CN113239956A - Injection mold state diagnosis method and device, computer equipment and storage medium - Google Patents
Injection mold state diagnosis method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a method for diagnosing the state of an injection mold, which comprises the steps of acquiring a data sequence of the injection mold, which is acquired by a sensor in a unit period; intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, and taking the data subsequence as a target sequence; acquiring a preset standard interval, comparing the standard interval with the target sequence by adopting a preset data comparison method to obtain a comparison result, and determining the state of the injection mold according to the comparison result. The data collected by the sensors are analyzed, so that the automatic diagnosis of the state of the injection mold is realized, and the data of different types of sensors are compared and analyzed according to the intercepted data sequences, so that the diagnosis efficiency of the state of the injection mold is improved. In addition, an injection mold state diagnosis device, a computer device and a storage medium are also provided.
Description
Technical Field
The invention relates to the technical field of injection molds, in particular to an injection mold state diagnosis method and device, computer equipment and a storage medium.
Background
The injection mold main body is a closed cavity formed by processing two steel materials, and the injection molding state in the mold cavity cannot be directly observed. The existing diagnosis methods mainly comprise two methods, the first method is to judge whether the product produced by injection molding of the mold is qualified, the method cannot detect the quality of the mold immediately in the injection molding process of the mold, and the production efficiency of injection molding of the mold is reduced; secondly, related parameters of the injection mold are obtained by utilizing sensor detection equipment in the mold of the injection mold according to the production requirements of the respective mold, and then are analyzed through manual experience, however, the existing sensor detection equipment is large in size and expensive, and the monitoring and diagnosis method is lack of objectivity. Both of these methods reduce the efficiency of the condition diagnosis of the injection mold.
Disclosure of Invention
In view of the above, it is desirable to provide an injection mold state diagnosis method, apparatus, computer device, and storage medium capable of improving the degree of intellectualization of mold state diagnosis.
A method of diagnosing a condition of an injection mold having a sensor provided inside the injection mold, the method comprising:
acquiring a data sequence of the injection mold acquired by the sensor in a unit period;
intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, and taking the data subsequence as a target sequence;
acquiring a preset standard interval, comparing the standard interval with the target sequence by adopting a preset data comparison method to obtain a comparison result, and determining the state of the injection mold according to the comparison result.
An injection mold condition diagnosing apparatus, the inside of an injection mold being provided with a sensor, the apparatus comprising:
the acquisition module is used for acquiring the data sequence of the injection mold acquired by the sensor in a unit period;
the intercepting module is used for intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence and taking the data subsequence as a target sequence;
and the diagnosis module is used for acquiring a preset standard interval, comparing the standard interval with the target sequence by adopting a preset data comparison method to obtain a comparison result, and determining the state of the injection mold according to the comparison result.
A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of:
acquiring a data sequence of the injection mold acquired by the sensor in a unit period;
intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, and taking the data subsequence as a target sequence;
acquiring a preset standard interval, comparing the standard interval with the target sequence by adopting a preset data comparison method to obtain a comparison result, and determining the state of the injection mold according to the comparison result.
A computer-readable medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
acquiring a data sequence of the injection mold acquired by the sensor in a unit period;
intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, and taking the data subsequence as a target sequence;
acquiring a preset standard interval, comparing the standard interval with the target sequence by adopting a preset data comparison method to obtain a comparison result, and determining the state of the injection mold according to the comparison result.
