A PP plastic cup production control system based on artificial intelligence

Abstract

The invention discloses a PP plastic cup production control system based on artificial intelligence, which belongs to the technical field of PP plastic cup production control, and comprises an acquisition module, a modular analysis module, a control module, a platform end and a replacement analysis module; the acquisition module is used for acquiring production monitoring data; the modular analysis module is used for analyzing the production monitoring data and obtaining control instructions; the control module is used for performing control adjustment according to the control instructions; the platform end comprises a channel information module and a replacement decision module; the channel information module is used for determining each candidate control mode and technical reference data; the replacement decision module is used for making a replacement decision, setting a new modular analysis module, and generating module function information; displaying each module function information to each user; the replacement analysis module is used for performing replacement analysis on the modular analysis module, determining the production difference value of each candidate control mode; and sending the production difference value of each candidate control mode to the replacement decision module.

Description

Technical Field

[0001] The invention belongs to the technical field of PP plastic cup production control, in particular to a PP plastic cup production control system based on artificial intelligence. Background Art

[0002] In the production process of PP plastic cups, traditional control systems often rely on fixed hardware and software architectures, and it is difficult to upgrade the control system according to technological development. As a result, when more advanced technologies emerge, the corresponding production equipment is easily eliminated directly, even if it still has certain use value; especially in the context of the rapid development of current production technology and market competition pressure; how to adapt to the development of new technologies and control production costs has become a problem in the current production of PP plastic cups; based on this, in order to achieve intelligent production control of PP plastic cups, the present invention provides a PP plastic cup production control system based on artificial intelligence. Summary of the invention

[0003] In order to solve the problems existing in the above-mentioned solutions, the present invention provides a PP plastic cup production control system based on artificial intelligence.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] An artificial intelligence-based PP plastic cup production control system, comprising an acquisition module, a modular analysis module, a control module, a platform end and a replacement analysis module;

[0006] The collection module is used to collect real-time data on PP plastic cup production according to preset collection items, obtain collection item data corresponding to each collection item, pre-process each collection item data, and integrate the pre-processed collection item data into production monitoring data.

[0007] Furthermore, the method for determining each collection item includes:

[0008] Determine each benchmark item according to the analysis requirements of the modular analysis module, wherein the benchmark item is a collection item with certain settings;

[0009] Obtain PP plastic cup information and production equipment information, and determine each candidate item according to the PP plastic cup information and production equipment information, wherein the candidate item refers to the collection item that has not been screened;

[0010] The candidate items are screened to obtain the screening items, where the screening items are the candidate items after screening; the collection items are determined according to the screening items and the benchmark items, and the corresponding collection equipment is installed based on the collection items.

[0011] Furthermore, the method for screening each candidate item includes:

[0012] Identify the audience device corresponding to each of the candidate items, where the audience device is a production device for data collection of the candidate items; identify the usage limit of each of the audience devices, and determine the representative years of each of the candidate items according to the usage limit;

[0013] estimating the application probability of each of the options according to each of the representative years, and estimating the implementation cost of each of the options;

[0014] According to the formula Calculating the screening value of each of the candidate items;

[0015] Where: PA is the screening value; e is the natural constant; CB is the implementation cost; η is the application probability; Q represents an infinite value;

[0016] Each filtering item is determined according to the filtering value corresponding to each of the candidate items.

[0017] Furthermore, the method for preprocessing the data of each collection item is:

[0018] Determine the data analysis requirements of the modular analysis module, and pre-process the data of each collection item according to the data analysis requirements.

[0019] The modular analysis module is used to analyze the production monitoring data according to the built-in production control method, obtain corresponding control instructions, and send the control instructions to the control module.

[0020] The control module is used to control and adjust the corresponding production equipment according to the received control instructions.

