Intelligent frequency modulation method and system for refrigeration compressor

Abstract

This application relates to a smart frequency regulation method and system for refrigeration compressors, belonging to the field of compressor control technology. The method includes acquiring the winding monitoring temperature and the equipment operating frequency; determining the internal over-limit temperature based on the winding monitoring temperature and the frequency reduction threshold temperature when the winding monitoring temperature exceeds the threshold temperature; constructing an operating interval on a time axis with the current time point as the endpoint and a preset operating duration as the width, and performing data analysis based on the operating interval to determine the internal heating coefficient and the internal heat dissipation coefficient; calculating and analyzing based on the internal heating coefficient and the internal heat dissipation coefficient to determine the theoretical equilibrium frequency; calculating based on the internal over-limit temperature to determine the urgency value, and calculating based on the urgency value, the equipment operating frequency, and the theoretical equilibrium frequency to determine the target adjustment frequency, and performing frequency regulation on the compressor based on the target adjustment frequency. This application has the function of improving the overall performance of the compressor.

Description

Technical Field

[0001] This application relates to the field of compressor control technology, and in particular to a smart frequency regulation method and system for refrigeration compressors. Background Technology

[0002] The refrigeration compressor is the core power component of a vapor compression refrigeration system, and its operational reliability and energy efficiency directly determine the performance of the entire system. During compressor operation, the motor windings become the hottest spot internally due to current loss and frictional heat generation; their temperature is a key indicator for assessing the compressor's thermal safety status. To ensure safe compressor operation, temperature sensors are installed at the compressor motor windings to monitor the winding temperature in real time.

[0003] In related technologies, when the temperature of the motor windings is detected to be higher than the set threshold, the frequency of the compressor will be reduced to the set target value. This reduces the operating intensity of the compressor by reducing the frequency, thereby reducing the heat generated by the compressor and achieving a temperature drop.

[0004] In the aforementioned technologies, when the monitored temperature exceeds the set threshold, an instruction to reduce the frequency to a preset target value is triggered. Under normal circumstances, this target value is relatively low. This one-size-fits-all adjustment method can effectively reduce the compressor temperature, but it is easy to cause a significant reduction in frequency without needing to do so. This may result in insufficient cooling capacity in some areas with cooling demand. Therefore, the overall performance of the current compressor is poor and there is still room for improvement. Summary of the Invention

[0005] To improve the overall performance of the compressor, this application provides a smart frequency regulation method and system for refrigeration compressors.

[0006] Firstly, this application provides a smart frequency regulation method for refrigeration compressors, employing the following technical solution: A smart frequency regulation method for refrigeration compressors, comprising: Acquire winding monitoring temperature and equipment operating frequency; When the winding monitoring temperature exceeds the preset frequency reduction threshold temperature, the internal over-limit temperature is determined by calculation based on the winding monitoring temperature and the frequency reduction threshold temperature. Construct a work interval on a preset timeline with the current time point as the endpoint and a width of a preset work duration, and perform data analysis based on the work interval to determine the internal heat generation coefficient and the internal heat dissipation coefficient; The theoretical equilibrium frequency is determined by calculation and analysis based on the internal heat generation coefficient and the internal heat dissipation coefficient. The urgency level is determined by calculation based on the internal over-limit temperature. The target adjustment frequency is then determined by calculation based on the urgency level, equipment operating frequency, and theoretical equilibrium frequency. The compressor is then frequency-adjusted based on the target adjustment frequency.

[0007] Optionally, the steps of determining the internal heat generation coefficient and internal heat dissipation coefficient based on data analysis of the operating area include: Within the work area, the interval is fixed according to the preset estimated time. Within a fixed range, sampling points are determined, and a single-point linear model is constructed based on the winding monitoring temperature and equipment operating frequency at each sampling point. The sampling matrix equation is determined based on all single-point linear models, and the internal heat generation coefficient and internal heat dissipation coefficient are determined by least squares estimation based on the sampling matrix equation.

[0008] Optionally, after determining the sampling points within a fixed interval, the intelligent frequency regulation method for refrigeration compressors also includes: Randomly select a sampling point and define it as the principal point, and define the sampling points in the fixed interval that are before and adjacent to the principal point as the predecessor points; The unit change frequency is determined by calculating the operating frequency of the equipment at the main points and the operating frequency of the equipment at the preceding points. When the unit change frequency is less than the preset excitation demand frequency, the key points will be eliminated.

