Method and device for determining operation locus curve, and electronic device

Abstract

The present application discloses a method and apparatus for determining an operation trajectory curve and an electronic device, the method includes the steps of: obtaining operation data and production data of multiple batches of a production facility; performing a combining operation on the operation data and production data corresponding to the same collection date in the multiple batches to obtain a combined data set; determining a similarity between the combined data in the combined data set, classifying the combined data in the combined data set according to the similarity, and obtaining multiple classes of data; selecting target-type data from the multiple classes of data, determining weight values ​​corresponding to operation parameters of different batches in the target-type data, and determining an operation trajectory curve corresponding to the target-type data according to at least the weight values ​​and the values ​​of the operation parameters in the batches corresponding to the weight values.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to a Chinese patent application filed with the China Patent Office on September 19, 2022, bearing priority number 202211137659.2 and entitled "Method and apparatus for determining operation trajectory curve and electronic device," the entire contents of which are incorporated herein by reference. [Technical field]

[0002] The present application relates to the field of production design in the process industry, and in particular to a method and apparatus for determining an operating trajectory curve, and an electronic device. [Background technology]

[0003] In the intermittent production of process industries, a standard curve of the operation trajectory is generally prepared before the production of each batch, and the underlying control system operates according to the standard curve of the operation trajectory. However, in the actual process, the initial state of the production equipment is not completely consistent for different batches, and at this time, the operator adjusts the operation trajectory during production based on experience, which causes the yield and quality of the final product of each batch to change. Such adjustment is subjective to a certain extent, and has the disadvantages of being mainly qualitative, not quantifiable, and not accurately adjusted. Therefore, it is necessary to obtain the optimal standard curve of the operation trajectory by data analysis of the production results and operation trajectories of multiple batches.

[0004] In the related art, the generation of the standard curve of the operation trajectory is generally performed manually, and cannot adapt to the gradual changes of the production equipment or the changes of the production needs. No effective solution to the above problem has been proposed yet. Summary of the Invention [Problem to be solved by the invention]

[0005] The embodiments of the present application provide a method and device for determining an operation trajectory curve, as well as an electronic device, in order to at least solve the problem that the generation of a standard curve of a conventional operation trajectory is generally performed manually and cannot adapt to gradual changes in production equipment or changes in production needs. [Means for solving the problem]

[0006] According to one aspect of an embodiment of the present application, there is provided a method for determining an operation trajectory curve, the method including: acquiring operation data and production data for multiple batches of a production facility, the operation data being data collected by different meters, the production data being product data related to products produced by the production facility, and each of the multiple batches corresponding to a different collection date; performing a combination operation on the operation data and the production data corresponding to the same collection date in the multiple batches to obtain a combination data set; determining a similarity between the combination data in the combination data set, classifying the combination data in the combination data set according to the similarity, and obtaining multiple classes of data in which data whose similarity is greater than a preset threshold is classified into one class; selecting target type data from the multiple classes of data, and determining weight values ​​corresponding to operation parameters of different batches in the target type data, the target type data being data of any one of the multiple classes of data, and the operation parameters being at least one controllable data of the operation data; and determining an operation trajectory curve corresponding to the target type data according to at least the weight value and the value of the operation parameter in the batch corresponding to the weight value.

[0007] In some embodiments of the present application, the step of performing a join operation on the operation data and production data corresponding to the same collection date in multiple batches includes the steps of obtaining an operation time corresponding to the operation data, aligning the operation data having the same operation time and sorting them according to the operation time to obtain first data, combining the first data with the production data corresponding to the same collection date to obtain combined data, and determining combined data corresponding to different collection dates to obtain a combined data set.

[0008] In some embodiments of the present application, the step of determining weight values ​​corresponding to the operational parameters of different batches of the target type data includes the steps of obtaining collection dates corresponding to the operational parameters of different batches of the target type data to obtain a plurality of collection dates, sorting the plurality of collection dates in order of proximity to a current date to obtain a target collection date set, and determining batch weight values ​​corresponding to the plurality of collection dates in the target collection date set.

[0009] In some embodiments of the present application, determining batch weight values ​​corresponding to a plurality of collection dates in the target collection date set includes determining batch weight values ​​corresponding to two adjacent dates in the target collection date set, where the batch weight corresponding to a first of the two adjacent dates is a preset multiple of the batch weight corresponding to a second of the two adjacent dates, the first date being closer to a current date of the two adjacent dates and the second date being farther from the current date of the two adjacent dates, and the preset multiple is any value greater than 1.

