Production process prediction model training method, prediction method, device and equipment

Abstract

The embodiment of the invention provides a training method, a prediction method, a device and equipment of a production process prediction model, and the method comprises the steps: for each production process, inputting a first process parameter of the production process and a second process parameter of a previous process before the production process into a prediction model corresponding to the production process; according to an output result of the prediction model, obtaining a first prediction result used for predicting whether the product flows into a subsequent process or not; and performing supervised training on a prediction model corresponding to the production process according to the first prediction result and a real result of whether the product flows into the subsequent process or not to obtain a trained prediction model. The flow direction of the product is predicted by combining the process parameters of the production process and the previous process, so that the interactive influence between the processes can be better balanced, the optimal process parameter value range is determined, the accuracy and flexibility of process parameter setting are improved, the rate of defective products is reduced, and the stability of the manufacturing process is improved.

Description

Technical Field

[0001] The present application relates to the field of production technology, and in particular to a training method, prediction method, device and equipment for a production process prediction model. Background Art

[0002] The manufacturing of products involves multiple production processes, and each production process involves the setting of one or more process parameters, such as temperature, pressure, speed, etc. The range of values ​​of process parameters will affect the quality of the product. In order to ensure that the product meets the specified standards and requirements, thresholds of process parameters are usually introduced to limit the range of values ​​of process parameters. Usually, these thresholds are fixed ranges based on experience or statistical data, also known as rigid thresholds, which represent the reasonable range of values ​​of process parameters within an acceptable range. By strictly controlling the process parameters within the thresholds, the stability and consistency of the product can be guaranteed.

[0003] However, in actual situations, the mutual influence between production processes and the dynamic changes of process parameters will lead to the limitations of rigid thresholds. In addition, there may be complex nonlinear relationships between different process parameters. Therefore, relying solely on rigid thresholds to define process parameters may not meet the requirements for product quality and process stability, and may result in a low product yield due to excessive adjustments. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a training method, prediction method, device and equipment for a production process prediction model, so as to achieve the technical effect of improving the range of process parameter values.

[0005] In a first aspect, an embodiment of the present application provides a method for training a production process prediction model, wherein multiple production processes of a product are sequential, each production process corresponds to a prediction model, and the prediction model is used to predict whether the product flows into a subsequent process after the corresponding production process; the method comprises:

[0006] For each of the production processes, inputting a first process parameter of the production process and a second process parameter of a previous process before the production process into a prediction model corresponding to the production process;

[0007] Obtaining, according to an output result of the prediction model, a first prediction result for predicting whether the product flows into the subsequent process;

[0008] According to the first prediction result and the actual result of whether the product flows into the subsequent process, the prediction model corresponding to the production process is supervisedly trained to obtain a trained prediction model.

[0009] In the above implementation process, by combining the process parameters of the production process and its previous process to predict the product flow, the interaction between the processes can be better weighed to determine the optimal range of process parameter values, thereby improving the accuracy and flexibility of process parameter setting, thereby reducing the defective rate and improving the stability of the manufacturing process.

[0010] Furthermore, the first prediction result for predicting whether the product will flow into the subsequent process is obtained according to the output result of the prediction model, including: obtaining a quality parameter for characterizing the quality of the product and a second prediction result corresponding to the prior process; determining the first prediction result according to the output result, the quality parameter and the second prediction result. By combining the process parameters of the production process and its prior process with the product quality to predict the flow of products, the interaction between the processes can be better weighed to determine the optimal range of process parameter values, improve the accuracy and flexibility of process parameter setting, thereby reducing the defective rate and improving the stability of the manufacturing process.

[0011] Further, determining the first prediction result according to the output result, the quality parameter and the second prediction result includes: determining the cumulative error of the output result according to the output result, the quality parameter and the second prediction result; and determining the first prediction result according to the output result and the cumulative error. By calculating the cumulative error of the output result and combining the output result of the prediction model with the cumulative error to determine the first prediction result, the accuracy of the first prediction result is improved.

[0012] Further, the first prediction result is determined based on the output result and the cumulative error, including: if the sum of the output result and the cumulative error is less than or equal to a preset threshold, determining that the first prediction result indicates that the product will flow into the subsequent process; if the difference between the output result and the cumulative error is greater than the preset threshold, determining that the first prediction result indicates that the product will not flow into the subsequent process; if the difference between the output result and the cumulative error is less than or equal to the preset threshold, and the sum of the output result and the cumulative error is greater than the preset threshold, determining that the first prediction result indicates that the product will not flow into the subsequent process. By combining the output result and the cumulative error to jointly determine whether the product continues to flow into the subsequent process after passing through the production process, the prediction accuracy of the prediction model is improved.

