Ceramsite material mixing degree intelligent evaluation system

Abstract

The invention relates to the technical field of ceramsite material mixing degree evaluation, and discloses an intelligent ceramsite material mixing degree evaluation system which comprises a material sampling device, a parameter acquisition unit, a feature analysis module and an evaluation judgment module. The parameter acquisition units are arranged at all mixing stations, each parameter acquisition unit comprises a record storage part and a detection assembly, historical mixing abnormal information (including abnormal time points, colors, granularity and vibration information) is stored, and the detection assemblies acquire real-time color and granularity data and vibration information. The feature analysis module divides abnormal and stable time intervals and determines color and granularity reference values and a vibration amplitude reference interval. The evaluation and judgment module adjusts the activation state of the sampling device and the response state of the detection assembly according to the characteristic time interval, judges the mixing anomaly tendency through real-time data, and determines whether the state of the sampling device is changed or not by combining the vibration amplitude. The system improves the accuracy and efficiency of mixing degree evaluation, and is suitable for intelligent monitoring of ceramsite production.

Description

Technical Field

[0001] The invention relates to the technical field of ceramsite material mixing degree evaluation, and in particular to an intelligent evaluation system for ceramsite material mixing degree. Background Art

[0002] In the production of ceramsite, material mixing is a critical step in ensuring consistent product quality. Ceramsite production typically involves multiple steps, including raw material proportioning, mixing, and calcination. The uniformity of material mixing at each mixing station directly impacts the final ceramsite's core properties, such as strength, density, and water absorption. Uneven mixing can lead to localized compositional deviations in the final ceramsite, causing quality fluctuations and increasing the defective rate. This can also waste raw materials and increase production costs.

[0003] In traditional ceramsite production, the assessment of material mixing often relies on manual sampling and testing, where the mixing effect is determined by visually observing material color differences and measuring particle size distribution through screening. This approach has significant limitations. On the one hand, manual testing is inefficient, making it difficult to achieve real-time monitoring of each mixing station on the assembly line, and transient mixing anomalies can be easily missed. On the other hand, test results are heavily influenced by human experience, are highly subjective, and lack a unified quantitative standard, resulting in insufficient assessment accuracy. Furthermore, manual sampling requires interrupting the production process, affecting production continuity. Historical data on mixing anomalies cannot be traced, making it difficult to develop a systematic quality improvement plan.

[0004] With the application of automation technology in the manufacturing industry, some expanded clay production lines have begun to introduce sensors for parameter collection. However, existing systems mostly use a fixed-frequency detection mode, continuously sampling regardless of whether the mixing state is stable, resulting in data redundancy and energy waste. At the same time, existing systems do not fully integrate historical abnormal data for dynamic analysis, making it difficult to distinguish between normal fluctuations and abnormal trends, resulting in a high rate of misjudgment. For example, when the color or particle size of the material fluctuates slightly, the system may directly determine that it is a mixing anomaly, triggering unnecessary downtime inspections and affecting production efficiency. When the mixing equipment's mixing effect is reduced due to abnormal vibration, the failure to promptly associate the vibration data may delay the abnormality handling.

[0005] How to build an intelligent evaluation system that can dynamically divide the detection cycle based on historical data, analyze the mixing state based on multi-parameter linkage, and realize adaptive adjustment of sampling strategy has become a key requirement for improving the quality stability of expanded clay production and reducing production costs. Summary of the Invention

[0006] The object of the present invention is to provide an intelligent evaluation system for the mixing degree of ceramsite materials to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: an intelligent evaluation system for the mixing degree of ceramsite materials, the system comprising: Material sampling device, used to obtain material samples at each mixing station of the ceramsite production line; A parameter collection unit is provided at each mixing station of the ceramsite production line, and includes a recording and storage component for storing historical mixing anomaly information at each mixing station, and multiple detection components for collecting material color data, particle size data, and mixing process vibration information at each station. The historical mixing anomaly information includes historical anomaly time points, historical color data, historical particle size data, and historical vibration information. a feature analysis module connected to the parameter acquisition unit, configured to divide a statistical period into a plurality of feature time intervals based on the historical mixing anomaly information of a single mixing station, determine a color reference value and a particle size reference value based on the historical color data and the historical particle size data of the single station, construct a time-domain vibration waveform diagram based on the vibration information to determine an amplitude distribution map of a stable period, and determine a vibration amplitude reference interval based on the amplitude distribution map; The characteristic time interval includes an abnormal time interval and a stable time interval; An evaluation and judgment module is respectively connected to each of the material sampling devices, the parameter acquisition unit and the feature analysis module, and is used to determine the operation activation status of the material sampling device at the corresponding station and the acquisition response status of the detection component according to the characteristic time interval of the single mixing station, determine the mixing abnormality tendency according to the real-time color data and real-time particle size data of the single mixing station to determine whether to analyze the vibration information, and determine whether to change the operation activation status of the corresponding material sampling device according to the mixing abnormality tendency and the real-time vibration amplitude.

[0008] Preferably, each mixing station is provided with a detection component, and each detection component includes a color identifier, a particle size measuring instrument and a vibration sensor.

[0009] Preferably, the feature analysis module determines the abnormal occurrence point of a single hybrid workstation based on its historical abnormal time point, determines a fixed time interval centered on the abnormal occurrence point as the abnormal time interval, and determines the absolute complement of the abnormal time interval within the statistical period as the stable time interval; If the duration of a single stable time interval is less than a set threshold, the stable time interval and the two adjacent abnormal time intervals are merged into one abnormal time interval.