[0010] In some embodiments of the present application, after determining the weight value between batches corresponding to two adjacent dates in the target collection date set, the method further includes the steps of obtaining a weight set of batches corresponding to all dates in the target collection date set, constructing an equation according to a numerical relationship between all 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.

[0011] In some embodiments of the present application, the step of determining an operation trajectory curve corresponding to the target type data includes the steps of: determining a target weight value of a batch corresponding to each date in the target collection date set according to a first weight value; and determining a target operation parameter according to the target weight value and a value of the operation parameter at different operating times of the batch corresponding to the target weight value, where the target operation parameter is a value of the operation parameter at different operating times of the operation trajectory curve corresponding to the target type data.

[0012] In some embodiments of the present application, the operating trajectory curve is a curve in a coordinate system established with operating time as the abscissa and the value of the operating parameter at the operating time as the ordinate.

[0013] According to another aspect of the present application, there is provided an acquisition module configured to acquire operation data and production data for a plurality of batches of a production facility, the operation data being data collected by different meters, and the production data being product data relating to products produced by the production facility, each of the plurality of batches corresponding to a different collection date; a combination module configured to perform a combination operation on the operation data and the production data corresponding to the same collection date in the plurality of batches to obtain a combined data set; and a combination module configured to determine a similarity between the combined data in the combined data set, classify the combined data in the combined data set according to the similarity, and classify data having a similarity greater than a preset threshold into one class. There is further provided an operation trajectory curve determining device, comprising: a classification module configured to obtain data of a class; a first determination module configured to select target-type data from the plurality of classes of data and determine weight values ​​corresponding to operation parameters of different batches in the target-type data, where the target-type data is data of any class of the plurality of classes of data, and the operation parameters are at least one controllable data of the operation data; and a second determination module configured to determine an operation trajectory curve corresponding to the target-type data according to at least the weight values ​​and the values ​​of the operation parameters in the batches corresponding to the weight values.

[0014] According to yet another aspect of an embodiment of the present application, there is further provided an electronic device including a memory configured to store program instructions and a processor connected to the memory, the processor being configured to execute program instructions to realize functions of: acquiring operation data and production data for multiple batches of a production facility, the operation data being data collected by different meters, the production data being product data related to products produced by the production facility, each of the multiple batches corresponding to a different collection date, performing a combination operation on the operation data and the production data corresponding to the same collection date in the multiple batches to obtain a combined data set, determining a similarity between the combined data in the combined data set, classifying the combined data in the combined data set according to the similarity, obtaining multiple classes of data in which data whose similarity is greater than a preset threshold is classified into one class, selecting target type data from the multiple classes of data, determining weight values ​​corresponding to operation parameters of different batches in the target type data, the target type data being data of any one of the multiple classes of data, the operation parameter being at least one controllable data of the operation data, and determining an operation trajectory curve corresponding to the target type data according to at least the weight value and a value of the operation parameter in the batch corresponding to the weight value.

[0015] According to yet another aspect of an embodiment of the present application, there is further provided a non-volatile storage medium including a stored computer program, wherein an apparatus in which the non-volatile storage medium is disposed executes the above-mentioned method for determining an operation trajectory curve by executing the computer program. Effect of the Invention

[0016] In the embodiment of the present application, the operation data and output data of multiple batches of production equipment are obtained, and a combination operation is performed on the operation data and output data corresponding to the same collection date in the multiple batches to obtain a combination data set, and the similarity between the combined data in the combination data set is determined, and the combined data in the combination data set is classified according to the similarity to obtain multiple classes of data, and then target-type data is selected from the multiple classes of data, and weight values ​​corresponding to the operation parameters of different batches in the target-type data are determined, and the operation trajectory curve corresponding to the target-type data is determined at least according to the weight values ​​and the values ​​of the operation parameters in the batches corresponding to the weight values, thereby achieving the purpose of automatically generating the operation trajectory during production, and realizing the technical effect of improving the generation efficiency of the operation trajectory, and further solving the problem that the generation of the standard curve of the operation trajectory in the conventional method is generally performed manually, and cannot adapt to the gradual changes of the production equipment and the changes in production needs. [Brief description of the drawings]