[0021] The platform end is used by the platform party, and includes a channel information module and a replacement decision module;

[0022] The channel information module is used to connect to the information channels selected by the platform party, collect various production control methods in real time through the information channels, and mark them as potential control methods; screen the potential control methods and determine the control methods to be selected;

[0023] Acquire control characteristic information of the control method to be selected, wherein the control characteristic information includes production reference quality data and production reference efficiency data; integrate each of the control methods to be selected and the control characteristic information into each technical reference data; and send each of the technical reference data to a replacement analysis module of each user.

[0024] Furthermore, the method of screening each potential control method includes:

[0025] An application evaluation model is established, and the expression of the application evaluation model is: ;

[0026] Where: s is the input data, the input data is the potential control method; the output data is the application evaluation value YP(s), the application evaluation value is 1 or -1;

[0027] Evaluate each of the potential control methods using the application evaluation model to obtain an application evaluation value of each of the potential control methods;

[0028] Each of the potential control methods with an application evaluation value of 1 is marked as a control method to be selected.

[0029] The replacement decision module is used to make a replacement decision, summarize the production difference values ​​of each user, process each of the production difference values, and obtain a representative difference value of each of the control modes to be selected;

[0030] Identify the estimated technical cost of each of the candidate control methods; calculate the replacement value of the corresponding candidate control method based on the estimated technical cost and the representative difference value;

[0031] Determine the target control mode according to each of the replacement values, set a new modular analysis module according to the target control mode, and generate module function information of the modular analysis module; display each of the module function information to each user, and let the user decide whether to replace the modular analysis module.

[0032] Furthermore, the replacement value is calculated as:

[0033] A replacement value calculation formula is preset, and the estimated technical cost and the representative difference value are input into the replacement value calculation formula for calculation to obtain the replacement value;

[0034] The replacement value calculation formula is: ; GH is the replacement value; YB is the representative difference value; DW is the estimated technical cost.

[0035] The replacement analysis module is used to perform replacement analysis on the modular analysis module and receive various technical reference data; obtain user technical data in real time according to the PP plastic cup production data, and the user technical data includes production application quality data and production application efficiency data;

[0036] The technical reference data are analyzed according to the user technical data to determine the production difference value of each of the control methods to be selected; and the production difference value of each of the control methods to be selected is sent to the replacement decision module.

[0037] Furthermore, the method for analyzing each technical reference data according to the user technical data includes:

[0038] Identify the production application quality data and production application efficiency data in the user technical data; identify the production reference quality data and production reference efficiency data in the technical reference data;

[0039] Evaluate the production reference quality data and production application quality data of the corresponding candidate control methods according to the preset quality evaluation items to obtain the single-item difference values corresponding to each quality evaluation item;

[0040] Calculate the efficiency difference value according to the production reference efficiency data and production application efficiency data of the corresponding candidate control methods;

[0041] According to the formula Calculate the production difference values of each candidate control method;

[0042] In the formula: CW is the production difference value; b1 and b2 are both proportionality coefficients, and the value ranges are 0 < b1 ≤ 1 and 0 < b2 ≤ 1; XL is the efficiency difference value; i represents the corresponding quality evaluation item, i = 1, 2,..., n, and n is a positive integer; DCi is the single-item difference value; λi is the weight coefficient.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] By adopting a modular data analysis module design, the flexible replacement and expansion of the data analysis function are realized. This design enables the system to easily adapt to the data processing requirements under different application scenarios without large-scale transformation or upgrade of the entire system. Users only need to select or replace the data analysis module most suitable for the current application scenario according to actual needs to quickly realize the optimization and upgrade of the data processing function. With the continuous progress of technology and the emergence of new algorithms, users can easily update the data analysis module to maintain the先进性 and competitiveness of the system. This design not only improves the accuracy and efficiency of data processing but also reduces the operation cost and maintenance difficulty of the system. Analyze and evaluate the data analysis module to ensure that the performance of the data analysis module always remains in the optimal state. When the module analysis module detects that the performance of the current data analysis module does not meet the requirements, it will trigger a replacement mechanism, allowing users or the system to automatically replace it with a more excellent data analysis module. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a block diagram of the principle of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0047] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] like Figure 1 As shown, a PP plastic cup production control system based on artificial intelligence includes a collection module, a modular analysis module, a control module, a platform end and a replacement analysis module;

[0049] The collection module is used to collect real-time data on PP plastic cup production according to preset collection items, obtain collection item data corresponding to each collection item, and integrate the collection item data into production monitoring data after pre-processing.

