CN115392773A - Method and device for determining operation track curve and electronic equipment - Google Patents
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Abstract
The application discloses a method and a device for determining an operation track curve and electronic equipment. Wherein, the method comprises the following steps: acquiring operation data and output data of production equipment in a plurality of batches; performing splicing operation on the running data and the output data corresponding to the same acquisition date in a plurality of batches to obtain a spliced data set; determining the similarity between spliced data in the spliced data set, and classifying the spliced data in the spliced data set according to the similarity to obtain various types of data; selecting target type data from the multiple types of data, and determining weight values corresponding to different batches of operating parameters in the target type data; and determining an operation track curve corresponding to the target type data at least according to the weight value and the value of the operation parameter in the batch corresponding to the weight value. The method and the device solve the technical problems that the generation of the existing operation track standard curve is generally specified manually and cannot adapt to gradual change of production equipment and change of production requirements.
Description
Technical Field
The application relates to the field of production design of process industries, in particular to a method and a device for determining an operation trajectory curve and electronic equipment.
Background
In the intermittent production in the process industry, an operation track standard curve is generally established before each batch of production, and a bottom layer control system operates according to the operation track standard curve. However, in an actual process, the initial states of the production devices in different batches are not completely consistent, and at this time, an operator can adjust the operation track in the production process according to experience, so that the yield and the quality of a final product in each batch can also change, and the adjustment has certain subjectivity and has the defects of being qualitative in main, incapable of being quantitative, incapable of being accurately adjusted and the like. Therefore, it is necessary to obtain an optimal operation trajectory standard curve by performing data analysis on the production results and the operation trajectories of a plurality of batches.
The generation of the existing operation track standard curve is generally specified manually and cannot adapt to the gradual change of production equipment and the change of production requirements, and an effective solution is not provided at present aiming at the problems.
Disclosure of Invention
The embodiment of the application provides a method and a device for determining an operation trajectory curve and electronic equipment, and aims to at least solve the technical problems that the generation of the existing operation trajectory standard curve is generally specified manually and cannot adapt to gradual change of production equipment and change of production requirements.
According to an aspect of an embodiment of the present application, there is provided a method for determining an operation trajectory curve, including: the method comprises the steps of obtaining operation data and output data of the production equipment in a plurality of batches, wherein the operation data are acquired through different instruments, the output data are product data related to products generated by the production equipment, and each batch in the plurality of batches corresponds to different acquisition dates; performing splicing operation on the running data and the output data corresponding to the same acquisition date in a plurality of batches to obtain a spliced data set; determining the similarity between spliced data in the spliced data set, classifying the spliced data in the spliced data set according to the similarity to obtain multiple types of data, wherein the data with the similarity larger than a preset threshold value is classified into one type; selecting target type data from the multiple types of data, and determining weight values corresponding to operating parameters of different batches in the target type data, wherein the target type data is any one type of data in the multiple types of data, and the operating parameters are at least one controllable data in the operating data; and determining an operation track curve corresponding to the target type data at least according to the weight value and the value of the operation parameter in the batch corresponding to the weight value.
Optionally, performing a splicing operation on the running data and the output data corresponding to the same collection date in a plurality of batches, including: acquiring the running time corresponding to the running data; aligning the running data with the same running time, and sequencing according to the running time to obtain first data; splicing the first data and the output data corresponding to the same acquisition date to obtain spliced data; and determining the splicing data corresponding to different acquisition dates to obtain a splicing data set.
Optionally, determining the weight values corresponding to the operation parameters of different batches in the target type data includes: acquiring acquisition dates corresponding to different batches of operation parameters in the target type data to obtain a plurality of acquisition dates; sequencing the multiple collection dates according to the sequence of the current date to obtain a target collection date set; and determining the weight values of the batches corresponding to the collection dates in the target collection date set.
Optionally, determining the weight values of the batches corresponding to the multiple collection dates in the target collection date set includes: determining a weight value between batches corresponding to two adjacent dates in the target collection date set, wherein the weight value of the batch corresponding to a first date in the two adjacent dates is a preset multiple of the weight value of the batch corresponding to a second date, the first date is the closer date to the current date in the two adjacent dates, the second date is the farther date from the current date in the two adjacent dates, and the preset multiple is any numerical value greater than 1.
