Cosmetic production line safety monitoring method and system based on data analysis

Abstract

The invention relates to the technical field of production safety and intelligent monitoring, in particular to a cosmetic production line safety monitoring method and system based on data analysis, and the method comprises the steps: obtaining real-time operation parameters through a multi-source data collection module, and generating a production environment dynamic mapping graph; extracting a fluctuation trend and abnormal distribution characteristics, and generating a region data adjustment instruction set; adjusting a signal acquisition strategy, and constructing a regional signal alignment state table; identifying the signal intensity variation, and forming a regional signal adjustment mark graph; and finally outputting a monitoring area state evaluation value list. According to the method, the signal fluctuation expression precision and the dynamic regulation and control capability can be remarkably improved, the adaptability and the response sensitivity of safety monitoring in a complex production environment are enhanced, and high-precision local compensation and equipment state quantitative judgment are realized.

Description

Technical Field

[0001] The present invention relates to the field of production safety and intelligent monitoring technology, and in particular to a cosmetics production line safety monitoring method and system based on data analysis. Background Art

[0002] Safety monitoring technology for cosmetics production lines is crucial for ensuring product quality and improving production efficiency. Monitoring methods based on big data analysis have become a research hotspot due to their ability to process massive amounts of data in real time and identify potential risks. However, existing technologies still exhibit limitations in data processing efficiency, monitoring accuracy, and system adaptability.

[0003] A search revealed a patent with publication number CN112214496B, which proposes a cosmetics production line safety monitoring method and cloud server based on big data analysis. This technical solution obtains first cosmetics production data and performs format analysis, mapping equipment operating parameters to second cosmetics production data to ensure format consistency. It also utilizes a preset data analysis thread to achieve safety monitoring of the target production line. However, this solution relies on fixed data mapping rules and preset analysis threads, potentially lacking data processing flexibility when faced with changing production environments and complex equipment interactions. Furthermore, this method pays little attention to the processing requirements of unstructured data (such as images and videos), which may affect the comprehensiveness and accuracy of monitoring results.

[0004] The above issues indicate that existing cosmetics production line safety monitoring technologies based on big data analysis still have room for improvement in terms of data processing flexibility, compatibility with unstructured data, and adaptability to complex production environments. Therefore, the present invention aims to provide a cosmetics production line safety monitoring method and system based on data analysis to enhance the level of intelligent data processing, strengthen support for multi-source heterogeneous data, and optimize adaptability and monitoring accuracy in dynamic production environments, thereby better meeting the demand for efficient and accurate safety monitoring in modern cosmetics production. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the embodiments of the present invention provide a cosmetics production line safety monitoring method and system based on data analysis. The technical solution is as follows:

[0006] The cosmetics production line safety monitoring method based on data analysis includes the following steps:

[0007] S1: The multi-source data acquisition module acquires real-time operating parameters of the production line, records the changing characteristics of equipment status information and unstructured data streams, identifies the initial response curves of key nodes based on signal differences, and generates a dynamic map of the production environment.

[0008] S2: Based on the dynamic mapping diagram of the production environment, the fluctuation trend and abnormal distribution characteristics of the equipment signal channel are extracted, the deviation is judged by combining the segment signal difference with the preset threshold range, the deviation segment is screened and the data processing direction is adjusted, and a regional data adjustment instruction set is generated;

[0009] S3: calling the regional data adjustment instruction set to adjust the signal acquisition strategy, synchronously recording the time axis response value and peak offset of the data acquisition module, and extracting the synchronization offset range of the corresponding region by comparing the signal time axis and amplitude changes before and after the adjustment to obtain the regional signal alignment status table;

[0010] S4: Call the synchronization stable segment signal sequence in the regional signal alignment state table, identify the signal intensity change in the regional differentiation time window, perform offset judgment with the change threshold set by the reconstruction unit, mark the area to be adjusted, and form a regional signal adjustment marking diagram.

[0011] As a further solution of the present invention, the dynamic mapping diagram of the production environment includes the equipment operation status distribution curve, signal fluctuation characteristic parameters, and channel classification identification; the regional data adjustment instruction set includes the data acquisition strategy parameter set, fluctuation trend control value, and segment signal compensation factor; the regional signal alignment status table includes the synchronization time offset, peak response difference value, and alignment status identification code; the regional signal adjustment mark diagram includes the segment position point to be adjusted, the intensity offset judgment result, and the structural change response identification.

[0012] As a further solution of the present invention, the steps for obtaining the dynamic map of the production environment are specifically as follows:

[0013] S111: Acquire real-time operating parameters of the production line through a multi-source data acquisition module, record the response values ​​of the equipment status information under different working conditions, and convert the signal strength according to the calibration coefficient to form an operating status file under the current working conditions, and obtain the equipment operating status parameter set;

[0014] S112: Based on the equipment operating status parameter set, record the response changes of equipment status information under differentiated operating conditions, analyze the mapping relationship between operating conditions and signal fluctuation rates, reconstruct the signal distribution characteristics in the operating environment, evaluate the stability of key components, and generate a dynamic mapping diagram of the production environment.

[0015] As a further solution of the present invention, the steps of obtaining the regional data adjustment instruction set are specifically as follows:

[0016] S211: Based on the production environment dynamic map, identify the fluctuation trend and abnormal distribution characteristics of the equipment signal channel, extract the signal change rate per unit time in the continuous channel, and perform interval classification based on the fluctuation gradient value to identify the response interval of the signal change on the channel to obtain the fluctuation response change interval;

[0017] S212: Call the fluctuation response change interval, scan the difference fluctuation amplitude and error trend of the channel segment according to the ratio of the channel segment signal difference and the upper and lower limit signals in the path, compare the matching degree of the real-time signal with the channel distribution trend, calculate the signal offset degree value, judge the abnormal distribution area in the channel, extract the path position group that needs to be adjusted, and generate the regional data adjustment instruction set.