[0009] Optionally, after constructing the single-point linear model, the intelligent frequency regulation method for the refrigeration compressor also includes: The rate of temperature change at key points is determined based on a single-point linear model. When the rate of temperature change exceeds the preset upper limit, the corresponding key points will be removed.

[0010] Optionally, after the internal heating coefficient is determined, the intelligent frequency regulation method for the refrigeration compressor also includes: The number of valid samples is determined by counting the number of sampling points retained within a fixed interval, depending on the circumstances. The effective sampling percentage is determined by calculating based on the number of valid samples and the preset interval sampling number. Determine whether the percentage of valid samples is greater than the preset baseline trust percentage; If the effective sampling percentage is greater than the baseline trust percentage, the currently determined internal heating coefficient will be maintained. If the effective sampling percentage is not greater than the baseline trust percentage, then within the working interval, a fixed interval is randomly defined based on the estimated duration to determine the internal heating coefficient at each time point, and the internal heating coefficient corresponding to when the effective sampling percentage is greater than the baseline trust percentage is defined as the representative heating coefficient. The internal heating coefficient is determined by fitting the representative heating coefficient at each time point, and then corrected and updated based on the fitted heating coefficient and the current internal heating coefficient.

[0011] Optionally, the step of determining the fitted calorific value by fitting the representative calorific value at each time point includes: The time point corresponding to the heat coefficient is defined as the representative point, and a significant interval is constructed based on the representative point that is furthest from the current time point and the current time point; Construct a historical interval on the timeline with the current time point as the endpoint and a width of a preset historical duration, and define the comparison interval within the historical interval based on the width of the significant interval; Within the comparison interval, comparison points are defined based on representative points, and the interval similarity is determined by analyzing the representative heating coefficient of the representative points and the internal heating coefficient of the comparison points. The comparison interval corresponding to the largest interval similarity is defined as the similar interval, and the internal heating coefficient at the latter end of the similar interval is determined as the fitted heating coefficient.

[0012] Optionally, the step of determining interval similarity by analyzing the representative heating coefficient of the representative point and the internal heating coefficient of the comparison point includes: The trust weight parameters are determined by calculating the effective sampling percentage at the representative points and the baseline trust percentage. The single-point deviation coefficient is determined by calculating the difference between the representative heating coefficient of the representative point and the internal heating coefficient of the corresponding comparison point. The similarity influence coefficient is determined by calculating the trust weight parameter and the single-point deviation coefficient, and the interval similarity is determined by calculating all the similarity influence coefficients.

[0013] Secondly, this application provides a smart frequency regulation system for a refrigeration compressor, employing the following technical solution: A smart frequency control system for refrigeration compressors, comprising: The acquisition module is used to acquire the winding monitoring temperature and the equipment operating frequency; The processing module, connected to the acquisition and judgment modules, is used for information storage and processing; The judgment module, connected to the acquisition and processing modules, is used for judging information. When the judgment module determines that the winding monitoring temperature is greater than the preset frequency reduction threshold temperature, the processing module calculates based on the winding monitoring temperature and the frequency reduction threshold temperature to determine the internal over-limit temperature. The processing module constructs a work interval on a preset time axis with the current time point as the end point and a width of a preset work duration, and performs data analysis based on the work interval to determine the internal heat generation coefficient and the internal heat dissipation coefficient. The processing module calculates and analyzes based on the internal heat generation coefficient and the internal heat dissipation coefficient to determine the theoretical equilibrium frequency; The processing module calculates the urgency level based on the internal over-limit temperature, and calculates the target adjustment frequency based on the urgency level, equipment operating frequency, and theoretical equilibrium frequency, and then adjusts the compressor frequency according to the target adjustment frequency.