[0017] The drawings described herein constitute a part of the present application for the purpose of providing a further understanding of the present application, and the schematic embodiments and the description thereof are for explaining the present application and are not intended to unduly limit the present application. [Figure 1] 1 is a hardware configuration block diagram of a computer terminal (or electronic device) that realizes a method for determining an operation trajectory curve according to an embodiment of the present application. [Diagram 2] 4 is a flowchart of a method for determining an operation locus curve according to an embodiment of the present application. [Diagram 3] 1 is a configuration diagram of an operation locus curve determination device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] In order to allow those skilled in the art to better understand the aspects of the present application, the technical aspects of the embodiments of the present application will be described below clearly and completely with reference to the drawings of the embodiments of the present application. It goes without saying that the described embodiments are only some of the embodiments of the present application, and are not all of them. Other embodiments that those skilled in the art can obtain based on the embodiments of the present application without requiring creative labor should also be included in the protection scope of the present application.

[0019] In addition, the terms "first", "second", and the like in the specification and claims of the present application and the drawings described above are intended to distinguish between similar objects, and are not intended to describe a specific order or priority. It should be understood that the numbers used in this manner can be interchanged where appropriate to allow the embodiments of the present application described herein to be performed in an order other than the order shown or described herein. In addition, the terms "including", "having" and any variations thereof are intended to cover what is included without being exclusive, for example, a process, method, system, product, or apparatus including a series of steps or units need not be limited to the steps or units explicitly shown, but may include steps or units not explicitly shown for these processes, methods, products, or apparatus, or other steps or units inherent thereto.

[0020] First, some of the nouns and terms appearing in the description of the embodiments of the present application are interpreted as follows.

[0021] Intermittent production process: also known as batch production process. It means that all the work steps are performed at the same place at different times, the operating conditions are unstable, and the parameters change over time. For example, a batch of raw materials is put into the equipment, it is operated, the product is discharged, then the equipment is cleaned, new materials are put in, and the process is repeated over and over again.

[0022] Operation Trajectory: The trajectory resulting from the change of an operation parameter over time.

[0023] In the related art, the standard curve of the operation trajectory is very important, and it is meaningful only to adjust based on a good standard curve of the operation trajectory. Meanwhile, the creation of the standard curve of the operation trajectory in the past is generally performed manually, and such a method is subjective to a certain extent, and with the successive changes of production equipment and changes in production needs, the method of the single and fixed standard curve of the operation trajectory cannot well adapt to these changes.

[0024] In order to solve the above problems, the embodiments of the present application provide corresponding solutions, which are described in detail below.

[0025] The embodiment of the method for determining an operation trajectory curve provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal, or a similar computing device. FIG. 1 shows a hardware configuration block diagram of a computer terminal (or an electronic device) that realizes the method for determining an operation trajectory curve. As shown in FIG. 1, the computer terminal 10 (or the electronic device 10) can include one or more processors (indicated as 102a, 102b, ..., 102n in the figure) (the processor can include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 configured to store data, and a transmission module 106 configured to have a communication function. In addition to these, the electronic device may further include a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the I / O interface), a network interface, a power source, and / or a camera. Those skilled in the art can understand that the configuration shown in FIG. 1 is merely schematic and does not limit the configuration of the electronic device. For example, computer terminal 10 may include more or fewer components than those shown in FIG. 1, or may have a different configuration than that shown in FIG.

[0026] It should be noted that the one or more processors and / or other data processing circuits described above may be generally referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination. The data processing circuits may also be a single independent processing module, or may be combined in whole or in part with any one of the other elements in the computer terminal 10 (or electronic device). As referred to in the embodiments of the present application, the data processing circuits, as a processor, for example, control the selection of paths of variable resistance terminals connected to an interface.

[0027] The memory 104 may be configured to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the method of determining an operation trajectory curve in the embodiments of the present application, and the processor executes various functional applications and data processing by executing the software programs and modules stored in the memory 104, that is, realizing the above-mentioned method of determining an operation trajectory curve. The memory 104 may include a high-speed random access memory, and may further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 may further include memories located remotely to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission module 106 is configured to transmit and receive data via a network. An example of the network may include a wireless network provided by a communication vendor of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, NIC) that is connected to other network devices via a base station and can communicate with the Internet. In one example, the transmission module 106 may be a radio frequency (RF) module configured to communicate with the Internet wirelessly.