Optionally, after determining a weight value between batches corresponding to two adjacent dates in the target collection date set, the method further includes: acquiring a weight set of batches corresponding to all dates in a target collection date set; constructing an equation according to the quantitative relation among all the weighted values in the weighted set; and solving the equation to obtain a first weight value between batches corresponding to two adjacent dates in the target collection date set.
Optionally, determining an operation trajectory curve corresponding to the data of the target type includes: determining a target weight value of a batch corresponding to each date in the target collection date set according to the first weight value; and determining a target operating parameter according to the target weight value and the values of the operating parameters of the batch corresponding to the target weight value at different running times, wherein the target operating parameter is the value of the operating parameters of the operating trajectory curve corresponding to the target type of data at different running times.
Optionally, the operation trajectory curve is a curve in a coordinate system established by taking the operation time as an abscissa and taking the value of the operation parameter at the operation time as an ordinate.
According to another aspect of the embodiments of the present application, there is also provided an apparatus for determining an operation trajectory curve, including: the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring operation data and output data of the production equipment in a plurality of batches, the operation data is acquired by different instruments, the output data is product data related to products generated by the production equipment, and each batch in the plurality of batches corresponds to different acquisition dates; the splicing module is used for executing splicing operation on the running data and the output data corresponding to the same acquisition date in a plurality of batches to obtain a spliced data set; the classification module is used for determining the similarity between the spliced data in the spliced data set and classifying the spliced data in the spliced data set according to the similarity to obtain multiple types of data, wherein the data with the similarity larger than a preset threshold value is classified into one type; the first determining module is used for selecting target type data from the multiple types of data and determining weight values corresponding to operating parameters of different batches in the target type data, wherein the target type data is any one of the multiple types of data, and the operating parameters are at least one controllable data in the operating data; and the second determining module is used for determining an operation track curve corresponding to the target type data at least according to the weight value and the value of the operation parameter in the batch corresponding to the weight value.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including: a memory for storing program instructions; a processor coupled to the memory for executing program instructions that implement the functions of: the method comprises the steps of obtaining operation data and output data of the production equipment in a plurality of batches, wherein the operation data are acquired through different instruments, the output data are product data related to products generated by the production equipment, and each batch in the plurality of batches corresponds to different acquisition dates; performing splicing operation on the running data and the output data corresponding to the same acquisition date in a plurality of batches to obtain a spliced data set; determining the similarity between spliced data in the spliced data set, classifying the spliced data in the spliced data set according to the similarity to obtain multiple types of data, wherein the data with the similarity larger than a preset threshold value is classified into one type; selecting target type data from the multiple types of data, and determining weight values corresponding to operating parameters of different batches in the target type data, wherein the target type data is any one type of data in the multiple types of data, and the operating parameters are at least one controllable data in the operating data; and determining an operation track curve corresponding to the target type data at least according to the weight value and the value of the operation parameter in the batch corresponding to the weight value.
According to still another aspect of the embodiments of the present application, a nonvolatile storage medium is further provided, where the nonvolatile storage medium includes a stored computer program, and a device in which the nonvolatile storage medium is located executes the method for determining an operation trajectory curve by running the computer program.
In the embodiment of the application, the operation data and the output data of the production equipment in a plurality of batches are obtained; performing splicing operation on the running data and the output data corresponding to the same acquisition date in a plurality of batches to obtain a spliced data set; determining the similarity between spliced data in the spliced data set, and classifying the spliced data in the spliced data set according to the similarity to obtain various types of data; selecting target type data from the multiple types of data, and determining weight values corresponding to different batches of operating parameters in the target type data; the operation track curve corresponding to the target type data is determined at least according to the weighted value and the value of the operation parameter in the batch corresponding to the weighted value, so that the aim of automatically generating the operation track in China in the production process is fulfilled, the technical effect of improving the generation efficiency of the operation track is achieved, and the technical problem that the generation of the existing operation track standard curve is generally manually specified and cannot adapt to the gradual change of production equipment and the change of production requirements is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a block diagram of a hardware structure of a computer terminal (or an electronic device) for implementing a method for determining an operation trajectory curve according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of determining an operational trajectory profile according to an embodiment of the present application;
fig. 3 is a block diagram of an operation trajectory curve determination device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all 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 application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the accompanying drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
First, some terms or terms appearing in the description of the embodiments of the present application are applicable to the following explanations:
the intermittent production process comprises the following steps: also known as batch processes. It means that all working steps are performed at the same place and at different times, the operation state is unstable, and the parameters change along with the time. For example: adding a batch of raw materials into the equipment, discharging the product through operation, cleaning the equipment, adding new materials, and circularly performing in cycles.