[0014] In summary, this application includes at least one of the following beneficial technical effects: During the operation of a refrigeration compressor, when an excessively high temperature is detected, the frequency that needs to be adjusted under theoretical conditions can be analyzed to reduce the occurrence of insufficient or excessive adjustment of the compressor frequency, thereby improving the overall performance of the compressor. During compressor operation, the temperature and heat dissipation of the compressor can be better estimated by observing the short-term parameters, thereby improving the accuracy of data analysis. Attached Figure Description

[0015] Figure 1 This is a flowchart of a smart frequency regulation method for refrigeration compressors.

[0016] Figure 2 This is a module flowchart for a smart frequency regulation method used in refrigeration compressors. Detailed Implementation

[0017] To make the purpose, technical solution, and advantages of this application clearer, the following is combined with Figures 1-2 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0018] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0019] This application discloses a smart frequency regulation method for refrigeration compressors, referring to... Figure 1 The method flow for intelligent frequency regulation of refrigeration compressors includes the following steps: Step S100: Obtain the winding monitoring temperature and the equipment operating frequency.

[0020] The winding temperature is the value obtained by the temperature sensor installed at the motor winding, and the equipment operating frequency is the operating frequency currently used by the compressor.

[0021] Step S101: When the winding monitoring temperature is greater than the preset frequency reduction threshold temperature, calculate based on the winding monitoring temperature and the frequency reduction threshold temperature to determine the internal over-limit temperature.

[0022] The frequency reduction threshold temperature is the minimum winding monitoring temperature that the operator sets when the internal temperature of the compressor is deemed too high. When the winding monitoring temperature is greater than the frequency reduction threshold temperature, it indicates that the current internal temperature of the compressor is too high, and frequency reduction is required. The internal over-limit temperature is the winding monitoring temperature minus the frequency reduction threshold temperature.

[0023] Step S102: Construct a work interval on a preset time axis with the current time point as the end point and a width of a preset work duration, and perform data analysis based on the work interval to determine the internal heat generation coefficient and the internal heat dissipation coefficient.

[0024] The time axis is a coordinate axis formed by combining various time points. This coordinate axis points from the time points that have been passed to the time points that have not yet been reached, with the direction of the time points that have been passed being forward. The operation duration is the total duration of the compressor during this start-up. By constructing the operation interval, it is possible to acquire and analyze the operation data after this start-up. The internal heating coefficient is a coefficient value representing the internal heating of the compressor, and the internal heat dissipation coefficient is a parameter value representing the internal heat dissipation capacity of the compressor. Both can be calibrated in advance through experiments, or determined by the method in steps S200-S202.

[0025] Step S103: Calculate and analyze based on the internal heating coefficient and internal heat dissipation coefficient to determine the theoretical equilibrium frequency.

[0026] The theoretical equilibrium frequency is the compressor's operating power at which heat generation and dissipation are theoretically balanced. The main heat source for the compressor is the heat loss from the motor windings; therefore, the compressor's heat generation power can be approximated as... ,in That is, the heating power of the compressor. That is, the internal heating coefficient. This refers to the operating frequency of the compressor; and the compressor's heat dissipation capacity is mainly achieved through refrigerant airflow and lubricating oil circulation. Therefore, the compressor's heat dissipation power can be approximately expressed as... ,in For the compressor's heat dissipation power, This is the internal heat dissipation coefficient; to maintain a balance between the two, therefore, the following is utilized: Perform calculations to obtain... ,Should That is, the theoretical equilibrium frequency.

[0027] Step S104: Calculate the urgency level based on the internal over-limit temperature, and calculate the target adjustment frequency based on the urgency level, equipment operating frequency, and theoretical equilibrium frequency, and adjust the compressor frequency according to the target adjustment frequency.

[0028] The urgency level value reflects the current internal temperature level. A higher value indicates a higher temperature, requiring more significant frequency reduction. The formula for calculating this urgency level value is as follows: ,in Indicates the urgency level value. This indicates the current winding monitoring temperature. The frequency reduction threshold temperature, That is, the internal temperature exceeds the limit. That is, the highest temperature that can theoretically occur, when hour, This means that there is currently no excessively high temperature, i.e., no urgent risk. hour, At this point, the risk is greatest; the target adjustment frequency is the frequency value that the compressor needs to be adjusted to, and the calculation formula is... ,in That is, the target adjustment frequency. This refers to the current operating frequency of the equipment. By adjusting the frequency according to the target, the compressor can be effectively cooled without excessively sacrificing its cooling capacity, resulting in better overall performance of the compressor.

