A sealing intelligent detection method for honey and water separation bottle cap

Abstract

The present application relates to the technical field of sealing test, and specifically to a sealing intelligent detection method for honey water separation bottle cap, the method comprising: injecting gas into a container equipped with a separation bottle cap, collecting voltage data of the side of the separation bottle cap at each moment by using a piezoelectric pressure sensor, obtaining the airflow amplitude change and the high-frequency significance of the airflow at each moment of the separation bottle cap; determining the bottle cap airflow characterization coefficient at each moment of the separation bottle cap; calculating the distribution anomaly coefficient of each abnormal point in the bottle cap airflow characterization coefficient at all moments; dividing the bottle cap airflow characterization coefficient at all moments into each period, and determining the abnormal order characterization value of each period based on the distribution anomaly coefficient; analyzing the distribution law of the abnormal order characterization value of all periods, determining the leakage risk index of the separation bottle cap, and detecting the sealing of the separation bottle cap. The present application improves the accuracy of the sealing detection of the separation bottle cap.

Description

Technical Field

[0001] The present application relates to the technical field of sealing testing, and in particular to an intelligent sealing detection method for a honey and water separation bottle cap. Background Art

[0002] Honey water separator bottle cap refers to a bottle cap used to help separate honey and water. It usually includes a bottle cap with a special structure to avoid direct contact between water and honey. By using the honey water separator bottle cap, honey can be used more conveniently without worrying about its deterioration or separation. It is commonly used in honey bottles or honey packaging containers to provide better protection for honey preservation and use. However, if the honey water separator bottle cap is not sealed well, it will cause product deterioration, leakage, and even affect the health of consumers. Therefore, it is necessary to strictly test the sealing of the honey water separator bottle cap.

[0003] In the existing method of testing the sealing performance of the honey water separation bottle cap under low pressure conditions, the pressure value in the test bottle is usually used to judge whether there is a significant decrease to judge whether the airtightness is good, or the bubble method is used for airtightness detection. However, the pressure data of the honey water separation bottle cap may not fluctuate over a large range in a short period of time. Therefore, the airtightness detection result cannot be accurately obtained only by the pressure data change characteristics of the honey water separation bottle cap collected in a short period of time. At the same time, since the pressure data collection process may be affected by interference factors such as airflow and temperature changes, the airtightness detection accuracy of the honey water separation bottle cap is low. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a sealing intelligent detection method for a honey and water separation bottle cap to solve the existing problems.

[0005] The present invention discloses a method for intelligently detecting the sealing of a honey and water separation bottle cap using the following technical solutions:

[0006] An embodiment of the present application provides a method for intelligently detecting the sealing of a honey and water separation bottle cap, the method comprising the following steps:

[0007] Inject gas into a container equipped with a honey and water separation bottle cap, and use a piezoelectric pressure sensor to collect voltage data at each moment on the side of the separation bottle cap;

[0008] A sliding window centered on the voltage data at each moment is constructed, and the volatility of all data in the sliding window is analyzed to obtain the change in the airflow amplitude at each moment of the separated bottle cap.

[0009] Analyze the frequency domain change trend of all data in the sliding window to obtain the high-frequency significance of the airflow at each moment of separating the bottle cap; determine the bottle cap airflow characterization coefficient at each moment of separating the bottle cap by combining the airflow amplitude change and the airflow high-frequency significance;

[0010] Analyze the abnormal degree of the bottle cap airflow characterization coefficient at all times, and determine the distribution abnormality coefficient of each abnormal point in the bottle cap airflow characterization coefficient at all times; divide the bottle cap airflow characterization coefficient at all times into each period, and determine the abnormal order characterization value of each period based on the distribution abnormality coefficient;

[0011] The distribution law of the abnormal order characterization values ​​of all cycles is analyzed, the leakage risk index of the separation bottle cap is determined, and the sealing of the separation bottle cap is tested.

[0012] In one embodiment, the airflow amplitude change is the coefficient of variation of all voltage data in the sliding window.

