CN117113107B - Rotor die casting abnormal error data processing method - Google Patents
Rotor die casting abnormal error data processing method Download PDFInfo
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- CN117113107B CN117113107B CN202311376699.7A CN202311376699A CN117113107B CN 117113107 B CN117113107 B CN 117113107B CN 202311376699 A CN202311376699 A CN 202311376699A CN 117113107 B CN117113107 B CN 117113107B
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- 230000002159 abnormal effect Effects 0.000 title claims abstract description 98
- 238000004512 die casting Methods 0.000 title claims abstract description 58
- 238000003672 processing method Methods 0.000 title claims abstract description 11
- 238000000034 method Methods 0.000 claims abstract description 71
- 230000001174 ascending effect Effects 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 abstract description 8
- 238000001514 detection method Methods 0.000 abstract description 8
- 238000007906 compression Methods 0.000 abstract description 7
- 230000006835 compression Effects 0.000 abstract description 7
- 238000006243 chemical reaction Methods 0.000 abstract description 4
- 238000013144 data compression Methods 0.000 abstract description 2
- 230000005856 abnormality Effects 0.000 description 5
- 230000007774 longterm Effects 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 5
- 231100000749 chronicity Toxicity 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000001788 irregular Effects 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 230000006837 decompression Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
- B22D17/00—Pressure die casting or injection die casting, i.e. casting in which the metal is forced into a mould under high pressure
- B22D17/20—Accessories: Details
- B22D17/32—Controlling equipment
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
- G06F18/15—Statistical pre-processing, e.g. techniques for normalisation or restoring missing data
Abstract
The invention relates to the technical field of data processing, in particular to a rotor die-casting abnormal error data processing method, which is characterized in that temperature data in the rotor die-casting process are collected, intra-group step length irregularities of each suspected sequence are obtained according to jump stability, regularity and data stability of step lengths among suspected data, adjacent normal sequences on the left side and the right side of each suspected sequence are compared according to the intra-group step length irregularities of each suspected sequence, adjacent suspected saliency of the suspected sequence relative to the normal sequence is obtained, weights of each suspected data point are obtained by combining the numerical value of each suspected data point, and rotor die-casting temperature data are compressed by combining Huffman coding. Therefore, the rotor die-casting temperature data compression is realized, the data transmission accuracy of abnormal data in the processes of data transmission, conversion, compression, decoding and the like is facilitated, and the accuracy of abnormal data point detection is facilitated.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a rotor die casting abnormal error data processing method.
Background
The abnormal error data in the rotor die casting process has great reference value, so that the abnormal condition in the current rotor die casting process can be monitored by using the abnormal data, and the method has important significance for adjusting parameters and the like in the die casting process.
Because the rotor die casting process is carried out under the conditions of high temperature and high pressure, the temperature can continuously change, and meanwhile, the temperature change is controlled in a normal range, so that the reasonability of the rotor die casting process can be ensured, and the quality of a rotor product is ensured. Because the abnormal errors in the temperature data have larger reference values for the present and future, the temperature data recorded in the rotor die casting process need to be compressed and stored so as to facilitate data transmission.
Abnormal data in temperature data of a conventional rotor die casting process occurs less frequently than normal data, and thus encoding of the abnormal data is long in a data compression process. However, in the processes of data transmission, compression, decoding and the like, the longer the data is encoded, the more bit errors are likely to occur in the data, so that the abnormal data identification is inaccurate, and the temperature data does not have higher reference value.
Disclosure of Invention
In order to solve the technical problems, the invention provides a rotor die casting abnormal error data processing method for solving the existing problems.
The invention relates to a rotor die casting abnormal error data processing method which adopts the following technical scheme:
an embodiment of the invention provides a rotor die casting abnormal error data processing method, which comprises the following steps:
collecting temperature data in the rotor die casting process; acquiring suspected data in the temperature data; taking temperature data except suspected data in the temperature data as normal data; obtaining each suspected sequence according to the acquisition time change of each suspected data; obtaining each normal sequence;
for each suspected sequence, obtaining the steady anomaly coefficient of each suspected data point in the suspected sequence according to the difference between adjacent elements in the suspected sequence; obtaining an intra-group gap value of each suspected data point in the suspected sequence according to the numerical value of each suspected data point; obtaining intra-group step irregularity of the suspected sequence according to the steady anomaly coefficient and the intra-group gap value of each suspected data point in the suspected sequence; obtaining the intra-group variation trend coefficient of the suspected sequence according to the intra-group step irregularity of the suspected sequence; acquiring intra-group variation trend coefficients of each normal sequence;
obtaining adjacent suspected saliency of each suspected sequence according to the intra-group variation trend coefficients of each suspected sequence and the adjacent normal sequence; obtaining the abnormal contribution degree of each suspected data point in each suspected sequence according to the adjacent suspected saliency of each suspected sequence; obtaining weights of the suspected data points according to the abnormal contribution degree of the suspected data points; taking the frequency of each normal data point as the weight of each normal data point;
and compressing the temperature data according to the weight of each data point and Huffman coding.
