Intelligent logistics monitoring system and method based on big data

Abstract

The invention relates to the technical field of intelligent logistics, in particular to an intelligent logistics monitoring system and control method based on big data, and the system comprises an intelligent logistics monitoring center, a warehousing coding system, an assembly process recording system, an intelligent logistics conveying system and a video monitoring module. The intelligent logistics monitoring center comprises a data operation module, a data storage module and a display module; the warehousing coding system is used for identifying and coding the material accessories entering the intelligent workshop and identifying and coding the assembled products, so that each material accessory and each assembled product have a unique identification code; wherein the assembled products are conveyed and assembled on the intelligent logistics conveying system; the intelligent logistics conveying system comprises a conveying assembly line, a plurality of assembly tables and a material accessory conveying line. According to the method, in reasonable assembly procedures, under the condition that assembly is not affected, the procedure sequence is adjusted firstly, and then combination is conducted.

Description

Technical Field

[0001] The present invention relates to the field of intelligent logistics technology, and in particular to an intelligent logistics monitoring system and method based on big data. Background Art

[0002] In traditional manufacturing factories, assembly lines are usually used in the production and assembly of products, that is, materials and accessories are provided from various assembly positions, assembled at assembly positions on the assembly line, and then products are produced. This requires reasonable distribution of materials and accessories so that the materials and accessories of the factory can be distributed in time to improve the production efficiency of the enterprise, and at the same time, to prevent the accumulation of objects and affect the production progress. In recent years, with the advancement of Industry 4.0, traditional factories have begun to turn to intelligence, forming smart factories with smart logistics and smart manufacturing.

[0003] The so-called smart factory refers to. Through the use of smart factories, production efficiency can be improved, factories can be optimized, and production costs such as personnel can be reduced. For example, in the prior art, there are also related smart factory technologies, such as: The invention patent with the publication number CN117973760A discloses a special vehicle production management information system, including: sales order management module, production plan module, production execution module, production management module, quality management module, after-sales management module, report center module, material management module, intelligent logistics module, basic information module, system management module. The present invention uses information technology to assist production management business, and combines the standardization and implementation of management systems and processes to establish a complete and comprehensive production information management system; with the help of a new software information technology framework, integrate and optimize the upstream and downstream business process systems, establish a comprehensive business data processing platform, and deeply integrate the PDM system and financial module system currently used by the enterprise to meet the management requirements of refined production, energy saving and cost reduction, and efficient business collaboration, so as to smooth the production site organization, speed up the production rhythm, reduce logistics costs, improve product quality, and enhance the overall competitiveness of the enterprise supply chain.

[0004] The invention patent with the publication number CN110968058A discloses a production system simulation device and a production system simulation method. The production system simulation device simulates the state transition based on the chain of discrete phenomena, and is equipped with: an equipment element information storage unit, which stores the specifications of the equipment elements as equipment element information; an operation element information storage unit, which stores the operation element information, and the operation element information includes the triggering conditions of the operation elements and the output destination after the operation is completed; and an associated information storage unit, which stores the associated information between the equipment elements and the operation elements, and performs simulation processing in the following manner: at least according to the satisfaction of the triggering conditions of the operation element information of the first operation element associated with the first equipment element through the associated information, the first equipment element executes the operation, and the output destination of the operation element information of the first operation element after the operation is completed is output.

[0005] The invention patent with the publication number CN108873830A discloses a production site data online collection and analysis and fault prediction system, which is built on a B / S architecture and includes a bottom-level sensor network unit, a lower-level computer collection station unit and a host-level computer server unit, wherein the lower-level computer collection station unit is connected to the bottom-level sensor network unit signal, and is used to obtain multiple real-time data including the equipment status, production process, product quality, logistics information, etc. of the intelligent manufacturing workshop in real time, and extract feature data at the same time; the host-level computer server unit is used to visualize the workshop posture and operation status based on real-time data, predict the fault location of each device based on feature data, and provide more targeted fault maintenance measures. It can realize the unified networking management of equipment in intelligent manufacturing workshops, completely eliminate information islands, and at the same time focus on providing targeted solutions to equipment failures and energy consumption.

[0006] The invention patent with publication number CN1113762597A discloses an intelligent AGV scheduling system and method based on cloud platform management. The system includes a 5G communication technology connection module, a data information acquisition module, an AGV status monitoring module, and a path intelligent control module. The method includes: using the grid map method to model the material transportation and processing scene in the factory; the cloud platform receives the status information of the CNC and AGV and stores the response in the form of a data table; using the row coordinate X as the foreign key to extract the operation status of each CNC and AGV on each row to form a new data table; selecting the CNC and AGV operation status that meet the standards, recording the demand signal issued by the CNC and responding; the response rules are the composite path dynamic scheduling strategy and priority principle, and the AGV moves to the corresponding CNC coordinate position that issues the demand according to the rules to transfer materials. It expands the application scope of AGV in actual scenarios by enabling the coordinated scheduling and multi-tasking of multiple AGVs.