[0029] Since the internal environment of the compressor changes in real time, its heat generation and heat dissipation capabilities also change. Therefore, if the internal heat generation and heat dissipation coefficients are calibrated in advance for data analysis, it is easy to lead to inaccurate data analysis. Hence, the following steps are introduced.

[0030] The steps for determining the internal heat generation coefficient and internal heat dissipation coefficient based on data analysis of the operating area include: Step S200: Within the work area, a fixed interval is defined based on the preset estimated duration.

[0031] The estimated duration is a fixed duration set by the staff. By constructing a fixed interval with the width of the estimated duration based on the end point of the work area, the time interval for the compressor to be used in the short term can be determined more effectively, which facilitates data analysis.

[0032] Step S201: Determine sampling points within a fixed interval and construct a single-point linear model based on the winding monitoring temperature and equipment operating frequency at each sampling point.

[0033] Sampling points are the time points within a fixed interval for data acquisition and monitoring, which can be determined based on the specific sampling frequency of each sensor; a single-point linear model is an operational model that reflects the temperature change at a single sampling point, specifically... ,in This represents the compressor's heat capacity, which is a known constant. That is, the first Temperature change rate at each sampling point; That is, the first The operating frequency of the device at each sampling point.

[0034] Step S202: Determine the sampling matrix equation based on all single-point linear models, and perform least squares estimation based on the sampling matrix equation to determine the internal heat generation coefficient and the internal heat dissipation coefficient.

[0035] The sampling matrix equation is ,in , , ,in That is, the total number of sampling points; performing least squares estimation on the sampling matrix equation is equivalent to solving a set of... This minimizes the sum of squared residuals, expressed as: Its analytical expression is That is, at this time ,Should That is, the internal heat generation coefficient at the current moment. That is, the internal heat dissipation coefficient at the current moment.

[0036] After determining the sampling points within a fixed interval, the intelligent frequency regulation method for refrigeration compressors also includes: Step S300: Randomly select a sampling point and define it as the principal point, and define the sampling points in the fixed interval that are before and adjacent to the principal point as the predecessor points.

[0037] Different sampling points are identified and distinguished by defining key points and preceding points, which facilitates subsequent analysis.

[0038] Step S301: Calculate and determine the unit change frequency based on the equipment operating frequency of the main point and the equipment operating frequency of the preceding point.

[0039] The unit change frequency is the absolute value of the difference between the operating frequencies of the equipment at two points in time.

[0040] Step S302: When the unit change frequency is less than the preset excitation demand frequency, the main points are eliminated.

[0041] The incentive demand frequency is set by the staff to determine whether adjacent sampling points have an incentive, so as to better distinguish them. and The minimum unit change frequency required for contribution status is, for example, 0.2Hz. If the unit change frequency is less than the required excitation frequency, it means that the data is not meaningful for analysis, so it is removed to improve the efficiency of data analysis.

[0042] Following the construction of the single-point linear model, the intelligent frequency regulation method for refrigeration compressors also includes: Step S400: Determine the rate of temperature change at the principal points based on the single-point linear model.

[0043] The rate of temperature change is as described above. .

[0044] Step S401: When the temperature change rate is greater than the preset upper limit change rate, the corresponding key points are eliminated.

[0045] The upper limit of the change rate is the minimum temperature change rate that needs to be achieved when the sensor experiences peak noise, set by the staff. For example, it may be 5°C / s. When the temperature change rate is greater than the upper limit of the change rate, it indicates that the data at that sampling point is inaccurate and should be removed to improve the accuracy of data analysis.

[0046] Once the internal heating coefficient is determined, the intelligent frequency regulation method for refrigeration compressors also includes: Step S500: Count the number of sampling points retained within the fixed interval as needed to determine the effective sampling quantity.

[0047] The effective sampling quantity is the number of sampling points remaining after processing by steps S300-S401 within the fixed interval.

[0048] Step S501: Calculate the effective sampling ratio based on the number of valid samples and the preset interval sampling number.

[0049] The number of samples in an interval is the total number of sampling points that can be determined in a fixed interval without removing sampling points. The percentage of valid samples can be determined by dividing the number of valid samples by the number of samples in the interval.

