A method and system for monitoring the operating status of toothpaste production equipment

Abstract

The present invention relates to the field of data processing technology, and in particular to a method and system for monitoring the operating status of toothpaste production equipment. The method comprises the following steps: obtaining a weight correction factor for each parameter data based on the deviation of the initial LOF value between each parameter data and historical parameter data; obtaining a corrected attenuation factor for each historical parameter data of each parameter data based on the gradient change of the initial LOF value of each historical parameter data; correcting each parameter data based on the corrected attenuation factor and the weight correction factor to obtain a corrected LOF value for each parameter data; and monitoring the operating status of the toothpaste production equipment based on the corrected LOF value of each parameter data in each dimensional parameter data sequence. The present invention improves the accuracy of monitoring the operating status of the toothpaste production equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for monitoring the operating status of toothpaste production equipment. Background Art

[0002] As an everyday consumer product, toothpaste production involves the coordinated production of multiple processes (such as paste making, filling, and packaging). Equipment stability directly impacts product quality and production efficiency. With technological advancements, toothpaste production equipment has achieved partial automation, accumulating a vast amount of operational data. However, this process lacks intelligent analysis tools. For example, historical data such as motor current and vacuum levels recorded by PLC control systems cannot be identified without algorithms to uncover potential correlations. This makes it difficult to identify abnormalities in equipment operation and translate them into fault warnings.

[0003] The Local Outlier Factor (LOF) algorithm can identify abnormalities in equipment operation by comparing the density difference between a data point and its neighborhood. For example, in toothpaste filling equipment, if the local density of parameters such as temperature, pressure, and flow rate significantly deviates from the normal range, the LOF algorithm can quickly locate potential fault points and avoid downtime caused by overheating or wear.

[0004] However, toothpaste production involves the coordinated work of parameter data in multiple dimensions, such as temperature, pressure, and flow, and there is a nonlinear coupling relationship between the parameter data (for example, an increase in temperature may change the viscosity of the material, thereby affecting the flow); and the LOF value is calculated based on the parameter space distribution. Because the original parameters of the toothpaste production equipment may dynamically drift due to equipment aging, differences in raw material batches, or environmental fluctuations (such as workshop temperature), the LOF value of the equipment parameter data deviates from the actual abnormal state. Directly using the uncorrected LOF value will reduce the accuracy of the toothpaste production equipment operation status monitoring. Summary of the Invention

[0005] In order to solve the technical problem that toothpaste production involves the coordinated work of multiple dimensional parameter data such as temperature, pressure, and flow, and there is a nonlinear coupling relationship between the parameter data, which causes the LOF value of the equipment parameter data to deviate from the actual abnormal state, and directly using the uncorrected LOF value will reduce the accuracy of toothpaste production equipment operation status monitoring, the present invention provides a toothpaste production equipment operation status monitoring method and system.

[0006] In a first aspect, the present invention provides a method for monitoring the operating status of toothpaste production equipment, which adopts the following technical solution:

[0007] A method for monitoring the operating status of toothpaste production equipment, comprising the steps of:

[0008] Collect multi-dimensional parameter data sequences during the operation of toothpaste production equipment;

[0009] Obtain the initial LOF value of each parameter data in each dimension parameter data sequence; obtain all historical parameter data of each parameter data; obtain the weight correction factor of each parameter data based on the deviation of the initial LOF value between each parameter data and the historical parameter data; obtain the correction attenuation factor of each historical parameter data of each parameter data based on the gradient change of the initial LOF value of each historical parameter data;

[0010] According to the modified attenuation factor and the weight correction factor, each parameter data is corrected to obtain the corrected LOF value of each parameter data;

[0011] The operating status of the toothpaste production equipment is monitored based on the corrected LOF value of each parameter data in each dimensional parameter data sequence.