[0018] As a further solution of the present invention, the step of obtaining the regional signal alignment status table is specifically as follows:

[0019] S311: Calling the regional data adjustment instruction set to adjust the signal acquisition strategy, comparing the current signal value according to the adjustment channel number and the target signal configuration, performing high- and low-priority signal difference adjustment, and recording the response start time, peak time, and peak amplitude to obtain an adjustment path response time series group;

[0020] S312: According to the adjustment path response time series group, extract the start time, peak time and amplitude of the channels before and after adjustment, identify the offset difference sequence, calculate the regional synchronization offset intensity value, map the intensity value to the channel distribution, filter the channel group within the synchronization range and arrange the signal timing sequence to obtain the regional signal alignment status table.

[0021] As a further solution of the present invention, the step of obtaining the regional signal adjustment signature map is specifically as follows:

[0022] S411: Calling the synchronization stable segment signal sequence in the regional signal alignment state table, extracting the signal strength per unit time of the synchronization stable segment, and comparing it with the strength sequence of the corresponding position of the adjacent regional signal segment, identifying the time deviation of the signal strength, and obtaining the regional signal offset value;

[0023] S412: Based on the regional signal offset value and the change threshold set by the reconstruction unit, determine the difference between the signal offset value of each region and the threshold, filter out the offset regions exceeding the threshold, and obtain the block change determination coefficient;

[0024] S413: Based on the block change judgment coefficient, detect the signal offset trend in the area and the continuity between the offset positions, mark the areas where the offset trend is stably rising or falling and spatially continuous, calculate the regional offset mark value, combine the spatial range of the offset area, identify the connected identification partition number and fill it into the mark map to form a regional signal adjustment mark map.

[0025] As a further solution of the present invention, the method further includes step S5:

[0026] S5: calling the positioning correction segment signal in the regional signal adjustment mark map, identifying the adjusted high and low priority signal combination value, reconstructing the regional equipment response state according to the fluctuation characteristic relationship corresponding to the combination signal, and outputting a monitoring area state assessment value list;

[0027] The monitoring area status assessment value list includes a high-priority and low-priority signal fluctuation comparison value, a regional response difference factor, and a response status level after reconstruction.

[0028] As a further solution of the present invention, the steps for obtaining the monitoring area status assessment value list are specifically as follows:

[0029] S511: calling the positioning correction segment signal in the regional signal adjustment mark map, extracting the high-priority and low-priority signal pairs of the positioning segment, matching the node coordinates with the channel numbers, and grouping the combined signal values ​​in a spatial sequence to generate a dual-channel positioning combination value set;

[0030] S512: Based on the dual-channel positioning combination value set, performing fluctuation difference discrimination on the combination value, extracting resolvable signal pairs, screening regional response groups of fluctuation characteristics according to the set dual-priority response threshold, and generating a dual-priority characteristic fluctuation rate sequence;

[0031] S513: Analyze the mapping relationship between fluctuation parameters and equipment status according to the dual-priority characteristic fluctuation rate sequence, perform dual-priority state inversion of regional nodes, identify the equipment status of the positioning segment, and output a monitoring area status assessment value list.

[0032] A cosmetics production line safety monitoring system based on data analysis, the system comprising:

[0033] The multi-source data acquisition module uses a sensor array to obtain real-time operating parameters of the production line, conducts horizontal comparisons of equipment status information under different working conditions, identifies signal response differences by channel grouping, and integrates all channel signal amplitude change segments to construct a dynamic map of the production environment;

[0034] The fluctuation positioning module extracts the regional dual-priority signal ratio sequence based on the dynamic mapping diagram of the production environment, identifies the response difference between channels and screens the deviation mutation segment, identifies the intersection point of the fluctuation trend, determines the range of the area that needs to be adjusted, and generates a fluctuation segment positioning set;

[0035] The signal synchronization module adjusts the ratio of high and low priority signal acquisition in the indicated area based on the fluctuation segment positioning set, records the time axis response value and peak offset amplitude sequence of the sensor array in the corresponding segment, compares the response difference between the channels before and after the adjustment, integrates the stable synchronization point group, and establishes a signal alignment synchronization state table;

[0036] The fluctuation structure identification module extracts the amplitude variation in the synchronization segment signal based on the signal alignment synchronization state table, compares the floating interval of the adjacent channel response according to the time window, maps the offset exceeding position to the two-dimensional imaging surface, marks the local fluctuation intensity mutation area, and outputs the fluctuation structure change layer;

[0037] The state parameter assessment module extracts the corresponding high and low priority signal combination values ​​based on the marked areas in the fluctuation structure change layer, calculates the unit area fluctuation response in combination with the set reference fluctuation rate value table, and outputs a list of monitoring area state assessment values.