[0050] Step S502: Determine whether the effective sampling ratio is greater than the preset baseline trust ratio.

[0051] The baseline trust ratio is the minimum effective sampling ratio set by staff to determine whether their data analysis is relatively reliable. The purpose of this judgment is to determine whether the currently determined data is relatively accurate and reliable.

[0052] Step S5021: If the effective sampling percentage is greater than the baseline trust percentage, then maintain the currently determined internal heating coefficient.

[0053] When the effective sampling ratio is greater than the baseline trust ratio, it indicates that the determination of the current internal heating coefficient is based on the analysis of data from a large number of points, which is relatively accurate and reliable. Therefore, it can be maintained.

[0054] Step S5022: If the effective sampling percentage is not greater than the baseline trust percentage, then within the working interval, a fixed interval is randomly defined according to the estimated duration to determine the internal heating coefficient at each time point, and the internal heating coefficient corresponding to when the effective sampling percentage is greater than the baseline trust percentage is defined as the representative heating coefficient.

[0055] When the effective sampling ratio is not greater than the baseline confidence ratio, it indicates that the data used to determine the internal heating coefficient is insufficient and has poor confidence, requiring further analysis. By continuously determining the internal heating coefficient at each time point within the operating range, it is possible to identify which data has confidence. Therefore, it is defined as the representative heating coefficient for identification, which facilitates subsequent analysis.

[0056] Step S503: Perform fitting processing based on the representative heating coefficient at each time point to determine the fitted heating coefficient, and calculate and correct the internal heating coefficient based on the fitted heating coefficient and the current internal heating coefficient.

[0057] The fitted heating coefficient is the internal heating coefficient that should appear under the current conditions, which is obtained by analyzing the representative heating coefficient with high confidence. For the specific fitting method, see steps S600-S603. At this time, the fitted heating coefficient and the current internal heating coefficient are averaged to determine a more suitable internal heating coefficient, thereby improving the accuracy of data analysis. Similarly, the internal heat dissipation coefficient can also be analyzed in steps S500-S503 to improve the accuracy of each data.

[0058] The steps for determining the fitted calorific value based on the representative calorific value at each time point include: Step S600: Define the time point corresponding to the representative heating coefficient as the representative point, and construct a significant interval based on the representative point that is furthest from the current time point and the current time point.

[0059] Different data can be identified and distinguished by defining representative points and constructing salient intervals.

[0060] Step S601: Construct a historical interval on the timeline with the current time point as the endpoint and a width of a preset historical duration, and define a comparison interval within the historical interval based on the width of the significant interval.

[0061] The historical duration is the total duration since the compressor was put into use, as set by the staff. Historical intervals are constructed to facilitate the acquisition and analysis of data within the historical duration. The comparison interval is a time interval within the historical interval that is randomly located and has the same width as the significant interval.

[0062] Step S602: Define comparison points in the comparison interval based on representative points, and analyze the representative heating coefficient of the representative points and the internal heating coefficient of the comparison points to determine the interval similarity.

[0063] The comparison point is the time point in the comparison interval where the position of the point is consistent with the position of the representative point in the relatively significant interval; the interval similarity is a numerical value that reflects the degree of similarity between the data of the significant interval and the comparison interval. For the specific determination method, please refer to steps S700-S702.

[0064] Step S603: Define the comparison interval corresponding to the largest interval similarity as the similar interval, and determine the internal heating coefficient at the end of the similar interval as the fitted heating coefficient.

[0065] When the interval similarity is the highest, it means that the data in the corresponding comparison interval is most similar to the significant interval. Therefore, a similar interval is defined and identified. At this time, the internal heating coefficient at the end of the similar interval can be fitted as the internal heating coefficient that needs to be expressed at the current time point. Therefore, it is determined as the fitted heating coefficient.

[0066] The steps for determining interval similarity based on the representative heating coefficient of representative points and the internal heating coefficient of comparison points include: Step S700: Calculate and determine the trust weight parameters based on the effective sampling ratio at the representative point and the baseline trust ratio.

[0067] The trust weight parameter is a parameter value that reflects the reliability of the data at each representative point. The larger the value, the more reliable the data. It is determined by subtracting the baseline trust ratio from the effective sampling ratio.