[0012] The innovation of the present invention lies in obtaining a weight correction factor for each parameter data based on the deviation of the initial LOF value between each parameter data and the historical parameter data; dynamically adjusting the corrected weight factor to make the correction result more accurate; obtaining a corrected attenuation factor for each historical parameter data of each parameter data based on the gradient change of the initial LOF value of each historical parameter data; thereby reducing their excessive influence on the current correction and ensuring greater reliance on the latest and more relevant parameter data during monitoring; correcting each parameter data based on the corrected attenuation factor and the weight correction factor to obtain a corrected LOF value for each parameter data; thereby the corrected LOF value can more accurately reflect the local outlier situation of the current parameter data relative to the historical parameter data; monitoring the operating status of the toothpaste production equipment based on the corrected LOF value, thereby improving the accuracy of monitoring the operating status of the toothpaste production equipment.

[0013] Preferably, the obtaining of all historical parameter data of each parameter data includes:

[0014] A historical parameter m is preset, and in any dimensional parameter data sequence when the toothpaste production equipment is running, the m parameter data before the t-th parameter data are all recorded as the historical parameter data of the t-th parameter data.

[0015] Preferably, obtaining the weight correction factor of each parameter data according to the deviation of the initial LOF value between each parameter data and the historical parameter data includes:

[0016] Get the offset value of each parameter data;

[0017] The inversely proportional normalized value of the ratio between the offset value of the t-th parameter data and the standard deviation of the initial LOF values ​​of all historical parameter data of the t-th parameter data is used as the weight correction factor of the t-th parameter data.

[0018] Beneficial effect: By calculating the deviation of the initial LOF value between the current parameter data and the historical parameter data, the difference in the initial LOF value between the current parameter data and the historical parameter data can be effectively identified, so as to dynamically adjust the correction weight factor to make the correction result more accurate and avoid over-reliance on the initial LOF value of the historical parameter data that does not conform to the current trend.

[0019] Preferably, obtaining the offset value of each parameter data includes:

[0020] The absolute value of the difference between the mean of the initial LOF values ​​of all historical parameter data of the t-th parameter data and the initial LOF value of the t-th parameter data is recorded as the offset value of the t-th parameter data.

[0021] Preferably, the step of obtaining the corrected attenuation factor of each historical parameter data according to the gradient change of the initial LOF value of each historical parameter data includes:

[0022] Obtain the gradient value of the initial LOF value of each historical parameter data of each parameter data;

[0023] The first The gradient of the historical parameter data is The normalized value of the product of the serial number values ​​of the historical parameter data is used as the first The corrected attenuation factor of historical parameter data.

[0024] Beneficial effect: By attenuating the weight of historical parameter data for each parameter data, their excessive influence on the current correction can be reduced, ensuring greater reliance on the latest and more relevant parameter data during monitoring.

[0025] Preferably, the step of obtaining the gradient value of the initial LOF value of each historical parameter data of each parameter data includes:

[0026] For the tth parameter data Historical parameter data, in Among all the historical parameter data of the historical parameter data, The absolute value of the difference between the bth historical parameter data and the b+1th historical parameter data is recorded as the first difference of the bth historical parameter data; The cumulative sum of the first differences of all historical parameter data of the first historical parameter data is used as the first The gradient value of the initial LOF value of the historical parameter data.

[0027] Preferably, the step of correcting each parameter data according to the corrected attenuation factor and the weight correction factor to obtain the corrected LOF value of each parameter data includes:

[0028] Obtaining a first correction value according to the weight correction factor;

[0029] Obtaining a second correction value according to the corrected attenuation factor;

[0030] The sum of the first correction value and the second correction value is used as the corrected LOF value of the t-th parameter data.

[0031] Beneficial effect: The corrected LOF value can more accurately reflect the local outliers of the current parameter data relative to the historical parameter data; if the influence of the historical parameter data is too large, the corrected attenuation factor can effectively reduce its influence, ensuring that the corrected LOF value can more accurately capture abnormal data.