According to the injection mold state diagnosis method, the injection mold state diagnosis system, the computer equipment and the storage medium, the data sequence of the injection mold collected by the sensor in a unit period is obtained; intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, and taking the data subsequence as a target sequence; acquiring a preset standard interval, comparing the standard interval with the target sequence by adopting a preset data comparison method to obtain a comparison result, and determining the state of the injection mold according to the comparison result. The data collected by the sensors are analyzed, so that the automatic diagnosis of the state of the injection mold is realized, and the data of different types of sensors are compared and analyzed according to the intercepted data sequences, so that the diagnosis efficiency of the state of the injection mold is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
FIG. 1 is a flow chart of a method of diagnosing injection mold conditions in one embodiment;
FIG. 2 is a flowchart of a method of diagnosing a condition of an injection mold according to another embodiment;
FIG. 3 is a flow diagram of a method for target sequence determination in one embodiment;
FIG. 4 is a flowchart of a method for diagnosing a condition of an injection mold according to still another embodiment;
FIG. 5 is a flowchart of a method for aligning a standard interval with a target sequence according to one embodiment;
FIG. 6 is a block diagram showing the structure of an injection mold state diagnosing apparatus according to an embodiment;
FIG. 7 is a block diagram of a computer device in one embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, in one embodiment, an injection mold state diagnosis method is provided, in which a sensor is disposed inside an injection mold, and the injection mold state diagnosis method is applicable to both a terminal and a server, and the embodiment is exemplified by being applied to the server. The injection mold state diagnosis method specifically comprises the following steps:
and 102, acquiring a data sequence of the injection mold acquired by the sensor in a unit period.
The unit cycle refers to a time period for completing injection molding of the injection mold, for example, the unit cycle may be 8 seconds, 10 seconds, and the like, and under different scenes, the time of the unit cycle is different, and is not limited specifically here. The data sequence refers to a set of discrete data of the injection mold during the injection molding process, which are acquired by a sensor at a plurality of sampling points in a period of time. A plurality of sensors such as a temperature sensor, a pressure and temperature integrated sensor, a position sensor and the like are arranged in the injection mold and used for acquiring process parameters, namely data sequences, in the production of the injection mold in real time. Specifically, the sensor is connected through the cable, data acquisition is carried out, and then the data acquisition is sent to the server, so that the server receives the data sequence, and the state of the injection mold is analyzed based on the data sequence.
And 104, intercepting a data subsequence from the data sequence according to the type and the data sequence of the sensor, and taking the data subsequence as a target sequence.
The data subsequence refers to a part of data in the data sequence, for example, the data sequence is divided into A, B, C and D to form 4 parts, the data subsequence may be at least one of the 4 parts, for example, the data subsequence may be a part a or two parts a and B, and the like. It will be appreciated that the complexity and effort of the calculations must be increased if the analysis is performed directly on the data sequence. In the injection molding process of the injection mold, the data sequences acquired by different types of sensors are different, for example, the data sequence of the temperature sensor is in a fixed range and is relatively stable; the data sequence of the pressure sensor has a large variation range, the data sequence collected by the position sensor reflects the size of a gap between injection molds, and the data sequence of the position sensor has large variation when the injection molds are abnormal. Therefore, according to the characteristics of the data sequences of different types of sensors, effective data, namely the target sequence, can be intercepted from the data sequences, so that the interference of invalid data is avoided, and the subsequent analysis efficiency is improved.
And 106, acquiring a preset standard interval, comparing the standard interval with the target sequence by adopting a preset data comparison method to obtain a comparison result, and determining the state of the injection mold according to the comparison result.
The preset standard interval refers to a preset standard data interval for measuring the state of the injection mold. The preset data comparison method is to analyze the state of the injection mold by means of data comparison, wherein the data comparison method may be a method of calculating a distance between a target sequence and a standard interval, such as a cosine distance, an euclidean distance, a minkowski distance, a manhattan distance, a chebyshev distance, or the like, that is, each data and corresponding time in the target sequence are respectively taken as coordinate points of a vertical coordinate and a horizontal coordinate, each data and corresponding time in the standard interval are respectively taken as coordinate points of the vertical coordinate and the horizontal coordinate, and a distance between each coordinate point in the data sequence and a corresponding coordinate point in the standard interval is calculated. Preferably, in this embodiment, a curve-based method is used for comparison to improve the intuitiveness and the calculation efficiency of data comparison, obtain a comparison result, and determine whether the state of the injection mold is normal according to the comparison result. By analyzing the target sequence, the automatic diagnosis of the state of the injection mold is realized, and the comparison analysis is performed according to the intercepted data sequences aiming at different types of sensor data, so that the diagnosis efficiency of the state of the injection mold is improved.