[0010] Preferably, the feature analysis module determines the color mean and color fluctuation range based on the historical color data corresponding to the stable time interval to determine the color reference value, and determines the particle size mean and particle size fluctuation range based on the historical particle size data corresponding to the stable time interval to determine the particle size reference value; The color reference value is the sum of the color mean and the lower limit of the color fluctuation range, and the particle size reference value is the sum of the particle size mean and the lower limit of the particle size fluctuation range.

[0011] Preferably, the feature analysis module determines the vibration amplitude reference interval according to the amplitude range corresponding to the preset reliability in the amplitude distribution spectrum of the stable time interval; The horizontal axis of the amplitude distribution graph is the vibration signal intensity, and the vertical axis of the amplitude distribution graph is the occurrence frequency.

[0012] Preferably, the evaluation and determination module determines the operation activation state of the material sampling device at the corresponding station and the collection response state of the detection component according to the characteristic time interval of a single mixing station, including: If the characteristic time interval is an abnormal time interval, the corresponding workstation material sampling device is in an activated state and the collection response state of the detection component is to only collect color data, particle size data and vibration data; If the characteristic time interval is a stable time interval, the corresponding workstation material sampling device is in a dormant state and the collection response state of the detection component is to collect color data, particle size data and vibration data, and the characteristic analysis module is controlled to determine the color reference value and the particle size reference value; The running activation state includes an activation state and a dormant state.

[0013] Preferably, the evaluation and determination module determines the abnormal mixing tendency of the station based on the result of determining that the material sampling device of a single mixing station is in a dormant state, in combination with the real-time color data and the real-time particle size data, including: If the real-time color data is less than or equal to the color reference value and the real-time particle size data is less than or equal to the particle size reference value, it is determined that there is no abnormal mixing tendency in the corresponding mixing station; If the real-time color data is greater than the color reference value and the real-time particle size data is greater than the particle size reference value, it is determined that a mixing abnormality exists in the corresponding mixing station and the corresponding material sampling device is adjusted to an activated state.

[0014] Preferably, the evaluation and determination module determines the abnormal mixing tendency of the station based on the result of determining that the material sampling device of a single mixing station is in a dormant state, in combination with the real-time color data and the real-time particle size data, and further includes: If the real-time color data is greater than the color reference value or the real-time particle size data is greater than the particle size reference value, it is determined that the corresponding mixing station has a mixing abnormality tendency and it is determined to analyze the vibration information.

[0015] Preferably, the evaluation and determination module controls the characteristic analysis module to determine a reference interval of vibration amplitude according to the result of the determination of the presence of abnormal mixing tendency, and determines whether to change the operation activation state of the material sampling device corresponding to the mixing station according to the real-time vibration amplitude; If the real-time vibration amplitude is not within the vibration amplitude reference range, it is determined that the material sampling device corresponding to the mixing station is changed from a dormant state to an active state; If the real-time vibration amplitude is within the vibration amplitude reference range, it is determined that the operation activation state of the material sampling device corresponding to the mixing station is not changed and that the device remains in the dormant state.

[0016] Preferably, when the feature analysis module determines the amplitude distribution spectrum of the stable time interval, it first samples the vibration information within the stable time interval in segments, with each segment of sampling data having a fixed length, and then performs frequency statistics on the amplitude values ​​of each segment of sampling data, and finally merges the statistical results of each segment to form an amplitude distribution spectrum covering the entire stable time interval.

[0017] Compared with the prior art, the present invention has the following beneficial effects: From the perspective of data collection and analysis, the system innovatively incorporates historical mixed anomaly information as the basis for analysis. Using a feature analysis module, the statistical cycle is divided into abnormal time intervals and stable time intervals, making the detection strategy more targeted. During abnormal time intervals, the material sampling device is activated and parameter collection is strengthened to ensure accurate capture of potential problems. During stable time intervals, a dormant sampling mode is adopted and color and particle size baseline values ​​are dynamically updated. This not only reduces invalid data collection but also ensures the timeliness of baseline values, resolving the data redundancy and baseline value rigidity issues caused by traditional fixed-frequency sampling.

[0018] In terms of assessment and judgment logic, the system achieves hierarchical judgment of mixed anomalies through multi-parameter linkage analysis. First, it preliminarily identifies mixed anomaly trends based on real-time color and particle size data. This is then verified again with vibration information, forming a triple verification mechanism of "color-particle size-vibration," significantly reducing the probability of misjudgment based on a single parameter. For example, when color or particle size fluctuates, the system does not directly determine an anomaly. Instead, it further confirms whether the vibration amplitude exceeds the reference range. This avoids misoperation caused by normal process fluctuations and improves the reliability of the assessment.

[0019] From the perspective of production efficiency and cost control, the system reduces unnecessary sampling and testing by adaptively adjusting the activation state of the material sampling device, thereby reducing equipment energy consumption and the need for manual intervention. Furthermore, a vibration amplitude reference range and baseline value update mechanism, built using historical data, enables the system to self-learn, continuously optimizing the evaluation model as production progresses and adapting to the changing characteristics of different batches of raw materials. This significantly improves the long-term stability of ceramsite product quality, reduces defective product losses due to mixing anomalies, and indirectly reduces production costs.