[0013] In one embodiment, the process of determining the high frequency significance of the airflow is:

[0014] All voltage data in the sliding window at each moment are converted into a spectrum diagram using a frequency domain conversion algorithm, and the changing trend of each voltage data in the sliding window at each moment in the spectrum diagram is calculated. The forward fusion result of all the changing trends in the sliding window at each moment is used as the high-frequency significance of the airflow.

[0015] In one embodiment, the process of determining the bottle cap airflow characterization coefficient is as follows:

[0016] The high-frequency significance of the airflow is non-negatively mapped, and the bottle cap airflow characterization coefficient is determined by combining the result of the non-negative mapping and the change in the airflow amplitude. The bottle cap airflow characterization coefficient is positively correlated with the result of the non-negative mapping and the change in the airflow amplitude.

[0017] In one embodiment, the process of determining the distribution anomaly coefficient is:

[0018] Obtain each mutation point in the airflow characterization coefficient of the bottle cap at all times, calculate the test result statistics of all the airflow characterization coefficients of the bottle cap in a local neighborhood centered on each mutation point, and determine the distribution abnormality coefficient based on the test result statistics;

[0019] The distribution anomaly coefficient is negatively correlated with the test result statistic.

[0020] In one embodiment, the division into cycles is specifically to take the length of time for the piezoelectric pressure sensor to rotate around the separation bottle cap once as one cycle.

[0021] In one embodiment, the abnormal order characterization value is the time corresponding to the maximum value of the distribution abnormality coefficient at all times in each cycle.

[0022] In one embodiment, the leakage risk index is determined as follows:

[0023] The difference between the abnormal order characterization values ​​between two adjacent cycles is calculated, and the leakage risk index of the separated bottle cap is determined based on the difference.

[0024] In one embodiment, the leakage risk index is a reverse mapping result of the discrete degrees of all the differences.

[0025] In one embodiment, the step of detecting the sealing property of the separation bottle cap comprises:

[0026] If the normalized value of the leakage risk index is greater than a preset threshold, it is determined that the sealing of the separation bottle cap is abnormal; otherwise, it is determined that the sealing of the separation bottle cap is normal.

[0027] This application has at least the following beneficial effects:

[0028] The present application injects gas into a container equipped with a separation bottle cap, uses a piezoelectric pressure sensor to collect voltage data at each moment on the side of the separation bottle cap, constructs a sliding window centered on the voltage data at each moment, analyzes the volatility of all data in the sliding window to obtain the airflow amplitude change of the separation bottle cap at each moment, reflects the degree of fluctuation of the voltage data at each moment, embodies the possibility of gas leakage in the separation bottle cap, and improves the reliability of the separation bottle cap sealing detection; analyzes the frequency domain change trend of all data in the sliding window, obtains the high-frequency significance of the airflow at each moment of the separation bottle cap, further amplifies the abnormality of the voltage data, avoids ignoring the small leakage of the separation bottle cap, and improves the timeliness of the detection of the abnormality of the separation bottle cap sealing; combines the airflow amplitude change and the The high-frequency significance of the airflow is determined, the airflow characterization coefficient of the bottle cap at each moment of the separation bottle cap is determined, the abnormal characteristics of the voltage data are integrated, and the accuracy of the sealing detection of the honey water separation bottle cap is improved; the abnormal degree of the bottle cap airflow characterization coefficient at all moments is analyzed, and the distribution abnormality coefficient of each abnormal point in the bottle cap airflow characterization coefficient at all moments is determined, which reduces the error influence degree of the sealing detection of the honey water separation bottle cap; the bottle cap airflow characterization coefficient at all moments is divided into each period, and based on the distribution abnormality coefficient, the abnormal order characterization value of each period is determined, the distribution law of the abnormal order characterization value of all periods is analyzed, and the leakage risk index of the separation bottle cap is determined, which reflects the possibility of abnormal sealing of the separation bottle cap and improves the accuracy of the sealing detection of the honey water separation bottle cap. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 A flowchart of the steps of a sealing intelligent detection method for a honey and water separation bottle cap provided in this application;

[0031] Figure 2 Schematic diagram of the installation of piezoelectric pressure sensor. DETAILED DESCRIPTION

[0032] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the sealing intelligent detection method of a honey water separation bottle cap proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0033] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0034] The specific scheme of the intelligent sealing detection method for the honey and water separation bottle cap provided by the present application is described in detail below with reference to the accompanying drawings.