[0013] Furthermore, the prediction model is a binary classification model; the first prediction result includes a first result for indicating that the product will flow into the subsequent process, and a second result for indicating that the product will not flow into the subsequent process. By using the binary classification model as the prediction model, it is possible to intuitively determine whether the product continues to flow into the subsequent process based on the output of the model.

[0014] A second aspect of the embodiment of the present application provides a method for predicting a production process, wherein the multiple production processes of a product are sequential, each of the production processes corresponds to a prediction model, and the prediction model is used to predict whether the product flows into a subsequent process after the corresponding production process; the method comprises:

[0015] Acquire a third process parameter of a current production process and a fourth process parameter of a previous process before the current production process;

[0016] The third process parameter and the fourth process parameter are input into a trained prediction model corresponding to the current production process to obtain an output prediction result; the prediction result is used to indicate whether the product flows into or out of the subsequent process.

[0017] In the above process, when predicting the flow of products, the process parameters of the current production process and the previous process are considered together. The value range of the process parameters of each process is no longer a rigid threshold, but a flexible threshold that can be flexibly adjusted according to the process parameters of the previous process. This solves the problem of "overkill" of products caused by rigid thresholds.

[0018] Furthermore, the method is applied to an edge server; the edge server and the production equipment of the product meet a preset geographical relationship. By shortening the geographical distance between the production equipment and the server, the data transmission time can be reduced. In the production process, when the input data required by the prediction model is obtained, it can be immediately sent to the edge server for calculation and prediction, thereby improving the prediction response efficiency and meeting the production timeliness.

[0019] In a third aspect, an embodiment of the present application provides a training device for a production process prediction model, wherein multiple production processes of a product are sequential, each production process corresponds to a prediction model, and the prediction model is used to predict whether the product flows into a subsequent process after the corresponding production process; the device comprises:

[0020] An input module, for inputting, for each of the production processes, a first process parameter of the production process and a second process parameter of a previous process before the production process into a prediction model corresponding to the production process;

[0021] A first prediction module, used for obtaining a first prediction result for predicting whether the product flows into the subsequent process according to an output result of the prediction model;

[0022] The training module is used to perform supervised training on the prediction model corresponding to the production process according to the first prediction result and the actual result of whether the product flows into the subsequent process to obtain a trained prediction model.

[0023] In a fourth aspect, an embodiment of the present application provides a production process prediction device, wherein the multiple production processes of a product are sequential, each production process corresponds to a prediction model, and the prediction model is used to predict whether the product flows into a subsequent process after the corresponding production process; the device comprises:

[0024] An acquisition module, used for acquiring a third process parameter of a current production process and a fourth process parameter of a previous process before the current production process;

[0025] The second prediction module is used to input the third process parameter and the fourth process parameter into a trained prediction model corresponding to the current production process to obtain an output prediction result; the prediction result is used to indicate whether the product flows into or out of the subsequent process.

[0026] A fifth aspect of an embodiment of the present application provides a computer program product, which includes a computer program, wherein the computer program, when executed by a processor, can implement a method as described in any one of the first aspect or the second aspect.

[0027] A sixth aspect of the embodiments of the present application provides an electronic device, the electronic device comprising:

[0028] processor;

[0029] a memory for storing processor-executable instructions;

[0030] Wherein, when the processor calls the executable instruction, it implements the operation of any method described in the first aspect or the second aspect.

[0031] A seventh aspect of an embodiment of the present application provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of any of the methods described in the first aspect or the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 A flowchart of a method for training a production process prediction model provided in an embodiment of the present application;

[0034] Figure 2A schematic flow chart of another method for training a production process prediction model provided in an embodiment of the present application;

[0035] Figure 3 A schematic flow chart of another method for training a production process prediction model provided in an embodiment of the present application;

[0036] Figure 4 This is a schematic diagram of outputting data in an embodiment of the present application;

[0037] Figure 5 A schematic diagram of a production process prediction method provided in an embodiment of the present application;

[0038] Figure 6 A flowchart of a production process prediction model training process and a production process prediction process provided in an embodiment of the present application;

[0039] Figure 7 A structural block diagram of a training device for a production process prediction model provided in an embodiment of the present application;

[0040] Figure 8 A structural block diagram of a production process prediction device provided in an embodiment of the present application;

[0041] Fig. 9 A hardware structure diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0044] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0045] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0046] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0047] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0048] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the embodiments of the present application.

[0049] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0050] Each production process of the product involves the setting of different process parameters, and the production processes will affect each other. For example, the output result of the previous production process may affect the value range of the process parameters of the next generation process. Taking the battery cell as an example, its manufacturing process involves multiple generation processes such as raw material cutting, component assembly, welding, etc. For example, the mechanical properties and dimensions of the raw materials may affect the value range of the process parameters used in the cutting process. For another example, in the assembly process, the gap size of the assembly parts may affect the value range of the process parameters used in the subsequent welding process, including welding speed, power, temperature, etc. Therefore, setting rigid thresholds for process parameters will bring limitations. Using rigid thresholds to over-adjust process parameters may cause products that originally meet quality requirements to be judged as defective products, making the product yield too low, that is, the "overkill" problem of the product.