[0020] The modular design of each system module facilitates integration into existing production lines, eliminating the need for large-scale modifications to existing equipment and lowering the barrier to entry. Through independent monitoring and centralized analysis of each mixing station, it can accurately locate anomalies in a single station and provide data support for optimizing the mixing process across the entire production line, helping ceramsite production transition from an experience-driven to a data-driven approach. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a working principle diagram of the intelligent evaluation system for ceramsite material mixing degree of the present invention; Figure 2 Flowchart for the division of characteristic time intervals; Figure 3 Flowchart for determination of color and particle size benchmark values; Figure 4 Flowchart for sampling device and detection component status control; Figure 5 Flowchart generated for the amplitude distribution map. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] See also Figure 1-Figure 5 The present invention relates to an intelligent evaluation system for the mixing degree of ceramsite materials, which includes: a material sampling device, a parameter acquisition unit, a feature analysis module, and an evaluation and judgment module. The specific implementation steps are as follows: A material sampling device is used to obtain material samples from each mixing station of a ceramsite production line.

[0024] The parameter acquisition unit, installed at each mixing station in the ceramsite production line, includes a recording and storage component and multiple detection components. The recording and storage component stores historical mixing anomaly information for each mixing station, including the time of the anomaly, color data, particle size data, and vibration information. The detection component collects material color data, particle size data, and vibration information from the mixing process at each station.

[0025] The feature analysis module, connected to the parameter acquisition unit, divides the statistical cycle into several characteristic time intervals based on historical mixing anomaly information from individual mixing stations. These characteristic time intervals include abnormal and stable time intervals. Furthermore, it determines color and particle size baseline values ​​based on historical color and particle size data for each station. It also constructs a time-domain vibration waveform diagram based on vibration information to determine the amplitude distribution during stable periods, and then determines the vibration amplitude reference interval based on the amplitude distribution.

[0026] The evaluation and judgment module is respectively connected to each material sampling device, parameter acquisition unit and feature analysis module. It can determine the operation activation status of the material sampling device of the corresponding station and the acquisition response status of the detection component according to the characteristic time interval of a single mixing station, determine the mixing abnormality tendency according to the real-time color data and real-time particle size data of a single mixing station to determine whether to analyze the vibration information, and determine whether to change the operation activation status of the corresponding material sampling device according to the mixing abnormality tendency and real-time vibration amplitude.

[0027] Example 1: In this embodiment, each mixing station is provided with a detection component, and a single detection component includes a color identifier, a particle size meter and a vibration sensor. The color identifier adopts the principle of optical induction, and realizes the capture and identification of the color characteristics of the material by capturing the spectral information reflected from the surface of the material. Specifically, a light source transmitting module and a spectrum receiving module are provided inside the color identifier. The light source transmitting module emits light of a specific wavelength range to the surface of the material, and the light is received by the spectrum receiving module after being reflected from the surface of the material. The spectrum receiving module converts the received light signal into an electrical signal, and amplifies, filters and processes the electrical signal through a signal processing circuit, and finally converts the material color into quantifiable color data, such as RGB value or HSV value. During the operation of the expanded clay production line, the color identifier works continuously, and collects the material color of each mixing station in real time at a fixed sampling frequency to ensure that the changes in the material color can be obtained in a timely manner.

[0028] A particle size analyzer uses the principle of laser diffraction to accurately measure the size distribution of material particles. The instrument primarily consists of a laser light source, a sample cell, and a detector. As the material passes through the sample cell, the laser beam emitted by the laser source strikes the material particles, causing diffraction. Particles of different sizes produce different diffraction patterns, which the detector captures and converts into electrical signals. By analyzing and calculating the electrical signals, the particle size analyzer can obtain particle size distribution data, including parameters such as the average particle size and particle size distribution range. In actual application, the particle size analyzer sets appropriate measurement parameters, such as laser wavelength and sampling time, based on the material characteristics and production process requirements to ensure accurate particle size data. The instrument monitors the material particle size at each mixing station in real time, providing critical particle size information for subsequent analysis of the degree of mixing.

[0029] Vibration sensors use the piezoelectric effect to sense vibrations during the mixing process and collect vibration signals in real time. When subjected to vibration, the piezoelectric crystal within the sensor generates an electric charge, which is proportional to the amplitude and frequency of the vibration. The vibration sensor amplifies and filters the generated charge signal and converts it into a standard electrical signal for output. This signal contains information such as the amplitude, frequency, and phase of the vibrations during the mixing process. In the production of ceramsite, the vibration of the mixing equipment is closely related to the mixing state of the materials. Therefore, the vibration sensor records the vibrations during the mixing process in real time, providing an important basis for determining whether the material mixing degree is abnormal.

[0030] During system operation, the color recognizer, particle size analyzer, and vibration sensor in the detection assembly at each mixing station work synchronously. The color recognizer continuously collects material color data, transmitting each acquired color data, such as RGB or HSV values, in real time to the recording and storage component of the parameter acquisition unit. The particle size analyzer monitors the material particle size in real time, also transmitting each measured particle size data, such as the average particle size and particle size distribution range, to the recording and storage component in real time. The vibration sensor records the vibration signals during the mixing process in real time, transmitting electrical signals indicating vibration amplitude, frequency, and other information to the recording and storage component for storage.

[0031] The recording and storage component categorizes and stores the data transmitted by each detection component, organizing and saving historical color data, particle size data, and vibration information by mixing station and time point. This allows for quick and accurate access to the corresponding historical data when analyzing the mixing state at a particular mixing station. For example, when determining the color and particle size baseline values, the feature analysis module can retrieve all historical color and particle size data for the mixing station's stable time period from the recording and storage component, providing the data foundation for subsequent statistical calculations.

