An environmentally friendly building block production control method and device

Abstract

The present invention relates to the technical field of production control of building materials, and particularly relates to an environmentally friendly building block production control method and device. Based on the differences in motor vibration data in different production cycles, suspected abnormal motor vibration data segments are screened out in the current production cycle. By comparing the comparison data segments composed of the suspected abnormal motor vibration data segments and the corresponding silo vibration data, the vibration trend components of the suspected abnormal motor vibration data are determined. Furthermore, by comparing the vibration trend components with the main components in the comparison data segments, the vibration consistency can be obtained. Further, by combining the information in the suspected abnormal motor vibration data segments, the abnormal degree at the abnormal moment is determined for controlling the motor. The present invention decomposes and screens components to compare the motor vibration data and the silo vibration data, and then determines the accurate abnormal degree for motor control. Through the quantification of the abnormal degree, the misjudgment of control caused by abnormal data caused by noise can be avoided, and the control accuracy can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building material production control, and particularly relates to a production control method and device for environment-friendly building blocks. Background Art

[0002] Environment-friendly building blocks are a kind of building materials with environmental protection characteristics and are widely used in the construction field. The existing production process flow of environment-friendly building blocks is raw materials, mixing, forming, curing, and stacking. Among them, mixing is an important link in production, and the mixer is an important device in the mixing link, and its operating state affects the efficiency of the entire production line. Therefore, it is necessary to analyze the vibration data of the mixer motor to identify abnormalities in a timely manner, so as to ensure the production efficiency of environment-friendly building blocks.

[0003] The prior art can judge whether the mixer has a fault by installing a vibration sensor at the mixing motor and using the vibration data during its operation. Since the equipment system of the building block production line is complex and there are many types of it, it is easy to cause electromagnetic interference to the vibration sensor. Strong electromagnetic interference may introduce noise during the signal transmission process of the sensor, making the collected vibration data contain noise. When performing fuzzy control, it is difficult to distinguish the real vibration data from the data affected by electromagnetic interference, reducing the accuracy of the control in the building block production process. Summary of the Invention

[0004] In order to solve the technical problem in the prior art that in the mixing process of building blocks, it is impossible to distinguish the real abnormal motor vibration data from the abnormal data caused by noise interference, the purpose of the present invention is to provide a production control method and device for environment-friendly building blocks, and the specific technical solutions adopted are as follows:

[0005] The present invention proposes a production control method for environment-friendly building blocks, and the method includes:

[0006] Obtain the vibration data of the to-be-controlled block production line in each production cycle; the vibration data includes motor vibration data and bin vibration data at different positions of the mixing bin, and each production cycle includes multiple vibration data segments;

[0007] Obtain the suspected abnormal motor vibration data segment in the current production cycle according to the data difference between the motor vibration data segments between the current production cycle and the historical production cycle;

[0008] Take the bin vibration data segment within the time range corresponding to the suspected abnormal motor vibration data segment as the comparison data segment; compare the similarity of the principal components between the suspected abnormal motor vibration data segment and the comparison data segment, and screen out the vibration trend component of the suspected abnormal motor vibration data; analyze the consistency between the vibration trend component and the principal component of the comparison data segment to obtain the vibration consistency of the suspected abnormal motor vibration data segment;

[0009] Based on the data distribution of the suspected abnormal motor vibration data segment and the vibration consistency, obtain the degree of abnormality at the abnormal moment in the suspected abnormal motor vibration data segment; control the motor speed according to the degree of abnormality at the abnormal moment.

[0010] Further, the screening method for the suspected abnormal motor vibration data segment includes:

[0011] Take any motor vibration data segment in the current production cycle as the target data segment; obtain the matching degree of the historical production cycle according to the difference between the target data segment and the motor vibration data segments at the same time sequence position in the historical production cycle, and screen out the matching cycle according to the matching degree.