[0051] To this end, the embodiment of the present application proposes to set a prediction model for each production process of the product to predict whether the product will continue to flow into the next production process after the current production process. Among them, in the training stage of the prediction model, the first process parameter known to the production process and the second process parameter known to the previous process are used as training data; in the use stage of the prediction model, the third process parameter of the current production process and the fourth process parameter of the previous process are also input into the prediction model. In this way, when predicting the flow of the product, the process parameters of the current production process and the previous process are considered together. In other words, for the range of values ​​of the process parameters of the production process that can make the product flow into the subsequent process, it will be affected by the process parameters of the previous process. Therefore, the range of values ​​of the process parameters of each process is no longer a rigid threshold, but a flexible threshold that can be flexibly adjusted according to the process parameters of the previous process. Thereby solving the problem of "overkill" of products caused by rigid thresholds.

[0052] According to some embodiments of the present application, a first aspect provides a training method for a production process prediction model. Among them, the production process of the product includes multiple, and each production process has a time sequence. That is, the production processes are executed in a certain time sequence. In addition, each production process corresponds to a prediction model, which is used to predict whether the product will continue to flow into the subsequent process after passing through the generation process. The training method provided in the embodiment of the present application can be used to train the prediction model corresponding to each production process.

[0053] See also Figure 1 The training method of a production process prediction model provided in the embodiment of the present application includes the following steps: Figure 1 Steps 110 to 130 are shown. The prediction model corresponding to each production process can be trained by executing steps 110 to 130.

[0054] Step 110: For each of the production processes, a first process parameter of the production process and a second process parameter of a previous process before the production process are input into a prediction model corresponding to the production process.

[0055] Model training requires training data. In this embodiment, historical products can be obtained as training data. The historical products can be the final products produced or semi-finished products that flow out of the production line due to process reasons during the production process. In addition, the historical products can include good products and / or defective products.

[0056] Specifically, known process parameters of historical products in each production process can be obtained as training data. The amount of training data can be set according to training requirements, and this embodiment does not limit the amount.

[0057] For each production process of the product, the known process parameters in the production process when the product is produced can be obtained as the first process parameters; and the known process parameters in the previous process when the product is produced can be obtained as the second process parameters. The previous process is relative to the production process corresponding to the current model training, and the previous process includes one or more. The second process parameters include all the known process parameters of the previous process.

[0058] Subsequently, the first process parameter and the second process parameter are input into the prediction model as training data of the prediction model corresponding to the production process.

[0059] Step 120: According to the output result of the prediction model, a first prediction result for predicting whether the product flows into the subsequent process is obtained.

[0060] After the training data is input into the prediction model, the output result of the prediction model can be obtained. Among them, in the value range of the output result, the closer the output result is to the lower limit of the value range, the fewer defects the product has after passing through the production process; similarly, the closer the output result is to the upper limit of the value range, the more defects the product has after passing through the production process. In this way, according to the output result, a first prediction result can be obtained, wherein the first prediction result is used to predict whether the product will flow into the subsequent process. The subsequent process is relative to the production process corresponding to the prediction model.

[0061] Optionally, the output result of the prediction model may be determined to be the first prediction result.

[0062] Optionally, the output result of the prediction model may be subjected to a preset process to obtain a first prediction result.

[0063] Step 130: Perform supervised training on the prediction model corresponding to the production process according to the first prediction result and the actual result of whether the product flows into the subsequent process to obtain a trained prediction model.

[0064] For historical products, the actual result of whether the product flows into the subsequent process after passing through the production process can be determined according to the actual situation. In this way, the first prediction result and the actual result obtained by the prediction model can be used to conduct supervised training on the prediction model.

[0065] Exemplarily, a loss function may be used to characterize the difference between the actual result and the first predicted result. The specific form of the loss function may be selected by a technician according to actual needs, and this embodiment is not limited thereto. Subsequently, the loss function may be used to update the parameters of the prediction model until the training end condition is met to obtain a trained prediction model. Exemplarily, the training result condition may include, but is not limited to, the number of training times being greater than a preset number threshold, the loss function converging, and the like.

[0066] In addition, for the first process in the product manufacturing process, since there is no previous process relative to the first process, only the first process parameter of the first process can be input into the prediction model corresponding to the first process to obtain the first prediction result.

