Intelligent maintenance method, system and equipment for aluminum foil cutting machine and medium

Abstract

The invention discloses an intelligent maintenance method, system and equipment for an aluminum foil cutting machine and a medium, and relates to the field of data processing. The method comprises the following steps: loading monitoring elements of the cutting machine; performing real-time monitoring according to the monitoring elements of the cutting machine to obtain a monitoring source of the cutting machine; performing standard state variation calculation on the monitoring source of the cutting machine to obtain a variation vector of the cutting machine; selecting the variation vector of the cutting machine to generate an abnormal variation vector of the cutting machine; a cutting machine maintenance channel is activated to carry out maintenance decision analysis, and a cutting machine maintenance scheme is generated; and the aluminum foil cutting machine is maintained according to the cutting machine maintenance scheme. By adopting the method, the technical problems of insufficient preventive maintenance and low maintenance efficiency of the cutting machine caused by excessive emphasis on repair after the fault occurs in the prior art are solved, and the technical effects of improving the preventive maintenance of the cutting machine and aiming at solving the fault of the cutting machine are achieved by enhancing the preventive maintenance and quoting the targeted decision of the variation vector.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to the field of cutting machine data processing, and specifically to an intelligent maintenance method, system, equipment and medium for an aluminum foil cutting machine. Background Art

[0002] The current intelligent maintenance method of aluminum foil cutting machines can monitor the equipment operation data in real time through sensors and other equipment. Through the Internet of Things technology, remote monitoring and maintenance of aluminum foil cutting machines can be realized. Engineers can diagnose and repair faults without going to the site. Based on historical data and algorithm models, some aluminum foil cutting machines can predict potential faults and perform maintenance in advance to avoid equipment downtime. Although aluminum foil cutting machines can monitor and collect a large amount of data, the accuracy and integrity of the data are sometimes difficult to guarantee due to factors such as sensor quality and environmental interference. This may lead to a decrease in the accuracy of fault prediction and affect the maintenance effect. There may be differences between different models of aluminum foil cutting machines, and the general model cannot fully adapt to all situations. Maintenance decisions still rely on manual judgment and experience, and lack an intelligent maintenance decision system.

[0003] In summary, the prior art has the technical problem of over-emphasizing repair after a fault occurs, resulting in insufficient preventive maintenance and low maintenance efficiency of the cutting machine. Summary of the invention

[0004] Based on this, it is necessary to provide an intelligent maintenance method, system, equipment and medium for aluminum foil cutting machines that can improve the preventive maintenance of cutting machines and solve cutting machine failures in response to the above technical problems.

[0005] In a first aspect, a smart maintenance method for an aluminum foil cutter is provided, the method comprising: loading a U-dimensional cutter monitoring element, wherein the U-dimensional cutter monitoring element comprises U preset cutter monitoring elements, wherein U is a positive integer greater than 1; performing real-time monitoring of the aluminum foil cutter according to the U-dimensional cutter monitoring element in combination with a pre-deployed Internet of Things sensor network to obtain a cutter monitoring source, wherein the cutter monitoring source comprises U cutter monitoring data streams corresponding to the U preset cutter monitoring elements; performing standard state variation calculation on the cutter monitoring source according to the U-dimensional cutter monitoring element to obtain a U-dimensional cutter variation vector; selecting the U-dimensional cutter variation vector according to a cutter variation identification channel to generate a T-dimensional cutter abnormal variation vector, wherein T is a positive integer and T is less than or equal to U; activating a U-dimensional cutter maintenance channel according to the T-dimensional cutter abnormal variation vector to perform maintenance decision analysis and generate a cutter maintenance plan; and maintaining the aluminum foil cutter according to the cutter maintenance plan.

[0006] In a second aspect, an intelligent maintenance system for an aluminum foil cutter is provided, the system comprising: a cutter monitoring element loading module, the cutter monitoring element loading module is used to load U-dimensional cutter monitoring elements, wherein the U-dimensional cutter monitoring elements include U preset cutter monitoring elements, wherein U is a positive integer greater than 1; a cutter monitoring source acquisition module, the cutter monitoring source acquisition module is used to perform real-time monitoring of the aluminum foil cutter according to the U-dimensional cutter monitoring elements in combination with a pre-deployed Internet of Things sensor network to obtain a cutter monitoring source, wherein the cutter monitoring source includes U cutter monitoring data streams corresponding to the U preset cutter monitoring elements; a cutter variation vector acquisition module, the cutter variation vector acquisition module The block is used to perform standard state variation calculation on the cutter monitoring source according to the U-dimensional cutter monitoring element to obtain the U-dimensional cutter variation vector; the cutter variation vector selection module is used to select the U-dimensional cutter variation vector according to the cutter variation identification channel to generate a T-dimensional cutter abnormal variation vector, wherein T is a positive integer and T is less than or equal to U; the cutter maintenance plan generation module is used to activate the U-dimensional cutter maintenance channel according to the T-dimensional cutter abnormal variation vector to perform maintenance decision analysis and generate a cutter maintenance plan; the cutter maintenance module is used to maintain the aluminum foil cutter according to the cutter maintenance plan.

[0007] According to a third aspect, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps according to the first aspect when executing the computer program.

[0008] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps described in the first aspect are implemented.

[0009] The above-mentioned intelligent maintenance method, system, equipment and medium of the aluminum foil cutter adopt this method to solve the technical problems in the prior art that too much emphasis is placed on repair after a fault occurs, resulting in insufficient preventive maintenance and low maintenance efficiency of the cutter. By strengthening preventive maintenance and referencing mutation vectors for targeted decision-making, the technical effect of improving the preventive maintenance of the cutter and solving the faults of the cutter is achieved.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1A schematic diagram of a flow chart of an intelligent maintenance method for an aluminum foil cutting machine in one embodiment; Figure 2 A schematic flow chart of constructing a U-dimensional standard state vector of an aluminum foil cutting machine in an intelligent maintenance method of an aluminum foil cutting machine in one embodiment; Figure 3 It is a structural block diagram of an intelligent maintenance system for an aluminum foil cutting machine in one embodiment; Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment.