[0012] Obtain the suspected abnormality coefficient of the target data segment according to the matching degree and quantity of the matching cycle, and judge whether the target data segment is a suspected abnormal motor vibration data segment according to the suspected abnormality coefficient.

[0013] Further, the method for obtaining the matching degree includes:

[0014] Take the absolute value of the difference in average amplitude between the target data segment and the motor vibration data segments at the same time sequence position in the historical production cycle as the amplitude difference; take the number of production cycles between the current production cycle and the historical production cycle as the time difference; perform a negative correlation mapping and normalization on the product of the amplitude difference and the time difference to obtain the matching degree.

[0015] Further, the method for obtaining the suspected abnormality coefficient includes:

[0016] Obtain the proportion of the number of the matching cycles in all production cycles, obtain the average matching degree of all matching cycles, perform a negative correlation mapping and normalization on the product of the proportion and the average matching degree to obtain the suspected abnormality coefficient.

[0017] Further, the screening method for the vibration trend component includes:

[0018] Take any principal component of the suspected abnormal motor vibration data segment as the target principal component; take the principal component of the comparison data segment as the comparison principal component, obtain the eigenvalue and value of the comparison principal component and the target principal component, and obtain the difference distance between the comparison principal component and the target principal component; take the ratio of the eigenvalue and value and the difference distance as the initial similarity factor, and select the comparison principal component with the largest initial similarity factor in each comparison data segment as the similar principal component of the target principal component; take the cumulative value of the initial similarity factors corresponding to the similar principal components of all comparison data segments as the component screening factor of the target principal component.

[0019] Among the principal components of the suspected abnormal motor vibration data segment, select the principal component with the largest component screening factor as the vibration trend component.

[0020] Further, the method for obtaining the vibration consistency includes:

[0021] Perform a negative correlation mapping on the position distance between the corresponding position of the comparison data segment and the motor to obtain the position confidence of each comparison data segment; take the product of the initial similarity factor between the vibration trend component and the similar principal component in the comparison data segment and the position confidence as the initial consistency of the comparison data segment; after accumulating the initial consistencies of all comparison data segments, multiply by the component screening factor corresponding to the vibration trend component to obtain the vibration consistency.

[0022] Further, the method for screening the abnormal moment includes:

[0023] Take the suspected abnormal motor vibration data segment with the vibration consistency greater than the preset consistency threshold as the abnormal data segment, and each moment in the abnormal data segment as the abnormal moment.

[0024] Further, the method for obtaining the degree of abnormality includes:

[0025] In the abnormal data segment, normalize the difference between the motor vibration data at the abnormal moment and the next moment to obtain the vibration instability at the abnormal moment; normalize the product of the vibration instability and the vibration consistency to obtain the degree of abnormality at the abnormal moment.

[0026] Further, the control of the motor speed according to the degree of abnormality at each moment includes:

[0027] If a continuous preset number of moments are all abnormal moments and the degree of abnormality is greater than the preset abnormality threshold, then control the motor to reduce the speed.

[0028] The present invention also proposes an environmentally friendly building block production control device, and the device includes:

[0029] A vibration data acquisition module for acquiring the vibration data of the block production line to be controlled in each production cycle; the vibration data includes motor vibration data and the vibration data of the silo body at different positions of the mixing silo, and each production cycle includes multiple vibration data segments;

[0030] A suspected abnormality screening module for obtaining the suspected abnormal motor vibration data segment in the current production cycle according to the data difference between the motor vibration data segments between the current production cycle and the historical production cycle;

[0031] A vibration trend component screening module is used to take the silo vibration data segment within the corresponding time range of the suspected abnormal motor vibration data segment as a comparison data segment; compare the similarity of the principal components between the suspected abnormal motor vibration data segment and the comparison data segment, and screen out the vibration trend components of the suspected abnormal motor vibration data.