[0038] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0039] In this invention, a multi-source data acquisition module acquires real-time production line operating parameters and constructs a complete dynamic map of the production environment. This module identifies signal fluctuation characteristics under different operating conditions and significantly improves the accuracy of signal transmission path fluctuation expression. By leveraging a linkage analysis mechanism that combines fluctuation trends with signal upper and lower limit differences, signal deviation segments are effectively identified and acquisition strategies fine-tuned, enabling higher resolution and dynamic control capabilities within signal acquisition channels within different priority segments. During the adjustment process, response time and peak value variations are simultaneously extracted to construct regional alignment status information, enhancing control over signal synchronization in densely structured areas and preventing the impact of time axis drift on subsequent recognition accuracy. Based on the alignment status, the difference between regional intensity changes and a preset threshold is further determined. By differentially identifying signal stable segments, areas requiring adjustment are precisely located, achieving local response compensation with high sensitivity. Finally, by combining signal fluctuation characteristics, the device state distribution is reconstructed, establishing a response hierarchy for high- and low-priority signals under multiple operating conditions, and outputting a state assessment value. This enhances the recognition of multi-layered structural details and the accuracy of quantitative state determination, significantly expanding the adaptability of safety monitoring applications in complex production environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of the method of the present invention;

[0041] Figure 2 This is a flowchart for obtaining a dynamic mapping diagram of the production environment of the present invention;

[0042] Figure 3 A flowchart for obtaining a regional data adjustment instruction set according to the present invention;

[0043] Figure 4 A flowchart for obtaining a regional signal alignment status table according to the present invention;

[0044] Figure 5 A flowchart for obtaining a regional signal adjustment marker map according to the present invention;

[0045] Figure 6 This is a flow chart for obtaining a list of monitoring area status assessment values ​​according to the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0047] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0048] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0049] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0050] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0051] See also Figure 1 The present invention provides a technical solution: a cosmetics production line safety monitoring method based on data analysis, comprising the following steps:

[0052] S1: The multi-source data acquisition module acquires real-time operating parameters of the production line, records the changing characteristics of equipment status information and unstructured data streams, identifies the initial response curves of key nodes based on signal differences, and generates a dynamic map of the production environment.

[0053] S2: Based on the dynamic mapping of the production environment, the fluctuation trend and abnormal distribution characteristics of the equipment signal channel are extracted. The deviation is judged by combining the segment signal difference with the preset threshold range. The deviated segments are screened and the data processing direction is adjusted to generate a regional data adjustment instruction set.

[0054] S3: Call the regional data adjustment instruction set to adjust the signal acquisition strategy, synchronously record the time axis response value and peak offset of the data acquisition module, and extract the synchronization offset range of the corresponding area by comparing the signal time axis and amplitude changes before and after the adjustment to obtain the regional signal alignment status table;

[0055] S4: calling the synchronization stable segment signal sequence in the regional signal alignment state table, identifying the signal strength change in the regional differentiation time window, performing offset judgment with the change threshold set by the reconstruction unit, marking the area to be adjusted, and forming a regional signal adjustment marking map;

[0056] S5: Call the positioning correction segment signal in the regional signal adjustment mark diagram, identify the adjusted high and low priority signal combination value, reconstruct the regional equipment response status according to the fluctuation characteristic relationship corresponding to the combination signal, and output the monitoring area status assessment value list.

[0057] The dynamic mapping diagram of the production environment includes the equipment operation status distribution curve, signal fluctuation characteristic parameters, and channel classification identification. The regional data adjustment instruction set includes the data acquisition strategy parameter set, fluctuation trend control value, and segment signal compensation factor. The regional signal alignment status table includes the synchronization time offset, peak response difference value, and alignment status identification code. The regional signal adjustment mark diagram includes the position point of the segment to be adjusted, the intensity offset judgment result, and the structural change response identification. The monitoring area status assessment value list includes the high and low priority signal fluctuation comparison value, the regional response difference factor, and the response status level after reconstruction.

[0058] See also Figure 2 , the specific steps for obtaining the dynamic mapping diagram of the production environment are:

[0059] S111: Acquire real-time operating parameters of the production line through a multi-source data acquisition module, record the response values ​​of the equipment status information under different working conditions, and convert the signal strength according to the calibration coefficient to form an operating status file under the current working conditions, and obtain the equipment operating status parameter set;

[0060] The multi-source data acquisition module, consisting of a temperature sensor (model: PT100), a pressure sensor (model: 3051S), and a current sensor (model: CS-500A) installed on the cosmetic emulsification tank (equipment number: EM-01), and a current sensor (model: CS-500A) on the stirring motor (equipment number: EM-M01), acquires real-time operating parameters of the production line at a frequency of 10 times per second. Under the "high-speed shear" operating condition, the collected raw voltage signal of the temperature sensor is 0.85V, the raw voltage signal of the pressure sensor is 1.25V, and the raw voltage signal of the current sensor is 2.10V. These raw signals are recorded and then converted to signal strength based on the calibration coefficient. The calibration coefficient is set based on periodic calibration experiments of the sensor. In this experiment, the sensor is measured at at least 10 different range points under a stable environment using standard measuring equipment. The measured values ​​are then linearly regressed with the standard values, and the slope of the regression line is taken as the calibration coefficient. For example, in the calibration experiment of the pressure sensor in the 0-1MPa range, the linear regression equation between its output voltage and the actual pressure value is: (where y is the voltage value V and x is the pressure value MPa), the calibration coefficient is 5V / MPa, so the actual pressure value corresponding to the 1.25V voltage signal after conversion is MPa. Similarly, convert the temperature and current signals into degrees Celsius (°C) and amperes (A) respectively. For example, if the temperature is 85°C and the current is 15A, all converted parameter values ​​under this working condition, including the equipment number, timestamp (for example, 2025-06-09 10:00:01), working condition name (high-speed shear), temperature (85°C), pressure (0.248MPa), motor current (15A), etc., are archived together to form an operating status file under the current working condition. The operating status files of all working conditions within a specific time period (for example, a production batch lasting 4 hours) are collected to obtain the equipment operating status parameter set.

