Industrial Internet of Things intelligent manufacturing information sensing cloud management method and system

Abstract

The invention discloses an industrial Internet of Things intelligent manufacturing information perception cloud management method and system, and relates to the field of industrial intelligent manufacturing, and the method comprises the steps: a sensor sends and counts related data of a mechanical mechanism to an analysis module, and the analysis module collects the transmission form and path information of a transmission module; the analysis module is used for analyzing the working duration of the transmission module; the analysis module calculates the transmission waiting time of the transmission module in production according to the transmission form of the transmission module, and the analysis module also analyzes the transmission waiting time of the transmission module according to the process path to count the rest frequency, the rest duration and the rest period of single production of the transmission module; and the analysis module calls the service life model corresponding to the transmission module, inputs sensor data corresponding to the transmission module, and analyzes the service life state of the transmission module. According to the invention, equipment faults and service life conditions are effectively monitored, and material loss and production line stop caused by faults are reduced.

Description

Technical Field

[0001] The present application relates to the field of industrial intelligent manufacturing, and specifically to an industrial Internet of Things intelligent manufacturing information perception cloud management method and system. Background Art

[0002] The Industrial Internet of Things is the continuous integration of various types of collection and control sensors or controllers with perception and monitoring capabilities, as well as mobile communications, intelligent analysis and other technologies into all aspects of the industrial production process, thereby greatly improving manufacturing efficiency, improving product quality, reducing product costs and resource consumption, and ultimately achieving the upgrading of traditional industries to a new stage of intelligence.

[0003] There are many types of sensors with sensing and collection functions, and their number is huge and their types are complicated. If the data collaboration between sensors cannot be well solved, it will seriously affect the manufacturing of the entire production line. For example, in a conveyor crawler industrial manufacturing production line, the reciprocating conveyor crawler brings differences due to transmission. During long-term operation, it even involves multi-step crawler reciprocating transmission. Since the motor sensor data is interfered with, the control end and the stepper motor cannot accurately fine-tune the differences brought about by this transmission. The differences may be caused by the unstable transportation caused by the crawler motor or the position disturbance of the conveyed material, which leads to the docking time consumption. These situations that affect the process operation time will bring differences. This difference affects the industrial manufacturing efficiency, and may affect the operation of the robot arm and the accuracy of the docking machinery, which will further increase the defective rate.

[0004] At the same time, these sensor data also reflect the durability of operating mechanisms such as robotic arms. Due to the rotation of product production, manual statistics and year estimates for mechanical mechanisms are inaccurate, and devices with usage characteristics such as mobile phones cannot be fully applied to mechanical mechanisms due to different product properties. In other words, mechanical use is specific, and there may be failures that make it unusable without being repaired. Therefore, for the mechanical usage data, it is unreliable to use simple production line opening and closing to time it.

[0005] Therefore, there is an urgent need for industrial Internet of Things intelligent manufacturing information perception cloud management methods and systems. Summary of the invention

[0006] The present application provides an industrial Internet of Things intelligent manufacturing information perception cloud management method and system to solve the problems of the prior art.

[0007] In the first aspect, the present application provides an industrial Internet of Things intelligent manufacturing information perception cloud management system, including a transmission module, a mechanical mechanism, a sensor and an analysis module; the transmission module is a structure on a mechanical structure for realizing the action of a mechanical structure;

[0008] in,

[0009] The sensor is used to collect and send relevant data of the mechanical mechanism to the analysis module;

[0010] The analysis module is used to collect the transmission form and path information of the corresponding transmission module for analyzing the working time of the transmission module;

[0011] The analysis module is also used to calculate the transmission waiting time of the transmission module in production based on the transmission form of the transmission module, and to analyze the transmission waiting time of the transmission module based on the process path to count the rest frequency, rest duration and rest cycle of a single production of the transmission module; the process path refers to the transmission path range required for the transmission module to complete the manufacturing action;

[0012] as well as,

[0013] The analysis module pre-stores a service life model of the corresponding transmission module. When the analysis module obtains relevant data of the mechanical mechanism, the relevant data of the mechanical mechanism, the working time of the transmission module, the transmission waiting time, the rest frequency, the rest time and the rest cycle are input into the service life model, and the life status of the transmission module is analyzed based on the service life model.