[0029] The display may be, for example, a touch screen liquid crystal display (LCD) that allows a user to interact with the user interface of the computer terminal 10 (or electronic device).

[0030] It should be noted that in some optional embodiments, the computer device (or electronic device) shown in Figure 1 above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of hardware and software elements. It should be noted that Figure 1 is merely an example of a particular implementation and is intended to illustrate the types of components that may be present in the computer device (or electronic device) described above.

[0031] In the above-mentioned operating environment, an embodiment of a method for determining an operation trajectory curve is provided in the embodiment of the present application. Note that the steps shown in the flowcharts in the drawings may be executed in a computer system as a series of computer-executable instructions, and although the flowcharts show a logical order, in some cases the steps shown or described may be executed in a different order from the order shown here.

[0032] FIG. 2 is a flowchart of a method for determining an operation trajectory curve according to an embodiment of the present application. As shown in FIG. 2, the method includes the following steps:

[0033] In step S202, operation data and production data for multiple batches of the production equipment are obtained, the operation data being data collected by different meters, the production data being product data related to products produced in the production equipment, and each of the multiple batches corresponding to a different collection date.

[0034] In an optional embodiment, the operation data can include process data such as pressure, temperature, flow rate, etc., and the operation data changes constantly at different times, for example, the operation data can be collected once per second. The output data can include yield, energy consumption per unit, product quality rate, etc., and the output data of the same batch is the same. It should be noted that the data included in the above operation data and output data are merely examples, and in the actual production process, other data are also included, but they will not be described one by one here.

[0035] In step S204, a join operation is performed on the operation data and production data corresponding to the same collection date in multiple batches to obtain a joined data set.

[0036] Before combining the data in step S204, data pre-processing is performed on the acquired operation data and production data to process abnormal values, missing values, etc., in the data. After the data pre-processing, operation data and production data having the same collection date in multiple batches are compiled, that is, the operation data having the same time is aligned according to the operation time, and combined with the production data having the same collection date (i.e., the same batch) to obtain the operation-production dataset, which is the combined dataset.

[0037] In step S206, the similarity between the combined data in the combined dataset is determined, and the combined data in the combined dataset is classified according to the similarity, to obtain multiple classes of data in which data with a similarity greater than a preset threshold is classified into one class.

[0038] In the above step S206, data in the combined data set whose similarity is greater than a preset threshold is classified into one class by a clustering algorithm, which may be, for example, a K-means algorithm, to obtain multiple classes of data after clustering.

[0039] In step S208, select target type data from the multiple classes of data, and determine weight values ​​corresponding to operation parameters of different batches in the target type data, where the target type data is data of any class among the multiple classes of data, and the operation parameter is at least one controllable data of the operation data.

[0040] In the above step S208, for the multiple classes of data, the class center data of each class is selected as a representative of the class, i.e., an example, and the examples are labeled, for example, low energy consumption examples, high yield examples, high quality examples, etc., and a collection of the multiple examples forms an example library. One type of example including n batches of data is selected from the example library, i.e., target type data is selected from the multiple classes of data, and weight values ​​corresponding to operation parameters of different batches in the target type data are determined.

[0041] In the above step S208, the operating parameter is at least one controllable data among the operating data. For example, if the operating data includes pressure, temperature, and flow rate, if the flow rate is adjustable by a valve, the flow rate can be one of the operating parameters, and if the pressure and temperature are not adjustable, the pressure and temperature cannot be the operating parameters.

[0042] In step S210, determine an operation trajectory curve corresponding to the target type data according to at least the weight value and the value of the operation parameter in the batch corresponding to the weight value.

[0043] In the embodiment of the present application, operation data and production data for different batches are collected, and abnormal values, missing values, etc. in the data are processed by data preprocessing, thereby improving the accuracy of the system in drawing the operation trajectory curve; and, by data integration, operation data and production data for different batches are combined to obtain an operation / production data set. Furthermore, similar operation / production data in the operation / production data set is classified by a clustering algorithm to obtain cases, the cases are labeled to form a case library, the weight of each batch in the selected case is calculated, and the standard curve of the operation trajectory is calculated, thereby making it possible to automatically generate the standard curve of the operation trajectory according to the production needs target without human intervention.

[0044] In step S204 of the above-mentioned method for determining an operation trajectory curve, the step of performing a combining operation on operation data and production data corresponding to the same collection date in multiple batches specifically includes: obtaining an operating time corresponding to the operation data; aligning the operation data having the same operating time and rearranging them according to the operating time to obtain first data; combining the first data with the production data corresponding to the same collection date to obtain combined data; and determining combined data corresponding to different collection dates to obtain a combined data set.