[0068] Step S701: Calculate the difference between the representative heating coefficient of the representative point and the internal heating coefficient of the corresponding comparison point to determine the single-point deviation coefficient.

[0069] The single-point deviation coefficient is the difference between the representative heating coefficient of the representative point and the internal heating coefficient of the corresponding comparison point, and this difference is an absolute value.

[0070] Step S702: Calculate the similarity influence coefficient based on the trust weight parameter and the single-point deviation coefficient, and calculate the interval similarity based on all the similarity influence coefficients.

[0071] The similarity influence coefficient is a parameter value that represents the degree of influence of a single point in time on the similarity of the interval. It is determined by dividing the trust weight parameter by the single-point deviation coefficient. At this point, the interval similarity can be obtained by averaging all the similarity influence coefficients.

[0072] Reference Figure 2 Based on the same inventive concept, embodiments of the present invention provide a smart frequency regulation system for a refrigeration compressor, comprising: The acquisition module is used to acquire the winding monitoring temperature and the equipment operating frequency; The processing module, connected to the acquisition and judgment modules, is used for information storage and processing; The judgment module, connected to the acquisition and processing modules, is used for judging information. When the judgment module determines that the winding monitoring temperature is greater than the preset frequency reduction threshold temperature, the processing module calculates based on the winding monitoring temperature and the frequency reduction threshold temperature to determine the internal over-limit temperature. The processing module constructs a work interval on a preset time axis with the current time point as the end point and a width of a preset work duration, and performs data analysis based on the work interval to determine the internal heat generation coefficient and the internal heat dissipation coefficient. The processing module calculates and analyzes based on the internal heat generation coefficient and the internal heat dissipation coefficient to determine the theoretical equilibrium frequency; The processing module calculates the urgency level based on the internal over-limit temperature, and calculates the target adjustment frequency based on the urgency level, equipment operating frequency, and theoretical equilibrium frequency, and then adjusts the compressor frequency according to the target adjustment frequency. The internal coefficient determination module is used to determine the internal heat generation coefficient and the internal heat dissipation coefficient. The frequency change analysis module is used to analyze and process the frequency changes at each sampling point; The temperature change analysis module is used to analyze and process the temperature changes at each sampling point; The internal heating coefficient update module is used to update the internal heating coefficient. The module for determining the fitted heating coefficient is used to determine the fitted heating coefficient. The interval similarity determination module is used to determine the interval similarity between two intervals.

[0073] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

Claims

1. A smart frequency regulation method for refrigeration compressors, characterized in that, include: Acquire winding monitoring temperature and equipment operating frequency; When the winding monitoring temperature exceeds the preset frequency reduction threshold temperature, the internal over-limit temperature is determined by calculation based on the winding monitoring temperature and the frequency reduction threshold temperature. Construct a work interval on a preset timeline with the current time point as the endpoint and a width of a preset work duration, and perform data analysis based on the work interval to determine the internal heat generation coefficient and the internal heat dissipation coefficient; The theoretical equilibrium frequency is determined by calculation and analysis based on the internal heat generation coefficient and the internal heat dissipation coefficient. The urgency level is determined by calculation based on the internal over-limit temperature. The target adjustment frequency is then determined by calculation based on the urgency level, equipment operating frequency, and theoretical equilibrium frequency. The compressor is then frequency-adjusted based on the target adjustment frequency.

2. The intelligent frequency regulation method for a refrigeration compressor according to claim 1, characterized in that, The steps for determining the internal heat generation coefficient and internal heat dissipation coefficient based on data analysis of the operating area include: Within the work area, the interval is fixed according to the preset estimated time. Within a fixed range, sampling points are determined, and a single-point linear model is constructed based on the winding monitoring temperature and equipment operating frequency at each sampling point. The sampling matrix equation is determined based on all single-point linear models, and the internal heat generation coefficient and internal heat dissipation coefficient are determined by least squares estimation based on the sampling matrix equation.

