CN117057630A - Intelligent management method and system for production line of food packaging workshop - Google Patents
Intelligent management method and system for production line of food packaging workshop Download PDFInfo
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Abstract
The application discloses an intelligent management method and system for a production line of a food packaging workshop, which relate to the technical field of intelligent management of the production line and comprise the following steps: referring to the related production data to obtain the daily maximum productivity of the food packaging workshop; inputting a production task, and evaluating the execution difficulty of the production task; constructing a production scheme generation model, and generating a production scheme corresponding to a production task by using the production scheme generation model; in the production process, a corresponding sensor is used for monitoring the production process; packaging quality inspection is carried out on the packaged food, and the actual production progress is counted; the application carries out real-time intelligent monitoring and control on the running state before, during and after the production of the food packaging workshop, can reduce human errors and decision delay, and improves the coordination and efficiency of the production line.
Description
Technical Field
The application relates to the technical field of intelligent management of production lines, in particular to an intelligent management method and system for a production line of a food packaging workshop.
Background
The traditional food packaging workshops lack informatization management means, cannot grasp the running condition of a production line in real time, cannot effectively analyze production data, and cannot realize the optimization of the production process. In recent years, with the development of new technologies such as big data and internet of things, more workshops begin to try digital management production lines, and the intelligent management workshop production line can effectively improve the efficiency and precision of workshop management, improve the product quality, reduce the production cost and energy consumption and improve the overall competitiveness of enterprises.
In the Chinese application with the application publication number of CN115330330A, an Internet-based production line operation management evaluation system is disclosed, which comprises a production line data acquisition module for acquiring and transmitting production line operation data, a production line information input module for inputting production line information, a cloud database for storing the production line operation data, a data identification module for data identification and transmission, an analysis evaluation module for analyzing the production line operation data and acquiring a production line operation evaluation result, an information push module based on a production line management scheme acquired according to the production line information and adopting an Internet technology, and a mobile display client for displaying on-line information.
In the above application, the closed-loop analysis and optimization, paperless operation and maintenance management and problem closed-loop management are realized by collecting the production line data and carrying out on-line analysis and evaluation and displaying the result by the mobile terminal, but only the production process is monitored and managed, and no corresponding management and intervention are carried out before and after the production line production, so that certain management defects still exist.
Therefore, the application provides an intelligent management method and system for a production line of a food packaging workshop.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the application provides an intelligent management method and system for a production line of a food packaging workshop, which are used for evaluating the productivity of the production workshop and the difficulty of production tasks before production and making corresponding production schemes, so that the cost required for completing the production tasks can be more accurately evaluated, and the production resources and manpower can be better arranged; the actual production progress is counted in real time after production, so that problems possibly occurring in the production process can be found out at the first time, and the risks of production delay and insufficient product supply are reduced. The intelligent management is carried out on the front, middle and rear parts of the workshop production, so that the technical problems recorded in the background technology are solved.
(II) technical scheme
In order to achieve the above purpose, the application is realized by the following technical scheme: an intelligent management method for a production line of a food packaging workshop comprises the following steps:
referring to related production data of a food packaging workshop, constructing a workshop production data set, and calculating to obtain the daily maximum production capacity of the food packaging workshop;
inputting a production task, analyzing and obtaining the daily minimum productivity required by completing the production task, evaluating the execution difficulty of the production task, and sending out a corresponding difficulty early warning signal according to the evaluation result;
constructing a production scheme generation model, generating a production scheme corresponding to a production task by using the production scheme generation model, and transmitting the production scheme to a control display end;
in the production process, the corresponding sensors are used for monitoring the residual raw materials, the on-duty staff and the running conditions of equipment in the production process, and if the abnormality occurs, corresponding processing strategies are adopted in time;
and (3) carrying out packaging quality inspection on the packaged food, counting to obtain the actual production progress, and sending out overtime early warning and regenerating the production scheme when the actual production progress is smaller than the planned threshold value.