[0050] In one embodiment, each collection item can be set by the platform staff, that is, each collection item is determined based on the existing technology to ensure the comprehensiveness of the collected data and avoid incomplete subsequent production monitoring data, which leads to the inability to perform control analysis.

[0051] In one embodiment, the method for determining each collection item includes:

[0052] Identify the data items corresponding to the data required by the modular analysis module of the current application, marked as benchmark items, which refer to the modular analysis module corresponding to the factory stage, and are required to ensure the normal operation of the current system;

[0053] Obtain PP plastic cup information and production equipment information, determine data items that may need to be collected based on the PP plastic cup information and production equipment information, and mark them as candidate items, that is, determine each data item based on the possible subsequent production requirements, material changes, etc. of the PP plastic cup, that is, the data items for monitoring after adjustment, which are mainly determined based on various high-tech technologies that are currently open but have not been applied. Because it often takes some time from theory to application, the above-mentioned technologies can be directly analyzed to determine each data item; then analyze based on the production equipment information to determine the various control methods that can be implemented and the data items corresponding to the corresponding control methods, that is, which data items are used to support the decision-making of the control method, compare the two, and determine each candidate item; in this process, existing intelligent technologies can be combined for analysis to realize intelligent determination of each data item, such as establishment based on intelligent algorithms such as neural networks; however, because a set of production lines only analyzes the collection items in sequence, each candidate item can also be determined by professionals; it is determined specifically according to the needs of the platform.

[0054] Each candidate item is evaluated and screened to obtain each screening item, each screening item and the benchmark item is marked as a collection item, and corresponding collection equipment is installed according to each collection item.

[0055] In one embodiment, the method for screening the candidate items is: screening based on the existing screening method.

[0056] In one embodiment, the method for screening each candidate item includes:

[0057] Identify which production equipment or equipment will be collected when each candidate item is applied, and mark them as target equipment, that is, determine the target equipment corresponding to each candidate item; obtain the usage limit of each target equipment, and determine it according to its design requirements, that is, the normal usage limit of the target equipment; set the corresponding representative years. If the candidate item corresponds to only one target equipment, the representative years are the usage limit years. If there are multiple target equipment, select the lowest usage limit years as the representative years.

[0058] The application probability is estimated based on the application status of the collected data corresponding to each candidate and the representative years. That is, the application probability within the representative years is determined according to the control analysis method corresponding to the candidate. Because the corresponding high-tech will be referred to when determining the candidate, the corresponding application estimate can be made to determine the corresponding application probability. For example, the possible application time can be understood based on the corresponding forums, expert opinions, etc., and then the application probability can be determined according to the representative years.

[0059] Estimate the implementation cost of each option; according to the formula Calculate the screening value of each candidate item;

[0060] Where: PA is the screening value; e is the natural constant; CB is the implementation cost; η is the application probability; Q means that the value is infinite and can be regarded as direct elimination;

[0061] Eliminate the candidate items whose screening values ​​are greater than the threshold value X1, and mark the remaining candidate items as screening items.

[0062] In one embodiment, each collected item data is preprocessed, which may include data cleaning, data integration, data transformation, data reduction and other processing steps, so that the preprocessed production monitoring data can be used by the modular analysis module; that is, the data analysis requirements of the modular analysis module are determined, such as format, transmission and other requirements; each collected item data is preprocessed according to the data analysis requirements, so the platform party needs to consider in advance the various possible data analysis requirements, and then determine the subsequent implementation of the preprocessing. Generally, when setting up a new modular analysis module, the platform party will simultaneously consider the limitations of the preprocessing to ensure that they work normally with each other.