[0067] It can be seen that the training method of a production process prediction model provided in this embodiment adopts the first process parameter known to the production process and the second process parameter known to the previous process as training data for the prediction model corresponding to each production process, so that when predicting whether the product flows into the subsequent process, the influence of the process parameters of the previous process on the value of the process parameters of the production process is also taken into account. In other words, whether the product flows into the subsequent process is jointly determined by the process parameters of the production process and the process parameters of the previous process. Then, for the value range of the process parameters of the production process that can make the product flow into the subsequent process, it will be affected by the process parameters of the previous process. Therefore, by combining the process parameters of the production process and its previous process to predict the flow of products, the interaction between the processes can be better weighed to determine the optimal range of process parameter values, improve the accuracy and flexibility of process parameter setting, thereby reducing the defective rate and improving the stability of the manufacturing process.

[0068] Based on the above embodiment, the first prediction result obtained according to the output result of the prediction model in step 120 may include: Figure 2 Steps 121-122 are shown.

[0069] Step 121: Acquire a quality parameter for characterizing the quality of the product and a second prediction result corresponding to the previous process;

[0070] Step 122: Determine the first prediction result according to the output result, the quality parameter and the second prediction result.

[0071] For historical products, the quality of the historical products can be tested and evaluated after production to obtain quality parameters that can characterize the product quality.

[0072] Optionally, the value range of the quality parameter includes a first value and a second value. For example, the first value is 0 and the second value is 1. Optionally, the first value may represent that the product quality is qualified; the second value may represent that the product quality is unqualified. Optionally, the first value may represent that the number of defects of the product is less than a preset quantity threshold, so the product quality is qualified; the second value may represent that the number of defects of the product is greater than the quantity threshold, so the product quality is unqualified.

[0073] In addition, as described above, each production process corresponds to a prediction model. For a prior process, the second prediction result corresponding to the prior process can be obtained through the prediction model corresponding to the prior process. Among them, the second prediction result is used to predict whether the product will flow into the subsequent process of the prior process. The process of obtaining the second prediction result can refer to the process of obtaining the first prediction result. For example, the output result of the prediction model corresponding to the prior process can be determined as the second prediction result. For another example, according to the method described in this embodiment, the output result of the prediction model corresponding to the prior process is preset and the second prediction result is obtained. If the prior process includes multiple processes, the second prediction result described in this embodiment includes the second prediction results output by the prediction models corresponding to the multiple prior processes.

[0074] Subsequently, the output result of the prediction model of the production process, the quality parameters, and the second prediction result corresponding to the previous process are processed in a preset manner to obtain a first prediction result for predicting whether the product will flow into the subsequent process.

[0075] It can be seen that after obtaining the output result of the prediction model, this embodiment again introduces the second prediction result of the previous process and the quality parameters of the product to process the output result, thereby obtaining the first prediction result of predicting whether the product will flow into the subsequent process, so that the first prediction result is affected by the second prediction result of the previous process and the product quality. Therefore, by combining the process parameters of the production process and its previous process with the product quality to predict the flow of products, the interaction between the processes can be better weighed to determine the optimal range of process parameter values, improve the accuracy and flexibility of process parameter setting, thereby reducing the defective rate and improving the stability of the manufacturing process.

[0076] Based on the above embodiment, the process of determining the first prediction result according to the output result, the quality parameter and the second prediction result in step 122 may specifically include the following steps: Figure 3 Steps 1221-1222 are shown.

[0077] Step 1221: Determine the cumulative error of the output result based on the output result, the quality parameter and the second prediction result.

[0078] As an example, the accumulated error may be a root mean square error.

[0079] For example, the root mean square error can be determined as the sum of the squares of the difference between the quality parameter and each second prediction result, the sum of the squares of the difference between the quality parameter and the output result, and the order of the production process. n It can be expressed as:

[0080]

[0081] Among them, y obs is the quality parameter; model,i The value of is y model,1 ,y model,2 ……y model,n , where y model,n is the output result of the prediction model to be trained this time, y model,1 ,y model,2 ……y model,n-1 is the second prediction result corresponding to the previous process; n is the execution order of the production process in the entire production process.

[0082] Step 1222: Determine the first prediction result according to the output result and the accumulated error.

[0083] The cumulative error represents the prediction error accumulated from the first production process to the nth production process. Therefore, the first prediction result can be determined according to the output result of the prediction model and the cumulative error.

[0084] It can be seen that this embodiment determines the first prediction result by calculating the cumulative error of the output result and combining the output result of the prediction model with the cumulative error, thereby improving the accuracy of the first prediction result.

[0085] On the basis of the above embodiment, in step 1222, the first prediction result is determined according to the output result and the accumulated error, which may specifically include the following three situations.

[0086] Case 1: If the sum of the output result and the accumulated error is less than or equal to a preset threshold, it is determined that the first prediction result indicates flowing into the subsequent process.