It should be noted that the states of the injection molding module may be further divided, for example, into 4 states, an optimal state, a standard state, a safe state, an abnormal state, etc., at this time, the standard interval correspondingly includes 3 standard intervals, the target sequence is continuously compared with the 3 standard intervals, and the standard interval in which the comparison result falls is determined, so that the corresponding state can be determined. Furthermore, in order to ensure the normal operation of the injection mold, for the injection mold in the safe state, the injection mold state diagnosis method in steps 102 to 106 may be adopted to continuously count the states of the injection mold in multiple unit periods, and if the diagnosis results indicate that all the injection mold states are in the safe state, the state of the injection module is determined to be abnormal, and the user side is notified in a message manner to perform shutdown protection, so as to ensure the safe operation of the injection module.
According to the injection mold state diagnosis method, the data sequence of the injection mold, which is acquired by the sensor in a unit period, is acquired; intercepting a data subsequence from the data sequence according to the type and the data sequence of the sensor, and taking the data subsequence as a target sequence; and acquiring a preset standard interval, comparing the standard interval with the target sequence by adopting a preset data comparison method to obtain a comparison result, and determining the state of the injection mold according to the comparison result. By analyzing the target data, the automatic diagnosis of the state of the injection mold is realized, and the comparison and analysis are performed according to the intercepted data sequences aiming at the sensor data of different types, so that the diagnosis efficiency of the state of the injection mold is improved, and the production efficiency of the injection mold is ensured.
In one embodiment, before intercepting the data subsequence from the data sequence according to the type of the sensor and the data sequence, the method further comprises: and drawing a target curve according to the unit period and the data sequence.
Specifically, each time point in the unit cycle may be plotted as an abscissa and each data in the data sequence as an ordinate to form a target curve, as shown in fig. 2, which is a schematic diagram of the target curve corresponding to the pressure sensor. It can be understood that, in this embodiment, by drawing the data sequence into a target curve, the change of the data sequence is more intuitively displayed, so as to improve the data comparison efficiency based on the target curve in the following.
As shown in FIG. 3, in one embodiment, the type of sensor includes at least one of a position sensor, a temperature sensor, and a pressure sensor; intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, and taking the data subsequence as a target sequence, wherein the method comprises the following steps:
104A, if the type of the sensor is a position sensor, intercepting a first curve according to the peak position of the target curve, and determining a target sequence based on the first curve;
step 104B, if the type of the sensor is a temperature sensor, intercepting a second curve with the slope smaller than a first preset threshold value in the target curve, and determining a target sequence based on the second curve;
and 104C, if the type of the sensor is a pressure sensor, intercepting a third curve and a fourth curve of which the absolute value of the slope in the target curve is greater than a second preset threshold value, and intercepting a fifth curve and a sixth curve of which the absolute value of the slope in the target curve is less than a third preset threshold value, intercepting a seventh curve according to the peak position of the target curve, and intercepting an eighth curve according to the valley position of the target curve, and determining a target sequence based on the first curve, the second curve, the third curve, the fourth curve, the fifth curve, the sixth curve, the seventh curve and the eighth curve, wherein the second preset threshold value is greater than the third preset threshold value.
In this embodiment, corresponding target sequences are acquired for different types of sensors, and specifically, when data in a data sequence acquired by a position sensor is larger, it is indicated that a gap of an injection mold is larger, which may cause glue leakage and affect production of the injection mold. When the type of the sensor is a temperature sensor, the change of a data sequence acquired by the temperature sensor is small, so that a second curve with the slope smaller than a first preset threshold value in the target curve is intercepted, and the target sequence is determined. When the type of the sensor is a pressure sensor, a data sequence with the pressure rapidly increasing, the pressure rapidly decreasing, the numerical value maximum and the numerical value minimum is effective data, therefore, a third curve and a fourth curve with the slope absolute value larger than a second preset threshold value in a target curve, a fifth curve and a sixth curve with the slope absolute value smaller than a third preset threshold value in the target curve are intercepted, a seventh curve is intercepted according to the peak position of the target curve, an eighth curve is intercepted according to the valley position of the target curve, a target sequence is determined based on the first curve, the second curve, the third curve, the fourth curve, the fifth curve, the sixth curve, the seventh curve and the eighth curve, and the second preset threshold value is larger than the third preset threshold value. Understandably, according to the type of the sensor, the effective data are intercepted, so that the target curve is more accurate, and the diagnosis accuracy of the injection mold is improved.