[0063] The modular analysis module is used to analyze the production monitoring data according to the built-in production control method, obtain corresponding control instructions, and send the obtained control instructions to the control module.

[0064] The control module is used to control and adjust the corresponding production equipment according to the received control instructions.

[0065] The platform end is used by the platform party and is set at the platform party, including a channel information module and a replacement decision module;

[0066] The channel information module is used to connect to the information channels selected by the platform, and is used to collect various production control methods that can be currently applied on the production line in real time through various information channels; collect various production control methods in real time through various information channels, mark them as potential control methods, screen each potential control method, determine the control method to be selected, and obtain the control feature information of the control method to be selected, the control feature information includes production quality data, production efficiency data, estimated technical cost and other related information; production quality data can be obtained through quality inspection pass rate, PP plastic cup properties and other quality-related data, such as high temperature resistance, ductility, hardness and other related data; estimated technical cost is the estimated implementation cost required to apply the control method to be selected; each control method to be selected and the corresponding control feature information are integrated into technical reference data; each technical reference data is sent to the replacement analysis module of each user.

[0067] In one embodiment, various potential control methods may be screened based on existing technologies, as long as it is determined that they can be applied on the current production line.

[0068] In one embodiment, the method for screening each potential control method includes:

[0069] Establish an application evaluation model. The expression of the application evaluation model is ; In the formula: s is the input data, the input data is the potential control method; the output data is the application evaluation value YP(s), the application evaluation value is 1 or -1; s meets the application requirements, which means that the potential control method can be applied on this production line, and the judgment is made based on the actual situation of the existing production line;

[0070] Evaluate each potential control method through the application evaluation model to obtain the application evaluation value corresponding to each potential control method;

[0071] Each potential control method with an application evaluation value of 1 is marked as a candidate control method.

[0072] The replacement decision module is used to make a replacement decision for the modular analysis module, summarize the production difference values ​​of each user, process each production difference value, and obtain a representative difference value of each control mode to be selected;

[0073] Identify the estimated technical cost of each candidate control method; calculate the replacement value of the corresponding candidate control method based on the estimated technical cost and the representative difference value;

[0074] The candidate control method with a replacement value greater than the threshold X2 and the largest replacement value is marked as the target control method, and a new modular analysis module is set according to the target control method, and module function information of the modular analysis module is generated, such as various advantage information; the function information of each module is displayed to each user, and the user decides whether to replace the new modular analysis module; it is mainly based on the platform's operating method. If it is a free replacement or there are corresponding contract requirements, the platform will generally contact the user for direct replacement.

[0075] In one embodiment, because the production difference value of each user is available, it can be compared with the modular analysis modules reserved by the platform to determine whether they need to be updated, so as to achieve personalized replacement for each user, and the corresponding replacement analysis results are sent to the user; and the user has the module function information of each reserved modular analysis module, so when the user himself believes that the applied modular analysis module cannot meet his needs, he can also directly apply to replace the selected modular analysis module.

[0076] In one embodiment, each production difference value is processed, and different regions, environments, years of use and other factors can be classified by clustering, classification and the like. Subsequently, corresponding representative difference values ​​are set for different classifications to eliminate the influence of environmental, geographical and other factors, so that all replacements do not need to be performed.

[0077] In one embodiment, a representative production difference value may be formed by using an average value, a mode, a variance, etc. and marked as a representative difference value.

[0078] In one embodiment, the method for calculating the replacement value of the corresponding candidate control mode according to the estimated technical cost and the representative difference value is:

[0079] A corresponding replacement value calculation formula is preset, and the estimated technical cost and the representative difference value are input into the replacement value calculation formula for calculation to obtain the replacement value; the estimated technical cost and the representative difference value are marked as YB and DW respectively.

