Method and apparatus for determining operation trajectory curve, and electronic device
By analyzing data from multiple batches of production equipment, operation trajectory curves are automatically generated, solving the problem that manual specifications cannot adapt to changes and improving production stability and efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SUPCON TECH CO LTD
- Filing Date
- 2022-09-19
- Publication Date
- 2026-06-26
AI Technical Summary
The existing standard curves for generating operational trajectories mainly rely on manual specification, which cannot adapt to changes in production equipment and production needs, resulting in unstable product output and quality.
By acquiring operational and output data from multiple batches of production equipment, the data is stitched together and classified. Clustering algorithms are used to identify similar data, calculate the weights of operational parameters for different batches, and automatically generate operational trajectory curves.
It enables the automatic generation of operation trajectories during the production process, improving production efficiency, adapting to changes in production equipment and demand, and ensuring the stability of product quality and output.
Smart Images
Figure CN115392773B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of production design in the process industry, and more specifically, to a method, apparatus, and electronic device for determining an operation trajectory curve. Background Technology
[0002] In process industries, intermittent production typically establishes a standard operating trajectory curve before each batch of production, and the underlying control system operates according to this curve. However, in practice, the initial state of the production equipment varies across different batches. Operators then adjust the operating trajectory based on experience during production, leading to variations in the final product output and quality for each batch. This adjustment is subjective, primarily qualitative, lacking quantification and precise adjustment capabilities. Therefore, data analysis of production results and operating trajectories from multiple batches is necessary to determine the optimal standard operating trajectory curve.
[0003] The existing standard curves for operation trajectories are generally generated manually, which cannot adapt to the gradual changes in production equipment and production needs. There is currently no effective solution to the above problems. Summary of the Invention
[0004] This application provides a method, apparatus, and electronic device for determining an operation trajectory curve, which at least solves the technical problem that the generation of existing operation trajectory standard curves is generally manually specified and cannot adapt to the gradual changes in production equipment and production needs.
[0005] According to one aspect of the embodiments of this application, a method for determining an operation trajectory curve is provided, comprising: acquiring operation data and output data of a production equipment in multiple batches, wherein the operation data is data collected by different instruments, the output data is product data related to the products generated by the production equipment, and each batch in the multiple batches corresponds to a different collection date; performing a splicing operation on the operation data and output data corresponding to the same collection date in the multiple batches to obtain a spliced dataset; determining the similarity between the spliced data in the spliced dataset, and classifying the spliced data in the spliced dataset according to the similarity to obtain multiple categories of data, wherein data with a similarity greater than a preset threshold are classified into one category; selecting target type data from the multiple categories of data, and determining the weight values corresponding to the operation parameters of different batches in the target type data, wherein the target type data is any one of the multiple categories of data, and the operation parameters are at least one controllable data in the operation data; and determining the operation trajectory curve corresponding to the target type data based at least on the weight values and the values of the operation parameters in the batches corresponding to the weight values.
[0006] Optionally, a splicing operation is performed on the running data and output data corresponding to the same collection date in multiple batches, including: obtaining the running time corresponding to the running data; aligning the running data with the same running time and sorting them according to the running time to obtain the first data; splicing the first data and the output data corresponding to the same collection date to obtain the spliced data; and determining the spliced data corresponding to different collection dates to obtain the spliced dataset.
[0007] Optionally, determining the weight values corresponding to the operation parameters of different batches in the target type data includes: obtaining the collection dates corresponding to the operation parameters of different batches in the target type data to obtain multiple collection dates; sorting the multiple collection dates according to their proximity to the current date to obtain a target collection date set; and determining the weight values of the batches corresponding to the multiple collection dates in the target collection date set.
[0008] Optionally, determining the weight values of batches corresponding to multiple collection dates in the target collection date set includes: determining the weight values between batches corresponding to two adjacent dates in the target collection date set, wherein the weight of the batch corresponding to the first date among the two adjacent dates is a preset multiple of the weight of the batch corresponding to the second date, the first date is the one closer to the current date among the two adjacent dates, the second date is the one farther from the current date among the two adjacent dates, and the preset multiple is any value greater than 1.
