Process parameter switching method and device for multi-variety production

Abstract

The invention discloses a process parameter switching method and device for multi-variety production, and a server side method comprises the steps: receiving a process parameter switching request sent by a manufacturing execution system used for monitoring a product production state, wherein the process parameter switching request carries production demand description information and a product batch model; according to the production demand description information, corresponding target process parameters are automatically obtained, the target process parameters are determined by adopting a preset comparison function, and the preset comparison function comprises a basic comparison term expression and a physical comparison term expression; pre-checking the target process parameters to obtain a checking result; and under the condition that the verification result indicates that the verification of the target process parameter is passed, binding the target process parameter to a product batch model, and issuing the product batch model to an equipment control layer. Therefore, by adopting the embodiment of the invention, the time required for searching and inputting the process parameters is avoided, so that the equipment can be switched to a new production task more quickly, and the production efficiency and the product quality are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of industrial automation technology, and in particular to a method and device for switching process parameters for multi-variety production. Background Art

[0002] In modern industries like chemical, pharmaceutical, and food and beverage, production equipment must frequently switch between different products to meet diverse and personalized market demands. For example, in the fine chemical industry, the same production equipment may need to switch between producing different products in a short period of time, with each product requiring unique process parameters (such as temperature, pressure, flow rate, reaction time, and feed sequence). This high-variety, small-batch production model places extremely high demands on equipment flexibility, stability, and safety.

[0003] In existing technologies, the control of production equipment typically relies on the equipment control layer (such as a DCS or PLC) and the upper-level Manufacturing Execution System (MES). Operators manually input new process parameters into the equipment control layer based on the production plan. This manual process of entering or selecting process parameters is not only time-consuming but also prone to human error, leading to parameter errors that affect product quality and production efficiency. Some automated systems are also slow to respond during automated operation due to the limitations of model computing power. Currently, there is no effective control system calibration method for the production of multiple products, multiple processes, multiple equipment, and multiple parameter control switching. Summary of the Invention

[0004] The embodiments of this application provide a method and apparatus for switching process parameters for multi-product production. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is provided below. This summary is not intended to be a comprehensive review, identify key or important elements, or delineate the scope of protection for these embodiments. Its sole purpose is to present some concepts in a simplified form, serving as a prelude to the detailed description that follows.

[0005] In a first aspect, an embodiment of the present application provides a method for switching process parameters for multi-variety production, which is applied to a server. The method includes: The server receives a process parameter switching request from the manufacturing execution system (MES) used to monitor product production status. The process parameter switching request carries production requirement description information and product batch model. The server automatically obtains the corresponding target process parameters based on the production requirement description information. The target process parameters are determined using a preset comparison function. The preset comparison function includes a basic comparison term expression and a physical comparison term expression. The basic comparison term expression is used to quantify the correlation between production requirement keyword sequences, and the physical comparison term expression is used to quantify the correlation between process flow charts. The server pre-checks the target process parameters and obtains the verification results; When the verification result indicates that the target process parameters have passed the verification, the server binds the target process parameters to the product batch model and sends it to the equipment control layer to switch the currently executed process parameters to the target process parameters.

[0006] In a second aspect, an embodiment of the present application provides a process parameter switching device for multi-variety production, the device comprising: A receiving module is used to receive a process parameter switching request sent by a manufacturing execution system for monitoring product production status, wherein the process parameter switching request carries production requirement description information and product batch model; An acquisition module is used to automatically acquire corresponding target process parameters based on the production requirement description information. The target process parameters are determined using a preset comparison function. The preset comparison function includes a basic comparison item expression and a physical comparison item expression. The basic comparison item expression is used to quantify the correlation between production requirement keyword sequences, and the physical comparison item expression is used to quantify the correlation between process flow charts. Pre-inspection module, used to pre-check target process parameters and obtain verification results; The parameter sending module is used to bind the target process parameters to the product batch model when the verification result indicates that the target process parameters have passed the verification, and send it to the equipment control layer to switch the currently executed process parameters to the target process parameters.

[0007] The technical solutions provided by the embodiments of the present application may have the following beneficial effects: In the embodiment of the present application, on the one hand, the manufacturing execution system can monitor the production status of the product in real time. When it is necessary to switch the production of the next variety, it can promptly send a process parameter switching request to the server, and the server can automatically obtain the corresponding target process parameters. This process does not require manual intervention, avoiding the time required to find and enter process parameters, allowing the equipment to switch to new production tasks more quickly, thereby significantly improving production efficiency and shortening the production cycle. On the other hand, the target process parameters are determined using a preset comparison function, which includes a basic comparison item expression and a physical comparison item expression. The basic comparison item expression is used to quantify the correlation between production requirement keyword sequences, and the physical comparison item expression is used to quantify the correlation between process flow charts. By comprehensively considering the correlation of production requirements and the correlation of process flows, it is possible to quickly obtain accurate process parameters based on the historical production data of each variety, thereby improving product quality.

[0008] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings, which are incorporated herein and constitute part of this specification, illustrate embodiments consistent with the application and, together with the description, further serve to explain the principles of the application.