Operation track: the operating parameter varies with time to produce a trace.
In the related art, the operation trajectory standard curve is very important, and it makes sense only to perform adjustment on a good operation trajectory standard curve. The existing generation of the standard curve of the operation track is generally specified manually, and the method has certain subjectivity, and along with gradual change of production equipment, change of production requirements and the like, the method of a single standard curve of the fixed operation track cannot be well adapted to the changes.
In order to solve the above problems, embodiments of the present application provide corresponding solutions, which are described in detail below.
The method for determining the operation trajectory curve provided by the embodiment of the application can be executed in a mobile terminal, a computer terminal or a similar operation device. Fig. 1 shows a hardware configuration block diagram of a computer terminal (or electronic device) for implementing the determination method of the operation trajectory curve. As shown in fig. 1, the computer terminal 10 (or electronic device 10) may include one or more processors (shown as 102a, 102b, \8230;, 102 n) which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuit may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computer terminal 10 (or electronic device). As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).
The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the determining method of the operation trajectory curve in the embodiment of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, so as to implement the determining method of the operation trajectory curve described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission module 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet via wireless.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or electronic device).
It should be noted that, in some alternative embodiments, the computer device (or electronic device) shown in fig. 1 may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that fig. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in the computer device (or electronic device) described above.
In the above operating environment, the embodiments of the present application provide an embodiment of a method for determining an operation trajectory curve, and it should be noted that the steps shown in the flowchart of the drawings may be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown or described.
Fig. 2 is a flowchart of a method for determining an operation trajectory profile according to an embodiment of the present application, and as shown in fig. 2, the method includes the following steps:
step S202, obtaining operation data and output data of the production equipment in a plurality of batches, wherein the operation data is data acquired by different meters, the output data is product data related to a product generated by the production equipment, and each batch in the plurality of batches corresponds to a different acquisition date.
In an alternative embodiment, the operation data may include, for example, process data such as pressure, temperature, flow rate, etc., the operation data is continuously changed at different times, for example, the operation data may be collected once in 1s, the production data may include, for example, yield, specific energy consumption, product goodness rate, etc., the production data in the same batch is the same, it should be noted that the above-mentioned data included in the operation data and the production data are only for illustration, and other data are included in the actual production process, which is not illustrated here.
And step S204, performing splicing operation on the running data and the output data corresponding to the same acquisition date in a plurality of batches to obtain a spliced data set.
Before the data is spliced in step S204, the acquired operation data and output data are subjected to data preprocessing, abnormal values, missing values, and the like in the data are processed, and after the data preprocessing, the operation data and output data in a plurality of batches with the same acquisition date are summarized, that is, the operation data at the same time are aligned according to the operation time and spliced with the output data in the same acquisition date (that is, the same batch), so as to obtain an operation-output data set, that is, the spliced data set.
Step S206, determining the similarity among the spliced data in the spliced data set, and classifying the spliced data in the spliced data set according to the similarity to obtain multiple types of data, wherein the data with the similarity larger than a preset threshold value is classified into one type.
In step S206, in the joined data set, the data with similarity greater than the preset threshold are classified into one class by a clustering algorithm, which may be, for example, a K-means algorithm, and multiple classes of data are obtained after clustering.
Step S208, selecting target type data from the multiple types of data, and determining weight values corresponding to the operating parameters of different batches in the target type data, wherein the target type data is any one of the multiple types of data, and the operating parameters are at least one controllable data in the operating data.
In step S208, for multiple types of data, the class center data of each type is selected as a representative of the type, i.e., a case, and the cases are labeled, for example, a low-energy-consumption case, a high-quality case, and the like, and a case library is formed by a set of a plurality of cases. One case is selected from the case library, namely target type data is selected from the multiple types of data, the selected case contains n batches of data, and weight values corresponding to operating parameters of different batches in the target type data are determined.
In step S208, the operation parameter is at least one controllable parameter in the operation data, for example, when the operation data includes pressure, temperature, and flow rate, the flow rate may be one of the operation parameters when the flow rate is adjustable by a valve, and the pressure and the temperature may not be the operation parameters when the pressure and the temperature are not adjustable.