[0045] In the embodiment of the present application, the intermittent production process is batch production, and the operation data of each batch has two dimensions, namely, variable and time, i.e., the value of the operation data and the operation time, while the output data only has one dimension, so when establishing the operation-output data set, the operation data and output data of one batch need to be combined into one set of operation-output data. The following example is taken as an example to illustrate.

[0046] The operating and production data for one batch is shown in Table 1 below.

[0047] (Table 1) Operation and production data for one batch TIFF2025511548000002.tif87170

[0048] By using a clustering algorithm to cluster the operation and production data in the combined data set whose similarity is greater than a preset threshold into one class, several classes, i.e., multiple classes of data, are obtained, and the class center data of each class is selected as a representative, i.e., an example, of that class. Table 2 shows the examples obtained after clustering in the combined data set.

[0049] Table 2. Examples obtained after clustering on the combined dataset. TIFF2025511548000003.tif117170

[0050] After labeling the generated examples above, we get something like Table 3.

[0051] (Table 3) Examples of labeling TIFF2025511548000004.tif112170

[0052] In step S208 of the above-mentioned method for determining the operation trajectory curve, the step of determining weight values ​​corresponding to operation parameters of different batches in the target type data specifically includes the steps of obtaining collection dates corresponding to operation parameters of different batches in the target type data, obtaining a plurality of collection dates, sorting the plurality of collection dates in order of proximity to the current date, obtaining a target collection date set, and determining weight values ​​of batches corresponding to the plurality of collection dates in the target collection date set.

[0053] In the above steps, the step of determining batch weight values ​​corresponding to multiple collection dates in the target collection date set specifically includes a step of determining weight values ​​between batches corresponding to two adjacent dates in the target collection date set, in which the batch weight corresponding to a first of the two adjacent dates is a preset multiple of the batch weight corresponding to a second date, the first date being the one of the two adjacent dates closer to the current date, the second date being the one of the two adjacent dates farthest from the current date, and the preset multiple being any value greater than 1.

[0054] In the embodiment of the present application, the batches are sorted in ascending order from the current time. The weight of the batch closest to the current time is assumed to be 1.5 times that of the batch slightly further from the current time, where 1.5 corresponds to the above-mentioned preset multiple, where the preset multiple can be any value greater than 1, and the preset multiple selected can be the same or different between different batches, and the specific value selected can be set by oneself according to the actual situation. If k1 is the weight of the batch farthest from the current time, then k2=1.5k1, k3=1.5k2=1.5 2 k1…, k n =1.5 n-1 If we set k1 and the sum of all the weights to 1, we get: k1+1.5k1+1.5 2 k1+ … +1.5 n-1 k1=1 Solving k1 gives k2, k3, … k n is obtained.

[0055] In the above step, after determining the weight value between batches corresponding to two adjacent dates in the target collection date set, the method further includes: obtaining a weight set of batches corresponding to all dates in the target collection date set; constructing an equation according to the numerical relationship between all 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.

[0056] In the embodiment of the present application, target type data is selected from multiple classes of data, for example, data of the low energy consumption case in Table 3 is selected. As shown in Tables 4 and 5, it is assumed that the low energy consumption case includes operation and production data of four batches.

[0057] (Table 4) Batch data included in low energy consumption examples TIFF2025511548000005.tif92170

[0058] (Table 5) Batch data included in low energy consumption cases (continued) TIFF2025511548000006.tif90170

[0059] Let k1 be the weight of the batch furthest from the current time. Under the condition that the weight of the batch closer to the current time is 1.5 times the weight of the batch slightly further from the current time, k2=1.5k1, k3=1.5k2=1.5 2 k1, k4 = 1.5k 3= 1.5 3 If we set k1 and the sum of all the weights to 1, we get the following equation: k1+1.5k1+1.5 2 k1+1.5 3 k1=1

[0060] Solving this gives k1 = 0.123, k2 = 0.185, k3 = 0.277, and k4 = 0.415. These calculated values ​​correspond to the first weight value mentioned above.