[0032] A vibration consistency analysis module is used to analyze the consistency between the vibration trend component and the principal component of the comparison data segment, and obtain the vibration consistency of the suspected abnormal motor vibration data segment.

[0033] A motor speed control module is used to obtain the abnormality degree of each moment in the suspected abnormal motor vibration data segment according to the data distribution of the suspected abnormal motor vibration data segment and the vibration consistency; control the motor speed according to the abnormality degree of each moment.

[0034] The present invention has the following beneficial effects:

[0035] Based on the differences in motor vibration data in different production cycles, the present invention screens out the suspected abnormal motor vibration data segments in the current production cycle. Considering that the abnormal vibration of the motor will be transmitted to other positions of the mixing silo due to the structure of the mixing silo, that is, the abnormal vibration should have a certain consistency with the silo vibration data of the mixing silo, and the noise is the noise data generated by electromagnetic interference, so the abnormal data shown does not match the silo vibration data. Therefore, the present invention determines the vibration trend components of the suspected abnormal motor vibration data by comparing the suspected abnormal motor vibration data segment with the comparison data segment composed of the corresponding silo vibration data. The vibration trend components can effectively characterize the vibration information in the suspected abnormal motor vibration data segment. By component decomposition, the interference of other components on the vibration consistency analysis can be avoided, and then the vibration consistency can be obtained by comparing the vibration trend components with the principal components in the comparison data segment. Further, combining the information in the suspected abnormal motor vibration data segment to determine the abnormality degree at the abnormal moment for controlling the motor. The present invention compares the motor vibration data and the silo data through component decomposition and component screening, and then determines the accurate abnormality degree for motor control. By quantifying the abnormality degree, the misjudgment of the control caused by the abnormal data caused by noise can be avoided, and the control accuracy can be improved. Description of the Drawings

[0036] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0037] Figure 1A flowchart of an environmentally friendly building block production control method provided by an embodiment of the present invention. Detailed implementation manners

[0038] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific implementation manners, structures, features and effects of an environmentally friendly building block production control method and device proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0040] The following specifically describes the specific solutions of an environmentally friendly building block production control method and device provided by the present invention with reference to the accompanying drawings.

[0041] Please refer to Figure 1 , which shows a flowchart of an environmentally friendly building block production control method provided by an embodiment of the present invention. The method includes:

[0042] Step S1: Obtain vibration data of the block production line to be controlled in each production cycle; the vibration data includes motor vibration data and bin vibration data at different positions of the mixing bin, and each production cycle includes multiple vibration data segments.

[0043] In the process of building block production, different production cycles are set according to production batches. In the embodiments of the present invention, the time for producing 50 blocks is used as one production cycle. Vibration sensors are installed on the motors of the mixing bin, and vibration sensors are evenly installed at different positions on the bin body of the mixing bin. The vibration data is collected through the vibration sensors. In the embodiments of the present invention, the acquisition frequency of the vibration sensors is once every 5 seconds, and the vibration data collected in one production cycle is divided into 30 data segments, thereby obtaining bin vibration data segments and motor vibration data segments.

[0044] Step S2: Obtain the suspected abnormal motor vibration data segment in the current production cycle according to the data difference between the motor vibration data segments in the current production cycle and the historical production cycles.

[0045] In the production process of building blocks, for the same production line, the wear process experienced by the mixer equipment in consecutive production cycles is gradual and relatively uniform. Moreover, the raw materials fed into the mixer each time are basically similar in terms of type, particle size, humidity, ratio, etc., and each production cycle is usually in the same working state. The similarity of the raw material characteristics makes the loads borne by the mixer motor during the mixing process similar, thus generating similar vibration data. Therefore, for the current production cycle, it is possible to screen out the suspected abnormal motor vibration data segments in the current production cycle by comparing the data differences between the motor vibration data segments of the historical production cycle and the current production cycle. The suspected abnormal motor vibration data segments contain obvious abnormal data characteristics, and these abnormal characteristics may be caused by abnormal vibrations or may be caused by noise, so further analysis is required in subsequent steps.