Step S210, determining an operation trajectory curve corresponding to the target type data at least according to the weight value and the value of the operation parameter in the batch corresponding to the weight value.
The method comprises the steps of collecting operation data and output data of different batches; abnormal values, missing values and the like in the data are processed through data preprocessing, so that the accuracy of drawing an operation track curve by the system is improved; summarizing the operation data and the output data of different batches through data integration to obtain an operation-output data set; obtaining cases from similar operation-output data in the operation-output data set through a clustering algorithm; marking the cases to form a case library; and calculating the weight of each batch in the selected case so as to calculate the operation track standard curve, namely automatically generating the operation track standard curve according to the production demand target without manual formulation.
In step S204 in the method for determining an operation trajectory curve, a splicing operation is performed on the operation data and the output data corresponding to the same collection date in a plurality of batches, and the method specifically includes the following steps: acquiring the running time corresponding to the running data; aligning the running data with the same running time, and sequencing the running data according to the running time to obtain first data; splicing the first data and the output data corresponding to the same acquisition date to obtain spliced data; and determining the splicing data corresponding to different acquisition dates to obtain a splicing data set.
In the embodiment of the present application, since the batch production process is batch production, and the operation data of each batch has two dimensions of variable and time, namely, the value of the operation data and the operation time, and the output data has only one dimension, when the operation-output data set is established, the operation data and the output data of one batch need to be spliced together to form a set of operation-output data. This is illustrated by the following example:
the run-to-yield data for a batch are shown in table 1 below:
TABLE 1 run-to-yield data for a batch
Clustering the operation-output data with the similarity larger than a preset threshold value in the spliced data set into a class by using a clustering algorithm, so as to obtain a plurality of classes, namely multi-class data, selecting class center data of each class as a representative of the class, namely a case, and obtaining the case after clustering in the spliced data set by using a table 2.
TABLE 2 cases obtained after clustering in the stitched dataset
After labeling the cases generated above, the contents shown in table 3 were obtained:
TABLE 3 labeled cases
In step S208 in the method for determining an operation trajectory curve, determining weight values corresponding to operation parameters of different batches in the target type data specifically includes the following steps: acquiring acquisition dates corresponding to different batches of operating parameters in the target type data to obtain a plurality of acquisition dates; sequencing the multiple collection dates according to the sequence of the distance from the current date to obtain a target collection date set; and determining the weight values of the batches corresponding to the collection dates in the target collection date set.
In the above step, determining the weight values of the batches corresponding to the multiple collection dates in the target collection date set specifically includes the following steps: determining a weight value between batches corresponding to two adjacent dates in the target collection date set, wherein the weight value of the batch corresponding to the first date in the two adjacent dates is a preset multiple of the weight value of the batch corresponding to the second date, the first date is the closer date to the current date in the two adjacent dates, the second date is the farther date from the current date in the two adjacent dates, and the preset multiple is any numerical value greater than 1.
In the embodiment of the present application, the batches are sorted according to the distance from the current time, and it is assumed that the weight of the time close is 1.5 times that of the time slightly far, and the 1.5 corresponds to the preset multiple, it should be noted that the preset multiple is any value greater than 1, and the preset multiple selected among different batches may be the same or different, and the specifically selected value may be set according to the actual situation. Let k 1 Is the weight of the farthest time batch, then there is k 2 ＝1.5k 1 ，k 3 ＝1.5k 2 ＝1.5 2 k 1 …，k n ＝1.5 n-1 k 1 If the sum of all weights is 1, then:
k 1 +1.5k 1 +1.5 2 k 1 +…+1.5 n-1 k 1 ＝1
solve to k 1 Then k is obtained 2 、k 3 、…k n 。
In the above step, after determining the weight value between the batches corresponding to two adjacent dates in the target collection date set, the method further includes the following steps: acquiring a weight set of batches corresponding to all dates in a target collection date set; establishing an equation according to the quantitative relation among all the weight values in the weight set; and solving the equation to obtain a first weight value between batches corresponding to two adjacent dates in the target collection date set.
In the embodiment of the present application, the target type data is selected from the multiple types of data, for example, the data in the low energy consumption case in table 3 is selected, and it is assumed that the low energy consumption case includes 4 batches of run-to-production data, as shown in tables 4 and 5.
