Enzyme preparation production key link monitoring method, system and equipment

Abstract

The invention relates to the technical field of production monitoring, in particular to a method, a system and equipment for monitoring key links of enzyme preparation production. The method comprises the following steps: acquiring equipment node information of an enzyme preparation production key link and equipment operation information corresponding to the equipment node information, and determining set operation parameters corresponding to each production equipment node in the production key link based on the equipment node information, determining an actual operation parameter corresponding to each production equipment node based on the equipment operation information, and performing backtracking calibration on the set operation parameter according to the actual operation parameter and a preset calibration algorithm to obtain a calibration operation parameter corresponding to each production equipment node, and determining whether the calibration operation parameters are consistent with the set operation parameters based on the production equipment nodes, if not, generating a parameter calibration instruction based on the calibration operation parameters and the set operation parameters, and controlling the production equipment corresponding to the set operation parameters to perform parameter calibration. The production quality of the enzyme preparation is improved.

Description

Technical Field

[0001] The present application relates to the technical field of model supervision, and in particular to a method, system and equipment for monitoring key links in the production of enzyme preparations. Background Art

[0002] The production process of enzyme preparations, namely enzyme engineering, is the technology for mass production and application of enzyme preparations. Enzyme engineering combines the basic principles of enzymology with chemical engineering, and uses the catalytic properties of enzymes in a certain reaction device to convert raw materials into products. With the rapid development of enzymology research and the promotion of enzyme applications, enzyme engineering has become an important technology in industrial production.

[0003] The sources of enzyme preparations include microorganisms, animals and plants, but the main source is microorganisms. Since microorganisms have the advantages of rapid reproduction, easy cultivation, and high enzyme production, excellent enzyme-producing strains are generally selected to produce enzymes through fermentation. In order to increase the enzyme concentration in the fermentation broth, it is necessary to select excellent strains, develop genetically engineered bacteria, and optimize fermentation conditions. In addition, industrial production also requires new enzymes with special properties, such as high-temperature resistant α-amylase, alkaline-resistant protease and lipase, etc., so it is necessary to study and develop strains that produce new enzymes with special properties.

[0004] There are many key links in the production process of enzyme preparations. The monitoring of the equipment corresponding to these links is crucial to ensure the quality and output of enzyme preparations. However, the current monitoring of link equipment only stays at the basic operation status monitoring and fault alarm level, but it is difficult to detect the error anomaly caused by the aging of the link equipment itself in time, which leads to the reduction of the production quality of enzyme preparations. Summary of the invention

[0005] In order to solve at least one of the above-mentioned technical problems, the present application provides a method, system and equipment for monitoring key links in enzyme preparation production.

[0006] In the first aspect, the present application provides a method for monitoring key links in the production of enzyme preparations, using the following technical solution: A method for monitoring key links in enzyme preparation production, comprising: Obtaining equipment node information corresponding to key links in enzyme preparation production and equipment operation information corresponding to the equipment node information; Determine the set operation parameters that each production equipment node in the key production link should correspond to based on the equipment node information; Determine the actual operation parameters corresponding to each production equipment node based on the equipment operation information; Performing back-tracking calibration on the set operating parameters according to the actual operating parameters and a preset calibration algorithm to obtain calibration operating parameters corresponding to each production equipment node; Based on the production equipment node, determine whether the calibration operation parameters are consistent with the setting operation parameters. If they are inconsistent, generate parameter calibration instructions based on the calibration operation parameters and the setting operation parameters to control the production equipment corresponding to the setting operation parameters to perform parameter calibration.

[0007] By adopting the above technical solution, the historical industry model and its corresponding historical industry data are obtained. The historical industry model is trained based on rich historical industry data, which is generated by different production monitoring equipment in the industry, ensuring the comprehensiveness and accuracy of the model. This step provides a solid foundation for the subsequent determination of industry data standards and model correction. The industry data standard is further determined based on the historical industry model. This standard, as a judgment benchmark, can accurately identify whether there are abnormal performance data in the historical industry data, thereby ensuring the accuracy and reliability of data analysis. The timely elimination of abnormal data avoids its interference with the subsequent data analysis results. The historical industry data is strictly analyzed, and after confirming that it meets the industry data standard, the corrected performance data is obtained, thereby realizing the correction of the industry equipment performance data. The industry data standard is updated based on the corrected performance data, and a more accurate and adapted to the current industry situation The corrected data standard is obtained. This makes the data standard closer to the actual application of the equipment, providing a more accurate judgment benchmark for the subsequent industry model correction. According to the corrected performance data and the corrected data standard, the historical industry model is corrected and updated, and finally the corrected industry model is obtained, thereby improving the supervision efficiency of the industry model.

[0008] In a preferred example, the present application may be further configured as follows: the set operation parameters are back-calibrated according to the actual operation parameters and the preset calibration algorithm to obtain the calibration operation parameters corresponding to each production equipment node, including: Compare the actual operation parameters and the set operation parameters one by one according to the parameter correspondence relationship to obtain a parameter comparison result; When there is inconsistency in the parameter comparison result, the abnormal device node is determined, and the operation parameter group of the abnormal device in different unit time is collected according to the operation cycle of the abnormal device node, and the operation parameter group is respectively input into the preset calibration algorithm to perform the set operation parameter back-calibration to obtain the calibration operation parameter corresponding to the abnormal device node, and the preset calibration algorithm is A=YB+C, wherein the Y and C constant values, A is the actual operation parameter in the operation parameter group, and B is the set operation parameter; When there is no inconsistency in the parameter comparison result, the set operation parameters are used as calibration operation parameters.