[0087] According to the accumulated error, the error bar of the prediction model output result can be determined. For example, Figure 4 As shown, it can be determined from the output result f(x1,x2,…,x n ) and the cumulative error δ n The difference f(x1,x2,…,x n )-δ n , until the output result and the sum of the accumulated errors f(x1,x2,…,x n )+δ n The range between is the error bar of the prediction model output. That is, the difference between the output result and the cumulative error f(x1,x2,…,x n )-δ n is the lower limit of the error bar, and the output result is the sum of the cumulative errors f(x1,x2,…,x n )+δ n is the upper limit of the error bar.

[0088] Continue to see Figure 4 , if the output result is equal to the sum of the cumulative errors f(x1,x2,…,x n )+δ n Less than or equal to the preset threshold value indicates that the error bar of the output result is less than or equal to the preset threshold value as a whole. At this time, the output result with the error bar is closer to the lower limit of the value range, indicating that the product has fewer defects after passing through this production process. Therefore, it can be determined that the first prediction result indicates that it will flow into the subsequent process.

[0089] The preset threshold value may be determined by a person skilled in the art according to the value range of the output result, and the art does not limit the value of the preset threshold value.

[0090] Case 2: If the difference between the output result and the accumulated error is greater than the preset threshold, it is determined that the first prediction result indicates that it does not flow into the subsequent process.

[0091] Continue to see Figure 4 , if the difference between the output result and the cumulative error f(x1,x2,…,x n )-δ n is greater than the preset threshold, indicating that the error bar of the output result is greater than the preset threshold as a whole. At this time, the output result with the error bar is closer to the upper limit of the value range, indicating that the product has more defects after passing through this production process. Therefore, it can be determined that the first prediction result indicates that it will not flow into the subsequent process.

[0092] Case three: if the difference between the output result and the accumulated error is less than or equal to the preset threshold, and the sum of the output result and the accumulated error is greater than the preset threshold, it is determined that the first prediction result indicates that it does not flow into the subsequent process.

[0093] Continue to see Figure 4 , if the difference between the output result and the cumulative error f(x1,x2,…,x n )-δ n is less than the preset threshold, and the output result is equal to the sum of the accumulated errors f(x1,x2,…,x n )+δ n If it is greater than the preset threshold, it means that the output result is near the threshold, indicating that after the product passes through the production process, the number of defects is just near the threshold that determines whether it will flow into the subsequent process. At this time, in order to avoid "missing" products, that is, potentially unqualified products flowing into the subsequent process, this situation can be set as not flowing into the subsequent process.

[0094] It can be seen that this embodiment combines the output result and the accumulated error to jointly determine whether the product continues to flow into the subsequent process after passing through the production process, thereby improving the prediction accuracy of the prediction model.

[0095] Based on any of the above embodiments, as a feasible example, the prediction model may be a binary classification model. The output result of the prediction model may include a first value and a second value. For example, the first value is 0 and the second value is 1. Optionally, the first value may represent that the product flows into a subsequent process; the second value may represent that the product does not flow into a subsequent process.

[0096] In this way, after obtaining the output results of the prediction model, according to Figure 2-Figure 3 The embodiment described can calculate the cumulative error of the output result. Figure 4 In the described embodiment, the first prediction result can be determined according to the output result of the prediction model and the accumulated error, wherein the first prediction result includes a first result for indicating that the product flows into a subsequent process, and a second result for indicating that the product does not flow into a subsequent process.

[0097] It can be seen that this embodiment uses a binary classification model as a prediction model, and according to the output of the model, it can be intuitively determined whether the product continues to flow into the subsequent process.

[0098] In addition, according to some embodiments of the present application, the second aspect further provides a method for predicting a production process. Among them, the production process of the product includes multiple processes, and each production process has a time sequence. That is, the production processes are executed in a certain time sequence. In addition, each production process corresponds to a prediction model, which is used to predict whether the product will continue to flow into the subsequent process after passing through the generation process. The prediction method provided in this embodiment can be used to predict whether the product will continue to flow into the subsequent process after passing through the current production process. That is, every time the product passes through a production process, the prediction method can be executed to determine whether the product will continue to flow into the next production process.

[0099] See also Figure 5 A method for predicting a production process provided in an embodiment of the present application includes: Figure 5 Steps 510 - 520 are shown.

[0100] Step 510: Acquire a third process parameter of a current production process and a fourth process parameter of a previous process before the current production process.

[0101] In this step, the third process parameter used by the product in the current production process and the fourth process parameter of the previous process are obtained. The previous process is relative to the current production process, and the previous process includes one or more. The fourth process parameter includes the process parameters of all previous processes.

[0102] Step 520: Input the third process parameter and the fourth process parameter into a trained prediction model corresponding to the current production process to obtain an output prediction result.

[0103] The prediction result is used to indicate whether the product flows into or out of the subsequent process.