[0007] The invention patent with the publication number CN113035376A discloses a smart factory based on the industrial Internet and its construction method. The construction method includes: constructing a network system to operate the working equipment in the factory through the Internet of Things, so that the working equipment can realize information interconnection in a unified network system; constructing a smart factory application system, developing the smart factory application system based on the B / S architecture, and the smart factory application system is used to customize the corresponding functional modules according to the factory needs; and constructing a hybrid cloud platform, the private cloud of the hybrid cloud platform is used to provide services for the smart factory application system, and the hybrid cloud platform is also connected to an external service system, which is used to call the interface of the corresponding external service system according to the request issued by the user in the smart factory application system to realize the coordination of the industrial chain. Realize the systematic, efficient and modular construction of smart factories.

[0008] The invention patent with the publication number CN115081728A discloses a multi-source heterogeneous textile equipment scheduling management and optimization system for a textile factory, which belongs to the field of textile industry automation technology, including textile equipment, which is used to carry out the specific actual production and production process of the factory; data acquisition module, which is used to obtain the data of multi-source heterogeneous textile equipment in the factory; data platform, which is used to separate the scheduling and control of the textile equipment during operation to the data level for processing; application optimization module, which is used to make autonomous optimization adjustments or give operators reasonable work suggestions through high-performance computing of the data platform. This application is highly connected to multi-source heterogeneous equipment, carrying digital models and microservices containing a lot of industrial knowledge, greatly improving the reuse level of industrial knowledge; promoting the reverse innovation and performance optimization of textile equipment itself, helping to explore and identify the sites and links of textile equipment improvement; supporting digital diversified structured data analysis, and accelerating the process from data to value.

[0009] It can be seen that in the existing technology, the intelligence and technology of smart factories still have the following technical defects: 1. In traditional smart factories, the assembly sequence is usually set and determined by the factory assembly line designers and will not change. The smart factory mainly reflects the intelligent transportation and assembly of objects, but the time consumed by each assembly process is usually different. Therefore, it is easy to cause some assembly processes to be completed too quickly, resulting in the accumulation of assembly items, which is not conducive to improving assembly efficiency.

[0010] 2. In traditional smart factories, the factory's assembly lines are not recorded and cannot be tracked. In the assembly of assembly lines in smart factories, mistakes are inevitable or errors may occur, resulting in quality problems in the assembled items. This inevitably increases the difficulty of quality inspection and is not easy to track.

[0011] 3. In traditional intelligent work, it has always been a problem to adjust and merge processes, and it has also been a problem to adjust various assembly processes.

[0012] In view of the above-mentioned technical problems, how to improve a better, more efficient and low-cost intelligent logistics assembly system is what people are looking forward to. To this end, a technical solution of this application is proposed. Summary of the invention

[0013] The purpose of the present invention is to provide an artificial intelligence-based inflatable unmanned water boat, a control system and a control method.

[0014] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: an intelligent logistics monitoring system based on big data, including an intelligent logistics monitoring center, a warehousing coding system, an assembly process recording system, an intelligent logistics transmission system and a video monitoring module; The intelligent logistics monitoring center includes a data calculation module, a data storage module and a display module; the warehousing coding system is used to identify and encode the material accessories entering the intelligent factory, and to identify and encode the assembly products at the same time, so that each material accessory and each assembly product has a unique identification code; wherein the assembly products are transported and assembled on the intelligent logistics transmission system; the intelligent logistics transmission system includes a transmission line, a plurality of assembly stations and a material accessory conveyor line; wherein a plurality of the assembly stations are sequentially arranged on the side of the transmission line, so as to assemble the assembly products flowing through the transmission line, so as to complete each assembly process, and the material accessory conveyor line is used to transport the material accessories to the assembly station; The assembly process recording system is arranged at each assembly station. When the material parts are conveyed by the conveying system, when the assembled product passes through the assembly station, the assembly process recording system records the time of the assembled product in the current assembly process, and then obtains the completion time T of a certain assembled product in the i-th assembly process. i and the average completion time T of the i-th assembly process in the assembly station i _averg, i takes 1, 2, 3, ..., N, a total of N assembly processes; and uses a sensor device to read the identification code of the assembled product, the identification code of the material accessories and the image of the material accessories, so as to obtain the data record set of the time when the assembled product is completed in each assembly process and the material object composition: U_assembly item identification code = {assembly item identification code, first assembly process completion time T1, installed material and accessory identification code, second assembly process completion time T2, installed material and accessory identification code, ... second assembly process completion time T N , installed material accessory identification code, material accessory image}; The data record set is stored in the data storage module; the data operation module is used to analyze and operate the data record set stored in the data storage module, generate logistics transportation scheduling instructions and operation diagrams, and send them to the display module for display.

