A sensor control system for a refrigerant recovery machine

Abstract

The present invention provides a sensor control system for a refrigerant recovery machine, and relates to the technical field of data processing. The method comprises: receiving an operation instruction of a user through a control panel, and controlling an AC motor to drive a compressor to operate according to the operation instruction, collecting temperature data, pressure data and flow data through a temperature sensor, a pressure sensor and a flow sensor, and calculating the temperature change amplitude, pressure change amplitude and flow fluctuation amplitude at adjacent time points, obtaining scores between two, constructing a correlation score matrix, judging the change trend of temperature, pressure and flow, adjusting a preset threshold range group, obtaining a dynamic threshold range group, and generating an alarm signal when the detection data set is not within the dynamic threshold range group, adjusting the operating parameters of the AC motor, and generating a maintenance signal and fault state operating data of the compressor. The present invention can control the operating state of the refrigerant recovery machine through the data detected by the sensor.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a sensor control system for a refrigerant recovery machine. Background Art

[0002] The existing refrigerant recovery machine sensor control system opens the control panel to drive the AC motor to start the compressor, and cooperates with the sensor for monitoring to ensure the normal operation of the equipment. The control system sets a fixed threshold for the sensor. When the data collected by the sensor exceeds this range, the system will automatically judge it as an abnormality, thereby triggering shutdown protection and alarm.

[0003] However, fixed abnormal thresholds may not be able to adapt to dynamic changes in the actual working environment. During the operation of the equipment, relying solely on the preset thresholds may result in underreporting. For example, although the equipment operating status has not exceeded the preset threshold, it is close to the limit state. The system fails to detect and take protective measures in time, which may cause the equipment to continue to operate until a failure occurs. Summary of the invention

[0004] The object of the present invention is to provide a refrigerant recovery machine sensor control system, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A refrigerant recovery machine sensor control system, the system comprising:

[0007] A control panel module, used for receiving operation instructions from a user through the control panel, and controlling the AC motor to drive the compressor to operate according to the operation instructions;

[0008] The data acquisition module is used to respectively collect temperature data, pressure data and flow data during the operation of the compressor through a temperature sensor, a pressure sensor and a flow sensor to obtain a detection data set;

[0009] A data processing module is used to calculate the temperature change amplitude, pressure change amplitude and flow fluctuation amplitude at adjacent time points according to the detection data set, and obtain temperature change data, pressure change data and flow fluctuation data;

[0010] The correlation analysis module is used to analyze the changing relationship between temperature, pressure and flow rate according to the temperature change data, pressure change data and flow rate fluctuation data, and calculate the scores between each two to obtain the correlation score matrix;

[0011] The trend analysis module is used to determine the changing trends of temperature, pressure and flow rate according to the correlation scoring matrix and obtain the predicted trend data;

[0012] A dynamic threshold module, used to adjust the preset threshold range group according to the predicted trend data to obtain a dynamic threshold range group, wherein the preset threshold range group includes a temperature threshold range, a pressure threshold range and a flow threshold range;

[0013] A data detection module is used to screen the detection data set according to the dynamic threshold range group, and generate an alarm signal when the detection data set is not within the dynamic threshold range group, including a low value alarm signal and a high value alarm signal;

[0014] A shutdown control module, used to adjust the operating parameters of the AC motor according to the alarm signal;

[0015] The equipment maintenance module is used to generate a maintenance signal and fault status operation data of the compressor according to the alarm signal.

[0016] Furthermore, according to the temperature change data, pressure change data and flow fluctuation data, the change relationship between temperature, pressure and flow is analyzed, and the coefficients between them are calculated to obtain the correlation score matrix, including:

[0017] According to the temperature change data and pressure change data, analyze the change range of temperature and pressure at the same time point, and calculate the correlation score between temperature and pressure;

[0018] According to the temperature change data and flow fluctuation data, the matching degree between the temperature change amplitude and the flow fluctuation amplitude is analyzed, and the fitting score between temperature and flow is calculated;

[0019] According to the pressure change data and flow fluctuation data, the response degree of the pressure change amplitude to the flow fluctuation amplitude is analyzed, and the sensitivity scores of pressure and flow are calculated;

[0020] According to the correlation score, fit score and sensitivity score, the changing relationship among temperature, pressure and flow rate is determined, and the correlation score matrix is ​​constructed.

[0021] Furthermore, based on the temperature change data and the pressure change data, the change range of temperature and pressure at the same time point is analyzed, and the correlation score of temperature and pressure is calculated, including:

[0022] The temperature change amplitude and the pressure change amplitude are normalized to eliminate the dimension difference, and they are arranged in chronological order to obtain the temperature change sequence and the pressure change sequence;

[0023] According to the temperature change sequence and the pressure change sequence, the ratio of the temperature change amplitude to the pressure change amplitude at each time point is calculated to obtain a ratio sequence;

[0024] According to the ratio sequence, the correlation score between temperature and pressure is calculated. The calculation formula of the correlation score is:

[0025] ;

[0026] in, Score the correlation between temperature and pressure, is the temperature, For pressure, is the total number of time points, is the index of the time point, For at time point The temperature variation range, For at time point The pressure variation range, for About time point The derivative of An infinitesimal point in time , and is a nonlinear mapping function, , .

[0027] Furthermore, based on the temperature change data and flow fluctuation data, the matching degree between the temperature change amplitude and the flow fluctuation amplitude is analyzed, and the fitting score between temperature and flow is calculated, including:

[0028] According to the temperature variation amplitude, the flow fluctuation amplitude is normalized to eliminate the dimension difference, and it is arranged in chronological order to obtain the flow fluctuation sequence;

[0029] Match the temperature value at each time point in the temperature change sequence with the flow value at the corresponding time point in the flow fluctuation sequence to obtain numerical matching data;

[0030] According to the numerical matching data, the fit score of temperature and flow is calculated. The calculation formula of the fit score is:

[0031] ;

[0032] in, Score the goodness of fit between temperature and flow, is the temperature, For flow, is the total number of time points, is the index of the time point, For at time point The temperature variation range, For at time point The flow fluctuation range, is the nonlinear index, and is the weight function, , , is the time decay coefficient.