Preferably, the obtaining suspected data in the temperature data specifically includes:
obtaining suspected data points in the temperature data through a Laida criterion; acquiring a normal range of the working temperature of the rotor, and taking a range of the normal range reduced by G degrees centigrade inwards as a corrected temperature range, wherein G is a preset reduced temperature; temperature data outside the corrected temperature range are also used as suspected data points.
Preferably, the obtaining each suspected sequence according to the collection time change of each suspected data specifically includes:
combining the suspected data connected with the acquisition time to obtain each suspected group; and (5) arranging the data in each suspected group according to the ascending order of the acquisition time to obtain each suspected sequence.
Preferably, the obtaining the steady anomaly coefficient of each suspected data point in the suspected sequence according to the difference between adjacent elements in the suspected sequence specifically includes: and taking the absolute value of the numerical difference between the ith and the (i+1) th suspected data points in the suspected sequence as the steady anomaly coefficient of the ith suspected data point.
Preferably, the obtaining the intra-group gap value of each suspected data point in the suspected sequence according to the numerical value of each suspected data point specifically includes: acquiring the numerical mean and variance of all suspected data points in a suspected sequence; calculating the absolute value of the difference between the value of each suspected data point and the mean value; and taking the product of the absolute value of the difference and the variance as an intra-group difference value of each suspected data point.
Preferably, the step length irregularity in the group of the suspected sequence is obtained according to the steady anomaly coefficient and the intra-group gap value of each suspected data point in the suspected sequence, and specifically includes:
calculating the sum of absolute values of differences of steady anomaly coefficients of all two adjacent suspected data points in the suspected sequence; calculating the sum of difference values in all suspected data point groups in the suspected sequence; and taking the sum of the two sum values as intra-group step irregularity of the suspected sequence.
Preferably, the obtaining the intra-group variation trend coefficient of the suspected sequence according to the intra-group step irregularity of the suspected sequence specifically includes: calculating a hurst index of the suspected sequence; and taking the ratio of the intra-group step irregularity of the suspected sequence to the hurst index as the intra-group variation trend coefficient of the suspected sequence.
Preferably, the obtaining the adjacent suspected saliency of each suspected sequence according to the intra-group variation trend coefficients of each suspected sequence and the adjacent normal sequence specifically includes:
for each suspected sequence, acquiring a normal sequence adjacent to the suspected sequence; calculating the product of the number of elements in the suspected sequence and the change trend coefficient in the group; for each normal sequence, calculating the ratio of the intra-group variation trend coefficient of the normal sequence to the number of corresponding elements; calculating the absolute value of the difference between the product and each ratio; calculating the sum of all the absolute values of the differences; and taking the sum value as the adjacent suspected prominence of the suspected sequence.
Preferably, the obtaining the abnormal contribution degree of each suspected data point in each suspected sequence according to the adjacent suspected saliency of each suspected sequence specifically includes:
for each suspected sequence, calculating the average value of the numerical values of all suspected data points in the suspected sequence; calculating the absolute value of the difference between the value of each suspected data point in the suspected sequence and the mean value; and taking the product of the adjacent suspected saliency of the suspected sequence and the absolute value of the difference value of each suspected data point as the abnormal contribution degree of each suspected data point.
Preferably, the obtaining the weight of each suspected data point according to the abnormal contribution degree of each suspected data point specifically includes: and taking the sum of the frequency and the abnormal contribution degree of each suspected data point as the weight of each suspected data point.