[0009] In a preferred example, the present application may be further configured as follows: the operation parameter groups are respectively input into a preset calibration algorithm to perform the set operation parameter backtracking calibration to obtain the calibration operation parameters corresponding to the abnormal device node, including: Acquire reference operation data of the production equipment node in a historical period, and determine that there is an abnormal parameter group in the reference operation data in which the actual operation parameters are inconsistent with the set operation parameters; Respectively grouping each group of abnormal parameters in the abnormal parameter group into groups of two according to the operation timeline of the production equipment node to obtain an improved parameter group corresponding to each abnormal operation parameter in the abnormal parameter group; Inputting the actual operation parameters and the set operation parameters in the improved parameter group into the preset calibration algorithm for calculation respectively, and obtaining the improved Y value and the improved C value in the preset calibration algorithm; Determine an improved calibration sequence table based on the improved Y value, the improved C value, the actual operating parameters in the improved parameter set, and the set operating parameters in the improved parameter set; Respectively grouping each group of operation parameters in the operation parameter group into two groups according to the operation timeline of the production equipment node to obtain an optimization parameter group corresponding to each operation parameter in the operation parameter group; The set operation parameters in the optimization parameter group are retroactively calibrated according to the improved calibration sequence table to obtain the calibration operation parameters corresponding to the abnormal device node.

[0010] In a preferred example, the present application can be further configured as follows: determining the improved calibration sequence table based on the improved Y value, the improved C value, the actual operating parameters in the improved parameter group, and the set operating parameters in the improved parameter group includes: Creating an initial calibration sequence table, and inputting the actual operation parameters in the improved parameter group and the set operation parameters in the improved parameter group into the initial calibration sequence table according to the operation timeline, and marking each operation timeline in the initial calibration sequence table to obtain a secondary calibration sequence table; The improved Y value and the improved C value are input into the secondary calibration sequence table accordingly, and the indexes of the actual operation parameters in the improved parameter group and the set operation parameters in the improved parameter group are established to obtain the improved calibration sequence table.

[0011] In a preferred example, the present application may be further configured as follows: performing retroactive calibration on the set operation parameters in the optimization parameter group according to the improved calibration sequence table to obtain the calibration operation parameters corresponding to the abnormal device node includes: Performing data difference calculation on the actual operating parameters and the set operating parameters in each row of operating parameter data in the improved calibration sequence table to obtain a first parameter difference corresponding to each row of operating parameter data in the improved calibration sequence table; Determine a second parameter difference between the actual operating parameter of the current optimization parameter group and the set operating parameter, and determine the comparison parameter data in the improved calibration sequence table that meets the preset parameter difference range based on the first parameter difference and the second parameter difference; Performing actual operation parameter screening on the comparison parameter data, determining a third parameter difference between the actual operation parameter in the comparison parameter data and the actual operation parameter of the optimization parameter group, and judging whether the third parameter difference meets a preset deviation range; If the third parameter difference meets the preset deviation range, the Y value and the C value in the comparison parameter data are respectively averaged to obtain a representative Y value and a representative C value; The actual operating parameters of the optimization parameter group, the representative Y value, and the representative C value are input into a preset calibration algorithm for calculation to obtain the calibration operating parameters corresponding to the abnormal device node.

[0012] In a preferred example, the present application may be further configured as follows: the actual operating parameters of the optimization parameter group, the representative Y value, and the representative C value are input into a preset calibration algorithm for calculation to obtain the calibration operating parameters corresponding to the abnormal device node, and then further comprising: The improved calibration sequence table is updated based on the corrected set operating parameters and the actual operating parameters in the optimization parameter group to obtain an updated improved calibration sequence table; The parameter difference between the set operating parameters and the actual operating parameters in the updated improved calibration sequence table is monitored in real time. When the parameter difference is greater than the parameter difference range standard, production equipment maintenance information is generated and sent to the target terminal device.

[0013] In a preferred example, the present application may be further configured as follows: the production equipment maintenance information is sent to the target terminal device, and then further includes: After detecting that the target terminal device has received the production equipment maintenance information, obtaining a receiving time node of the target terminal device and equipment image information around the production equipment to be maintained; A maintenance deadline time node is determined based on the receiving time node and a preset maintenance time period, and when the real-time time node reaches the maintenance deadline time node, the device image information is analyzed to determine whether there are operation features of maintenance personnel performing production equipment maintenance in the device image information, and if not, an emergency maintenance instruction is generated to control the emergency maintenance equipment to respond.