[0015] Furthermore, the assembly process recording system also records the assembly time T of each material accessory. i , and sent to the data storage module, the data calculation module reads the assembly time T stored in the material accessories i , where i is the completion time of the i-th assembly process, and the calculation is performed to judge: if the assembly time of two adjacent assembly processes satisfies: T i +T i+1 <T i+2 , then the two assembly processes of the i-th and i+1-th are merged into one assembly station, thereby reducing one assembly station.

[0016] Furthermore, the data operation module also determines the interchangeability of the assembly process; if any two assembly processes can be swapped, the assembly time of the process after the process swap is calculated again if the assembly time of the two adjacent assembly processes satisfies: T i +T i+1 <T i+2 , then the two assembly processes of the i-th and i+1-th are merged into one assembly station, and the swapping and merging of assembly processes are repeated until the number of assembly stations is the minimum.

[0017] Furthermore, the data storage module also stores the quantity of each material accessory, and when the quantity of the material is reduced to a preset quantity, an alarm is prompted through the display module so that the material accessories can be put into storage in time.

[0018] Furthermore, the data operation module reads the number of completed steps C of each assembly process stored in the data storage module. i , if C i / C i-1 If it is less than the preset value, it indicates that the C i If there are too few assembly stations for a process, resulting in a pile-up of assembly items, then additional assembly stations are added to complete the i-th process.

[0019] Furthermore, the data operation module reads the completion time T of each process stored in the data storage module. i , if T i / T i-1 When it is greater than 2, it indicates that the assembly station of the i-th assembly process consumes too much or too little time, which will lead to the accumulation of assembly parts, so the assembly station that completes the i-th assembly process is increased.

[0020] Furthermore, the data operation module reads the completion time T of each assembly process stored in the data storage module. i , and statistically analyze the number of assembly processes completed in each cycle, and draw a curve chart of the cycle-assembly process completion number of each assembly process completed in each cycle, and send it to the display module for display, and based on the quantity curve and T i _averg is used to determine whether there is an abnormality in the number of completed assembly processes. When the number of completed assembly processes is abnormal, it is determined that the assembly process needs maintenance, and the maintenance personnel are notified to go to the assembly station for investigation and inspection.

[0021] Furthermore, a process detection module is also provided on the assembly station, which scans the identification code of the assembly part, reads the assembly flow information, and determines whether there is any missing process based on the current position. If there is any omission, the process detection module will alarm and remind to rework.

[0022] On the other hand, the present application also provides an intelligent logistics monitoring method based on big data, including an intelligent logistics monitoring system based on big data, characterized in that the specific intelligent logistics monitoring method based on big data is as follows; Step S1, the warehousing coding system is used to identify and code the material accessories entering the smart factory, and also to identify and code the assembled products, so that each material accessory and each assembled product has a unique identification code; Step S2, the assembled product is conveyed on the intelligent logistics conveying system to the i-th assembly station for assembly, so as to complete each assembly process, and the material and accessories conveying line conveys the material and accessories to the assembly station; Step S3, when the material accessories are conveyed by the conveying system, when the assembled product passes through the assembly station, the assembly process recording system records the time of the assembled product in the current assembly process, and then obtains the completion time T of a certain assembled product in the i-th assembly process i and the average completion time T of the i-th assembly process in the assembly station i _averg, i takes 1, 2, 3, ... N, with a total of N assembly processes; and uses a sensor device to read the identification code of the assembled product, the identification code of the material accessories, and the image of the material accessories, so as to obtain a data record set of the time when the assembled product is completed in each assembly process and the material object composition: U_assembly item identification code = {assembly item identification code, first assembly process completion time T1, installed material and accessory identification code, second assembly process completion time T2, installed material and accessory identification code, ... second assembly process completion time T N, installed material accessory identification code, material accessory image}; and storing the data record set in the data storage module; Step S4, the data operation module is used to analyze and operate the data record set stored in the data storage module, generate logistics transportation scheduling instructions and operation diagrams, and send them to the display module for display; Step S5: The assembly process recording system also records the assembly time T of each material accessory. i , and sent to the data storage module, the data calculation module reads the assembly time T stored in the material accessories i , where i is the completion time of the i-th assembly process, and the calculation is performed to judge: if the assembly time of two adjacent assembly processes satisfies: T i +T i+1 <T i+2 , then the two assembly processes of the i-th and i+1-th are merged into one assembly station, thereby reducing one assembly station; Step S5, the data calculation module further determines the interchangeability of the assembly process; if any two assembly processes can be swapped, the assembly time of the process after the process swap is calculated again if the assembly time of the two adjacent assembly processes satisfies: T i +T i+1 <T i+2 , then merge the two assembly processes of the i-th and i+1-th into one assembly station, and repeat the swapping and merging of assembly processes until the number of assembly stations is the minimum; Step S6, the data operation module reads the assembly time T of each assembly process stored in the data storage module. i , if T i / T i-1 When it is greater than 2, it indicates that the assembly station of the i-th assembly process consumes too much time, which will lead to the accumulation of assembly parts, so the assembly station that completes the i-th assembly process is added; Step S7, the data operation module reads the completion time T of each assembly process stored in the data storage module i , and statistically analyze the number of assembly processes completed in each cycle, and draw a curve chart of the cycle-assembly process completion number of the completed number of each assembly process in each cycle, and send it to the display module for display, and based on the quantity curve and T i _averg is used to determine whether there is an abnormality in the number of completed assembly processes. If there is an abnormality in the number of completed assembly processes, it is determined that the assembly process needs maintenance, and the maintenance personnel are notified to go to the assembly station for investigation and inspection; Step S8, a process detection module is also provided on the assembly station, the process detection module scans the identification code of the assembly part, reads the assembly flow information, and determines whether there is any missing process based on the current position. If there is any omission, an alarm is issued through the process detection module, and a reminder is given to rework.

