globally optimal collaborative control strategy. The execution module is communicatively connected to the collaborative control center and is equipped with a hierarchical response mechanism for adjusting the compressor capacity, condenser heat exchange efficiency, and throttling element opening in real time according to the priority of the control strategy. The priority is dynamically determined based on the load mutation rate and component safety threshold.

2. The condensing unit control system according to claim 1, characterized in that, The invasive sensing unit includes a pressure sensor installed at the compressor inlet and outlet, a temperature sensor for the motor windings, and a flow sensor for the condenser piping; the non-invasive sensing unit includes an infrared array sensor and an acoustic wave collector deployed on the unit casing, which are used to acquire the surface temperature field distribution of components and the acoustic wave characteristics of abnormal vibrations, respectively.

3. A condensing unit control system according to claim 1, characterized in that, The feature association engine of the edge computing unit mines parameter associations in the following ways: Establish a multidimensional correlation matrix of operating parameters, environmental parameters, and energy efficiency indicators; Analysis of the phase difference and amplitude coupling of parameter changes based on sliding time window; Extract the parameter response hysteresis characteristics during load mutations.

4. A condensing unit control system according to claim 1, characterized in that, The early fault warning includes: By comparing the real-time vibration acoustic wave characteristics with the acoustic wave spectrum template under normal operating conditions, abnormal frequency components can be identified. Analyze the gradient anomalies in the temperature field distribution to determine the local scaling or blockage trend of heat exchange components; Based on the fluctuation variance of pressure parameters, the risk of jamming of throttling elements is predicted.

5. A condensing unit control system according to claim 1, characterized in that, The basic energy efficiency model library is categorized according to the following dimensions: Optimization models are categorized according to refrigerant type into Freon-based, natural refrigerant-based, and mixed refrigerant-based models. Control models are categorized based on load characteristics into steady-state load, pulse load, and periodic load. The models are categorized according to environmental conditions, including high-temperature environments, high-humidity environments, and dusty environments.

6. A condensing unit control system according to claim 1, characterized in that, The online iteration of the dynamic correction module includes: Based on the environmental deviation between the current environmental parameters and the benchmark model, the environmental impact coefficients in the model are corrected. Based on the deviation between recent measured energy efficiency values ​​and model predictions, the parameter weighting factors are dynamically adjusted. A component aging factor is introduced, which is calculated based on the running time and performance degradation trend.

7. A condensing unit control system according to claim 1, characterized in that, The hierarchical response mechanism of the execution module includes: Level 1 response: When the load change rate exceeds the preset threshold, the opening degree of the compressor's capacity regulation component and throttling element is adjusted first, and the response delay is controlled within the preset range; Level 2 response: When the change in environmental parameters exceeds the preset value, the fan speed and condenser flow rate of the condenser should be adjusted as the main focus. Level 3 response: When the system energy efficiency ratio is lower than the baseline value, the operating parameter combination of all core components is optimized simultaneously.

8. A condensing unit control system according to claim 1, characterized in that, It also includes a human-machine interaction module, which displays real-time operating parameters, energy efficiency curves and fault warning information, and supports manual adjustment of the weight parameters of the collaborative control strategy.

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