Further, referring to a historical production record, an equipment specification and a manpower system of the food packaging workshop, obtaining the number Yg of packaging staff of the food packaging workshop, the maximum number Cs of food transmitted by the transmission equipment per hour and the average number Bz of food packaged by staff per hour after finishing, and constructing a workshop production data set.
Further, the number Yg of packing staff in the food packing workshop, the maximum number Cs of food transferred by the transferring equipment per hour and the average number Bz of food packed per hour are obtained, and the daily maximum productivity Cn of the food packing workshop is calculated and obtained:
；
the calculation formula of the maximum daily production capacity Cn of the corresponding food packaging workshop is as above.
Further, inputting a production task, including a completion period Rq and the quantity of packaged foods Sl, and calculating to obtain the daily minimum production capacity Xn required for completing the production task:
；
the corresponding calculation formula of the daily minimum production capacity Xn required for completing the production task is as above.
Further, the relationship between the minimum daily production capacity Xn required for completing the production task and the maximum daily production capacity Cn of the food packaging workshop is judged, the execution difficulty of the production task is evaluated, and a corresponding difficulty early warning signal is sent outwards according to the evaluation result, specifically:
when Xn is not more than Cn, the difficulty of the current production task is small, the productivity of the food packaging workshop can realize the task in the date, and correspondingly, no early warning signal is sent outwards;
when Xn is larger than Cn and smaller than 1.5Cn, the current production task difficulty is medium, if the task can be realized in the date, the productivity of the food packaging workshop is required to be upgraded, and correspondingly, a secondary difficulty early warning signal is sent outwards;
when Xn is not less than 1.5Cn, the current production task difficulty is high, the productivity of a food packaging workshop can not be realized, and a primary difficulty early warning signal is correspondingly sent outwards.
Further, a workshop production data set and a historical production record are obtained, partial data is selected from the data set to serve as sample data to be imported into a constructed machine learning model, and after training test is conducted, a production scheme generation model is exported.
Further, the production tasks are input into a production scheme generating model, the corresponding production scheme is generated by using the production scheme generating model, the production scheme is transmitted to a control display end, and the production scheme comprises daily planned production capacity, daily required staff number and equipment transmission speed.
Further, the quality Zc of the remaining packaging materials on each station is monitored in real time by using a quality sensor, and when the quality Zc of the remaining packaging materials on the station is lower than a material threshold value, a transport forklift is used for distributing the materials, and the distribution quality is uploaded to the cloud.
Further, a camera is used for acquiring a production picture of a food packaging workshop in real time, an artificial intelligence technology is used for monitoring actual on-duty conditions of staff on a station, and when the fact that the staff leaves the duty is detected, the transmission speed of equipment is adjusted.
Further, vibration, temperature, voltage and current data of the equipment in the running process are collected through vibration, temperature, voltage and current sensors, the fault state of the equipment is identified through comparing and analyzing characteristic information of the data, and fault warning is timely sent out.
Further, a camera and an image recognition algorithm are arranged on a production line, whether the food is packaged well is detected, and if the food is found to be incompletely packaged or damaged, the food is marked as unqualified;
the actual daily package quantity Sj and the unqualified daily package quantity Bg are obtained through monitoring by using a counter, the food package qualification rate Hg is obtained through calculation, and the corresponding calculation formula of the food package qualification rate Hg is as follows:
；
and when the food packaging qualification rate Hg is less than 90%, sending out abnormal package qualification rate early warning.