The invention has at least the following beneficial effects:
according to the method, the temperature data in the rotor die casting process are acquired, the suspected temperature data are acquired, the intra-group step irregularity of each suspected sequence is obtained according to the jump stability, regularity and data stability of step sizes among the suspected data, so that whether the data in each suspected sequence are abnormal data can be evaluated, the data with larger step size change are initially taken as key monitoring objects, and the accurate identification of subsequent abnormal data is facilitated; meanwhile, the historical chronicity trend in each suspected sequence is combined to influence the intra-group change trend of each suspected sequence, whether the change trend has chronicity is analyzed on the basis of the step change rule, and the method is helpful for judging the characteristic of abnormal data mutation;
next, comparing the adjacent normal sequences on the left side and the right side of each suspected sequence to obtain adjacent suspected saliency of the suspected sequence relative to the normal sequence, which is helpful for judging the abnormality degree of the suspected sequence by combining the surrounding normal temperature information, so that the mode of evaluating abnormal data points is more comprehensive, accurate judgment of each abnormal data is helpful, and smaller error occurs; according to the method, the abnormal data in the rotor die-casting temperature data can be coded in a shorter time, so that the detection efficiency is improved in the subsequent recognition and processing processes of the abnormal data, and meanwhile, bit errors are not easy to occur in the processes of data transmission, conversion, compression, decoding and the like in the shorter code length of the abnormal data, so that the accuracy of abnormal data point detection is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for processing abnormal error data of rotor die casting according to the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to specific implementation, structure, characteristics and effects of a rotor die casting anomaly error data processing method according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the rotor die casting abnormal error data processing method provided by the invention with reference to the accompanying drawings.
The embodiment of the invention provides a rotor die casting abnormal error data processing method.
Specifically, the following method for processing abnormal error data of rotor die casting is provided, referring to fig. 1, the method includes the following steps:
and S001, collecting temperature data in the rotor die casting process.
Because the rotor die-casting environment is a high-temperature and high-pressure environment, when monitoring the temperature data in the rotor die-casting process, a temperature sensor is set in the die-casting die, and the temperature data of the rotor are collected. Each temperature data is acquired by taking 1s as a time interval, and the temperature data within N=200 seconds from the current moment is acquired to obtain time-series temperature data, and N temperature data are measured in total. It should be noted that the time interval of the collection and the number of the collected temperature data can be set by the user, and the embodiment is not particularly limited.
In the process of monitoring temperature data, there may be a missing value in the temperature data due to signal interference, equipment failure or human operation error, etc., and in this embodiment, the missing value is filled by using a mean value compensation method, which is a known technique, and a specific process is not described again.
So far, the temperature data of rotor die casting can be obtained according to the steps, so that the abnormal data in the temperature data can be conveniently analyzed.
And step S002, analyzing the temperature data to obtain the abnormal contribution degree of each suspected data.
For temperature data in the rotor die casting process, as the temperature is critical to the quality of rotor die casting, if the temperature cannot be effectively controlled within a certain range, the efficiency of rotor die casting is reduced, and the effect of a finished product of the rotor is affected to a certain extent. Therefore, the abnormal data is particularly important, so that the abnormal data can embody the abnormality in the current rotor die casting process and can be used as an important basis for evaluating and adjusting the die casting process in the future.
However, in the conventional Huffman coding process, because the abnormal data is less, the abnormal data is easy to code into a longer code length when the Huffman coding is carried out on the abnormal data, and in the processes of data transmission, storage, compression and decompression, the longer coding is easy to generate bit errors, so that the temperature evaluation in the rotor die casting process is inaccurate, and the rotor quality is poor.
Therefore, it is necessary to analyze abnormal data for temperature data in the rotor die casting process, and the contribution of these data to the recognition of abnormal conditions for the entire temperature data is large.
Use 3The principle (also called Laida criterion) obtains the suspected data point in the temperature data, and at the same time, obtains the +.>Wherein->For the lower boundary temperature of the normal range, +.>For the upper boundary temperature of the normal range, the upper boundary temperature and the lower boundary temperature are respectively reduced by G degrees celsius inwards, the reduced temperature range is taken as a corrected temperature range, and temperature data outside the corrected temperature range is taken as a suspected data point, wherein the value of G can be set by a value operator of G, and the value of G is set to be 4 in the embodiment. And taking a set formed by all suspected data points as a suspected data set.
Combining the suspected data connected with the acquisition time in the suspected data set to obtain each suspected group; arranging the data in each suspected group according to time ascending order to obtain each suspected sequence; and taking the data except the suspected data points in the temperature data as normal data points, taking a set formed by the data except the suspected data points as a normal data set, and acquiring each normal sequence in the normal data set by the method.