[0035] An embodiment of the present application provides a method for detecting the sealing of a honey water separation bottle cap. Specifically, the following method for detecting the sealing of a honey water separation bottle cap is provided. Figure 1 , the method comprises the following steps:

[0036] Step S001, injecting gas into a container equipped with a honey and water separation bottle cap, and using a piezoelectric pressure sensor to collect voltage data on the side of the separation bottle cap at each moment.

[0037] Since the existing sealing detection method relies on the accuracy of the pressure detection instrument, if there is a slight leak in the bottle cap, the pressure inside the bottle may not drop significantly in a short period of time, and the pressure data analysis cannot detect the slight leak in the separation bottle cap in a timely and accurate manner. To address this problem, the present application uses a method based on piezoelectric pressure sensor measurement to detect the sealing of the honey water separation bottle cap.

[0038] First, inject gas into the container equipped with the honey water separation bottle cap to be tested to increase the pressure inside the bottle, so that the pressure inside the container is greater than the external air pressure. Install a piezoelectric pressure sensor around the separation bottle cap to detect whether there is an air pressure leak. The piezoelectric pressure sensor can convert the detected air flow changes into voltage data. Specifically, a piezoelectric pressure sensor that can rotate around the separation bottle cap is installed on the side of the separation bottle cap. The schematic diagram of the piezoelectric pressure sensor installation is shown in the figure below. Figure 2 As shown in the figure, the sensing surface of the piezoelectric pressure sensor always faces the bottle cap. Figure 2 Rotate in the middle direction, Figure 2 The middle circle represents the separated bottle cap, and the rectangle represents the piezoelectric pressure sensor.

[0039] The voltage data generated by the piezoelectric pressure sensor during the rotation process is collected. As an embodiment of the present application, the data collection time interval is set to 0.01 seconds, the data collection time length is 30 seconds, and the time for the piezoelectric pressure sensor to separate the bottle cap is 6 seconds. All the collected voltage data are organized into a voltage data sequence in time sequence, among which the data collection time interval, time length, and the time for one rotation can be set by the implementer according to the actual situation, and this application does not impose any restrictions on this.

[0040] Step S002, constructing a sliding window centered on the voltage data at each moment, analyzing the volatility of all data in the sliding window to obtain the change in the airflow amplitude at each moment of separating the bottle cap.

[0041] If there is a sealing failure in the separation bottle cap, the compressed gas in the container will be ejected outward through the leak hole, forming a leaking airflow. When the sensing surface of the piezoelectric pressure sensor is not subjected to external force, its output signal is a small amplitude, stable random signal. When the sensing surface of the piezoelectric pressure sensor is impacted by the leaking airflow, the impact force will generate an electric charge, thereby generating a transient process signal with a large amplitude. Therefore, the sealing of the separation bottle cap can be judged by analyzing the voltage data sequence.

[0042] The separation bottle cap may not be sealed tightly at the bottle mouth due to damage or missing sealing ring, defects may occur in the manufacturing process of the separation bottle cap, and leakage may occur due to uneven materials, molds and other problems. As a result, the pressure changes during the sealing detection of the honey water separation bottle cap have obvious characteristics. However, if the existing damage defects are small, the pressure change characteristics caused by leakage problems in a short period of time are not obvious, affecting the accuracy of sealing analysis based on pressure changes.

[0043] Therefore, in the case of minor leakage, the present application analyzes the collected voltage data. In the process of analyzing the voltage data, the collected voltage data sequence has glitches or small oscillations due to the leakage airflow caused by minor fluctuations. If a leakage airflow is detected, the transient process signal of the voltage data sequence shows a trend of a large drop followed by a rise. Therefore, the sealing of the separation bottle cap can be reflected by the change characteristics of the data in the voltage data sequence.

[0044] For the voltage data sequence, a sliding window with a length of N is constructed with each voltage data as the center, and the sliding step is 1. It moves from left to right in the voltage data sequence. If there is missing data in the sliding window, the missing value filling algorithm is used to fill it.