[0014] In the second aspect, the present application provides a monitoring system for key links in enzyme preparation production, which adopts the following technical solutions: A monitoring system for key links in enzyme preparation production, including: An information acquisition module is used to obtain the equipment node information corresponding to the key links of enzyme preparation production and the equipment operation information corresponding to the equipment node information; A first determination module is used to determine the set operation parameters that each production equipment node in the key production link should correspond to based on the equipment node information; A second determination module is used to determine the actual operation parameters corresponding to each production equipment node based on the equipment operation information; A parameter calibration module, used to perform back-calibration on the set operating parameters according to the actual operating parameters and a preset calibration algorithm to obtain calibration operating parameters corresponding to each production equipment node; A parameter judgment module is used to determine whether the calibration operation parameters are consistent with the set operation parameters based on the production equipment node. If they are inconsistent, a parameter calibration instruction is generated based on the calibration operation parameters and the set operation parameters to control the production equipment corresponding to the set operation parameters to perform parameter calibration.

[0015] In a possible implementation, when the parameter calibration module performs back-tracking calibration on the set operation parameters according to the actual operation parameters and a preset calibration algorithm to obtain the calibration operation parameters corresponding to each production equipment node, it is specifically used to: Compare the actual operation parameters and the set operation parameters one by one according to the parameter correspondence relationship to obtain a parameter comparison result; When there is inconsistency in the parameter comparison result, the abnormal device node is determined, and the operation parameter group of the abnormal device in different unit time is collected according to the operation cycle of the abnormal device node, and the operation parameter group is respectively input into the preset calibration algorithm to perform the set operation parameter back-calibration to obtain the calibration operation parameter corresponding to the abnormal device node, and the preset calibration algorithm is A=YB+C, wherein the Y and C constant values, A is the actual operation parameter in the operation parameter group, and B is the set operation parameter; When there is no inconsistency in the parameter comparison result, the set operation parameters are used as calibration operation parameters.

[0016] In another possible implementation, when the parameter calibration module inputs the operation parameter groups into a preset calibration algorithm to perform the set operation parameter backtracking calibration and obtains the calibration operation parameters corresponding to the abnormal device node, it is specifically used to: Acquire reference operation data of the production equipment node in a historical period, and determine that there is an abnormal parameter group in the reference operation data in which the actual operation parameters are inconsistent with the set operation parameters; Respectively grouping each group of abnormal parameters in the abnormal parameter group into groups of two according to the operation timeline of the production equipment node to obtain an improved parameter group corresponding to each abnormal operation parameter in the abnormal parameter group; Inputting the actual operation parameters and the set operation parameters in the improved parameter group into the preset calibration algorithm for calculation respectively, and obtaining the improved Y value and the improved C value in the preset calibration algorithm; Determine an improved calibration sequence table based on the improved Y value, the improved C value, the actual operating parameters in the improved parameter set, and the set operating parameters in the improved parameter set; Respectively grouping each group of operation parameters in the operation parameter group into two groups according to the operation timeline of the production equipment node to obtain an optimization parameter group corresponding to each operation parameter in the operation parameter group; The set operation parameters in the optimization parameter group are retroactively calibrated according to the improved calibration sequence table to obtain the calibration operation parameters corresponding to the abnormal device node.

[0017] In another possible implementation, when the parameter calibration module determines the improved calibration sequence table based on the improved Y value, the improved C value, the actual operating parameters in the improved parameter group, and the set operating parameters in the improved parameter group, it is specifically used to: Creating an initial calibration sequence table, and inputting the actual operation parameters in the improved parameter group and the set operation parameters in the improved parameter group into the initial calibration sequence table according to the operation timeline, and marking each operation timeline in the initial calibration sequence table to obtain a secondary calibration sequence table; The improved Y value and the improved C value are input into the secondary calibration sequence table accordingly, and the indexes of the actual operation parameters in the improved parameter group and the set operation parameters in the improved parameter group are established to obtain the improved calibration sequence table.

[0018] In another possible implementation, when the parameter calibration module performs retroactive calibration on the set operation parameters in the optimization parameter group according to the improved calibration sequence table to obtain the calibration operation parameters corresponding to the abnormal device node, it is specifically used to: Performing data difference calculation on the actual operating parameters and the set operating parameters in each row of operating parameter data in the improved calibration sequence table to obtain a first parameter difference corresponding to each row of operating parameter data in the improved calibration sequence table; Determine a second parameter difference between the actual operating parameter of the current optimization parameter group and the set operating parameter, and determine the comparison parameter data in the improved calibration sequence table that meets the preset parameter difference range based on the first parameter difference and the second parameter difference; Performing actual operation parameter screening on the comparison parameter data, determining a third parameter difference between the actual operation parameter in the comparison parameter data and the actual operation parameter of the optimization parameter group, and judging whether the third parameter difference meets a preset deviation range; If the third parameter difference meets the preset deviation range, the Y value and the C value in the comparison parameter data are respectively averaged to obtain a representative Y value and a representative C value; The actual operating parameters of the optimization parameter group, the representative Y value, and the representative C value are input into a preset calibration algorithm for calculation to obtain the calibration operating parameters corresponding to the abnormal device node.

[0019] In another possible implementation, the system further includes: a sequence table updating module and an equipment maintenance module, wherein: The sequence table updating module is used to update the improved calibration sequence table based on the corrected set operating parameters and the actual operating parameters in the optimization parameter group to obtain an updated improved calibration sequence table; The equipment maintenance module is used to monitor the parameter difference between the set operation parameters and the actual operation parameters in the updated improved calibration sequence table in real time. When the parameter difference is greater than the parameter difference range standard, production equipment maintenance information is generated and sent to the target terminal device.

