Efficiency analysis methods, devices, and computer equipment for beverage production lines

Abstract

This application relates to a method, apparatus, and computer equipment for efficiency analysis of a beverage production line. The method includes: acquiring historical data and current production progress information of the beverage production line; identifying the beverage production process and production duration information for each beverage type based on the historical data; identifying the production timeliness impact information of each production process node for each beverage type; and, based on the production timeliness impact information of each production process node for each beverage type, identifying a timeliness impact inference scheme for each beverage type through a production efficiency analysis strategy; generating current timeliness inference information for each beverage type based on the current production progress information; and generating current predicted production timeliness information for each beverage type based on the current timeliness inference information for each beverage type through a timeliness impact inference scheme for each beverage type. This method improves the accuracy of efficiency analysis of beverage production lines.

Description

Technical Field

[0001] This application relates to the field of production line efficiency analysis technology, and in particular to an efficiency analysis method, apparatus and computer equipment for a beverage production line. Background Technology

[0002] Efficiency analysis of beverage production lines currently relies on manual calculations of data such as machine start-up time, downtime, and filling quantity. This method is not only time-consuming and labor-intensive but also prone to inaccurate results due to human error (such as missing downtime for model changes or incorrect filling quantities). Furthermore, for multi-product production scenarios (such as switching between two bottle / can types within the same shift), traditional calculation methods struggle to quickly differentiate production data under different rated capacities and accurately break down the efficiency ratio between single-product and dual-product production. This makes it difficult for managers to identify efficiency bottlenecks (such as low equipment efficiency during the production of a particular product) and adjust production strategies accordingly. Therefore, improving the accuracy of efficiency analysis for beverage production lines is a current research focus.

[0003] Current technology still relies on integrating and formulating calculations based on a large amount of scattered data acquired after the beverage production line has finished production. However, this method is inefficient, the data is scattered, and there is a lack of unified time stamps and data tags. This means that when tracing data for a specific production period, it is necessary to check each data point one by one, and there is also the problem of data omission. As a result, the accuracy of efficiency analysis of beverage production lines is low. Summary of the Invention

[0004] Therefore, it is necessary to provide an efficiency analysis method, apparatus, computer equipment, computer-readable storage medium, and computer program product for a beverage production line to address the aforementioned technical problems.

[0005] Firstly, this application provides an efficiency analysis method for a beverage production line, including:

[0006] The system acquires historical data of the beverage production line and current production progress information of the beverage production line, and based on the historical data, identifies the beverage production process for each beverage type and the production time information for each beverage type.

[0007] Based on the beverage production process and production time information of each beverage type, identify the impact information of production timeliness of each production process node of each beverage type, and based on the impact information of production timeliness of each production process node of each beverage type, identify the timeliness impact reasoning scheme of each beverage type through production efficiency analysis strategy.

[0008] Based on the current production progress information, current timeliness inference information for each beverage type is generated, and based on the current timeliness inference information for each beverage type, current predicted production timeliness information for each beverage type is generated through the timeliness impact inference scheme for each beverage type.

[0009] Optionally, the step of identifying the beverage production process and production time information for each beverage type based on the historical record information includes:

[0010] The historical record information is broken down into sub-historical record information for each beverage type, and for each beverage type, based on the sub-historical record information for each beverage type, the production record information for each beverage type at each production process node is identified;

[0011] Based on the production record information of each production process node, the production process information of each production process node and the time information of each step corresponding to each production process information are identified. The production process information of each production process node is then arranged in the order of each production process node to obtain the beverage production process of the beverage type.

[0012] The time taken for each step of the production process is used as the production time information for the beverage type.

[0013] Optionally, the step of identifying the impact information on production timeliness of each production process node for each beverage type based on the beverage production process and production time information of each beverage type includes:

[0014] For each production process information, based on the time information of each link corresponding to the production process information, the sub-production time of each production link is identified, and the reasons for the time consumption of each production link are collected.

[0015] Based on the sub-production time of each production stage and the reasons for the time consumption of each production stage, the time consumption ratio distribution information of each production stage is identified through the time consumption analysis model.

[0016] The distribution information of the time consumption ratio of each production link is used as the production timeliness impact information of the production process node corresponding to the production process information.