[0061] S112: Based on the equipment operating status parameter set, record the response changes of equipment status information under differentiated operating conditions, analyze the mapping relationship between operating conditions and signal fluctuation rates, reconstruct the signal distribution characteristics in the operating environment, evaluate the stability of key components, and generate a dynamic map of the production environment;

[0062] Based on the acquisition of a set of equipment operating status parameters containing data from multiple production batches, the current signal response changes of the stirring motor when processing matrices with different viscosities (differential operating conditions, such as a lotion with a viscosity of 1000 cP and a cream with a viscosity of 15000 cP) are extracted. Specifically, the current value sequence of 100 consecutive sampling points under the viscosity of 1000 cP is retrieved. And the current value sequence under the viscosity of 15000cP , calculate the respective signal volatility, which is defined as the ratio of the sequence standard deviation to the sequence mean. For example, the mean of the low viscosity sequence is 10.2A and the standard deviation is 0.5A, then its volatility is , the mean value of the high viscosity sequence is 25.5A, and the standard deviation is 2.8A, so its volatility is The analysis shows that there is a positive correlation mapping relationship between the operating conditions (matrix viscosity) and the signal fluctuation rate (motor current fluctuation rate), that is, the higher the viscosity, the more severe the current fluctuation. This mapping relationship and the signal distribution characteristics of other key parameters such as pressure and temperature under different working conditions are reconstructed. For example, the pressure signal presents a normal distribution with a mean of 0.25MPa and a standard deviation of 0.02MPa under high-speed shearing. In this way, the intrinsic correlation and distribution law of each signal in the operating environment are quantified. Further, by analyzing the wear records of key components (such as stirring blades) and the historical data of the motor current signal fluctuation rate during their operation, a correlation model between the two is established. When the real-time fluctuation rate exceeds 15% of the fluctuation rate range under the historical normal wear state, it is determined that the stability has decreased. The mapping relationship of all parameters and the stability evaluation results are combined to generate a dynamic mapping diagram of the production environment.

[0063] See also Figure 3 , the steps for obtaining the regional data adjustment instruction set are as follows:

[0064] S211: Based on the dynamic mapping of the production environment, identify the fluctuation trend and abnormal distribution characteristics of the equipment signal channel, extract the signal change rate per unit time in the continuous channel, and classify the intervals based on the fluctuation gradient value to identify the response interval of the signal change on the channel and obtain the fluctuation response change interval;

[0065] Retrieve the normal fluctuation trend of the piston pump pressure signal channel (channel number: CH-P01) of the filling machine (equipment number: FM-02) defined in the dynamic mapping diagram of the production environment. This trend is defined as that when filling a standard viscosity (5000cP) emulsion, the pressure signal should be between 0.15MPa and 0.20MPa and show periodic changes. At the same time, identify the abnormal distribution characteristics of its real-time signal, such as the continuous occurrence of pressure peaks exceeding 0.22MPa. Then extract the signal change rate per unit time in this continuous channel. The unit time is set to 100 milliseconds (ms). If the pressure value at time T1 is 0.18MPa and the pressure value at time T1+100ms is 0.21MPa, the signal change rate is MPa / s, and then combine the fluctuation gradient value to perform interval classification. The calculation method of the fluctuation gradient value is the average of 5 consecutive rate values. For example, if 5 consecutive rate values ​​are {0.3, 0.32, 0.35, 0.33, 0.31} MPa / s, the gradient value is 0.322 MPa / s. The interval is set according to the experimental data: below 0.1 MPa / s is the stable interval, 0.1 to 0.4 MPa / s is the normal fluctuation interval, and above 0.4 MPa / s is the violent fluctuation interval. Since 0.322 MPa / s is in the normal fluctuation interval, the current period is classified as normal. When the calculated gradient value, such as 0.45 MPa / s, falls into the violent fluctuation interval, the response interval of the signal change on the channel is identified, and the fluctuation response change interval is obtained.

[0066] S212: Calling the fluctuation response change interval, scanning the channel segment difference fluctuation amplitude and error trend based on the channel segment signal difference and the upper and lower limit signal ratio within the path, comparing the matching degree of the real-time signal with the channel distribution trend, calculating the signal offset value, determining the abnormal distribution area in the channel, extracting the path position group that needs to be adjusted, and generating the regional data adjustment instruction set;

[0067] The fluctuation response change interval identified by calling the fluctuation response change interval is the interval of severe fluctuation response change that occurred on the filling machine piston pump pressure signal channel CH-P01 (timestamp: 10:05:30 to 10:05:35). Based on the channel segment signal difference, that is, the difference between the maximum signal value of 0.28MPa and the minimum signal value of 0.15MPa in this interval is 0.13MPa, and combined with the upper and lower limit signal ratio within the path, this ratio is based on the statistical analysis of data from similar equipment in the historical database that has been running stably for one year under similar working conditions. The upper limit signal reference value is 0.25MPa, and the lower limit signal reference value is 0.12MPa. The ratio is , and the real-time signal ratio is , scan the difference fluctuation amplitude and error trend of the channel segment, further compare the real-time signal (for example, the pressure value at 10:05:32 is 0.26MPa) with the channel distribution trend (the expected pressure mean at this moment is 0.18MPa), and calculate the signal deviation value. The calculation method of this value is (real-time value - expected mean) / expected mean, that is, The offset threshold is set to 0.30. This threshold is determined by analyzing the data 30 minutes before 100 historical equipment failures and taking the 85th percentile of the offset value of the data before the failure. Since the calculated offset value of 0.44 is greater than the threshold of 0.30, it is determined that there is an abnormal distribution area in the channel. The path position group corresponding to the abnormal area is extracted. This position group contains the device number (FM-02), channel number (CH-P01), and abnormal timestamp (10:05:32), and a regional data adjustment instruction set is generated.

