Production efficiency prediction method and device, storage medium and program product

Abstract

The invention provides a production efficiency prediction method and device, a storage medium and a program product, and the method comprises the steps: obtaining production transfer information, determining that the production efficiency of a product before transfer is the water level information of the historical production efficiency of the product before transfer, and obtaining the first historical data of each product in a production task under the water level information, a first to-be-fitted function is constructed according to the first historical data, fitting is carried out on the first to-be-fitted function, influence degree parameters of the first process are obtained, the production efficiency of the transferred product is determined according to the influence degree parameters of the first process, and the production efficiency of the transferred product is used for adjusting the production task. Through the scheme provided by the invention, the accuracy and the reliability of predicting the production efficiency can be realized, so that when the production task is adjusted based on the predicted production efficiency, the accuracy and the effectiveness of adjustment can be improved, and the technical effects of saving production resources and meeting the production requirements of users can be realized.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a method, device, storage medium and program product for predicting production efficiency. Background Art

[0002] With the automation and intelligence of product production, there are higher requirements for the prediction of production efficiency of products on manufacturing production lines. For example, when a production line switches from producing one product A to producing another product B, the prediction of the production efficiency of product B has attracted much attention. In the scenario of Advanced Planning and Scheduling (APS), if the production efficiency after the production line switch cannot be accurately determined, the feasibility of factory scheduling will be affected.

[0003] In the related art, two methods are usually used to predict the production efficiency of the production line after the transfer. One method is manual judgment, and the other method is to determine it according to the number of repeated processes.

[0004] However, the manual judgment method is too dependent on the ability of experts and is easily affected by human subjective factors, so the accuracy is relatively low. If it is determined by the number of repeated processes, since the production of products usually involves many processes, and the processes involved in different products are usually different, and the complexity of the processes may vary greatly, it is difficult to accurately determine the production similarities between two different products, which leads to the relatively low accuracy of the determined production efficiency.

[0005] The content of the background technology section is only the information known to the inventor personally, and does not mean that the above information has entered the public domain before the application date of this disclosure, nor does it mean that it can become the prior art of the present disclosure. Summary of the invention

[0006] The present disclosure provides a production efficiency prediction method, device, storage medium and program product to solve at least one of the above technical problems.

[0007] In a first aspect, the present disclosure provides a method for predicting production efficiency, comprising:

[0008] Obtaining production transfer information, wherein the production transfer information is used to indicate switching from producing a product before the production transfer in the production task to producing a product after the production transfer;

[0009] Determine that the production efficiency of the pre-transfer product at the time of transfer is at the water level information of the historical production efficiency of the pre-transfer product, and obtain the first historical data of each product in the production task under the water level information, wherein the first historical data includes a first process and a first production efficiency;

[0010] constructing a first function to be fitted according to the first historical data, where the first function to be fitted is used to characterize the relationship between the first production efficiency, the preset variables of the first process, and a parameter of the degree of influence of the first process on the first production efficiency, and fitting the first function to be fitted to obtain the parameter of the degree of influence of the first process;

[0011] The production efficiency of the transferred product is determined according to the influence degree parameter of the first process, wherein the production efficiency of the transferred product is used to adjust the production task.

[0012] In some embodiments, determining the production efficiency of the transferred product according to the influence degree parameter of the first process includes:

[0013] Determine the repeated processes and non-repeated processes between the product before the transfer and the product after the transfer;

[0014] Obtaining the influence degree parameter of the repeated process from the influence degree parameter of the first process;

[0015] The production efficiency of the transferred product is determined according to the influence degree parameter of the repeated process and the non-repetitive process.

[0016] In some embodiments, determining the production efficiency of the transferred product according to the influence degree parameter of the repeated process and the non-repetitive process includes:

[0017] The production efficiency of the transferred product is determined according to the impact degree parameter of the repeated process and the historical production efficiency of the non-repetitive process.

[0018] In some embodiments, determining the production efficiency of the transferred product according to the impact degree parameter of the repeated process and the historical production efficiency of the non-repetitive process includes:

[0019] Determining a second production efficiency less than a preset threshold from historical production efficiencies of the non-repetitive process;

[0020] Constructing a second function to be fitted according to the non-repetitive process and the second production efficiency, wherein the second function to be fitted is used to characterize the relationship between the second production efficiency, the preset variables of the non-repetitive process, and the parameter of the degree of influence of the non-repetitive process on the second production efficiency, and fitting the second function to be fitted to obtain the parameter of the degree of influence of the non-repetitive process;

[0021] The production efficiency of the transferred product is determined according to the parameters of the influence degree of the repeated process and the influence degree of the non-repetitive process.

[0022] In some embodiments, determining the production efficiency of the transferred product according to the influence degree of the repeated process and the influence degree parameter of the non-repeated process includes:

[0023] The sum of the impact degree parameter of the repeated process and the impact degree parameter of the non-repetitive process is calculated, and the sum is determined as the production efficiency of the transferred product.