[0017] Optionally, the step of identifying a timeliness impact inference scheme for each beverage type based on the production timeliness impact information of each production process node for each beverage type through a production efficiency analysis strategy includes:

[0018] For each beverage type, based on the distribution information of the time consumption ratio of each production link in each production process node of the beverage type, the time consumption ratio information of each time-consuming factor in each production link is identified.

[0019] Based on the time consumption percentage of each time-consuming factor in each production stage, the time consumption inference ratio of each production stage is identified.

[0020] The time consumption ratio of all production stages is used as the reasoning scheme for the time-sensitivity impact of the beverage type.

[0021] Optionally, generating current timeliness inference information for each beverage type based on the current production progress information includes:

[0022] The current production progress information is broken down into sub-current production progress for each beverage type, and the current production parameters for each beverage type are identified based on the sub-current production progress of each beverage type.

[0023] Based on the current production parameters of each beverage type, predict the production parameters of each production stage for each beverage type, and use the predicted production parameters of each production stage for each beverage type as the current timeliness inference information for each beverage type.

[0024] Optionally, the step of generating current predicted production timeliness information for each beverage type based on the current timeliness inference information for each beverage type, through a timeliness impact inference scheme for each beverage type, includes:

[0025] For each beverage type, based on the predicted production parameters of each production stage of the beverage type, and through the time consumption ratio of each production stage of the beverage type, the time consumption information of each production stage is predicted according to the production sequence of each production stage.

[0026] Based on the time consumption information of each production stage, the current predicted production time information of the beverage type is calculated using a timeliness prediction algorithm.

[0027] Secondly, this application also provides an efficiency analysis device for a beverage production line, comprising:

[0028] The acquisition module is used to acquire historical information of the beverage production line and current production progress information of the beverage production line, and based on the historical information, to identify the beverage production process of each beverage type and the production time information of each beverage type.

[0029] The identification module is used to identify the impact information of production timeliness of each production process node of each beverage type based on the beverage production process and production time information of each beverage type, and to identify the timeliness impact reasoning scheme of each beverage type based on the impact information of production timeliness of each production process node of each beverage type through production efficiency analysis strategy.

[0030] The generation module is used to generate current timeliness inference information for each beverage type based on the current production progress information, and to generate current predicted production timeliness information for each beverage type based on the current timeliness inference information for each beverage type and the timeliness impact inference scheme for each beverage type.

[0031] Optionally, the acquisition module is specifically used for:

[0032] The historical record information is broken down into sub-historical record information for each beverage type, and for each beverage type, based on the sub-historical record information for each beverage type, the production record information for each beverage type at each production process node is identified;

[0033] Based on the production record information of each production process node, the production process information of each production process node and the time information of each step corresponding to each production process information are identified. The production process information of each production process node is then arranged in the order of each production process node to obtain the beverage production process of the beverage type.

[0034] The time taken for each step of the production process is used as the production time information for the beverage type.

[0035] Optionally, the identification module is specifically used for:

[0036] For each production process information, based on the time information of each link corresponding to the production process information, the sub-production time of each production link is identified, and the reasons for the time consumption of each production link are collected.

[0037] Based on the sub-production time of each production stage and the reasons for the time consumption of each production stage, the time consumption ratio distribution information of each production stage is identified through the time consumption analysis model.

[0038] The distribution information of the time consumption ratio of each production link is used as the production timeliness impact information of the production process node corresponding to the production process information.

[0039] Optionally, the identification module is specifically used for:

[0040] For each beverage type, based on the distribution information of the time consumption ratio of each production link in each production process node of the beverage type, the time consumption ratio information of each time-consuming factor in each production link is identified.

[0041] Based on the time consumption percentage of each time-consuming factor in each production stage, the time consumption inference ratio of each production stage is identified.

[0042] The time consumption ratio of all production stages is used as the reasoning scheme for the time-sensitivity impact of the beverage type.

[0043] Optionally, the generation module is specifically used for:

[0044] The current production progress information is broken down into sub-current production progress for each beverage type, and the current production parameters for each beverage type are identified based on the sub-current production progress of each beverage type.

[0045] Based on the current production parameters of each beverage type, predict the production parameters of each production stage for each beverage type, and use the predicted production parameters of each production stage for each beverage type as the current timeliness inference information for each beverage type.

[0046] Optionally, the generation module is specifically used for:

[0047] For each beverage type, based on the predicted production parameters of each production stage of the beverage type, and through the time consumption ratio of each production stage of the beverage type, the time consumption information of each production stage is predicted according to the production sequence of each production stage.