Analyzing the kth suspected sequence, and recording the data number in the kth suspected sequence as。
Because of the difference in possible transitions of the abnormal temperature data, if such transitions are more frequent in each suspected sequence, this indicates a greater likelihood of an abnormal condition in each suspected sequence. For an ith suspected data point within a kth suspected sequence, calculating a steady anomaly coefficient for the suspected data point:
in the method, in the process of the invention,is the steady anomaly coefficient of the ith suspected data point in the kth suspected sequence, +.>、/>The values of the ith and the (i+1) th suspected data points in the kth suspected sequence are respectively obtained.
When the numerical difference between each adjacent suspected data point in the suspected sequence is larger, the jump difference between the two adjacent suspected data points is larger, andthe larger the jump difference of the temperature data at the ith suspected data point is larger, the more abnormal the suspected data point is, and the larger the steady abnormal coefficient of the suspected data point is.
Meanwhile, based on steady abnormal coefficients between every two adjacent data points, similar difference changes of the temperatures can occur in the normal rotor die casting process, and the difference of the changes between the two adjacent data points is not enough to explain the abnormal condition of the data points. However, for normal temperature changes, the changes have a regular and steadily increasing trend, and if the change trend between every two adjacent data points does not steadily increase but rather has larger changes, the abnormal surrounding of the data points can be indicated; in addition, if the temperature does not change too much within a period of time, that is, the change in the suspected sequence is not severe, it may be indirectly indicated that the suspected sequence may not be the starting cause of the abnormal error data, and the abnormal condition of the suspected sequence may be reduced.
Based on this, intra-group step irregularities of the kth suspected sequence are calculated for the suspected sequence.
In the method, in the process of the invention,intra-group step irregularity for kth suspected sequence, +.>For the number of suspected data points of the kth suspected sequence, < +.>、/>The steady anomaly coefficients of the ith and the (i+1) th suspected data points in the kth suspected sequence are respectively +.>Is the intra-group gap value of the ith suspected data point in the kth suspected sequence, +.>The temperature value of the ith suspected data point in the kth suspected sequence,/for the kth suspected data point>Is the average value of the temperature values of all suspected data points in the kth suspected sequence, +.>Is the variance of the temperature values of all the suspected data points in the kth suspected sequence.
The larger the difference value between the steady abnormal coefficients between every two adjacent data points in the kth suspected sequence is, the less the temperature change between the data points in the group has the steady increasing trend, namely the temperature change has no larger regularity, the irregular temperature change indicates the abnormality in the rotor die casting process, and the rotor die casting has quality problems; meanwhile, if the difference between the data points is found to be smaller in the suspected sequence, that is, the suspected sequence has a trend of stable step length, the more stable the temperature change in the suspected sequence in the time period is, the suspected sequence may not need to be used as an important suspected sequence, and the suspected sequence before or after the larger change may occur may need to be used as an important monitoring object.
Through the calculation, the intra-group step irregularity of each suspected sequence can be obtained, and the intra-group step irregularity is calculated based on the difference between the step sizes; in the temperature data change process, the long-term change trend of the temperature data can reflect whether the temperature in the group has a long-term change rule to be circulated, if a suspected sequence with a longer duration exists, the abnormal condition of the group can be judged according to whether the whole suspected sequence has a historical long-term trend, so that the Hurst index of each suspected sequence is calculated to obtain the change trend coefficient in the group of the suspected sequence.
In the method, in the process of the invention,is the kth suspected sequenceIntra-group trend coefficient of>Intra-group step irregularity for kth suspected sequence, +.>Is the hurst index of the kth suspected sequence.
It should be noted that, if the irregularity of the step length in the group of the suspected sequence is larger and the historical long-term variation trend of the suspected sequence is less obvious, the variation difference between the step lengths in the suspected sequence is more irregular and does not have long-term variation trend, the larger the intra-group variation of the suspected sequence is, that is, the larger the intra-group variation trend coefficient of the suspected sequence is, the more abnormal the suspected sequence is.
Repeating the steps to obtain the intra-group variation trend coefficients of each suspected sequence in the temperature data, and calculating the intra-group variation trend coefficients of each normal sequence in the temperature data based on the same method.