[0033] Furthermore, based on the pressure change data and flow fluctuation data, the response degree of the pressure change amplitude to the flow fluctuation amplitude is analyzed, and the sensitivity scores of pressure and flow are calculated, including:

[0034] According to the pressure change data and the flow rate fluctuation data, the pressure change rate and the flow rate fluctuation rate are calculated to obtain the pressure rate sequence and the flow rate sequence;

[0035] Match the pressure rate sequence and the flow rate sequence according to the time points to obtain rate matching data;

[0036] According to the rate matching data, the response degree of the pressure change rate to the flow change rate is analyzed to obtain the time offset;

[0037] According to the time offset, the sensitivity scores of pressure and flow are calculated. The calculation formula of sensitivity score is:

[0038] ;

[0039] in, Score the sensitivity of pressure and flow, For pressure, For flow, is the total number of time points, is the index of the time point, For at time point The rate of change of pressure, For at time point The flow rate change rate, is the time offset, is the rate attenuation coefficient, is an asymmetric kernel function, .

[0040] Furthermore, according to the correlation score, fit score and sensitivity score, the change relationship between temperature, pressure and flow rate is determined, and a correlation score matrix is ​​constructed, including:

[0041] According to all the time points of the detection data set, different time lengths are determined with the current time point as the end point to obtain a time length sequence;

[0042] According to the time length sequence, the correlation score, fit score and sensitivity score within different time lengths are calculated to obtain the dynamic correlation sequence;

[0043] According to the dynamic correlation sequence, the changing relationship between temperature, pressure and flow rate at different time lengths is analyzed to construct a dynamic scoring matrix;

[0044] The dynamic rating matrices of different time lengths are merged to obtain the correlation rating matrix.

[0045] Furthermore, the dynamic rating matrices of different time lengths are merged to obtain the associated rating matrix, including:

[0046] Set the time length to Relevance score , fit score and sensitivity scores , construct a dynamic scoring matrix, the expression of the dynamic scoring matrix is:

[0047] ;

[0048] in, The time length is The dynamic scoring matrix, the rows of the dynamic scoring matrix are temperature data, pressure data, and flow data, and the columns of the dynamic coefficient matrix are temperature data, pressure data, and flow data;

[0049] By merging the dynamic scoring matrices of different time lengths, the correlation scoring matrix is ​​obtained. The expression of the correlation scoring matrix is:

[0050] ;

[0051] in, is the correlation score matrix, They are dynamic scoring matrices of different time lengths. , is the total number of time points of the detection data set, that is, the maximum time length of the detection data set.

[0052] Furthermore, according to the correlation score matrix, the changing trends of temperature, pressure and flow are determined to obtain the predicted trend data, including:

[0053] According to the scores of the correlation score matrix, the linkage changes of temperature, pressure and flow in different time lengths are analyzed to obtain linkage change data;

[0054] According to the linkage change data, determine the change trends of temperature, pressure and flow, and obtain the change trend data;

[0055] Based on the change trend data and real-time detection data set, the change amount of temperature, pressure and flow is determined to obtain the predicted trend data.

[0056] Furthermore, according to the predicted trend data, the preset threshold range group is adjusted to obtain a dynamic threshold range group, including:

[0057] According to the predicted trend data and the preset threshold range group, the difference between the predicted trend data and the preset threshold range group is calculated to obtain a range difference matrix;

[0058] According to the range difference matrix, combined with the direction and amplitude of the predicted trend data, the dynamic adjustment value is calculated. The calculation formula of the dynamic adjustment value is:

[0059] ;

[0060] in, To dynamically adjust the value, is the range difference matrix, and are the upper and lower limit matrices of the preset threshold range group, To predict trend data, is the middle value of the preset threshold range group, is the adjustment factor;

[0061] The dynamic adjustment value is combined with the preset threshold range group to obtain a dynamic threshold range group.

[0062] Further, according to the predicted trend data and the preset threshold range group, the difference between the predicted trend data and the preset threshold range group is calculated to obtain a range difference matrix, including:

[0063] According to the predicted trend data and the preset threshold range group, the difference between the predicted trend value and the upper and lower limits of the range is calculated to obtain the difference sequence of temperature, pressure and flow respectively. The expression of the difference sequence is:

[0064] , , ;

[0065] in, is the temperature difference sequence, They are respectively the temperature prediction trend value, the lower limit value of the temperature threshold range and the upper limit value of the temperature threshold range. is the pressure difference sequence, They are respectively the pressure prediction trend value, the lower limit value of the pressure threshold range and the upper limit value of the pressure threshold range, is the flow difference sequence, They are the flow prediction trend value, the flow threshold range lower limit value and the flow threshold range upper limit value respectively;

[0066] According to the difference sequence, the range difference matrix is ​​constructed. The expression of the range difference matrix is:

[0067] ;

[0068] in, is the range difference matrix, the rows of which are temperature, pressure, and flow, respectively, and the columns of which are the differences between the lower and upper limits of the threshold range and the predicted trend value, respectively. and are the differences between the lower and upper limits of the temperature threshold range and the predicted trend value, , , and are the differences between the lower and upper limits of the pressure threshold range and the predicted trend value, , , and are the difference between the lower limit and upper limit of the flow threshold range and the predicted trend value, , .

[0069] The above solution of the present invention includes at least the following beneficial effects:

[0070] The present invention further improves the accuracy of fault prediction by analyzing temperature, pressure and flow data and calculating the change range of adjacent time points. Unlike fixed thresholds, this dynamic data processing method can identify potential risks in advance before the equipment is about to fail. This process can accurately identify subtle changes in the operation of the equipment, providing an accurate basis for subsequent dynamic threshold adjustments and alarms. This refined detection method effectively improves the response speed of the system, allowing the equipment to take protective measures immediately when problems occur, avoiding the high cost of equipment damage and production line shutdowns.