[0024] In some embodiments, the method further comprises:

[0025] Obtaining product information of the production task, the product information including the product and the process, the product including the product before the transfer and the product after the transfer;

[0026] A product list and a process list are created according to the product information, and a correspondence between products and processes is determined according to the product list and the process list, wherein the correspondence characterizes products under the same process, the correspondence is used to determine the repetitive process and the non-repetitive process, and the correspondence is also used to construct the first function to be fitted and the second function to be fitted.

[0027] In some embodiments, the production task includes production scheduling information for products to be produced, and the products to be produced include the transferred products and products produced after the transferred products; the method further includes:

[0028] Determine the forecast production schedule information of the product to be produced according to the production efficiency of the product after the transfer;

[0029] Matching the predicted production scheduling information with the formulated production scheduling information to obtain a matching result;

[0030] The production scheduling information is adjusted based on the matching result to adjust the production task.

[0031] In a second aspect, the present disclosure provides a production efficiency prediction device, comprising:

[0032] An obtaining unit, used for obtaining production transfer information, wherein the production transfer information is used for indicating switching from producing the pre-production transfer product in the production task to producing the post-production transfer product;

[0033] A determination unit, used for determining that the production efficiency of the pre-transfer product at the time of transfer is at the level of the historical production efficiency of the pre-transfer product;

[0034] The obtaining unit is further used to obtain first historical data of each product in the production task under the water level information, wherein the first historical data includes a first process and a first production efficiency;

[0035] a construction unit, configured to construct a first function to be fitted according to the first historical data, wherein the first function to be fitted is used to characterize a relationship between the first production efficiency, a preset variable of the first process, and a parameter of an influence degree of the first process on the first production efficiency;

[0036] A fitting unit, used for fitting the first function to be fitted to obtain an influence degree parameter of the first process;

[0037] The determination unit is further used to determine the production efficiency of the transferred product according to the influence degree parameter of the first process, wherein the production efficiency of the transferred product is used to adjust the production task.

[0038] In some embodiments, the determination unit is specifically used to determine the repeated processes and non-repetitive processes between the product before transfer and the product after transfer; and obtain the influence degree parameter of the repeated process from the influence degree parameter of the first process; and determine the production efficiency of the product after transfer based on the influence degree parameter of the repeated process and the non-repetitive process.

[0039] In some embodiments, the determination unit is specifically used to determine the production efficiency of the transferred product according to the impact degree parameter of the repetitive process and the historical production efficiency of the non-repetitive process.

[0040] In some embodiments, the determining unit is specifically configured to determine a second production efficiency that is less than a preset threshold from the historical production efficiency of the non-repetitive process;

[0041] Constructing a second function to be fitted according to the non-repetitive process and the second production efficiency, wherein the second function to be fitted is used to characterize the relationship between the second production efficiency, the preset variables of the non-repetitive process, and the parameter of the degree of influence of the non-repetitive process on the second production efficiency, and fitting the second function to be fitted to obtain the parameter of the degree of influence of the non-repetitive process;

[0042] The production efficiency of the transferred product is determined according to the parameters of the influence degree of the repeated process and the influence degree of the non-repetitive process.

[0043] In some embodiments, the determination unit is specifically used to calculate the sum of the impact degree parameter of the repetitive process and the impact degree parameter of the non-repetitive process, and determine the sum as the production efficiency of the transferred product.

[0044] In some embodiments, the obtaining unit is further used to obtain product information of the production task, the product information includes products and processes, and the products include the pre-transfer product and the post-transfer product;

[0045] The prediction device also includes:

[0046] A creation unit, used for creating a product list and a process list according to the product information;

[0047] The determination unit is also used to determine the correspondence between products and processes based on the product list and the process list, wherein the correspondence characterizes products under the same process, the correspondence is used to determine the repetitive process and the non-repetitive process, and the correspondence is also used to construct the first function to be fitted and the second function to be fitted.

[0048] In some embodiments, the production task includes production scheduling information for products to be produced, and the products to be produced include the transferred products and products produced after the transferred products; the determination unit is further used to determine the predicted production scheduling information of the products to be produced according to the production efficiency of the transferred products;

[0049] The prediction device also includes:

[0050] A matching unit, used for matching the predicted production scheduling information with the formulated production scheduling information to obtain a matching result;

[0051] An adjustment unit is used to adjust the production scheduling information based on the matching result to adjust the production task.

[0052] In a third aspect, the present disclosure provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, wherein the computer program is used to enable the processor to execute the method described in the first aspect above.

[0053] In a fourth aspect, the present disclosure provides a computer program product, including a computer program, which implements the method described in the first aspect when executed by a processor.