[0048] Based on the time consumption information of each production stage, the current predicted production time information of the beverage type is calculated using a timeliness prediction algorithm.

[0049] Thirdly, this application provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the first aspects.

[0050] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0051] Fifthly, this application provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0052] The aforementioned efficiency analysis method, apparatus, and computer equipment for beverage production lines acquire historical data and current production progress information of the beverage production line. Based on the historical data, they identify the production process and production duration information for each beverage type. Based on this information, they identify the impact of each production process node on the production timeliness of each beverage type. Using a production efficiency analysis strategy, they identify a timeliness impact inference scheme for each beverage type. Based on the current production progress information, they generate current timeliness inference information for each beverage type. Using this current timeliness inference information and the timeliness impact inference scheme, they generate current predicted production timeliness information for each beverage type. This solution, by combining historical data from the beverage production line, analyzes the impact of each beverage type's production timeliness from the perspective of its production process and production duration, and from the perspective of each production process node. This refines the timeliness impact analysis process for each production process, improving the accuracy and comprehensiveness of the analysis. Furthermore, this solution analyzes and infers the impact of timeliness on various beverage types, thereby enabling one-click intelligent prediction and generation of production line processes for different beverage types. This avoids the inefficiency, scattered and missing data, and difficulty in tracing production links of existing technologies, thus effectively improving the accuracy of efficiency analysis of beverage production lines. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating an efficiency analysis method for a beverage production line in one embodiment.

[0055] Figure 2 This is a flowchart illustrating an example of efficiency analysis of a beverage production line in one embodiment;

[0056] Figure 3 This is a structural block diagram of an efficiency analysis device for a beverage production line in one embodiment;

[0057] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] The efficiency analysis method for beverage production lines provided in this application embodiment can be applied to a beverage production line efficiency analysis system. This system can be applied to a terminal, which can be, but is not limited to, various personal computers, laptops, mid-range computers, etc. The terminal, by combining historical data from the beverage production line, analyzes the impact of production timeliness on each beverage type from the perspective of each production process node, considering the beverage production process and production duration information for each beverage type. This refines the timeliness impact analysis process for each production process, improving the accuracy and comprehensiveness of the analysis. Furthermore, this solution uses a timeliness impact inference scheme for each beverage type to analyze and infer different beverage types, enabling one-click intelligent prediction and generation of production line processes for different beverage types. This avoids the inefficiency, data fragmentation and omissions, and difficulties in tracing production links of existing technologies, thereby effectively improving the accuracy of efficiency analysis for beverage production lines.

[0060] In one exemplary embodiment, such as Figure 1 As shown, an efficiency analysis method for a beverage production line is provided. Taking the application of this method to a terminal as an example, the method includes the following steps S101 to S103. Wherein:

[0061] Step S101: Obtain historical data of the beverage production line and current production progress information of the beverage production line, and based on the historical data, identify the beverage production process and production time information of each beverage type.

[0062] In this embodiment, the terminal retrieves historical data from the production database, including the production time, parameters, and results of each beverage type recorded during past production runs. This historical data characterizes the parameter changes and their correlation with production time for each beverage type. The terminal then obtains the current production parameters and stages for each beverage type currently being produced, thus determining the current production progress. These production parameters include, but are not limited to, start-up time, number of bottles, rated capacity, bottle / can type, estimated output, and wastage. The beverage type refers to different beverage types that the production line can simultaneously produce, such as sparkling, non-sparkling, alcoholic, and non-alcoholic beverages. The production time information includes the sub-production times for different production process nodes of each beverage type, and the correspondence between these sub-production times and production efficiency. Based on the historical data, the terminal identifies the beverage production process and production time information for each beverage type. The specific identification process will be explained in detail later.

[0063] Step S102: Based on the beverage production process and production time information of each beverage type, identify the production timeliness impact information of each production process node of each beverage type, and based on the production timeliness impact information of each production process node of each beverage type, identify the timeliness impact reasoning scheme of each beverage type through production efficiency analysis strategy.