An intelligent management system for a food packaging workshop production line, comprising:
the workshop productivity evaluation module is used for consulting related production data of the food packaging workshop, constructing a workshop production data set and calculating to obtain the daily maximum production capacity of the food packaging workshop;
the task difficulty judging module inputs a production task, analyzes and obtains the minimum daily production capacity required by completing the production task, evaluates the execution difficulty of the production task, and sends out a corresponding difficulty early warning signal according to the evaluation result;
the scheme generation module is used for constructing a production scheme generation model, generating a production scheme corresponding to a production task by using the production scheme generation model, and transmitting the production scheme to the control display end;
the material monitoring module is used for monitoring the quality Zc of the residual packaging materials on each station in real time by using a quality sensor in the production process, and when the quality Zc of the residual packaging materials on the stations is lower than a material threshold value, a transport forklift is used for distributing the materials and uploading the distribution quality to the cloud;
the on-duty monitoring module of staff acquires production pictures of the food packaging workshop in real time by using a camera, monitors actual on-duty conditions of staff on a station by using an artificial intelligence technology, and adjusts the transmission speed of equipment when the fact that staff leave the duty is detected;
the equipment fault monitoring module is used for acquiring vibration, temperature, voltage and current data of equipment in the running process through vibration, temperature, voltage and current sensors, identifying the fault state of the equipment through comparing and analyzing characteristic information of the data, and timely giving out fault warning;
the packaging qualification detection module is used for setting a camera and an image recognition algorithm on a production line to detect whether food is packaged well or not, marking the food as unqualified packaging if the food is found to be incompletely packaged or damaged, analyzing to obtain the food packaging qualification rate, and sending out abnormal packaging qualification rate early warning when the food packaging qualification rate is less than 90%;
the progress judging module is used for comparing the size relation between the actual production progress and the planned production progress, sending out overtime early warning and regenerating the production scheme when the actual production progress is smaller than 0.8 times of the planned production progress;
and the control display end receives the data and the early warning signals from each module and displays and outputs the data and the early warning signals.
(III) beneficial effects
The application provides an intelligent management method and system for a production line of a food packaging workshop, and the intelligent management method and system have the following beneficial effects:
1. the production data set of the workshop is constructed by consulting the related production data of the food packaging workshop, the daily production maximum capacity of the food packaging workshop is calculated and obtained, the running condition of the production line of the food packaging workshop can be mastered more accurately, the production flow is predicted and optimized, the downtime of the production line is reduced, the utilization rate of equipment is improved, and the production cost is reduced;
2. the minimum daily production capacity required for completing the production task is obtained through analysis, the execution difficulty of the production task is evaluated, the completeness of the production task can be determined, the cost required for completing the production task is estimated more accurately, the production resource and manpower are arranged better, the overall production efficiency is improved, the waste of the resource is reduced, and the production delay is avoided;
3. by constructing a production scheme generation model and using the production scheme generation model to generate a production scheme corresponding to a production task, the requirements and the limitations of resources such as personnel, equipment, raw materials and the like can be comprehensively considered, the resource allocation is automatically optimized according to the requirements of the production task, the resource utilization efficiency is improved, human errors and decision delays are reduced, and the harmony and the efficiency of a production line are improved, so that the production cost is reduced;
4. in the production process, the surplus raw materials, the on-duty conditions of staff and the running conditions of equipment are monitored in real time, so that a production plan can be better optimized, delay and interruption of the production plan caused by material shortage, personnel shortage and equipment faults are avoided, the production efficiency of a food packaging workshop is improved, and the product quality is guaranteed;
5. the packaging integrity and the safety of the product can be ensured through the packaging quality inspection, and the quality level of the product is improved; the real production progress is counted in real time, the overtime early warning is sent outwards in time, the possible problems in the production process can be found out at the first time, and the problems can be corrected in time, so that the problems are prevented from being enlarged, and the risks of production delay and insufficient product supply are reduced.
Drawings
FIG. 1 is a schematic flow chart of an intelligent management method for a production line of a food packaging workshop;
fig. 2 is a schematic structural diagram of an intelligent management system for a production line of a food packaging workshop.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. 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.
Referring to fig. 1, the application provides an intelligent management method for a production line of a food packaging workshop, which comprises the following steps:
step one, referring to related production data of a food packaging workshop, constructing a workshop production data set, and calculating to obtain the daily maximum production capacity Cn of the food packaging workshop.