[0061] In step S210 of the above-mentioned method for determining an operation trajectory curve, the step of determining an operation trajectory curve corresponding to the target type data specifically includes the steps of: determining a target weight value of a batch corresponding to each date in the target collection date set according to a first weight value; and determining a target operation parameter according to the target weight value and the value of the operation parameter at different operating times of the batch corresponding to the target weight value, where the target operation parameter is the value of the operation parameter at different operating times of the operation trajectory curve corresponding to the target type data.

[0062] In the present embodiment, the values ​​of the operation parameters at each time in the standard curve of the operation trajectory are calculated as follows: TIFF2025511548000007.tif23170

[0063] Here, s p,t is the value of the operation parameter p on the operation trajectory curve at time t, and x p,i,t is the value of the operational parameter p at time t in the i-th batch, and k i is the weight of the i-th batch.

[0064] In an optional embodiment, if the above low energy consumption case contains 4 batches of operation and production data, for example, the current date is May 1, 2022, batch 1 is the furthest from the current date and its corresponding weight is the smallest. Thus, the value of operation parameter 1 at the first hour of the operation trajectory is as follows: k4*28.8+k3*22.5+k2*24+k1*25=0.415*28.8+0.277*22.5+0.185*24+0.123*25=25.7

[0065] Operation parameter 2 to operation parameter n are calculated by the above method, and similarly, operation parameter 1 to operation parameter n for the 2nd hour to the mth hour are calculated by the above method.

[0066] In the above method for determining an operation trajectory curve, the operation trajectory curve is a curve in a coordinate system established with the operating time as the abscissa and the value of the operation parameter at the operating time as the ordinate.

[0067] In the present embodiment, the weighted average of each operating parameter at each time is calculated with the batch weight, and these weighted averages are concatenated to obtain the standard curve of the operating trajectory.

[0068] In this application, for intermittent production scenes in the process industry, the operation data of historical batches and the output data of the final product are data-analyzed to generate a standard curve of the operation trajectory from multiple optimal batches according to the goal to be achieved in production. The method for determining the operation trajectory curve provided in the embodiment of this application has the following advantages: 1. There is no need to manually create the standard curve of the operation trajectory in advance. 2. Actual historical data is used to obtain cases, and the cases are used as the basis for creating the standard curve of the operation trajectory. 3. Considering that the closer the batch is to the current time, the greater the reference significance, a method for calculating the weights of different batches in cases of the same class is designed. 4. The standard curve of the operation trajectory is obtained from the weighted average value of different batches in cases of the same class.

[0069] 3 is a block diagram of an operation trajectory curve determining device according to an embodiment of the present application. As shown in FIG. 3, the device includes an acquisition module 302, a combination module 304, a classification module 306, a first determination module 308, and a second determination module 310.

[0070] The acquisition module 302 is configured to acquire operational data and production data for multiple batches of a production facility, where the operational data is data collected by different meters and the production data is product data related to products produced at the production facility, each of the multiple batches corresponding to a different collection date.

[0071] The combine module 304 is configured to perform a combine operation on the operational data and production data corresponding to the same collection date for multiple batches to obtain a combined data set.

[0072] The classification module 306 is configured to determine a similarity between the combined data in the combined dataset, classify the combined data in the combined dataset according to the similarity, and obtain multiple classes of data in which data whose similarity is greater than a preset threshold is classified into one class.

[0073] The first determination module 308 is configured to select a target type data from the multiple classes of data, and determine weight values ​​corresponding to operation parameters of different batches of the target type data, where the target type data is data of any class among the multiple classes of data, and the operation parameter is at least one controllable data of the operational data.

[0074] The second determination module 310 is configured to determine an operation trajectory curve corresponding to the target type of data according to at least the weight values ​​and the values ​​of the operation parameters in the batch corresponding to the weight values.

[0075] In addition, since the device 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, the relevant description of the method for determining an operation trajectory curve is also applied to this device for determining an operation trajectory curve, and therefore the description thereof will be omitted here.

[0076] In an embodiment of the present application, a non-volatile storage medium including a stored computer program, and an apparatus in which the non-volatile storage medium is disposed executes the computer program to obtain operation data and production data for a plurality of batches of a production facility, the operation data being data collected by different meters, and the production data being product data related to products produced by the production facility, each of the plurality of batches corresponding to a different collection date; performing a join operation on the operation data and the production data corresponding to the same collection date in the plurality of batches to obtain a joined data set; and determining a similarity between the joined data in the joined data set and classifying the joined data in the joined data set according to the similarity. There is further provided a non-volatile storage medium for executing a method for determining an operation trajectory curve, the method including: obtaining a plurality of classes of data by classifying data whose similarity is greater than a preset threshold into one class; selecting target type data from the plurality of classes of data and determining weight values ​​corresponding to operation parameters of different batches in the target type data, where the target type data is data of any one of the plurality of classes of data and the operation parameter is at least one controllable data of the operation data; and determining an operation trajectory curve corresponding to the target type data according to at least the weight value and the value of the operation parameter in the batch corresponding to the weight value.