[0068] See also Figure 4 , the steps for obtaining the regional signal alignment status table are as follows:

[0069] S311: Call the regional data adjustment instruction set to adjust the signal acquisition strategy, compare the current signal value according to the adjustment channel number and the target signal configuration, perform high- and low-priority signal difference adjustment, and record the response start time, peak time, and peak amplitude to obtain the adjustment path response time series group;

[0070] The generated regional data adjustment instruction set is called. This instruction set specifies the CH-P01 channel of the adjustment device FM-02. Based on this, the signal acquisition strategy is adjusted, increasing the sampling frequency of this channel from 10Hz to 100Hz. The current signal value of the adjustment channel number CH-P01 is compared with the target signal (pressure signal) and the difference between the high and low priority signals is adjusted. Here, the pressure signal (CH-P01) is set to high priority, and the servo motor current signal (channel number: CH-C02) that drives the piston pump is set to low priority. The adjustment action is to activate the signal when the pressure signal value exceeds 0. When the pressure reaches 25 MPa, the synchronous high-frequency acquisition of the current signal channel CH-C02 is immediately triggered, and the response start time of the two channels is recorded (for example, 10:05:32.150), the time when the pressure signal reaches the peak value of 0.29 MPa (peak time: 10:05:32.250), and the corresponding peak amplitude of 0.29 MPa. At the same time, the time when the current signal reaches the corresponding peak value (for example, 3.5 A) is recorded (10:05:32.280). This series of data points containing timestamps, channel numbers, and amplitudes are collected to obtain the regulation path response time series group.

[0071] S312: Based on the adjustment path response time series group, extract the start time, peak time, and amplitude of the channels before and after adjustment, identify the offset difference sequence, calculate the regional synchronization offset strength value, map the strength value to the channel distribution number, filter the channel groups within the synchronization range, and arrange the signal timing sequence to obtain the regional signal alignment status table;

[0072] Based on the acquired adjustment path response time series group, the signal sequences of the pre-adjustment channel (sampling frequency 10 Hz) and the post-adjustment channel (sampling frequency 100 Hz) were extracted. Specifically, the abnormal point (10:05:32.200, value 0.28 MPa) was found in the pre-adjustment sequence, and the peak point (10:05:32.250, value 0.29 MPa) was found in the post-adjustment high-frequency sequence. It was identified that the start time of the two did not change, the peak time had a 50 ms offset, and the amplitude had a 0.01 MPa offset. This formed an offset difference sequence {time offset: 50 ms, amplitude offset: 0.01 MPa}. The regional synchronization offset intensity value was further calculated. This value is defined as the weighted sum of the time offset (unit: seconds) and the amplitude offset (unit: MPa). The weight coefficients were set based on expert experience and historical data analysis. The time weight was 0.7 and the amplitude weight was 0.3. The setting basis is that time asynchrony has a greater impact on fault judgment than a small deviation in amplitude. Therefore, the intensity value is , the calculated intensity values ​​and channel distributions are mapped by number, as shown in Table 1.

[0073] Table 1: Regional synchronization offset strength and channel mapping

[0074] Channel number Device Name Signal Type Synchronization offset strength value CH-P01 filling machine pressure 0.038 CH-C02 filling machine Current 0.041 CH-S01 conveyor belt speed 0.005

[0075] Table 1 lists the synchronization offset strength values ​​calculated for different channels. A strength threshold is set to 0.01 to filter channel groups within the synchronization range. This threshold is based on the 95th percentile of the synchronization offset strength values ​​for 500 data sets collected during normal device operation. Since the strength values ​​of channels CH-P01 and CH-C02 are both greater than 0.01, while the strength values ​​of CH-S01 are less than 0.01, CH-P01 and CH-C02 are filtered as non-synchronized channels. Channel groups such as CH-S01 that are below the threshold are considered to have stable synchronization. The signals of these stable channels are then precisely arranged by timestamp to obtain the regional signal alignment status table.

[0076] See also Figure 5 , the steps for obtaining the regional signal adjustment mark map are as follows:

[0077] S411: Calling the synchronization stable segment signal sequence in the regional signal alignment state table, extracting the signal strength per unit time of the synchronization stable segment, and comparing it with the strength sequence of the corresponding position of the adjacent regional signal segment, identifying the time deviation of the signal strength, and obtaining the regional signal offset value;

[0078] Call the signal sequence marked as the synchronous stable segment in the generated regional signal alignment state table, such as the conveyor belt speed signal (CH-S01) and the stirring motor current signal (CH-EM-C01) of the upstream equipment emulsification tank, and extract the signal intensity per unit time (1 second) of the synchronous stable segment in the time window 10:06:00 to 10:06:10 to obtain the conveyor belt speed sequence m / s, and stirring motor current sequence A, the intensity sequence of this current sequence and the adjacent area signal segment, that is, the torque signal (channel number: CH-T01) of the downstream device capping machine (device number: CM-01) in the same time window Nm is compared to identify the time deviation of signal intensity. The specific comparison process is to calculate the cross-correlation function of the signal sequence. It is found that the stirring motor current sequence I has an intensity mutation at time point T=6 (jumping from 10.1A to 15.5A), while other signal sequences remain stable. This inconsistency on the time axis is the time deviation. By calculating the difference between the mutation point and the corresponding position of other signal sequences, the regional signal offset value is obtained. Here, the offset value is A.