The first step comprises the following steps:
step 101, consulting historical production records, equipment specifications and a manpower system of a food packaging workshop, obtaining the number Yg of packaging staff of the food packaging workshop, the maximum number Cs of food transmitted by transmission equipment per hour and the average number Bz of food packaged by staff per hour after finishing, and constructing a workshop production data set.
Step 102, obtaining the number Yg of packing staff in a food packing workshop, the maximum number Cs of food transported by a transporting device per hour and the average number Bz of food packed per hour by staff, and calculating to obtain the daily maximum production capacity Cn of the food packing workshop:
；
the calculation formula of the maximum daily production capacity Cn of the corresponding food packaging workshop is as above.
In use, the contents of steps 101 and 102 are combined:
the production data set of the workshop is constructed by consulting the related production data of the food packaging workshop, the daily production maximum capacity of the food packaging workshop is calculated and obtained, the running condition of the production line of the food packaging workshop can be mastered more accurately, the production flow is predicted and optimized, the downtime of the production line is reduced, the utilization rate of equipment is improved, and the production cost is reduced.
Inputting a production task, analyzing and obtaining the daily minimum productivity Xn required by completing the production task, evaluating the execution difficulty of the production task, and sending a corresponding difficulty early warning signal outwards according to the evaluation result.
The second step comprises the following steps:
step 201, inputting a production task, including a completion period Rq and the quantity Sl of packaged foods, and calculating to obtain the daily minimum production capacity Xn required by completing the production task:
；
the corresponding calculation formula of the daily minimum production capacity Xn required for completing the production task is as above.
Step 202, judging the relationship between the minimum daily production capacity Xn required for completing the production task and the maximum daily production capacity Cn of the food packaging workshop, evaluating the execution difficulty of the production task, and sending out a corresponding difficulty early warning signal according to the evaluation result, wherein the method specifically comprises the following steps:
when Xn is not more than Cn, the difficulty of the current production task is small, the productivity of the food packaging workshop can realize the task in the date, and correspondingly, no early warning signal is sent outwards;
when Xn is larger than Cn and smaller than 1.5Cn, the current production task difficulty is medium, if the task can be realized in the date, the productivity of the food packaging workshop is required to be upgraded, and correspondingly, a secondary difficulty early warning signal is sent outwards;
when Xn is not less than 1.5Cn, the current production task difficulty is high, the productivity of a food packaging workshop can not be realized, and a primary difficulty early warning signal is correspondingly sent outwards.
In use, the contents of steps 201 and 202 are combined:
the minimum daily production capacity required for completing the production task is obtained through analysis, the execution difficulty of the production task is evaluated, the completeness of the production task can be determined, the cost required for completing the production task is estimated more accurately, the production resource and manpower are arranged better, the overall production efficiency is improved, the waste of the resource is reduced, and the production delay is avoided.
And thirdly, constructing a production scheme generation model, generating a production scheme corresponding to the production task by using the production scheme generation model, and transmitting the production scheme to a control display end.
The third step comprises the following steps:
step 301, acquiring a workshop production data set and a historical production record, selecting part of data from the data set as sample data, importing the sample data into a constructed machine learning model, and exporting a production scheme generation model after training test;
and 302, inputting the production task into a production scheme generation model, generating a corresponding production scheme by using the production scheme generation model, and transmitting the production scheme to a control display end, wherein the production scheme comprises daily planned production capacity, daily required staff number and equipment transmission speed.
In use, the contents of steps 301 and 302 are combined:
by constructing a production scheme generation model and using the production scheme generation model to generate a production scheme corresponding to a production task, the requirements and the limitations of personnel, equipment, raw materials and other resources can be comprehensively considered, the resource allocation is automatically optimized according to the requirements of the production task, the resource utilization efficiency is improved, human errors and decision delays are reduced, the coordination and the efficiency of a production line are improved, and therefore the production cost is reduced.