[0046] Preferably, in the embodiments of the present invention, for the current production cycle, all the production cycles before it are historical production cycles. Therefore, there are relatively many historical production cycles. In order to increase the accuracy of vibration data comparison, the embodiments of the present invention first screen out the matching cycles of the current production cycle, and then determine the suspected abnormal motor vibration data segments by comparing the motor vibration data between the current production cycle and the matching cycles. The specific screening method for the suspected abnormal motor vibration data segments includes:

[0047] Take any motor vibration data segment in the current production cycle as the target data segment; obtain the matching degree of the historical production cycle according to the difference between the target data segment and the motor vibration data segment at the same time sequence position in the historical production cycle, and screen out the matching cycles according to the matching degree. That is, the greater the matching degree, the more similar the motor vibration data segments between the two production cycles, and the more matching cycles, the more similar the vibration characteristics of the target data segment in the current production cycle, and the less likely the target data segment is an abnormal vibration.

[0048] Further, obtain the suspected abnormal coefficient of the target data segment according to the matching degree and the number of the matching cycles, and judge whether the target data segment is a suspected abnormal motor vibration data segment according to the suspected abnormal coefficient. That is, the suspected abnormal coefficient should be negatively correlated with the matching degree and the number of the matching cycles. The greater the matching degree and the number of the matching cycles, the smaller the suspected abnormal coefficient.

[0049] In the embodiments of the present invention, after normalizing the suspected abnormal coefficient, set the abnormal coefficient threshold to 0.7, and take the target data segment with the suspected abnormal coefficient greater than the abnormal coefficient threshold as the suspected abnormal motor vibration data segment.

[0050] Furthermore, in the embodiments of the present invention, the method for obtaining the matching degree includes:

[0051] The absolute value of the difference in average amplitude between the target data segment and the motor vibration data segment at the same time sequence position within the historical production cycle is used as the amplitude difference. The larger the amplitude difference, the greater the difference in vibration data between the two motor vibration data segments, and the less matching the historical production cycle is with the current production cycle.

[0052] The number of production cycles between the current production cycle and the historical production cycle is used as the time difference. That is, the larger the time difference, the greater the time distance between the two production cycles. The losses during the stirring process conform to this, and the characteristics generated are less similar, so the matching degree should be lower.

[0053] Therefore, the product of the amplitude difference and the time difference is subjected to negative correlation mapping and normalization to obtain the matching degree. In the embodiments of the present invention, the method of negative correlation mapping and normalization is: taking the opposite number of the data as the power of the exponential function with the natural constant as the base, and the function output result is the result of negative correlation mapping and normalization of the corresponding data. Those skilled in the art can choose other basic mathematical means to achieve negative correlation mapping and normalization, which will not be limited and elaborated here, nor will it be repeated in the subsequent content.

[0054] In the embodiments of the present invention, the matching degree threshold is set to 0.7, and the historical production cycle with a matching degree greater than the matching degree threshold is used as the matching cycle of the current production cycle.

[0055] Further, the method for obtaining the suspected abnormality coefficient includes:

[0056] Obtain the proportion of the number of matching cycles in all production cycles, obtain the average matching degree of all matching cycles, and perform negative correlation mapping and normalization on the product of the proportion and the average matching degree to obtain the suspected abnormality coefficient.

[0057] Step S3: Use the silo vibration data segment within the time range corresponding to the suspected abnormal motor vibration data segment as the comparison data segment; compare the similarity of the principal components between the suspected abnormal motor vibration data segment and the comparison data segment, and screen out the vibration trend components of the suspected abnormal motor vibration data; analyze the consistency between the vibration trend components and the principal components of the comparison data segment to obtain the vibration consistency of the suspected abnormal motor vibration data segment.