[0032] Preferably, obtaining the first correction value according to the weight correction factor includes:

[0033] The product of the weight correction factor of the t-th parameter data and the initial LOF value of the t-th parameter data is recorded as the first correction value.

[0034] Preferably, obtaining the second correction value according to the corrected attenuation factor includes:

[0035] The difference between 1 and the weight correction factor of the t-th parameter data is recorded as the correction difference; the t-th parameter data is recorded as The correction attenuation factor of the historical parameter data is The product of the initial LOF values ​​of the historical parameter data is recorded as the first product of the historical parameter data of the tth parameter data; and the product of the cumulative sum of the first products of all the historical parameter data of the tth parameter data and the corrected difference value, which is recorded as the second corrected value.

[0036] Beneficial effect: With the continuous updating of parameter data, historical parameter data may cause unnecessary interference to the judgment of the monitoring system. By correcting the attenuation factor, the impact of these outdated historical parameter data can be effectively reduced, making the monitoring system more dependent on parameter data that is highly correlated with the current parameter data.

[0037] In a second aspect, the present invention provides a toothpaste production equipment operation status monitoring system, which adopts the following technical solution:

[0038] A toothpaste production equipment operation status monitoring system includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned toothpaste production equipment operation status monitoring method is implemented.

[0039] By adopting the above technical solution, the above-mentioned method for monitoring the operating status of toothpaste production equipment is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0040] The present invention has the following technical effects: the present invention obtains a weight correction factor for each parameter data according to the deviation of the initial LOF value between each parameter data and the historical parameter data; thereby dynamically adjusting the correction weight factor to make the correction result more accurate; obtains a correction attenuation factor for each historical parameter data of each parameter data according to the gradient change of the initial LOF value of each historical parameter data; thereby reducing their excessive influence on the current correction and ensuring greater reliance on the latest and more relevant parameter data during monitoring; according to the correction attenuation factor and the weight correction factor, corrects each parameter data to obtain a corrected LOF value for each parameter data; thereby, the corrected LOF value can more accurately reflect the local outlier situation of the current parameter data relative to the historical parameter data; and monitors the operating status of the toothpaste production equipment based on the corrected LOF value, thereby improving the accuracy of monitoring the operating status of the toothpaste production equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The present invention is a flowchart of a method for monitoring the operating status of toothpaste production equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0043] The embodiment of the present invention discloses a method for monitoring the operating status of a toothpaste production device, referring to Figure 1 , including steps S1 to S4:

[0044] S1: Collect multi-dimensional parameter data sequences during the operation of toothpaste production equipment.

[0045] In the implementation of the present invention, the preset sampling frequency is 1 second / time. Temperature sensors, pressure sensors, and flow sensors are installed on the heating pipes, storage tank inlets and outlets, and raw material pipelines of the toothpaste production equipment. During the operation of the toothpaste production equipment, temperature data, pressure data, and flow data are collected at each moment for a total of two hours.

[0046] The three dimensional data of temperature, pressure and flow at each sampling moment are used as a multi-dimensional parameter data sequence during the operation of the toothpaste production equipment.

[0047] S2: Obtain the initial LOF value of each parameter data in each dimension parameter data sequence; obtain all historical parameter data of each parameter data; obtain the weight correction factor of each parameter data according to the deviation of the initial LOF value between each parameter data and the historical parameter data; obtain the correction attenuation factor of each historical parameter data of each parameter data according to the gradient change of the initial LOF value of each historical parameter data.

[0048] It should be noted that toothpaste production involves the coordinated work of multiple dimensional parameter data such as temperature, pressure, and flow, and there is a nonlinear coupling relationship between the parameter data (for example, an increase in temperature may change the viscosity of the material, thereby affecting the flow); and the LOF value is calculated based on the parameter space distribution. Because the original parameters of the toothpaste production equipment may dynamically drift due to equipment aging, differences in raw material batches or environmental fluctuations (such as workshop temperature), the LOF value of the equipment parameter data deviates from the actual abnormal state. Directly using the uncorrected LOF value will reduce the accuracy of the toothpaste production equipment operation status monitoring; therefore, it is necessary to introduce a correction mechanism to correct the LOF values ​​of the multiple dimensional parameter data during the operation of the toothpaste production equipment to ensure its accuracy and avoid misjudgment.