And step four, in the production process, using corresponding sensors to monitor the residual raw materials, on-duty staff and equipment operation conditions in the production process, and if abnormality occurs, adopting corresponding processing strategies in time.
The fourth step comprises the following steps:
in the production process, monitoring the quality Zc of the remaining packaging materials on each station in real time by using a quality sensor, and when the quality Zc of the remaining packaging materials on the station is lower than a preset material threshold value, distributing the materials by using a transport forklift and uploading the distribution quality to a cloud;
step 402, acquiring a production picture of a food packaging workshop in real time by using a camera, monitoring actual on-duty conditions of staff on a station by using an artificial intelligence technology, and adjusting the transmission speed of equipment when the fact that the staff leaves the on-duty is detected;
step 403, vibration, temperature, voltage and current data of the equipment in the running process are collected through vibration, temperature, voltage and current sensors, the fault state of the equipment is identified through comparing and analyzing characteristic information of the data, and fault warning is timely sent out.
In use, the contents of steps 401 to 403 are combined:
in the production process, the surplus of raw materials, on-duty conditions of staff and equipment operation conditions are monitored in real time, so that a production plan can be optimized better, delay and interruption of the production plan caused by material shortage, personnel shortage and equipment faults are avoided, the production efficiency of a food packaging workshop is improved, and the product quality is guaranteed.
And fifthly, carrying out packaging quality inspection on the packaged food, counting to obtain the actual production progress, and sending out overtime early warning and regenerating the production scheme when the actual production progress is smaller than the planned threshold value.
The fifth step comprises the following steps:
step 501, setting a camera and an image recognition algorithm on a production line, detecting whether food is packaged well, and if the food is found to be incompletely packaged or damaged, marking the food as unqualified;
step 502, monitoring and obtaining the daily actual package quantity Sj and the daily package unqualified quantity Bg by using a counter, and calculating and obtaining the food package qualification rate Hg, wherein the calculation formula of the corresponding food package qualification rate Hg is as follows:
；
and when the food packaging qualification rate Hg is less than 90%, sending out abnormal package qualification rate early warning.
And 503, counting the number of qualified daily packaged foods, obtaining an actual production progress, comparing the actual production progress with the planned production progress, and sending out a timeout early warning and regenerating the production scheme when the actual production progress is smaller than 0.8 times of the planned production progress.
In use, the contents of steps 501 and 503 are combined:
the packaging integrity and the safety of the product can be ensured through the packaging quality inspection, and the quality level of the product is improved; the real production progress is counted in real time, the overtime early warning is sent outwards in time, the possible problems in the production process can be found out at the first time, and the problems can be corrected in time, so that the problems are prevented from being enlarged, and the risks of production delay and insufficient product supply are reduced.
Referring to fig. 2, the present application provides an intelligent management system for a production line of a food packaging workshop, comprising:
the workshop productivity evaluation module is used for consulting related production data of the food packaging workshop, constructing a workshop production data set and calculating to obtain the daily maximum production capacity of the food packaging workshop;
the task difficulty judging module inputs a production task, analyzes and obtains the minimum daily production capacity required by completing the production task, evaluates the execution difficulty of the production task, and sends out a corresponding difficulty early warning signal according to the evaluation result;
the scheme generation module is used for constructing a production scheme generation model, generating a production scheme corresponding to a production task by using the production scheme generation model, and transmitting the production scheme to the control display end;
the material monitoring module is used for monitoring the quality Zc of the residual packaging materials on each station in real time by using a quality sensor in the production process, and when the quality Zc of the residual packaging materials on the stations is lower than a material threshold value, a transport forklift is used for distributing the materials and uploading the distribution quality to the cloud;
the on-duty monitoring module of staff acquires production pictures of the food packaging workshop in real time by using a camera, monitors actual on-duty conditions of staff on a station by using an artificial intelligence technology, and adjusts the transmission speed of equipment when the fact that staff leave the duty is detected;
the equipment fault monitoring module is used for acquiring vibration, temperature, voltage and current data of equipment in the running process through vibration, temperature, voltage and current sensors, identifying the fault state of the equipment through comparing and analyzing characteristic information of the data, and timely giving out fault warning;
the packaging qualification detection module is used for setting a camera and an image recognition algorithm on a production line to detect whether food is packaged well or not, marking the food as unqualified packaging if the food is found to be incompletely packaged or damaged, analyzing to obtain the food packaging qualification rate, and sending out abnormal packaging qualification rate early warning when the food packaging qualification rate is less than 90%;
the progress judging module is used for comparing the size relation between the actual production progress and the planned production progress, sending out overtime early warning and regenerating the production scheme when the actual production progress is smaller than 0.8 times of the planned production progress;
and the control display end receives the data and the early warning signals from each module and displays and outputs the data and the early warning signals.