[0054] The present disclosure provides a method, device, storage medium and program product for predicting production efficiency, including: obtaining production transfer information, wherein the production transfer information is used to indicate the switch from producing pre-transfer products in a production task to producing post-transfer products, determining that the production efficiency of the pre-transfer products at the time of transfer is at the water level information of the historical production efficiency of the pre-transfer products, and obtaining first historical data of each product in the production task under the water level information, wherein the first historical data includes a first process and a first production efficiency, constructing a first function to be fitted based on the first historical data, the first function to be fitted is used to characterize the relationship between the first production efficiency, the preset variables of the first process, and the influence degree parameter of the first process on the first production efficiency, fitting the first function to be fitted to obtain the influence degree parameter of the first process, determining the production efficiency of the post-transfer products based on the influence degree parameter of the first process, wherein the production efficiency of the post-transfer products is used to adjust the production task. In this embodiment, by determining the influence degree parameters of each process on the production efficiency and predicting the production efficiency of the transferred product based on the influence degree parameters, the disadvantage of low accuracy caused by manual prediction or prediction based on the number of repeated processes in related technologies can be avoided, thereby achieving the accuracy and reliability of predicted production efficiency. Furthermore, when the production tasks are adjusted based on the predicted production efficiency, the accuracy and effectiveness of the adjustment can be improved, and the technical effect of saving production resources and meeting user production needs can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0056] Figure 1 A schematic diagram of a process flow of a method for predicting production efficiency provided in an embodiment of the present disclosure;

[0057] Figure 2 When the production efficiency prediction device provided by the embodiment of the present disclosure is an electronic device, a schematic diagram of the structure of the electronic device;

[0058] Figure 3 A schematic diagram of a module of a production efficiency prediction device provided in an embodiment of the present disclosure.

[0059] The above drawings show clear embodiments of the present disclosure, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present disclosure in any way, but to illustrate the concepts of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0060] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0061] It should be understood that the terms "including" and "having" and any variations thereof in the embodiments of the present disclosure are intended to cover but not exclude inclusion. For example, a product or device comprising a series of components is not necessarily limited to those components explicitly listed, but may include other components not explicitly listed or inherent to these products or devices.

[0062] In the embodiments of the present disclosure, the term "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0063] The term "plurality" in the embodiments of the present disclosure refers to two or more than two, and other quantifiers are similar thereto.

[0064] The terms "first", "second", "third", etc. in the present disclosure are used to distinguish similar or similar objects or entities, and are not necessarily meant to limit a specific order or sequence, unless otherwise indicated. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, for example, they can be implemented in an order other than those given in the diagrams or descriptions of the embodiments of the present disclosure.

[0065] The term "unit / module" as used in this disclosure refers to any known or later developed hardware, software, firmware, artificial intelligence, fuzzy logic or a combination of hardware and / or software code that is capable of performing the functions associated with that element.

[0066] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0067] See also Figure 1 , Figure 1 The following is a flow chart of a method for predicting production efficiency (hereinafter referred to as the prediction method) provided in an embodiment of the present disclosure. Figure 1 As shown, the method includes S101 to S104:

[0068] S101: Obtaining production transfer information, wherein the production transfer information is used to indicate switching from producing pre-production transfer products in a production task to producing post-production transfer products.

[0069] For example, the prediction method of this embodiment can be executed by a prediction device for production efficiency (hereinafter referred to as a prediction device). The prediction device can be a server (such as a local server or a cloud server), an electronic device, a processor, a chip, etc., which is not limited in this embodiment.

[0070] If the prediction device is an electronic device, then in some embodiments, the structure diagram of the electronic device can refer to Figure 2 . Electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit implementations of the present disclosure described and / or claimed herein.

[0071] like Figure 2 As shown, the electronic device 200 includes a computing unit 201, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 202 or a computer program loaded from a storage unit 208 into a random access memory (RAM) 203. In the RAM 203, various programs and data required for the operation of the electronic device 200 can also be stored. The computing unit 201, the ROM 202, and the RAM 203 are connected to each other via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0072] A number of components in the electronic device 200 are connected to the I / O interface 205, including: an input unit 206, such as a keyboard, a mouse, etc.; an output unit 207, such as various types of displays, speakers, etc.; a storage unit 208, such as a disk, an optical disk, etc.; and a communication unit 209, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 209 allows the electronic device 200 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0073] The computing unit 201 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 201 performs the prediction method and processing described in the present disclosure. For example, in some embodiments, the prediction method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 208. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 200 via ROM 202 and / or communication unit 209. When the computer program is loaded into RAM 203 and executed by the computing unit 201, one or more steps of the prediction method of the present disclosure may be performed. Alternatively, in other embodiments, the computing unit 201 may be configured to perform the prediction method of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0074] It is worth noting that this embodiment does not limit the way in which the prediction device obtains the production transfer information. For example, the prediction device may include a display device, and may output an interactive interface through the display device, and the user may initiate the production transfer information through the interactive interface.

[0075] The prediction device can display production tasks through an interactive interface, and the production tasks include products and production status, such as products that have been produced, products that are being produced, and products that have not been produced. Products before production transfer are products that have been produced or are being produced, and products after production transfer are products that have not been produced, and specifically are products that need to be produced after the production of products before production transfer is completed.

[0076] S102: Determine that the production efficiency of the product before the transfer is at the water level information of the historical production efficiency of the product before the transfer, and obtain the first historical data of each product in the production task under the water level information, wherein the first historical data includes the first process and the first production efficiency.