[0010] Figure 1 is a method flow diagram of a process parameter switching method for multi-variety production provided by an embodiment of the application; Figure 2 is a flow diagram of a pre-verification process provided by an embodiment of the application; Figure 3 is a process flow diagram of a process parameter switching process for multi-variety production provided by an embodiment of the application; Figure 4 is a flow diagram of a model fine-tuning method of a keyword extraction model provided by an embodiment of the application; Figure 5 is a structural diagram of a process parameter switching device for multi-variety production provided by an embodiment of the application; Figure 6 is a structural diagram of an electronic device provided by an embodiment of the application. DETAILED DESCRIPTION

[0011] The following description and drawings are illustrative of the specific embodiments of the present application and are not intended to be limiting thereof.

[0012] It should be noted that the described embodiments are merely a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor fall within the scope of protection of the present application.

[0013] The following description refers to the accompanying drawings, wherein like numbers refer to like or similar elements throughout. The embodiments described in the following examples do not represent all of the embodiments consistent with the application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the application as detailed in the appended claims.

[0014] In the description of this application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances. In addition, in the description of this application, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship.

[0015] Currently, production equipment control typically relies on the equipment control layer (such as a DCS or PLC) and the upper-level Manufacturing Execution System (MES). Operators manually input new process parameters into the equipment control layer based on the production plan. Manually entering or selecting process parameters is not only time-consuming but also prone to operator error, resulting in parameter errors that affect product quality and production efficiency.

[0016] The applicants of this application recognize that this method relies entirely on the operator's work experience and attention, and is prone to monitoring errors due to operator fatigue or negligence. Furthermore, manual observation makes it difficult to accurately capture and record key details during the operation. This monitoring method suffers from low timeliness and accuracy, resulting in unstable product quality.

[0017] In order to solve the above problems, the present application provides a process parameter switching method and device for multi-variety production to solve the problems existing in the above-mentioned related technical problems. In an embodiment of the present application, on the one hand, the manufacturing execution system can monitor the production status of the product in real time. When it is necessary to switch the production of the next variety, it can promptly send a process parameter switching request to the server, and the server can automatically obtain the corresponding target process parameters. This process does not require manual intervention, avoids the time required to find and input process parameters, and enables the equipment to switch to new production tasks more quickly, thereby significantly improving production efficiency and shortening the production cycle. On the other hand, the target process parameters are determined using a preset comparison function. The preset comparison function includes a basic comparison item expression and a physical comparison item expression. The basic comparison item expression is used to quantify the correlation between production requirement keyword sequences, and the physical comparison item expression is used to quantify the correlation between process flow charts. By comprehensively considering the correlation of production requirements and the correlation of process flows, it is possible to quickly obtain accurate process parameters based on the historical production data of each variety, thereby improving product quality. The following is a detailed description using an exemplary embodiment.

[0018] The following will be combined with the Figure 1 -Attached Figure 4This article details the process parameter switching method for multi-variety production provided by an embodiment of this application. This method can be implemented using a computer program and run on a process parameter switching device for multi-variety production based on a von Neumann architecture. This computer program can be integrated into an application or run as a standalone tool application.

[0019] See Figure 1 , provides a flow chart of a process parameter switching method for multi-variety production for the embodiment of the present application, which is applied to the server. Figure 1 As shown, the method of the embodiment of the present application includes the following steps: S101: A server receives a process parameter switching request from a manufacturing execution system (MES) for monitoring product production status. The process parameter switching request carries production requirement description information and product batch model. In a production system, the server is a software system running on a server that manages and controls the production process. A Manufacturing Execution System (MES) is a software system used to monitor and manage the production process. By collecting production data in real time, it can monitor the status of production equipment and ensure the smooth progress of production tasks. A process parameter switch request is a request sent by the MES system to the server to indicate the need to switch the current process parameters. The production requirement description information is a detailed description of the current production task, including the product type, specifications, quality requirements, and production process requirements. The product batch model is a code or name used to identify different batches of products.

[0020] In one possible implementation, the MES system, based on the production plan or real-time monitoring data, determines that the current production task requires a change in process parameters. The MES system then generates a process parameter change request, which includes a description of the production requirements and the product batch number. The server receives the process parameter change request from the MES system via a network interface (such as HTTP or TCP / IP). The server parses the request and extracts the production requirements and product batch number.

[0021] A JSON format process parameter switching request, for example: {"request_id":"REQ123456789","timestamp": "2025-06-17T10:30:00Z","production_demand_info": {"Product Name": "Chemical Product A", "Product Specifications": {"Chemical Composition":{"Component A": "50%","Component B": "30%","Component C": "20%"},"Purity Requirement": "≥99.5%","Appearance":"Colorless and Transparent Liquid","Quality Requirement": {"Heavy Metal Content": "<10ppm","Water Content": "<0.1%"}},"Process Requirements": {"Reaction Temperature": "100-120°C","Reaction Pressure": "1-2MPa","Reaction Time": "4 hours","Stirring Speed": "50-100rpm"}},"Batch Model": "Batch_20250617_Chemical Product A","Additional Notes": "This batch of products requires strict control of reaction conditions to ensure that product quality meets standards."}

[0022] S102: The server automatically obtains corresponding target process parameters based on the production requirement description information. The target process parameters are determined using a preset comparison function. The preset comparison function includes a basic comparison term expression and a physical comparison term expression. The basic comparison term expression is used to quantify the correlation between production requirement keyword sequences, and the physical comparison term expression is used to quantify the correlation between process flow charts. Among them, target process parameters refer to the operating parameters of production equipment that need to be set in order to meet production needs. Operating parameters include temperature, pressure, time, flow, etc., which directly affect the quality of the product.