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. 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.
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.
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.
Claims (10)
1. An intelligent management method for a production line of a food packaging workshop is characterized by comprising the following steps of: the method comprises the following steps:
referring to related production data of a food packaging workshop, constructing a workshop production data set, and calculating to obtain the daily maximum production capacity of the food packaging workshop; inputting a production task, analyzing and obtaining the daily minimum productivity required by completing the production task, evaluating the execution difficulty of the production task, and sending out a corresponding difficulty early warning signal according to the evaluation result;
the method comprises the steps of obtaining the number Yg of packing staff in a food packing workshop, the maximum number Cs of food transported by transport equipment per hour and the average number Bz of food packed by staff per hour, and calculating to obtain the daily maximum productivity Cn of the food packing workshop:
；
the calculation formula of the maximum daily production capacity Cn of the corresponding food packaging workshop is as above;
constructing a production scheme generation model, generating a production scheme corresponding to a production task by using the production scheme generation model, and transmitting the production scheme to a control display end; in the production process, the corresponding sensors are used for monitoring the residual raw materials, the on-duty staff and the running conditions of equipment in the production process, and if the abnormality occurs, corresponding processing strategies are adopted in time;
and carrying out packaging quality inspection on the packaged food, counting the number of the packaged food passing the quality inspection, obtaining the actual production progress, and sending out overtime early warning and regenerating the production scheme when the actual production progress is smaller than the planned threshold value.
2. The intelligent management method for a food packaging workshop production line according to claim 1, wherein:
and (3) referring to a historical production record, an equipment specification and a manpower system of the food packaging workshop, obtaining the number Yg of packaging staff of the food packaging workshop, the maximum number Cs of food transmitted by the transmission equipment per hour and the average number Bz of food packaged by staff per hour after finishing, and constructing a workshop production data set.
3. The intelligent management method for a production line of a food packaging workshop according to claim 2, wherein:
inputting a production task, namely calculating and obtaining the daily minimum production capacity Xn required by completing the production task, wherein the production task comprises a completion period Rq and the quantity Sl of packaged foods:
；
the corresponding calculation formula of the daily minimum production capacity Xn required for completing the production task is as above.
4. A method for intelligent management of a food packaging plant production line according to claim 3, wherein:
judging the relationship between the minimum daily production capacity Xn required for completing the production task and the maximum daily production capacity Cn of the food packaging workshop, evaluating the execution difficulty of the production task, and sending out a corresponding difficulty early warning signal according to the evaluation result, wherein the specific method comprises the following steps:
when Xn is not more than Cn, the difficulty of the current production task is small, the productivity of the food packaging workshop can realize the task in the date, and correspondingly, no early warning signal is sent outwards;
when Xn is larger than Cn and smaller than 1.5Cn, the current production task difficulty is medium, if the task can be realized in the date, the productivity of the food packaging workshop is required to be upgraded, and correspondingly, a secondary difficulty early warning signal is sent outwards;
when Xn is not less than 1.5Cn, the current production task difficulty is high, the productivity of a food packaging workshop can not be realized, and a primary difficulty early warning signal is correspondingly sent outwards.