TABLE 4 batch data contained in Low energy consumption cases
TABLE 5 batch data contained in Low energy consumption cases (continuation Table)
Let k 1 Is the weight of the batch farthest in time, and under the condition that the weight of the batch near in time is 1.5 times the weight of the batch slightly farther in time, k is 2 ＝1.5k 1 ，k 3 ＝1.5k 2 ＝1.5 2 k 1 ，k 4 ＝1.5k 3＝ 1.5 3 k 1 The sum of all weights is 1, and the following equation is given:
k 1 +1.5k 1 +1.5 2 k 1 +1.5 3 k 1 ＝1
solved to k 1 If k is not less than 0.123, k is 2 ＝0.185，k 3 ＝0.277，k 4 =0.415. The solved value corresponds to the first weight value.
In step S210 in the method for determining an operation trajectory curve, determining an operation trajectory curve corresponding to data of a target type specifically includes the following steps: determining a target weight value of a batch corresponding to each date in the target collection date set according to the first weight value; and determining a target operating parameter according to the target weight value and the value of the operating parameter of the batch corresponding to the target weight value at different running times, wherein the target operating parameter is the value of the operating parameter of the operating trajectory curve corresponding to the target type data at different running times.
In the embodiment of the present application, the value of the operation parameter in the operation trajectory standard curve at each time is calculated as shown in the following formula:
wherein s is p,t Is the value of an operating parameter p in an operating track curve at the time t, x p,i,t Is the value of the operating parameter p in the ith batch at time t, k i Is the weight of the ith batch.
In an alternative embodiment, taking the run-to-yield data including 4 batches in the low energy consumption case as an example, taking the current date of 2022 year, 5 month and 1 day as an example, the batch 1 is farthest from the current date and has the smallest corresponding weight, and so on, the value of the operating parameter 1 at the 1 st hour of the operating track is:
k 4 *28.8+k 3 *22.5+k 2 *24+k 1 *25＝0.415*28.8+0.277*22.5+0.185*24+0.123*25＝25.7
the operation parameters 2 to n were calculated according to the above-described method, and similarly, the operation parameters 1 to n at the 2 nd to m th hours were calculated according to the above-described method.
In the method for determining the operation trajectory curve, the operation trajectory curve is a curve in a coordinate system established by taking the operation time as an abscissa and taking the value of the operation parameter at the operation time as an ordinate.
In the embodiment of the application, the weighted average value of each operation parameter at each moment is calculated according to the batch weight, and the operation track standard curve is obtained by connecting the weighted average values.
According to the method and the device, aiming at the intermittent production scene of the process industry, the operation track standard curve is generated from a plurality of optimal batches by carrying out data analysis on the operation data of the historical batches and the output data of the final product according to the target achieved by the production requirement. The method for determining the operation track curve provided by the embodiment of the application has the following advantages: 1. an operation track standard curve does not need to be manually formulated in advance; 2. obtaining a case by adopting real historical data, and taking the case as a formulation basis of an operation track standard curve; 3. considering that the batch reference meaning is larger when the time is closer to the current time, designing weight calculation methods of different batches under the same type of cases; 4. and obtaining an operation track standard curve according to the weighted average values of different batches under the same type of cases.
Fig. 3 is a block diagram of an operation trajectory graph determining apparatus according to an embodiment of the present application, as shown in fig. 3, the apparatus including:
an obtaining module 302, configured to obtain operation data and output data of a production device in multiple batches, where the operation data is data acquired through different meters, the output data is product data related to a product generated by the production device, and each batch in the multiple batches corresponds to a different acquisition date;
the splicing module 304 is configured to perform splicing operation on the running data and the output data corresponding to the same acquisition date in multiple batches to obtain a spliced data set;
the classification module 306 is configured to determine similarity between the spliced data in the spliced data sets, and classify the spliced data in the spliced data sets according to the similarity to obtain multiple types of data, where the data with the similarity greater than a preset threshold are classified into one type;
the first determining module 308 is configured to select target type data from the multiple types of data, and determine weight values corresponding to operation parameters of different batches in the target type data, where the target type data is any one of the multiple types of data, and the operation parameter is at least one controllable data in the running data;
the second determining module 310 is configured to determine an operation trajectory curve corresponding to the target type of data according to at least the weight value and the value of the operation parameter in the batch corresponding to the weight value.