[0058] The motor of the mixer is the core driving component that drives the overall system, and the abnormal vibration it generates will have a direct impact on the entire mixer. The mixer motor is the power source for the operation of the mixer housing. The motor is connected to the mixer housing through various connecting components. The power generated during the operation of the motor will be directly transmitted to the mixing device, driving the materials inside the housing to be mixed. Therefore, under normal circumstances, due to factors such as power transmission and structural correlation, the vibrations of the motor and the housing have a high degree of consistency. Therefore, the vibration data of the housing at different positions of the mixer housing will have the same vibration characteristics as the abnormal vibration of the motor, while the abnormal motor vibration data caused by noise will not have an obvious correlation with the housing vibration data. Therefore, in order to analyze the consistency between the two types of data, it is first necessary to screen out the suspected abnormal motor vibration data segments for the characteristic data of vibration trend analysis.

[0059] Because the purpose of screening out the vibration trend is to compare the housing vibration and the motor vibration, in the embodiments of the present invention, the similarity of the principal components between the suspected abnormal motor vibration data segment and the comparison data segment is compared, and the vibration trend components of the suspected abnormal motor vibration data are screened out. That is, the more similar the principal components are, the more suitable they are for analyzing the consistency of the vibration trend, and the greater the reference significance of their vibration trend. Therefore, the vibration trend components in the suspected abnormal motor vibration data can be screened out based on this similarity. Furthermore, the vibration consistency is determined by using the consistency between the vibration trend components and the principal components in the comparison data segment. The greater the vibration consistency, the more the abnormal vibration in the suspected abnormal motor vibration data belongs to the true abnormal vibration.

[0060] In the embodiments of the present invention, the principal components of all data segments are obtained through the PCA principal component analysis algorithm, and each obtained principal component has a corresponding eigenvalue. The larger the eigenvalue, the more important the information of the principal component in the current data segment.

[0061] Preferably, in the embodiments of the present invention, the method for screening the vibration trend components includes:

[0062] Taking any principal component of the suspected abnormal motor vibration data segment as the target principal component; taking the principal components of the comparison data segment as the comparison principal components.

[0063] Obtaining the sum of the eigenvalues of the comparison principal component and the target principal component. The larger the sum of the eigenvalues, the more important the information of the two principal components in the corresponding data segments, and the more suitable they are for trend analysis.

[0064] Obtaining the difference distance between the comparison principal component and the target principal component. The smaller the difference distance, the more similar the two principal components are, and the more likely the corresponding target principal component is the vibration trend component used to characterize the vibration trend.

[0065] Take the ratio of the eigenvalue, value, and difference distance as the initial similarity factor. The larger the initial similarity factor, the more similar the comparison principal component is to the target principal component for the target principal component. Select the comparison principal component with the largest initial similarity factor in each comparison data segment as the similar principal component of the target principal component.

[0066] Take the accumulated value of the initial similarity factors corresponding to the similar principal components of all comparison data segments as the component screening factor of the target principal component. The larger the component screening factor, the more similar the trend of the target principal component is to the vibration data of the bin body at all positions, and the more the target principal component can represent the vibration trend information of the motor.

[0067] Therefore, among the principal components of the suspected abnormal motor vibration data segment, select the principal component with the largest component screening factor as the vibration trend component.

[0068] Furthermore, in the embodiment of the present invention, the method for obtaining vibration consistency includes:

[0069] Perform a negative correlation mapping on the position distance between the corresponding position of the comparison data segment and the motor to obtain the position confidence of each comparison data segment. That is, the closer the sensor for collecting the bin body vibration data is to the motor, the more consistent the vibration characteristics are, and thus the greater the position confidence.

[0070] Take the product of the initial similarity factor between the vibration trend component and the similar principal component in the comparison data segment and the position confidence as the initial consistency of the comparison data segment. The greater the initial consistency, the more consistent the vibration trend component is with the vibration trend in the comparison data segment.