[0077] The water level information can be understood as a percentage. The production efficiency of the product before the transfer, relative to the historical production efficiency of the product before the transfer, reaches a percentage of the historical production efficiency. For example, it reaches 80% of the historical production efficiency.

[0078] Since this article involves historical data under different circumstances, the “first” and “second” methods are used to distinguish the historical data under different circumstances, and they cannot be understood as limitations on the historical data. The descriptions of other “first” and “second” are similar and will not be explained later.

[0079] Correspondingly, in combination with the above example, if the water level information is 80%, the prediction device can obtain the historical data of all products in the production task at 80% (ie, the first historical data). For example, the prediction device includes a memory, and the memory stores data, including historical data. Among them, the first historical data includes two dimensions of content: process (ie, the first process) and production efficiency (ie, the first production efficiency). For example, what are the processes for a certain product at 80%, and what is the corresponding production efficiency. That is, the first process is the process for producing all the products.

[0080] S103: constructing a first function to be fitted according to the first historical data, where the first function to be fitted is used to characterize the relationship between the first production efficiency, the preset variables of the first process, and the influence degree parameter of the first process on the first production efficiency, and fitting the first function to be fitted to obtain the influence degree parameter of the first process.

[0081] Accordingly, when the first historical data is obtained, the prediction device can construct a function (i.e., a first function to be fitted) according to the first historical data. For example, the first function to be fitted can be represented by Formula 1:

[0082] y=p1*X1+p2*X2+……+pn*Xn

[0083] Wherein, y is the first production efficiency, X1 to Xn are preset variables corresponding to each first process, and p1 to pn are parameters of the degree of influence of each process on the corresponding first production efficiency. For example, different processes may have different degrees of difficulty, and therefore, the degree of influence of different processes on production efficiency may also be different. Therefore, in this embodiment, the degree of influence of the process on production efficiency is characterized by the degree of influence parameter.

[0084] In this embodiment, fitting can be understood as finding a function that can best describe or predict the pattern or trend in the data. This embodiment does not limit the method of fitting. For example, linear programming method can be used for fitting, Lasso regression can be used for fitting, or a combination of linear programming method and Lasso regression can be used for fitting. This embodiment focuses on the application of fitting in production efficiency prediction, and specifically on the application of the degree of influence of the process on production efficiency, so as to further apply it to the prediction of production efficiency, and further apply it to the adjustment of production tasks.

[0085] S104: Determine the production efficiency of the product after the transfer according to the influence degree parameter of the first process, wherein the production efficiency of the product after the transfer is used to adjust the production task.

[0086] After determining the degree of influence of each process on production efficiency, the prediction device can predict the production efficiency of the transferred product based on the influence degree parameters corresponding to each process to obtain the production efficiency of the transferred product. On this basis, the prediction device can adjust the production task based on the production efficiency of the transferred product, such as adjusting the start time of a product in the production task.

[0087] In some embodiments, based on the above examples, it can be known that the prediction device can output an interactive interface. Accordingly, when the prediction device determines the production efficiency of the product after the transfer, the effectiveness efficiency of the product after the transfer can be output through the interactive interface.

[0088] Combined with the above analysis of S101 to S104, it can be seen that in this embodiment, the prediction device can determine the impact degree parameters of each process on the production efficiency, and predict the production efficiency of the transferred product based on the impact degree parameters, which can avoid the disadvantages of low accuracy caused by manual prediction or prediction based on the number of repeated processes in related technologies, thereby achieving accuracy and reliability in predicting production efficiency. When the production task is adjusted based on the predicted production efficiency, the accuracy and effectiveness of the adjustment can be improved, and the technical effect of saving production resources and meeting user production needs can be achieved.

[0089] In some embodiments, S104 may include the following steps 1 to 3:

[0090] Step 1: Identify the duplicate and non-duplicate processes between the product before and after the transfer.

[0091] Based on the above analysis, it can be seen that each product is completed based on the corresponding process, and there are usually multiple processes to produce each product.

[0092] Exemplarily, the prediction device can determine the various processes for producing products before the transfer, and can also determine the various processes for producing products after the transfer, and can compare the various processes for producing products before the transfer and the various processes for producing products after the transfer to determine the processes required for producing both the products before the transfer and the products after the transfer (i.e., repeated processes), and can determine the processes other than the repeated processes among the processes for producing products after the transfer as non-repetitive processes.

[0093] Step 2: Obtain the influence degree parameters of the repeated process from the influence degree parameters of the first process.

[0094] According to the above analysis, the first process is a process for producing all the products, and therefore, the first process includes a repeated process. Therefore, the prediction device can obtain the influence degree parameter of the repeated process from the influence degree parameter of the first process.

[0095] Step 3: Determine the production efficiency of the transferred product based on the impact parameters of the repeated process and the non-repetitive process.