And comparing the intra-group variation trend coefficient of the obtained normal sequence with the intra-group variation trend coefficient of the suspected sequence according to the characteristics of the normal sequence and the suspected sequence, wherein the intra-group variation trend coefficient is smaller in general, but no abnormal condition exists, namely the difference of the intra-group variation trend coefficients between the normal sequences on the left side and the right side of the k-th suspected sequence is smaller, namely the suspected sequence can be reflected to have no abnormal condition from the other aspect.
Accordingly, the adjacent suspected prominence of each suspected sequence is obtained for the difference between each adjacent suspected sequence and the normal sequence.
In the method, in the process of the invention,adjacent suspected prominence for the kth suspected sequence,/->Is an exponential function based on natural constants, < ->、/>Respectively the intra-group variation trend coefficients of the normal sequences adjacent to the left and right of the kth suspected sequence,is the intra-group trend coefficient of the kth suspected sequence,>、/>the number of elements in the normal sequence adjacent to the k suspected sequence on the left and right respectively,/->The number of elements in the kth suspected sequence. And when the suspected sequence is the first or last suspected sequence, the normal sequence adjacent to the right or left is not present, and only the normal sequence adjacent to the left or right is calculated, so that the adjacent suspected saliency of the suspected sequence is obtained.
It should be noted that, the greater the difference of the intra-group variation trend coefficients between the kth suspected sequence and the normal sequences adjacent to the left and right sides thereof, the more dissimilar the variation trend of the suspected sequence compared with the normal sequences adjacent to the two sides is, so that the greater the adjacent suspected saliency of the suspected sequence is, that is, the greater the difference between the suspected sequence and the normal sequences at the two sides is.
And combining the temperature values of the suspected data points in the suspected sequences to obtain the abnormal contribution degree of each data point in the suspected data, namely, each data point can be corrected based on the statistically calculated probability of the temperature data of the data point during encoding, so that the code length of the abnormal data is shortened during encoding of the temperature data in the rotor die casting process, and the abnormal data is not easy to generate errors in the subsequent abnormality detection and identification process.
In the method, in the process of the invention,abnormal contribution degree of ith suspected data point in kth suspected sequence, +.>As a linear normalization function>Adjacent suspected prominence for the kth suspected sequence,/->Temperature value for the ith data point of the kth suspected sequence, +.>Is the mean value of the temperature data in the kth suspected sequence.
The abnormal contribution degree of each suspected data point is obtained by combining the adjacent suspected saliency of each suspected sequence and the difference between the average value of the temperature data of each data point in the suspected sequence and the whole temperature data, and if the difference between the suspected data point and the average value is larger and the change trend of the suspected sequence of the suspected data point is more obvious relative to the change trend between the left and right adjacent normal sequences, the abnormal state of the suspected data point is indicated, the higher the value of the suspected data point to the whole temperature data is, and the higher the contribution degree of the abnormal state is judged.
And step S003, finishing Huffman coding according to the abnormal contribution degree of each suspected data, and compressing the rotor die-casting temperature data.
The abnormal contribution degree of each suspected data point in the rotor die casting temperature data can be obtained through the last step.
Counting the occurrence frequency of each data point in the temperature data, and adding the frequency of each suspected data point and the abnormal contribution degree of each suspected data point to obtain the weight of each suspected data point; and directly taking the frequency of each normal data as the weight of each normal data. And carrying out Huffman coding according to the weight of each data point of the temperature data, and carrying out compression processing on the rotor die casting temperature data according to the Huffman coding condition.
The abnormal data in the rotor die-casting temperature data obtained in this way has shorter codes, is beneficial to accelerating the detection efficiency in the subsequent recognition and processing process of the abnormal data, and meanwhile, the shorter code length of the abnormal data is not easy to cause bit errors in the processes of data transmission, conversion, compression, decoding and the like, so that the accuracy of abnormal data point detection is facilitated.