5. The intelligent management method for a food packaging workshop production line according to claim 1, wherein:
monitoring the quality Zc of the residual packaging materials on each station in real time by using a quality sensor, and when the quality Zc of the residual packaging materials on the stations is lower than a material threshold value, distributing the materials by using a transport forklift and uploading the distribution quality to the cloud;
and acquiring a production picture of the food packaging workshop in real time by using a camera, monitoring the actual on-duty condition of staff on a station, and adjusting the transmission speed of equipment when the staff is detected to leave the on-duty condition.
6. The intelligent management method for a food packaging workshop production line according to claim 1, wherein:
vibration, temperature, voltage and current data of the equipment in the running process are collected through vibration, temperature, voltage and current sensors, the fault state of the equipment is identified through comparing and analyzing characteristic information of the data, and fault warning is timely sent out.
7. The intelligent management method for a food packaging workshop production line according to claim 6, wherein:
a camera and an image recognition algorithm are arranged on a production line, whether the food is packaged well is detected, and if the food is found to be incompletely packaged or damaged, the food is marked as unqualified;
the actual daily package quantity Sj and the unqualified daily package quantity Bg are obtained through monitoring by using a counter, the food package qualification rate Hg is obtained through calculation, and the corresponding calculation formula of the food package qualification rate Hg is as follows:
；
and when the food packaging qualification rate Hg is less than 90%, sending out abnormal package qualification rate early warning.
8. An intelligent management system for a production line of a food packaging workshop, applying the method of any one of claims 1 to 7, characterized in that: comprising the following steps:
the workshop productivity evaluation module is used for consulting related production data of the food packaging workshop, constructing a workshop production data set and calculating to obtain the daily maximum production capacity of the food packaging workshop;
the task difficulty judging module inputs a production task, analyzes and obtains the minimum daily production capacity required by completing the production task, evaluates the execution difficulty of the production task, and sends out a corresponding difficulty early warning signal according to the evaluation result;
the scheme generation module is used for constructing a production scheme generation model, generating a production scheme corresponding to a production task by using the production scheme generation model, and transmitting the production scheme to the control display end;
and the material monitoring module is used for monitoring the quality Zc of the residual packaging materials on each station in real time by using a quality sensor in the production process, and when the quality Zc of the residual packaging materials on the stations is lower than a material threshold value, using a transport forklift to dispatch the materials and uploading the dispatching quality to the cloud.
9. The intelligent management system for a food packaging plant production line of claim 8, wherein: further comprises:
the on-duty monitoring module of staff acquires production pictures of the food packaging workshop in real time by using a camera, monitors actual on-duty conditions of staff on a station by using an artificial intelligence technology, and adjusts the transmission speed of equipment when the fact that staff leave the duty is detected;
the equipment fault monitoring module collects vibration, temperature, voltage and current data of equipment in the operation process through vibration, temperature, voltage and current sensors, and recognizes the fault state of the equipment through comparing and analyzing characteristic information of the data, and timely sends out fault warning.
10. The intelligent management system for a food packaging plant production line according to claim 9, wherein: further comprises:
the packaging qualification detection module is used for setting a camera and an image recognition algorithm on a production line to detect whether food is packaged well or not, marking the food as unqualified packaging if the food is found to be incompletely packaged or damaged, analyzing to obtain the food packaging qualification rate, and sending out abnormal packaging qualification rate early warning when the food packaging qualification rate is less than 90%;
the progress judging module is used for comparing the size relation between the actual production progress and the planned production progress, sending out overtime early warning and regenerating the production scheme when the actual production progress is smaller than 0.8 times of the planned production progress;
and the control display end receives the data and the early warning signals from each module and displays and outputs the data and the early warning signals.
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