3. The intelligent frequency regulation method for a refrigeration compressor according to claim 2, characterized in that, After determining the sampling points within a fixed interval, the intelligent frequency regulation method for refrigeration compressors also includes: Randomly select a sampling point and define it as the principal point, and define the sampling points in the fixed interval that are before and adjacent to the principal point as the predecessor points; The unit change frequency is determined by calculating the operating frequency of the equipment at the main points and the operating frequency of the equipment at the preceding points. When the unit change frequency is less than the preset excitation demand frequency, the key points will be eliminated.

4. The intelligent frequency regulation method for a refrigeration compressor according to claim 3, characterized in that, Following the construction of the single-point linear model, the intelligent frequency regulation method for refrigeration compressors also includes: The rate of temperature change at key points is determined based on a single-point linear model. When the rate of temperature change exceeds the preset upper limit, the corresponding key points will be removed.

5. The intelligent frequency regulation method for a refrigeration compressor according to claim 4, characterized in that, Once the internal heating coefficient is determined, the intelligent frequency regulation method for refrigeration compressors also includes: The number of valid samples is determined by counting the number of sampling points retained within a fixed interval, depending on the circumstances. The effective sampling percentage is determined by calculating based on the number of valid samples and the preset interval sampling number. Determine whether the percentage of valid samples is greater than the preset baseline trust percentage; If the effective sampling percentage is greater than the baseline trust percentage, the currently determined internal heating coefficient will be maintained. If the effective sampling percentage is not greater than the baseline trust percentage, then within the working interval, a fixed interval is randomly defined based on the estimated duration to determine the internal heating coefficient at each time point, and the internal heating coefficient corresponding to when the effective sampling percentage is greater than the baseline trust percentage is defined as the representative heating coefficient. The internal heating coefficient is determined by fitting the representative heating coefficient at each time point, and then corrected and updated based on the fitted heating coefficient and the current internal heating coefficient.

6. The intelligent frequency regulation method for a refrigeration compressor according to claim 5, characterized in that, The steps for determining the fitted calorific value based on the representative calorific value at each time point include: The time point corresponding to the heat coefficient is defined as the representative point, and a significant interval is constructed based on the representative point that is furthest from the current time point and the current time point; Construct a historical interval on the timeline with the current time point as the endpoint and a width of a preset historical duration, and define the comparison interval within the historical interval based on the width of the significant interval; Within the comparison interval, comparison points are defined based on representative points, and the interval similarity is determined by analyzing the representative heating coefficient of the representative points and the internal heating coefficient of the comparison points. The comparison interval corresponding to the largest interval similarity is defined as the similar interval, and the internal heating coefficient at the latter end of the similar interval is determined as the fitted heating coefficient.

7. The intelligent frequency regulation method for a refrigeration compressor according to claim 6, characterized in that, The steps for determining interval similarity based on the representative heating coefficient of representative points and the internal heating coefficient of comparison points include: The trust weight parameters are determined by calculating the effective sampling percentage at the representative points and the baseline trust percentage. The single-point deviation coefficient is determined by calculating the difference between the representative heating coefficient of the representative point and the internal heating coefficient of the corresponding comparison point. The similarity influence coefficient is determined by calculating the trust weight parameter and the single-point deviation coefficient, and the interval similarity is determined by calculating all the similarity influence coefficients.

8. A smart frequency regulation system for a refrigeration compressor, used to implement the smart frequency regulation method for a refrigeration compressor as described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire the winding monitoring temperature and the equipment operating frequency; The processing module, connected to the acquisition and judgment modules, is used for information storage and processing; The judgment module, connected to the acquisition and processing modules, is used for judging information. When the judgment module determines that the winding monitoring temperature is greater than the preset frequency reduction threshold temperature, the processing module calculates based on the winding monitoring temperature and the frequency reduction threshold temperature to determine the internal over-limit temperature. The processing module constructs a work interval on a preset time axis with the current time point as the end point and a width of a preset work duration, and performs data analysis based on the work interval to determine the internal heat generation coefficient and the internal heat dissipation coefficient. The processing module calculates and analyzes based on the internal heat generation coefficient and the internal heat dissipation coefficient to determine the theoretical equilibrium frequency; The processing module calculates the urgency level based on the internal over-limit temperature, and calculates the target adjustment frequency based on the urgency level, equipment operating frequency, and theoretical equilibrium frequency, and then adjusts the compressor frequency according to the target adjustment frequency.

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