In summary, in the embodiment of the invention, the suspected temperature data is obtained by collecting the temperature data in the rotor die casting process, and the intra-group step irregularity of each suspected group is obtained according to the jump stability, regularity and data stability of step sizes among the suspected data, so that whether the data in each group are abnormal data can be evaluated, and the data with larger step size change can be initially used as key monitoring objects, thereby being beneficial to accurately identifying the subsequent abnormal data; meanwhile, the historical chronicity trend in each group is combined to influence the change trend in each suspected group, and whether the change trend has chronicity is analyzed on the basis of the step change rule, so that the method is helpful for judging the characteristic of abnormal data mutation;
next, comparing the adjacent normal groups on the left side and the right side of each suspected group to obtain adjacent suspected prominence of the suspected group relative to the normal group, which is helpful for judging the abnormality degree of the suspected group by combining the surrounding normal temperature information, so that the mode of evaluating abnormal data points is more comprehensive, accurate judgment of each abnormal data is helpful, and smaller error occurs; according to the method, the abnormal data in the rotor die-casting temperature data can be coded in a shorter time, so that the detection efficiency is improved in the subsequent recognition and processing processes of the abnormal data, and meanwhile, bit errors are not easy to occur in the processes of data transmission, conversion, compression, decoding and the like in the shorter code length of the abnormal data, so that the accuracy of abnormal data point detection is improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment mainly describes differences from other embodiments.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; the technical solutions described in the foregoing embodiments are modified or some of the technical features are replaced equivalently, so that the essence of the corresponding technical solutions does not deviate from the scope of the technical solutions of the embodiments of the present application, and all the technical solutions are included in the protection scope of the present application.
Claims (10)
1. The rotor die casting abnormal error data processing method is characterized by comprising the following steps:
collecting temperature data in the rotor die casting process; acquiring suspected data in the temperature data; taking temperature data except suspected data in the temperature data as normal data; obtaining each suspected sequence according to the acquisition time change of each suspected data; obtaining each normal sequence;
for each suspected sequence, obtaining the steady anomaly coefficient of each suspected data point in the suspected sequence according to the difference between adjacent elements in the suspected sequence; obtaining an intra-group gap value of each suspected data point in the suspected sequence according to the numerical value of each suspected data point; obtaining intra-group step irregularity of the suspected sequence according to the steady anomaly coefficient and the intra-group gap value of each suspected data point in the suspected sequence; obtaining the intra-group variation trend coefficient of the suspected sequence according to the intra-group step irregularity of the suspected sequence; acquiring intra-group variation trend coefficients of each normal sequence;
obtaining adjacent suspected saliency of each suspected sequence according to the intra-group variation trend coefficients of each suspected sequence and the adjacent normal sequence; obtaining the abnormal contribution degree of each suspected data point in each suspected sequence according to the adjacent suspected saliency of each suspected sequence; obtaining weights of the suspected data points according to the abnormal contribution degree of the suspected data points; taking the frequency of each normal data point as the weight of each normal data point;
and compressing the temperature data according to the weight of each data point and Huffman coding.
2. The method for processing abnormal error data of rotor die casting according to claim 1, wherein the suspected data in the obtained temperature data specifically comprises:
obtaining suspected data points in the temperature data through a Laida criterion; acquiring a normal range of the working temperature of the rotor, and taking a range of the normal range reduced by G degrees centigrade inwards as a corrected temperature range, wherein G is a preset reduced temperature; temperature data outside the corrected temperature range are also used as suspected data points.
3. The method for processing abnormal error data of rotor die casting according to claim 1, wherein the obtaining each suspected sequence according to the time variation of collection of each suspected data specifically comprises:
combining the suspected data connected with the acquisition time to obtain each suspected group; and (5) arranging the data in each suspected group according to the ascending order of the acquisition time to obtain each suspected sequence.
4. The method for processing abnormal error data of die casting of a rotor according to claim 1, wherein the obtaining the steady abnormal coefficient of each suspected data point in the suspected sequence according to the difference between adjacent elements in the suspected sequence specifically comprises: and taking the absolute value of the numerical difference between the ith and the (i+1) th suspected data points in the suspected sequence as the steady anomaly coefficient of the ith suspected data point.
5. The method for processing abnormal error data of die casting of a rotor according to claim 1, wherein the obtaining the intra-group gap value of each suspected data point in the suspected sequence according to the numerical value of each suspected data point specifically comprises: acquiring the numerical mean and variance of all suspected data points in a suspected sequence; calculating the absolute value of the difference between the value of each suspected data point and the mean value; and taking the product of the absolute value of the difference and the variance as an intra-group difference value of each suspected data point.
6. The method for processing abnormal error data of die casting of a rotor according to claim 1, wherein the step irregularity in the group of the suspected sequence is obtained according to the steady anomaly coefficient and the intra-group gap value of each suspected data point in the suspected sequence, specifically comprising:
calculating the sum of absolute values of differences of steady anomaly coefficients of all two adjacent suspected data points in the suspected sequence; calculating the sum of difference values in all suspected data point groups in the suspected sequence; and taking the sum of the two sum values as intra-group step irregularity of the suspected sequence.