[0071] The present invention can discover potential abnormal data by analyzing the relationship between different sensor data. According to the change relationship between different sensor data, the system can predict the occurrence of faults in advance and intervene in the early stage. Different from the traditional system that only relies on a single data point, the association analysis improves the accuracy and reliability of fault detection through multi-dimensional data association. This function can provide more detailed abnormal judgment criteria, making the system more sensitive to minor changes, preventing the occurrence of missed reports and false alarms, and greatly improving the overall stability of the system.

[0072] The present invention predicts the changing trends of temperature, pressure and flow through the correlation scoring matrix, further improving the intelligence level of equipment operation. By predicting the trend of historical data, the system can predict the potential failure points of the equipment and send out early warning signals when the equipment status has not reached the threshold. This function improves the system's active monitoring capability of the equipment, avoids the downtime protection for maintenance after a failure occurs in the traditional system, reduces production downtime, and through intelligent prediction, the production process can be more stable, the production efficiency is improved, and the service life of the equipment is extended.

[0073] The present invention avoids the problem of missed reports or false reports that may be caused by fixed thresholds by adjusting the preset threshold range group according to the predicted trend data. The equipment operating environment is often uncertain and dynamically changing. The traditional fixed threshold system cannot adapt to this environment. The dynamic threshold range group can adjust the detection standard in real time according to the actual operating data, making the system more flexible and intelligent. Through this method, the system can respond to changes in the equipment status in real time to ensure that the equipment can be monitored in a timely and accurate manner under different working conditions. This flexibility maximizes the operating efficiency of the equipment, effectively reduces the probability of equipment failure, and improves the overall performance of the equipment.

[0074] The present invention receives user instructions and controls the start-up of the compressor, and can conveniently perform operations such as starting, stopping, and adjusting the operating status of the equipment, thereby making the system more flexible and easy to use. This function automates the manual operations in traditional mechanical systems and reduces the occurrence of human errors. The automated operation enables the compressor to be adjusted more accurately according to real-time feedback, reduces energy consumption, and extends the service life of the equipment. Especially in industrial environments, it reduces the direct intervention of operators in the equipment, reduces operational risks, and improves the stability and reliability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a flow chart of a refrigerant recovery machine sensor control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0077] like Figure 1 As shown, an embodiment of the present invention provides a refrigerant recovery machine sensor control system, the system comprising:

[0078] A control panel module, used for receiving operation instructions from a user through the control panel, and controlling the AC motor to drive the compressor to operate according to the operation instructions;

[0079] The data acquisition module is used to respectively collect temperature data, pressure data and flow data during the operation of the compressor through a temperature sensor, a pressure sensor and a flow sensor to obtain a detection data set;

[0080] A data processing module is used to calculate the temperature change amplitude, pressure change amplitude and flow fluctuation amplitude at adjacent time points according to the detection data set, and obtain temperature change data, pressure change data and flow fluctuation data;

[0081] The correlation analysis module is used to analyze the changing relationship between temperature, pressure and flow rate according to the temperature change data, pressure change data and flow rate fluctuation data, and calculate the scores between each two to obtain the correlation score matrix;

[0082] The trend analysis module is used to determine the changing trends of temperature, pressure and flow rate according to the correlation scoring matrix and obtain the predicted trend data;

[0083] A dynamic threshold module, used to adjust the preset threshold range group according to the predicted trend data to obtain a dynamic threshold range group, wherein the preset threshold range group includes a temperature threshold range, a pressure threshold range and a flow threshold range;

[0084] A data detection module is used to screen the detection data set according to the dynamic threshold range group, and generate an alarm signal when the detection data set is not within the dynamic threshold range group, including a low value alarm signal and a high value alarm signal;

[0085] A shutdown control module, used to adjust the operating parameters of the AC motor according to the alarm signal;

[0086] The equipment maintenance module is used to generate a maintenance signal and fault status operation data of the compressor according to the alarm signal.

[0087] In an embodiment of the present invention, the user's operating instructions are received through a control panel, and the AC motor is controlled according to the instructions to drive the compressor to operate, providing a real-time and intuitive operation interface, ensuring that the device can respond to user needs in different situations, increasing the intuitiveness and simplicity of operation, and reducing the operation error rate; through temperature sensors, pressure sensors and flow sensors, temperature data, pressure data and flow data during the operation of the compressor are respectively collected to obtain a detection data set, providing all-round equipment monitoring to ensure that the system can capture every change in the equipment status; based on the detection data set, the temperature change amplitude, pressure change amplitude and flow fluctuation amplitude at adjacent time points are calculated to obtain temperature change data, pressure change data and flow fluctuation data, providing real-time operation status judgment for the system, and making predictions about the future equipment status based on historical data.

[0088] According to the temperature change data, pressure change data and flow fluctuation data, the change relationship between temperature, pressure and flow is analyzed, and the scores between the two are calculated to obtain the correlation score matrix. Through the correlation analysis of multi-dimensional data, the accuracy and timeliness of equipment fault detection are improved, thereby more effectively avoiding missed reports or false alarms and improving the safety of the system; according to the correlation score matrix, the change trends of temperature, pressure and flow are judged to obtain the predicted trend data, so that the system has the prediction ability, can actively discover the potential failures of the equipment, and take measures in advance to avoid the equipment from running under extreme working conditions; according to the predicted trend data, the preset threshold range group is adjusted to obtain the dynamic threshold range group, which enhances the system's real-time adaptability to the equipment status and ensures the long-term stable operation of the equipment.

[0089] The detection data set is screened according to the dynamic threshold range group. When the detection data set is not within the dynamic threshold range group, an alarm signal is generated, ensuring that the equipment can be monitored in real time at any time and an alarm can be issued in time when the equipment status is abnormal; according to the alarm signal, the operating parameters of the AC motor are adjusted, and through intelligent shutdown control, the system can minimize the risks in equipment operation; according to the alarm signal, the maintenance signal and fault status operation data of the compressor are generated, which improves the maintainability of the equipment, reduces maintenance costs, and can improve fault handling efficiency and avoid production losses caused by long-term shutdown.