[0077] The numbers of the above examples of the present application are merely for illustrative purposes and do not indicate superiority or inferiority of the examples.

[0078] In the above-mentioned embodiments of the present application, the description of each embodiment has its own emphasis, and for the parts that are not described in detail in one embodiment, reference can be made to the relevant descriptions of other embodiments.

[0079] In some embodiments provided in the present application, it should be understood that the disclosed technical contents may be realized in other forms. The above-mentioned device embodiments are merely illustrative, and for example, the division of the units may be a logical functional division, and in actual implementation, other division forms may be used, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented. In addition, the shown or discussed couplings or direct couplings or communication connections between each other may be indirect couplings or communication connections through some interfaces, units or modules, and may be electrical or other forms.

[0080] The units described as separate members may or may not be physically separate, and the members shown as units may or may not be physical units, i.e., they may be located in one place or distributed in multiple units. Some or all of the units may be selected according to actual needs to achieve the objectives of the aspects of the present embodiment.

[0081] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist physically alone, or two or more units may be integrated into one unit. The integrated units may be realized in the form of hardware or software functional units.

[0082] When the integrated units are realized in the form of software functional units and sold or used as independent products, they may be stored in one computer-readable storage medium. Based on such understanding, the essence of the technical aspects of the present application or a part of the contribution to the prior art, or all or a part of the technical aspects, may be realized in the form of a software product, and the computer software product is stored in a storage medium including a plurality of instructions that cause a computer device (which may be a personal computer, a server, a network device, etc.) to execute all or a part of the steps of the method described in each embodiment of the present application. The above-mentioned storage medium includes various media capable of storing program code, such as a USB, a read-only memory (ROM), a random access memory (RAM), a portable HDD, a magnetic disk, or an optical disk.

[0083] The above are only preferred embodiments of the present application, however, those skilled in the art may make some improvements and modifications without departing from the principles of the present application, and these improvements and modifications should also fall within the scope of protection of the present application. [Industrial Applicability]

[0084] The technical aspects provided in the embodiments of the present application can be applied to the field of production design in process industry. In the embodiments of the present application, the operation data and output data of multiple batches of production equipment are obtained, and a combination operation is performed on the operation data and output data corresponding to the same collection date in the multiple batches to obtain a combination data set, and the similarity between the combined data in the combination data set is determined, and the combined data in the combination data set is classified according to the similarity to obtain multiple classes of data, and then target type data is selected from the multiple classes of data, and weight values ​​corresponding to operation parameters of different batches in the target type data are determined, and an operation trajectory curve corresponding to the target type data is determined according to at least the weight values ​​and the values ​​of the operation parameters in the batches corresponding to the weight values, thereby achieving the purpose of automatically generating an operation trajectory during production, and realizing the technical effect of improving the generation efficiency of the operation trajectory, and further solving the problem that the generation of the standard curve of the traditional operation trajectory is generally performed manually, and cannot adapt to the gradual changes of production equipment and the changes in production needs.

Claims

1. acquiring operation data and production data for a plurality of batches of a production facility, the operation data being data collected by different meters, the production data being product data relating to products produced at the production facility, each of the plurality of batches corresponding to a different collection date; performing a join operation on the operation data and production data corresponding to the same collection date in the plurality of batches to obtain a joined data set; determining a similarity between the combined data in the combined data set, classifying the combined data in the combined data set according to the similarity, and obtaining a plurality of classes of data in which data having a similarity greater than a preset threshold is classified into one class; selecting a target type data from the plurality of classes of data, and determining weight values ​​corresponding to operation parameters of different batches of the target type data, the target type data being one of the classes of data among the plurality of classes of data, and the operation parameter being at least one controllable data among the operational data; determining an operation trajectory curve corresponding to the target type of data according to at least the weight values ​​and values ​​of operation parameters in the batch corresponding to the weight values; A method for determining an operation trajectory curve, including:

2. The step of performing a join operation on the operation data and the production data corresponding to the same collection date in the plurality of batches includes: acquiring an operation time corresponding to the operation data; aligning operation data having the same operation time and rearranging the operation data according to the operation time to obtain first data; combining the first data with production data corresponding to the same collection date to obtain the combined data; and determining combined data corresponding to the different collection dates to obtain the combined data set.