[0009] Optionally, after determining the weight values between batches corresponding to two adjacent dates in the target collection date set, the method further includes: obtaining the weight set of all batches corresponding to all dates in the target collection date set; constructing an equation based on the quantitative relationship between all weight values in the weight set; and solving the equation to obtain the first weight value between batches corresponding to two adjacent dates in the target collection date set.
[0010] Optionally, determining the operation trajectory curve corresponding to the target type of data includes: determining the target weight value of the batch corresponding to each date in the target collection date set based on the first weight value; determining the target operation parameters based on the target weight value and the operation parameter values of the batch corresponding to the target weight value at different running times, wherein the target operation parameters are the values of the operation parameters of the operation trajectory curve corresponding to the target type of data at different running times.
[0011] Optionally, the operation trajectory curve is a curve in a coordinate system with running time as the horizontal axis and the values of the operation parameters during the running time as the vertical axis.
[0012] According to another aspect of the embodiments of this application, an apparatus for determining an operation trajectory curve is also provided, comprising: an acquisition module, configured to acquire operation data and output data of a production equipment in multiple batches, wherein the operation data is data collected by different instruments, the output data is product data related to the products generated by the production equipment, and each batch in the multiple batches corresponds to a different collection date; a splicing module, configured to perform a splicing operation on the operation data and output data corresponding to the same collection date in the multiple batches to obtain a spliced dataset; a classification module, configured to determine the similarity between the spliced data in the spliced dataset, and classify the spliced data in the spliced dataset according to the similarity to obtain multiple types of data, wherein data with a similarity greater than a preset threshold are classified into one type; a first determination module, configured to select target type data from the multiple types of data, and determine the weight values corresponding to the operation 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 operation parameters are at least one controllable data in the operation data; and a second determination module, configured to determine the operation trajectory curve corresponding to the target type data based at least on the weight values and the values of the operation parameters in the batches corresponding to the weight values.
[0013] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory for storing program instructions; and a processor connected to the memory for executing program instructions to perform the following functions: acquiring operating data and output data of a production device in multiple batches, wherein the operating data is data collected by different instruments, the output data is product data related to the products generated by the production device, and each batch in the multiple batches corresponds to a different collection date; performing a splicing operation on the operating data and output data corresponding to the same collection date in the multiple batches to obtain a spliced dataset; determining the similarity between the spliced data in the spliced dataset, and classifying the spliced data in the spliced dataset according to the similarity to obtain multiple types of data, wherein data with a similarity greater than a preset threshold are classified into one type; selecting target type data from the multiple types of data, and determining the weight values corresponding to the operation 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 operation parameters are at least one controllable data in the operating data; and determining the operation trajectory curve corresponding to the target type data based at least on the weight values and the values of the operation parameters in the batches corresponding to the weight values.
[0014] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned method for determining the operation trajectory curve by running the computer program.
[0015] In this embodiment, by acquiring the operating data and output data of the production equipment in multiple batches; performing a splicing operation on the operating data and output data corresponding to the same collection date in multiple batches to obtain a spliced dataset; determining the similarity between the spliced data in the spliced dataset, and classifying the spliced data in the spliced dataset according to the similarity to obtain multiple types of data; selecting target type data from the multiple types of data, and determining the weight values corresponding to the operating parameters of different batches in the target type data; and determining the operation trajectory curve corresponding to the target type data based at least on the weight values and the values of the operating parameters in the batches corresponding to the weight values, the purpose of automatically generating operation trajectories in the production process is achieved, thereby realizing the technical effect of improving the generation efficiency of operation trajectories, and solving the technical problem that the generation of existing standard curves for operation trajectories is generally specified manually and cannot adapt to the gradual changes in production equipment and changes in production needs. Attached Figure Description
[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:
[0017] Figure 1 This is a hardware structure block diagram of a computer terminal (or electronic device) for implementing a method for determining an operation trajectory curve according to an embodiment of this application.
[0018] Figure 2 This is a flowchart of a method for determining an operation trajectory curve according to an embodiment of this application;
[0019] Figure 3 This is a structural diagram of an operation trajectory curve determination device according to an embodiment of this application. Detailed Implementation
[0020] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.
[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0022] First, some nouns or terms that appear in the description of the embodiments of this application shall be interpreted as follows:
[0023] Intermittent production process: also known as batch production process. This refers to a process where all work steps are performed at the same location but at different times, resulting in unstable operation and parameters that change over time. For example: a batch of raw materials is added to the equipment, the process is completed, the product is discharged, the equipment is cleaned, and then new materials are added, and this cycle repeats continuously.