[0012] Explanation of the reference numerals: a cutting machine monitoring element loading module 11 , a cutting machine monitoring source obtaining module 12 , a cutting machine variation vector obtaining module 13 , a cutting machine variation vector selecting module 14 , a cutting machine maintenance plan generating module 15 , and a cutting machine maintenance module 16 . DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] like Figure 1 As shown, the present application provides an intelligent maintenance method for an aluminum foil cutting machine, the method comprising: Loading a U-dimensional clipper monitoring element, wherein the U-dimensional clipper monitoring element includes U preset clipper monitoring elements, wherein U is a positive integer greater than 1; The cutting machine is controlled by system software, and then directly cuts the product. The corresponding parameters are set on the operating platform, and the computer transmits the corresponding instructions to the cutting machine. The cutting machine quickly cuts according to the accepted design drawings. The aluminum foil cutting machine is a mechanical equipment specially used for cutting aluminum foil. This application provides an intelligent maintenance method for the aluminum foil cutting machine, which effectively improves the operating efficiency and stability of the aluminum foil cutting machine and reduces the failure rate.

[0015] Loading U-dimensional cutter monitoring elements refers to combining U preset aluminum foil cutter monitoring elements, where U is a positive integer greater than 1. The cutter monitoring elements include motor current, voltage, cutter temperature, pressure, vibration, ambient temperature, ambient humidity, etc. Combining U cutter monitoring elements forms a multi-dimensional monitoring system, which can more comprehensively reflect the operating status of the cutter, thereby improving the accuracy and efficiency of maintenance.

[0016] According to the U-dimensional cutting machine monitoring elements, the aluminum foil cutting machine is monitored in real time in combination with the pre-deployed Internet of Things sensor network to obtain a cutting machine monitoring source, wherein the cutting machine monitoring source includes U cutting machine monitoring data streams corresponding to the U preset cutting machine monitoring elements; According to the U-dimensional cutting machine monitoring elements, the aluminum foil cutting machine is monitored in real time in combination with the pre-deployed Internet of Things sensor network. The Internet of Things sensor network is composed of a series of sensor nodes, which are deployed in the key parts and key links of the aluminum foil cutting machine to collect the operating data of the cutting machine in real time, including temperature sensors, humidity sensors, pressure sensors, vibration sensors, etc., which are used to monitor various status parameters of the cutting machine. When the Internet of Things sensor network starts working, it will continuously collect data corresponding to U cutting machine monitoring elements, including motor current, voltage, temperature, etc. The U-dimensional cutting machine monitoring elements correspond to one or more sensor nodes respectively, and the sensor nodes transmit the collected data to the data processing center in real time; in the data processing center, the received data is analyzed to obtain a cutting machine monitoring source, and the cutting machine monitoring source includes U cutting machine monitoring data streams corresponding to the U preset cutting machine monitoring elements, that is, the cutting machine monitoring data groups corresponding to the U preset cutting machine monitoring elements. By real-time monitoring and analysis of the cutting machine monitoring data streams, abnormal conditions or potential problems of the cutting machine can be discovered in time. Combined with the pre-deployed Internet of Things sensor network, the aluminum foil cutting machine is monitored in real time, and U cutting machine monitoring data streams are obtained, which can provide real-time and accurate data support, and provide strong guarantees for the state monitoring, fault warning and maintenance optimization of the cutting machine.

[0017] Performing standard state variation calculation on the clipper monitoring source according to the U-dimensional clipper monitoring element to obtain a U-dimensional clipper variation vector; The standard state variation calculation of the cutting machine monitoring source is performed according to the U-dimensional cutting machine monitoring element, wherein the standard state is usually determined comprehensively based on the design parameters of the cutting machine, historical operation data and industry experience, and represents the performance indicators and state parameters that the cutting machine should achieve under normal working conditions. The U cutting machine monitoring data streams corresponding to the U-dimensional cutting machine monitoring element are compared with the standard state, and the deviation or variation degree between the real-time data and the standard state is calculated. For the U-dimensional cutting machine monitoring element, a numerical value representing its variation degree is obtained, and the numerical values ​​are combined to form a U-dimensional cutting machine variation vector. This vector is a multi-dimensional data set, which comprehensively reflects the variation of the cutting machine relative to the standard state under the real-time monitoring state. The acquisition of the U-dimensional cutting machine variation vector provides a basis for in-depth analysis of the operating state of the aluminum foil cutting machine. By observing and analyzing this vector, it is possible to find problems and hidden dangers that may exist in the operation of the aluminum foil cutting machine, and then take corresponding maintenance measures.

[0018] Performing a standard state sample search on the aluminum foil cutting machine according to the U-dimensional cutting machine monitoring element to generate a U-dimensional cutting machine monitoring standard sample set; According to the U-dimensional cutting machine monitoring element, a standard state sample retrieval is performed on the same family equipment of the aluminum foil cutting machine to generate a U-dimensional same family expanded monitoring standard sample set, wherein the same family equipment includes multiple aluminum foil cutting machines of the same model corresponding to the aluminum foil cutting machine; Calculate the standard state vector according to the U-dimensional clipper monitoring standard sample set and the U-dimensional homology expanded monitoring standard sample set to construct a U-dimensional clipper standard state vector; Performing standardization processing according to the cutting machine monitoring source to construct a U-dimensional cutting machine monitoring vector; The deviation of the U-dimensional clipper monitoring vector is calculated according to the U-dimensional clipper standard state vector to generate the U-dimensional clipper variation vector.