3. Determining weight values ​​corresponding to operational parameters of different batches of the target type data includes: obtaining collection dates corresponding to operation parameters of different batches of the target type data to obtain a plurality of collection dates; sorting the plurality of collection dates in ascending order from a current date to obtain a target collection date set; determining batch weight values ​​corresponding to a plurality of collection dates in the target collection date set.

4. The step of determining batch weight values ​​corresponding to a plurality of collection dates in the target collection date set includes:

4. The method of claim 3, comprising: determining a weight value between batches corresponding to two adjacent dates in the target collection date set, wherein a weight of a batch corresponding to a first of the two adjacent dates is a preset multiple of a weight of a batch corresponding to a second of the two adjacent dates, the first date being closer to the current date of the two adjacent dates and the second date being farther from the current date of the two adjacent dates, and the preset multiple being any value greater than 1.

5. After determining a weight value between batches corresponding to two adjacent dates in the target collection date set, obtaining a set of batch weights corresponding to all dates in the target collection date set; constructing an equation according to numerical relationships between all weight values ​​in the weight set; The method of claim 4 , further comprising: solving the equation to obtain a first weight value between batches corresponding to two adjacent dates in the target collection date set.

6. The step of determining an operation trajectory curve corresponding to the target type data includes: determining a target weight value for a batch corresponding to each date in the set of target collection dates according to the first weight value; 6. The method of claim 5, comprising: determining a target operating parameter according to the target weight value and a value of an operating parameter at different run times of a batch corresponding to the target weight value, wherein the target operating parameter is a value of an operating parameter at different run times of an operating trajectory curve corresponding to data of the target type.

7. 7. The method of claim 6, wherein the operating trajectory curve is a curve in a coordinate system established with operating time as the abscissa and a value of an operating parameter at the operating time as the ordinate.

8. an acquisition module configured to acquire operational data and production data for a plurality of batches of a production facility, the operational data being data collected by different meters, the production data being product data relating to products produced at the production facility, each of the plurality of batches corresponding to a different collection date; a combining module configured to perform a combining operation on the operation data and the production data corresponding to the same collection date in the plurality of batches to obtain a combined data set; a classification module configured to determine a similarity between combined data in the combined dataset, classify the combined data in the combined dataset according to the similarity, and obtain a plurality of classes of data in which data having a similarity greater than a preset threshold is classified into one class; A first determination module configured to select a target type of data from the plurality of classes of data and determine weight values ​​corresponding to operation parameters of different batches of the target type of data, the target type of data being one of the classes of data, and the operation parameter being at least one controllable data of the operational data; a second determination module configured to determine an operation trajectory curve corresponding to the target type of data according to at least the weight value and a value of an operation parameter in the batch corresponding to the weight value; An operation trajectory curve determining device comprising:

9. a memory configured to store program instructions; a processor coupled to the memory, the processor comprising: Acquiring operation data and production data for multiple batches of a production facility, the operation data being data collected by different meters, the production data being product data relating to products produced at the production facility, each of the multiple batches corresponding to a different collection date; performing a join operation on the operational data and the production data corresponding to the same collection date in the plurality of batches to obtain a joined data set; determining a similarity between the binding data in the binding data set, classifying the binding data in the binding data set according to the similarity, and obtaining a plurality of classes of data in which data having a similarity greater than a preset threshold is classified into one class; Selecting target-type data from the plurality of classes of data, and determining weight values ​​corresponding to operation parameters of different batches of the target-type data, the target-type data being any one of the plurality of classes of data, and the operation parameter being at least one controllable data of the operational data; and an electronic device configured to execute program instructions implementing a function of determining an operation trajectory curve corresponding to the target type of data according to at least the weight values ​​and values ​​of operation parameters in the batch corresponding to the weight values.

10. A non-volatile storage medium including a stored computer program, wherein a device in which the non-volatile storage medium is disposed executes the method for determining an operation trajectory curve according to any one of claims 1 to 7 by executing the computer program.

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