[0071] Further, statistically analyze all comparison data segments. After accumulating the initial consistencies of all comparison data segments, multiply them by the component screening factor corresponding to the vibration trend component to obtain the vibration consistency. That is, the corresponding component screening factor can be used as a reference weight. The larger the component screening factor, the more the vibration trend component can represent the vibration trend of the motor, and the result obtained by multiplying it with the accumulated value of the initial consistency can be regarded as the weighted adjustment result.

[0072] Step S4: According to the data distribution of the suspected abnormal motor vibration data segment and the vibration consistency, obtain the abnormal degree of the abnormal moment in the suspected abnormal motor vibration data segment; control the motor speed according to the abnormal degree of the abnormal moment.

[0073] For the suspected abnormal motor vibration data segment, the more obvious the abnormal fluctuation represented by its data and the greater the vibration consistency, the greater the degree of abnormal vibration of the motor. Therefore, the abnormal degree of the abnormal moment in the suspected abnormal motor vibration data segment can be obtained according to the data distribution of the suspected abnormal motor vibration data segment and the vibration consistency. Then, control the motor speed according to the abnormal degree of the abnormal moment.

[0074] Preferably, in the embodiments of the present invention, since the vibration consistency can represent the abnormal conditions of the suspected abnormal motor vibration data segment, the suspected abnormal motor vibration data segment can be screened based on the vibration consistency. The suspected abnormal motor vibration data segment with a vibration consistency greater than the preset consistency threshold is used as the abnormal data segment, and each moment in the abnormal data segment is used as an abnormal moment. That is, the abnormal data segment is caused by the abnormal vibration of the motor, and the suspected abnormal motor vibration data segment with a vibration consistency not greater than the preset consistency threshold is the abnormality caused by noise. In the embodiments of the present invention, the consistency threshold is set to 0.7.

[0075] Preferably, in the embodiments of the present invention, the method for obtaining the degree of abnormality includes:

[0076] In the abnormal data segment, the difference between the motor vibration data at the abnormal moment and the next moment is normalized to obtain the vibration instability at the abnormal moment; the product of the vibration instability and the vibration consistency is normalized to obtain the degree of abnormality at the abnormal moment. That is, for an abnormal moment, if it has a significant abnormal fluctuation change and the vibration consistency of its corresponding data segment is large, it indicates that its degree of abnormality is greater.

[0077] Preferably, in the embodiments of the present invention, for the current motor vibration data segment in the current production cycle, if it is an abnormal data segment, and there are continuously a preset number of moments that are all abnormal moments, and the degree of abnormality is greater than the preset abnormality threshold, then the motor is controlled to reduce the speed. In the embodiments of the present invention, the preset number is set to 10, and the abnormality threshold is set to 0.6.

[0078] In other embodiments of the present invention, the positive integer 1 can be used as the weight of the motor vibration data at each moment in all normal periods of the current production cycle, and the result obtained by adding 1 to the degree of abnormality is used as the weight of the motor vibration data at the kth abnormal moment in the current production cycle, thereby obtaining the weight of the motor vibration data at each moment in the current production cycle. Then, the weighted motor vibration data time series sequence of the current production cycle is input into the fuzzy control system to output a control instruction for the motor speed. The fuzzy control system can dynamically adjust the production control strategy. When the system detects that there are high interferences or abnormal vibration data in some periods, the system can adjust the weights so that the data with less interference and high reliability has a greater impact on the decision-making. Therefore, the fuzzy control system can respond to the state of the production line in real time and make more accurate adjustments to ensure the quality and efficiency of building block production.