[0024] Operation trajectory: The trajectory generated by the change of operation parameters over time.
[0025] In related technologies, the standard curve of the operating trajectory is very important; adjustments are only meaningful when made based on a good standard curve. However, the existing standard curves of the operating trajectory are generally generated manually, which introduces a certain degree of subjectivity. Furthermore, with the gradual changes in production equipment and production needs, the practice of using a single, fixed standard curve of the operating trajectory cannot adequately adapt to these changes.
[0026] To address the above problems, this application provides corresponding solutions, which are described in detail below.
[0027] The method for determining the operation trajectory curve provided in this application can be executed in a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal (or electronic device) for implementing a method for determining an operation trajectory curve is shown. Figure 1As shown, the computer terminal 10 (or electronic device 10) may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, 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, it may also include: 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 supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.
[0028] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits can be implemented wholly or partially as software, hardware, firmware, or any other combination. Furthermore, the data processing circuits can be a single, independent processing module, or wholly or partially integrated into any other element within the computer terminal 10 (or electronic device). As involved in the embodiments of this application, the data processing circuit serves as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).
[0029] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for determining the operation trajectory curve in this embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the above-mentioned method for determining the operation trajectory curve. 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 instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
[0030] The transmission module 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.
[0031] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or electronic device).
[0032] It should be noted here that, in some optional embodiments, the above... Figure 1 The computer device (or electronic device) shown 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... Figure 1 This is only one instance of a particular specific instance, and is intended to illustrate the types of components that may exist in the aforementioned computer equipment (or electronic equipment).
[0033] In the above operating environment, this application provides an embodiment of a method for determining an operation trajectory curve. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.
[0034] Figure 2 This is a flowchart of a method for determining an operation trajectory curve according to an embodiment of this application, such as... Figure 2 As shown, the method includes the following steps:
[0035] Step S202: Obtain the operating data and output data of the production equipment in multiple batches. The operating data is data collected by different instruments, and the output data is product data related to the products generated by the production equipment. Each batch in the multiple batches corresponds to a different collection date.
[0036] In one optional embodiment, the operating data may include process data such as pressure, temperature, and flow rate. This operating data is constantly changing at different times, for example, the operating data may be collected once every 1 second. The output data may include output, unit energy consumption, product premium rate, etc. The output data in the same batch is the same. It should be noted that the data included in the above-mentioned operating data and output data are only illustrative examples. In the actual production process, other data are also included, which will not be described here.
[0037] Step S204: Perform a splicing operation on the running data and output data corresponding to the same collection date in multiple batches to obtain a spliced dataset.
[0038] Before stitching the data in step S204 above, the acquired running data and output data are preprocessed to handle outliers and missing values. After data preprocessing, the running data and output data from the same collection date in multiple batches are summarized, that is, the running data at the same time are aligned according to the running time and stitched with the output data from the same collection date (i.e., the same batch) to obtain the running-output dataset, which is the stitched dataset mentioned above.
[0039] Step S206: Determine the similarity between the spliced data in the spliced dataset, and classify the spliced data in the spliced dataset according to the similarity to obtain multiple classes of data. Among them, data with similarity greater than a preset threshold are classified into one class.
[0040] In step S206 above, in the spliced dataset, data with similarity greater than a preset threshold are grouped into one class using a clustering algorithm, such as the K-means algorithm, and multi-class data are obtained after clustering.
[0041] Step S208: Select target type data from multiple types of data, and determine the weight values corresponding to the operation parameters of different batches in the target type data. 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.
[0042] In step S208 above, for multiple types of data, the class center data of each type is selected as the representative of that type, i.e., a case. The cases are labeled, such as low-energy consumption cases, high-yield cases, high-quality cases, etc. The collection of several cases forms a case library. One type of case is selected from the case library, that is, the target type of data is selected from the multiple types of data. The selected case contains n batches of data, and the weight values corresponding to the operation parameters of different batches in the target type of data are determined.
[0043] In step S208 above, the operating parameter is at least one controllable data in the operating data. For example, when the operating data includes pressure, temperature and flow rate, the flow rate can be used as an operating parameter if it can be adjusted by a valve. If the pressure and temperature are not adjustable, they cannot be used as operating parameters.