[0023] In some embodiments of the present application, the process of automatically obtaining corresponding target process parameters based on production requirement description information includes: inputting the production requirement description information into a pre-fine-tuned keyword extraction model, and outputting a production requirement keyword sequence corresponding to the production requirement description information, the pre-fine-tuned keyword extraction model is obtained by fine-tuning a large language model using professional knowledge for the multi-variety production industry; using a preset comparison function to analyze the overall comparison value between the production requirement keyword sequence and the historical production data of each variety in a preset multi-variety database, the preset multi-variety database is obtained by collecting and preprocessing the historical production data of each variety; extracting the currently executed process parameters from the historical production data of the variety corresponding to the largest non-zero overall comparison value; adjusting the redundant parameters in the currently executed process parameters to obtain the target process parameters corresponding to the production requirement description information.

[0024] The pre-tuned keyword extraction model is a fine-tuned model used to extract keyword sequences from production demand descriptions. This model is fine-tuned based on large language models (such as BERT and GPT), utilizing specialized knowledge from the multi-variety production industry. Production demand keyword sequences are extracted from production demand descriptions and represent the core content of production requirements. The pre-set multi-variety database contains historical production data for each variety and provides a reference for historical production data. Redundant parameters are parameters that are irrelevant or unnecessary to current production requirements.

[0025] In an embodiment of the present application, by inputting the production demand description information into a pre-fine-tuned keyword extraction model, the system can quickly extract the core keyword sequence of the production demand. Using a preset comparison function, the system further analyzes the similarity between these keyword sequences and the historical production data in the preset multi-variety database to find the most matching historical production data. On this basis, the system extracts the currently executed process parameters and adjusts the redundant parameters therein, and finally generates target process parameters that accurately match the current production needs. This process not only improves the accuracy and efficiency of process parameter selection, but also reduces errors in human operation, ensures the stability of production and consistency of product quality, and is very suitable for situations where there are many types of products, many processes, many parameter controls, and frequent switching of equipment control programs in the production process. Among them, the historical production data in the preset multi-variety database are shown in Table 1.

[0026] Table 1

[0027] Among them, the function expression of the preset comparison function is:

[0028] in, is the overall comparison value, is the semantic similarity weight, with a value range of ,default value , is the topological similarity weight, with a value range of ,default , is the basic comparison value, is the physical comparison item value; The basic comparison term expression is:

[0029] in, is the basic comparison item value, is the current vector set of production demand keywords, is the historical vector set corresponding to each variety, The number of production demand keywords, is the number of historical keywords corresponding to each variety, The number of semantic intersections between production demand keywords and historical keywords corresponding to each variety; The physical comparison term expression is:

[0030] in, is the physical comparison item value, is the first process topology directed graph, where nodes represent different reactors and edges represent material flows. is the directed graph of the historical process flow chart, for The total number of nodes, for The total number of nodes, is the graph edit distance between the first process topology directed graph and each second process topology directed graph, The total number of nodes in the directed graph of the process topology with the most nodes or edges.

[0031] It should be noted that in order to ensure the accuracy of matching, , so that the semantics of the production demand keywords and the historical keywords corresponding to each variety are not intersected is 0. At this time, the basic comparison item value Can be 0, resulting in the final When the value is 0, the historical production data of the variety corresponding to the zero contrast value can be accurately screened out, so that the currently executed process parameters can be extracted from the historical production data of the variety corresponding to the largest non-zero overall contrast value.

[0032] In the embodiment of the present application, by designing basic comparison term expressions, the parameter generalization problem caused by relying solely on the process topology directed graph can be avoided.

[0033] In some embodiments of the present application, the specific process of analyzing the comparison value between the production demand keyword sequence and the historical production data of each variety in the preset multi-variety database includes: obtaining the historical production data of each variety in the preset multi-variety database; inputting the historical production data of each variety into a pre-fine-tuned keyword extraction model, and outputting the historical production demand keyword sequence corresponding to each variety; calculating the basic comparison value between the production demand keyword sequence and the historical production demand keyword sequence corresponding to each variety based on the basic comparison item expression; obtaining the current process flow chart of the required production variety based on the production demand keyword sequence; calculating the physical comparison item value between the current process flow chart and the historical process flow chart of each variety based on the physical comparison item expression; and weightedly product the basic comparison value and the physical comparison item value to obtain the overall comparison value between the production demand keyword sequence and the historical production data of each variety in the preset multi-variety database.

[0034] The historical production demand keyword sequence is a keyword sequence extracted from the production data of each product, which is used to represent the core content of the historical production demand. The process flow chart is a process flow chart generated based on production demand, which describes the specific process steps and processes of the production task.