[0045] In one embodiment of the present application, the length of the sliding window N=31, which can be set by the implementer according to actual conditions, and the present application does not impose any restrictions on this. The missing value filling algorithm adopts a mirror filling method, which is a well-known technology and is not described in detail in the present application. The implementer can choose other existing feasible missing value filling algorithms according to actual conditions, and the present application does not impose any restrictions on this.

[0046] In the voltage data sequence, the coefficient of variation of all voltage data in the sliding window of any voltage data is calculated as the change in the airflow amplitude of the separation bottle cap at the corresponding moment of any voltage data. The change in the airflow amplitude reflects the volatility and discreteness of the voltage data in the sliding window. The larger the change in the airflow amplitude, the more obvious the fluctuation of the voltage data in the sliding window, and the more likely the sealing of the separation bottle cap is abnormal.

[0047] Step S003, analyzing the frequency domain change trend of all data in the sliding window to obtain the high-frequency significance of the airflow at each moment of separating the bottle cap; combining the airflow amplitude change and the high-frequency significance of the airflow to determine the bottle cap airflow characterization coefficient at each moment of separating the bottle cap.

[0048] As an embodiment of the present application, a discrete Fourier transform (DFT) is used to obtain the frequency domain information of all voltage data in a sliding window of each voltage data. The input of the DFT algorithm is all voltage data in the sliding window, and the output is the frequency amplitude spectrum of all voltage data in the sliding window. DFT is an existing well-known technology, and the specific process is not described in detail in this application.

[0049] It should be noted that this embodiment only provides a frequency domain analysis algorithm, namely, the discrete Fourier transform algorithm. On the premise that the frequency domain conversion of voltage data can be achieved, the implementer can choose other existing feasible frequency domain analysis algorithms according to actual conditions, and this application does not impose any restrictions on this.

[0050] The changing trend of each voltage data in the sliding window at each moment in the frequency amplitude spectrum is calculated, and the forward fusion result of all the changing trends in the sliding window at each moment is used as the high-frequency significance of the airflow at each moment of separating the bottle cap.

[0051] It should be noted that the change trend indicates the trend direction of the data, such as a downward trend, an upward trend, etc., which can be specifically expressed by a slope, a rate of change, etc.; forward fusion means combining multiple variables with an overall increasing trend, such as addition, multiplication, etc.

[0052] In one embodiment of the present application, the slope of each voltage data in the sliding window at each moment in the frequency amplitude spectrum is calculated, and the cumulative sum of all the slopes in the sliding window is used as the high-frequency significance of the airflow at the corresponding moment of the separation bottle cap. The greater the high-frequency significance of the airflow, the more frequent the airflow fluctuation of the separation bottle cap at the corresponding moment, and the more likely there is a problem with the sealing of the separation bottle cap.

[0053] The high-frequency significance of the airflow at each moment of separating the bottle cap is non-negatively mapped, and the bottle cap airflow characterization coefficient at each moment of separating the bottle cap is determined by combining the non-negative mapping result and the change in the airflow amplitude. The bottle cap airflow characterization coefficient at each moment of separating the bottle cap is positively correlated with the non-negative mapping result and the change in the airflow amplitude.

[0054] It should be understood that non-negative mapping means mapping all high-frequency significance of airflow at all times of separating bottle caps to positive values ​​while ensuring monotonicity, such as exponential function; positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases, such as additive relationship, multiplicative relationship, etc.

[0055] In one embodiment of the present application, the ratio of the high frequency significance of the airflow at each moment of separating the bottle cap to the preset value M is calculated, and the ratio is used as the exponent of an exponential function with a natural constant as the base, and the product of the calculation result of the exponential function and the change in the airflow amplitude at the corresponding moment of separating the bottle cap is used as the bottle cap airflow characterization coefficient at each moment of separating the bottle cap. The larger the bottle cap airflow characterization coefficient, the greater the airflow fluctuation detected at that moment.

[0056] In this embodiment, M=10, the purpose is to reduce the growth rate of the exponential function and avoid increasing the amount of calculation due to excessive data. The implementer can set the value of M according to the actual situation, and this application does not impose any restrictions here.