[0079] In summary, based on the differences in motor vibration data during different production cycles, the embodiments of the present invention screen out suspected abnormal motor vibration data segments in the current production cycle. By comparing the comparison data segments composed of the suspected abnormal motor vibration data segments and the corresponding silo vibration data, the vibration trend components of the suspected abnormal motor vibration data are determined. Furthermore, by comparing the vibration trend components with the principal components in the comparison data segments, the vibration consistency can be obtained. Further, by combining the information in the suspected abnormal motor vibration data segments, the abnormal degree at the abnormal moment is determined to control the motor. The present invention decomposes and screens components to compare the motor vibration data and the silo vibration data, and then determines the accurate abnormal degree for motor control. By quantifying the abnormal degree, the misjudgment of control caused by abnormal data caused by noise can be avoided, and the control accuracy can be improved.

[0080] Based on the same inventive concept, the present invention also provides an environmentally friendly building block production control device, which includes:

[0081] A vibration data acquisition module, configured to acquire vibration data of the block production line to be controlled in each production cycle; the vibration data includes motor vibration data and silo vibration data at different positions of the mixing silo, and each production cycle includes multiple vibration data segments;

[0082] A suspected abnormality screening module, configured to obtain suspected abnormal motor vibration data segments in the current production cycle according to the data differences between the motor vibration data segments between the current production cycle and the historical production cycle;

[0083] A vibration trend component screening module, configured to use the silo vibration data segment within the corresponding time range of the suspected abnormal motor vibration data segment as a comparison data segment; compare the similarity of the principal components between the suspected abnormal motor vibration data segment and the comparison data segment, and screen out the vibration trend components of the suspected abnormal motor vibration data;

[0084] A vibration consistency analysis module, configured to analyze the consistency between the vibration trend components and the principal components of the comparison data segment, and obtain the vibration consistency of the suspected abnormal motor vibration data segment;

[0085] A motor speed control module, configured to obtain the abnormal degree of each moment in the suspected abnormal motor vibration data segment according to the data distribution of the suspected abnormal motor vibration data segment and the vibration consistency; control the motor speed according to the abnormal degree of each moment.

[0086] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the advantages or disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. An environmentally friendly building block production control method, characterized in that The method includes: Obtaining vibration data of the to-be-controlled block production line in each production cycle; the vibration data includes motor vibration data and silo vibration data at different positions of the mixing silo, and each production cycle includes multiple vibration data segments; Obtaining a suspected abnormal motor vibration data segment in the current production cycle according to the data difference between the motor vibration data segments between the current production cycle and the historical production cycle; Taking the silo vibration data segment within the time range corresponding to the suspected abnormal motor vibration data segment as a comparison data segment; comparing the similarity of the principal components between the suspected abnormal motor vibration data segment and the comparison data segment, screening out the vibration trend component of the suspected abnormal motor vibration data; analyzing the consistency between the vibration trend component and the principal component of the comparison data segment to obtain the vibration consistency of the suspected abnormal motor vibration data segment; Obtaining the abnormal degree of the abnormal moment in the suspected abnormal motor vibration data segment according to the data distribution of the suspected abnormal motor vibration data segment and the vibration consistency; controlling the motor speed according to the abnormal degree of the abnormal moment.

2. The environmentally friendly building block production control method according to claim 1, wherein The screening method of the suspected abnormal motor vibration data segment includes: Taking any motor vibration data segment in the current production cycle as a target data segment; obtaining the matching degree of the historical production cycle according to the difference between the target data segment and the motor vibration data segment at the same time sequence position in the historical production cycle, and screening out the matching cycle according to the matching degree; Obtaining the suspected abnormal coefficient of the target data segment according to the matching degree and quantity of the matching cycle, and judging whether the target data segment is a suspected abnormal motor vibration data segment according to the suspected abnormal coefficient.

3. The environmentally friendly building block production control method according to claim 2, characterized in that, The method for obtaining the matching degree includes: Taking the absolute value of the difference in average amplitude between the target data segment and the motor vibration data segment at the same time sequence position in the historical production cycle as the amplitude difference; taking the number of production cycles between the current production cycle and the historical production cycle as the time difference; performing negative correlation mapping and normalization on the product of the amplitude difference and the time difference to obtain the matching degree.