[0104] In this step, the third process parameter and the fourth process parameter are used as input data of the model and input into the trained prediction model corresponding to the current production process, and the prediction model can output a prediction result.

[0105] The prediction result indicates whether the product flows into or out of the subsequent process. The subsequent process is relative to the current production process, and refers to the next production process of the current production process. According to the indication of the prediction result, the product that has completed the current production process can flow into the subsequent process or flow out of the production line.

[0106] It can be seen that this embodiment proposes to set a prediction model for each production process of the product to predict whether the product will continue to flow into the subsequent process after passing through the current production process. When predicting, the third process parameter of the current production process and the fourth process parameter of the previous process are jointly input into the prediction model. In this way, when predicting the flow of the product, the process parameters of the current production process and the previous process are considered together. In other words, for the range of values ​​of the process parameters of the production process that can make the product flow into the subsequent process, it will be affected by the process parameters of the previous process. Therefore, the range of values ​​of the process parameters of each process is no longer a rigid threshold, but a flexible threshold that can be flexibly adjusted according to the process parameters of the previous process. Thereby solving the problem of "overkill" of products caused by rigid thresholds.

[0107] Based on the above embodiment, optionally, the prediction method can be applied to an edge server. The edge server and the production equipment of the product meet a preset geographical relationship.

[0108] Among them, the production equipment can be connected to the server cluster for communication. The server cluster may include multiple servers, each server is used to implement different functions. According to the geographical distribution of each server in the server cluster, the server that satisfies the preset geographical relationship with the production equipment can be divided as an edge server. Exemplarily, the preset geographical relationship can be that the distance between the production equipment and the server is less than a preset distance threshold. In this way, by shortening the geographical distance between the production equipment and the server, the data transmission time can be reduced. In the production process, when the input data required by the prediction model is obtained, it can be immediately sent to the edge server for calculation and prediction, thereby improving the prediction response efficiency to meet the production timeliness.

[0109] According to some embodiments of the present application, a production process prediction model training method and a production process prediction method are also provided. Figure 6 As shown in the figure, each production process of the product corresponds to a prediction model. The prediction model is a binary classification model. For the first process of the product, the known process parameter x1 of the historical product in the first production process can be obtained as the first process parameter and input into the corresponding prediction model. Then the output result f(x1) of the prediction model is obtained, and the cumulative error δ1=y of the output result f(x1) is calculated. obs -f(x1). The value range of the output result includes the first value and the second value. For example, the first value is 0, which means that the product has fewer defects; the second value is 1, which means that the product has more defects. obs is the quality parameter of the historical product, and its value range includes the first value and the second value. For example, the first value is 0, indicating that the product quality is qualified; the second value is 1, indicating that the product quality is unqualified. It can be seen that if the output result f(x1) of the prediction model is consistent with the product quality parameter y obs The values ​​of are consistent, for example, the values ​​are all 1, which means that the prediction model has predicted that the product has many defects after the current production process, and in fact the final quality of the product is also unqualified. At this time, the prediction accuracy of the prediction model is high and the cumulative error is small, for example, 0. On the contrary, if the output result f(x1) of the prediction model is consistent with the quality parameter y of the product obs The values ​​of are inconsistent, indicating that the prediction accuracy of the prediction model is low at this time, and the corresponding cumulative error is large, for example, 1. It can be seen that the value range of the cumulative error is [0, 1].

[0110] Then, it can be determined whether the output result f(x1) and the cumulative error δ1 meet one of the situations 1 to 3. If the situation 1 is met, that is, the sum of the output result and the cumulative error f(x1)+δ1 is less than or equal to the preset threshold, for example, 0.5, then it is determined that the first prediction result indicates that the product flows into the subsequent process, that is, the second production process. If the situation 2 is met, that is, the sum of the output result and the cumulative error f(x1)-δ1 is greater than the preset threshold, or if the situation 3 is met, that is, the sum of the output result and the cumulative error f(x1)+δ1 is greater than the preset threshold, and the sum of the output result and the cumulative error f(x1)-δ1 is less than the preset threshold, then it is determined that the first prediction result indicates that the product does not flow into the subsequent process.

[0111] Subsequently, supervised training can be performed on the prediction model based on the first prediction result and the actual result of whether the historical product flows into the subsequent process. In this way, the training of the prediction model corresponding to the first process can be completed by using the first process parameters of a large number of historical products in the first process as training data.

[0112] For the non-first process of the product, the known process parameters x of the historical product in the non-first production process can be obtained. n As the first process parameter, the known process parameters x1, x2, ..., x n-1 As the second process parameter. The first process parameter x n and the second process parameters x1, x2,…, x n-1 As input data, it can be input into the prediction model of the non-first production process. Then the output result of the prediction model f(x1, x2,…, x n ), and calculate the output result f(x1,x2,…,x n ) Then, we can determine the output result f(x1,x2,…,x n ) and the cumulative error δ n Which of the situations 1 to 3 is satisfied and the corresponding first prediction result is determined.