[0023] On the other hand, the present application also provides a computer storage medium, which stores a computer program, and the computer program is used to execute an intelligent logistics monitoring method based on big data.

[0024] Compared with the prior art, the present invention has the following beneficial effects: 1. In this application, by counting the time consumed by each assembly process, and then appropriately merging them based on the assembly time, the use of assembly tables can be reduced, the assembly can be reasonably coordinated, and the efficiency of the project can be improved.

[0025] 2. In this application, by adjusting the process sequence in a reasonable assembly process without affecting the assembly, and then merging them, the assembly process is further coordinated and adjusted to further improve the assembly efficiency.

[0026] 3. In this application, by collecting statistics on the completion time of each assembly process, a traceable big data model is formed, thereby realizing a big data-based intelligent factory, analyzing the assembly process based on big data, discovering faults in a timely manner, and providing system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of an intelligent logistics monitoring system and method based on big data; Figure 2 This is a structural diagram of an intelligent logistics monitoring system and method based on big data.

[0028] The description of the accompanying drawings is as follows: 1. Intelligent logistics monitoring center; 2. Warehouse coding system; 3. Assembly process recording system; 4. Intelligent logistics transmission system; 5. Video monitoring module; 6. Process detection module; 7. Data calculation module; 8. Data storage module; 9. Display module; 10. Conveyor line; 11. Assembly table; 12. Material and accessories conveyor line. DETAILED DESCRIPTION

[0029] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Specific embodiment one: like Figure 1-2 As shown, an intelligent logistics monitoring system based on big data includes an intelligent logistics monitoring center 1, a warehousing coding system 2, an assembly process recording system 3, an intelligent logistics transmission system 4 and a video monitoring module 5; The intelligent logistics monitoring center 1 includes a data calculation module 7, a data storage module 8 and a display module 9; the warehousing coding system 2 is used to identify and code the material accessories entering the intelligent factory, and identify and code the assembly products at the same time, so that each material accessory and each assembly product has a unique identification code; wherein the assembly products are transported and assembled on the intelligent logistics transmission system 4; the intelligent logistics transmission system 4 includes a transmission line 10, a plurality of assembly tables 11 and a material accessory conveying line 12; wherein the plurality of assembly tables 11 are sequentially arranged on the side of the transmission line 10, so as to assemble the assembly products flowing through the transmission line 10, so as to complete each assembly process, and the material accessory conveying line 12 is used to transport the material accessories to the assembly table 11; The assembly process recording system 3 is arranged at each assembly station 11. When the material accessories are transported by the intelligent logistics conveying system 4, when the assembled product passes through the assembly station, the assembly process recording system 3 records the time of the assembled product in the current assembly process, and then obtains the completion time T of a certain assembled product in the i-th assembly process. i and the average completion time T of the i-th assembly process in the assembly station i _averg, i takes 1, 2, 3, ... N, with a total of N assembly processes; and uses a sensor device to read the identification code of the assembled product, the identification code of the material accessories, and the image of the material accessories, so as to obtain a data record set of the time when the assembled product is completed in each assembly process and the material object composition: U_assembly item identification code = {assembly item identification code, first assembly process completion time T1, installed material and accessory identification code, second assembly process completion time T2, installed material and accessory identification code, ... second assembly process completion time T N , installed material accessory identification code, material accessory image}; The data record set is stored in the data storage module 8; the data calculation module 7 is used to analyze and calculate the data record set stored in the data storage module 8, generate logistics transportation scheduling instructions and operation diagrams, and send them to the display module 9 for display.