[0044] Step S210: Determine the operation trajectory curve corresponding to the target type of data based at least on the weight value and the values of the operation parameters in the corresponding batch.
[0045] This application embodiment collects operational and output data from different batches; processes outliers and missing values in the data through data preprocessing to improve the accuracy of the system in drawing operation trajectory curves; integrates operational and output data from different batches to obtain an operational-output dataset; clusters similar operational-output data in the operational-output dataset to obtain cases; labels the cases to form a case library; calculates the weight of each batch within the selected cases, thereby calculating the standard curve of the operation trajectory. This enables the automatic generation of standard curves of the operation trajectory based on production needs, without the need for manual formulation.
[0046] In step S204 of the above method for determining the operation trajectory curve, a splicing operation is performed on the running data and output data corresponding to the same collection date in multiple batches. Specifically, the steps include: obtaining the running time corresponding to the running data; aligning the running data with the same running time and sorting them according to the running time to obtain the first data; splicing the first data and the output data corresponding to the same collection date to obtain the spliced data; and determining the spliced data corresponding to different collection dates to obtain the spliced dataset.
[0047] In this embodiment, since the intermittent production process is batch production, and the operational data of each batch has two dimensions—variables and time, i.e., the values of the operational data and the operational time—while the output data only has one dimension, when establishing the operational-output dataset, it is necessary to concatenate the operational data and output data of a batch together to form a set of operational-output data. The following example illustrates this:
[0048] The run-output data for a certain batch is shown in Table 1 below:
[0049] Table 1. Run-output data for a batch
[0050]
[0051] Clustering algorithms are used to cluster the run-output data in the spliced dataset that have a similarity greater than a preset threshold into one class, thus obtaining several classes, i.e., multiple classes of data. The class center data of each class is selected as the representative of that class, i.e., the case. Table 2 shows the cases obtained after clustering in the spliced dataset.
[0052] Table 2. Cases obtained from the concatenated dataset after clustering.
[0053]
[0054] After marking the cases generated above, the results are shown in Table 3:
[0055] Table 3 Cases after labeling
[0056]
[0057] In step S208 of the above method for determining the operation trajectory curve, determining the weight values corresponding to the operation parameters of different batches in the target type data specifically includes the following steps: obtaining the collection dates corresponding to the operation parameters of different batches in the target type data to obtain multiple collection dates; sorting the multiple collection dates according to their proximity to the current date to obtain a target collection date set; and determining the weight values of the batches corresponding to the multiple collection dates in the target collection date set.
[0058] In the above steps, determining the weight values of batches corresponding to multiple collection dates in the target collection date set specifically includes the following steps: determining the weight values between batches corresponding to two adjacent dates in the target collection date set, wherein the weight of the batch corresponding to the first date among the two adjacent dates is a preset multiple of the weight of the batch corresponding to the second date, the first date is the one closer to the current date among the two adjacent dates, the second date is the one farther from the current date among the two adjacent dates, and the preset multiple is any value greater than 1.
[0059] In this embodiment, batches are sorted according to their distance from the current time. It is assumed that the weight of more recent batches is 1.5 times that of more distant batches. This 1.5 corresponds to the aforementioned preset multiple. It should be noted that this preset multiple can be any value greater than 1, and the preset multiple selected for different batches can be the same or different. The specific value selected can be set according to the actual situation. k 1 If the weight is the oldest batch, then... k 2 =1.5k 1 , k 3 =1.5k 2 =1.52 k 1 … , k n = 1.5 n-1 k 1 If the sum of all weights is 1, then:
[0060] k 1 +1.5k 1 +1.5 2 k 1 + … + 1.5 n-1 k 1 =1
[0061] Solve k 1 , and then I got k 2 、k 3 、… k n .
[0062] In the above steps, after determining the weight values between batches corresponding to two adjacent dates in the target collection date set, the method further includes the following steps: obtaining the weight set of all batches corresponding to all dates in the target collection date set; constructing an equation based on the quantitative relationship between all weight values in the weight set; solving the equation to obtain the first weight value between batches corresponding to two adjacent dates in the target collection date set.