[0035] In some embodiments of the present application, the process of calculating the basic comparison value between the production demand keyword sequence and the historical production demand keyword sequence corresponding to each variety includes: converting the production demand keyword sequence into a vector set to obtain a current vector set; converting the historical production demand keyword sequence corresponding to each variety into a vector set to obtain a historical vector set corresponding to each variety; calculating the semantic similarity between the current vector set and the historical vector set corresponding to each variety; and using the semantic similarity as the basic comparison value between the production demand keyword sequence and the historical production demand keyword sequence corresponding to each variety.

[0036] For example, the production demand keyword sequence is [granulation, tableting], and a certain historical production demand keyword sequence is [granulation, coating]. It can be seen that only "granulation" , so .

[0037]

[0038] In some embodiments of the present application, the specific process of calculating the physical comparison item value between the current process flow chart and the historical process flow charts of each variety includes: converting the current process flow chart into a first process topology directed graph composed of nodes and edges; converting the historical process flow charts of each variety into a second process topology directed graph composed of nodes and edges; calculating the minimum number of editing operations required for adding, deleting, and modifying nodes or edges between the first process topology directed graph and each second process topology directed graph, and obtaining the graph editing distance between the first process topology directed graph and each second process topology directed graph; counting the total number of nodes in the process topology directed graph with the most nodes or edges in the first process topology directed graph and each second process topology directed graph; calculating the difference between the ratio of the graph editing distance and the total number of nodes, and obtaining the process topology similarity between the current process flow chart and the historical process flow charts of each variety; and using the process topology similarity as the physical comparison item value between the current process flow chart and the historical process flow charts of each variety.

[0039] For example, the directed graph of the first process topology is: mixing → reaction → drying (3 nodes), and the directed graph of a second process topology is mixing → reaction → filtration → packaging (4 nodes). In this case, GED = 2 (1 node needs to be added + 1 edge needs to be modified).

[0040]

[0041] Furthermore, if the overall comparison values ​​of the matches are all 0, it means that there are no matching similar varieties in the preset multi-variety database. At this time, manual operation can be switched to, and a prompt message is generated and sent to the warning client for warning reminder.

[0042] In other embodiments, the specific process of automatically obtaining the corresponding target process parameters based on the production requirement description information includes: loading a pre-established mapping relationship between the process parameter version number and the process parameter; querying the target process parameter version number in the production requirement description information; obtaining the target process parameters corresponding to the target process parameter version number from the mapping relationship; and using the target process parameters as the target process parameters corresponding to the production requirement description information.

[0043] In the embodiment of the present application, a one-to-one relationship is established between the pre-established mapping relationship between the process parameter version number and the process parameter, so the corresponding process parameters can be accurately found with high accuracy, but the workload of the initial parameter configuration is large.

[0044] S103, the server pre-checks the target process parameters and obtains the verification results; Among them, pre-verification ensures that the target process parameters meet the operating requirements of the production equipment, the standards of the production process and safety specifications, so as to avoid production failure or equipment damage due to parameter errors.

[0045] Each target process parameter includes a target process identifier and a target attribute value.

[0046] In some embodiments, the specific process of pre-verifying the target process parameters and obtaining the verification results includes: reading multiple parameter ranges supported by the device control layer from the device configuration file or database, each parameter range including a process identifier and an attribute value range; judging from the process identifier and attribute value range included in each parameter range whether the attribute value range corresponding to the process identifier identical to each target process identifier contains the target attribute value; if so, generating a verification result indicating that the target process parameter verification has passed; if not, generating a verification result indicating that the target process parameter verification has failed.

[0047] For example, the target process parameters are: Chemical composition: Component A: 50%, Component B: 30%, Component C: 20%, Purity requirement: ≥99.5%, Appearance: colorless transparent liquid, Reaction temperature: 100-120°C, Reaction time: 3.8 hours, Reaction pressure: 1-2Mpa, Stirring speed: 50-100rpm.

[0048] For example, some parameter ranges include: Reaction temperature: must be within the equipment's permitted temperature range (e.g., 80-150°C). Reaction pressure: must be within the equipment's permitted pressure range (e.g., 0.5-3 MPa). Reaction time: must be within a reasonable production cycle (e.g., 1-6 hours). Stirring speed: must be within the equipment's permitted speed range (e.g., 30-120 rpm). Through comparison, relevant calibration results can be determined.

[0049] For example Figure 2 As shown, Figure 2 This is a flowchart of a pre-verification process provided by the present application, which reads multiple parameter ranges supported by the device control layer, each parameter range including a process identifier and an attribute value range, and determines whether the process identifier and attribute value range included in each parameter range contain target process parameters. If so, a verification result indicating that the target process parameter verification has passed is generated; if not, a verification result indicating that the target process parameter verification has failed is generated.

[0050] S104 , when the verification result indicates that the target process parameters have passed the verification, the server binds the target process parameters to the product batch model and sends it to the equipment control layer to switch the currently executed process parameters to the target process parameters.

[0051] The product batch model refers to the code or name used to identify different batches of products. The equipment control layer refers to the system or software that directly controls the operation of production equipment.