7. The method for processing abnormal error data of rotor die casting according to claim 1, wherein the method for obtaining the intra-group variation trend coefficient of the suspected sequence according to the intra-group step irregularity of the suspected sequence comprises the following steps: calculating a hurst index of the suspected sequence; and taking the ratio of the intra-group step irregularity of the suspected sequence to the hurst index as the intra-group variation trend coefficient of the suspected sequence.
8. The method for processing abnormal error data of die casting of a rotor according to claim 1, wherein the obtaining the adjacent suspected saliency of each suspected sequence according to the intra-group variation trend coefficients of each suspected sequence and the adjacent normal sequence specifically comprises:
for each suspected sequence, acquiring a normal sequence adjacent to the suspected sequence; calculating the product of the number of elements in the suspected sequence and the change trend coefficient in the group; for each normal sequence, calculating the ratio of the intra-group variation trend coefficient of the normal sequence to the number of corresponding elements; calculating the absolute value of the difference between the product and each ratio; calculating the sum of all the absolute values of the differences; and taking the sum value as the adjacent suspected prominence of the suspected sequence.
9. The method for processing abnormal error data of die casting of a rotor according to claim 1, wherein the obtaining the abnormal contribution degree of each suspected data point in each suspected sequence according to the adjacent suspected saliency of each suspected sequence specifically comprises:
for each suspected sequence, calculating the average value of the numerical values of all suspected data points in the suspected sequence; calculating the absolute value of the difference between the value of each suspected data point in the suspected sequence and the mean value; and taking the product of the adjacent suspected saliency of the suspected sequence and the absolute value of the difference value of each suspected data point as the abnormal contribution degree of each suspected data point.
10. The method for processing abnormal error data of die casting of a rotor according to claim 1, wherein the step of obtaining the weight of each suspected data point according to the abnormal contribution degree of each suspected data point comprises the following steps: and taking the sum of the frequency and the abnormal contribution degree of each suspected data point as the weight of each suspected data point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311376699.7A CN117113107B (en) | 2023-10-24 | 2023-10-24 | Rotor die casting abnormal error data processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111260125A (en) * | 2020-01-13 | 2020-06-09 | 西南交通大学 | Temperature anomaly detection method for rail vehicle component |
| WO2020143475A1 (en) * | 2019-01-10 | 2020-07-16 | 杭州海康威视数字技术股份有限公司 | Temperature abnormality detection method and apparatus, computer device, and storage medium |
| CN114912852A (en) * | 2022-07-18 | 2022-08-16 | 山东鑫亚装备制造有限公司 | Abnormity monitoring and early warning system for storage tank of refrigeration bin |
| CN114972337A (en) * | 2022-07-26 | 2022-08-30 | 启东市固德防水布有限公司 | Anomaly identification method based on waterproof cloth Hough space data processing |
| CN115858630A (en) * | 2023-02-21 | 2023-03-28 | 新风光电子科技股份有限公司 | Abnormity detection method for energy storage data of energy storage power station |
| CN116610682A (en) * | 2023-07-21 | 2023-08-18 | 山东盈动智能科技有限公司 | Temperature tester data classification method based on data storage |
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020143475A1 (en) * | 2019-01-10 | 2020-07-16 | 杭州海康威视数字技术股份有限公司 | Temperature abnormality detection method and apparatus, computer device, and storage medium |
| CN111260125A (en) * | 2020-01-13 | 2020-06-09 | 西南交通大学 | Temperature anomaly detection method for rail vehicle component |
| CN114912852A (en) * | 2022-07-18 | 2022-08-16 | 山东鑫亚装备制造有限公司 | Abnormity monitoring and early warning system for storage tank of refrigeration bin |
| CN114972337A (en) * | 2022-07-26 | 2022-08-30 | 启东市固德防水布有限公司 | Anomaly identification method based on waterproof cloth Hough space data processing |
| CN115858630A (en) * | 2023-02-21 | 2023-03-28 | 新风光电子科技股份有限公司 | Abnormity detection method for energy storage data of energy storage power station |
| CN116610682A (en) * | 2023-07-21 | 2023-08-18 | 山东盈动智能科技有限公司 | Temperature tester data classification method based on data storage |
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