[0096] Exemplarily, the process for producing the transferred product includes a repetitive process and a non-repetitive process. Therefore, the production efficiency of the transferred product is mainly affected by the repetitive process and the non-repetitive process, and the influence of the repetitive process on the production efficiency can be characterized by the influence degree parameter of the repetitive process. Therefore, in this embodiment, the prediction device can determine the production efficiency of the transferred product based on the influence degree parameter of the repetitive process. In addition, since the production of the transferred product also involves a non-repetitive process, the prediction device also considers the non-repetitive process on the basis of considering the influence degree parameter of the repetitive process, so as to determine the production efficiency of the transferred product by combining the considerations of the two dimensions, so as to improve the effectiveness and accuracy of the determination of the production efficiency of the transferred product.

[0097] In some embodiments, step 3 may include: determining the production efficiency of the transferred product based on the impact degree parameter of the repeated process and the historical production efficiency of the non-repetitive process.

[0098] Exemplarily, in combination with the above analysis, the prediction device may include a memory, and data may be stored in the memory, such as the historical production efficiency of a non-repetitive process.

[0099] Accordingly, the prediction device can obtain the historical production efficiency of the non-repetitive process from the memory, and combine the impact degree parameter of the repetitive process and the historical production efficiency of the non-repetitive process obtained from the memory to predict the production efficiency of the product after the transfer.

[0100] Relatively speaking, the same process may have different production efficiencies when operated by the same worker or different workers, but the differences will not be too large. Therefore, in this embodiment, the production efficiency of the transferred product is determined by combining the historical production efficiency of the non-repetitive process and the influence degree parameter of the repetitive process, which can improve the accuracy and reliability of determining the production efficiency of the transferred product.

[0101] In some embodiments, the above “determining the production efficiency of the transferred product according to the impact degree parameter of the repeated process and the historical production efficiency of the non-repeated process” may include the following sub-steps 1 and 3:

[0102] Sub-step 1: Determine a second production efficiency that is less than a preset threshold from historical production efficiencies of non-repetitive processes.

[0103] Exemplarily, the memory of the prediction device may store the production efficiency of the products produced based on the non-repetitive process in history (i.e., the historical production efficiency of the non-repetitive process). The prediction device may compare all the historical production efficiencies of the non-repetitive process with a preset threshold value to obtain the historical production efficiency that is less than the preset threshold value.

[0104] The present embodiment does not limit the size of the preset threshold. For example, the prediction device may determine the preset threshold based on demand, historical records, experiments, etc. Specifically, the preset threshold may be a relatively small value, such as 20%.

[0105] Sub-step 2: construct a second function to be fitted based on the non-repetitive process and the second production efficiency, the second function to be fitted is used to characterize the relationship between the second production efficiency, the preset variables of the non-repetitive process, and the parameters of the degree of influence of the non-repetitive process on the second production efficiency, and fit the second function to be fitted to obtain the parameters of the degree of influence of the non-repetitive process.

[0106] It is understandable that in order to avoid cumbersome description, similar contents to those described above will not be described in detail in this embodiment.

[0107] For example, for understanding the second fitting function in sub-step 2, reference may be made to the description of the first fitting function in the above example, which will not be repeated here.

[0108] Similarly, the prediction device fits the second fitting function to obtain the influence degree parameter of the non-repetitive process.

[0109] In other embodiments, the prediction device can pre-construct a function that fits different water level information to obtain corresponding impact degree parameters, and store each impact degree parameter in a memory. When needed, the impact degree parameters of the corresponding needs can be obtained from the memory.

[0110] Sub-step 3: Determine the production efficiency of the transferred product based on the parameters of the impact degree of the repeated process and the impact degree of the non-repetitive process.

[0111] Combined with the above analysis, it can be seen that the production of transferred products involves both repetitive processes and non-repetitive processes. Accordingly, when the influence degree parameters corresponding to the repetitive process and the non-repetitive process are obtained respectively, the prediction device can combine the influence degree parameters corresponding to the repetitive process and the non-repetitive process to predict the production efficiency of the transferred products. The production efficiency of the transferred products can be associated with each process for producing the transferred products, thereby improving the accuracy of determining the production efficiency of the transferred products. Moreover, in the present embodiment, the influence degree parameter of the non-repetitive process is determined based on the second production efficiency under a relatively small preset threshold value, and the possible adaptability of the transfer can be taken into account, thereby further improving the accuracy and reliability of the determined production efficiency of the transferred products.

[0112] In some embodiments, sub-step 3 may include: calculating the sum of the impact degree parameters of the repeated process and the impact degree parameters of the non-repetitive process, and determining the sum as the production efficiency of the product after the transfer.

[0113] Exemplarily, the prediction device calculates the sum of the influence degree parameters of each repeated process and the influence degree parameters of each non-repetitive process, and uses the sum value obtained by the sum calculation as the production efficiency of the product after the transfer.

[0114] In some embodiments, in order to facilitate the construction and fitting of the first function to be fitted and the second function to be fitted, and to facilitate the determination of the relationship between the processes corresponding to the respective products (such as which processes are the same, which processes are different, etc.), the prediction method further includes the following steps 1 and 2:

[0115] Step 1: Get the product information of the production task. The product information includes the product and process. The product includes the product before transfer and the product after transfer.