[0052] Furthermore, after receiving the process parameters sent by the server, the equipment control layer applies them to the production equipment, completing the process parameter switch. For example, after the PLC system receives the parameters, it updates the equipment's operating parameters and starts operating according to the new parameters.

[0053] For example Figure 3 As shown, Figure 3 This is a process schematic diagram of a process parameter switching process for multi-variety production provided by this application. First, the manufacturing execution system sends a process parameter switching request to the server. The server analyzes the information in the request and the production requirement keywords and process flow chart in the preset multi-variety database based on the preset comparison function to determine the target process parameters. After verification, it is sent to the equipment control layer for production process parameter switching.

[0054] In the embodiment of the present application, on the one hand, the manufacturing execution system can monitor the production status of the product in real time. When it is necessary to switch the production of the next variety, it can promptly send a process parameter switching request to the server, and the server can automatically obtain the corresponding target process parameters. This process does not require manual intervention, avoiding the time required to find and enter process parameters, allowing the equipment to switch to new production tasks more quickly, thereby significantly improving production efficiency and shortening the production cycle. On the other hand, the target process parameters are determined using a preset comparison function, which includes a basic comparison item expression and a physical comparison item expression. The basic comparison item expression is used to quantify the correlation between production requirement keyword sequences, and the physical comparison item expression is used to quantify the correlation between process flow charts. By comprehensively considering the correlation of production requirements and the correlation of process flows, it is possible to quickly obtain accurate process parameters based on the historical production data of each variety, thereby improving product quality.

[0055] See Figure 4 , which is a flow chart of a method for fine-tuning a keyword extraction model provided in the embodiment of the present application. Figure 4 As shown, the method of the embodiment of the present application may include the following steps: S201, collects and pre-sets professional knowledge for multi-variety production industries; Among them, professional knowledge for the multi-variety production industry refers to knowledge in technology, process, management and other aspects related to multi-variety production, including production process parameters, quality control standards, equipment operation requirements, etc.

[0056] In some embodiments of the present application, the types of professional knowledge required for collection include production process parameters (such as temperature, pressure, time, etc.), quality control standards (such as purity requirements, appearance requirements, etc.), equipment operation requirements (such as the temperature range and pressure range allowed by the equipment), production flowcharts, and historical production data (including successful and failed cases). The data sources include internal documents of the enterprise, historical production records, expert experience, industry standards and specifications, and external databases.

[0057] S202, adjusting model parameters of the large language model to obtain a keyword extraction model; S203, inputting preset professional knowledge for multi-variety production industries into the keyword extraction model for model fine-tuning to obtain a pre-fine-tuned keyword extraction model.

[0058] In the embodiments of the present application, on the one hand, the manufacturing execution system can monitor the product production state in real time, and when it is necessary to switch to the production of the next variety, it can timely issue a process parameter switching request to the server, and the server can automatically obtain the corresponding target process parameters. This process does not require manual intervention, avoids the time required for searching and inputting process parameters, enables the equipment to switch to a new production task more quickly, and thus significantly improves the production efficiency and shortens the production cycle. On the other hand, the target process parameters are determined by using a preset comparison function, the preset comparison function includes a basic comparison term expression and a physical comparison term expression, the basic comparison term expression is used to quantify the correlation between the production demand keyword sequences, and the physical comparison term expression is used to quantify the correlation between the process flowcharts. By comprehensively considering the correlation of production demands and the correlation of process flowcharts, accurate process parameters can be quickly obtained depending on the historical production data of each variety, and thus the product quality is improved.

[0059] The following is an apparatus embodiment of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the apparatus embodiments of the present application, please refer to the method embodiments of the present application.

[0060] Please refer to Figure 5 which shows a structure schematic diagram of a process parameter switching device for multi-variety production provided by an example embodiment of the present application. The process parameter switching device for multi-variety production can be realized by software, hardware, or a combination of both to become all or part of an electronic device. The device 1 includes a receiving module 10, an obtaining module 20, a pre-inspection module 30, and a parameter issuing module 40.

[0061] The receiving module 10 is configured to receive a process parameter switching request sent by a manufacturing execution system for monitoring the product production state, the process parameter switching request carrying production demand description information and a product batch model; An acquisition module 20 is configured to automatically acquire corresponding target process parameters based on the production requirement description information. The target process parameters are determined using a preset comparison function. The preset comparison function includes a basic comparison term expression and a physical comparison term expression. The basic comparison term expression is used to quantify the correlation between production requirement keyword sequences, and the physical comparison term expression is used to quantify the correlation between process flow charts. A pre-inspection module 30 is used to pre-check target process parameters and obtain a verification result; The parameter sending module 40 is used to bind the target process parameters to the product batch model when the verification result indicates that the target process parameters have passed the verification, and send it to the equipment control layer to switch the currently executed process parameters to the target process parameters.

[0062] It should be noted that the process parameter switching device for multi-variety production provided in the above embodiment only uses the division of the above-mentioned functional modules as an example when executing the process parameter switching method for multi-variety production. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the process parameter switching device for multi-variety production provided in the above embodiment and the process parameter switching method for multi-variety production are of the same concept. The implementation process is detailed in the method embodiment and will not be repeated here.

