CN116757648B - Production and manufacturing management system and method based on artificial intelligence - Google Patents
Production and manufacturing management system and method based on artificial intelligence Download PDFInfo
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Abstract
The application discloses an artificial intelligence-based production and manufacturing management system and method, which relate to the technical field of equipment management systems, wherein after multi-source data are acquired through a data analysis module, the multi-source data are substituted into an intelligent prediction model to analyze whether future use of a punching machine tool can be failed, when an analysis result received by an inventory analysis module is that the future use of the punching machine tool can be failed, the inventory analysis module acquires the reserve quantity of parts related to the punching machine tool in a warehouse log, and when the reserve quantity is smaller than a reserve threshold value, the inventory analysis module sends replenishment information to replenishment staff. According to the application, the fault prediction processing can be carried out on the punching machine tool in the operation process of the punching machine tool, and whether the goods need to be supplemented in advance is judged by combining the reserve quantity of parts related to the punching machine tool in a warehouse, so that the maintenance time of the punching machine tool can be shortened, the working efficiency of the punching machine tool is effectively improved, and the maintenance cost of the punching machine tool is reduced.
Description
Technical Field
The application relates to the technical field of equipment management systems, in particular to an artificial intelligence-based production and manufacturing management system and method.
Background
The hardware processing machine tool is special equipment for processing metal materials, and comprises a milling machine, a lathe, a drilling machine, a grinding machine, a punching machine and the like, wherein the machine tools play key roles in the hardware processing industry and are used for cutting, forming, punching, grinding and other processes to convert the metal materials into parts or products with specific shapes and sizes, and the hardware processing machine tool management system is an informatization management system aiming at the hardware processing industry and aims at improving the utilization rate and the production efficiency of the machine tools and realizing the optimization and the automation of the production process.
The prior art has the following defects:
the existing management system does not predict faults of the punching machine tool, if parts required to be replaced by the punching machine tool are not stored when the punching machine tool breaks down, the maintenance time of the punching machine tool can be increased, so that the working efficiency of the punching machine tool is reduced, and the faults of the punching machine tool can be possibly worsened and developed along with the increase of the maintenance time, so that the maintenance cost of the punching machine tool is increased.
Disclosure of Invention
The application aims to provide an artificial intelligence-based production and manufacturing management system and method, which are used for solving the defects in the background technology.
In order to achieve the above object, the present application provides the following technical solutions: the production and manufacturing management system based on the artificial intelligence comprises a data acquisition module, a data analysis module, an early warning module and an inventory analysis module;
and a data acquisition module: in the running process of the punching machine tool, multi-source data of the punching machine tool are collected at fixed time, and the multi-source data are preprocessed;
and a data analysis module: after multi-source data are acquired, substituting the multi-source data into an intelligent prediction model, and analyzing whether future use of the punching machine tool can be failed;
and the early warning module is used for: judging whether an early warning signal needs to be sent according to an analysis result of the data analysis module, and sending the early warning signal to a remote management center based on the Internet of things when the early warning signal is sent;
inventory analysis module: and when the received analysis result is that the punching machine tool fails in future use, acquiring the reserve quantity of parts related to the punching machine tool in a warehouse log, and when the reserve quantity is smaller than a reserve threshold value, sending replenishment information to replenishment staff.
In a preferred embodiment, the data acquisition module acquires multi-source data of the punching machine at regular time during the operation of the punching machine, wherein the multi-source data acquired by the data acquisition module comprises a main shaft deformation standard deviation, transmission case noise, punching head integrity and cooling pipeline pressure fluctuation amplitude.
In a preferred embodiment, the building of the intelligent prediction model comprises the following steps:
removing the dimension of the deformation standard deviation of the main shaft, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline, and taking the value of the deformation standard deviation to carry out comprehensive calculation to obtain a fault coefficientThe computational expression is:
；
in the method, in the process of the application,is the standard deviation of the deformation of the main shaft>Noise of transmission case>For the integrity of the punching head->For the magnitude of the pressure fluctuation of the cooling line, < > for>、/>、/>、/>The ratio coefficients of the standard deviation of the deformation of the main shaft, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline are respectively +.>、/>、/>、/>Are all greater than 0;
obtaining failure coefficientsAfter that, the failure coefficient is->And fault threshold->Comparing to finishAnd establishing an intelligent prediction model.