[0032] The three parts of the detection component work together to complete the collection of parameters related to the materials in the mixing station. The color identifier focuses on the color changes of the material, the particle size measuring instrument grasps the size distribution of the material particles, and the vibration sensor monitors the mechanical vibration during the mixing process. These three aspects of data reflect the mixing state of the materials from different dimensions, providing comprehensive information support for the analysis and judgment of the entire intelligent evaluation system. Through the collaborative work of these three components, the system can obtain material data of each mixing station in real time and accurately, laying a solid foundation for subsequent feature analysis and evaluation judgment. Whether in abnormal time intervals or stable time intervals, the detection component works continuously to ensure the continuity and integrity of the data, so that the system can promptly detect abnormal conditions in the mixing process and take appropriate measures to make adjustments.

[0033] Example 2: In this example, the feature analysis module processes historical abnormality time points for a single mixing station to determine the point at which the abnormality occurred and, based on this information, divides the abnormal time interval into a stable time interval. Specifically, the feature analysis module extracts historical mixing abnormality information for the mixing station from the record storage component of the parameter acquisition unit, including the historical abnormality time points. These historical abnormality time points are recorded at specific moments in past production processes when the system detected a mixing abnormality, such as the year, month, day, hour, minute, and second.

[0034] The feature analysis module identifies each historical abnormal time point as the abnormal occurrence point. With the abnormal occurrence point as the center, a fixed period of time is extended forward and backward to form a time interval, which is the abnormal time interval. The fixed time here is a value pre-set by the system. For example, it can be set to 10 minutes, 15 minutes, etc. The specific time can be determined according to the process characteristics and actual needs of ceramsite production. For example, if the abnormal occurrence point is 10:30 on June 25, 2025, and the fixed time is set to 10 minutes, then the abnormal time interval is the time period from 10:20 to 10:40 on June 25, 2025.

[0035] After determining all abnormal time intervals, the feature analysis module needs to determine the stable time interval within the entire statistical period. The statistical period refers to the time range for the system to perform analysis, such as a day, a shift, or a week. The stable time interval is the portion of the statistical period excluding all abnormal time intervals. For example, if the statistical period is 24 hours on June 25, 2025, and the mixed workstation has two abnormal time intervals on this day, namely 10:20 to 10:40 and 14:30 to 14:50, then the stable time interval is the remaining time period in the 24 hours excluding these two abnormal time intervals.

[0036] In addition, the feature analysis module also needs to judge the duration of the stable time interval. If the duration of a single stable time interval is less than the set threshold, the time interval needs to be merged. The set threshold is also a time length value pre-set by the system, for example, it can be set to 5 minutes, 8 minutes, etc. When the duration of a stable time interval is less than the set threshold, it means that the stable time interval is too short and may not truly represent a stable state, or it is difficult to reflect a stable mixed state in such a short time. At this time, the feature analysis module will merge the stable time interval and the two adjacent abnormal time intervals into a new abnormal time interval.

[0037] For example, suppose there's a 4-minute stable interval with a 5-minute threshold. The previous abnormal interval is from 10:20 to 10:40, and the next abnormal interval is from 11:00 to 11:20. The feature analysis module will merge these two abnormal intervals with the intermediate stable interval to form a new abnormal interval, from 10:20 to 11:20. This new abnormal interval encompasses the two original abnormal intervals and the short stable interval, better reflecting actual production conditions and avoiding analysis errors caused by excessively short stable intervals.

[0038] Throughout the entire processing process, the feature analysis module must accurately extract and process historical anomaly time points to ensure accurate identification of anomaly occurrence points. Furthermore, the setting of fixed durations and thresholds requires comprehensive consideration of factors such as the characteristics of mixing anomalies during ceramsite production, the operating characteristics of the equipment, and production process requirements. For example, different mixing stations may have different fixed durations and thresholds depending on the equipment type and material characteristics.

[0039] The characteristic analysis module divides the statistical period in this way to obtain characteristic time intervals, including abnormal time intervals and stable time intervals. The division of these time intervals is crucial for the subsequent work of the evaluation and judgment module. The evaluation and judgment module uses the characteristic time intervals to determine the operating activation status of the material sampling device and the collection response status of the detection component, thereby realizing intelligent assessment and corresponding control of the ceramsite material mixing degree.

[0040] In actual applications, the feature analysis module continuously processes new historical anomaly time points and updates the division between abnormal and stable time intervals. As production data accumulates, the system's time interval divisions become more accurate, more precisely reflecting the actual operating status of the mixed workstations and providing a more reliable time basis for subsequent analysis and judgment. Throughout the entire process, the feature analysis module strictly follows pre-set rules and procedures to ensure the scientific and rational division of time intervals, thereby guaranteeing the accuracy and effectiveness of the entire intelligent assessment system.

[0041] Embodiment 3: In this embodiment, the feature analysis module determines the color reference value and the granularity reference value based on the historical color data and the historical granularity data corresponding to the stable time interval.

[0042] The feature analysis module retrieves all historical color data for a single mixing station within a stable time interval from the parameter acquisition unit's record storage component. This historical color data is collected and stored by the color recognizer in the detection component during a stable time interval and includes quantifiable color parameters such as RGB values ​​and HSV values ​​at different time points.