In one embodiment, before the standard interval and the target sequence are aligned by using the preset data alignment method, the method further comprises: and drawing the standard interval into a standard area.
Specifically, the standard region is drawn according to the data corresponding to the standard interval and the time corresponding to the target sequence, so that the subsequent comparison with the target sequence is facilitated, and the comparison efficiency is improved.
As shown in fig. 4, in one embodiment, the standard interval and the target sequence are aligned by a preset data alignment method, which includes:
and step 106B, obtaining a comparison result based on the position relation between the comparison curve and the standard area.
In this embodiment, the target sequence is drawn in the standard coordinate system to obtain the alignment curve, and the alignment result can be determined quickly and intuitively based on the position relationship between the alignment curve and the standard region.
As shown in fig. 5, in an embodiment, obtaining the alignment result based on the position relationship between the alignment curve and the standard region includes:
and step 106E, if the cross area does not meet the comparison rule, determining that the comparison result is not consistent and the state of the injection mold is abnormal.
In this embodiment, the intersection region refers to a region where the comparison curve is associated with the standard region, the comparison rule refers to a rule for determining whether the intersection region meets the comparison logic, when the intersection region meets the comparison rule, it is determined that the comparison result is consistent, the state of the injection mold is normal, when the intersection region does not meet the comparison rule, it is determined that the comparison result is consistent, and the state of the injection mold is abnormal, thereby implementing automatic diagnosis of the mold state.
In one embodiment, after determining the state of the injection mold according to the comparison result, the method further includes: and if the state of the injection mold is abnormal, adjusting the data sequence based on the standard interval so as to enable the state of the injection mold to be normal.
In this embodiment, when the state of the injection mold is abnormal, the data sequence is adjusted according to the standard interval, so that the state of the injection mold is normal, the safe production of the injection mold is further ensured, and the production efficiency is improved.
As shown in fig. 6, in one embodiment, there is provided an injection mold state diagnosis apparatus including:
an obtaining module 602, configured to obtain a data sequence of the injection mold, which is acquired by the sensor in a unit cycle;
an intercepting module 604, configured to intercept a data subsequence from the data sequence according to the type of the sensor and the data sequence, and use the data subsequence as a target sequence;
the diagnosis module 606 is configured to obtain a preset standard interval, compare the standard interval with the target sequence by using a preset data comparison method to obtain a comparison result, and determine the state of the injection mold according to the comparison result.
In one embodiment, the injection mold state diagnosing apparatus further includes: and the first drawing module is used for drawing a target curve according to the unit period and the data sequence.
In one embodiment, the type of sensor includes at least one of a position sensor, a temperature sensor, and a pressure sensor; the intercept module comprises:
the first intercepting unit is used for intercepting a first curve according to the peak position of the target curve and determining the target sequence based on the first curve if the type of the sensor is a position sensor;
the second intercepting unit is used for intercepting a second curve with the slope smaller than a first preset threshold value in the target curve if the type of the sensor is a temperature sensor, and determining the target sequence based on the second curve;
a third intercepting unit, configured to intercept a third curve and a fourth curve in the target curve, where an absolute value of a slope of the third curve is greater than a second preset threshold, and a fifth curve and a sixth curve in the target curve, where the absolute value of the slope of the third curve is less than a third preset threshold, intercept the seventh curve according to a peak position of the target curve, intercept the eighth curve according to a valley position of the target curve, determine the target sequence based on the first curve, the second curve, the third curve, the fourth curve, the fifth curve, the sixth curve, the seventh curve, and the eighth curve, where the second preset threshold is greater than the third preset threshold.