In a preferred embodiment, the data analysis module acquires the standard deviation of the spindle deformation, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline, which are acquired by the data acquisition module at regular time, and then substitutes the standard deviation of the spindle deformation, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline into a failure coefficient calculation formula to acquire a failure coefficient。
In a preferred embodiment, if the failure coefficient isMore than or equal to fault threshold->The data analysis module analyzes that the punching machine tool cannot malfunction in future use;
if the failure coefficient is< failure threshold->The data analysis module analyzes that future use of the punching machine tool can be failed.
In a preferred embodiment, the analysis result of the data analysis module is that the punching machine tool can be failed in future use, and the early warning module sends an early warning signal to a remote management center based on the internet of things;
when the remote management center receives the early warning signal, if the punching machine tool is in the process of punching the hardware, the machine tool needs to be controlled to stop after the current punching operation is completed;
if the punching machine tool is in a condition that one hardware is just processed, directly controlling the punching machine tool to stop; judging whether the punching machine tool is in a machining state or not, and acquiring the punching machine tool through an industrial camera arranged at a workbench of the punching machine tool.
In a preferred embodiment, the logic for obtaining the standard deviation of the spindle deformation is: because the data acquisition module acquires punching machine tool data once every 30 minutes, the calculation expression of the main shaft deformation standard deviation is as follows:
；
in the method, in the process of the application,mean value of deformation measurement values, +.>Represents the ith deformation measurement, n being the number of deformation measurements at different time points.
In a preferred embodiment, the calculation expression of the integrity of the punching head is:
；
in the method, in the process of the application,for real-time corrosion depth->The real-time corrosion depth refers to current corrosion depth data acquired through an on-line monitor or sensor for an initial geometry, which refers to an initial geometry of the punch head or a preset reference size.
In a preferred embodiment, the logic for obtaining the magnitude of the pressure fluctuation of the cooling conduit is: marking a cooling duct stabilizing pressure range asThe cooling line pressure acquired in real time is marked +.>If->＜Pressure fluctuation amplitude of cooling pipeline>If->＞/>Pressure fluctuation amplitude of cooling pipeline>。
The application also provides an artificial intelligence-based production and manufacturing management method, which comprises the following steps:
s1: in the operation process of the punching machine tool, the acquisition end acquires multi-source data of the punching machine tool at fixed time and preprocesses the multi-source data;
s2: the processing end acquires the preprocessed multi-source data, substitutes the multi-source data into the intelligent prediction model, and analyzes whether the punching machine tool is faulty in future use;
s3: judging whether an early warning signal needs to be sent according to the analysis result, and sending the early warning signal to a remote management center based on the Internet of things when the early warning signal is sent;
s4: when the remote management center receives the early warning signal, selecting whether to control the punching machine to stop according to the current working state of the punching machine;
s5: when the analysis result shows that the future use of the punching machine tool can be failed, the reserve quantity of parts related to the punching machine tool in the warehouse log is obtained;
s6: and if the reserve amount is smaller than the reserve threshold value, the processing end sends the replenishment information to a replenishment person.