[0116] Exemplarily, in combination with the above analysis, the production task is used to indicate the production of different products. Accordingly, the product information includes the production task used to indicate the various products to be produced, as well as the various processes for producing each product. The product before the transfer and the product after the transfer are two different styles of products in the production task. Generally speaking, the processes involved in the two different products are at least partially different.

[0117] Step 2: Create a product list and a process list based on the product information, and determine the correspondence between products and processes based on the product list and the process list, wherein the correspondence characterizes products under the same process, the correspondence is used to determine repeated processes and non-repetitive processes, and the correspondence is also used to construct the first function to be fitted and the second function to be fitted.

[0118] Exemplarily, the prediction device may create a product list as shown in Table 1 based on the products in the product information.

[0119] Table 1

[0120] Product 1 Product 2 …… Product m K1 K2 …… Km

[0121] As can be seen from Table 1, the production task includes m (m is an integer greater than or equal to 2) types of products, namely product 1 to product m, and can be represented by K1 to Km.

[0122] The prediction device can create a process list as shown in Table 2 based on the processes in the product information.

[0123] Table 2

[0124] Process 1 Craft 2 …… Technology G1 G2 …… Gn

[0125] From Table 2, it can be seen that the production of m products involves n (n is an integer greater than or equal to 2) processes, namely process 1 to process n, and can be represented by G1 to Gn.

[0126] The prediction device can analyze the product list and the process list to obtain the corresponding relationship as shown in Table 3.

[0127] Table 3

[0128] Technology product G1 K1, K2, K3 G2 K2, K4, K5, K6 …… ……

[0129] Combined with Table 3, it can be seen that the production of products K1, K2, and K3 involves process G1, and the production of products K2, K4, K5, and K6 involves process G2.

[0130] In combination with the above examples, in some embodiments, the corresponding relationship shown in Table 3 can be used to determine the repeated process and the non-repeated process. For example, the prediction device can determine the repeated process and the non-repeated process by querying Table 3.

[0131] In other embodiments, the corresponding relationship shown in Table 3 can also be used to construct the first function to be fitted and the second function to be fitted. For example, the corresponding relationship shown in Table 3 can also be used to construct the first function to be fitted:

[0132] The prediction device can obtain the historical data of all products in the production task at 80% (ie, the first historical data), and construct Table 4 of the products and the first production efficiency at 80%.

[0133] Table 4

[0134] product Production efficiency K1 0.6 K2 1.2 …… …… Km 0.9

[0135] From Table 4, it can be seen that the production efficiency of product K1 at 80% is 0.6, the production efficiency of product K2 at 80% is 1.2, and the production efficiency of Km at 80% is 0.9.

[0136] The prediction device is constructed based on Table 3 and Table 4 to obtain Table 5 to characterize the corresponding production efficiency of each product under each process.

[0137] Table 5

[0138] G1 G2 G3 G4 G5 …… Gn Production efficiency K1 1 1 1 0 0 …… 0 0.6 K2 0 1 1 1 1 …… 0 1.2 …… …… …… …… …… …… …… …… …… Km 1 0 1 0 0 …… 1 0.9

[0139] Among them, 1 means that the process is involved, and 0 means that the process is not involved. Combining Table 5, it can be seen that taking the production of product K1 as an example, the processes involved in the production of product K1 include G1, G2, G3, etc., and the production efficiency under 80% water level information is 0.6. Other information in Table 5 is similar to this, and will not be listed here one by one.

[0140] The prediction device can assign a variable (i.e., a preset variable) to processes G1 to Gn, and can initialize an influence degree parameter for processes G1 to Gn (this step is not necessary) to construct Formula 1 as described in the above example. Accordingly, the prediction device can fit Formula 1 to obtain the influence degree parameters corresponding to processes G1 to Gn.

[0141] It is worth noting that in this example, the prediction device determines the corresponding relationship by constructing the table above, and constructs and fits the first fitting function and the second fitting function based on the corresponding relationship, which can improve the efficiency of determining the corresponding influence degree parameters, thereby improving the efficiency of predicting the production efficiency of the product after the production change. In addition, through the above table and the corresponding relationship, the prediction device can quickly and accurately determine the repeated process and the non-repeated process, and can also improve the efficiency of predicting the production efficiency of the product after the production change.

[0142] In some embodiments, the production task includes production scheduling information for the products to be produced, and the products to be produced include products after the transfer of funds and products produced after the products after the transfer of funds. Accordingly, the prediction method disclosed in the present invention also includes: determining the predicted production scheduling information of the products to be produced according to the production efficiency of the products after the transfer of funds, matching the predicted production scheduling information with the formulated production scheduling information to obtain a matching result, and adjusting the formulated production scheduling information based on the matching result to adjust the production task.

[0143] Exemplarily, the production scheduling information may be understood as pre-determined production scheduling information, which may represent information such as the start time of production, the production duration, and the end time of production for each product in the production task.