In one embodiment, the injection mold state diagnosing apparatus further includes: and the second drawing module is used for drawing the standard interval into a standard area.
In one embodiment, the diagnostic module comprises:
the generating unit is used for drawing the target sequence in a standard coordinate system to obtain a comparison curve;
and the determining unit is used for obtaining the comparison result based on the position relation between the comparison curve and the standard area.
In one embodiment, the diagnostic module further comprises:
the acquisition unit is used for acquiring a comparison rule corresponding to the preset data comparison method;
the first diagnosis unit is used for acquiring a cross region of the comparison curve and the standard region, and if the cross region meets the comparison rule, the comparison result is determined to be consistent, and the state of the injection mold is normal;
and the second diagnosis unit is used for determining that the comparison result is not in accordance and the state of the injection mold is abnormal if the cross region does not meet the comparison rule.
In one embodiment, the injection mold state diagnosing apparatus further includes: and the adjusting module is used for adjusting the data sequence based on the standard interval if the state of the injection mold is abnormal so as to enable the state of the injection mold to be normal.
FIG. 7 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device may specifically be a server including, but not limited to, a high performance computer and a cluster of high performance computers. As shown in fig. 7, the computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program that, when executed by the processor, causes the processor to implement the injection mold state diagnosis method. The internal memory may also have a computer program stored therein, which when executed by the processor, causes the processor to perform the injection mold state diagnostic method. Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the injection mold condition diagnosing method provided herein may be implemented in the form of a computer program that is executable on a computer device as shown in fig. 7. The memory of the computer device may store therein respective program templates constituting the injection mold state diagnosing apparatus. Such as an acquisition module 602, an intercept module 604, and a diagnostic module 606.
A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the following steps when executing the computer program: acquiring a data sequence of the injection mold acquired by the sensor in a unit period; intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, and taking the data subsequence as a target sequence; acquiring a preset standard interval, comparing the standard interval with the target sequence by adopting a preset data comparison method to obtain a comparison result, and determining the state of the injection mold according to the comparison result.
In one embodiment, before the intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, the method further includes: and drawing a target curve according to the unit period and the data sequence.
In one embodiment, the type of sensor includes at least one of a position sensor, a temperature sensor, and a pressure sensor; the intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, and taking the data subsequence as a target sequence, comprises: if the type of the sensor is a position sensor, intercepting a first curve according to the peak position of the target curve, and determining the target sequence based on the first curve; if the type of the sensor is a temperature sensor, intercepting a second curve with the slope smaller than a first preset threshold value in the target curve, and determining the target sequence based on the second curve; if the type of the sensor is a pressure sensor, intercepting a third curve and a fourth curve of which the absolute value of the slope in the target curve is greater than a second preset threshold value, and intercepting a fifth curve and a sixth curve of which the absolute value of the slope in the target curve is less than a third preset threshold value, intercepting a seventh curve according to the peak position of the target curve, and intercepting an eighth curve according to the valley position of the target curve, determining the target sequence based on the first curve, the second curve, the third curve, the fourth curve, the fifth curve, the sixth curve, the seventh curve and the eighth curve, wherein the second preset threshold value is greater than the third preset threshold value.
In one embodiment, before the comparing the standard interval with the target sequence by using the preset data comparison method, the method further comprises: and drawing a standard area for the standard interval.
In one embodiment, the comparing the standard interval and the target sequence by using a preset data comparison method includes: drawing the target sequence in a standard coordinate system to obtain a comparison curve; and obtaining the comparison result based on the position relation between the comparison curve and the standard area.
In one embodiment, the obtaining the alignment result based on the position relationship between the alignment curve and the standard region includes: acquiring a comparison rule corresponding to the preset data comparison method; acquiring a cross area of the comparison curve and the standard area, and if the cross area meets the comparison rule, determining that the comparison result is in line and the state of the injection mold is normal; and if the cross area does not meet the comparison rule, determining that the comparison result is not in accordance and the state of the injection mold is abnormal.