In the technical scheme, the application has the technical effects and advantages that:
1. according to the application, after multi-source data are acquired through the data analysis module, the multi-source data are substituted into the intelligent prediction model to analyze whether future use of the punching machine tool is failed, when the analysis result received by the inventory analysis module is that the future use of the punching machine tool is failed, the inventory analysis module acquires the reserve quantity of parts related to the punching machine tool in a warehouse log, and when the reserve quantity is smaller than a reserve threshold value, the reserve quantity information is sent to a replenishment person, and the management system can perform failure prediction processing on the punching machine tool in the operation process of the punching machine tool and judge whether the reserve quantity of parts related to the punching machine tool in a warehouse is needed to be replenished in advance or not by combining the reserve quantity of the parts related to the punching machine tool, so that the maintenance time of the punching machine tool can be shortened, the working efficiency of the punching machine tool is effectively improved, and the maintenance cost of the punching machine tool is reduced;
2. according to the application, the deformation standard deviation of the main shaft, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline are removed, the values of the deformation standard deviation are taken for comprehensive calculation, the fault coefficient is obtained, the multisource data is comprehensively analyzed, the data analysis efficiency is effectively improved, the analysis is more comprehensive, and the obtained fault coefficient is compared with the fault threshold value to judge whether the punching machine tool is faulty in future use, so that the stable operation of the punching machine tool is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a block diagram of a system according to the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1: referring to fig. 1, the production and manufacturing management system based on artificial intelligence according to the present embodiment includes a data acquisition module, a data analysis module, an early warning module, and an inventory analysis module;
the data acquisition module acquires multi-source data of the punching machine tool at regular time in the operation process of the punching machine tool, and sends the multi-source data to the data analysis module after preprocessing;
after the data analysis module acquires multi-source data, substituting the multi-source data into the intelligent prediction model, analyzing whether the punching machine tool fails in future use, and sending an analysis result to the early warning module and the inventory analysis module;
the early warning module judges whether an early warning signal needs to be sent according to the analysis result of the data analysis module, when the early warning signal is sent, the early warning signal is sent to a remote management center based on the Internet of things, and when the remote management center receives the early warning signal, whether the punching machine is controlled to stop is selected according to the current working state of the punching machine;
when the analysis result received by the inventory analysis module is that the future use of the punching machine tool can be failed, the inventory analysis module acquires the reserve quantity of parts related to the punching machine tool in a warehouse log, and when the reserve quantity is smaller than a reserve threshold value, the inventory analysis module sends replenishment information to replenishment staff.
According to the application, after multi-source data are acquired through the data analysis module, the multi-source data are substituted into the intelligent prediction model to analyze whether future use of the punching machine tool can occur, when the analysis result received by the inventory analysis module is that the future use of the punching machine tool can occur, the inventory analysis module acquires the reserve quantity of parts related to the punching machine tool in a warehouse log, and when the reserve quantity is smaller than a reserve threshold value, the reserve quantity information is sent to a replenishment person.
The data acquisition module is used for acquiring multi-source data of the punching machine tool at regular time in the operation process of the punching machine tool, wherein the multi-source data acquired by the data acquisition module comprise a main shaft deformation standard deviation, transmission case noise, punching head integrity and cooling pipeline pressure fluctuation amplitude;
the data acquisition module preprocesses the multi-source data, and comprises the following steps:
data cleaning: the method comprises the steps of processing abnormal values, processing noise data and the like, wherein the abnormal values can be extreme values in the data or values which do not accord with business rules, identification and processing are required, and the noise data can be interference data caused by sensor errors, equipment faults or data transmission problems and are required to be subjected to filtering or smoothing processing;
data conversion: converting the data to meet the requirements of analysis or modeling, wherein the conversion can comprise operations such as feature extraction, feature selection, data normalization and the like to extract useful features and reduce the dimension of the data;
and (3) data verification: the cleaned data is verified, the accuracy and consistency of the data are ensured, the data verification can be performed by means of a statistical method, data visualization and the like, and the distribution, the relevance and the rationality of the data are checked.
Example 2: after the data analysis module acquires multi-source data, substituting the multi-source data into the intelligent prediction model, analyzing whether the punching machine tool fails in future use, and sending an analysis result to the early warning module and the inventory analysis module;
in the application, the establishment of the intelligent prediction model comprises the following steps:
removing the dimension of the deformation standard deviation of the main shaft, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline, and taking the value of the deformation standard deviation to carry out comprehensive calculation to obtain a fault coefficientThe computational expression is:
；
in the method, in the process of the application,is the standard deviation of the deformation of the main shaft>Noise of transmission case>For the integrity of the punching head->For the magnitude of the pressure fluctuation of the cooling line, < > for>、/>、/>、/>The ratio coefficients of the standard deviation of the deformation of the main shaft, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline are respectively +.>、/>、/>、/>Are all greater than 0.