[0057] Step S004, analyzing the abnormal degree of the bottle cap airflow characterization coefficient at all times, determining the distribution abnormality coefficient of each abnormal point in the bottle cap airflow characterization coefficient at all times; dividing the bottle cap airflow characterization coefficient at all times into periods, and determining the abnormal order characterization value of each period based on the distribution abnormality coefficient.

[0058] The airflow characterization coefficients of the bottle caps at all times are arranged in ascending order of time to form an airflow characterization sequence. During the detection process, if there is a defect in the separated bottle cap and airflow leaks, the sensor can detect a certain degree of airflow change before and after moving to the defective position. Therefore, in the airflow characterization sequence, abnormal data points may appear in multiple local areas.

[0059] In one embodiment of the present application, in order to obtain each mutation point in the airflow characterization sequence, a sliding T algorithm is used to detect mutation points. The input of the sliding T algorithm is the airflow characterization sequence. The window size in the sliding T algorithm is set to 11 in this embodiment. The implementer can set it according to the actual situation. This application does not limit this. The output of the sliding T algorithm is all the mutation points in the airflow characterization sequence. The sliding T algorithm is an existing well-known technology, and the specific process will not be described in detail in this application.

[0060] It should be noted that, under the premise that the purpose of detecting mutation points in the airflow characterization sequence can be achieved, that is, the sliding T algorithm is used for mutation point detection, the implementer can choose other existing feasible mutation point detection algorithms according to actual conditions, such as LOF anomaly detection algorithm, Pettitt mutation detection algorithm, etc., and this application does not impose any restrictions on this.

[0061] According to the above analysis, the high airflow characterization coefficient of the bottle cap at the mutation position may be caused by leakage of the separated bottle cap. Combined with the distribution characteristics of the airflow characterization coefficient of the bottle cap near the mutation position, the distribution anomaly coefficient corresponding to each mutation point is calculated.

[0062] Calculate the test result statistics of all the airflow characterization coefficients of the bottle caps in a local neighborhood centered on each mutation point, and determine the distribution anomaly coefficient of each mutation point based on the test result statistics;

[0063] The distribution anomaly coefficient of each mutation point is negatively correlated with the test result statistic.

[0064] It should be understood that a negative correlation means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases, such as a subtraction relationship, a division relationship, etc.

[0065] In one embodiment of the present application, for the airflow characterization sequence, each mutation point is taken as the center, and L bottle cap airflow characterization coefficients are extended to both sides as the local neighborhood of each mutation point. If there is missing data in the local neighborhood, the missing value filling algorithm is used to fill it. All bottle cap airflow characterization coefficients in the local neighborhood of each mutation point are used as the input of the Kolmogorov-Smirnov (KS) test algorithm, and the output is the test result statistic of each mutation point, wherein the distribution is assumed to be a Gaussian distribution, the KS test algorithm is an existing well-known technology, and the specific process is not repeated in this application; the inverse of the test result statistic of each mutation point is used as the distribution anomaly coefficient of each mutation point.

[0066] In this embodiment, L=10, and the implementer can set it according to the actual situation. This application does not impose any restrictions on this. The missing value filling algorithm adopts linear interpolation to fill in the missing value. The implementer can select other existing feasible missing value filling algorithms according to the actual situation. This application does not impose any restrictions on this. For the Kolmogorov-Smirnov (KS) test algorithm, the implementer can also select other existing feasible hypothesis testing algorithms according to the actual situation, such as the Anderson-Darling test algorithm, the Cramér–von Mises test algorithm, etc., and this application does not impose any restrictions on this.

[0067] If the test result statistic is smaller, it means that the data in the local neighborhood of the mutation point is closer to the Gaussian distribution, and the corresponding distribution anomaly coefficient is larger, then the possibility of abnormal airflow at the sensor location at the time corresponding to the mutation point in the airflow characterization sequence is greater, and the separation bottle cap is more likely to leak.