[0090] The control panel receives the user's operation instructions, and controls the AC motor to drive the compressor to operate according to the user's operation instructions, which specifically includes:

[0091] The user inputs control commands to the system through a physical or touch panel, such as starting or stopping the compressor, adjusting operating parameters, etc. The system transmits these operating instructions to the control circuit and controls the start and stop of the AC motor through a drive signal, thereby controlling the operation of the compressor.

[0092] Among them, according to the detection data set, the temperature change amplitude, pressure change amplitude and flow fluctuation amplitude at adjacent time points are calculated to obtain temperature change data, pressure change data and flow fluctuation data, specifically including:

[0093] According to the temperature data, the temperature difference between adjacent time points is calculated to obtain the temperature change amplitude, and the temperature change data is obtained based on the temperature value and the temperature change amplitude at each time point; according to the pressure data, the pressure difference between adjacent time points is calculated to obtain the pressure change amplitude, and the pressure change data is obtained based on the pressure value and the pressure change amplitude at each time point; according to the flow data, the flow difference between adjacent time points is calculated to obtain the flow fluctuation amplitude, and the flow fluctuation data is obtained based on the flow value and the flow fluctuation amplitude at each time point.

[0094] In a preferred embodiment of the present invention, the relationship between the temperature, pressure and flow rate is analyzed according to the temperature change data, the pressure change data and the flow rate fluctuation data, and the coefficients between the two are calculated to obtain a correlation score matrix, including:

[0095] According to the temperature change data and pressure change data, analyze the change range of temperature and pressure at the same time point, and calculate the correlation score between temperature and pressure;

[0096] According to the temperature change data and flow fluctuation data, the matching degree between the temperature change amplitude and the flow fluctuation amplitude is analyzed, and the fitting score between temperature and flow is calculated;

[0097] According to the pressure change data and flow fluctuation data, the response degree of the pressure change amplitude to the flow fluctuation amplitude is analyzed, and the sensitivity scores of pressure and flow are calculated;

[0098] According to the correlation score, fit score and sensitivity score, the changing relationship among temperature, pressure and flow rate is determined, and the correlation score matrix is ​​constructed.

[0099] In an embodiment of the present invention, the collected temperature change data, pressure change data and flow fluctuation data are comprehensively analyzed. The data collected by each sensor reflects the change of an important parameter in the operation of the equipment. A single sensor data may not be sufficient to accurately reflect the overall state of the equipment. Therefore, the change trend of these data is comprehensively analyzed. By analyzing the change relationship between these data, the system can identify the possible intrinsic connection between the various parameters. Parameters such as temperature, pressure and flow are usually interrelated. For example, an increase in temperature may cause a change in pressure, and a change in pressure will also affect the flow. Through this multi-dimensional data analysis, the system can more accurately judge potential problems in the operation of the equipment and avoid misdiagnosis that may be caused by a single data source. The comprehensive analysis not only improves the accuracy of fault detection, but also helps to predict possible faults or abnormalities of the equipment in advance, and improves the system's sensitivity to faults.

[0100] According to the correlation score, fit score and sensitivity score, the change relationship between temperature, pressure and flow is determined, and the correlation score matrix is ​​constructed. Each element in the correlation score matrix is ​​calculated by a mathematical algorithm. The element value reveals the correlation and interaction between different sensor data. Each element reflects the degree of correlation between two data points. The larger the score value, the closer the change relationship between the two parameters. Through this method, the system can identify the potential correlation between temperature, pressure and flow, and then provide more information and basis for fault detection. The correlation score matrix provides a more refined judgment standard for subsequent trend analysis and fault diagnosis, so that the system can accurately capture the slight fluctuations in equipment operation. By linking these data, the system can effectively improve the overall judgment ability of the equipment status and avoid the omissions or false alarms that may exist in traditional monitoring methods.

[0101] In a preferred embodiment of the present invention, based on the temperature change data and the pressure change data, the change amplitude of the temperature and the pressure at the same time point is analyzed, and the correlation score of the temperature and the pressure is calculated, including:

[0102] The temperature change amplitude and the pressure change amplitude are normalized to eliminate the dimension difference, and they are arranged in chronological order to obtain the temperature change sequence and the pressure change sequence;

[0103] According to the temperature change sequence and the pressure change sequence, the ratio of the temperature change amplitude to the pressure change amplitude at each time point is calculated to obtain a ratio sequence;

[0104] According to the ratio sequence, the correlation score between temperature and pressure is calculated. The calculation formula of the correlation score is:

[0105] ;

[0106] in, Score the correlation between temperature and pressure, is the temperature, For pressure, is the total number of time points, is the index of the time point, For at time point The temperature variation range, For at time point The pressure variation range, for About time point The derivative of An infinitesimal point in time , and is a nonlinear mapping function, , .

[0107] In an embodiment of the present invention, the temperature change amplitude and the pressure change amplitude are normalized to eliminate dimensional differences, and are arranged in chronological order to obtain a temperature change sequence and a pressure change sequence, which provide standardized and unified input for subsequent ratio calculation and correlation analysis, ensure data consistency and reliability, and thereby improve the accuracy of fault detection; based on the temperature change sequence and the pressure change sequence, the ratio of the temperature change amplitude to the pressure change amplitude at each time point is calculated to obtain a ratio sequence, which helps to reveal potential equipment problems and enhance the system's fault diagnosis capability by providing the relative relationship between temperature and pressure changes; based on the ratio sequence, the temperature and pressure correlation score is calculated, and by accurately evaluating the correlation between temperature and pressure and digitizing it, the accuracy and timeliness of system fault detection are improved, and the equipment monitoring and maintenance strategies are further optimized.