[0113] Subsequently, supervised training can be performed on the prediction model based on the first prediction result and the actual result of whether the historical product flows into the subsequent process. In this way, the training of the prediction model corresponding to the non-first process can be completed by using the first process parameters of a large number of historical products in non-first processes and the second process parameters of the previous process as training data.

[0114] In the application stage of the prediction model, similarly, if the current production process of the product is the first process, the process parameters of the first process can be input as the third process parameters into the trained prediction model. According to the prediction results output by the prediction model, it is determined whether the product that has passed the first process flows into the subsequent production process or flows out of the production line. If the product continues to flow into the second process, the process parameters of the first process can be used as the fourth process parameters, and the process parameters of the second process can be used as the third process parameters. The process of inputting the product that has passed the second process into the subsequent production process or flowing out of the production line is determined according to the prediction results output by the prediction model. And so on, until the product flows out of the production line or completes the last process.

[0115] It can be seen that the prediction result f(x1,x2,…,x n ), due to the process parameters x1, x2, …x n-1 is known, so f(x1,x2,…,x n )+δ n Is x n is a function of the only variable. For the above situation 1, that is, the product can flow into the next process, if the preset threshold is 0.5, let f(x1,x2,…,x n )+δ n ≤0.5, we can calculate the x that allows the product to flow into the subsequent process n From this, we can know that the process parameter x of the current production process n The value range of is not a fixed value, but is affected by the process parameter values ​​of the previous process. It is a flexible threshold that changes in real time. Compared with the rigid threshold, the flexible threshold can greatly reduce the "overkill" problem in the industrial manufacturing process.

[0116] According to some embodiments of the present application, a third aspect further provides a training device for a production process prediction model. There is a time sequence between the multiple production processes of a product, and each production process corresponds to a prediction model, which is used to predict whether the product will flow into the subsequent process after the corresponding production process. Figure 7 As shown, the training device 700 includes:

[0117] An input module 710, for inputting, for each of the production processes, a first process parameter of the production process and a second process parameter of a previous process before the production process into a prediction model corresponding to the production process;

[0118] A first prediction module 720, configured to obtain a first prediction result for predicting whether the product flows into the subsequent process according to an output result of the prediction model;

[0119] The training module 730 is used to perform supervised training on the prediction model corresponding to the production process according to the first prediction result and the actual result of whether the product flows into the subsequent process to obtain a trained prediction model.

[0120] In some embodiments, the first prediction module 720 is specifically used to:

[0121] Acquire a quality parameter for characterizing the quality of the product and a second prediction result corresponding to the prior process;

[0122] The first prediction result is determined according to the output result, the quality parameter and the second prediction result.

[0123] In some embodiments, the first prediction module 720 is specifically used to:

[0124] Determining a cumulative error of the output result according to the output result, the quality parameter, and the second prediction result;

[0125] The first prediction result is determined according to the output result and the accumulated error.

[0126] In some embodiments, the first prediction module 720 is specifically used to:

[0127] If the sum of the output result and the accumulated error is less than or equal to a preset threshold, determining that the first prediction result indicates flowing into the subsequent process;

[0128] If the difference between the output result and the accumulated error is greater than the preset threshold, determining that the first prediction result indicates that the first prediction result does not flow into the subsequent process;

[0129] If the difference between the output result and the accumulated error is less than or equal to the preset threshold, and the sum of the output result and the accumulated error is greater than the preset threshold, it is determined that the first prediction result indicates that it does not flow into the subsequent process.

[0130] In some embodiments, the prediction model is a binary classification model; the first prediction result includes a first result for indicating that the product will flow into the subsequent process, and a second result for indicating that the product will not flow into the subsequent process.

[0131] The implementation process of the functions and effects of each module in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, which will not be repeated here.

[0132] According to some embodiments of the present application, a fourth aspect further provides a production process prediction device. There is a time sequence between the multiple production processes of the product, and each production process corresponds to the prediction model, which is used to predict whether the product will flow into the subsequent process after the corresponding production process. Figure 8 As shown, the prediction device 800 includes:

[0133] An acquisition module 810 is used to acquire a third process parameter of a current production process and a fourth process parameter of a previous process before the current production process;

[0134] The second prediction module 820 is used to input the third process parameter and the fourth process parameter into a trained prediction model corresponding to the current production process to obtain an output prediction result; the prediction result is used to indicate whether the product flows into or out of the subsequent process.

[0135] In some embodiments, the prediction device 800 is applied to an edge server; the edge server and the production equipment of the product satisfy a preset geographical relationship.

[0136] The implementation process of the functions and effects of each module in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, which will not be repeated here.