[0019] Standard state sample retrieval refers to collecting data of the aluminum foil cutter under normal operating conditions through the U-dimensional cutter monitoring element, reflecting the performance parameters of the cutter under optimal working conditions, integrating the parameters, and combining the sorted standard state data into a U-dimensional cutter monitoring standard sample set as a benchmark for subsequent analysis and comparison; identifying other cutters of the same model as the aluminum foil cutter, these devices are regarded as the same family devices, and performing the same standard state sample retrieval process as the aluminum foil cutter on the same family devices, collecting their data under normal operating conditions, and adding the standard state data of the same family devices to the U-dimensional cutter monitoring standard sample set to form an expanded monitoring standard sample set. This expanded sample set can improve the diversity and representativeness of the standard, making the subsequent analysis more accurate and comprehensive. Extract key features from the U-dimensional cutter monitoring standard sample set and the U-dimensional same family expanded monitoring standard sample set. These features should be able to fully reflect the operating status of the cutter, and calculate the standard state vector based on the extracted features. This vector is a multi-dimensional data set, each dimension corresponds to the standard state value of a monitoring element. The monitoring data of the aluminum foil cutter is collected in real time through the IoT sensor network, and the collected real-time monitoring data is standardized to ensure that the dimension and range of the data are consistent with the standard state vector. The standardized real-time monitoring data is combined into a U-dimensional cutter monitoring vector. The U-dimensional cutter monitoring vector is compared with the U-dimensional cutter standard state vector, and the deviation between the real-time monitoring state and the standard state of each monitoring element is calculated. The calculated deviation values ​​are combined into a U-dimensional cutter variation vector, which reflects the abnormal situation of the aluminum foil cutter relative to the standard state during actual operation. The U-dimensional cutter variation vector is obtained to provide strong data support for subsequent fault diagnosis, performance evaluation and maintenance decisions.

[0020] like Figure 2 As shown, the U-dimensional cutting machine monitoring standard sample set and the U-dimensional homology expanded monitoring standard sample set are standardized to obtain a cutting machine standard sample library; Partition the standard sample library of the cutting machine according to the U-dimensional cutting machine monitoring element to generate U element standard sample partitions; Performing concentrated value calculations on the U element standard sample partitions respectively to generate U element standard concentrated value calculation results; The U-dimensional clipper standard state vector is constructed according to the calculation results of the standard concentrated values ​​of the U elements.

[0021] The U-dimensional clipper monitoring standard sample set and the U-dimensional homogeneous expanded monitoring standard sample set are standardized. According to the nature of the data and the purpose of analysis, a suitable standardization method is selected, including data scaling and translation, so that it conforms to the standard normal distribution or a specific range, and all standardized sample data are integrated into a unified clipper standard sample library for subsequent analysis and calculation. According to the definition and characteristics of the U-dimensional clipper monitoring element, determine how to partition the standard sample library, each partition will contain all sample data related to a specific monitoring element; according to the determined partition basis, the clipper standard sample library is partitioned to generate U element standard sample partitions. Each partition corresponds to a monitoring element and contains all standardized sample data of the element; according to the distribution characteristics of the data and the analysis requirements, a suitable concentration value indicator is selected, such as the mean, median, mode, etc., and the selected concentration value indicator is applied to the U element standard sample partitions for calculation, and the concentration value of each monitoring element is obtained. The concentration value of each monitoring element is used as the standard state value of the element to form U element standard concentration value calculation results, which are used to construct the U-dimensional clipper standard state vector. The standard concentrated values ​​of U elements are combined in the order of monitoring elements to form a multidimensional vector, and the combined multidimensional vector is used as the standard state vector of the U-dimensional cutting machine, as the benchmark for subsequent variation analysis and state monitoring, and used to compare the difference between real-time monitoring data and standard state. Through the above method, the standard state vector of the U-dimensional cutting machine is obtained, which accurately reflects the operating characteristics of the aluminum foil cutting machine under the standard state, and provides a reliable basis for subsequent state monitoring and fault warning.

[0022] Selecting the U-dimensional clipper variation vector according to the clipper variation identification channel to generate a T-dimensional clipper abnormal variation vector, wherein T is a positive integer, and T is less than or equal to U; The clipper variation identification channel refers to a model for identifying the abnormal state of the clipper determined according to the structural characteristics, working principle and historical fault data of the clipper. Selecting the U-dimensional clipper variation vector according to the clipper variation identification channel refers to inputting the U-dimensional clipper variation vector into the clipper variation identification channel for screening to form a T-dimensional clipper abnormal variation vector, that is, arranging the screened T elements according to their variation severity in the variation identification channel to generate a T-dimensional clipper abnormal variation vector; the value of T should be less than or equal to U to ensure that a variation vector with a higher risk of abnormal variation vector is selected. Effectively extract features with a higher risk of causing abnormal state of the clipper from multi-dimensional variation information to provide more accurate and effective support for subsequent diagnosis and maintenance.

[0023] Extracting a first cropper variation vector according to the U-dimensional cropper variation vector; The clipper variation identification channel includes a clipper variation risk prediction network and a clipper variation selector; Performing risk prediction on the first clipper variation vector according to the clipper variation risk prediction network to obtain a first variation risk index; inputting the first mutation risk index into the clipper mutation selector, wherein the clipper mutation selector includes a mutation risk index threshold; If the first mutation risk index is greater than / equal to the mutation risk index threshold, setting the first clipper mutation vector as the first clipper abnormal mutation vector; The respective clipper variation vectors in the U-dimensional clipper variation vector are continuously selected according to the clipper variation identification channel, and combined with the first clipper abnormal variation vector to generate the T-dimensional clipper abnormal variation vector.