[0063] In this application embodiment, target type data is selected from multiple types of data, such as data from the low-energy consumption case in Table 3. It is assumed that the low-energy consumption case contains four batches of operation-output data, as shown in Tables 4 and 5.
[0064] Table 4 Batch data included in low-energy consumption cases
[0065]
[0066] Table 5. Batch data included in low-energy consumption cases (continued)
[0067]
[0068] set up k 1It is the weight of the oldest batch. Under the condition that "the weight of the most recent batch is 1.5 times that of the slightly older batch", then we have: k 2 = 1.5 k 1 , k 3 = 1.5 k 2 = 1.5 2 k 1 , k 4 = 1.5 k 3= 1.5 3 k 1 If the sum of all weights is 1, then the following equation holds:
[0069] k 1 + 1.5 k 1 + 1.5 2 k 1 +1.5 3 k 1 = 1
[0070] Solving k 1 =0.123, then we have k 2 =0.185, k 3 =0.277, k 4 =0.415. This calculated value corresponds to the first weight value mentioned above.
[0071] In step S210 of the above method for determining the operation trajectory curve, determining the operation trajectory curve corresponding to the target type of data specifically includes the following steps: based on the first weight value, determining the target weight value of the batch corresponding to each date in the target collection date set; based on the target weight value and the values of the operation parameters of the batch corresponding to the target weight value at different running times, determining the target operation parameters, wherein the target operation parameters are the values of the operation parameters of the operation trajectory curve corresponding to the target type of data at different running times.
[0072] In this embodiment, the values of the operation parameters in the standard curve of the operation trajectory at each moment are calculated using the following formula:
[0073]
[0074] in, s p,t Operating parameters in the operating trajectory curve p exist t The value at time, x p,i,t For operating parameters p In the i In each batch t The value at time, k i For the first i The weight of each batch.
[0075] In an optional embodiment, taking the above low-energy consumption case containing four batches of operation-output data as an example, and taking the current date as May 1, 2022 as an example, batch 1 is the furthest from the current date and has the smallest corresponding weight, and so on. Therefore, the value of operation parameter 1 for the first hour of the operation trajectory is:
[0076] 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
[0077] Operation parameters 2 to n are calculated using the method described above. Similarly, operation parameters 1 to n from the 2nd hour to the mth hour are calculated using the method described above.
[0078] In the above method for determining the operation trajectory curve, the operation trajectory curve is a curve in a coordinate system with running time as the horizontal axis and the values of the operation parameters during the running time as the vertical axis.
[0079] In this embodiment of the application, the weighted average value of each operation parameter at each time point is calculated according to the batch weight, and the standard curve of the operation trajectory is obtained by connecting these weighted average values.
[0080] This application addresses intermittent production scenarios in the process industry. Through data analysis of historical batch operation data and final product output data, it generates a standard operation trajectory curve from multiple optimal batches based on production goals. The method for determining the operation trajectory curve provided in this application has the following advantages: 1. It eliminates the need for manually pre-determining the standard operation trajectory curve; 2. It uses real historical data to obtain case studies, which serve as the basis for determining the standard operation trajectory curve; 3. Considering that batches closer to the current time have greater reference significance, a method for calculating the weights of different batches under similar case studies is designed; 4. The standard operation trajectory curve is obtained based on the weighted average of different batches under similar case studies.
[0081] Figure 3 This is a structural diagram of an operation trajectory curve determination device according to an embodiment of this application, such as... Figure 3 As shown, the device includes:
[0082] The acquisition module 302 is used to acquire the operating data and output data of the production equipment in multiple batches. The operating data is data collected by different instruments, and the output data is product data related to the products generated by the production equipment. Each batch in the multiple batches corresponds to a different collection date.
[0083] The splicing module 304 is used to perform splicing operations on running data and output data corresponding to the same collection date in multiple batches to obtain a spliced dataset;
[0084] The classification module 306 is used to determine the similarity between the spliced data in the spliced dataset, and classify the spliced data in the spliced dataset according to the similarity to obtain multiple classes of data. Among them, data with similarity greater than a preset threshold are classified into one class.
[0085] The first determining module 308 is used to select target type data from multiple types of data and determine the weight values corresponding to the operation 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 operation parameter is at least one controllable data in the running data;
[0086] The second determining module 310 is used to determine the operation trajectory curve corresponding to the target type of data based at least on the weight value and the values of the operation parameters in the corresponding batch.