[0108] In a preferred embodiment of the present invention, according to the temperature change data and the flow fluctuation data, the matching degree between the temperature change amplitude and the flow fluctuation amplitude is analyzed, and the fitting score between the temperature and the flow is calculated, including:

[0109] According to the temperature variation amplitude, the flow fluctuation amplitude is normalized to eliminate the dimension difference, and it is arranged in chronological order to obtain the flow fluctuation sequence;

[0110] Match the temperature value at each time point in the temperature change sequence with the flow value at the corresponding time point in the flow fluctuation sequence to obtain numerical matching data;

[0111] According to the numerical matching data, the fit score of temperature and flow is calculated. The calculation formula of the fit score is:

[0112] ;

[0113] in, Score the goodness of fit between temperature and flow, is the temperature, For flow, is the total number of time points, is the index of the time point, For at time point The temperature variation range, For at time point The flow fluctuation range, is the nonlinear index, and is the weight function, , , is the time decay coefficient.

[0114] In an embodiment of the present invention, the flow fluctuation amplitude is normalized according to the temperature change amplitude to eliminate the dimensional difference, and it is arranged in chronological order to obtain a flow fluctuation sequence, which provides standardized input data for subsequent fitting analysis, improves the accuracy of the analysis process, and ensures that the temperature and flow data can be effectively connected and compared; the temperature value at each time point in the temperature change sequence is matched with the flow value at the corresponding time point in the flow fluctuation sequence to obtain numerical matching data, which provides a matching data set based on time alignment, helps to reveal the operating laws and abnormal modes of the equipment, and further provides strong support for fault warning and equipment optimization; based on the numerical matching data, the fit score of temperature and flow is calculated to quantify the fit between the two, so that the system can provide more accurate equipment status analysis, further enhance fault detection and prevention capabilities, and improve equipment reliability and stability.

[0115] In a preferred embodiment of the present invention, based on the pressure change data and the flow fluctuation data, the response degree of the pressure change amplitude to the flow fluctuation amplitude is analyzed, and the sensitivity score of the pressure and flow is calculated, including:

[0116] According to the pressure change data and the flow rate fluctuation data, the pressure change rate and the flow rate fluctuation rate are calculated to obtain the pressure rate sequence and the flow rate sequence;

[0117] Match the pressure rate sequence and the flow rate sequence according to the time points to obtain rate matching data;

[0118] According to the rate matching data, the response degree of the pressure change rate to the flow change rate is analyzed to obtain the time offset;

[0119] According to the time offset, the sensitivity scores of pressure and flow are calculated. The calculation formula of sensitivity score is:

[0120] ;

[0121] in, Score the sensitivity of pressure and flow, For pressure, For flow, is the total number of time points, is the index of the time point, For at time point The rate of change of pressure, For at time point The flow rate change rate, is the time offset, is the rate attenuation coefficient, is an asymmetric kernel function, .

[0122] In an embodiment of the present invention, the pressure change rate and the flow fluctuation rate are calculated based on the pressure change data and the flow fluctuation data to obtain the pressure rate sequence and the flow rate sequence, which can reveal the state changes of the device at different time points and can also capture the instantaneous fluctuations in the operation of the device; the pressure rate sequence and the flow rate sequence are matched according to the time points to obtain rate matching data, so as to provide reliable pairing data for the subsequent analysis process, and ensure that the changes of different parameters can be accurately corresponded and compared, thereby ensuring the accuracy and timeliness of the analysis; based on the rate matching data, the response degree of the pressure change rate to the flow change rate is analyzed to obtain the time offset, revealing the timing relationship between the pressure change and the flow change. By analyzing the response time difference, the system can determine the delay degree of the pressure change on the flow when the device is operating normally; based on the time offset, the pressure and flow sensitivity scores are calculated, and the response performance of the device is measured by the score value to determine whether the operating parameters of the device need to be adjusted to avoid failures or performance degradation caused by response delays.

[0123] Among them, according to the pressure change data and the flow fluctuation data, the pressure change rate and the flow fluctuation rate are calculated to obtain the pressure rate sequence and the flow rate sequence, which specifically includes:

[0124] The pressure change data and flow fluctuation data are obtained from the sensors of the equipment. These data are usually presented in the form of time series. Then, the pressure change rate at each time point is obtained by calculating the difference in pressure changes, that is, the pressure difference between adjacent time points divided by the time interval. The same method is applied to the flow fluctuation data to calculate the flow change rate, that is, the change in flow at adjacent time points divided by the time interval, thereby obtaining the pressure rate series and flow rate series.

[0125] Among them, according to the rate matching data, the response degree of the pressure change rate to the flow change rate is analyzed to obtain the time offset, which specifically includes:

[0126] Analyze the relationship between the pressure rate and the flow rate, compare their changing trends and calculate the time offset. If the change in the flow rate lags behind the change in the pressure rate, the system will record the time difference, that is, the time offset. For example, certain components in the equipment may cause a certain delay in the flow change to the pressure change, or the flow change may be affected by other factors.

[0127] In a preferred embodiment of the present invention, the relationship between the change of temperature, pressure and flow is determined according to the correlation score, the fit score and the sensitivity score, and a correlation score matrix is ​​constructed, including:

[0128] According to all the time points of the detection data set, different time lengths are determined with the current time point as the end point to obtain a time length sequence;

[0129] According to the time length sequence, the correlation score, fit score and sensitivity score within different time lengths are calculated to obtain the dynamic correlation sequence;

[0130] According to the dynamic correlation sequence, the changing relationship between temperature, pressure and flow rate at different time lengths is analyzed to construct a dynamic scoring matrix;

[0131] The dynamic rating matrices of different time lengths are merged to obtain the correlation rating matrix.