In one embodiment, after the determining the state of the injection mold according to the comparison result, the method further includes: and if the state of the injection mold is abnormal, adjusting the data sequence based on the standard interval so as to enable the state of the injection mold to be normal.
A computer-readable storage medium storing a computer program, the computer program when executed by a processor implementing the steps of: acquiring a data sequence of the injection mold acquired by the sensor in a unit period; intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, and taking the data subsequence as a target sequence; acquiring a preset standard interval, comparing the standard interval with the target sequence by adopting a preset data comparison method to obtain a comparison result, and determining the state of the injection mold according to the comparison result.
In one embodiment, before the intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, the method further includes: and drawing a target curve according to the unit period and the data sequence.
In one embodiment, the type of sensor includes at least one of a position sensor, a temperature sensor, and a pressure sensor; the intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, and taking the data subsequence as a target sequence, comprises: if the type of the sensor is a position sensor, intercepting a first curve according to the peak position of the target curve, and determining the target sequence based on the first curve; if the type of the sensor is a temperature sensor, intercepting a second curve with the slope smaller than a first preset threshold value in the target curve, and determining the target sequence based on the second curve; if the type of the sensor is a pressure sensor, intercepting a third curve and a fourth curve of which the absolute value of the slope in the target curve is greater than a second preset threshold value, and intercepting a fifth curve and a sixth curve of which the absolute value of the slope in the target curve is less than a third preset threshold value, intercepting a seventh curve according to the peak position of the target curve, and intercepting an eighth curve according to the valley position of the target curve, determining the target sequence based on the first curve, the second curve, the third curve, the fourth curve, the fifth curve, the sixth curve, the seventh curve and the eighth curve, wherein the second preset threshold value is greater than the third preset threshold value.
In one embodiment, before the comparing the standard interval with the target sequence by using the preset data comparison method, the method further comprises: and drawing a standard area for the standard interval.
In one embodiment, the comparing the standard interval and the target sequence by using a preset data comparison method includes: drawing the target sequence in a standard coordinate system to obtain a comparison curve; and obtaining the comparison result based on the position relation between the comparison curve and the standard area.
In one embodiment, the obtaining the alignment result based on the position relationship between the alignment curve and the standard region includes: acquiring a comparison rule corresponding to the preset data comparison method; acquiring a cross area of the comparison curve and the standard area, and if the cross area meets the comparison rule, determining that the comparison result is in line and the state of the injection mold is normal; and if the cross area does not meet the comparison rule, determining that the comparison result is not in accordance and the state of the injection mold is abnormal.
In one embodiment, after the determining the state of the injection mold according to the comparison result, the method further includes: and if the state of the injection mold is abnormal, adjusting the data sequence based on the standard interval so as to enable the state of the injection mold to be normal.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of diagnosing a condition of an injection mold having a sensor provided therein, the method comprising:
acquiring a data sequence of the injection mold acquired by the sensor in a unit period;
intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, and taking the data subsequence as a target sequence;
acquiring a preset standard interval, comparing the standard interval with the target sequence by adopting a preset data comparison method to obtain a comparison result, and determining the state of the injection mold according to the comparison result.
2. The injection mold state diagnostic method as set forth in claim 1, further comprising, before said intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence:
and drawing a target curve according to the unit period and the data sequence.
3. The injection mold state diagnostic method according to claim 2, wherein the type of the sensor includes at least one of a position sensor, a temperature sensor, and a pressure sensor; the intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence, and taking the data subsequence as a target sequence, comprises:
if the type of the sensor is a position sensor, intercepting a first curve according to the peak position of the target curve, and determining the target sequence based on the first curve;
if the type of the sensor is a temperature sensor, intercepting a second curve with the slope smaller than a first preset threshold value in the target curve, and determining the target sequence based on the second curve;
if the type of the sensor is a pressure sensor, intercepting a third curve and a fourth curve of which the absolute value of the slope in the target curve is greater than a second preset threshold value, and intercepting a fifth curve and a sixth curve of which the absolute value of the slope in the target curve is less than a third preset threshold value, intercepting a seventh curve according to the peak position of the target curve, and intercepting an eighth curve according to the valley position of the target curve, determining the target sequence based on the first curve, the second curve, the third curve, the fourth curve, the fifth curve, the sixth curve, the seventh curve and the eighth curve, wherein the second preset threshold value is greater than the third preset threshold value.