[0064] In this embodiment, the terminal identifies the production timeliness impact information of each production process node for each beverage type based on the beverage production process and production time information for each beverage type. Based on this impact information, the terminal uses a production efficiency analysis strategy to identify a timeliness impact inference scheme for each beverage type. The production timeliness impact information refers to the time consumption ratio of each production stage in the beverage production process of each beverage type. This time consumption ratio is a set of proportional values ​​between the production parameters corresponding to each production efficiency of that stage and the production time. This set of proportional values ​​includes the ratio between each production efficiency and the production market. The production efficiency analysis strategy analyzes the time consumption inference ratio of each production stage for each beverage type. This time consumption inference ratio characterizes the time consumption ratio of the production stage corresponding to the input parameters of the current production stage after the production result of the previous production stage is input into the current production stage. The specific analysis process will be explained in detail later.

[0065] Step S103: Based on the current production progress information, generate current timeliness reasoning information for each beverage type, and based on the current timeliness reasoning information for each beverage type, generate current predicted production timeliness information for each beverage type through the timeliness impact reasoning scheme for each beverage type.

[0066] In this embodiment, the terminal generates current timeliness inference information for each beverage type based on the current production progress information, and generates current predicted production timeliness information for each beverage type through a timeliness impact inference scheme. The current timeliness inference information characterizes the predicted production parameters of each production stage (i.e., the production parameters of the current production stage after the production result of the previous stage is input into it) obtained by inferring and predicting the production sequence based on the current production progress information. The current predicted production timeliness information characterizes the predicted production duration of the entire process for the predicted beverage type.

[0067] Based on the above solution, by combining historical data from the beverage production line, this solution analyzes the impact of production timeliness on each beverage type from the perspective of its production process and duration, and from each node in the production process. This refines the timeliness impact analysis process for each production process, improving the accuracy and comprehensiveness of the analysis for each beverage type. Furthermore, this solution uses a timeliness impact reasoning scheme for each beverage type to analyze and reason for different beverage types. This enables one-click intelligent prediction and generation of production line processes for different beverage types, avoiding the inefficiency, data fragmentation and omissions, and difficulties in tracing production links of existing technologies, thus effectively improving the accuracy of efficiency analysis for beverage production lines.

[0068] Optionally, based on historical record information, the beverage production process and production time information for each beverage type are identified, including: breaking down the historical record information into sub-historical record information for each beverage type, and for each beverage type, identifying the production record information for each production process node based on the sub-historical record information; identifying the production process information for each production process node and the time information for each step corresponding to each production process information based on the production record information for each production process node, and arranging the production process information for each production process node according to the chronological order of each production process node to obtain the beverage production process for the beverage type; and using the time information for each step corresponding to each production process information as the production time information for the beverage type.

[0069] In this embodiment, the terminal breaks down historical record information into sub-historical record information for each beverage type. For each beverage type, based on the sub-historical record information, it identifies the production record information for each beverage type at each production process node. Here, each production process node refers to a process node within the production process of each beverage type. Each process node may correspond to one or more production steps, and each production step is a task performed during the production of the beverage type. These tasks include, for example, can cleaning, beverage filling, bottle pressurization, sealing, and beverage preparation.

[0070] Then, based on the production record information of each production process node, the terminal identifies the production process information of each node and the time information of each step corresponding to each production process information. It then arranges the production process information of each node according to their chronological order to obtain the beverage production process for that beverage type. Finally, the terminal uses the time information of each step corresponding to each production process information as the production duration information for that beverage type. Each production process information includes various production parameters for that node, such as conveyor belt speed, beverage dispensing volume, beverage loading rate, washing time, and drying time.

[0071] Based on the above scheme, by breaking down the production process of each beverage type into the production links of each process node according to the production flow, and then analyzing the production process information and the time information of each link, the comprehensiveness and accuracy of the analysis of each beverage type are improved.

[0072] Optionally, based on the beverage production process and production time information of each beverage type, identify the impact information on production timeliness of each production process node for each beverage type, including: for each production process information, based on the time information of each step corresponding to the production process information, identify the sub-production time of each production step, and collect the reasons for the time consumption of each production step; based on the sub-production time of each production step and the reasons for the time consumption of each production step, identify the distribution information of the proportion of time consumption of each production step through a time consumption analysis model; and use the distribution information of the proportion of time consumption of each production step as the impact information on production timeliness of the production process node corresponding to the production process information.