[0137] Based on the method described in any of the above embodiments, an embodiment of the present application further provides a computer program product, wherein the computer program product includes a computer program, wherein the computer program can implement any of the methods described in the above embodiments when executed by a processor.

[0138] Based on the method described in any of the above embodiments, the present application also provides the following Fig. 9 A schematic diagram of the structure of an electronic device is shown in FIG. Fig. 9 At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the method described in any of the above embodiments.

[0139] An embodiment of the present application further provides a computer storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, it can be used to execute the method described in any of the above embodiments.

[0140] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0141] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0142] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0143] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0144] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0145] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A training method for a production process prediction model, characterized in that: There is a time sequence between the multiple production processes of the product, and each production process corresponds to the prediction model, and the prediction model is used to predict whether the product flows into the subsequent process after the corresponding production process; the method includes: For each of the production processes, inputting a first process parameter of the production process and a second process parameter of a previous process before the production process into a prediction model corresponding to the production process; Obtaining, according to an output result of the prediction model, a first prediction result for predicting whether the product flows into the subsequent process; According to the first prediction result and the actual result of whether the product flows into the subsequent process, the prediction model corresponding to the production process is supervisedly trained to obtain a trained prediction model.

2. The method according to claim 1, characterized in that The step of obtaining a first prediction result for predicting whether the product flows into the subsequent process according to the output result of the prediction model includes: Acquire a quality parameter for characterizing the quality of the product and a second prediction result corresponding to the prior process; The first prediction result is determined according to the output result, the quality parameter and the second prediction result.

3. The method according to claim 2, characterized in that The determining the first prediction result according to the output result, the quality parameter and the second prediction result includes: Determining a cumulative error of the output result according to the output result, the quality parameter, and the second prediction result; The first prediction result is determined according to the output result and the accumulated error.

4. The method according to claim 3, characterized in that The determining the first prediction result according to the output result and the accumulated error includes: If the sum of the output result and the accumulated error is less than or equal to a preset threshold, determining that the first prediction result indicates flowing into the subsequent process; If the difference between the output result and the accumulated error is greater than the preset threshold, determining that the first prediction result indicates that the first prediction result does not flow into the subsequent process; If the difference between the output result and the accumulated error is less than or equal to the preset threshold, and the sum of the output result and the accumulated error is greater than the preset threshold, it is determined that the first prediction result indicates that it does not flow into the subsequent process.

5. The method according to any one of claims 1 to 4, characterized in that: The prediction model is a binary classification model; the first prediction result includes a first result for indicating that the product will flow into the subsequent process, and a second result for indicating that the product will not flow into the subsequent process.

6. A method for predicting a production process, characterized in that: There is a time sequence between the multiple production processes of the product, and each production process corresponds to the prediction model, and the prediction model is used to predict whether the product flows into the subsequent process after the corresponding production process; the method includes: Acquire a third process parameter of a current production process and a fourth process parameter of a previous process before the current production process; The third process parameter and the fourth process parameter are input into a trained prediction model corresponding to the current production process to obtain an output prediction result; the prediction result is used to indicate whether the product flows into or out of the subsequent process.

7. The method according to claim 6, characterized in that The method is applied to an edge server; the edge server and the production equipment of the product satisfy a preset geographical relationship.

8. A training device for a production process prediction model, characterized in that: There is a time sequence between the multiple production processes of the product, and each production process corresponds to the prediction model, and the prediction model is used to predict whether the product flows into the subsequent process after the corresponding production process; the device includes: An input module, for inputting, for each of the production processes, a first process parameter of the production process and a second process parameter of a previous process before the production process into a prediction model corresponding to the production process; A first prediction module, used for obtaining a first prediction result for predicting whether the product flows into the subsequent process according to an output result of the prediction model; The training module is used to perform supervised training on the prediction model corresponding to the production process according to the first prediction result and the actual result of whether the product flows into the subsequent process to obtain a trained prediction model.

9. A production process prediction device, characterized in that: There is a time sequence between the multiple production processes of the product, and each production process corresponds to the prediction model, and the prediction model is used to predict whether the product flows into the subsequent process after the corresponding production process; the device includes: An acquisition module, used for acquiring a third process parameter of a current production process and a fourth process parameter of a previous process before the current production process; The second prediction module is used to input the third process parameter and the fourth process parameter into a trained prediction model corresponding to the current production process to obtain an output prediction result; the prediction result is used to indicate whether the product flows into or out of the subsequent process.

10. A computer program product, characterized in that The computer program product comprises a computer program, wherein the computer program can implement the method according to any one of claims 1 to 7 when executed by a processor.

11. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing processor-executable instructions; Wherein, when the processor calls the executable instructions, the operation of any method described in claims 1-7 is implemented.

12. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed by a processor, the steps of any method described in claims 1-7 are implemented.

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