[0031] Furthermore, the assembly process recording system 3 also records the assembly time T of each material accessory. i , and sent to the data storage module 8, the data calculation module 7 reads the assembly time T stored in the material accessories i , where i is the completion time of the i-th assembly process, and the calculation is performed to judge: if the assembly time of two adjacent assembly processes satisfies: T i +T i+1 <T i+2, then the two assembly processes of the i-th and i+1-th are merged into one assembly station, thereby reducing one assembly station.

[0032] Furthermore, the data operation module 7 also determines the interchangeability of the assembly process; if any two assembly processes can be swapped, the assembly time of the process after the process swap is calculated again if the assembly time of the two adjacent assembly processes satisfies: T i +T i+1 <T i+2 , then the two assembly processes of the i-th and i+1-th are merged into one assembly station, and the swapping and merging of assembly processes are repeated until the number of assembly stations is the minimum.

[0033] Furthermore, the data storage module 8 also stores the quantity of each material accessory, and when the quantity of the material is reduced to a preset quantity, the display module 9 is used to remind an alarm so that the material accessories can be put into storage in time. The reminder alarm through the display module 9 also includes: The reduction rate of the material in the data storage module 8 is calculated using the following formula:

[0034] in, Indicates a decrease in speed. Indicates the duration of the reduction of the material in the historical period. Indicates the reduced quantity of the material within the reduced duration; According to the reduction speed, the reduction time of the remaining quantity of the material in the data storage module 8 is calculated using the following formula:

[0035] in, Indicates the duration of the decrease in the remaining quantity of the material. Indicates a decrease in speed. Indicates the remaining quantity; Collect the storage time of the material in the data storage module 8; calculate the storage time threshold of the material in the data storage module 8 according to the storage time using the following formula:

[0036]

[0037] in, Indicates the storage time threshold, Indicates the duration of decrease of the first remaining quantity of the material, Indicates the storage time. Indicates the start time of reduction of the first remaining quantity; The delay time from the alarm time of the display module 9 to the threshold of the storage time is collected; according to the delay time, the threshold of the alarm time of the display module 9 is calculated using the following formula:

[0038] in, Indicates the alarm time threshold, Indicates the storage time threshold, Indicates the delay duration; When the time when the display module 9 reminds the alarm is less than the alarm time threshold, the display module 9 reminds the alarm so that the material accessories can be put into the warehouse in time.

[0039] The warehousing time refers to the total time it takes to transport the material from other places to the data storage module 8, and the warehousing time threshold refers to the latest start time of transporting the material from other places.

[0040] Furthermore, the data operation module 7 reads the number of completed steps C of each assembly process stored in the data storage module 8. i , if C i / C i-1 If it is less than the preset value, it indicates that the C i If there are too few assembly stations for a process, resulting in a pile-up of assembly items, then additional assembly stations are added to complete the i-th process.

[0041] The method further comprises: Obtain the minimum number of assembly stations to be increased; use the minimum number of assembly stations to increase the number of assembly stations to complete the i-th process, and obtain the first round of increased assembly stations; collect the number of completed steps of each assembly process under the first round of increased assembly stations ; Use the minimum increase number to determine the second round of additional assembly stations and the third round of additional assembly stations to complete the i-th process; collect the number of completed steps of each assembly process under the second round of additional assembly stations and the third round of additional assembly stations respectively and ; According to the first round of adding assembly platforms, the second round of adding assembly platforms, the third round of adding assembly platforms, , and , the influence coefficient between the assembly station and the number of completed steps of the i-th process is calculated using the following formula:

[0042]

[0043]

[0044]

[0045]

[0046] in, , represents the influence coefficient, Indicates the number of assembly stations added in the first round, Indicates the number of assembly stations increased in the second round, Indicates that the number of assembly stations is increased in the third round, , Indicates the sign when the influence coefficient is an unknown number, express , , Any parameter in express , , Any parameter in express , , The sequence number of any parameter in express , , The number of The value is 3; Based on the influence coefficient, the additional number of assembly stations for completing the i-th process is calculated using the following formula:

[0047]

[0048]

[0049] in, Indicates the number of assembly stations increased, , represents the influence coefficient, Represents C i / C i-1 When C is less than the preset value i The number of corresponding assembly stations, Represents C i / C i-1 The threshold value when it is less than the preset value; The number of assembly stations is increased by using the assembly stations to complete the i-th process.

[0050] The minimum number of additional assembly stations refers to the minimum number of assembly stations added each time. For example, if one assembly station is added, the first round of additional assembly stations refers to all the assembly stations put into use after adding the minimum number of assembly stations on the basis of the original assembly stations. The second round of additional assembly stations refers to all the assembly stations put into use after adding the minimum number of assembly stations on the basis of the first round of additional assembly stations. The third round of additional assembly stations refers to all the assembly stations put into use after adding the minimum number of assembly stations on the basis of the second round of additional assembly stations. The number of assembly stations added refers to C i / C i-1 When it is less than the preset value, the total number of assembly stations that need to be increased based on the original assembly stations.