Obtaining failure coefficientsThen, the fault is tiedCount->And fault threshold->And (5) comparing to finish the establishment of the intelligent prediction model.
After the data analysis module acquires the standard deviation of the spindle deformation, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline, which are acquired by the data acquisition module at regular time (every 30 minutes), the standard deviation of the spindle deformation, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline are substituted into a fault coefficient calculation formula to acquire a fault coefficient；
If the failure coefficient isMore than or equal to fault threshold->The data analysis module analyzes that the punching machine tool cannot malfunction in future use;
if the failure coefficient is< failure threshold->The data analysis module analyzes that future use of the punching machine tool can be failed.
According to the application, the deformation standard deviation of the main shaft, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline are removed, the values of the deformation standard deviation are taken for comprehensive calculation, the fault coefficient is obtained, the multisource data is comprehensively analyzed, the data analysis efficiency is effectively improved, the analysis is more comprehensive, and the obtained fault coefficient is compared with the fault threshold value to judge whether the punching machine tool is faulty in future use, so that the stable operation of the punching machine tool is ensured.
The early warning module judges whether an early warning signal needs to be sent according to the analysis result of the data analysis module, when the early warning signal is sent, the early warning signal is sent to a remote management center based on the Internet of things, and when the remote management center receives the early warning signal, whether the punching machine is controlled to stop is selected according to the current working state of the punching machine.
When the analysis result of the data analysis module is that the punching machine tool can be failed in future use, the early warning module sends an early warning signal to the remote management center based on the Internet of things;
when the remote management center receives the early warning signal, if the punching machine tool is in the process of punching the hardware, the machine tool needs to be controlled to stop after the current punching operation is completed;
if the punching machine tool is in a condition that one hardware is just processed, directly controlling the punching machine tool to stop; judging whether the punching machine tool is in a machining state or not, and acquiring the punching machine tool through an industrial camera arranged at a workbench of the punching machine tool.
When the analysis result received by the inventory analysis module is that the punching machine tool fails in future use, the inventory analysis module acquires the reserve quantity of parts related to the punching machine tool in a warehouse log, and when the reserve quantity is smaller than a reserve threshold value, the inventory analysis module sends replenishment information to replenishment staff;
the inventory analysis module obtains the reserve quantity of parts related to the punching machine tool in the warehouse log, and comprises the following steps:
1) Obtaining information of parts related to the punching machine from a log record of a warehouse management system, wherein the information can comprise names, numbers, warehouse-in dates, warehouse-out dates, inventory numbers and the like of the parts;
2) And screening out the data of the parts related to the punching machine according to the specific identification (such as part numbers, names and the like) of the parts related to the punching machine. Thus, other irrelevant part data can be filtered out for subsequent processing;
3) The data of the relevant parts obtained through screening are aggregated, the data are grouped according to the names or numbers of the parts, and the total reserve quantity of each part is calculated;
4) And calculating the reserve quantity of each part according to the result of the inventory variation analysis. The reserve may be obtained by subtracting the current stock quantity from the shipment, i.e., reserve = current stock quantity-shipment.
Since there is more than one punching machine in the shop, the stock quantity = current stock quantity-shipment quantity, which is the number of parts required by other punching machines.
And when the reserve quantity of the remaining parts is smaller than the reserve threshold value, sending replenishment information to a replenishment person, wherein the replenishment person needs to timely replenish the parts with the reserve quantity smaller than the reserve threshold value to the warehouse.