4. The injection mold state diagnosis method according to claim 1, further comprising, before the comparing the standard interval with the target sequence by using a preset data comparison method:
and drawing a standard area for the standard interval.
5. The injection mold state diagnosis method according to claim 4, wherein the comparing the standard interval and the target sequence by using a preset data comparison method comprises:
drawing the target sequence in a standard coordinate system to obtain a comparison curve;
and obtaining the comparison result based on the position relation between the comparison curve and the standard area.
6. The injection mold state diagnosis method according to claim 5, wherein the obtaining of the comparison result based on the positional relationship between the comparison curve and the standard region includes:
acquiring a comparison rule corresponding to the preset data comparison method;
acquiring a cross area of the comparison curve and the standard area, and if the cross area meets the comparison rule, determining that the comparison result is in line and the state of the injection mold is normal;
and if the cross area does not meet the comparison rule, determining that the comparison result is not in accordance and the state of the injection mold is abnormal.
7. The injection mold state diagnostic method according to claim 6, further comprising, after the determining the state of the injection mold according to the comparison result:
and if the state of the injection mold is abnormal, adjusting the data sequence based on the standard interval so as to enable the state of the injection mold to be normal.
8. An injection mold state diagnosing device, the inside of an injection mold is provided with a sensor, characterized in that the injection mold state diagnosing device comprises:
the acquisition module is used for acquiring the data sequence of the injection mold acquired by the sensor in a unit period;
the intercepting module is used for intercepting a data subsequence from the data sequence according to the type of the sensor and the data sequence and taking the data subsequence as a target sequence;
and the diagnosis module is used for acquiring a preset standard interval, comparing the standard interval with the target sequence by adopting a preset data comparison method to obtain a comparison result, and determining the state of the injection mold according to the comparison result.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the injection mold state diagnosis method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the injection mold state diagnosis method according to any one of claims 1 to 7.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11339195A (en) * | 1998-05-22 | 1999-12-10 | Kansei Corp | Lane abnormality detection circuit, and rear-end collision alarm device using the same |
| DE10154252A1 (en) * | 2001-11-05 | 2003-05-15 | Siemens Ag | Detection and compensation circuit for bit-slip-errors during transmission of digital data is installed in receiver and transmitter produces equal-width bit sequences with identification markings |
| KR20150082976A (en) * | 2014-01-08 | 2015-07-16 | 주식회사 비스텔 | Method for analyzing wafer yield rate using sensor data in semiconductor manufacturing process |
| CN104819107A (en) * | 2015-05-13 | 2015-08-05 | 北京天源科创风电技术有限责任公司 | Diagnostic method and system for abnormal shift of wind turbine generator power curve |
| JP5771317B1 (en) * | 2014-08-26 | 2015-08-26 | 株式会社日立パワーソリューションズ | Abnormality diagnosis apparatus and abnormality diagnosis method |
| US20150276557A1 (en) * | 2014-03-28 | 2015-10-01 | Hitachi High-Technologies Corporation | State monitoring system, state monitoring method and medium |
| WO2016153164A1 (en) * | 2015-03-26 | 2016-09-29 | 주식회사 한일티앤씨 | System and method for sensing abnormality of stage facility and predicting failure using same |