[0063] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0064] In the embodiment of the present application, on the one hand, the manufacturing execution system can monitor the production status of the product in real time. When it is necessary to switch the production of the next variety, it can promptly send a process parameter switching request to the server, and the server can automatically obtain the corresponding target process parameters. This process does not require manual intervention, avoiding the time required to find and enter process parameters, allowing the equipment to switch to new production tasks more quickly, thereby significantly improving production efficiency and shortening the production cycle. On the other hand, the target process parameters are determined using a preset comparison function, which includes a basic comparison item expression and a physical comparison item expression. The basic comparison item expression is used to quantify the correlation between production requirement keyword sequences, and the physical comparison item expression is used to quantify the correlation between process flow charts. By comprehensively considering the correlation of production requirements and the correlation of process flows, it is possible to quickly obtain accurate process parameters based on the historical production data of each variety, thereby improving product quality.

[0065] The present application also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implements the process parameter switching method for multi-variety production provided by the above-mentioned various method embodiments.

[0066] The present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the process parameter switching method for multi-variety production according to the above-mentioned various method embodiments.

[0067] See Figure 6 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0068] The communication bus 1002 is used to implement the connection and communication between these components.

[0069] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0070] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0071] The processor 1001 may include one or more processing cores. The processor 1001 utilizes various interfaces and circuits to connect various components within the electronic device 1000. It executes instructions, programs, code sets, or instruction sets stored in the memory 1005, and accesses data stored in the memory 1005 to perform various functions and process data within the electronic device 1000. Optionally, the processor 1001 may be implemented in hardware using at least one of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 1001 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display; and the modem handles wireless communications. It is understood that the modem may also be implemented independently of the processor 1001 and implemented on a separate chip.

[0072] Among them, the memory 1005 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may also be optionally at least one storage system located away from the aforementioned processor 1001. As Figure 6 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a process parameter switching application for multi-variety production.

[0073] exist Figure 6 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain user input data; and the processor 1001 can be used to call the process parameter switching application for multi-variety production stored in the memory 1005 and specifically perform the following operations: Receive a process parameter switching request sent by a manufacturing execution system for monitoring product production status, the process parameter switching request carrying production requirement description information and product batch model; Automatically obtain the corresponding target process parameters based on the production requirement description information. The target process parameters are determined using a preset comparison function. The preset comparison function includes a basic comparison term expression and a physical comparison term expression. The basic comparison term expression is used to quantify the correlation between production requirement keyword sequences, and the physical comparison term expression is used to quantify the correlation between process flow charts. Pre-check target process parameters and obtain verification results; When the verification result indicates that the target process parameters have passed the verification, the target process parameters are bound to the product batch model and sent to the equipment control layer to switch the currently executed process parameters to the target process parameters.

[0074] In one embodiment, when the processor 1001 automatically obtains the corresponding target process parameters according to the production requirement description information, it specifically performs the following operations: Input the production demand description information into a pre-tuned keyword extraction model, which outputs a production demand keyword sequence corresponding to the production demand description information. The pre-tuned keyword extraction model is obtained by fine-tuning a large language model using professional knowledge for the multi-variety production industry. Using a preset comparison function, analyze the overall comparison value between the production demand keyword sequence and the historical production data of each variety in the preset multi-variety database. The preset multi-variety database is obtained by collecting and pre-processing the historical production data of each variety; Extract the currently executed process parameters from the historical production data of the product corresponding to the largest non-zero overall contrast value; Adjust the redundant parameters in the currently executed process parameters to obtain the target process parameters corresponding to the production demand description information.

[0075] In one embodiment, when the processor 1001 analyzes the comparison value between the production demand keyword sequence and the historical production data of each variety in the preset multi-variety database, it specifically performs the following operations: Obtain historical production data for each variety in the preset multi-variety database; Input the historical production data of each variety into the pre-adjusted keyword extraction model, and output the historical production demand keyword sequence corresponding to each variety; Calculate the basic comparison value between the production demand keyword sequence and the historical production demand keyword sequence corresponding to each variety based on the basic comparison item expression; Based on the production requirement keyword sequence, obtain the current process flow chart of the required production variety; According to the physical comparison item expression, calculate the physical comparison item value between the current process flow chart and the historical process flow chart of each variety; The basic comparison value and the physical comparison item value are weighted and multiplied to obtain the overall comparison value between the production demand keyword sequence and the historical production data of each variety in the preset multi-variety database.

[0076] In one embodiment, when calculating the basic comparison value between the production demand keyword sequence and the historical production demand keyword sequence corresponding to each product, the processor 1001 specifically performs the following operations: Convert the production demand keyword sequence into a vector set to obtain the current vector set; Convert the historical production demand keyword sequence corresponding to each variety into a vector set to obtain the historical vector set corresponding to each variety; Calculate the semantic similarity between the current vector set and the historical vector set corresponding to each variety; Semantic similarity is used as the basic comparison value between the production demand keyword sequence and the historical production demand keyword sequence corresponding to each variety.