The acquisition logic of the spindle deformation standard deviation is as follows: because the data acquisition module acquires punching machine tool data once every 30 minutes, the calculation expression of the main shaft deformation standard deviation is as follows:
；
in the method, in the process of the application,mean value of deformation measurement values, +.>The i-th deformation measured value is represented, n is the number of deformation measured values at different time points, the larger the standard deviation of the deformation of the main shaft is, the larger the discrete degree of the deformation measured values is, the more unstable the deformation of the main shaft is, and the main shaft deformation measured value is measured on line through a laser interferometer arranged at the main shaft;
the noise of the transmission case is obtained in real time through a decibel meter arranged at the transmission case, and the larger the noise of the transmission case is, the more serious the mechanical abrasion inside the transmission case is;
the calculation expression of the integrity of the punching head is as follows:
；
in the method, in the process of the application,for real-time corrosion depth->For the initial geometry, the real-time corrosion depth refers to the current corrosion depth data obtained through on-line monitoring or a sensor, the initial geometry refers to the initial geometry of the punching head or a preset reference size, and the greater the integrity of the punching head is, the longer the service life of the punching head is.
The logic for obtaining the pressure fluctuation amplitude of the cooling pipeline is as follows: marking a cooling duct stabilizing pressure range asThe cooling line pressure acquired in real time is marked +.>If->＜/>Pressure fluctuation amplitude of cooling pipeline>If->＞/>Amplitude of pressure fluctuation of cooling pipelineThe larger the pressure fluctuation amplitude of the cooling pipeline is, the farther the cooling pipeline pressure obtained in real time is from the stable pressure range of the cooling pipeline is, and the more the cooling system is easy to break down.
Example 3: the production and manufacturing management method based on artificial intelligence in this embodiment includes the following steps:
in the operation process of the punching machine tool, the acquisition end acquires multi-source data of the punching machine tool at regular time, the multi-source data are preprocessed, the processing end acquires the preprocessed multi-source data, the multi-source data are substituted into the intelligent prediction model, whether future use of the punching machine tool can be failed is analyzed, whether an early warning signal needs to be sent or not is judged according to an analysis result, when the early warning signal is sent, the early warning signal is sent to a remote management center based on the Internet of things, when the remote management center receives the early warning signal, whether the punching machine tool is controlled to stop is selected according to the current working state of the punching machine tool, when the analysis result shows that the future use of the punching machine tool can be failed, the reserve quantity of parts related to the punching machine tool in a warehouse log is acquired, and when the reserve quantity is smaller than a reserve threshold value, replenishment information is sent to replenishment staff.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that the term "and/or" is merely an association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B may mean: there are three cases, a alone, a and B together, and B alone, wherein a, B may be singular or plural. In addition, the character "/" herein generally indicates that the associated object is an "or" relationship, but may also indicate an "and/or" relationship, and may be understood by referring to the context.
In the present application, "at least one" means one or more, and "a plurality" means two or more. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. Production manufacturing management system based on artificial intelligence, its characterized in that: the system comprises a data acquisition module, a data analysis module, an early warning module and an inventory analysis module;
and a data acquisition module: in the running process of the punching machine tool, multi-source data of the punching machine tool are collected at fixed time, and the multi-source data are preprocessed;
and a data analysis module: after multi-source data are acquired, substituting the multi-source data into an intelligent prediction model, and analyzing whether future use of the punching machine tool can be failed;
and the early warning module is used for: judging whether an early warning signal needs to be sent according to an analysis result of the data analysis module, and sending the early warning signal to a remote management center based on the Internet of things when the early warning signal is sent;
inventory analysis module: when the received analysis result shows that the punching machine tool fails in future use, the storage quantity of parts related to the punching machine tool in the warehouse log is obtained, and when the storage quantity is smaller than a storage threshold value, the storage quantity is sent to a replenishment person for replenishment information;
the data acquisition module is used for acquiring multi-source data of the punching machine tool at regular time in the operation process of the punching machine tool, wherein the multi-source data acquired by the data acquisition module comprise a main shaft deformation standard deviation, transmission case noise, punching head integrity and cooling pipeline pressure fluctuation amplitude;
the establishment of the intelligent prediction model comprises the following steps:
removing the dimension of the deformation standard deviation of the main shaft, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline, and taking the value of the deformation standard deviation to carry out comprehensive calculation to obtain a fault coefficient gz x The computational expression is:
wherein bx is z Is the standard deviation of the deformation of the main shaft, zs c Wz, which is the noise of the gear box d Yb for punch head integrity g For the pressure fluctuation amplitude of the cooling pipeline, alpha, beta, gamma and omega are respectively the standard deviation of main shaft deformation, noise of a transmission case, the integrity of a punching head and the proportionality coefficient of the pressure fluctuation amplitude of the cooling pipeline, and the alpha, beta, gamma and omega are all larger than 0;
obtaining fault coefficient gz x Then, the fault coefficient gz x And fault threshold gz y Performing comparison to complete the establishment of an intelligent prediction model;
after the data analysis module acquires the standard deviation of the spindle deformation, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline, which are acquired by the data acquisition module at regular time, the standard deviation of the spindle deformation, the noise of the transmission case, the integrity of the punching head and the pressure fluctuation amplitude of the cooling pipeline are substituted into a fault coefficient calculation formula to acquire a fault coefficient gz x ；
If the fault coefficient gz x Not less than fault threshold gz y The data analysis module analyzes that the punching machine tool cannot malfunction in future use;
if the fault coefficient gz x < failure threshold gz y The data analysis module analyzes that future use of the punching machine tool can be failed.