[0043] The feature analysis module statistically processes this historical color data, first calculating the color mean. This is calculated by summing the values ​​for each color channel of all historical color data within a stable time interval and dividing the sum by the number of data points. For example, in the RGB color model, assuming there are n color data points within a stable time interval, each containing values ​​for the R, G, and B channels, the R mean is the sum of all R values ​​divided by n. The G and B mean values ​​are calculated in the same way. The resulting color mean reflects the average color state of the material within that stable time interval.

[0044] After calculating the color mean, the feature analysis module also needs to determine the color fluctuation range. The color fluctuation range refers to the difference between the maximum and minimum values ​​of the historical color data within a stable time interval. Using the RGB model as an example, the maximum and minimum values ​​of the R, G, and B channels are calculated separately. The fluctuation range of each channel is the difference between the maximum and minimum values ​​of that channel, thus obtaining the fluctuation range of the entire color data. This range reflects the extent of material color variation within the stable time interval.

[0045] Based on the color mean and color fluctuation range, the feature analysis module determines the color reference value. This value is calculated as the sum of the color mean and the lower limit of the color fluctuation range. The lower limit of the color fluctuation range is the minimum value of the color data. For example, in the RGB model, the lower limit of the R channel is the minimum value of that channel, and the same applies to the G and B channels. The color mean is added to the lower limit of each channel to obtain the color reference value, which is used to measure whether subsequent real-time color data is within the normal range.

[0046] The feature analysis module uses a similar approach to process historical particle size data. First, it retrieves all historical particle size data from the record storage component within a stable time interval. This data, collected by the particle size measuring instrument, includes parameters such as the material's average particle size and particle size distribution. The feature analysis module then statistically analyzes this historical particle size data and calculates the mean particle size value, which is the average value of all particle size data within the stable time interval. This mean value reflects the average particle size level of the material under steady-state conditions.

[0047] The feature analysis module determines the particle size fluctuation range, defined as the difference between the maximum and minimum values ​​of historical particle size data within a stable time interval. This range reflects the degree of particle size fluctuation within that stable time interval. Then, based on the mean particle size and the lower limit of the particle size fluctuation range (i.e., the minimum particle size), a particle size reference value is calculated. Specifically, this value is the sum of the mean particle size and the lower limit of the particle size fluctuation range. This reference value is used to determine whether subsequent real-time particle size data is abnormal.

[0048] In practice, the feature analysis module must ensure that the historical color and particle size data it acquires are from accurately defined stable time intervals. These stable time intervals are determined by processing historical abnormal time points within the feature analysis module. This involves removing abnormal time intervals from the statistical period and merging short stable time intervals to ensure that the stable time intervals truly reflect the stable state of the material mix.

[0049] When processing data, the feature analysis module screens and verifies it to eliminate any possible abnormal data points. For example, if a piece of historical color data or historical particle size data significantly deviates from other data, this could be due to a fault in the detection component or an error in the transmission process. The feature analysis module will mark and eliminate this data to ensure the accuracy of the calculated color mean, color fluctuation range, particle size mean, and particle size fluctuation range.

[0050] The determination of color and particle size benchmarks provides an important reference for the evaluation and judgment module. Upon receiving real-time color and particle size data, the evaluation and judgment module compares them with the corresponding benchmarks to determine whether there is a tendency for abnormal mixing at the mixing station. For example, if the real-time color data exceeds the color benchmark or the real-time particle size data exceeds the particle size benchmark, the evaluation and judgment module will deem a possible abnormal mixing situation and further analyze the vibration information for confirmation.

[0051] In addition, the feature analysis module regularly updates the color and particle size baseline values ​​as production data accumulates. For example, if an update cycle is set, when the system reaches that cycle, the feature analysis module will reacquire historical data from the most recent stable time interval, recalculate the color mean, color fluctuation range, particle size mean, and particle size fluctuation range, and update the baseline values ​​accordingly to accommodate changes in material properties or process adjustments that may occur during production, ensuring that the baseline values ​​always accurately reflect the material characteristics in the current stable state.

[0052] Throughout the process of determining color and particle size benchmarks, the feature analysis module strictly adheres to pre-set statistical methods and calculation rules, ensuring that each step has clear logic and basis. By scientifically processing historical data within a stable time interval, the resulting benchmark values ​​accurately reflect the normal state of material mixing, providing a reliable judgment standard for subsequent intelligent assessment of mixing degree. This enables the system to promptly detect anomalies in the mixing process and take appropriate measures to ensure the quality and stability of ceramsite production.

[0053] Example 4: In this embodiment, the feature analysis module needs to determine the vibration amplitude reference interval based on the amplitude range corresponding to the preset reliability in the amplitude distribution map of the stable time interval, and the construction of the amplitude distribution map needs to go through the steps of segmented sampling, frequency statistics and result merging. Taking the stable time interval of a certain hybrid workstation as an example, it is assumed that the stable time interval is from 8:00 to 9:00 on June 25, 2025, with a duration of 1 hour. The feature analysis module performs segmented sampling of the vibration information in the interval, and the length of each sampling data segment is set to a fixed duration, for example, 5 minutes, so the entire 1-hour stable time interval can be divided into 12 segments, each segment corresponding to 5 minutes of vibration information.

[0054] During the segmented sampling process, the feature analysis module retrieves the vibration information of the mixing station within the stable time interval from the recording storage component of the parameter acquisition unit. These vibration information are collected by the vibration sensor in the detection component and stored in the form of electrical signals, including parameters such as vibration amplitude and frequency. For each 5-minute sampling data segment, the feature analysis module extracts the amplitude value, that is, the numerical value of the vibration signal intensity. For example, the first segment of sampling data is from 8:00 to 8:05. During this period, the vibration sensor collects the amplitude value once per second, for a total of 300 data points. Each data point corresponds to an amplitude value, such as 0.5g, 0.6g, etc. (g is the unit of gravitational acceleration).