[0132] In an embodiment of the present invention, different time lengths are determined based on all time points of the detection data set and with the current time point as the end point, to obtain a time length sequence. By defining multiple time windows, it is convenient to perform multi-scale analysis on the equipment. According to the time length sequence, the correlation score, fit score and sensitivity score within different time lengths are calculated to obtain a dynamic correlation sequence. By comprehensively calculating various scores, the system can comprehensively evaluate the interaction of various parameters at different time scales. According to the dynamic correlation sequence, the changing relationship between temperature, pressure and flow at different time lengths is analyzed, and a dynamic scoring matrix is ​​constructed. By constructing the dynamic scoring matrix, the system can capture the complex relationship between various parameters of the equipment at different time scales. The dynamic scoring matrices of different time lengths are merged to obtain a correlation scoring matrix. By merging data at different time scales, the system can provide a more comprehensive and flexible evaluation data, which is helpful to identify the long-term trend of the equipment, potential failure risks and optimization space of operation.

[0133] In a preferred embodiment of the present invention, dynamic scoring matrices of different time lengths are merged to obtain a correlation scoring matrix, including:

[0134] Set the time length to Relevance score , fit score and sensitivity scores , construct a dynamic scoring matrix, the expression of the dynamic scoring matrix is:

[0135] ;

[0136] in, The time length is The dynamic scoring matrix, the rows of the dynamic scoring matrix are temperature data, pressure data, and flow data, and the columns of the dynamic coefficient matrix are temperature data, pressure data, and flow data;

[0137] By merging the dynamic scoring matrices of different time lengths, the correlation scoring matrix is ​​obtained. The expression of the correlation scoring matrix is:

[0138] ;

[0139] in, is the correlation score matrix, They are dynamic scoring matrices of different time lengths. , is the total number of time points of the detection data set, that is, the maximum time length of the detection data set.

[0140] In an embodiment of the present invention, by constructing dynamic scoring matrices of different time lengths and merging them into an associated scoring matrix, the relationship between parameters is quantified, so that the system can provide early warning when potential failures occur in the equipment, thereby improving the accuracy and timeliness of fault diagnosis. The system can have an in-depth understanding of the changing trends of temperature, pressure and flow, which helps to optimize the automatic adjustment strategy of the equipment, ensure that the system can perform adaptive adjustments under different working conditions, and avoid the equipment from continuing to operate when it is close to the limit state, thereby extending the service life of the equipment and improving operating efficiency.

[0141] In a preferred embodiment of the present invention, the change trends of temperature, pressure and flow are judged according to the correlation score matrix to obtain the predicted trend data, including:

[0142] According to the scores of the correlation score matrix, the linkage changes of temperature, pressure and flow in different time lengths are analyzed to obtain linkage change data;

[0143] According to the linkage change data, determine the change trends of temperature, pressure and flow, and obtain the change trend data;

[0144] Based on the change trend data and real-time detection data set, the change amount of temperature, pressure and flow is determined to obtain the predicted trend data.

[0145] In an embodiment of the present invention, according to the scores of the association scoring matrix, the linkage changes of temperature, pressure and flow in different time lengths are analyzed to obtain linkage change data, and the mutual influence relationship of temperature, pressure and flow in different time lengths is analyzed to provide basic data for subsequent prediction and adjustment; according to the linkage change data, the change trend of temperature, pressure and flow is determined to obtain change trend data, and the change trend of each parameter of the equipment is identified to help the system understand the long-term change pattern and predict the future operating status; according to the change trend data and the real-time detection data set, the change amount of temperature, pressure and flow is determined to obtain prediction trend data, and the change trend and real-time detection data are combined to calculate the change amount of each parameter, and generate prediction data to provide real-time decision data for equipment management.

[0146] Among them, according to the score of the correlation score matrix, the linkage changes of temperature, pressure and flow in different time lengths are analyzed to obtain linkage change data, including:

[0147] The system applies these scores to different time periods to evaluate the joint change patterns of these three parameters in each time window. For example, the system may compare the changes between temperature and pressure in the past few minutes, hours, or days to see if they fluctuate synchronously or if there is a lag in a certain time period. This analysis helps the system understand the mutual influence and dynamic relationship between temperature, pressure, and flow at different time scales, thereby obtaining linkage change data.

[0148] Among them, according to the linkage change data, the change trends of temperature, pressure and flow are determined, and the change trend data are obtained, which specifically includes:

[0149] The change trend within each time window is determined based on the linkage change data. Through trend analysis, the long-term change direction of temperature, pressure and flow during equipment operation can be identified. For example, the changes in temperature and pressure may be synchronous and show an upward trend, which may indicate an increase in equipment workload or a preliminary signal of some abnormality. Trend analysis can identify potential abnormal situations and obtain change trend data.

[0150] Among them, according to the change trend data and the real-time detection data set, the change amount of temperature, pressure and flow is determined to obtain the predicted trend data, which specifically includes:

[0151] The real-time monitoring data is combined with the change trend data, and the algorithm is used to predict the possible changes in these parameters in the future. For example, if the current temperature and pressure are both on an upward trend, the system can predict that these two parameters may continue to rise in the short term, and based on this, determine the possible increase in value, thereby obtaining the predicted trend data.

[0152] In a preferred embodiment of the present invention, the preset threshold range group is adjusted according to the predicted trend data to obtain a dynamic threshold range group, including:

[0153] According to the predicted trend data and the preset threshold range group, the difference between the predicted trend data and the preset threshold range group is calculated to obtain a range difference matrix;

[0154] According to the range difference matrix, combined with the direction and amplitude of the predicted trend data, the dynamic adjustment value is calculated. The calculation formula of the dynamic adjustment value is:

[0155] ;

[0156] in, To dynamically adjust the value, is the range difference matrix, and are the upper and lower limit matrices of the preset threshold range group, To predict trend data, is the middle value of the preset threshold range group, is the adjustment factor;

[0157] The dynamic adjustment value is combined with the preset threshold range group to obtain a dynamic threshold range group.