[0144] When the production efficiency of the product after the transfer is obtained, the prediction device can obtain information such as the production duration and the end time of the product after the transfer under the production efficiency. Accordingly, the prediction device can match the information such as the production duration and the end time of production with the relevant information in the production scheduling information, and adjust the relevant information in the production scheduling information based on the matching result.

[0145] For example, if the production time for the post-transfer product in the production scheduling information is XX hours, and based on the predicted production efficiency of the post-transfer product, it is known that the production efficiency of the post-transfer product is relatively fast, which may shorten the production time of the post-transfer product, then the prediction device can adjust the production time for the post-transfer product in the production scheduling information, so that after the production of the post-transfer product is completed, the production of the product after the post-transfer product in the production generation task can be quickly entered, thereby reducing time consumption and improving overall production efficiency.

[0146] In other embodiments, if the matching result indicates that the production efficiency of the post-transfer product obtained based on the prediction cannot meet the production schedule of the post-transfer product in the production scheduling information, the prediction device can generate and output prompt information so that the management personnel can take corresponding measures according to the prompt information to avoid delaying the execution of the production task.

[0147] According to another aspect of the embodiments of the present disclosure, the present disclosure also provides a prediction device. In some embodiments, the structure of the prediction device can refer to Figure 2 In other embodiments, the structure of the prediction device can refer to Figure 3 .

[0148] like Figure 3 As shown, the prediction device 300 includes:

[0149] The obtaining unit 301 is used to obtain production transfer information, wherein the production transfer information is used to indicate switching from producing the pre-production transfer product in the production task to producing the post-production transfer product.

[0150] The determination unit 302 is used to determine that the production efficiency of the pre-transfer product at the time of transfer is at the level of the historical production efficiency of the pre-transfer product.

[0151] The obtaining unit 301 is further used to obtain first historical data of each product in the production task under the water level information, wherein the first historical data includes a first process and a first production efficiency.

[0152] The construction unit 303 is used to construct a first function to be fitted according to the first historical data, wherein the first function to be fitted is used to characterize the relationship between the first production efficiency, the preset variables of the first process, and the parameter of the degree of influence of the first process on the first production efficiency.

[0153] The fitting unit 304 is used to fit the first function to be fitted to obtain the influence degree parameter of the first process.

[0154] The determining unit 302 is further configured to determine the production efficiency of the transferred product according to the influence degree parameter of the first process, wherein the production efficiency of the transferred product is used to adjust the production task.

[0155] In some embodiments, the determination unit 302 is specifically used to determine the repeated processes and non-repetitive processes between the product before the transfer and the product after the transfer; and obtain the influence degree parameter of the repeated process from the influence degree parameter of the first process; and determine the production efficiency of the product after the transfer based on the influence degree parameter of the repeated process and the non-repetitive process.

[0156] In some embodiments, the determining unit 302 is specifically configured to determine a second production efficiency that is less than a preset threshold from the historical production efficiency of the non-repetitive process;

[0157] Constructing a second function to be fitted according to the non-repetitive process and the second production efficiency, wherein the second function to be fitted is used to characterize the relationship between the second production efficiency, the preset variables of the non-repetitive process, and the parameter of the degree of influence of the non-repetitive process on the second production efficiency, and fitting the second function to be fitted to obtain the parameter of the degree of influence of the non-repetitive process;

[0158] The production efficiency of the transferred product is determined according to the parameters of the influence degree of the repeated process and the influence degree of the non-repetitive process.

[0159] In some embodiments, the determination unit 302 is specifically used to calculate the sum of the impact degree parameter of the repeated process and the impact degree parameter of the non-repetitive process, and determine the sum as the production efficiency of the transferred product.

[0160] In some embodiments, the obtaining unit 301 is further used to obtain product information of the production task, the product information includes products and processes, and the products include the pre-transfer product and the post-transfer product;

[0161] The prediction device also includes:

[0162] A creation unit 305, used to create a product list and a process list according to the product information;

[0163] The determination unit 302 is also used to determine the correspondence between products and processes based on the product list and the process list, wherein the correspondence represents products under the same process, the correspondence is used to determine the repetitive process and the non-repetitive process, and the correspondence is also used to construct the first function to be fitted and the second function to be fitted.

[0164] In some embodiments, the production task includes production scheduling information for products to be produced, and the products to be produced include the transferred products and products produced after the transferred products; the determination unit 302 is further used to determine the predicted production scheduling information of the products to be produced according to the production efficiency of the transferred products;

[0165] The prediction device also includes:

[0166] A matching unit 306, configured to match the predicted production scheduling information with the formulated production scheduling information to obtain a matching result;

[0167] The adjusting unit 307 is used to adjust the production scheduling information based on the matching result to adjust the production task.

[0168] According to an embodiment of the present disclosure, the present disclosure further provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, wherein the computer program is used to enable the processor to execute the prediction method as described above.