[0020] In another possible implementation, the system further includes: an image acquisition module and a maintenance monitoring module, wherein: The image acquisition module is used to acquire the receiving time node of the target terminal device and the device image information around the production equipment to be maintained after detecting that the target terminal device has received the production equipment maintenance information; The maintenance monitoring module is used to determine the maintenance deadline time node based on the receiving time node and the preset maintenance time period, and when the real-time time node reaches the maintenance deadline time node, analyze the equipment image information to determine whether there are operation characteristics of maintenance personnel performing production equipment maintenance in the equipment image information, and if not, generate an emergency maintenance instruction to control the emergency maintenance equipment to respond.

[0021] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned method for monitoring key links in enzyme preparation production.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute a method for monitoring key links in enzyme preparation production.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: The equipment node information and operation information corresponding to the key links of enzyme preparation production were obtained to ensure that the key links and key equipment in the production process were accurately identified, providing a solid foundation for subsequent parameter setting and calibration. The accuracy of equipment node information directly affects the rationality of subsequent parameter setting and provides a premise for the stability and optimization of the production process. By determining the set operation parameters based on the equipment node information and determining the actual operation parameters based on the equipment operation information, real-time monitoring and accurate evaluation of the operation status of the production equipment are realized, so that each link in the production process can be carried out according to the preset optimal parameters, improving production efficiency and product quality. At the same time, the acquisition of actual operation parameters provides data support for subsequent parameter calibration. The calibration operation parameters are obtained by back-tracking the set operation parameters according to the actual operation parameters and the preset calibration algorithm. It ensures that the operation parameters of the production equipment are always kept in the optimal state, avoids parameter deviations caused by factors such as equipment aging, and further improves production efficiency and product quality. By determining the consistency of the calibration operation parameters and the set operation parameters based on the production equipment nodes, and generating parameter calibration instructions when they are inconsistent, the production equipment corresponding to the set operation parameters is controlled to perform parameter calibration, and the dynamic adjustment and optimization of the production equipment operation parameters are realized. It ensures the continuous stability and controllability of the production process, providing a strong guarantee for the high-quality production of enzyme preparations. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of a method for monitoring key links in enzyme preparation production provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a monitoring system for key links in enzyme preparation production provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following is combined with Figure 1 To Attachment Figure 3 This application is described in further detail.

[0026] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed, but such modifications are protected by the patent law as long as they are within the scope of the claims of the present application.

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can 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, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0029] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0030] The embodiment of the present application provides a method for monitoring key links in the production of enzyme preparations, which is performed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. Figure 1 As shown, the method includes: Step S10: Obtaining equipment node information corresponding to key links in enzyme preparation production and equipment operation information corresponding to the equipment node information.

[0031] Specifically, the key links in enzyme production refer to the steps or stages in the enzyme production process that have a key impact on product quality, production efficiency or cost control. These links usually include but are not limited to raw material pretreatment, fermentation, extraction, purification and post-processing. Equipment node information refers to the basic information of production equipment associated with these key links, including the name, model, location number and pre-set operating parameters of the equipment, which are used to identify each key equipment in the production process. Equipment operation information refers to the operating parameters and status of these devices when performing specific production tasks, such as temperature, pressure, speed, operating time, etc. This information is used to indicate the actual working conditions of the equipment.

[0032] For the embodiment of the present application, by utilizing an integrated information system such as a manufacturing execution system (MES) or an enterprise resource planning system (ERP), equipment usage and operation information are automatically extracted from production scheduling, plan execution, material management, etc., to achieve comprehensive integration and efficient use of production data.

[0033] Step S11: Determine the set operation parameters that each production equipment node in the key production link should correspond to based on the equipment node information.

[0034] Specifically, setting operating parameters refers to the pre-set equipment operating conditions or values, such as temperature, pressure, speed, flow rate, time, etc., in order to ensure that the production equipment can operate as expected in the key production links. In the embodiment of the present application, the set operating parameters corresponding to each production equipment node are extracted and determined from the equipment node information by data extraction.

[0035] Step S12: Determine the actual operation parameters corresponding to each production equipment node based on the equipment operation information.

[0036] Specifically, equipment operation information refers to the real-time data and real-time information generated by production equipment during operation, including the equipment's operating status, working hours, workload, energy consumption, etc., which is used to reflect the real-time working conditions of production equipment nodes. Actual operating parameters refer to the actual operating conditions or values ​​of equipment nodes during the actual operation of production equipment, such as temperature, pressure, speed, flow, etc., based on equipment operation information and production requirements.

[0037] Step S13: back-calibrate the set operating parameters according to the actual operating parameters and the preset calibration algorithm to obtain the calibration operating parameters corresponding to each production equipment node.

[0038] Specifically, the actual operating parameters and the set operating parameters are compared one by one according to the parameter correspondence to obtain the parameter comparison results. When there is inconsistency in the parameter comparison results, the abnormal device node is determined, and the operating parameter group of the abnormal device in different unit times is collected according to the operation cycle of the abnormal device node. The operating parameter groups are respectively input into the preset calibration algorithm to perform back calibration of the set operating parameters to obtain the calibration operating parameters corresponding to the abnormal device node. The preset calibration algorithm is A=YB+C, where Y and C are constant values, A is the actual operating parameter in the operating parameter group, and B is the set operating parameter. When there is no inconsistency in the parameter comparison results, the set operating parameters are used as the calibration operating parameters.