[0080] In one embodiment, the replacement value calculation formula is: GH=YB÷DW; GH is the replacement value.

[0081] In one embodiment, the replacement value calculation formula is: GH=YB÷DW 0.5 ; GH is the replacement value.

[0082] In one embodiment, the replacement value calculation formula is: ; GH is the replacement value.

[0083] In one embodiment, the replacement value calculation formula may also be other existing formulas.

[0084] The replacement analysis module is used to perform replacement analysis on the modular analysis module, receive various technical reference data; perform real-time statistical analysis on the current PP plastic cup production, obtain corresponding production quality data, production efficiency data, etc., and integrate them into user technical data; in order to distinguish, the production quality data and production efficiency data in the technical reference data are marked as production reference quality data and production reference efficiency data respectively; the production quality data and production efficiency data in the user technical data are marked as production application quality data and production application efficiency data respectively;

[0085] Analyze each technical reference data according to the user technical data to determine the production difference value corresponding to each candidate control method; send the production difference value of each candidate control method to the replacement decision module.

[0086] In one embodiment, the method for analyzing each technical reference data according to the user technical data includes:

[0087] Identify the production application quality data and production application efficiency data in the user technical data; identify the production reference quality data and production reference efficiency data in the technical reference data;

[0088] The quality assessment items corresponding to the preset quality assessment and the weight coefficients of each quality assessment item are generally set by the platform; the quality deviation proportion corresponding to each quality assessment item is determined based on the production reference quality data and the production application quality data, and marked as a single item difference value, specifically, the production reference quality data is compared with the production application quality data, that is, when the production reference quality is better, the single item difference value is positive, otherwise it is negative;

[0089] For example, the deviation of quality inspection pass rate and hardness deviation can be quantified and compared based on the existing data processing method to determine the single difference value corresponding to each quality assessment item;

[0090] The single difference value is marked as DCi, i represents the corresponding quality evaluation item, i=1, 2, ..., n, n is a positive integer; the weight coefficient of each quality evaluation item is marked as λi;

[0091] Calculate the efficiency difference value based on the production reference efficiency data and the production application efficiency data;

[0092] According to the formula Calculate the production variance value of each candidate control method;

[0093] Where: CW is the production difference value; b1 and b2 are both proportionality coefficients, and the value ranges are 0 < b1 ≤ 1, 0 < b2 ≤ 1; XL is the efficiency difference value.

[0094] The above formulas are all calculated by removing the dimension and taking their numerical values. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulating a large amount of data.

[0095] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A PP plastic cup production control system based on artificial intelligence, characterized in that: It includes acquisition module, modular analysis module, control module, platform end and replacement analysis module; The collection module is used to collect real-time data on the production of PP plastic cups according to the preset collection items, obtain the collection item data corresponding to each collection item, pre-process the collection item data, and integrate the pre-processed collection item data into production monitoring data; The modular analysis module is used to analyze the production monitoring data according to the built-in production control method, obtain corresponding control instructions, and send the control instructions to the control module; The control module is used to control and adjust the corresponding production equipment according to the received control instructions; The platform end is used by the platform party, and includes a channel information module and a replacement decision module; The channel information module is used to connect to the information channels selected by the platform, collect various production control methods in real time through the information channels, and mark them as potential control methods; screen the potential control methods and determine the control methods to be selected; Acquire control characteristic information of the control method to be selected, wherein the control characteristic information includes production reference quality data and production reference efficiency data; integrate the control methods to be selected and the control characteristic information into technical reference data; and send the technical reference data to the replacement analysis module of each user; The replacement decision module is used to make a replacement decision, summarize the production difference values ​​of each user, process each of the production difference values, and obtain a representative difference value of each of the control modes to be selected; Identify the estimated technical cost of each of the candidate control methods; calculate the replacement value of the corresponding candidate control method based on the estimated technical cost and the representative difference value; Determine a target control mode according to each replacement value, set a new modular analysis module according to the target control mode, and generate module function information of the modular analysis module; display each module function information to each user, and let the user decide whether to replace the modular analysis module; The replacement analysis module is used to perform replacement analysis on the modular analysis module and receive various technical reference data; obtain user technical data in real time according to the PP plastic cup production data, and the user technical data includes production application quality data and production application efficiency data; The technical reference data are analyzed according to the user technical data to determine the production difference value of each of the control methods to be selected; and the production difference value of each of the control methods to be selected is sent to the replacement decision module.