[0049] The embodiment of the present invention is described by taking any dimensional parameter data during the operation of a toothpaste production device as an example;

[0050] In an embodiment of the present invention, the LOF algorithm is used to obtain the LOF value of each parameter data in any one dimensional parameter data sequence when the toothpaste production equipment is running, and the LOF value is recorded as the initial LOF value of each parameter data.

[0051] The LOF algorithm is an existing technology and will not be described in detail in this embodiment.

[0052] It should be noted that the LOF value is used to measure the density of a data point in a local area compared with the density of its neighborhood; if the LOF value of a data point deviates significantly from the LOF value of historical data, it may indicate that the point is abnormal; when the LOF value of a data point is found to be abnormal, it is necessary to correct it based on historical data to ensure that it is within an appropriate range; in the corresponding correction method, the weight of the data point should be appropriately reduced, in order to avoid abnormal fluctuations from having too much impact on the overall data analysis during the subsequent correction process; by reducing the weight, the impact of outliers can be reduced to avoid a cumulative amplification effect on the overall data.

[0053] In the embodiment of the present invention, the specific method for obtaining the weight correction factor of each parameter data according to the deviation of the initial LOF value between each parameter data and the historical parameter data is as follows:

[0054] A historical parameter m=5 is preset. In any dimension parameter data sequence during the operation of the toothpaste production equipment, the m parameter data before the t-th parameter data are recorded as the historical parameter data of the t-th parameter data.

[0055] The absolute value of the difference between the mean of the initial LOF values ​​of all historical parameter data of the t-th parameter data and the initial LOF value of the t-th parameter data is recorded as the offset value of the t-th parameter data; the inversely proportional normalized value of the ratio between the offset value of the t-th parameter data and the standard deviation of the initial LOF values ​​of all historical parameter data of the t-th parameter data is used as the weight correction factor of the t-th parameter data;

[0056] The specific formula is:

[0057]

[0058] Where, Represents the weight correction factor of the tth parameter data; Indicates the initial LOF value of the t-th parameter data; Represents the mean of the initial LOF values ​​of all historical parameter data for the t-th parameter data; Represents the preset hyperparameters. This implementation presets , used to prevent the denominator from being 0; Represents an exponential function with a natural constant as the base, and the embodiment adopts Model to present inverse proportional relationship and normalization processing, As the input of the model, the implementer can choose the inverse proportional function and normalization function according to the actual situation.

[0059] in, Indicates the degree of deviation of the t-th parameter data; when the initial LOF value of the t-th parameter data deviates greatly from the mean of the initial LOF values ​​of all its historical parameter data, it indicates that there is a certain abnormal data fluctuation in the t-th parameter data, that is, the initial LOF value of the t-th parameter data is likely to be distorted, and it is necessary to make a large degree of correction to the initial LOF value of the t-th parameter data through the initial LOF value of the historical parameter data of the t-th parameter data, but the weight parameter corresponding to the initial LOF value of the t-th parameter data also needs to be reduced to avoid this deviation from being accumulated and amplified in the subsequent correction process.

[0060] It should be noted that when the initial LOF value gradient of all historical parameter data of a certain parameter data is large, it means that its historical parameter data shows an upward trend, which may indicate that the parameter data of this dimension of the toothpaste production equipment in the past has changed greatly or has abnormal fluctuations; in this case, the current correction work should rely more on recent data, because recent data can better reflect the current operating status of the equipment, so it is necessary to accelerate the attenuation of historical data; the attenuation factor determines the degree of influence of historical data on the correction of current data, which means that the higher the attention paid to recent data.