[0158] In an embodiment of the present invention, based on the predicted trend data and the preset threshold range group, the difference between the predicted trend data and the preset threshold range group is calculated to obtain a range difference matrix. By quantifying the difference between the predicted trend and the safety threshold, the system can promptly discover the potential risks of the equipment; based on the range difference matrix, and in combination with the direction and amplitude of the predicted trend data, the preset threshold range is adjusted by the difference, so that the system can stop the operation of the equipment in advance and avoid the equipment from entering the extreme state; the dynamic adjustment value is merged with the preset threshold range group to obtain a dynamic threshold range group, which can put the equipment into a shutdown state in advance, so as to perform maintenance on the equipment in time.

[0159] In a preferred embodiment of the present invention, according to the predicted trend data and the preset threshold range group, the difference between the predicted trend data and the preset threshold range group is calculated to obtain a range difference matrix, including:

[0160] According to the predicted trend data and the preset threshold range group, the difference between the predicted trend value and the upper and lower limits of the range is calculated to obtain the difference sequence of temperature, pressure and flow respectively. The expression of the difference sequence is:

[0161] , , ;

[0162] in, is the temperature difference sequence, They are respectively the temperature prediction trend value, the lower limit value of the temperature threshold range and the upper limit value of the temperature threshold range. is the pressure difference sequence, They are respectively the pressure prediction trend value, the lower limit value of the pressure threshold range and the upper limit value of the pressure threshold range, is the flow difference sequence, They are the flow prediction trend value, the flow threshold range lower limit value and the flow threshold range upper limit value respectively;

[0163] According to the difference sequence, the range difference matrix is ​​constructed. The expression of the range difference matrix is:

[0164] ;

[0165] in, is the range difference matrix, the rows of which are temperature, pressure, and flow, respectively, and the columns of which are the differences between the lower and upper limits of the threshold range and the predicted trend value, respectively. and are the differences between the lower and upper limits of the temperature threshold range and the predicted trend value, , , and are the differences between the lower and upper limits of the pressure threshold range and the predicted trend value, , , and are the difference between the lower limit and upper limit of the flow threshold range and the predicted trend value, , .

[0166] In an embodiment of the present invention, the difference between the predicted trend value and the upper and lower limits of the range is calculated based on the predicted trend data and the preset threshold range group, and the difference sequences of temperature, pressure and flow are obtained respectively, which help the system to accurately identify and quantify the gap between the current operating state of the equipment and its safety range. Through these difference sequences, the system can timely discover the changing trend of the equipment status and provide a strong basis for early warning and maintenance; according to the difference sequence, a range difference matrix is ​​constructed. By constructing the range difference matrix, the system can intuitively present the difference between the operating state of each parameter of the equipment and the set threshold, providing a clear data structure for subsequent decision support and fault prevention, which can help maintenance personnel make decisions quickly.

[0167] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A refrigerant recovery machine sensor control system, characterized in that: The system comprises: A control panel module, used for receiving operation instructions from a user through the control panel, and controlling the AC motor to drive the compressor to operate according to the operation instructions; The data acquisition module is used to respectively collect temperature data, pressure data and flow data during the operation of the compressor through a temperature sensor, a pressure sensor and a flow sensor to obtain a detection data set; A data processing module is used to calculate the temperature change amplitude, pressure change amplitude and flow fluctuation amplitude at adjacent time points according to the detection data set, and obtain temperature change data, pressure change data and flow fluctuation data; The correlation analysis module is used to analyze the changing relationship between temperature, pressure and flow rate according to the temperature change data, pressure change data and flow rate fluctuation data, and calculate the scores between each two to obtain the correlation score matrix; The trend analysis module is used to determine the changing trends of temperature, pressure and flow rate according to the correlation scoring matrix and obtain the predicted trend data; A dynamic threshold module, used to adjust the preset threshold range group according to the predicted trend data to obtain a dynamic threshold range group, wherein the preset threshold range group includes a temperature threshold range, a pressure threshold range and a flow threshold range; A data detection module is used to screen the detection data set according to the dynamic threshold range group, and generate an alarm signal when the detection data set is not within the dynamic threshold range group, including a low value alarm signal and a high value alarm signal; A shutdown control module, used to adjust the operating parameters of the AC motor according to the alarm signal; An equipment maintenance module, used to generate a maintenance signal and fault status operation data of the compressor according to the alarm signal; According to the temperature change data, pressure change data and flow fluctuation data, the change relationship between temperature, pressure and flow is analyzed, and the coefficients between them are calculated to obtain the correlation score matrix, including: According to the temperature change data and pressure change data, analyze the change range of temperature and pressure at the same time point, and calculate the correlation score between temperature and pressure; According to the temperature change data and flow fluctuation data, the matching degree between the temperature change amplitude and the flow fluctuation amplitude is analyzed, and the fitting score between temperature and flow is calculated; According to the pressure change data and flow fluctuation data, the response degree of the pressure change amplitude to the flow fluctuation amplitude is analyzed, and the sensitivity scores of pressure and flow are calculated; According to the correlation score, fit score and sensitivity score, the changing relationship between temperature, pressure and flow rate is determined, and the correlation score matrix is ​​constructed; According to the temperature change data and pressure change data, analyze the change range of temperature and pressure at the same time point, and calculate the correlation score of temperature and pressure, including: The temperature change amplitude and the pressure change amplitude are normalized to eliminate the dimension difference, and they are arranged in chronological order to obtain the temperature change sequence and the pressure change sequence; According to the temperature change sequence and the pressure change sequence, the ratio of the temperature change amplitude to the pressure change amplitude at each time point is calculated to obtain a ratio sequence; According to the ratio sequence, the correlation score between temperature and pressure is calculated. The calculation formula of the correlation score is: ； in, Score the correlation between temperature and pressure, is the temperature, For pressure, is the total number of time points, is the index of the time point, For at time point The temperature variation range, For at time point The pressure variation range, for About time point The derivative of An infinitesimal point in time , and is a nonlinear mapping function, , .