2. The artificial intelligence based manufacturing management system of claim 1, wherein: the data analysis module analyzes that the punching machine tool can be failed in future use, and the early warning module sends an early warning signal to a remote management center based on the Internet of things;
when the remote management center receives the early warning signal, if the punching machine tool is in the process of punching the hardware, the machine tool needs to be controlled to stop after the current punching operation is completed;
if the punching machine tool is in a condition that one hardware is just processed, directly controlling the punching machine tool to stop; judging whether the punching machine tool is in a machining state or not, and acquiring the punching machine tool through an industrial camera arranged at a workbench of the punching machine tool.
3. The artificial intelligence based manufacturing management system of claim 2, wherein: the acquisition logic of the main shaft deformation standard deviation is as follows: the data acquisition module acquires punching machine tool data once every 30 minutes, and the calculation expression of the main shaft deformation standard deviation is as follows:
in the method, in the process of the application,the average value of the deformation measurement values is represented, xi represents the ith deformation measurement value, and n is the number of deformation measurement values at different time points.
4. The artificial intelligence based manufacturing management system of claim 3, wherein: the calculation expression of the integrity of the punching head is as follows:
in fs d For real-time corrosion depth cs t The real-time corrosion depth refers to current corrosion depth data acquired through an on-line monitor or sensor for an initial geometry, which refers to an initial geometry of the punch head or a preset reference size.
5. The artificial intelligence based manufacturing management system of claim 4, wherein: the logic for acquiring the pressure fluctuation amplitude of the cooling pipeline is as follows: marking the cooling line steady pressure range as yl min ～yl max The cooling pipeline pressure obtained in real time is marked as yl g If yl g ＜yl min Amplitude yb of pressure fluctuation of cooling pipeline g ＝|yl g -yl min I, if yl g ＞yl max Amplitude yb of pressure fluctuation of cooling pipeline g ＝|yl g -yl max |。
6. Production and manufacturing management method based on artificial intelligence, implemented based on the management system according to any one of claims 1-5, characterized in that: the management method comprises the following steps:
s1: in the operation process of the punching machine tool, the acquisition end acquires multi-source data of the punching machine tool at fixed time and preprocesses the multi-source data;
s2: the processing end acquires the preprocessed multi-source data, substitutes the multi-source data into the intelligent prediction model, and analyzes whether the punching machine tool is faulty in future use;
s3: judging whether an early warning signal needs to be sent according to the analysis result, and sending the early warning signal to a remote management center based on the Internet of things when the early warning signal is sent;
s4: when the remote management center receives the early warning signal, selecting whether to control the punching machine to stop according to the current working state of the punching machine;
s5: when the analysis result shows that the future use of the punching machine tool can be failed, the reserve quantity of parts related to the punching machine tool in the warehouse log is obtained;
s6: and if the reserve amount is smaller than the reserve threshold value, the processing end sends the replenishment information to a replenishment person.
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