It should be noted that the apparatus for determining an operation trajectory curve shown in fig. 3 is configured to execute the method for determining an operation trajectory curve shown in fig. 2, and therefore, the explanation in the method for determining an operation trajectory curve is also applicable to the apparatus for determining an operation trajectory curve, and is not described herein again.
The embodiment of the present application further provides a nonvolatile storage medium, where the nonvolatile storage medium includes a stored computer program, and a device where the nonvolatile storage medium is located executes a method for determining the following operation trajectory curve by running the computer program: the method comprises the steps of obtaining operation data and output data of the production equipment in a plurality of batches, wherein the operation data are acquired through different instruments, the output data are product data related to products generated by the production equipment, and each batch in the plurality of batches corresponds to different acquisition dates; performing splicing operation on the running data and the output data corresponding to the same acquisition date in a plurality of batches to obtain a spliced data set; determining the similarity between spliced data in the spliced data set, classifying the spliced data in the spliced data set according to the similarity to obtain multiple types of data, wherein the data with the similarity larger than a preset threshold value is classified into one type; selecting target type data from the multiple types of data, and determining weight values corresponding to operating parameters of different batches in the target type data, wherein the target type data is any one type of data in the multiple types of data, and the operating parameters are at least one controllable data in the operating data; and determining an operation track curve corresponding to the target type data at least according to the weight value and the value of the operation parameter in the batch corresponding to the weight value.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be an indirect coupling or communication connection through some interfaces, units or modules, and may be electrical or in other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A method for determining an operation trajectory curve, comprising:
acquiring operation data and output data of production equipment in a plurality of batches, wherein the operation data are acquired by different instruments, the output data are product data related to products generated by the production equipment, and each batch in the plurality of batches corresponds to different acquisition dates;
performing splicing operation on the running data and the output data corresponding to the same acquisition date in the plurality of batches to obtain a spliced data set;
determining the similarity among the spliced data in the spliced data set, classifying the spliced data in the spliced data set according to the similarity to obtain multiple types of data, wherein the data with the similarity larger than a preset threshold value is classified into one type;
selecting target type data from the multi-class data, and determining weight values corresponding to operating parameters of different batches in the target type data, wherein the target type data is any one of the multi-class data, and the operating parameters are at least one controllable data in the operating data;
and determining an operation track curve corresponding to the target type data at least according to the weight value and the value of the operation parameter in the batch corresponding to the weight value.
2. The method of claim 1, wherein performing a stitching operation on the run data and the production data corresponding to the same collection date in the plurality of batches comprises:
acquiring the running time corresponding to the running data;
aligning the running data with the same running time, and sequencing according to the running time to obtain first data;
splicing the first data and the output data corresponding to the same acquisition date to obtain spliced data;
and determining splicing data corresponding to different acquisition dates to obtain the splicing data set.
3. The method of claim 1, wherein determining the weight values corresponding to the different batches of operating parameters in the target type data comprises:
acquiring acquisition dates corresponding to the operation parameters of different batches in the target type data to obtain a plurality of acquisition dates;
sequencing the multiple collection dates according to the sequence of the current date to obtain a target collection date set;
determining the weight values of batches corresponding to a plurality of collection dates in the target collection date set.
4. The method of claim 3, wherein determining the weight value for batches corresponding to a plurality of collection dates in the target collection date set comprises:
determining a weight value between batches corresponding to two adjacent dates in the target collection date set, wherein the weight value of the batch corresponding to a first date in the two adjacent dates is a preset multiple of the weight value of the batch corresponding to a second date, the first date is the closer date to the current date in the two adjacent dates, the second date is the farther date from the current date in the two adjacent dates, and the preset multiple is any value greater than 1.
5. The method of claim 4, wherein after determining the weight value between batches corresponding to two adjacent dates in the target collection date set, the method further comprises:
acquiring a weight set of batches corresponding to all dates in the target collection date set;
establishing an equation according to the quantitative relation among all the weight values in the weight set;
and solving the equation to obtain a first weight value between batches corresponding to two adjacent dates in the target collection date set.