[0073] In this embodiment, the terminal identifies the sub-production time of each production process based on the time information of each stage corresponding to the production process information, and collects the reasons for the time consumption of each production stage. The reasons for the time consumption of each production stage are used to characterize the impact of changes in various production parameters within that production stage on the sub-production time of that stage.

[0074] Then, based on the sub-production time of each production stage and the reasons for the time consumption in each stage, the terminal uses a time consumption analysis model to identify the proportional distribution information of the time consumption ratio of each production stage. This proportional distribution information represents the ratio between the changes in each production parameter of that stage and the changes in the sub-production time of that stage. The time consumption analysis model is a deep learning-based artificial neural network. After acquiring the sub-production time of each production stage and the reasons for the time consumption in each stage, the model comprehensively analyzes the impact of each influencing factor on the production time obtained from historical training, and outputs the proportional value between the changes in each production parameter of each stage and the changes in the sub-production time of that stage (this proportional value can be a fixed value or a curve / gradient value, which represents the proportional value corresponding to different ranges of change).

[0075] Finally, the terminal uses the distribution information of the time consumption ratio of each production link as the production timeliness impact information of the corresponding production process nodes.

[0076] Based on the above scheme, by analyzing the reasons for the impact of timeliness on each production link, the distribution information of the time consumption ratio of each production link is obtained, thereby improving the accuracy of the timeliness correlation analysis of each production link.

[0077] Optionally, based on the production timeliness impact information of each production process node for each beverage type, a timeliness impact inference scheme for each beverage type is identified through a production efficiency analysis strategy. This includes: for each beverage type, identifying the time consumption proportion of each time-consuming factor in each production process node based on the distribution information of the time consumption ratio of each production link; identifying the time consumption inference ratio of each production link based on the time consumption ratio information of each time-consuming factor in each production link; and using the time consumption inference ratio of all production links as the timeliness impact inference scheme for the beverage type.

[0078] In this embodiment, the terminal identifies the time-consuming proportion of each time-consuming factor in each production stage based on the time-consuming proportion distribution information of each production process node for each beverage type. Each time-consuming factor corresponds to a production parameter in each production stage that affects the change in sub-production time. That is, in the time-consuming proportion distribution information of the production stages obtained above, there are cases where the ratio between the change in some production parameters and the change in sub-production time is consistently 0. In this case, the production parameter is a non-time-consuming factor; otherwise, it is a time-consuming factor. The time-consuming proportion information of non-time-consuming factors is the proportion of the change in sub-production time corresponding to a unit change in each production parameter to the sum of the changes in sub-production time corresponding to a unit change in all production parameters.

[0079] Then, based on the time-consuming percentage information of each time-consuming factor in each production stage, the terminal identifies the time-consuming inference ratio for each production stage. This time-consuming inference ratio is obtained by normalizing the time-consuming percentage information to obtain the time-consuming weight value corresponding to each time-consuming factor. The terminal then uses the time-consuming weight value corresponding to each time-consuming factor, along with the time-consuming percentage distribution information of each stage corresponding to each time-consuming factor, as the time-consuming inference ratio for each production stage. Finally, the terminal uses the time-consuming inference ratios of all production stages as the inference scheme for the timeliness impact on beverage types.

[0080] Based on the above scheme, since changes in production parameters are often not isolated but correlated, meaning that when the production efficiency of a single production stage is adjusted, all production parameters will change. Therefore, calculating the distribution of the time consumption ratio of each time-consuming factor in each production stage individually has the problem of excessive local influence of a single variable, without considering the mutual influence between different production parameters. Therefore, by combining the time consumption weight value corresponding to each time-consuming factor, and then weighting and summing the distribution of the time consumption ratio of each time-consuming factor before calculating the production time, the influencing factors between production parameters can be eliminated. This allows us to focus on the correlation between each production factor and the time consumption, thereby effectively improving the prediction accuracy of the production time of each production stage.

[0081] Optionally, based on the current production progress information, current timeliness inference information for each beverage type is generated, including: breaking down the current production progress information into sub-current production progress for each beverage type, and identifying the current production parameters for each beverage type based on the sub-current production progress of each beverage type; predicting the predicted production parameters for each production stage of each beverage type based on the current production parameters of each beverage type, and using the predicted production parameters for each production stage of each beverage type as the current timeliness inference information for each beverage type.