[0079] S412: Based on the regional signal offset value and the change threshold set by the reconstruction unit, the difference between the signal offset value of each region and the threshold is determined, and the offset regions exceeding the threshold are screened to obtain the block change determination coefficient;

[0080] According to the obtained regional signal offset value of 5.4A, combined with the change threshold set by the reconstruction unit, the change threshold is based on the provisions of the motor rated current in the equipment maintenance manual and the analysis of historical overload event data. For example, for the stirring motor, its rated current is 20A, the normal operating current average is 10A, and the standard deviation is 1A. The change threshold is set to the average plus 5 times the standard deviation, that is, A, this is an absolute value threshold, and a relative change rate threshold is set at the same time, its value is 50%. This value is analyzed by analyzing 1000 batches of normal production data. The current change rate between any adjacent seconds does not exceed 40%, so 50% is taken as the threshold to judge the numerical difference between the signal offset and the threshold in each area. The real-time current value of 15.5A does not exceed the absolute threshold of 15A, but its relative change rate is , this value exceeds the relative change rate threshold of 50%, so the offset area (emulsification tank stirring motor) is screened out, and the block change judgment coefficient is obtained. The coefficient is quantified as 1, indicating that the threshold is exceeded, and 0 if it is not exceeded.

[0081] S413: Based on the block change judgment coefficient, the signal offset trend and the continuity between offset positions in the region are detected, and regions with stable increasing or decreasing offset trends and spatial continuity are marked. The regional offset mark value is calculated. Based on the spatial range of the offset region, the connectivity identification partition number is identified and filled into the mark map to form a regional signal adjustment mark map.

[0082] Based on the obtained block change judgment coefficient 1, the signal offset trend and the continuity between offset positions in the area (emulsification tank stirring motor) are detected. Specifically, the signal values ​​of three consecutive sampling points (10:06:06, 10:06:07, 10:06:08) after the time point (10:06:05) at which the coefficient 1 is obtained are checked. They are 15.5A, 15.6A, and 15.5A respectively. These values ​​are significantly higher than the previous level of 10.1A, showing spatial continuity (all occurring on the same motor) and temporal persistence. Therefore, the offset trend is marked as a stable increase, and the regional offset mark value is calculated. The calculation method of this value is: Block change judgment coefficient Continuous overtime duration (seconds) The average excess amplitude (A), that is, , combined with the spatial range of the offset area (emulsification tank EM-01), identify its connectivity identification partition number (for example, EM01-M01-C), and fill the number and the tag value 16.29 into a data structure to form a regional signal adjustment tag map.

[0083] See also Figure 6 , the specific steps for obtaining the monitoring area status assessment value list are:

[0084] S511: Call the positioning correction segment signal in the regional signal adjustment mark map, extract the high-priority and low-priority signal pairs of the positioning segment, match the node coordinates with the channel number, and aggregate the combined signal values ​​according to the spatial sequence to generate a dual-channel positioning combination value set;

[0085] The resulting regional signal adjustment marking diagram is called, in which the emulsification tank motor (EM01-M01-C) is marked as the area requiring positioning correction. The high-priority signal (motor current) and low-priority signal (tank temperature) pairs of this positioning correction segment are extracted. The priority is determined based on the direct correlation between the signal and the core process parameters. Current directly reflects the stirring state and has a high priority, while temperature is an auxiliary monitoring parameter and has a low priority. Matching is performed based on the node coordinates (timestamp) and the channel number (CH-EM-C01, CH-EM-T01). The combined signal values ​​are aggregated in spatial sequence, that is, in chronological order. For example, in the time period from 10:06:05 to 10:06:08, the current and temperature data pairs collected are {(15.5A, 85.1℃), (15.6A, 85.2℃), (15.5A, 85.3℃)}. These data pairs are combined to generate a dual-channel positioning combination value set.

[0086] S512: Based on the dual-channel positioning combination value set, performing fluctuation difference discrimination on the combination value, extracting resolvable signal pairs, screening the regional response group of the fluctuation feature according to the set dual-priority response threshold, and generating a dual-priority feature fluctuation rate sequence;

[0087] Based on the generated dual-channel positioning combination value set {(15.5A, 85.1℃), (15.6A, 85.2℃), (15.5A, 85.3℃)}, the fluctuation difference of the combination value is judged. The specific operation is to calculate the normalized difference between the two internal signals of each data pair. First, the two signals are normalized. The normal range of the current signal is [5A, 15A], and the normal range of the temperature is [80℃, 90℃]. The normalized value of the first data point is {current: ,temperature: }, extract the signal pairs with resolvability, that is, the resolvability is defined as the signal pair with the absolute difference between the two normalized values ​​greater than 0.2, where , so it is distinguishable. The regional response group of the fluctuation feature is screened according to the set dual-priority response threshold. The threshold is set as the normalized value of the high-priority signal (current) is greater than 1.0, and the normalized value of the low-priority signal (temperature) is less than 0.6. The current data point {1.05, 0.51} meets this condition. The high-priority signal values ​​(current values) in all regional response groups that meet the conditions are extracted to form a sequence {15.5, 15.6, 15.5}, ​​generating a dual-priority feature volatility sequence.