[0077] In one embodiment, when calculating the physical comparison item value between the current process flow chart and the historical process flow charts of each product, the processor 1001 specifically performs the following operations: Converting the current process flow graph into a first process topology directed graph consisting of nodes and edges; Convert the historical process flow chart of each product into a second process topology directed graph consisting of nodes and edges; Calculating the minimum number of edit operations required for adding, deleting, or modifying nodes or edges between the first process topology directed graph and each second process topology directed graph to obtain a graph edit distance between the first process topology directed graph and each second process topology directed graph; Counting the total number of nodes of the process topology directed graph with the most nodes or edges in the first process topology directed graph and each of the second process topology directed graphs; The difference between the graph edit distance and the ratio of the total number of nodes is calculated to obtain the process topology similarity between the current process flow diagram and the historical process flow diagram of each variety; The process topology similarity is used as the physical comparison item value between the current process flow chart and the historical process flow chart of each variety.

[0078] In one embodiment, when executing the generation of the pre-fine-tuned keyword extraction model, the processor 1001 specifically performs the following operations: Collect preset expertise for multi-variety production industries; Adjust the model parameters of the large language model to obtain a keyword extraction model; The preset professional knowledge for the multi-variety production industry is input into the keyword extraction model to fine-tune the model, thereby obtaining a pre-fine-tuned keyword extraction model.

[0079] In one embodiment, when the processor 1001 automatically obtains the corresponding target process parameters according to the production requirement description information, it specifically performs the following operations: Load the pre-established mapping relationship between process parameter version number and process parameter; In the production requirement description information, query the target process parameter version number; From the mapping relationship, obtain the target process parameter corresponding to the target process parameter version number; The target process parameters are used as the target process parameters corresponding to the production requirement description information.

[0080] In one embodiment, when the processor 1001 pre-checks the target process parameters and obtains the check result, it specifically performs the following operations: Read multiple parameter ranges supported by the device control layer from the device configuration file or database, each parameter range includes a process identifier and an attribute value range; From the process identifiers and attribute value ranges included in each parameter range, determining whether the attribute value range corresponding to the process identifier identical to each target process identifier includes the target attribute value; If yes, generating a verification result indicating that the target process parameter verification passed; If not, a verification result indicating that the target process parameter verification fails is generated.

[0081] In the embodiment of the present application, on the one hand, the manufacturing execution system can monitor the production status of the product in real time. When it is necessary to switch the production of the next variety, it can promptly send a process parameter switching request to the server, and the server can automatically obtain the corresponding target process parameters. This process does not require manual intervention, avoiding the time required to find and enter process parameters, allowing the equipment to switch to new production tasks more quickly, thereby significantly improving production efficiency and shortening the production cycle. On the other hand, the target process parameters are determined using a preset comparison function, which includes a basic comparison item expression and a physical comparison item expression. The basic comparison item expression is used to quantify the correlation between production requirement keyword sequences, and the physical comparison item expression is used to quantify the correlation between process flow charts. By comprehensively considering the correlation of production requirements and the correlation of process flows, it is possible to quickly obtain accurate process parameters based on the historical production data of each variety, thereby improving product quality.

[0082] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program for switching process parameters for multi-variety production can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The storage medium for the program for switching process parameters for multi-variety production can be a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0083] The above disclosure is only a preferred embodiment of the present application, and certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A process parameter switching method for multi-variety production, characterized in that: Applied to the server, the method includes: receiving a process parameter switching request sent by a manufacturing execution system for monitoring product production status, wherein the process parameter switching request carries production requirement description information and a product batch model; Automatically obtain corresponding target process parameters based on the production requirement description information, wherein the target process parameters are determined using a preset comparison function, wherein the preset comparison function includes a basic comparison term expression and a physical comparison term expression. The basic comparison term expression is used to quantify the correlation between production requirement keyword sequences, and the physical comparison term expression is used to quantify the correlation between process flow charts. Pre-checking the target process parameters to obtain a check result; When the verification result indicates that the target process parameter has passed the verification, the target process parameter is bound to the product batch model and sent to the equipment control layer to switch the currently executed process parameter to the target process parameter.

2. The method according to claim 1, characterized in that The automatically obtaining corresponding target process parameters according to the production requirement description information includes: Inputting the production demand description information into a pre-fine-tuned keyword extraction model, and outputting a production demand keyword sequence corresponding to the production demand description information, wherein the pre-fine-tuned keyword extraction model is obtained by fine-tuning a large language model using professional knowledge for the multi-variety production industry; Using a preset comparison function, analyzing the overall comparison value between the production demand keyword sequence and the historical production data of each variety in a preset multi-variety database, wherein the preset multi-variety database is obtained by collecting and pre-processing the historical production data of each variety; Extract the currently executed process parameters from the historical production data of the product corresponding to the largest non-zero overall contrast value; Redundant parameters in the currently executed process parameters are adjusted to obtain target process parameters corresponding to the production requirement description information.