[0082] In this embodiment, the terminal breaks down the current production progress information into sub-current production progress for each beverage type, and identifies the current production parameters for each beverage type based on the sub-current production progress. Then, based on the current production parameters for each beverage type, the terminal predicts the predicted production parameters for each production stage of each beverage type, and uses these predicted production parameters as the current timeliness inference information for each beverage type. Specifically, the current production parameters for each beverage type refer to the production parameters of the current production stage. The terminal first obtains the current production efficiency of each production stage, and then, based on the current production efficiency and current production parameters of each production stage, queries the production results and probability values ​​for each production stage according to the order of production stages from beginning to end and the historical production records of each stage. Then, based on the probability values ​​of each production result, the terminal calculates the sum of all production results multiplied by their probability values ​​to obtain the predicted production result for each production stage. Finally, based on the predicted production results for each production stage, the terminal identifies the predicted production parameters for each stage according to the preset parameter correspondence between production stages on the terminal.

[0083] Based on the above scheme, by reasoning and predicting the production sequence of each production stage for each beverage type, the predicted production parameters for each production stage are predicted, thereby improving the accuracy of the prediction for each production stage.

[0084] Optionally, based on the current timeliness inference information for each beverage type, and through the timeliness impact inference scheme for each beverage type, current predicted production timeliness information for each beverage type is generated, including: for each beverage type, based on the predicted production parameters of each production stage of the beverage type, and through the time consumption inference ratio of each production stage of the beverage type, predicting the stage consumption information of each production stage according to the production sequence of each production stage; and based on the stage consumption information of each production stage, calculating the current predicted production timeliness information for the beverage type through a timeliness prediction algorithm.

[0085] In this embodiment, for each beverage type, the terminal predicts the time consumption information of each production stage based on the predicted production parameters of each production stage and the time consumption inference ratio of each production stage, according to the production sequence of each stage. Specifically, this prediction method involves multiplying the time consumption inference ratio of each production stage by the predicted production parameters of that stage to obtain the time consumption information for each stage. Then, based on the time consumption information of each production stage, the terminal calculates the current predicted production timeliness information for the beverage type using a timeliness prediction algorithm. This timeliness prediction algorithm involves summing the time consumption information of each production stage with the preset flow time between each production stage to obtain the current predicted production timeliness information for the beverage type.

[0086] Based on the above scheme, by reasoning and calculating the time consumption information of each production stage, and then predicting the current production timeliness information of the entire beverage type, the accuracy of the prediction for the entire beverage type is improved.

[0087] This application also provides an example of efficiency analysis for a beverage production line, such as... Figure 2 As shown, the specific processing procedure includes the following steps:

[0088] Step S201: Obtain historical data of the beverage production line and current production progress information of the beverage production line.

[0089] Step S202: The historical record information is broken down into sub-historical record information for each beverage type, and for each beverage type, based on the sub-historical record information of the beverage type, the production record information of each beverage type at each production process node is identified.

[0090] Step S203: Based on the production record information of each production process node, identify the production process information of each production process node and the time information of each step corresponding to each production process information, and arrange the production process information of each production process node according to the order of each production process node to obtain the beverage production process of the beverage type.

[0091] Step S204: Use the time information of each step corresponding to each production process information as the production time information of the beverage type.

[0092] Step S205: For each production process information, based on the time information of each link corresponding to the production process information, identify the sub-production time of each production link, and collect the reasons for the time consumption of each production link.

[0093] Step S206: Based on the sub-production time of each production stage and the reasons for the time consumption of each production stage, the time consumption ratio distribution information of each production stage is identified through the time consumption analysis model.

[0094] Step S207: The distribution information of the time consumption ratio of each production link is used as the production timeliness impact information of the production process node corresponding to the production process information.

[0095] Step S208: For each beverage type, based on the distribution information of the time consumption ratio of each production link in each production process node of the beverage type, identify the time consumption ratio information of each time consumption factor in each production link.

[0096] Step S209: Based on the time consumption ratio information of each time consumption factor in each production stage, identify the time consumption inference ratio of each production stage.

[0097] Step S210: The time consumption ratio of all production stages is used as the time-sensitive impact inference scheme for beverage types.

[0098] Step S211: The current production progress information is broken down into sub-current production progress for each beverage type, and the current production parameters for each beverage type are identified based on the sub-current production progress for each beverage type.

[0099] Step S212: Based on the current production parameters of each beverage type, predict the predicted production parameters of each production stage of each beverage type, and use the predicted production parameters of each production stage of each beverage type as the current timeliness inference information of each beverage type.