[0039] Specifically, the operation parameter groups are respectively input into the preset calibration algorithm to perform retrospective calibration of the set operation parameters to obtain the calibration operation parameters corresponding to the abnormal equipment nodes, including: obtaining reference operation data of the production equipment nodes in the historical period, and determining the abnormal parameter groups in which the actual operation parameters are inconsistent with the set operation parameters in the reference operation data, grouping each group of abnormal parameters in the abnormal parameter group in pairs according to the operation timeline of the production equipment node, and obtaining an improved parameter group corresponding to each abnormal operation parameter in the abnormal parameter group, inputting the actual operation parameters and the set operation parameters in the improved parameter group into the preset calibration algorithm for calculation, and obtaining an improved Y value and an improved C value in the preset calibration algorithm, and determining an improved calibration sequence table based on the improved Y value, the improved C value, the actual operation parameters in the improved parameter group, and the set operation parameters in the improved parameter group, grouping each group of operation parameters in the operation parameter group in pairs according to the operation timeline of the production equipment node, and obtaining an optimized parameter group corresponding to each operation parameter in the operation parameter group, and retrospectively calibrating the set operation parameters in the optimized parameter group according to the improved calibration sequence table to obtain the calibration operation parameters corresponding to the abnormal equipment nodes.

[0040] Specifically, an improved calibration sequence table is determined based on the improved Y value, the improved C value, the actual operating parameters in the improved parameter group and the set operating parameters in the improved parameter group, including: creating an initial calibration sequence table, and inputting the actual operating parameters in the improved parameter group and the set operating parameters in the improved parameter group into the initial calibration sequence table according to the operating timeline, and marking each operating timeline in the initial calibration sequence table to obtain a secondary calibration sequence table, inputting the improved Y value and the improved C value into the secondary calibration sequence table accordingly, and establishing indexes of the actual operating parameters in the improved parameter group and the set operating parameters in the improved parameter group to obtain an improved calibration sequence table.

[0041] Specifically, the set operating parameters in the optimization parameter group are retroactively calibrated according to the improved calibration sequence table to obtain the calibration operating parameters corresponding to the abnormal device node, including: performing data difference calculation on the actual operating parameters and the set operating parameters in each row of operating parameter data in the improved calibration sequence table to obtain the first parameter difference corresponding to each row of operating parameter data in the improved calibration sequence table, determining the second parameter difference between the actual operating parameters of the current optimization parameter group and the set operating parameters, and determining the comparison parameter data in the improved calibration sequence table that meets the preset parameter difference range based on the first parameter difference and the second parameter difference, performing actual operating parameter screening on the comparison parameter data, determining the third parameter difference between the actual operating parameters in the comparison parameter data and the actual operating parameters of the optimization parameter group, and judging whether the third parameter difference meets the preset deviation range, if the third parameter difference meets the preset deviation range, respectively calculating the mean of the Y value and the C value in the comparison parameter data to obtain the representative Y value and the representative C value, and inputting the actual operating parameters, the representative Y value and the representative C value of the optimization parameter group into the preset calibration algorithm for calculation to obtain the calibration operating parameters corresponding to the abnormal device node.

[0042] Specifically, the preset parameter difference range and the preset deviation range are ranges set by maintenance personnel according to actual conditions and are not limited here.

[0043] Step S14: Determine whether the calibration operation parameters are consistent with the set operation parameters based on the production equipment node. If not, generate parameter calibration instructions based on the calibration operation parameters and the set operation parameters to control the production equipment corresponding to the set operation parameters to perform parameter calibration.

[0044] In the present application embodiment, the equipment node information and operation information corresponding to the key links of enzyme preparation production are obtained to ensure that the key links and key equipment in the production process are accurately identified, providing a solid foundation for subsequent parameter setting and calibration. The accuracy of the equipment node information directly affects the rationality of the subsequent parameter setting, and provides a premise for the stability and optimization of the production process. By determining the set operation parameters based on the equipment node information, and determining the actual operation parameters according to the equipment operation information, the real-time monitoring and accurate evaluation of the operation status of the production equipment are realized, so that each link in the production process can be carried out according to the preset optimal parameters, improving production efficiency and product quality. At the same time, the acquisition of the actual operation parameters provides data support for subsequent parameter calibration. By back-tracking the set operation parameters according to the actual operation parameters and the preset calibration algorithm, the calibration operation parameters are obtained. It is ensured that the operating parameters of the production equipment are always kept in the optimal state, avoiding parameter deviations caused by factors such as equipment aging, and further improving production efficiency and product quality. By determining the consistency of the calibration operation parameters and the set operation parameters based on the production equipment nodes, and generating parameter calibration instructions when inconsistent, the production equipment corresponding to the set operation parameters is controlled to perform parameter calibration, and the dynamic adjustment and optimization of the production equipment operation parameters are realized. It ensures the continuous stability and controllability of the production process, providing a strong guarantee for the high-quality production of enzyme preparations.