[0061] In the embodiment of the present invention, the specific method for obtaining the modified attenuation factor of each historical parameter data of each parameter data according to the gradient change of the initial LOF value of each historical parameter data is as follows:

[0062] For the tth parameter data Historical parameter data, in Among all the historical parameter data of the historical parameter data, The absolute value of the difference between the bth historical parameter data and the b+1th historical parameter data is recorded as the first difference of the bth historical parameter data; The cumulative sum of the first differences of all historical parameter data of the first historical parameter data is used as the first The gradient value of the initial LOF value of the historical parameter data;

[0063] The first The gradient of the historical parameter data is The normalized value of the product of the serial number values ​​of the historical parameter data is used as the first Corrected attenuation factor of historical parameter data;

[0064] The specific formula is:

[0065]

[0066] Where, Indicates the tth parameter data Corrected attenuation factor of historical parameter data; Indicates the tth parameter data The serial number of each historical parameter data; Indicates the tth parameter data The gradient value of the initial LOF value of the historical parameter data; represents the linear normalization function.

[0067] S3: Correct each parameter data according to the corrected attenuation factor and the weight correction factor to obtain a corrected LOF value for each parameter data.

[0068] It should be noted that the operating parameters of toothpaste production equipment (such as temperature, pressure, and flow) may fluctuate due to factors such as raw material batches, ambient temperature and humidity, and equipment wear. Exponential smoothing, by assigning higher weights to recent data and lower weights to longer-term data, can quickly capture short-term trends in parameter data and avoid lags in LOF value corrections caused by interference from historical parameter data. For example, if a flow parameter abnormality is detected due to changes in raw material viscosity, exponential smoothing can quickly adjust the weights, bringing the revised LOF value closer to the current state.

[0069] In the embodiment of the present invention, each parameter data is corrected according to the corrected attenuation factor and the weight correction factor, and the specific method for obtaining the corrected LOF value of each parameter data is as follows:

[0070] The product of the weight correction factor of the t-th parameter data and the initial LOF value of the t-th parameter data is recorded as the first correction value;

[0071] The difference between 1 and the weight correction factor of the t-th parameter data is recorded as the correction difference; the product of the correction attenuation factor of the r-th historical parameter data of the t-th parameter data and the initial LOF value of the r-th historical parameter data is recorded as the first product of the r-th historical parameter data; the product of the cumulative sum of the first products of all historical parameter data of the t-th parameter data and the correction difference is recorded as the second correction value;

[0072] The sum of the first correction value and the second correction value is used as the corrected LOF value of the t-th parameter data;

[0073] The specific formula is:

[0074]

[0075] Where, represents the corrected LOF value of the tth parameter data; Represents the weight correction factor of the tth parameter data; Indicates the initial LOF value of the t-th parameter data; The number of all historical parameter data representing the t-th parameter data; Indicates the tth parameter data Corrected attenuation factor of historical parameter data; Indicates the tth parameter data The initial LOF value of historical parameter data.

[0076] It should be noted that the above method corrects the initial LOF value of the t-th parameter data by using the weight correction factor of the t-th parameter data and the correction attenuation factor of each of its historical parameter data; wherein, the weight correction factor indicates the weight of the initial LOF value of the t-th parameter data in its corrected LOF value, and the larger the weight correction factor, the higher the credibility of the LOF value of the t-th parameter data, corresponding to the larger proportion of its initial LOF value in the correction process of the LOF value of the t-th parameter data, and the smaller the proportion of the initial LOF value of the historical parameter data; in addition, middle, The overall weight of the initial LOF value of the historical parameter data is controlled, and the correction attenuation factor determines its internal distribution. The larger the correction attenuation factor of the historical parameter data, the more emphasis is placed on the impact of recent historical production parameters during the correction process.

[0077] S4: Monitor the operating status of the toothpaste production equipment based on the corrected LOF value of each parameter data in each dimensional parameter data sequence.