[0068] In the detection process of the present application, the piezoelectric pressure sensor rotates around the honey water separation bottle cap for multiple times. If there is a fault in the separation bottle cap and air leakage occurs, the reflection on the distribution abnormality coefficient will appear periodically in the sequence. Each rotation of the piezoelectric pressure sensor around the separation bottle cap is regarded as a cycle. Since the duration of one rotation is set to 6 seconds in this embodiment, the airflow characterization sequence in this embodiment is divided into 5 cycles.

[0069] The sampling time corresponding to the maximum value of all the distribution anomaly coefficients in the first cycle of the airflow characterization sequence is recorded as the abnormal order characterization value P1, and the sampling time corresponding to the maximum value of all the distribution anomaly coefficients in the second cycle of the airflow characterization sequence is recorded as the abnormal order characterization value P2, and so on, to obtain the abnormal order characterization value P3, abnormal order characterization value P4 and abnormal order characterization value P5. If there is a leaking airflow, the values ​​of the obtained abnormal order characterization values ​​P1 to P5 will show a regular feature of nearly equal spacing.

[0070] Step S005, analyzing the distribution law of the abnormal order characterization values ​​of all cycles, determining the leakage risk index of the separation bottle cap, and detecting the sealing of the separation bottle cap.

[0071] The difference between two adjacent data in the sequence {P1, P2, P3, P4, P5} is calculated, and the leakage risk index of the separated bottle cap is determined based on the difference.

[0072] The leakage risk index of the separated bottle caps is the reverse mapping result of the inverse of the discrete degrees of all the differences.

[0073] It should be understood that the difference indicates the degree of difference between two data, which can be calculated by difference, absolute value of difference, ratio, etc. The reverse mapping means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases, for example, subtraction, division, etc. The degree of dispersion can be calculated by variance, standard deviation, etc.

[0074] In one embodiment of the present application, the difference between two adjacent data in the sequence {P1, P2, P3, P4, P5} is calculated, and the reciprocal of the standard deviation of all the differences in the sequence {P1, P2, P3, P4, P5} is used as the leakage risk index of the separated bottle cap.

[0075] If the leakage risk index is larger, it means that airflow with similar abnormality is detected at the same position in each rotation cycle, and the possibility of leakage in the honey water separation bottle cap is greater.

[0076] Calculate the normalized value of the leakage risk index of the separation bottle cap. If the normalized value is greater than the preset threshold, it is determined that there is gas leakage in the honey water separation bottle cap and the sealing is abnormal. If the normalized value is less than or equal to the preset threshold, it is determined that there is no gas leakage in the honey water separation bottle cap and the sealing is normal.

[0077] In one embodiment of the present application, the preset threshold is manually set to 0.8, and the implementer can set it according to the actual situation, and the present application does not limit it here.

[0078] In summary, the present application injects gas into a container equipped with a separation bottle cap, uses a piezoelectric pressure sensor to collect voltage data at each moment on the side of the separation bottle cap, constructs a sliding window centered on the voltage data at each moment, and analyzes the volatility of all data in the sliding window to obtain the change in the airflow amplitude of the separation bottle cap at each moment, which reflects the degree of fluctuation of the voltage data at each moment, embodies the possibility of gas leakage in the separation bottle cap, and improves the reliability of the detection of the sealing of the separation bottle cap; analyzes the frequency domain change trend of all data in the sliding window, obtains the high-frequency significance of the airflow at each moment of the separation bottle cap, further amplifies the anomaly of the voltage data, avoids ignoring the tiny leakage of the separation bottle cap, and improves the timeliness of the detection of the anomaly of the sealing of the separation bottle cap; combined with the airflow amplitude change The amount and high-frequency significance of the airflow are used to determine the airflow characterization coefficient of the bottle cap at each moment of the separation bottle cap, which integrates the abnormal characteristics of the voltage data and improves the accuracy of the sealing detection of the honey water separation bottle cap; the abnormal degree of the bottle cap airflow characterization coefficient at all moments is analyzed, and the distribution abnormality coefficient of each abnormal point in the bottle cap airflow characterization coefficient at all moments is determined, which reduces the error influence degree of the sealing detection of the honey water separation bottle cap; the bottle cap airflow characterization coefficient at all moments is divided into each period, and based on the distribution abnormality coefficient, the abnormal order characterization value of each period is determined, the distribution law of the abnormal order characterization value of all periods is analyzed, and the leakage risk index of the separation bottle cap is determined, which reflects the possibility of abnormal sealing of the separation bottle cap and improves the accuracy of the sealing detection of the honey water separation bottle cap.