[0055] The feature analysis module performs frequency statistics on the amplitude values ​​of each segment of sampled data. Taking the first 5-minute sampled data as an example, assuming that the amplitude value range is between 0.4g and 0.8g, the feature analysis module divides this range into several amplitude intervals. For example, with an interval of 0.1g, it is divided into four intervals: 0.4-0.5g, 0.5-0.6g, 0.6-0.7g, and 0.7-0.8g. Then, the number of data points in each amplitude interval is counted, that is, the frequency of occurrence. For example, if there are 120 data points in the 0.5-0.6g interval, then the frequency of this interval is 120 / 300=40%. Similarly, the frequency statistics of this segment of sampled data are completed, and each amplitude interval and its corresponding frequency of occurrence are obtained.

[0056] After completing the frequency statistics for all 12 segments of sampled data, the feature analysis module merges the statistical results for each segment to form an amplitude distribution spectrum covering the entire stable time interval. During merging, the statistical results for each segment are superimposed or accumulated in the spectrum, with the amplitude value as the horizontal axis (unit: g) and the frequency of occurrence as the vertical axis (unit: %). For example, for the amplitude range of 0.5-0.6g, the frequency of the first segment is 40%, the frequency of the second segment is 35%, the frequency of the third segment is 38%, and the frequency of the twelfth segment is 42%. After merging, the total frequency of this segment in the entire amplitude distribution spectrum may be calculated by weighted average or direct accumulation, depending on the system's preset merging rules.

[0057] After obtaining the amplitude distribution map, the feature analysis module determines the vibration amplitude reference interval based on the preset confidence level. The preset confidence level is a probability value pre-set by the system, such as 90% or 95%, which is used to indicate the credibility of the vibration amplitude within a certain range. Taking 95% confidence level as an example, the feature analysis module finds the amplitude range that covers 95% of the frequencies in the amplitude distribution map, and this range is the vibration amplitude reference interval. For example, it can be seen from the amplitude distribution map that when the amplitude value is between 0.55g and 0.75g, the corresponding frequency reaches 95% cumulatively, then 0.55g to 0.75g is the vibration amplitude reference interval for this stable time interval.

[0058] In practical applications, the fixed duration of segmented sampling can be adjusted according to the frequency of change of vibration information and system requirements. If the vibration frequency of the mixing equipment is high, the segment duration can be shortened, such as setting it to 1 minute, to more accurately capture vibration changes; if the vibration frequency is low, the segment duration can be appropriately extended, such as setting it to 10 minutes, to reduce the amount of calculation. The choice of preset confidence level also needs to be combined with the production process requirements and equipment characteristics. For example, for processes with high requirements for mixing uniformity, a higher confidence level, such as 95%, can be set to more strictly define the normal vibration amplitude range.

[0059] Furthermore, the feature analysis module filters out abnormal amplitude data when constructing the amplitude distribution map. For example, if a certain segment of sampled data contains amplitude values ​​that significantly deviate from the normal range, this could be due to a vibration sensor failure or external interference. The feature analysis module will then remove this data based on pre-set outlier detection rules (e.g., values ​​exceeding ±3 standard deviations from the mean) to ensure the accuracy of the amplitude distribution map.

[0060] Once the vibration amplitude reference range is determined, it is used by the Assessment and Decision Module to analyze the real-time vibration amplitude. For example, if the Assessment and Decision Module determines a mixing anomaly based on real-time color or particle size data, it retrieves the current real-time vibration amplitude and compares it with the reference range. If the real-time vibration amplitude is not within this range, it indicates an abnormal vibration state during the mixing process, potentially indicating a problem with the mixing degree. The Assessment and Decision Module will adjust the operational activation state of the material sampling device accordingly, such as switching it from a dormant state to an active state, to facilitate further sampling and analysis.

[0061] As production continues, the feature analysis module regularly updates the amplitude distribution map and vibration amplitude reference range. For example, at the end of each day or production shift, the feature analysis module resamples, counts frequencies, and merges the vibration information within the latest stable time interval to generate a new amplitude distribution map. It then updates the vibration amplitude reference range accordingly to accommodate changes in vibration conditions caused by factors such as equipment wear and changes in material properties, ensuring that the vibration amplitude reference range always accurately reflects the current normal vibration state.

[0062] Throughout the entire process, the feature analysis module systematically processes vibration information within the stable time interval, from segmented sampling to frequency statistics, to spectrum construction and reference interval determination. Each step follows clear logic and preset rules to ensure the scientific and reliable nature of the vibration amplitude reference interval. This analysis method, based on actual production data, can effectively capture the characteristics of the vibration state during the mixing process, providing a key vibration characteristic reference for intelligent assessment of the degree of mixing. This enables the system to promptly detect potential mixing anomalies through changes in vibration information, thereby achieving accurate assessment and control of the degree of mixing of the ceramsite material.

[0063] Example 5: In this example, the evaluation and determination module controls the operational activation state of the material sampling device and the acquisition response state of the detection component based on multiple information such as characteristic time intervals, real-time color data, real-time particle size data, and real-time vibration amplitude, thereby achieving intelligent assessment of the mixing degree of the ceramsite material. The following describes its implementation in detail with reference to specific examples.