[0078] In an embodiment of the present invention, a specific method for monitoring the operating status of a toothpaste production device based on the corrected LOF value of each parameter data in each dimensional parameter data sequence is as follows:

[0079] A threshold parameter T=1 is preset. In other embodiments, the implementer may preset the value of the threshold parameter T according to specific implementation conditions.

[0080] If the corrected LOF value of any parameter data in any dimensional parameter data sequence is greater than the threshold parameter T, it means that abnormal data fluctuations have occurred in the parameter data sequence, and there may be potential failure risks during the operation of the toothpaste production equipment. Relevant technical personnel must be notified in time for inspection and maintenance.

[0081] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for monitoring the operating status of toothpaste production equipment, characterized in that: Including steps: Collect multi-dimensional parameter data sequences during the operation of toothpaste production equipment; Obtain the initial LOF value of each parameter data in each dimension parameter data sequence; obtain all historical parameter data of each parameter data; obtain the offset value of each parameter data; take the inverse proportional normalized value of the ratio between the offset value of the t-th parameter data and the standard deviation of the initial LOF value of all historical parameter data of the t-th parameter data as the weight correction factor of the t-th parameter data; for the t-th parameter data Historical parameter data, in Among all the historical parameter data of the historical parameter data, The absolute value of the difference between the bth historical parameter data and the b+1th historical parameter data is recorded as the first difference of the bth historical parameter data; The cumulative sum of the first differences of all historical parameter data of the first historical parameter data is used as the first The gradient value of the initial LOF value of the historical parameter data; The gradient of the historical parameter data is The normalized value of the product of the serial number values ​​of the historical parameter data is used as the first Corrected attenuation factor of historical parameter data; According to the modified attenuation factor and the weight correction factor, each parameter data is corrected to obtain the corrected LOF value of each parameter data; The operating status of the toothpaste production equipment is monitored based on the corrected LOF value of each parameter data in each dimensional parameter data sequence.

2. A method for monitoring the operating status of toothpaste production equipment according to claim 1, characterized in that: The acquisition of all historical parameter data for each parameter data includes: A historical parameter m is preset, and in any dimensional parameter data sequence when the toothpaste production equipment is running, the m parameter data before the t-th parameter data are all recorded as the historical parameter data of the t-th parameter data.

3. The method for monitoring the operating status of toothpaste production equipment according to claim 1, wherein: The obtaining of the offset value of each parameter data includes: The absolute value of the difference between the mean of the initial LOF values ​​of all historical parameter data of the t-th parameter data and the initial LOF value of the t-th parameter data is recorded as the offset value of the t-th parameter data.

4. The method for monitoring the operating status of toothpaste production equipment according to claim 1, wherein: The step of correcting each parameter data according to the corrected attenuation factor and the weight correction factor to obtain a corrected LOF value for each parameter data includes: Obtaining a first correction value according to the weight correction factor; Obtaining a second correction value according to the corrected attenuation factor; The sum of the first correction value and the second correction value is used as the corrected LOF value of the t-th parameter data.

5. A method for monitoring the operating status of toothpaste production equipment according to claim 4, characterized in that: The obtaining of the first correction value according to the weight correction factor includes: The product of the weight correction factor of the t-th parameter data and the initial LOF value of the t-th parameter data is recorded as the first correction value.

6. A method for monitoring the operating status of toothpaste production equipment according to claim 4, characterized in that: The obtaining of the second correction value according to the corrected attenuation factor includes: The difference between 1 and the weight correction factor of the t-th parameter data is recorded as the correction difference; the t-th parameter data is recorded as The correction attenuation factor of the historical parameter data is The product of the initial LOF values ​​of the historical parameter data is recorded as the first product of the historical parameter data of the tth parameter data; and the product of the cumulative sum of the first products of all the historical parameter data of the tth parameter data and the corrected difference value, which is recorded as the second corrected value.

7. A toothpaste production equipment operation status monitoring system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for monitoring the operating status of a toothpaste production device according to any one of claims 1 to 6 is implemented.

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