[0079] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0080] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0081] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for intelligently detecting the sealing of a honey and water separation bottle cap, It is characterized in that The method comprises the following steps: Inject gas into a container equipped with a honey and water separation bottle cap, and use a piezoelectric pressure sensor to collect voltage data at each moment on the side of the separation bottle cap; A sliding window centered on the voltage data at each moment is constructed, and the volatility of all data in the sliding window is analyzed to obtain the change in the airflow amplitude at each moment of the separated bottle cap. Analyze the frequency domain change trend of all data in the sliding window to obtain the high-frequency significance of the airflow at each moment of separating the bottle cap; determine the bottle cap airflow characterization coefficient at each moment of separating the bottle cap by combining the airflow amplitude change and the airflow high-frequency significance; Analyze the abnormal degree of the bottle cap airflow characterization coefficient at all times, and determine the distribution abnormality coefficient of each abnormal point in the bottle cap airflow characterization coefficient at all times; divide the bottle cap airflow characterization coefficient at all times into each period, and determine the abnormal order characterization value of each period based on the distribution abnormality coefficient; Analyze the distribution law of the abnormal order characterization values ​​of all cycles, determine the leakage risk index of the separation bottle cap, and test the sealing of the separation bottle cap; The process of determining the high frequency significance of the airflow is as follows: All voltage data in the sliding window at each moment are converted into a frequency spectrum using a frequency domain conversion algorithm, and the changing trend of each voltage data in the sliding window at each moment in the frequency spectrum is calculated, and the forward fusion result of all the changing trends in the sliding window at each moment is used as the high-frequency significance of the airflow; The process of determining the bottle cap airflow characterization coefficient is as follows: The high-frequency significance of the airflow is non-negatively mapped, and the bottle cap airflow characterization coefficient is determined by combining the result of the non-negative mapping and the change in the airflow amplitude, wherein the bottle cap airflow characterization coefficient is positively correlated with the result of the non-negative mapping and the change in the airflow amplitude; The determination process of the distribution anomaly coefficient is: Obtain each mutation point in the airflow characterization coefficient of the bottle cap at all times, calculate the test result statistics of all the airflow characterization coefficients of the bottle cap in a local neighborhood centered on each mutation point, and determine the distribution abnormality coefficient based on the test result statistics; The distribution anomaly coefficient is negatively correlated with the test result statistic; The abnormal order characterization value is the time corresponding to the maximum value of the distribution abnormality coefficient at all times in each cycle.

2. A method for detecting the sealing of a honey-water separation bottle cap as claimed in claim 1, It is characterized in that The airflow amplitude variation is the coefficient of variation of all voltage data within the sliding window.

3. A sealing intelligent detection method for a honey water separation bottle cap as claimed in claim 1, It is characterized in that The division into cycles specifically refers to the length of time for the piezoelectric pressure sensor to rotate around the separation bottle cap once as one cycle.

4. A sealing intelligent detection method for a honey water separation bottle cap as claimed in claim 1, It is characterized in that The process of determining the leakage risk index is as follows: The difference between the abnormal order characterization values ​​between two adjacent cycles is calculated, and the leakage risk index of the separated bottle cap is determined based on the difference.

5. A sealing intelligent detection method for a honey-water separation bottle cap as claimed in claim 4, It is characterized in that The leakage risk index is a reverse mapping result of the discrete degrees of all the differences.

6. A method for detecting the sealing of a honey-water separation bottle cap as claimed in claim 1, It is characterized in that The method of testing the sealing performance of the separation bottle cap comprises: If the normalized value of the leakage risk index is greater than a preset threshold, it is determined that the sealing of the separation bottle cap is abnormal; otherwise, it is determined that the sealing of the separation bottle cap is normal.

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