[0064] Assuming a mixing station is within the statistical period of 10:00 on June 25, 2025, the feature analysis module determines that the station's abnormal time interval is from 10:15 to 10:25, and the rest of the time is a stable time interval. When the time is 10:16, that is, within the abnormal time interval, the evaluation and judgment module will set the material sampling device of the corresponding station to an active state, and at the same time control the collection response state of the detection component to only collect color data, particle size data, and vibration data. At this time, the material sampling device starts to sample the material at the mixing station. The color identifier, particle size meter, and vibration sensor in the detection component begin to work, collecting the material's color data, particle size data, and vibration information in real time, and transmitting this data to the evaluation and judgment module.

[0065] At 10:30 a.m., within the stable time interval, the assessment and judgment module switches the material sampling device to a dormant state. The detection component's collection response state remains to collect color data, particle size data, and vibration data. Simultaneously, the feature analysis module determines the color and particle size baseline values. For example, the feature analysis module retrieves historical color and particle size data from the record storage component during the stable time interval and calculates the color mean to be RGB(150, 160, 170). The color fluctuation range lower limit is RGB(140, 150, 160). Therefore, the color baseline value is RGB(150 + 140, 160 + 150, 170 + 160), or RGB(290, 310, 330). (This is only an example calculation; actual color value calculations depend on the specific model.) The particle size mean is 50 μm, the particle size fluctuation range lower limit is 40 μm, and the particle size baseline value is 50 + 40 = 90 μm.

[0066] When the material sampling device is in the dormant state, the assessment and judgment module combines real-time color data and real-time particle size data to determine the mixing abnormality tendency of the workstation. Assume that at 10:40, the real-time color data is RGB (300, 320, 340) and the real-time particle size data is 95μm. Because the real-time color data is greater than the color reference value RGB (290, 310, 330) and the real-time particle size data is greater than the particle size reference value of 90μm, the assessment and judgment module determines that the mixing station has a mixing abnormality and adjusts the material sampling device to the active state to sample and analyze the material.

[0067] For example, at 10:45, the real-time color data is RGB (295, 315, 335) and the real-time particle size data is 85 μm. At this point, the real-time color data is greater than the color reference value, while the real-time particle size data is less than the particle size reference value. The assessment and judgment module determines that the mixing station has a tendency to mix abnormally and decides to analyze the vibration information.

[0068] When the assessment and judgment module determines that there is a tendency for mixed abnormalities, it will control the feature analysis module to determine the reference range of the vibration amplitude. Assume that the feature analysis module determines that the reference range of the vibration amplitude is 0.6g to 0.8g at a confidence level of 95% based on the amplitude distribution map of the stable time interval. At this time, the assessment and judgment module obtains the real-time vibration amplitude. If the real-time vibration amplitude is 0.5g, which is not within the reference range, the assessment and judgment module will determine to change the material sampling device from the dormant state to the active state; if the real-time vibration amplitude is 0.7g, which is within the reference range, it will be determined not to change the operating activation state of the material sampling device, and it will remain in the dormant state.

[0069] In another example, during a stable time interval at a mixing station, the material sampling device remains dormant while the detection component continues to collect data. Assuming the real-time color data is RGB (280, 300, 320), which is less than the color reference value RGB (290, 310, 330), and the real-time particle size data is 80 μm, which is less than the particle size reference value 90 μm, the assessment module determines that the mixing station does not have a tendency to mix abnormally. The material sampling device remains dormant, while the detection component continues to collect data normally.

[0070] During operation, the Assessment and Decision Module monitors changes in the characteristic time interval in real time. If the Characteristic Analysis Module updates the characteristic time interval at any point, the Assessment and Decision Module immediately adjusts the operational activation state of the material sampling device and the collection response state of the detection component based on the new characteristic time interval. For example, if the previously stable time interval is reclassified as an abnormal time interval, the Assessment and Decision Module will immediately switch the material sampling device from a dormant state to an active state, and the collection response state of the detection component will change to data collection only.

[0071] Furthermore, the assessment module dynamically adjusts the state of the material sampling device based on changes in real-time vibration amplitude. While the material sampling device is active, if, after a period of sampling and analysis, both real-time color data and real-time particle size data are less than or equal to the baseline value, and the real-time vibration amplitude is within the reference range, the assessment module determines that the mixing anomaly has resolved and switches the material sampling device back to sleep, saving energy and equipment loss.

[0072] Throughout the assessment process, the module strictly follows pre-set rules and logic to ensure accurate and timely assessment of the mixing station's status. By analyzing characteristic time intervals, comparing real-time data with baseline values, and assisting with vibration information, the module accurately identifies abnormal mixing trends and phenomena. By adjusting the operating status of the material sampling device, it achieves intelligent assessment and control of the ceramsite material's mixing degree, ensuring the stability of the ceramsite production process and product quality.