[0024] A mutation vector is extracted from the U-dimensional cutter mutation vector as the first cutter mutation vector. According to the staff's own selection, the selected cutter mutation vector is recorded as the first cutter mutation vector, which represents the variation of the aluminum foil cutter in a certain specific monitoring feature. The first cutter mutation vector is input into the cutter mutation risk prediction network. The cutter mutation risk prediction network can predict the corresponding mutation risk according to the characteristics of the mutation vector. The first cutter mutation vector is input to obtain the first mutation risk index. The first mutation risk index represents the abnormal risk degree of the first cutter mutation vector. The higher the first mutation risk index, the higher the risk of the mutation corresponding to the mutation vector may lead to the abnormal state of the cutter. The first mutation risk index is input into the cutter mutation selector. The cutter mutation selector includes a mutation risk index threshold, which is set according to historical data, expert experience and system requirements, and is used to determine whether the mutation risk has reached the level that needs attention; if the first mutation risk index is greater than or equal to the mutation risk index threshold, it means that the mutation corresponding to the mutation vector has a higher risk and may cause the cutter to be abnormal. Therefore, the first clipper mutation vector is set as the first clipper abnormal mutation vector. Use the same clipper mutation identification channel to continue to select other mutation vectors within the U-dimensional clipper mutation vector, repeat the above method, perform risk prediction and selection on the U-dimensional clipper mutation vector, add the mutation vector that meets the selection conditions to the set of abnormal mutation vectors, combine all identified abnormal mutation vectors, including the first clipper abnormal mutation vector and other abnormal mutation vectors that meet the conditions, and generate a T-dimensional clipper abnormal mutation vector. The T-dimensional clipper abnormal mutation vector reflects the compilation information with higher risk of abnormal state of the clipper, which can be used for subsequent fault diagnosis, early warning and maintenance decisions. Extract the T-dimensional abnormal mutation vector that is most relevant to the abnormal state of the clipper from the U-dimensional clipper mutation vector to provide strong data support for the intelligent maintenance of the clipper.

[0025] According to the T-dimensional cutting machine abnormal variation vector, the U-dimensional cutting machine maintenance channel is activated to perform maintenance decision analysis and generate a cutting machine maintenance plan; The U-dimensional cutting machine maintenance channel refers to a maintenance model for different abnormalities of the cutting machine. It is constructed according to the neural network model, analyzes the abnormal variation vector of the T-dimensional cutting machine, obtains the variation represented by the abnormal variation vector of the T-dimensional cutting machine and the impact on the performance of the cutting machine; performs a detailed maintenance decision analysis based on the abnormal variation vector of the T-dimensional cutting machine, including determining the maintenance priority, measures and methods according to the specific situation of the abnormal variation vector. For example, for the abnormal variation of some key components, it may be necessary to shut down for maintenance immediately; and for the variation of some minor components, parameter adjustment or regular observation is required. According to the results of the maintenance decision analysis, a specific cutting machine maintenance plan is generated. This plan should include the specific content, steps, time, personnel and other information of the maintenance to ensure the effectiveness and safety of the maintenance work. At the same time, the plan should also take into account the cost and benefits of the maintenance work to ensure the economy and rationality of the maintenance work. According to the abnormal variation vector of the T-dimensional cutting machine, the corresponding maintenance channel is activated, a targeted maintenance decision analysis is performed, and a practical cutting machine maintenance plan is generated. It helps to timely discover and solve the abnormal problems of the cutting machine, ensure its normal operation and extend its service life.

[0026] The U-dimensional clipper maintenance channel includes U element abnormal variation predictors and a clipper maintenance decision registration model; According to the T-dimensional clipper abnormal variation vector, T matching element abnormal variation predictors in the U-dimensional clipper maintenance channel are activated, and fault prediction is performed on the T-dimensional clipper abnormal variation vector according to the T matching element abnormal variation predictors to generate T abnormal variation prediction results; Perform fault fusion according to the T abnormal variation prediction results to generate a cutting machine fault prediction result; The maintenance decision analysis of the clipper fault prediction result is performed according to the clipper maintenance decision registration model to obtain the clipper maintenance plan.

[0027] According to the elements in the T-dimensional clipper abnormal variation vector, T element abnormal variation predictors matching it are activated from the U-dimensional clipper maintenance channel. The element abnormal variation predictors all perform abnormal variation prediction for specific clipper elements and are constructed based on a neural network model to predict the fault condition of the clipper abnormal variation vector. The T activated element abnormal variation predictors are used to perform fault prediction on the T-dimensional clipper abnormal variation vector. The T matching element abnormal variation predictors analyze the corresponding elements in the vector and generate an abnormal variation prediction result. After obtaining the T abnormal variation prediction results, fault fusion is performed to integrate the results of each predictor to form a fault prediction result. A comprehensive prediction result of the cutting machine fault is formed. The fault fusion can adopt the maximum voting, Bayesian fusion and other methods to obtain the complete fault prediction of the aluminum foil cutting machine. The cutting machine fault prediction result is input into the cutting machine maintenance decision registration model. The cutting machine maintenance decision registration model refers to a neural network model that generates a specific cutting machine maintenance plan based on the fault prediction result and the preset maintenance strategy. The cutting machine maintenance plan includes a variety of measures such as replacing parts, adjusting parameters, and optimizing process flow, aiming to eliminate or reduce the impact of faults on the performance of the cutting machine. Through the cutting machine maintenance decision registration model, a maintenance plan for the current abnormal state of the cutting machine is finally output. Using the function of the U-dimensional cutting machine maintenance channel, an effective maintenance plan is quickly generated according to the T-dimensional cutting machine abnormal variation vector to ensure the stable operation and efficient production of the cutting machine.

[0028] The clipper maintenance decision registration model includes a clipper maintenance record library, a maintenance registration identifier and a maintenance registration selector, wherein the clipper maintenance record library includes multiple groups of clipper maintenance records, each group of clipper maintenance records includes sample aluminum foil clipper fault data and sample aluminum foil clipper maintenance decisions; Performing maintenance decision selection on the fault prediction result of the cutting machine according to the cutting machine maintenance record library, the maintenance registration identifier and the maintenance registration selector, and generating a plurality of selected maintenance decisions that meet the selection capacity constraint; Collecting maintenance evaluation record data of the plurality of selection maintenance decisions to obtain a plurality of selection decision maintenance evaluation record sets; Performing maintenance evaluation balance calculation according to the plurality of selection decision maintenance evaluation record sets to generate a plurality of selection decision evaluation coefficients; According to the multiple selection decision evaluation coefficients, the optimal selection decision evaluation coefficient is screened, and the multiple selection maintenance decisions are matched according to the optimal selection decision evaluation coefficient to generate the cutting machine maintenance plan.