[0087] It should be noted that, Figure 3 The device for determining the operation trajectory curve shown is used to perform... Figure 2 The method for determining the operation trajectory curve shown above is also applicable to the device for determining the operation trajectory curve, and will not be repeated here.
[0088] This application embodiment also provides a non-volatile storage medium, which includes a stored computer program. The device containing the non-volatile storage medium executes the computer program to perform the following method for determining a trajectory curve: acquiring operational data and output data of a production equipment in multiple batches, wherein the operational data is data collected by different instruments, and the output data is product data related to the products generated by the production equipment, with each batch corresponding to a different collection date; performing a splicing operation on the operational data and output data corresponding to the same collection date in the multiple batches to obtain a spliced dataset; determining the similarity between the spliced data in the spliced dataset, and classifying the spliced data in the spliced dataset according to the similarity to obtain multiple categories of data, wherein data with a similarity greater than a preset threshold are grouped into one category; selecting target type data from the multiple categories of data, and determining the weight values corresponding to the operation parameters of different batches in the target type data, wherein the target type data is any one of the multiple categories of data, and the operation parameters are at least one controllable data in the operational data; and determining the operation trajectory curve corresponding to the target type data based at least on the weight values and the values of the operation parameters in the corresponding batches.
[0089] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0090] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0091] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.
[0092] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0093] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0094] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.
[0095] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.
Claims
1. A method for determining an operation trajectory curve, characterized in that, include: The operation data and output data of the production equipment in multiple batches are obtained, wherein the operation data is data collected by different instruments, the output data is product data related to the products generated by the production equipment, and each batch in the multiple batches corresponds to a different collection date; Perform a splicing operation on the running data and output data corresponding to the same collection date in the multiple batches to obtain a spliced dataset; The similarity between the spliced data in the spliced dataset is determined, and the spliced data in the spliced dataset is classified according to the similarity to obtain multiple classes of data. Among them, data with similarity greater than a preset threshold are classified into one class. Select target type data from the multiple types of data, and determine the weight values corresponding to the operation 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 operation parameter is at least one controllable data in the running data; Based at least on the weight value and the values of the operation parameters in the corresponding batch, the operation trajectory curve corresponding to the target type of data is determined, wherein the operation trajectory is the trajectory generated by the change of operation parameters over time, and the operation trajectory curve is a curve in a coordinate system with the running time as the horizontal axis and the values of the operation parameters under the running time as the vertical axis. Determining the weight values corresponding to the operation parameters of different batches in the target type data includes: obtaining the collection dates corresponding to the operation parameters of different batches in the target type data to obtain multiple collection dates; sorting the multiple collection dates according to their proximity to the current date to obtain a target collection date set; and determining the weight values of the batches corresponding to the multiple collection dates in the target collection date set. Determining the weight values of batches corresponding to multiple collection dates in the target collection date set includes: determining the weight values between batches corresponding to two adjacent dates in the target collection date set, wherein the weight of the batch corresponding to the first date among the two adjacent dates is a preset multiple of the weight of the batch corresponding to the second date, the first date is the one closer to the current date among the two adjacent dates, the second date is the one farther from the current date among the two adjacent dates, and the preset multiple is any value greater than 1.
2. The method according to claim 1, characterized in that, Perform a concatenation operation on the running data and output data corresponding to the same collection date from the multiple batches, including: Obtain the running time corresponding to the running data; Align running data with the same running time and sort them according to the running time to obtain the first data; The first data and the output data corresponding to the same collection date are spliced together to obtain the spliced data; The spliced dataset is obtained by determining the spliced data corresponding to different collection dates.
3. The method according to claim 1, characterized in that, After determining the weight values between batches corresponding to two adjacent dates in the target collection date set, the method further includes: Obtain the weight set of all batches corresponding to the target collection dates in the target collection date set; Based on the quantitative relationships between all weight values in the weight set, construct an equation; Solving the equation yields the first weight value between batches corresponding to two adjacent dates in the target collection date set.