2. The artificial intelligence-based PP plastic cup production control system according to claim 1 is characterized in that: The methods for determining each collection item include: Determine each benchmark item according to the analysis requirements of the modular analysis module, wherein the benchmark item is a collection item with certain settings; Obtain PP plastic cup information and production equipment information, and determine each candidate item according to the PP plastic cup information and production equipment information, wherein the candidate item refers to the collection item that has not been screened; The candidate items are screened to obtain the screening items, which are the candidate items after screening; the collection items are determined according to the screening items and the benchmark items, and the corresponding collection equipment is installed based on the collection items.

3. The artificial intelligence-based PP plastic cup production control system according to claim 2 is characterized in that: Methods for screening the options include: Identify the audience devices corresponding to each of the to-be-selected options, where the audience devices are production devices for data collection for the to-be-selected options; identify the service life limits of each audience device, and determine the representative life spans of each of the to-be-selected options according to the service life limits. Estimate the application probabilities of each of the to-be-selected options according to the representative life spans, and estimate the implementation costs of each of the to-be-selected options. According to the formula Calculating the screening value of each of the candidate items; In the formula: PA is the screening value; e is the natural constant; CB is the implementation cost; η is the application probability; Q represents infinitely large. Determine each screening option according to the screening values corresponding to each of the to-be-selected options.

4. The artificial intelligence-based PP plastic cup production control system according to claim 1 is characterized in that: The method for preprocessing the data of each collection item is as follows: Determine the data analysis requirements of the modular analysis module, and preprocess the data of each collection item according to the data analysis requirements.

5. The artificial intelligence-based PP plastic cup production control system according to claim 1 is characterized in that: The method for screening each potential control method includes: An application evaluation model is established, and the expression of the application evaluation model is: ; In the formula: s is the input data, and the input data is the potential control method; the output data is the application evaluation value YP(s), and the application evaluation value is 1 or -1. Evaluate each of the potential control methods through the application evaluation model to obtain the application evaluation values of each of the potential control methods. Mark each of the potential control methods with an application evaluation value of 1 as the to-be-selected control methods.

6. The artificial intelligence-based PP plastic cup production control system according to claim 1 is characterized in that: The calculation method of the replacement value is as follows: Preset the replacement value calculation formula, input the estimated technical cost and the representative difference value into the replacement value calculation formula for calculation to obtain the replacement value. The replacement value calculation formula is: ; GH is the replacement value; YB is the representative difference value; DW is the estimated technical cost.

7. The artificial intelligence-based PP plastic cup production control system according to claim 1 is characterized in that: The method for analyzing each technical reference data according to the user technical data includes: Identify the production application quality data and production application efficiency data in the user technical data; identify the production reference quality data and production reference efficiency data in the technical reference data. Evaluate the production reference quality data and production application quality data of the corresponding to-be-selected control methods according to the preset quality evaluation items to obtain the single-item difference values corresponding to each quality evaluation item. Calculate the efficiency difference value according to the production reference efficiency data and production application efficiency data of the corresponding to-be-selected control methods. According to the formula Calculate the production variance value of each candidate control method; In the formula: CW is the production difference value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; XL is the efficiency difference value; i represents the corresponding quality evaluation item, i = 1, 2,..., n, and n is a positive integer; DCi is the single-item difference value; λi is the weight coefficient.

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