[0029] The maintenance record library, maintenance registration identifier and maintenance registration selector of the cutting machine fault prediction results are used to select maintenance decisions. The cutting machine maintenance record library contains multiple sets of cutting machine maintenance records, each set of records contains the fault data of the sample aluminum foil cutting machine and the corresponding maintenance decision. The maintenance registration identifier will make a preliminary match based on the similarity between the fault prediction results and the sample fault data, and then the maintenance registration selector will further screen according to the preset selection conditions, such as cost, time, feasibility, etc., to generate multiple selected maintenance decisions that meet the selection capacity constraints. The selection capacity constraint refers to the maintenance decision that meets the fault prediction results, that is, the threshold of the number of selected maintenance decisions. The maintenance evaluation record data of multiple selected maintenance decisions are collected, including the time, cost, personnel input of the maintenance process, and the performance improvement of the cutting machine after maintenance. By collecting these data, multiple selection decision maintenance evaluation record sets can be formed. According to multiple selection decision maintenance evaluation record sets, maintenance evaluation balance calculation is performed, and the performance of different maintenance decisions in various aspects, such as cost, efficiency, effect, etc., can be comprehensively considered. A weighted method can be used for calculation to generate multiple selection decision evaluation coefficients. After obtaining multiple selection decision evaluation coefficients, the optimal selection decision evaluation coefficient is selected according to preset screening conditions, such as maximizing comprehensive benefits and minimizing costs. The maintenance decision corresponding to the optimal selection decision evaluation coefficient has reached a relatively ideal state in all aspects. According to the optimal selection decision evaluation coefficient, multiple selection maintenance decisions are matched to generate a final cutting machine maintenance plan. The cutting machine maintenance decision registration model can effectively utilize the information in the cutting machine maintenance record library, combined with the current fault prediction results, to generate a cutting machine maintenance plan that meets actual needs. This will help improve the maintenance efficiency and operating stability of the cutting machine and reduce maintenance costs.

[0030] Extracting a first group of cutting machine maintenance records according to the cutting machine maintenance record library, wherein the first group of cutting machine maintenance records includes first sample aluminum foil cutting machine fault data and first sample aluminum foil cutting machine maintenance decision; Performing similarity recognition on the cutter fault prediction result and the first sample aluminum foil cutter fault data according to the maintenance registration identifier to generate a first maintenance registration recognition coefficient; Inputting the first maintenance registration identification coefficient into the maintenance registration selector, wherein the maintenance registration selector includes a maintenance registration selection threshold; If the first maintenance registration recognition coefficient is greater than / equal to the maintenance registration selection threshold, the first sample aluminum foil cutting machine maintenance decision is used as the first selected maintenance decision; Taking the selection capacity constraint as the maintenance decision selection target, the maintenance decision selection is continued on the cutting machine fault prediction result according to the cutting machine maintenance record library, the maintenance registration identifier and the maintenance registration selector, and the multiple selected maintenance decisions are generated in combination with the first selected maintenance decision.

[0031] Extract the first group of cutting machine maintenance records from the cutting machine maintenance record library. This group of records should include the fault data of the first sample aluminum foil cutting machine and the corresponding maintenance decision. The fault data of the first sample aluminum foil cutting machine and the maintenance decision of the first sample aluminum foil cutting machine refer to the maintenance records arbitrarily selected from the cutting machine maintenance record library, which are recorded as the first group of cutting machine maintenance records. Use the maintenance registration identifier to perform similarity recognition on the cutting machine fault prediction results and the first sample aluminum foil cutting machine fault data. This step is to evaluate the applicability of the sample maintenance decision to the current fault condition by comparing the similarity between the current cutting machine fault condition and the sample fault data. After the recognition is completed, a first maintenance registration recognition coefficient is generated. The coefficient reflects the similarity between the current fault and the sample fault, and the first maintenance registration recognition coefficient is input into the maintenance registration selector. A maintenance registration selection threshold is preset in the maintenance registration selector to determine whether the recognition coefficient meets the selection criteria. If the first maintenance registration recognition coefficient is greater than or equal to the maintenance registration selection threshold, it means that the maintenance decision of the first sample aluminum foil cutting machine is more matched with the fault condition of the current cutting machine, so it is determined as the first selected maintenance decision. Taking the selection capacity constraint as the target of maintenance decision selection, the quantity and quality of maintenance decisions need to be considered in the selection process to ensure that the multiple selected maintenance decisions finally generated can meet the actual needs. According to the cutting machine maintenance record library, the maintenance registration identifier and the maintenance registration selector, the maintenance decision selection of the cutting machine fault prediction results continues. Combined with the first selected maintenance decision and other candidate maintenance decisions generated in the subsequent selection process, the final multiple selected maintenance decisions are formed. The multiple selected maintenance decisions are all based on the historical data in the cutting machine maintenance record library and the fault prediction results of the current cutting machine, and are screened out through similar identification and selection processes. The information in the cutting machine maintenance record library is used, combined with the functions of the maintenance registration identifier and the maintenance registration selector, and the maintenance decision selection of the cutting machine fault prediction results is performed to generate multiple selected maintenance decisions that meet the actual needs. This will provide strong support for the formulation of subsequent maintenance plans.

[0032] The aluminum foil cutter is maintained according to the cutter maintenance plan.

[0033] According to the cutting machine maintenance plan, carry out maintenance measures one by one, and carefully maintain the aluminum foil cutting machine according to the cutting machine maintenance plan, which can ensure the stable operation and efficient performance of the aluminum foil cutting machine.

[0034] In summary, the beneficial effects of this method include: 1. All kinds of data of the cutting machine can be completely saved to realize fault prediction, and all devices can be connected to the network, which effectively avoids the problem of dynamic data loss, greatly improves the efficiency of fault handling, and comprehensively solves the problem of low efficiency of traditional work; 2. Smart operation and maintenance can simplify complex workflows and reduce work costs. Once various loopholes occur, solutions can be quickly provided and specific problems can be identified, so that the management process can be fully optimized; 3. Able to accurately judge the equipment status, timely warn of potential risks, and ensure the stable operation of the cutting machine.