[0051] Furthermore, the data operation module 7 reads the completion time T of each process stored in the data storage module 8. i , if T i / T i-1 When it is greater than 2, it indicates that the assembly station of the i-th assembly process consumes too much or too little time, which will lead to the accumulation of assembly parts, so the assembly station that completes the i-th assembly process is increased.

[0052] Furthermore, the data operation module 7 reads the completion time T of each assembly process stored in the data storage module 8. i , and statistically analyze the number of assembly processes completed in each cycle, and draw a curve chart of the cycle-assembly process completion number of each assembly process in each cycle, and send it to the display module 9 for display, and based on the quantity curve and T i _averg is used to determine whether there is an abnormality in the number of completed assembly processes. When the number of completed assembly processes is abnormal, it is determined that the assembly process needs maintenance, and the maintenance personnel are notified to go to the assembly station for investigation and inspection.

[0053] Furthermore, a process detection module 6 is also provided on the assembly station. The process detection module 6 scans the identification code of the assembly part, reads the assembly flow information, and determines whether there is any missing process based on the current position. If there is any omission, an alarm is issued through the process detection module 6, and a reminder is given to rework. Specific embodiment 2: An intelligent logistics monitoring method based on big data, including an intelligent logistics monitoring system based on big data, characterized in that the specific intelligent logistics monitoring method based on big data is as follows; Step S1, the warehousing coding system 2 is used to identify and code the material accessories entering the smart factory, and also to identify and code the assembled products, so that each material accessory and each assembled product has a unique identification code; Step S2, the assembled product is conveyed on the intelligent logistics conveying system 4 to the i-th assembly station for assembly, so as to complete each assembly process, and the material and accessories conveying line 12 conveys the material and accessories to the assembly station 11; Step S3, when the material accessories are conveyed by the conveying system 5, when the assembled product passes through the assembly station, the assembly process recording system 3 records the time of the assembled product in the current assembly process, and then obtains the completion time T of a certain assembled product in the i-th assembly process. i and the average completion time T of the i-th assembly process in the assembly station i _averg, i takes 1, 2, 3, ... N, with a total of N assembly processes; and uses a sensor device to read the identification code of the assembled product, the identification code of the material accessories, and the image of the material accessories, so as to obtain a data record set of the time when the assembled product is completed in each assembly process and the material object composition: U_assembly item identification code = {assembly item identification code, first assembly process completion time T1, installed material and accessory identification code, second assembly process completion time T2, installed material and accessory identification code, ... second assembly process completion time T N , installed material accessory identification code, material accessory image}; and storing the data record set in the data storage module 8; Step S4, the data operation module 7 is used to analyze and operate the data record set stored in the data storage module 8, generate logistics transportation scheduling instructions and operation diagrams, and send them to the display module 9 for display; Step S5, the assembly process recording system 3 also records the assembly time T of each material accessory i , and sent to the data storage module 8, the data calculation module 7 reads the assembly time T stored in the material accessories i , where i is the completion time of the i-th assembly process, and the calculation is performed to judge: if the assembly time of two adjacent assembly processes satisfies: T i +T i+1 <T i+2 , then the two assembly processes of the i-th and i+1-th are merged into one assembly station, thereby reducing one assembly station; Step S5, the data calculation module 7 further determines the interchangeability of the assembly process; if any two assembly processes can be swapped, the assembly time of the process after the process swap is calculated again if the assembly time of the two adjacent assembly processes satisfies: T i +T i+1 <T i+2, then merge the two assembly processes of the i-th and i+1-th into one assembly station, and repeat the swapping and merging of assembly processes until the number of assembly stations is the minimum; Step S6: the data calculation module 7 reads the completion time T of each process stored in the data storage module 8. i , if T i / T i-1 When it is greater than 2, it indicates that the assembly station of the i-th assembly process consumes too much time, which will lead to the accumulation of assembly parts, so the assembly station that completes the i-th assembly process is added; Step S7, the data operation module 7 reads the completion time T of each assembly process stored in the data storage module 8 i , and statistically analyze the number of assembly processes completed in each cycle, and draw a curve chart of the cycle-assembly process completion number of each assembly process in each cycle, and send it to the display module 9 for display, and based on the quantity curve and T i _averg is used to determine whether there is an abnormality in the number of completed assembly processes. If there is an abnormality in the number of completed assembly processes, it is determined that the assembly process needs maintenance, and the maintenance personnel are notified to go to the assembly station for investigation and inspection; Step S8, a process detection module 6 is also provided on the assembly station, and the process detection module 6 scans the identification code of the assembly part, reads the assembly flow information, and determines whether there is any missing process based on the current position. If there is any omission, an alarm is issued through the process detection module 6, and a reminder is given to rework. Specific embodiment three: A computer storage medium stores a computer program for executing an intelligent logistics monitoring method based on big data.