[0073] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0074] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent evaluation system for the mixing degree of ceramsite materials, characterized in that: include: Material sampling device, used to obtain material samples at each mixing station of the ceramsite production line; A parameter collection unit is provided at each mixing station of the ceramsite production line, and includes a recording and storage component for storing historical mixing anomaly information at each mixing station, and multiple detection components for collecting material color data, particle size data, and mixing process vibration information at each station. The historical mixing anomaly information includes historical anomaly time points, historical color data, historical particle size data, and historical vibration information. a feature analysis module connected to the parameter acquisition unit, configured to divide a statistical period into a plurality of feature time intervals based on the historical mixing anomaly information of a single mixing station, determine a color reference value and a particle size reference value based on the historical color data and the historical particle size data of the single station, construct a time-domain vibration waveform diagram based on the vibration information to determine an amplitude distribution map of a stable period, and determine a vibration amplitude reference interval based on the amplitude distribution map; The characteristic time interval includes an abnormal time interval and a stable time interval; An evaluation and judgment module is respectively connected to each of the material sampling devices, the parameter acquisition unit and the feature analysis module, and is used to determine the operation activation status of the material sampling device at the corresponding station and the acquisition response status of the detection component according to the characteristic time interval of the single mixing station, determine the mixing abnormality tendency according to the real-time color data and real-time particle size data of the single mixing station to determine whether to analyze the vibration information, and determine whether to change the operation activation status of the corresponding material sampling device according to the mixing abnormality tendency and the real-time vibration amplitude.

2. The intelligent evaluation system for ceramsite material mixing degree according to claim 1, wherein: Each mixing station is provided with a detection component, and a single detection component includes a color identifier, a particle size measuring instrument and a vibration sensor.

3. The intelligent evaluation system for ceramsite material mixing degree according to claim 1, wherein: The feature analysis module determines the abnormal occurrence point of a single hybrid workstation based on its historical abnormal time points, determines a fixed time interval centered on the abnormal occurrence point as the abnormal time interval, and determines the absolute complement of the abnormal time interval within the statistical period as the stable time interval; If the duration of a single stable time interval is less than a set threshold, the stable time interval and the two adjacent abnormal time intervals are merged into one abnormal time interval.

4. The intelligent evaluation system for ceramsite material mixing degree according to claim 1, wherein: The feature analysis module determines the color mean and color fluctuation range based on the historical color data corresponding to the stable time interval to determine the color reference value, and determines the particle size mean and particle size fluctuation range based on the historical particle size data corresponding to the stable time interval to determine the particle size reference value; The color reference value is the sum of the color mean and the lower limit of the color fluctuation range, and the particle size reference value is the sum of the particle size mean and the lower limit of the particle size fluctuation range.

5. The intelligent evaluation system for ceramsite material mixing degree according to claim 1, wherein: The feature analysis module determines a vibration amplitude reference interval based on an amplitude range corresponding to a preset confidence level in an amplitude distribution graph of a stable time interval; The horizontal axis of the amplitude distribution graph is the vibration signal intensity, and the vertical axis of the amplitude distribution graph is the occurrence frequency.

6. The intelligent evaluation system for ceramsite material mixing degree according to claim 1, wherein: The evaluation and determination module determines the operation activation state of the material sampling device at the corresponding workstation and the collection response state of the detection component according to the characteristic time interval of the single mixing workstation, including: If the characteristic time interval is an abnormal time interval, the corresponding workstation material sampling device is in an activated state and the collection response state of the detection component is to only collect color data, particle size data and vibration data; If the characteristic time interval is a stable time interval, the corresponding workstation material sampling device is in a dormant state and the collection response state of the detection component is to collect color data, particle size data and vibration data, and the characteristic analysis module is controlled to determine the color reference value and the particle size reference value; The running activation state includes an activation state and a dormant state.

7. The intelligent evaluation system for ceramsite material mixing degree according to claim 6, wherein: The evaluation and determination module determines the abnormal mixing tendency of the station based on the result of determining that the material sampling device of a single mixing station is in a dormant state, combined with the real-time color data and the real-time particle size data, including: If the real-time color data is less than or equal to the color reference value and the real-time particle size data is less than or equal to the particle size reference value, it is determined that there is no abnormal mixing tendency in the corresponding mixing station; If the real-time color data is greater than the color reference value and the real-time particle size data is greater than the particle size reference value, it is determined that a mixing abnormality exists in the corresponding mixing station and the corresponding material sampling device is adjusted to an activated state.

8. The intelligent evaluation system for ceramsite material mixing degree according to claim 7, wherein: The evaluation and determination module determines the abnormal mixing tendency of the station based on the result of determining that the material sampling device of a single mixing station is in a dormant state, combined with the real-time color data and the real-time particle size data, and further includes: If the real-time color data is greater than the color reference value or the real-time particle size data is greater than the particle size reference value, it is determined that the corresponding mixing station has a mixing abnormality tendency and it is determined to analyze the vibration information.

9. The intelligent evaluation system for ceramsite material mixing degree according to claim 8, characterized in that: The evaluation and determination module controls the feature analysis module to determine a reference interval of vibration amplitude based on the result of the determination of the presence of abnormal mixing tendency, and determines whether to change the operation activation state of the material sampling device corresponding to the mixing station based on the real-time vibration amplitude; If the real-time vibration amplitude is not within the vibration amplitude reference range, it is determined that the material sampling device corresponding to the mixing station is changed from a dormant state to an active state; If the real-time vibration amplitude is within the vibration amplitude reference range, it is determined that the operation activation state of the material sampling device corresponding to the mixing station is not changed and that the device remains in the dormant state.

10. The intelligent evaluation system for ceramsite material mixing degree according to claim 5, characterized in that: When the feature analysis module determines the amplitude distribution spectrum of the stable time interval, it first samples the vibration information within the stable time interval in segments, with each segment of sampling data having a fixed length. Then, frequency statistics are performed on the amplitude values ​​of each segment of sampling data, and finally, the statistical results of each segment are merged to form an amplitude distribution spectrum covering the entire stable time interval.

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