[0035] like Figure 3 As shown, the embodiment of the present application includes an intelligent maintenance system for an aluminum foil cutting machine, and the system includes: A clipper monitoring element loading module 11, wherein the clipper monitoring element loading module 11 is used to load U-dimensional clipper monitoring elements, wherein the U-dimensional clipper monitoring elements include U preset clipper monitoring elements, wherein U is a positive integer greater than 1; A cutting machine monitoring source acquisition module 12, the cutting machine monitoring source acquisition module 12 is used to perform real-time monitoring of the aluminum foil cutting machine according to the U-dimensional cutting machine monitoring elements in combination with a pre-deployed Internet of Things sensor network to obtain a cutting machine monitoring source, wherein the cutting machine monitoring source includes U cutting machine monitoring data streams corresponding to the U preset cutting machine monitoring elements; A clipper variation vector obtaining module 13, the clipper variation vector obtaining module 13 is used to perform standard state variation calculation on the clipper monitoring source according to the U-dimensional clipper monitoring element to obtain a U-dimensional clipper variation vector; A clipper variation vector selection module 14 is used to select the U-dimensional clipper variation vector according to the clipper variation identification channel to generate a T-dimensional clipper abnormal variation vector, wherein T is a positive integer, and T is less than or equal to U; A cutting machine maintenance plan generating module 15, the cutting machine maintenance plan generating module 15 is used to activate the U-dimensional cutting machine maintenance channel to perform maintenance decision analysis and generate a cutting machine maintenance plan according to the T-dimensional cutting machine abnormal variation vector; The cutting machine maintenance module 16 is used to maintain the aluminum foil cutting machine according to the cutting machine maintenance plan.

[0036] Furthermore, the embodiment of the present application also includes: A standard state sample retrieval module, the standard state sample retrieval module is used to perform a standard state sample retrieval on the aluminum foil cutting machine according to the U-dimensional cutting machine monitoring element, and generate a U-dimensional cutting machine monitoring standard sample set; A homologous extended monitoring standard sample set generation module, the homologous extended monitoring standard sample set generation module is used to perform standard state sample retrieval on homologous devices of the aluminum foil cutting machine according to the U-dimensional cutting machine monitoring element, and generate a U-dimensional homologous extended monitoring standard sample set, wherein the homologous devices include multiple aluminum foil cutting machines of the same model corresponding to the aluminum foil cutting machine; A standard state vector calculation module, the standard state vector calculation module is used to calculate the standard state vector according to the U-dimensional clipper monitoring standard sample set and the U-dimensional homogeneous expanded monitoring standard sample set, and construct a U-dimensional clipper standard state vector; A standardization processing module, the standardization processing module is used to perform standardization processing according to the cutting machine monitoring source to construct a U-dimensional cutting machine monitoring vector; A deviation calculation module is used to perform deviation calculation on the U-dimensional clipper monitoring vector according to the U-dimensional clipper standard state vector to generate the U-dimensional clipper variation vector.

[0037] Furthermore, the embodiment of the present application also includes: A cutting machine standard sample library acquisition module, the cutting machine standard sample library acquisition module is used to perform standardization processing on the U-dimensional cutting machine monitoring standard sample set and the U-dimensional homogeneous expanded monitoring standard sample set to obtain a cutting machine standard sample library; An element standard sample partition generation module, the element standard sample partition generation module is used to partition the sample of the cutting machine standard sample library according to the U-dimensional cutting machine monitoring element, and generate U element standard sample partitions; A concentrated value calculation module, the concentrated value calculation module is used to perform concentrated value calculations on the U element standard sample partitions respectively to generate U element standard concentrated value calculation results; A standard state vector construction module is used to construct the U-dimensional cutting machine standard state vector according to the calculation results of the standard concentrated values ​​of the U elements.

[0038] Furthermore, the embodiment of the present application also includes: A clipper variation vector extraction module, the clipper variation vector extraction module is used to extract a first clipper variation vector according to the U-dimensional clipper variation vector; The clipper variation identification channel includes a module, and the clipper variation identification channel includes a module for the clipper variation identification channel including a clipper variation risk prediction network and a clipper variation selector; A risk prediction module, the risk prediction module is used to perform risk prediction on the first clipper variation vector according to the clipper variation risk prediction network to obtain a first variation risk index; a variation risk index input module, the variation risk index input module being used to input the first variation risk index into the clipper variation selector, wherein the clipper variation selector comprises a variation risk index threshold; a variation risk index judgment module, wherein the variation risk index judgment module is used to set the first cutter variation vector as the first cutter abnormal variation vector if the first variation risk index is greater than / equal to the variation risk index threshold; A mutation vector selection module is used to continue to select each clipper mutation vector in the U-dimensional clipper mutation vector according to the clipper mutation identification channel, and generate the T-dimensional clipper abnormal mutation vector in combination with the first clipper abnormal mutation vector.

[0039] Furthermore, the embodiment of the present application also includes: A clipper maintenance channel module, the clipper maintenance channel module is used for the U-dimensional clipper maintenance channel and includes U element abnormal variation predictors and a clipper maintenance decision registration model; A matching element abnormal variation predictor activation module, the matching element abnormal variation predictor activation module is used to activate T matching element abnormal variation predictors in the U-dimensional cutting machine maintenance channel according to the T-dimensional cutting machine abnormal variation vector, and perform fault prediction on the T-dimensional cutting machine abnormal variation vector according to the T matching element abnormal variation predictors to generate T abnormal variation prediction results; A fault fusion module, the fault fusion module is used to perform fault fusion according to the T abnormal variation prediction results to generate a cutting machine fault prediction result; A maintenance decision analysis module is used to perform maintenance decision analysis on the fault prediction result of the cutting machine according to the maintenance decision registration model of the cutting machine to obtain the maintenance plan of the cutting machine.