[0045] Furthermore, the actual operating parameters, representative Y values ​​and representative C values ​​of the optimized parameter group are input into a preset calibration algorithm for calculation to obtain the calibration operating parameters corresponding to the abnormal equipment node, and then include: updating the improved calibration sequence table based on the corrected set operating parameters and the actual operating parameters in the optimized parameter group to obtain an updated improved calibration sequence table, and monitoring the parameter difference between the set operating parameters and the actual operating parameters in the updated improved calibration sequence table in real time. When the parameter difference is greater than the parameter difference range standard, generating production equipment maintenance information, and sending the production equipment maintenance information to the target terminal device.

[0046] Furthermore, the production equipment maintenance information is sent to the target terminal device, and then it also includes: after detecting that the target terminal device has received the production equipment maintenance information, obtaining the receiving time node of the target terminal device and the equipment image information around the production equipment to be maintained, determining the maintenance deadline time node based on the receiving time node and the preset maintenance time period, and when the real-time time node reaches the maintenance deadline time node, analyzing the equipment image information to determine whether there are operating characteristics of maintenance personnel performing production equipment maintenance in the equipment image information, if not, generating an emergency maintenance instruction to control the emergency maintenance equipment to respond.

[0047] The above embodiment introduces a method for monitoring key links in enzyme preparation production from the perspective of method flow, and the following embodiment introduces a system for monitoring key links in enzyme preparation production from the perspective of virtual modules or virtual units. Please refer to the following embodiments for details.

[0048] The present application embodiment provides a key link monitoring system 20 for enzyme preparation production, such as Figure 2 As shown, Figure 2 A schematic diagram of a monitoring system for key links in enzyme preparation production provided in an embodiment of the present application. The system 20 may specifically include: The information acquisition module 21 is used to obtain the equipment node information corresponding to the key links of enzyme preparation production and the equipment operation information corresponding to the equipment node information; A first determination module 22 is used to determine the set operation parameters that each production equipment node in the key production link should correspond to based on the equipment node information; A second determination module 23, used to determine the actual operation parameters corresponding to each production equipment node based on the equipment operation information; The parameter calibration module 24 is used to perform back-tracking calibration on the set operation parameters according to the actual operation parameters and the preset calibration algorithm to obtain the calibration operation parameters corresponding to each production equipment node; The parameter judgment module 25 is used to determine whether the calibration operation parameters are consistent with the set operation parameters based on the production equipment node. If they are inconsistent, a parameter calibration instruction is generated based on the calibration operation parameters and the set operation parameters to control the production equipment corresponding to the set operation parameters to perform parameter calibration.

[0049] In a possible implementation of the embodiment of the present application, the parameter calibration module 24 performs back-tracking calibration on the set operation parameters according to the actual operation parameters and the preset calibration algorithm to obtain the calibration operation parameters corresponding to each production equipment node, specifically for: Compare the actual operation parameters and the set operation parameters one by one according to the parameter correspondence relationship to obtain the parameter comparison result; When there is inconsistency in the parameter comparison results, the abnormal device node is determined, and the operation parameter group of the abnormal device in different unit time is collected according to the operation cycle of the abnormal device node, and the operation parameter group is respectively input into the preset calibration algorithm to set the operation parameter back calibration, and the calibration operation parameter corresponding to the abnormal device node is obtained. The preset calibration algorithm is A=YB+C, where Y and C are constant values, A is the actual operation parameter in the operation parameter group, and B is the set operation parameter; When there is no inconsistency in the parameter comparison results, the set operation parameters are used as calibration operation parameters.

[0050] In another possible implementation of the embodiment of the present application, the parameter calibration module 24 inputs the operation parameter groups into the preset calibration algorithm to perform the operation parameter retrospective calibration, and obtains the calibration operation parameters corresponding to the abnormal device node, specifically for: Obtain reference operation data of production equipment nodes in a historical period, and determine abnormal parameter groups in which actual operation parameters are inconsistent with set operation parameters in the reference operation data; Each group of abnormal parameters in the abnormal parameter group is grouped in pairs according to the operation timeline of the production equipment node, and an improved parameter group corresponding to each abnormal operation parameter in the abnormal parameter group is obtained; The actual operating parameters and the set operating parameters in the improved parameter group are respectively input into the preset calibration algorithm for calculation to obtain the improved Y value and the improved C value in the preset calibration algorithm; Determine an improved calibration sequence table based on the improved Y value, the improved C value, the actual operating parameters in the improved parameter set, and the set operating parameters in the improved parameter set; Each group of operation parameters in the operation parameter group is grouped in pairs according to the operation timeline of the production equipment node, and an optimization parameter group corresponding to each operation parameter in the operation parameter group is obtained; According to the improved calibration sequence table, the set operating parameters in the optimization parameter group are retroactively calibrated to obtain the calibration operating parameters corresponding to the abnormal device nodes.

[0051] In another possible implementation of the embodiment of the present application, when the parameter calibration module 24 determines the improved calibration sequence table based on the improved Y value, the improved C value, the actual operating parameters in the improved parameter group, and the set operating parameters in the improved parameter group, it is specifically used to: An initial calibration sequence table is created, and the actual operation parameters in the improved parameter group and the set operation parameters in the improved parameter group are respectively input into the initial calibration sequence table according to the operation timeline, and each operation timeline is marked in the initial calibration sequence table accordingly to obtain a secondary calibration sequence table; The improved Y value and the improved C value are input into the secondary calibration sequence table correspondingly, and the indexes of the actual operation parameters in the improved parameter group and the set operation parameters in the improved parameter group are established to obtain the improved calibration sequence table.