4. The environmentally friendly building block production control method according to claim 2, characterized in that The method for obtaining the suspected abnormal coefficient includes: Obtaining the proportion of the number of the matching cycles in all production cycles, obtaining the average matching degree of all matching cycles, performing negative correlation mapping and normalization on the product of the proportion and the average matching degree to obtain the suspected abnormal coefficient.

5. An environmentally friendly building block production control method according to claim 1, characterized in that, The screening method of the vibration trend component includes: Taking any principal component of the suspected abnormal motor vibration data segment as a target principal component; taking the principal component of the comparison data segment as a comparison principal component, obtaining the eigenvalue and value of the comparison principal component and the target principal component, and obtaining the difference distance between the comparison principal component and the target principal component; taking the ratio of the eigenvalue and value and the difference distance as an initial similarity factor, and selecting the comparison principal component with the largest initial similarity factor in each comparison data segment as the similar principal component of the target principal component; taking the cumulative value of the initial similarity factors corresponding to the similar principal components of all comparison data segments as the component screening factor of the target principal component; Selecting the principal component with the largest component screening factor among the principal components of the suspected abnormal motor vibration data segment as the vibration trend component.

6. The environmentally friendly building block production control method according to claim 5, characterized in that, The method for obtaining the vibration consistency includes: Performing a negative correlation mapping on the position distance between the corresponding positions of the comparison data segments and the motor to obtain the position confidence of each comparison data segment; taking the product of the initial similarity factor between the vibration trend component and the similar principal component in the comparison data segment and the position confidence as the initial consistency of the comparison data segment; after accumulating the initial consistencies of all comparison data segments, multiplying by the component screening factor corresponding to the vibration trend component to obtain the vibration consistency.

7. An environmentally friendly building block production control method according to claim 1, characterized in that, The method for screening abnormal moments includes: Regarding the suspected abnormal motor vibration data segments with vibration consistency greater than the preset consistency threshold as abnormal data segments, and each moment in the abnormal data segments as an abnormal moment.

8. The production control method of an environment-friendly building block according to claim 7, characterized in that The method for obtaining the degree of abnormality includes: In the abnormal data segment, normalizing the difference between the motor vibration data at the abnormal moment and the next moment to obtain the vibration instability at the abnormal moment; normalizing the product of the vibration instability and the vibration consistency to obtain the degree of abnormality at the abnormal moment.

9. The environmentally friendly building block production control method according to claim 1, characterized in that, Controlling the motor speed according to the degree of abnormality at each moment includes: If a continuous preset number of moments are all abnormal moments and the degree of abnormality is greater than the preset abnormal threshold, then control the motor to reduce the speed.

10. An environmentally friendly building block production control device, characterized in that, The device includes: A vibration data acquisition module for acquiring vibration data in each production cycle of the block production line to be controlled; the vibration data includes motor vibration data and silo vibration data at different positions of the mixing silo, and each production cycle includes multiple vibration data segments; A suspected abnormality screening module for obtaining the suspected abnormal motor vibration data segments in the current production cycle according to the data difference between the motor vibration data segments in the current production cycle and the historical production cycle; A vibration trend component screening module for using the silo vibration data segments within the corresponding time range of the suspected abnormal motor vibration data segments as comparison data segments; comparing the similarity of the principal components between the suspected abnormal motor vibration data segments and the comparison data segments, and screening out the vibration trend components of the suspected abnormal motor vibration data; A vibration consistency analysis module for analyzing the consistency between the vibration trend component and the principal component of the comparison data segment to obtain the vibration consistency of the suspected abnormal motor vibration data segment; A motor speed control module for obtaining the degree of abnormality at each moment in the suspected abnormal motor vibration data segment according to the data distribution of the suspected abnormal motor vibration data segment and the vibration consistency; controlling the motor speed according to the degree of abnormality at each moment.

Images