[0040] Furthermore, the embodiment of the present application also includes: A clipper maintenance decision registration module, wherein the clipper maintenance decision registration module is used for the clipper maintenance decision registration model and includes a clipper maintenance record library, a maintenance registration identifier and a maintenance registration selector, wherein the clipper maintenance record library includes multiple groups of clipper maintenance records, and each group of clipper maintenance records includes sample aluminum foil clipper fault data and sample aluminum foil clipper maintenance decisions; A maintenance decision selection module, the maintenance decision selection module is used to select maintenance decisions for the fault prediction results of the cutting machine according to the cutting machine maintenance record library, the maintenance registration identifier and the maintenance registration selector, and generate a plurality of selected maintenance decisions that meet the selection capacity constraint; A maintenance evaluation record data collection module, the maintenance evaluation record data collection module is used to collect the maintenance evaluation record data of the plurality of selected maintenance decisions to obtain a plurality of selection decision maintenance evaluation record sets; A selection decision evaluation coefficient generation module, the selection decision evaluation coefficient generation module is used to perform maintenance evaluation balance calculation according to the multiple selection decision maintenance evaluation record sets to generate multiple selection decision evaluation coefficients; A cutting machine maintenance plan generation module is used to screen the optimal selection decision evaluation coefficient according to the multiple selection decision evaluation coefficients, and match the multiple selection maintenance decisions according to the optimal selection decision evaluation coefficient to generate the cutting machine maintenance plan.

[0041] Furthermore, the embodiment of the present application also includes: A clipper maintenance record extraction module, the clipper maintenance record extraction module is used to extract a first group of clipper maintenance records according to the clipper maintenance record library, wherein the first group of clipper maintenance records includes a first sample aluminum foil clipper fault data and a first sample aluminum foil clipper maintenance decision; A similarity recognition module, the similarity recognition module is used to perform similarity recognition on the cutter fault prediction result and the first sample aluminum foil cutter fault data according to the maintenance registration identifier, and generate a first maintenance registration recognition coefficient; A maintenance registration recognition coefficient input module, the maintenance registration recognition coefficient input module is used to input the first maintenance registration recognition coefficient into the maintenance registration selector, wherein the maintenance registration selector includes a maintenance registration selection threshold; A maintenance registration recognition coefficient judgment module, wherein the maintenance registration recognition coefficient judgment module is used to take the first sample aluminum foil cutting machine maintenance decision as the first selected maintenance decision if the first maintenance registration recognition coefficient is greater than / equal to the maintenance registration selection threshold; A plurality of maintenance decision module selection generation modules are provided, wherein the plurality of maintenance decision module selection generation modules are used to take the selection capacity constraint as the maintenance decision selection target, continue to perform maintenance decision selection on the cutting machine fault prediction result according to the cutting machine maintenance record library, the maintenance registration identifier and the maintenance registration selector, and generate the plurality of maintenance decisions in combination with the first selected maintenance decision.

[0042] The specific embodiments of the intelligent maintenance system for aluminum foil cutting machines can be referred to the embodiments of the intelligent maintenance method for aluminum foil cutting machines described above, which will not be described in detail here. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0043] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store news data and data such as time decay factors. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the intelligent maintenance method of the aluminum foil cutting machine.

[0044] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0045] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, steps of a smart maintenance method for an aluminum foil cutting machine are implemented.

[0046] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the intelligent maintenance method of the aluminum foil cutting machine are implemented.

[0047] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.

[0048] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. The intelligent maintenance method of the aluminum foil cutting machine is characterized by: The method comprises: Loading a U-dimensional clipper monitoring element, wherein the U-dimensional clipper monitoring element includes U preset clipper monitoring elements, wherein U is a positive integer greater than 1; According to the U-dimensional cutting machine monitoring elements, the aluminum foil cutting machine is monitored in real time in combination with the pre-deployed Internet of Things sensor network to obtain a cutting machine monitoring source, wherein the cutting machine monitoring source includes U cutting machine monitoring data streams corresponding to the U preset cutting machine monitoring elements; Performing standard state variation calculation on the clipper monitoring source according to the U-dimensional clipper monitoring element to obtain a U-dimensional clipper variation vector; Selecting the U-dimensional clipper variation vector according to the clipper variation identification channel to generate a T-dimensional clipper abnormal variation vector, wherein T is a positive integer, and T is less than or equal to U; According to the T-dimensional cutting machine abnormal variation vector, the U-dimensional cutting machine maintenance channel is activated to perform maintenance decision analysis and generate a cutting machine maintenance plan; The aluminum foil cutter is maintained according to the cutter maintenance plan.

2. The method according to claim 1, characterized in that The standard state variation calculation of the clipper monitoring source is performed according to the U-dimensional clipper monitoring element to obtain a U-dimensional clipper variation vector, including: Performing a standard state sample search on the aluminum foil cutting machine according to the U-dimensional cutting machine monitoring element to generate a U-dimensional cutting machine monitoring standard sample set; According to the U-dimensional cutting machine monitoring element, a standard state sample retrieval is performed on the same family equipment of the aluminum foil cutting machine to generate a U-dimensional same family expanded monitoring standard sample set, wherein the same family equipment includes multiple aluminum foil cutting machines of the same model corresponding to the aluminum foil cutting machine; Calculate the standard state vector according to the U-dimensional clipper monitoring standard sample set and the U-dimensional homology expanded monitoring standard sample set to construct a U-dimensional clipper standard state vector; Performing standardization processing according to the cutting machine monitoring source to construct a U-dimensional cutting machine monitoring vector; The deviation of the U-dimensional clipper monitoring vector is calculated according to the U-dimensional clipper standard state vector to generate the U-dimensional clipper variation vector.

3. The method according to claim 2, characterized in that The standard state vector is calculated according to the U-dimensional clipper monitoring standard sample set and the U-dimensional homogeneous expanded monitoring standard sample set to construct the U-dimensional clipper standard state vector, including: Standardizing the U-dimensional cutting machine monitoring standard sample set and the U-dimensional homology expanded monitoring standard sample set to obtain a cutting machine standard sample library; Partition the standard sample library of the cutting machine according to the U-dimensional cutting machine monitoring element to generate U element standard sample partitions; Performing concentrated value calculations on the U element standard sample partitions respectively to generate U element standard concentrated value calculation results; The U-dimensional clipper standard state vector is constructed according to the calculation results of the standard concentrated values ​​of the U elements.