4. The method according to claim 3, characterized in that, Determining the operation trajectory curve corresponding to the target type of data includes: Based on the first weight value, determine the target weight value for each batch corresponding to each date in the target collection date set; Based on the target weight value and the values of the operation parameters corresponding to the target weight value at different running times, the target operation parameters are determined, wherein the target operation parameters are the values of the operation parameters of the operation trajectory curve corresponding to the target type of data at different running times.
5. A device for determining an operation trajectory curve, characterized in that, include: The acquisition module is used to acquire the operating data and output data of the production equipment in multiple batches. The operating data is data collected by different instruments, and the output data is product data related to the products generated by the production equipment. Each batch in the multiple batches corresponds to a different collection date. The splicing module is used to perform splicing operations on the running data and output data corresponding to the same collection date in the multiple batches to obtain a spliced dataset; The classification module is used to determine the similarity between the spliced data in the spliced dataset, and classify the spliced data in the spliced dataset according to the similarity to obtain multiple classes of data, wherein data with a similarity greater than a preset threshold are classified into one class; The first determining module is used to select target type data from the multiple types of data and determine the weight values corresponding to the operation 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 operation parameters are at least one controllable data in the running data; determining the weight values corresponding to the operation parameters of different batches in the target type data includes: obtaining the collection dates corresponding to the operation parameters of different batches in the target type data to obtain multiple collection dates; sorting the multiple collection dates according to their proximity to the current date to obtain a target collection date set; determining the weight values of the batches corresponding to the multiple collection dates in the target collection date set; determining the weight values of the batches corresponding to the multiple collection dates in the target collection date set includes: determining the weight values between the batches corresponding to two adjacent dates in the target collection date set, wherein the weight of the batch corresponding to the first date in the two adjacent dates is a preset multiple of the weight of the batch corresponding to the second date, the first date is the one closer to the current date among the two adjacent dates, the second date is the one farther from the current date among the two adjacent dates, and the preset multiple is any value greater than 1; The second determining module is used to determine the operation trajectory curve corresponding to the target type of data based at least on the weight value and the values of the operation parameters in the batch corresponding to the weight value. The operation trajectory is the trajectory generated by the change of operation parameters over time. The operation trajectory curve is a curve in a coordinate system with the running time as the horizontal axis and the values of the operation parameters under the running time as the vertical axis.
6. An electronic device, characterized in that, include: Memory, used to store program instructions; A processor, connected to the memory, is configured to execute program instructions to perform the following functions: acquiring operational and output data of a production device in multiple batches, wherein the operational data is data collected by different instruments, the output data is product data related to the products generated by the production device, and each batch corresponds to a different collection date; performing a splicing operation on the operational and output data corresponding to the same collection date in the multiple batches to obtain a spliced dataset; determining the similarity between the spliced data in the spliced dataset, and classifying the spliced data in the spliced dataset according to the similarity to obtain multiple categories of data, wherein data with a similarity greater than a preset threshold are grouped into one category; selecting target type data from the multiple categories of data, and determining the weight values corresponding to the operation parameters of different batches in the target type of data, wherein the target type of data is any one of the multiple categories of data, and the operation parameters are at least one controllable data in the operational data; determining the operation trajectory curve corresponding to the target type of data based at least on the weight values and the values of the operation parameters in the batches corresponding to the weight values, wherein... The operation trajectory is the trajectory generated by the change of operation parameters over time. The operation trajectory curve is a curve in a coordinate system with the running time as the horizontal axis and the values of the operation parameters under the running time as the vertical axis. Determining the weight values corresponding to the operation parameters of different batches in the target type data includes: obtaining the collection dates corresponding to the operation parameters of different batches in the target type data to obtain multiple collection dates; sorting the multiple collection dates in order of their distance from the current date to obtain a target collection date set; determining the weight values of the batches corresponding to the multiple collection dates in the target collection date set; determining the weight values of the batches corresponding to the multiple collection dates in the target collection date set includes: determining the weight values between the batches corresponding to two adjacent dates in the target collection date set, wherein the weight of the batch corresponding to the first date in the two adjacent dates is a preset multiple of the weight of the batch corresponding to the second date, the first date is the one closer to the current date among the two adjacent dates, the second date is the one farther from the current date among the two adjacent dates, and the preset multiple is any value greater than 1.
7. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored computer program, wherein the device containing the non-volatile storage medium executes the method for determining the operation trajectory curve according to any one of claims 1 to 4 by running the computer program.