[0100] Step S213: For each beverage type, based on the predicted production parameters of each production stage of the beverage type, and by inferring the time consumption ratio of each production stage of the beverage type, predict the time consumption information of each production stage according to the production sequence of each production stage.

[0101] Step S214: Based on the time consumption information of each production stage, calculate the current predicted production time information of the beverage type using a timeliness prediction algorithm.

[0102] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0103] Based on the same inventive concept, this application also provides an efficiency analysis device for a beverage production line to implement the efficiency analysis method for the beverage production line described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the efficiency analysis device for a beverage production line provided below can be found in the limitations of the efficiency analysis method for a beverage production line described above, and will not be repeated here.

[0104] In one exemplary embodiment, such as Figure 3 As shown, an efficiency analysis device for a beverage production line is provided, comprising: an acquisition module 310, an identification module 320, and a generation module 330, wherein:

[0105] The acquisition module 310 is used to acquire historical information of the beverage production line and current production progress information of the beverage production line, and based on the historical information, identify the beverage production process of each beverage type and the production time information of each beverage type.

[0106] The identification module 320 is used to identify the production timeliness impact information of each production process node of each beverage type based on the beverage production process of each beverage type and the production time information of each beverage type, and to identify the timeliness impact reasoning scheme of each beverage type based on the production timeliness impact information of each production process node of each beverage type through the production efficiency analysis strategy.

[0107] The generation module 330 is used to generate current timeliness reasoning information for each beverage type based on the current production progress information, and to generate current predicted production timeliness information for each beverage type based on the current timeliness reasoning information for each beverage type and the timeliness impact reasoning scheme for each beverage type.

[0108] Optionally, the acquisition module 310 is specifically used for:

[0109] The historical record information is broken down into sub-historical record information for each beverage type, and for each beverage type, based on the sub-historical record information for each beverage type, the production record information for each beverage type at each production process node is identified;

[0110] Based on the production record information of each production process node, the production process information of each production process node and the time information of each step corresponding to each production process information are identified. The production process information of each production process node is then arranged in the order of each production process node to obtain the beverage production process of the beverage type.

[0111] The time taken for each step of the production process is used as the production time information for the beverage type.

[0112] Optionally, the identification module 320 is specifically used for:

[0113] For each production process information, based on the time information of each link corresponding to the production process information, the sub-production time of each production link is identified, and the reasons for the time consumption of each production link are collected.

[0114] Based on the sub-production time of each production stage and the reasons for the time consumption of each production stage, the time consumption ratio distribution information of each production stage is identified through the time consumption analysis model.

[0115] The distribution information of the time consumption ratio of each production link is used as the production timeliness impact information of the production process node corresponding to the production process information.

[0116] Optionally, the identification module 320 is specifically used for:

[0117] For each beverage type, based on the distribution information of the time consumption ratio of each production link in each production process node of the beverage type, the time consumption ratio information of each time-consuming factor in each production link is identified.

[0118] Based on the time consumption percentage of each time-consuming factor in each production stage, the time consumption inference ratio of each production stage is identified.

[0119] The time consumption ratio of all production stages is used as the reasoning scheme for the time-sensitivity impact of the beverage type.

[0120] Optionally, the generation module 330 is specifically used for:

[0121] The current production progress information is broken down into sub-current production progress for each beverage type, and the current production parameters for each beverage type are identified based on the sub-current production progress of each beverage type.

[0122] Based on the current production parameters of each beverage type, predict the production parameters of each production stage for each beverage type, and use the predicted production parameters of each production stage for each beverage type as the current timeliness inference information for each beverage type.

[0123] Optionally, the generation module 330 is specifically used for:

[0124] For each beverage type, based on the predicted production parameters of each production stage of the beverage type, and through the time consumption ratio of each production stage of the beverage type, the time consumption information of each production stage is predicted according to the production sequence of each production stage.

[0125] Based on the time consumption information of each production stage, the current predicted production time information of the beverage type is calculated using a timeliness prediction algorithm.

[0126] The various modules in the efficiency analysis device of the aforementioned beverage production line can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0127] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an efficiency analysis method for a beverage production line. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0128] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0129] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of an efficiency analysis method for a beverage production line.