4. The method according to claim 1, characterized in that The U-dimensional clipper variation vector is selected according to the clipper variation identification channel to generate a T-dimensional clipper abnormal variation vector, including: Extracting a first cropper variation vector according to the U-dimensional cropper variation vector; The clipper variation identification channel includes a clipper variation risk prediction network and a clipper variation selector; Performing risk prediction on the first clipper variation vector according to the clipper variation risk prediction network to obtain a first variation risk index; inputting the first mutation risk index into the clipper mutation selector, wherein the clipper mutation selector includes a mutation risk index threshold; If the first mutation risk index is greater than / equal to the mutation risk index threshold, setting the first clipper mutation vector as the first clipper abnormal mutation vector; The respective clipper variation vectors in the U-dimensional clipper variation vector are continuously selected according to the clipper variation identification channel, and combined with the first clipper abnormal variation vector to generate the T-dimensional clipper abnormal variation vector.

5. The method according to claim 1, characterized in that According to the T-dimensional cutting machine abnormal variation vector, the U-dimensional cutting machine maintenance channel is activated to perform maintenance decision analysis and generate a cutting machine maintenance plan, including: The U-dimensional clipper maintenance channel includes U element abnormal variation predictors and a clipper maintenance decision registration model; According to the T-dimensional clipper abnormal variation vector, T matching element abnormal variation predictors in the U-dimensional clipper maintenance channel are activated, and fault prediction is performed on the T-dimensional clipper abnormal variation vector according to the T matching element abnormal variation predictors to generate T abnormal variation prediction results; Perform fault fusion according to the T abnormal variation prediction results to generate a cutting machine fault prediction result; The maintenance decision analysis of the clipper fault prediction result is performed according to the clipper maintenance decision registration model to obtain the clipper maintenance plan.

6. The method according to claim 5, characterized in that Performing maintenance decision analysis on the fault prediction result of the cutting machine according to the maintenance decision registration model of the cutting machine to obtain the maintenance plan of the cutting machine, including: The clipper maintenance decision registration model includes a clipper maintenance record library, a maintenance registration identifier and a maintenance registration selector, wherein the clipper maintenance record library includes multiple groups of clipper maintenance records, each group of clipper maintenance records includes sample aluminum foil clipper fault data and sample aluminum foil clipper maintenance decisions; Performing maintenance decision selection on the fault prediction result of the cutting machine according to the cutting machine maintenance record library, the maintenance registration identifier and the maintenance registration selector, and generating a plurality of selected maintenance decisions that meet the selection capacity constraint; Collecting maintenance evaluation record data of the plurality of selection maintenance decisions to obtain a plurality of selection decision maintenance evaluation record sets; Performing maintenance evaluation balance calculation according to the plurality of selection decision maintenance evaluation record sets to generate a plurality of selection decision evaluation coefficients; According to the multiple selection decision evaluation coefficients, the optimal selection decision evaluation coefficient is screened, and the multiple selection maintenance decisions are matched according to the optimal selection decision evaluation coefficient to generate the cutting machine maintenance plan.

7. The method according to claim 6, characterized in that The maintenance decision selection is performed on the fault prediction result of the cutting machine according to the cutting machine maintenance record library, the maintenance registration identifier and the maintenance registration selector to generate a plurality of selected maintenance decisions that meet the selection capacity constraint, including: Extracting a first group of cutting machine maintenance records according to the cutting machine maintenance record library, wherein the first group of cutting machine maintenance records includes first sample aluminum foil cutting machine fault data and first sample aluminum foil cutting machine maintenance decision; Performing similarity recognition on the cutter fault prediction result and the first sample aluminum foil cutter fault data according to the maintenance registration identifier to generate a first maintenance registration recognition coefficient; Inputting the first maintenance registration identification coefficient into the maintenance registration selector, wherein the maintenance registration selector includes a maintenance registration selection threshold; If the first maintenance registration recognition coefficient is greater than / equal to the maintenance registration selection threshold, the first sample aluminum foil cutting machine maintenance decision is used as the first selected maintenance decision; Taking the selection capacity constraint as the maintenance decision selection target, the maintenance decision selection is continued on the cutting machine fault prediction result according to the cutting machine maintenance record library, the maintenance registration identifier and the maintenance registration selector, and the multiple selected maintenance decisions are generated in combination with the first selected maintenance decision.

8. The intelligent maintenance system of aluminum foil cutting machine is characterized by: The system is used to perform the method according to any one of claims 1 to 7, and the system comprises: A clipper monitoring element loading module, the clipper monitoring element loading module is used to load U-dimensional clipper monitoring elements, wherein the U-dimensional clipper monitoring elements include U preset clipper monitoring elements, wherein U is a positive integer greater than 1; A cutting machine monitoring source acquisition module, the cutting machine monitoring source acquisition module is used to perform real-time monitoring of the aluminum foil cutting machine according to the U-dimensional cutting machine monitoring elements in combination with a pre-deployed Internet of Things sensor network to obtain a cutting machine monitoring source, wherein the cutting machine monitoring source includes U cutting machine monitoring data streams corresponding to the U preset cutting machine monitoring elements; A clipper variation vector acquisition module, the clipper variation vector acquisition module is used to perform standard state variation calculation on the clipper monitoring source according to the U-dimensional clipper monitoring element to obtain a U-dimensional clipper variation vector; A clipper variation vector selection module is used to select the U-dimensional clipper variation vector according to the clipper variation identification channel to generate a T-dimensional clipper abnormal variation vector, wherein T is a positive integer, and T is less than or equal to U; A cutting machine maintenance plan generation module, the cutting machine maintenance plan generation module is used to activate the U-dimensional cutting machine maintenance channel to perform maintenance decision analysis and generate a cutting machine maintenance plan according to the T-dimensional cutting machine abnormal variation vector; A cutting machine maintenance module, wherein the cutting machine maintenance module is used to maintain the aluminum foil cutting machine according to the cutting machine maintenance plan.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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