[0052] In another possible implementation of the embodiment of the present application, the parameter calibration module 24 performs retroactive calibration on the set operation parameters in the optimization parameter group according to the improved calibration sequence table to obtain the calibration operation parameters corresponding to the abnormal device node, specifically for: Performing data difference calculation on the actual operating parameters and the set operating parameters in each row of operating parameter data in the improved calibration sequence table to obtain a first parameter difference corresponding to each row of operating parameter data in the improved calibration sequence table; Determine a second parameter difference between the actual operating parameter of the current optimized parameter group and the set operating parameter, and determine the comparison parameter data in the improved calibration sequence table that meets the preset parameter difference range based on the first parameter difference and the second parameter difference; Performing actual operation parameter screening by comparing the parameter data, determining a third parameter difference between the actual operation parameter in the comparison parameter data and the actual operation parameter of the optimization parameter group, and judging whether the third parameter difference meets a preset deviation range; If the third parameter difference meets the preset deviation range, the Y value and the C value in the comparison parameter data are respectively averaged to obtain a representative Y value and a representative C value; The actual operating parameters, representative Y values, and representative C values ​​of the optimized parameter group are input into a preset calibration algorithm for calculation to obtain the calibration operating parameters corresponding to the abnormal device node.

[0053] In another possible implementation of the embodiment of the present application, the system 20 further includes: a sequence table update module and a device maintenance module, wherein: A sequence table updating module, used to update the improved calibration sequence table based on the corrected set operating parameters and the actual operating parameters in the optimized parameter group to obtain an updated improved calibration sequence table; The equipment maintenance module is used to monitor the parameter difference between the set operating parameters and the actual operating parameters in the updated improved calibration sequence table in real time. When the parameter difference is greater than the parameter difference range standard, the production equipment maintenance information is generated and sent to the target terminal device.

[0054] In another possible implementation of the embodiment of the present application, the system 20 further includes: an image acquisition module and a maintenance monitoring module, wherein: An image acquisition module is used to acquire the receiving time node of the target terminal device and the device image information around the production equipment to be maintained after detecting that the target terminal device has received the production equipment maintenance information; The maintenance monitoring module is used to determine the maintenance deadline time node based on the receiving time node and the preset maintenance time period, and when the real-time time node reaches the maintenance deadline time node, analyze the equipment image information to determine whether there are operation characteristics of maintenance personnel performing production equipment maintenance in the equipment image information. If not, an emergency maintenance instruction is generated to control the emergency maintenance equipment to respond.

[0055] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the enzyme preparation production key link monitoring system 20 described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0056] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0057] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0058] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0059] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0060] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0061] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0062] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0063] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0064] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for monitoring key links in enzyme preparation production, characterized in that: include: Obtaining equipment node information corresponding to key links in enzyme preparation production and equipment operation information corresponding to the equipment node information; Determine the set operation parameters that each production equipment node in the key production link should correspond to based on the equipment node information; Determine the actual operation parameters corresponding to each production equipment node based on the equipment operation information; Performing back-tracking calibration on the set operating parameters according to the actual operating parameters and a preset calibration algorithm to obtain calibration operating parameters corresponding to each production equipment node; Based on the production equipment node, determine whether the calibration operation parameters are consistent with the setting operation parameters. If they are inconsistent, generate parameter calibration instructions based on the calibration operation parameters and the setting operation parameters to control the production equipment corresponding to the setting operation parameters to perform parameter calibration.

2. The method for monitoring key links in enzyme preparation production according to claim 1, characterized in that: The step of back-calibrating the set operating parameters according to the actual operating parameters and a preset calibration algorithm to obtain calibration operating parameters corresponding to each production equipment node includes: Compare the actual operation parameters and the set operation parameters one by one according to the parameter correspondence relationship to obtain a parameter comparison result; When there is inconsistency in the parameter comparison result, the abnormal device node is determined, and the operation parameter group of the abnormal device in different unit time is collected according to the operation cycle of the abnormal device node, and the operation parameter group is respectively input into the preset calibration algorithm to perform the set operation parameter back-calibration to obtain the calibration operation parameter corresponding to the abnormal device node, and the preset calibration algorithm is A=YB+C, wherein the Y and C constant values, A is the actual operation parameter in the operation parameter group, and B is the set operation parameter; When there is no inconsistency in the parameter comparison result, the set operation parameters are used as calibration operation parameters.