[0056] 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 present application. It should be pointed out that, for ordinary technicians in this field, 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.

Claims

1. An intelligent logistics monitoring system based on big data, comprising an intelligent logistics monitoring center (1), a warehousing coding system (2), an assembly process recording system (3), an intelligent logistics transmission system (4) and a video monitoring module (5); The intelligent logistics monitoring center (1) includes a data operation module (7), a data storage module (8) and a display module (9); the warehousing coding system (2) is used to identify and code the material accessories entering the intelligent factory, and to identify and code the assembled products, so that each material accessory and each assembled product has a unique identification code; wherein the assembled products are transported and assembled on the intelligent logistics conveying system (4); the intelligent logistics conveying system (4) includes a conveying line (10), a plurality of assembly stations (11) and a material accessory conveying line (12); in, A plurality of the assembly stations (11) are sequentially arranged beside the conveying line (10) so as to assemble the assembly products flowing through the conveying line (10) to complete each assembly process, and the material and accessory conveying line (12) is used to convey the material and accessory to the assembly stations (11); The assembly process recording system (3) is arranged at each of the assembly stations (11). When the material accessories are transported by the intelligent logistics conveying system (4), when the assembled product passes through the assembly station, the assembly process recording system (3) records the time of the assembled product in the current assembly process, and stores the time data records of all assembly processes in the data storage module (8); the data operation module (7) is used to analyze and operate the data record set stored in the data storage module (8), generate logistics transportation scheduling instructions and operation diagrams, and send them to the display module (9) for display, and control the configuration of the intelligent logistics conveying system (4).

2. According to the big data-based intelligent logistics monitoring system of claim 1, It is characterized in that The assembly process recording system (3) records the time of the assembly product in the current assembly process, and obtains the completion time T of a certain assembly product in the ith assembly process. i and the average completion time T of the i-th assembly process in the assembly station i _averg, i takes 1, 2, 3, ... N, with a total of N assembly processes; and uses a sensor device to read the identification code of the assembled product, the identification code of the material accessories, and the image of the material accessories, so as to obtain a data record set of the time when the assembled product is completed in each assembly process and the material object composition: U_assembly item identification code = {assembly item identification code, first assembly process completion time T 1 , the identification code of the installed material and accessories, the completion time of the second assembly process T 2 , the identification code of the installed material accessories, ... The second assembly process completion time T N , installed material accessory identification code, material accessory image}; The data record set is stored in the data storage module (8); the data operation module (7) is used to analyze and operate the data record set stored in the data storage module (8), generate logistics transportation scheduling instructions and operation diagrams, and send them to the display module (9) for display.

3. According to the big data-based intelligent logistics monitoring system of claim 2, It is characterized in that The assembly process recording system (3) also records the assembly time T of each material accessory. i , and sent to the data storage module (8), the data calculation module (7) reads the assembly time T stored in the material accessories i , where i is the completion time of the i-th assembly process, and the calculation is performed to judge: if the assembly time of two adjacent assembly processes satisfies: T i +T i+1 <T i+2 , then the two assembly processes of the i-th and i+1-th are merged into one assembly station, thereby reducing one assembly station.

4. According to the big data-based intelligent logistics monitoring system of claim 3, It is characterized in that The data operation module (7) further determines the interchangeability of the assembly process; if any two assembly processes can be swapped, the assembly time of the process after the process swap is calculated again if the assembly time of the two adjacent assembly processes satisfies: T i +T i+1 <T i+2 , then the two assembly processes of the i-th and i+1-th are merged into one assembly station, and the swapping and merging of assembly processes are repeated until the number of assembly stations is the minimum.

5. According to the big data-based intelligent logistics monitoring system of claim 1, It is characterized in that The data operation module (7) reads the number of completed steps C of each assembly process stored in the data storage module (8) i , if C i / C i-1 is less than the preset value, it indicates that the C i If there are too few assembly stations for a process, resulting in a pile-up of assembly items, an additional assembly station is added to complete the i-th process.

6. According to the big data-based intelligent logistics monitoring system of claim 1, It is characterized in that The data operation module (7) reads the completion time T of each process stored in the data storage module (8) i , if T i / T i-1 When it is greater than 2, it indicates that the assembly station of the i-th assembly process consumes too much time, which will cause the assembly parts to pile up, so the assembly station to complete the i-th assembly process is increased.