[0088] S513: Analyze the mapping relationship between fluctuation parameters and equipment status based on the dual-priority characteristic fluctuation rate sequence, perform dual-priority state inversion of regional nodes, identify the equipment status of the positioning segment, and output a list of monitoring area status assessment values;

[0089] According to the generated dual-priority characteristic volatility sequence {15.5, 15.6, 15.5}, ​​the mapping relationship between this fluctuation parameter (high current accompanied by normal temperature) and the equipment status established in S112 is analyzed. The mapping relationship indicates that when the matrix viscosity is constant, the sudden and continuous increase in current without corresponding drastic change in temperature corresponds to the state of "stirring blade foreign matter stuck" or "motor load abnormality". The dual-priority state inversion of the regional node is performed, that is, the most likely state of the equipment is reversely inferred based on the real-time data, and the equipment state of the positioning section (emulsification tank motor) is identified as "moderate load abnormality". Finally, this state is quantified into an evaluation value. The setting rule of the evaluation value is: normal = 0, mild abnormality = 1, moderate abnormality = 2, severe abnormality = 3, fault = 4. Therefore, the evaluation value corresponding to the current state "moderate load abnormality" is 2. The equipment number (EM-01), monitoring area (motor), evaluation state (moderate load abnormality), and evaluation value (2) are taken as a record, and the monitoring area state evaluation value list is output.

[0090] The cosmetics production line safety monitoring system based on data analysis includes:

[0091] The multi-source data acquisition module uses a sensor array to obtain real-time operating parameters of the production line, conducts horizontal comparisons of equipment status information under different working conditions, identifies signal response differences by channel grouping, and integrates all channel signal amplitude change segments to construct a dynamic map of the production environment;

[0092] The fluctuation positioning module extracts the regional dual-priority signal ratio sequence based on the dynamic mapping of the production environment, identifies the response differences between channels and screens the deviation mutation segments, identifies the intersection points of fluctuation trends, determines the scope of the area that needs adjustment, and generates the fluctuation segment positioning set;

[0093] The signal synchronization module adjusts the acquisition ratio of high- and low-priority signals in the indicated area based on the fluctuation segment positioning set, records the time axis response value and peak offset amplitude sequence of the sensor array in the corresponding segment, compares the response difference between the channels before and after adjustment, integrates the stable synchronization point group, and establishes the signal alignment synchronization state table;

[0094] The fluctuation structure recognition module extracts the amplitude variation in the synchronization segment signal based on the signal alignment synchronization state table, compares the floating interval of the adjacent channel response according to the time window, maps the offset exceeding position to the two-dimensional imaging surface, marks the local fluctuation intensity mutation area, and outputs the fluctuation structure change layer;

[0095] The state parameter assessment module extracts the corresponding high- and low-priority signal combination values ​​based on the marked areas in the fluctuation structure change layer, calculates the unit area fluctuation response based on the set reference fluctuation rate value table, and outputs a list of monitoring area state assessment values.

[0096] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A cosmetics production line safety monitoring method based on data analysis, characterized in that: The following steps are involved: S1: The multi-source data acquisition module acquires real-time operating parameters of the production line, records the changing characteristics of equipment status information and unstructured data streams, identifies the initial response curves of key nodes based on signal differences, and generates a dynamic map of the production environment. S2: Based on the dynamic mapping diagram of the production environment, the fluctuation trend and abnormal distribution characteristics of the equipment signal channel are extracted, the deviation is judged by combining the segment signal difference with the preset threshold range, the deviation segment is screened and the data processing direction is adjusted, and a regional data adjustment instruction set is generated; S3: calling the regional data adjustment instruction set to adjust the signal acquisition strategy, synchronously recording the time axis response value and peak offset of the data acquisition module, and extracting the synchronization offset range of the corresponding region by comparing the signal time axis and amplitude changes before and after the adjustment to obtain the regional signal alignment status table; S4: Call the synchronization stable segment signal sequence in the regional signal alignment state table, identify the signal intensity change in the regional differentiation time window, perform offset judgment with the change threshold set by the reconstruction unit, mark the area to be adjusted, and form a regional signal adjustment marking diagram.

2. The cosmetics production line safety monitoring method based on data analysis according to claim 1, characterized in that: The dynamic mapping diagram of the production environment includes the equipment operation status distribution curve, signal fluctuation characteristic parameters, and channel classification identification; the regional data adjustment instruction set includes the data acquisition strategy parameter set, fluctuation trend control value, and segment signal compensation factor; the regional signal alignment status table includes the synchronization time offset, peak response difference value, and alignment status identification code; the regional signal adjustment mark diagram includes the position point of the segment to be adjusted, the intensity offset judgment result, and the structural change response identification.

3. The cosmetics production line safety monitoring method based on data analysis according to claim 1, characterized in that: The steps for obtaining the dynamic map of the production environment are specifically as follows: S111: Acquire real-time operating parameters of the production line through a multi-source data acquisition module, record the response values ​​of the equipment status information under different working conditions, and convert the signal strength according to the calibration coefficient to form an operating status file under the current working conditions, and obtain the equipment operating status parameter set; S112: Based on the equipment operating status parameter set, record the response changes of equipment status information under differentiated operating conditions, analyze the mapping relationship between operating conditions and signal fluctuation rates, reconstruct the signal distribution characteristics in the operating environment, evaluate the stability of key components, and generate a dynamic mapping diagram of the production environment.