| CN106678069A (en) * | 2017-03-13 | 2017-05-17 | 重庆江增船舶重工有限公司 | Detection method for preventing surge of centrifugal compressor from occurring |
| CN206568189U (en) * | 2017-01-21 | 2017-10-20 | 天津胜琦隆电子科技有限公司 | Mechanical arm for injection molding machine with failure warning system |
| CN109049590A (en) * | 2018-09-21 | 2018-12-21 | 广州市振兴塑料模具有限公司 | A kind of automation hot flow path injection molding manufacturing monitoring system |
| US20200183946A1 (en) * | 2018-12-11 | 2020-06-11 | EXFO Solutions SAS | Anomaly Detection in Big Data Time Series Analysis |
| CN111483125A (en) * | 2020-04-08 | 2020-08-04 | 宁波中国科学院信息技术应用研究院 | Hydraulic fault early warning system for injection molding machine |
| CN112060523A (en) * | 2020-09-28 | 2020-12-11 | 珠海格力智能装备有限公司 | Control method and device of injection molding equipment and injection molding equipment |
| US20200393812A1 (en) * | 2019-06-14 | 2020-12-17 | Renesas Electronics Corporation | Abnormality detection system, abnormality detection apparatus, and abnormality detection method |
| CN112446601A (en) * | 2020-11-20 | 2021-03-05 | 国网江苏省电力有限公司营销服务中心 | Data diagnosis method and system for non-computable region |
-
2021
- 2021-04-08 CN CN202110377349.7A patent/CN113239956B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11339195A (en) * | 1998-05-22 | 1999-12-10 | Kansei Corp | Lane abnormality detection circuit, and rear-end collision alarm device using the same |
| DE10154252A1 (en) * | 2001-11-05 | 2003-05-15 | Siemens Ag | Detection and compensation circuit for bit-slip-errors during transmission of digital data is installed in receiver and transmitter produces equal-width bit sequences with identification markings |
| KR20150082976A (en) * | 2014-01-08 | 2015-07-16 | 주식회사 비스텔 | Method for analyzing wafer yield rate using sensor data in semiconductor manufacturing process |
| US20150276557A1 (en) * | 2014-03-28 | 2015-10-01 | Hitachi High-Technologies Corporation | State monitoring system, state monitoring method and medium |
| JP5771317B1 (en) * | 2014-08-26 | 2015-08-26 | 株式会社日立パワーソリューションズ | Abnormality diagnosis apparatus and abnormality diagnosis method |
| WO2016153164A1 (en) * | 2015-03-26 | 2016-09-29 | 주식회사 한일티앤씨 | System and method for sensing abnormality of stage facility and predicting failure using same |
| CN104819107A (en) * | 2015-05-13 | 2015-08-05 | 北京天源科创风电技术有限责任公司 | Diagnostic method and system for abnormal shift of wind turbine generator power curve |
| CN206568189U (en) * | 2017-01-21 | 2017-10-20 | 天津胜琦隆电子科技有限公司 | Mechanical arm for injection molding machine with failure warning system |
| CN106678069A (en) * | 2017-03-13 | 2017-05-17 | 重庆江增船舶重工有限公司 | Detection method for preventing surge of centrifugal compressor from occurring |
| CN109049590A (en) * | 2018-09-21 | 2018-12-21 | 广州市振兴塑料模具有限公司 | A kind of automation hot flow path injection molding manufacturing monitoring system |
| US20200183946A1 (en) * | 2018-12-11 | 2020-06-11 | EXFO Solutions SAS | Anomaly Detection in Big Data Time Series Analysis |
| US20200393812A1 (en) * | 2019-06-14 | 2020-12-17 | Renesas Electronics Corporation | Abnormality detection system, abnormality detection apparatus, and abnormality detection method |
| CN111483125A (en) * | 2020-04-08 | 2020-08-04 | 宁波中国科学院信息技术应用研究院 | Hydraulic fault early warning system for injection molding machine |
| CN112060523A (en) * | 2020-09-28 | 2020-12-11 | 珠海格力智能装备有限公司 | Control method and device of injection molding equipment and injection molding equipment |
| CN112446601A (en) * | 2020-11-20 | 2021-03-05 | 国网江苏省电力有限公司营销服务中心 | Data diagnosis method and system for non-computable region |
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| Publication number | Publication date |
|---|---|
| CN113239956B (en) | 2024-01-19 |
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