6. The method of claim 5, wherein determining the operation trajectory curve corresponding to the data of the target type comprises:
according to the first weight value, determining a target weight value of a batch corresponding to each date in the target collection date set;
and determining a target operating parameter according to the target weight value and the operating parameter values of the batches corresponding to the target weight value at different running times, wherein the target operating parameter is the value of the operating parameter of the operating trajectory curve corresponding to the target type of data at different running times.
7. The method of claim 6, wherein the operation trajectory curve is a curve in a coordinate system established with a runtime as an abscissa and values of operation parameters at the runtime as an ordinate.
8. An operation trajectory curve determination device, characterized by comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring operation data and output data of production equipment in a plurality of batches, the operation data is acquired by different instruments, the output data is product data related to products generated by the production equipment, and each batch in the plurality of batches corresponds to different acquisition dates;
the splicing module is used for executing splicing operation on the running data and the output data corresponding to the same acquisition date in the batches to obtain a spliced data set;
the classification module is used for determining the similarity between the spliced data in the spliced data set and classifying the spliced data in the spliced data set according to the similarity to obtain multiple types of data, wherein the data with the similarity larger than a preset threshold value is classified into one type;
a first determining module, configured to select target type data from the multiple types of data, and determine weight values corresponding to operation parameters of different batches in the target type data, where the target type data is any one of the multiple types of data, and the operation parameter is at least one controllable data in the operation data;
and the second determining module is used for determining an operation track curve corresponding to the target type data at least according to the weight value and the value of the operation parameter in the batch corresponding to the weight value.
9. An electronic device, comprising:
a memory for storing program instructions;
a processor, coupled to the memory, for executing program instructions that implement the following functions: acquiring operation data and output data of production equipment in a plurality of batches, wherein the operation data are acquired by different instruments, the output data are product data related to products generated by the production equipment, and each batch in the plurality of batches corresponds to different acquisition dates; performing splicing operation on the running data and the output data corresponding to the same acquisition date in the plurality of batches to obtain a spliced data set; determining the similarity among the spliced data in the spliced data set, and classifying the spliced data in the spliced data set according to the similarity to obtain multiple types of data, wherein the data with the similarity larger than a preset threshold value is classified into one type; selecting target type data from the multi-class data, and determining weight values corresponding to operating parameters of different batches in the target type data, wherein the target type data is any one of the multi-class data, and the operating parameters are at least one controllable data in the operating data; and determining an operation track curve corresponding to the target type of data at least according to the weight value and the value of the operation parameter in the batch corresponding to the weight value.
10. A non-volatile storage medium, comprising a stored computer program, wherein the apparatus on which the non-volatile storage medium is installed executes the method for determining an operation trajectory profile according to any one of claims 1 to 7 by executing the computer program.
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| CN202211137659.2A CN115392773A (en) | 2022-09-19 | 2022-09-19 | Method and device for determining operation track curve and electronic equipment |
| PCT/CN2022/141693 WO2024060440A1 (en) | 2022-09-19 | 2022-12-23 | Operation trajectory curve determination method and apparatus, and electronic device |
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| CN202211137659.2A CN115392773A (en) | 2022-09-19 | 2022-09-19 | Method and device for determining operation track curve and electronic equipment |
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| WO2024060440A1 (en) * | 2022-09-19 | 2024-03-28 | 中控技术股份有限公司 | Operation trajectory curve determination method and apparatus, and electronic device |
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| US9046882B2 (en) * | 2010-06-30 | 2015-06-02 | Rockwell Automation Technologies, Inc. | Nonlinear model predictive control of a batch reaction system |
| EP3726317B1 (en) * | 2019-04-17 | 2022-08-10 | ABB Schweiz AG | Computer-implemented determination of a quality indicator of a production batch-run of a production process |
| CN111679636B (en) * | 2020-05-11 | 2021-11-09 | 杭州睿疆科技有限公司 | System, method and computer equipment for processing production process parameters |
| CN114330647A (en) * | 2021-12-09 | 2022-04-12 | 浙江中控技术股份有限公司 | Model training method, device and silicon rod weight prediction method |
| CN115392773A (en) * | 2022-09-19 | 2022-11-25 | 浙江中控技术股份有限公司 | Method and device for determining operation track curve and electronic equipment |
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| WO2024060440A1 (en) * | 2022-09-19 | 2024-03-28 | 中控技术股份有限公司 | Operation trajectory curve determination method and apparatus, and electronic device |
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