3. The method according to claim 2, characterized in that The analyzing the comparison value between the production demand keyword sequence and the historical production data of each variety in the preset multi-variety database includes: Obtain historical production data for each variety in the preset multi-variety database; Input the historical production data of each variety into a pre-adjusted keyword extraction model, and output a historical production demand keyword sequence corresponding to each variety; Calculating a basic comparison value between the production demand keyword sequence and the historical production demand keyword sequence corresponding to each variety according to the basic comparison item expression; Based on the production requirement keyword sequence, obtain the current process flow chart of the required product; Calculating the physical comparison item value between the current process flow chart and the historical process flow chart of each variety according to the physical comparison item expression; The basic comparison value and the physical comparison item value are weighted and multiplied to obtain an overall comparison value between the production demand keyword sequence and the historical production data of each variety in the preset multi-variety database.

4. The method according to claim 3, characterized in that The calculating of the basic comparison value between the production demand keyword sequence and the historical production demand keyword sequence corresponding to each variety includes: Converting the production demand keyword sequence into a vector set to obtain a current vector set; Converting the historical production demand keyword sequence corresponding to each variety into a vector set to obtain a historical vector set corresponding to each variety; Calculating semantic similarity between the current vector set and the historical vector sets corresponding to each variety; The semantic similarity is used as a basic comparison value between the production demand keyword sequence and the historical production demand keyword sequence corresponding to each variety.

5. The method according to claim 3, characterized in that The calculating of the physical comparison item value between the current process flow chart and the historical process flow charts of each variety includes: Converting the current process flow graph into a first process topology directed graph consisting of nodes and edges; Converting the historical process flow chart of each product into a second process topology directed graph consisting of nodes and edges; Calculating the minimum number of edit operations required for adding, deleting, or modifying nodes or edges between the first process topology directed graph and each second process topology directed graph to obtain a graph edit distance between the first process topology directed graph and each second process topology directed graph; Counting the total number of nodes of the process topology directed graph with the most nodes or edges in the first process topology directed graph and each second process topology directed graph; Calculating the difference between the graph edit distance and the ratio of the total number of nodes to obtain the process topology similarity between the current process flow chart and the historical process flow charts of each variety; The process topology similarity is used as a physical comparison item value between the current process flow chart and the historical process flow charts of each variety.

6. The method according to claim 2, characterized in that The function expression of the preset comparison function is: in, is the overall comparison value, is the semantic similarity weight, with a value range of ,default value , is the topological similarity weight, with a value range of ,default , is the basic comparison value, is the physical comparison item value; The basic comparison term expression is: in, is the basic comparison item value, is the current vector set of production demand keywords, is the historical vector set corresponding to each variety, The number of production demand keywords, is the number of historical keywords corresponding to each variety, The number of semantic intersections between production demand keywords and historical keywords corresponding to each variety; The physical comparison term expression is: in, is the physical comparison item value, is the first process topology directed graph, where nodes represent different reactors and edges represent material flows. is the directed graph of the historical process flow chart, for The total number of nodes, for The total number of nodes, is the graph edit distance between the first process topology directed graph and each second process topology directed graph, The total number of nodes in the directed graph of the process topology with the most nodes or edges.

7. The method according to claim 2, characterized in that Follow these steps to generate a pre-tuned keyword extraction model, including: Collect preset expertise for multi-variety production industries; Adjust the model parameters of the large language model to obtain a keyword extraction model; The preset professional knowledge for the multi-variety production industry is input into the keyword extraction model to perform model fine-tuning to obtain a pre-fine-tuned keyword extraction model.

8. The method according to any one of claims 1 to 7, characterized in that The automatically obtaining corresponding target process parameters according to the production requirement description information includes: Load the pre-established mapping relationship between process parameter version number and process parameter; In the production requirement description information, query the target process parameter version number; Obtaining the target process parameter corresponding to the target process parameter version number from the mapping relationship; The target process parameters are used as the target process parameters corresponding to the production requirement description information.

9. The method according to any one of claims 1 to 7, characterized in that Each target process parameter includes a target process identifier and a target attribute value; The pre-verification of the target process parameters to obtain a verification result includes: Reading multiple parameter ranges supported by the device control layer from a configuration file or database of the device, each parameter range including a process identifier and a property value range; From the process identifiers and property value ranges included in each parameter range, determining whether the property value range corresponding to the process identifier identical to each target process identifier includes the target property value; If yes, generating a verification result indicating that the target process parameter verification has passed; If not, a verification result indicating that the target process parameter verification fails is generated.

10. A process parameter switching device for multi-variety production, characterized in that: The device comprises: a receiving module, configured to receive a process parameter switching request sent by a manufacturing execution system for monitoring product production status, wherein the process parameter switching request carries production requirement description information and product batch model; an acquisition module, configured to automatically acquire corresponding target process parameters based on the production requirement description information, wherein the target process parameters are determined using a preset comparison function, wherein the preset comparison function includes a basic comparison term expression and a physical comparison term expression, wherein the basic comparison term expression is used to quantify the correlation between production requirement keyword sequences, and the physical comparison term expression is used to quantify the correlation between process flow charts; A pre-verification module is used to pre-verify the target process parameters and obtain verification results; The parameter sending module is used to bind the target process parameters to the product batch model when the verification result indicates that the target process parameters have passed the verification, and send it to the equipment control layer to switch the currently executed process parameters to the target process parameters.

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