[0169] According to an embodiment of the present disclosure, the present disclosure also provides a computer program product, which includes: a computer program, the computer program is stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0170] Various embodiments of the devices and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0171] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0172] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0173] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0174] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0175] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0176] It should be understood by those skilled in the art that the embodiments of the present disclosure may be provided as methods, devices, or computer program products, etc. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0177] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, apparatuses, and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer executable instructions. These computer executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0178] These processor executable instructions may also be stored in a processor readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the processor readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0179] These processor-executable instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0180] Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is also intended to include these modifications and variations.

Claims

1. A method for predicting production efficiency, characterized in that: The method comprises: Obtaining production transfer information, wherein the production transfer information is used to indicate switching from producing a product before the production transfer in the production task to producing a product after the production transfer; Determine that the production efficiency of the pre-transfer product at the time of transfer is at the water level information of the historical production efficiency of the pre-transfer product, and obtain the first historical data of each product in the production task under the water level information, wherein the first historical data includes a first process and a first production efficiency; constructing a first function to be fitted according to the first historical data, where the first function to be fitted is used to characterize the relationship between the first production efficiency, the preset variables of the first process, and a parameter of the degree of influence of the first process on the first production efficiency, and fitting the first function to be fitted to obtain the parameter of the degree of influence of the first process; The production efficiency of the transferred product is determined according to the influence degree parameter of the first process, wherein the production efficiency of the transferred product is used to adjust the production task.

2. The method according to claim 1, characterized in that Determining the production efficiency of the transferred product according to the influence degree parameter of the first process includes: Determine the repeated processes and non-repeated processes between the product before the transfer and the product after the transfer; Obtaining the influence degree parameter of the repeated process from the influence degree parameter of the first process; The production efficiency of the transferred product is determined according to the influence degree parameter of the repeated process and the non-repetitive process.

3. The method according to claim 2, characterized in that The step of determining the production efficiency of the transferred product according to the influence degree parameter of the repeated process and the non-repeated process includes: The production efficiency of the transferred product is determined according to the impact degree parameter of the repeated process and the historical production efficiency of the non-repetitive process.

4. The method according to claim 3, characterized in that: The step of determining the production efficiency of the transferred product according to the influence degree parameter of the repeated process and the historical production efficiency of the non-repeated process includes: Determining a second production efficiency less than a preset threshold from historical production efficiencies of the non-repetitive process; Constructing a second function to be fitted according to the non-repetitive process and the second production efficiency, wherein the second function to be fitted is used to characterize the relationship between the second production efficiency, the preset variables of the non-repetitive process, and the parameter of the degree of influence of the non-repetitive process on the second production efficiency, and fitting the second function to be fitted to obtain the parameter of the degree of influence of the non-repetitive process; The production efficiency of the transferred product is determined according to the parameters of the influence degree of the repeated process and the influence degree of the non-repetitive process.

5. The method according to claim 4, characterized in that The step of determining the production efficiency of the transferred product according to the influence degree of the repeated process and the influence degree parameters of the non-repeated process includes: The sum of the impact degree parameter of the repeated process and the impact degree parameter of the non-repetitive process is calculated, and the sum is determined as the production efficiency of the transferred product.

6. The method according to claim 4, characterized in that The method further comprises: Obtaining product information of the production task, the product information including the product and the process, the product including the product before the transfer and the product after the transfer; A product list and a process list are created according to the product information, and a correspondence between products and processes is determined according to the product list and the process list, wherein the correspondence characterizes products under the same process, the correspondence is used to determine the repetitive process and the non-repetitive process, and the correspondence is also used to construct the first function to be fitted and the second function to be fitted.

7. The method according to claim 1, characterized in that The production task includes production scheduling information for products to be produced, and the products to be produced include the transferred products and products produced after the transferred products; the method also includes: Determine the forecast production schedule information of the product to be produced according to the production efficiency of the product after the transfer; Matching the predicted production scheduling information with the formulated production scheduling information to obtain a matching result; The production scheduling information is adjusted based on the matching result to adjust the production task.

8. A production efficiency prediction device, characterized in that: include: An obtaining unit, used for obtaining production transfer information, wherein the production transfer information is used for indicating switching from producing the pre-production transfer product in the production task to producing the post-production transfer product; A determination unit, used for determining that the production efficiency of the pre-transfer product at the time of transfer is at the level of the historical production efficiency of the pre-transfer product; The obtaining unit is further used to obtain first historical data of each product in the production task under the water level information, wherein the first historical data includes a first process and a first production efficiency; a construction unit, configured to construct a first function to be fitted according to the first historical data, wherein the first function to be fitted is used to characterize a relationship between the first production efficiency, a preset variable of the first process, and a parameter of an influence degree of the first process on the first production efficiency; A fitting unit, used for fitting the first function to be fitted to obtain an influence degree parameter of the first process; The determination unit is further used to determine the production efficiency of the transferred product according to the influence degree parameter of the first process, wherein the production efficiency of the transferred product is used to adjust the production task.

9. A processor-readable storage medium, characterized in that: The processor-readable storage medium stores a computer program, and the computer program is used to enable the processor to execute the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

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