2. A refrigerant recovery machine sensor control system according to claim 1, characterized in that: According to the temperature change data and flow fluctuation data, the matching degree between the temperature change amplitude and the flow fluctuation amplitude is analyzed, and the fitting score between temperature and flow is calculated, including: According to the temperature variation amplitude, the flow fluctuation amplitude is normalized to eliminate the dimension difference, and it is arranged in chronological order to obtain the flow fluctuation sequence; Match the temperature value at each time point in the temperature change sequence with the flow value at the corresponding time point in the flow fluctuation sequence to obtain numerical matching data; According to the numerical matching data, the fit score of temperature and flow is calculated. The calculation formula of the fit score is: ； in, Score the goodness of fit between temperature and flow, is the temperature, For flow, is the total number of time points, is the index of the time point, For at time point The temperature variation range, For at time point The flow fluctuation range, is the nonlinear index, and is the weight function, , , is the time decay coefficient.

3. A refrigerant recovery machine sensor control system according to claim 2, characterized in that: Based on the pressure change data and flow fluctuation data, analyze the response of the pressure change amplitude to the flow fluctuation amplitude, and calculate the sensitivity scores of pressure and flow, including: According to the pressure change data and the flow rate fluctuation data, the pressure change rate and the flow rate fluctuation rate are calculated to obtain the pressure rate sequence and the flow rate sequence; Match the pressure rate sequence and the flow rate sequence according to the time points to obtain rate matching data; According to the rate matching data, the response degree of the pressure change rate to the flow change rate is analyzed to obtain the time offset; According to the time offset, the sensitivity scores of pressure and flow are calculated. The calculation formula of sensitivity score is: ； in, Score the sensitivity of pressure and flow, For pressure, For flow, is the total number of time points, is the index of the time point, For at time point The rate of change of pressure, For at time point The flow rate change rate, is the time offset, is the rate attenuation coefficient, is an asymmetric kernel function, .

4. A refrigerant recovery machine sensor control system according to claim 3, characterized in that: According to the correlation score, fit score and sensitivity score, the changing relationship between temperature, pressure and flow is determined, and the correlation score matrix is ​​constructed, including: According to all the time points of the detection data set, different time lengths are determined with the current time point as the end point to obtain a time length sequence; According to the time length sequence, the correlation score, fit score and sensitivity score within different time lengths are calculated to obtain the dynamic correlation sequence; According to the dynamic correlation sequence, the changing relationship between temperature, pressure and flow rate at different time lengths is analyzed to construct a dynamic scoring matrix; The dynamic rating matrices of different time lengths are merged to obtain the correlation rating matrix.

5. A refrigerant recovery machine sensor control system according to claim 4, characterized in that: The dynamic scoring matrices of different time lengths are combined to obtain the correlation scoring matrix, including: Set the time length to Relevance score , fit score and sensitivity scores , construct a dynamic scoring matrix, the expression of the dynamic scoring matrix is: ； in, The time length is The dynamic scoring matrix, the rows of the dynamic scoring matrix are temperature data, pressure data, and flow data, and the columns of the dynamic coefficient matrix are temperature data, pressure data, and flow data; By merging the dynamic scoring matrices of different time lengths, the correlation scoring matrix is ​​obtained. The expression of the correlation scoring matrix is: ； in, is the correlation score matrix, They are dynamic scoring matrices of different time lengths. , is the total number of time points of the detection data set, that is, the maximum time length of the detection data set.

6. A refrigerant recovery machine sensor control system according to claim 5, characterized in that: According to the correlation score matrix, the changing trends of temperature, pressure and flow are judged to obtain the predicted trend data, including: According to the scores of the correlation score matrix, the linkage changes of temperature, pressure and flow in different time lengths are analyzed to obtain linkage change data; According to the linkage change data, determine the change trends of temperature, pressure and flow, and obtain the change trend data; Based on the change trend data and real-time detection data set, the change amount of temperature, pressure and flow is determined to obtain the predicted trend data.

7. A refrigerant recovery machine sensor control system according to claim 6, characterized in that: According to the forecast trend data, the preset threshold range group is adjusted to obtain a dynamic threshold range group, including: According to the predicted trend data and the preset threshold range group, the difference between the predicted trend data and the preset threshold range group is calculated to obtain a range difference matrix; According to the range difference matrix, combined with the direction and amplitude of the predicted trend data, the dynamic adjustment value is calculated. The calculation formula of the dynamic adjustment value is: ； in, To dynamically adjust the value, is the range difference matrix, and are the upper and lower limit matrices of the preset threshold range group, To predict trend data, is the middle value of the preset threshold range group, is the adjustment factor; The dynamic adjustment value is combined with the preset threshold range group to obtain a dynamic threshold range group.

8. A refrigerant recovery machine sensor control system according to claim 7, characterized in that: According to the predicted trend data and the preset threshold range group, the difference between the predicted trend data and the preset threshold range group is calculated to obtain a range difference matrix, including: According to the predicted trend data and the preset threshold range group, the difference between the predicted trend value and the upper and lower limits of the range is calculated to obtain the difference sequence of temperature, pressure and flow respectively. The expression of the difference sequence is: ， ， ； in, is the temperature difference sequence, They are respectively the temperature prediction trend value, the lower limit value of the temperature threshold range and the upper limit value of the temperature threshold range. is the pressure difference sequence, They are respectively the pressure prediction trend value, the lower limit value of the pressure threshold range and the upper limit value of the pressure threshold range, is the flow difference sequence, They are the flow prediction trend value, the flow threshold range lower limit value and the flow threshold range upper limit value respectively; According to the difference sequence, the range difference matrix is ​​constructed. The expression of the range difference matrix is: ； in, is the range difference matrix, the rows of which are temperature, pressure, and flow, respectively, and the columns of which are the differences between the lower and upper limits of the threshold range and the predicted trend value, respectively. and are the differences between the lower and upper limits of the temperature threshold range and the predicted trend value, , , and are the differences between the lower and upper limits of the pressure threshold range and the predicted trend value, , , and are the difference between the lower limit and upper limit of the flow threshold range and the predicted trend value, , .

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