7. According to the big data-based intelligent logistics monitoring system of claim 6, It is characterized in that The data operation module (7) reads the completion time T of each assembly process stored in the data storage module (8) i , and statistically analyze the number of assembly processes completed in each cycle, and draw a curve chart of the cycle-assembly process completion number of each assembly process completed in each cycle, and send it to the display module (9) for display, and based on the quantity curve and T i _averg is used to determine whether there is an abnormality in the number of completed assembly processes. When the number of completed assembly processes is abnormal, it is determined that the assembly process needs maintenance, and the maintenance personnel are notified to go to the assembly station for investigation and inspection.

8. According to the big data-based intelligent logistics monitoring system of claim 7, It is characterized in that The assembly station is also provided with a process detection module (6), which scans the identification code of the assembly part, reads the assembly flow information, and determines whether there is any omission of the process based on the current position. If there is any omission, the process detection module (6) issues an alarm and reminds the user to rework. The data storage module (8) also stores the quantity of each material accessory, and when the quantity of the material is reduced to a preset quantity, an alarm is issued through the display module (9) so that the material accessories can be put into storage in time.

9. A method for intelligent logistics monitoring based on big data, comprising the intelligent logistics monitoring system based on big data as claimed in claim 8, It is characterized in that The specific intelligent logistics monitoring methods based on big data are as follows; Step S1, the warehousing coding system (2) is used to identify and code the material accessories entering the smart factory, and also to identify and code the assembled products, so that each material accessory and each assembled product has a unique identification code; Step S2, the assembled product is conveyed on the intelligent logistics conveying system (4) to the i-th assembly station for assembly, so as to complete each assembly process, and the material and parts conveying line (12) conveys the material and parts to the assembly station (11); Step S3, when the material parts are transported by the intelligent logistics conveying system (4), when the assembled product passes through the assembly station, the assembly process recording system (3) records the time of the assembled product in the current assembly process, and then obtains the completion time T of a certain assembled product in the i-th assembly process. i and the average completion time T of the i-th assembly process in the assembly station i _averg, i takes 1, 2, 3, ... N, with a total of N assembly processes; and uses a sensor device to read the identification code of the assembled product, the identification code of the material accessories, and the image of the material accessories, so as to obtain a data record set of the time when the assembled product is completed in each assembly process and the material object composition: U_assembly item identification code = {assembly item identification code, first assembly process completion time T 1 , the identification code of the installed material and accessories, the completion time of the second assembly process T 2 , the identification code of the installed material accessories, ... The second assembly process completion time T N , installed material accessory identification code, material accessory image}; and storing the data record set in the data storage module (8); Step S4, the data operation module (7) is used to analyze and operate the data record set stored in the data storage module (8), generate logistics transportation scheduling instructions and operation diagrams, and send them to the display module (9) for display; Step S5, the assembly process recording system (3) also records the assembly time T of each material accessory i , and sent to the data storage module (8), the data calculation module (7) reads the assembly time T stored in the material accessories i , where i is the completion time of the i-th assembly process, and the calculation is performed to judge: if the assembly time of two adjacent assembly processes satisfies: T i +T i+1 <T i+2 , then the two assembly processes of the i-th and i+1-th are merged into one assembly station, thereby reducing one assembly station; Step S5, the data calculation module (7) further determines the interchangeability of the assembly process; if any two assembly processes can be swapped, the assembly time of the process after the process swap is calculated again if the assembly time of the two adjacent assembly processes satisfies: T i +T i+1 <T i+2 , then merge the two assembly processes of the i-th and i+1-th into one assembly station, and repeat the swapping and merging of assembly processes until the number of assembly stations is the minimum; Step S6, the data calculation module (7) reads the completion time T of each process stored in the data storage module (8) i , if T i / T i-1 When it is greater than 2, it indicates that the assembly station of the i-th assembly process consumes too much time, which will lead to the accumulation of assembly parts, so the assembly station that completes the i-th assembly process is added; Step S7, the data calculation module (7) reads the completion time T of each assembly process stored in the data storage module (8) i , and statistically analyze the number of assembly processes completed in each cycle, and draw a curve chart of the cycle-assembly process completion number of each assembly process completed in each cycle, and send it to the display module (9) for display, and based on the quantity curve and T i _averg is used to determine whether there is an abnormality in the number of completed assembly processes. If there is an abnormality in the number of completed assembly processes, it is determined that the assembly process needs maintenance, and the maintenance personnel are notified to go to the assembly station for investigation and inspection; Step S8, a process detection module (6) is also provided on the assembly station, and the process detection module (6) scans the identification code of the assembly part, reads the assembly flow information, and determines whether there is any omission of the process based on the current position. If there is any omission, the process detection module (6) will alarm and remind to rework.

10. A computer storage medium storing a computer program, wherein the computer program is used to execute the intelligent logistics monitoring method based on big data according to claim 9.

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