4. The cosmetics production line safety monitoring method based on data analysis according to claim 1, characterized in that: The steps for obtaining the regional data adjustment instruction set are specifically as follows: S211: Based on the production environment dynamic map, identify the fluctuation trend and abnormal distribution characteristics of the equipment signal channel, extract the signal change rate per unit time in the continuous channel, and perform interval classification based on the fluctuation gradient value to identify the response interval of the signal change on the channel to obtain the fluctuation response change interval; S212: Call the fluctuation response change interval, scan the difference fluctuation amplitude and error trend of the channel segment according to the ratio of the channel segment signal difference and the upper and lower limit signals in the path, compare the matching degree of the real-time signal with the channel distribution trend, calculate the signal offset degree value, judge the abnormal distribution area in the channel, extract the path position group that needs to be adjusted, and generate the regional data adjustment instruction set.

5. The cosmetics production line safety monitoring method based on data analysis according to claim 1, characterized in that: The steps for obtaining the regional signal alignment status table are specifically as follows: S311: Calling the regional data adjustment instruction set to adjust the signal acquisition strategy, comparing the current signal value according to the adjustment channel number and the target signal configuration, performing high- and low-priority signal difference adjustment, and recording the response start time, peak time, and peak amplitude to obtain an adjustment path response time series group; S312: According to the adjustment path response time series group, extract the start time, peak time and amplitude of the channels before and after adjustment, identify the offset difference sequence, calculate the regional synchronization offset intensity value, map the intensity value to the channel distribution, filter the channel group within the synchronization range and arrange the signal timing sequence to obtain the regional signal alignment status table.

6. The cosmetics production line safety monitoring method based on data analysis according to claim 1, characterized in that: The steps for obtaining the regional signal adjustment mark map are specifically as follows: S411: Calling the synchronization stable segment signal sequence in the regional signal alignment state table, extracting the signal strength per unit time of the synchronization stable segment, and comparing it with the strength sequence of the corresponding position of the adjacent regional signal segment, identifying the time deviation of the signal strength, and obtaining the regional signal offset value; S412: Based on the regional signal offset value and the change threshold set by the reconstruction unit, determine the difference between the signal offset value of each region and the threshold, filter out the offset regions exceeding the threshold, and obtain the block change determination coefficient; S413: Based on the block change judgment coefficient, detect the signal offset trend in the area and the continuity between the offset positions, mark the areas where the offset trend is stably rising or falling and spatially continuous, calculate the regional offset mark value, combine the spatial range of the offset area, identify the connected identification partition number and fill it into the mark map to form a regional signal adjustment mark map.

7. The cosmetics production line safety monitoring method based on data analysis according to claim 1, characterized in that: The method further comprises step S5: S5: calling the positioning correction segment signal in the regional signal adjustment mark map, identifying the adjusted high and low priority signal combination value, reconstructing the regional equipment response state according to the fluctuation characteristic relationship corresponding to the combination signal, and outputting a monitoring area state assessment value list; The monitoring area status assessment value list includes a high-priority and low-priority signal fluctuation comparison value, a regional response difference factor, and a response status level after reconstruction.

8. The cosmetics production line safety monitoring method based on data analysis according to claim 1, characterized in that: The steps for obtaining the monitoring area status assessment value list are specifically as follows: S511: calling the positioning correction segment signal in the regional signal adjustment mark map, extracting the high-priority and low-priority signal pairs of the positioning segment, matching the node coordinates with the channel numbers, and grouping the combined signal values ​​in a spatial sequence to generate a dual-channel positioning combination value set; S512: Based on the dual-channel positioning combination value set, performing fluctuation difference discrimination on the combination value, extracting resolvable signal pairs, screening regional response groups of fluctuation characteristics according to the set dual-priority response threshold, and generating a dual-priority characteristic fluctuation rate sequence; S513: Analyze the mapping relationship between fluctuation parameters and equipment status according to the dual-priority characteristic fluctuation rate sequence, perform dual-priority state inversion of regional nodes, identify the equipment status of the positioning segment, and output a monitoring area status assessment value list.

9. The cosmetics production line safety monitoring system based on data analysis is characterized by: The system is used in the cosmetics production line safety monitoring method based on data analysis according to any one of claims 1 to 7, and the system comprises: The multi-source data acquisition module uses a sensor array to obtain real-time operating parameters of the production line, conducts horizontal comparisons of equipment status information under different working conditions, identifies signal response differences by channel grouping, and integrates all channel signal amplitude change segments to construct a dynamic map of the production environment; The fluctuation positioning module extracts the regional dual-priority signal ratio sequence based on the dynamic mapping diagram of the production environment, identifies the response difference between channels and screens the deviation mutation segment, identifies the intersection point of the fluctuation trend, determines the range of the area that needs to be adjusted, and generates a fluctuation segment positioning set; The signal synchronization module adjusts the ratio of high and low priority signal acquisition in the indicated area based on the fluctuation segment positioning set, records the time axis response value and peak offset amplitude sequence of the sensor array in the corresponding segment, compares the response difference between the channels before and after the adjustment, integrates the stable synchronization point group, and establishes a signal alignment synchronization state table; The fluctuation structure identification module extracts the amplitude variation in the synchronization segment signal based on the signal alignment synchronization state table, compares the floating interval of the adjacent channel response according to the time window, maps the offset exceeding position to the two-dimensional imaging surface, marks the local fluctuation intensity mutation area, and outputs the fluctuation structure change layer; The state parameter assessment module extracts the corresponding high and low priority signal combination values ​​based on the marked areas in the fluctuation structure change layer, calculates the unit area fluctuation response in combination with the set reference fluctuation rate value table, and outputs a list of monitoring area state assessment values.

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