3. The method for monitoring key links in enzyme preparation production according to claim 2, characterized in that: The step of inputting the operation parameter groups into a preset calibration algorithm to perform back-calibration of the set operation parameters to obtain calibration operation parameters corresponding to the abnormal device node includes: Acquire reference operation data of the production equipment node in a historical period, and determine that there is an abnormal parameter group in the reference operation data in which the actual operation parameters are inconsistent with the set operation parameters; Respectively grouping each group of abnormal parameters in the abnormal parameter group into groups of two according to the operation timeline of the production equipment node to obtain an improved parameter group corresponding to each abnormal operation parameter in the abnormal parameter group; Inputting the actual operation parameters and the set operation parameters in the improved parameter group into the preset calibration algorithm for calculation respectively, and obtaining the improved Y value and the improved C value in the preset calibration algorithm; Determine an improved calibration sequence table based on the improved Y value, the improved C value, the actual operating parameters in the improved parameter set, and the set operating parameters in the improved parameter set; Respectively grouping each group of operation parameters in the operation parameter group into two groups according to the operation timeline of the production equipment node to obtain an optimization parameter group corresponding to each operation parameter in the operation parameter group; The set operation parameters in the optimization parameter group are retroactively calibrated according to the improved calibration sequence table to obtain the calibration operation parameters corresponding to the abnormal device node.

4. The method for monitoring key links in enzyme preparation production according to claim 3, characterized in that: The step of determining an improved calibration sequence table based on the improved Y value, the improved C value, the actual operating parameters in the improved parameter group, and the set operating parameters in the improved parameter group includes: Creating an initial calibration sequence table, and inputting the actual operation parameters in the improved parameter group and the set operation parameters in the improved parameter group into the initial calibration sequence table according to the operation timeline, and marking each operation timeline in the initial calibration sequence table to obtain a secondary calibration sequence table; The improved Y value and the improved C value are input into the secondary calibration sequence table accordingly, and the indexes of the actual operation parameters in the improved parameter group and the set operation parameters in the improved parameter group are established to obtain the improved calibration sequence table.

5. The method for monitoring key links in enzyme preparation production according to claim 3, characterized in that: The step of retroactively calibrating the set operation parameters in the optimization parameter group according to the improved calibration sequence table to obtain the calibration operation parameters corresponding to the abnormal device node includes: Performing data difference calculation on the actual operating parameters and the set operating parameters in each row of operating parameter data in the improved calibration sequence table to obtain a first parameter difference corresponding to each row of operating parameter data in the improved calibration sequence table; Determine a second parameter difference between the actual operating parameter of the current optimization parameter group and the set operating parameter, and determine the comparison parameter data in the improved calibration sequence table that meets the preset parameter difference range based on the first parameter difference and the second parameter difference; Performing actual operation parameter screening on the comparison parameter data, determining a third parameter difference between the actual operation parameter in the comparison parameter data and the actual operation parameter of the optimization parameter group, and judging whether the third parameter difference meets a preset deviation range; If the third parameter difference meets the preset deviation range, the Y value and the C value in the comparison parameter data are respectively averaged to obtain a representative Y value and a representative C value; The actual operating parameters of the optimization parameter group, the representative Y value, and the representative C value are input into a preset calibration algorithm for calculation to obtain the calibration operating parameters corresponding to the abnormal device node.

6. A method for monitoring key links in enzyme preparation production according to claim 5, characterized in that: The actual operating parameters of the optimization parameter group, the representative Y value, and the representative C value are input into a preset calibration algorithm for calculation to obtain the calibration operating parameters corresponding to the abnormal device node, and then further include: The improved calibration sequence table is updated based on the corrected set operating parameters and the actual operating parameters in the optimization parameter group to obtain an updated improved calibration sequence table; The parameter difference between the set operating parameters and the actual operating parameters in the updated improved calibration sequence table is monitored in real time. When the parameter difference is greater than the parameter difference range standard, production equipment maintenance information is generated and sent to the target terminal device.

7. The method for monitoring key links in enzyme preparation production according to claim 6, characterized in that: The step of sending the production equipment maintenance information to the target terminal device further comprises: After detecting that the target terminal device has received the production equipment maintenance information, obtaining a receiving time node of the target terminal device and equipment image information around the production equipment to be maintained; A maintenance deadline time node is determined based on the receiving time node and a preset maintenance time period, and when the real-time time node reaches the maintenance deadline time node, the device image information is analyzed to determine whether there are operation features of maintenance personnel performing production equipment maintenance in the device image information, and if not, an emergency maintenance instruction is generated to control the emergency maintenance equipment to respond.

8. A monitoring system for key links in enzyme production, characterized in that: include: An information acquisition module is used to acquire the equipment node information corresponding to the key links of enzyme preparation production and the equipment operation information corresponding to the equipment node information; A first determination module is used to determine the set operation parameters that each production equipment node in the key production link should correspond to based on the equipment node information; A second determination module is used to determine the actual operation parameters corresponding to each production equipment node based on the equipment operation information; A parameter calibration module, used to perform back-calibration on the set operating parameters according to the actual operating parameters and a preset calibration algorithm to obtain calibration operating parameters corresponding to each production equipment node; A parameter judgment module is used to determine whether the calibration operation parameters are consistent with the set operation parameters based on the production equipment node. If they are inconsistent, a parameter calibration instruction is generated based on the calibration operation parameters and the set operation parameters to control the production equipment corresponding to the set operation parameters to perform parameter calibration.

9. An electronic device, characterized in that: It includes: One or more processors; Memory; One or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to: execute a method for monitoring key links in enzyme preparation production according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a method for monitoring key links in enzyme preparation production as described in any one of claims 1 to 7 is implemented.

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