[0130] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program being executed by a processor to implement the steps of an efficiency analysis method for a beverage production line.

[0131] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of an efficiency analysis method for a beverage production line.

[0132] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0133] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0134] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0135] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for efficiency analysis of a beverage production line, characterized in that, The method includes: The system acquires historical data of the beverage production line and current production progress information of the beverage production line, and based on the historical data, identifies the beverage production process for each beverage type and the production time information for each beverage type. Based on the beverage production process and production time information of each beverage type, identify the impact information of production timeliness of each production process node of each beverage type, and based on the impact information of production timeliness of each production process node of each beverage type, identify the timeliness impact reasoning scheme of each beverage type through production efficiency analysis strategy. Based on the current production progress information, current timeliness inference information for each beverage type is generated, and based on the current timeliness inference information for each beverage type, current predicted production timeliness information for each beverage type is generated through the timeliness impact inference scheme for each beverage type.

2. The method according to claim 1, characterized in that, The process of identifying the beverage production process and production time information for each beverage type based on the historical record information includes: The historical record information is broken down into sub-historical record information for each beverage type, and for each beverage type, based on the sub-historical record information for each beverage type, the production record information for each beverage type at each production process node is identified; Based on the production record information of each production process node, the production process information of each production process node and the time information of each step corresponding to each production process information are identified. The production process information of each production process node is then arranged in the order of each production process node to obtain the beverage production process of the beverage type. The time taken for each step of the production process is used as the production time information for the beverage type.

3. The method according to claim 2, characterized in that, The process of identifying the impact of each production process node on the production timeliness of each beverage type, based on the beverage production process of each beverage type and the production time information of each beverage type, includes: For each production process information, based on the time information of each link corresponding to the production process information, the sub-production time of each production link is identified, and the reasons for the time consumption of each production link are collected. Based on the sub-production time of each production stage and the reasons for the time consumption of each production stage, the time consumption ratio distribution information of each production stage is identified through the time consumption analysis model. The distribution information of the time consumption ratio of each production link is used as the production timeliness impact information of the production process node corresponding to the production process information.

4. The method according to claim 3, characterized in that, The production timeliness impact information based on each production process node of each beverage type, through a production efficiency analysis strategy, identifies a timeliness impact inference scheme for each beverage type, including: For each beverage type, based on the distribution information of the time consumption ratio of each production link in each production process node of the beverage type, the time consumption ratio information of each time-consuming factor in each production link is identified. Based on the time consumption percentage of each time-consuming factor in each production stage, the time consumption inference ratio of each production stage is identified. The time consumption ratio of all production stages is used as the reasoning scheme for the time-sensitivity impact of the beverage type.

5. The method according to claim 4, characterized in that, The process of generating current timeliness inference information for each beverage type based on the current production progress information includes: The current production progress information is broken down into sub-current production progress for each beverage type, and the current production parameters for each beverage type are identified based on the sub-current production progress of each beverage type. Based on the current production parameters of each beverage type, predict the production parameters of each production stage for each beverage type, and use the predicted production parameters of each production stage for each beverage type as the current timeliness inference information for each beverage type.

6. The method according to claim 5, characterized in that, The current timeliness inference information based on each beverage type, through the timeliness impact inference scheme for each beverage type, generates the current predicted production timeliness information for each beverage type, including: For each beverage type, based on the predicted production parameters of each production stage of the beverage type, and through the time consumption ratio of each production stage of the beverage type, the time consumption information of each production stage is predicted according to the production sequence of each production stage. Based on the time consumption information of each production stage, the current predicted production time information of the beverage type is calculated using a timeliness prediction algorithm.

7. An efficiency analysis device for a beverage production line, characterized in that, The device includes: The acquisition module is used to acquire historical information of the beverage production line and current production progress information of the beverage production line, and based on the historical information, to identify the beverage production process of each beverage type and the production time information of each beverage type. The identification module is used to identify the impact information of production timeliness of each production process node of each beverage type based on the beverage production process and production time information of each beverage type, and to identify the timeliness impact reasoning scheme of each beverage type based on the impact information of production timeliness of each production process node of each beverage type through production efficiency analysis strategy. The generation module is used to generate current timeliness inference information for each beverage type based on the current production progress information, and to generate current predicted production timeliness information for each beverage type based on the current timeliness inference information for each beverage type and the timeliness impact inference scheme for each beverage type.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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