[0014] Based on the above technical solution, the analysis module pre-stores a service life model of the corresponding transmission module. When the analysis module obtains the relevant data of the mechanical mechanism, the relevant data of the mechanical mechanism, the working time, rest frequency, rest time and rest cycle of the transmission module are input into the service life model, and the life status of the transmission module is analyzed based on the service life model, specifically:

[0015] The life model generates a corresponding module according to the basic parameters of the transmission module, disassembles the mechanical structure involved in the module and obtains the common life span of the corresponding transmission module, and counts the common life span on each mechanical structure to simulate the use and occupancy of each mechanical structure when the corresponding transmission module is put into production for transmission operation; the common life span is the upper limit of the indicator of the service life of the corresponding transmission module;

[0016] A common lifespan consumed by a mechanical structure in use and a preset coefficient consumed by a mechanical structure in an occupied state, wherein the preset coefficient is a preset coefficient greater than 0 and less than 1 set by manually judging the load and operation proportion of the mechanical structure in different occupancy conditions.

[0017] Based on the above technical solution, when the working time of the transmission module is input into the life model, the ratio of the usage and occupancy of each mechanical structure in the working time is analyzed, and after conversion, the common life corresponding to each mechanical structure is consumed.

[0018] Based on the above technical solution, when the waiting time data is input into the service life model, the common life consumed in the same production phase is offset for all mechanical structures of the service life model according to the rest duration and rest cycle.

[0019] Based on the above technical solution, when the transmission waiting time data is input into the service life model, the difference between the common life consumption and the common life offset corresponding to the same production stage is multiplied by the failure coefficient according to the rest frequency of a single production. The failure coefficient is converted by the rest frequency, and the conversion method adopts an empirical formula. The empirical formula is used to control the relationship between the rest frequency of a single production and the corresponding failure coefficient of the transmission module. The empirical formula is obtained by artificial calculation based on the long-term operating equipment failure rate provided by the transmission module manufacturer.

[0020] Based on the above technical solution, when the sensor adopts a monitoring camera, the number and position of the transmission module are marked by image recognition, and the working time of the transmission module in the video image is obtained according to the image acquisition period.

[0021] Based on the above technical solution, the sensor outputs video image data to the management system. The management system parses the transmission module in the video image into multiple parts and marks the transmission waiting time and working time corresponding to different image parts. The management system loads the service life model, identifies and allocates the mechanical structures corresponding to different parts, and also includes calculating the values ​​of working time and transmission waiting time on different mechanical structures after parsing the video image.

[0022] Based on the above technical solution, when the sensor is a laser sensor, the start and stop time periods are analyzed by sending and receiving lasers, and the working time and transmission waiting time of the corresponding transmission module are output.

[0023] In the second aspect, the present application provides an industrial Internet of Things intelligent manufacturing information perception cloud management method, which is based on the above-mentioned industrial Internet of Things intelligent manufacturing information perception cloud management system, and the specific steps are as follows:

[0024] Acquire a video image, and identify a transmission module based on the video image;

[0025] Analyze the transmission module. When the transmission module consists of multiple parts, analyze the transmission waiting time and working time of each part of the transmission module. The transmission waiting time is the time period when the transmission module pauses at a position on the production line and waits for the next material input;

[0026] Analyze the mechanical structure of the transmission module corresponding to different parts, and assign the transmission waiting time and working time of the mechanical structure;

[0027] Load the data into the life model and output the life status of the transmission module.

[0028] In this method, the lifespan model specifically includes:

[0029] The life model generates a corresponding module according to the basic parameters of the transmission module, disassembles the mechanical structure involved in the module and obtains the common life span of the corresponding transmission module, and counts the common life span on each mechanical structure to simulate the use and occupancy of each mechanical structure when the corresponding transmission module is put into production for transmission operation; the common life span is the upper limit of the indicator of the service life of the corresponding transmission module;

[0030] A preset coefficient for consuming a common lifespan of a mechanical structure in use and a common lifespan of a mechanical structure in occupation, wherein the preset coefficient is a preset coefficient greater than 0 and less than 1 set by manually judging the load and operation ratio of the mechanical structure in different occupation conditions;

[0031] When the working hours are input, the usage and occupancy ratio of each mechanical structure in the working hours are analyzed, and after conversion, the corresponding common life of each mechanical structure is consumed;

[0032] When inputting the transmission waiting time data, the common life consumed in the same production phase is offset for all mechanical structures of the life model according to the rest time and rest cycle;

[0033] When inputting the transmission waiting time data, based on the rest frequency of a single production, the difference between the consumption and the offset corresponding to the same production stage is multiplied by the failure coefficient. The failure coefficient is converted through the rest frequency. The conversion method adopts an empirical formula. The empirical formula is used to control the relationship between the rest frequency of a single production and the corresponding failure coefficient of the transmission module. The empirical formula is obtained by manual calculation based on the long-term operating equipment failure rate provided by the transmission module manufacturer.

[0034] The industrial Internet of Things intelligent manufacturing information perception cloud management method and system provided by this application uses the life model in conjunction with sensor settings to achieve life monitoring of non-universal and obsolete equipment, data collection, and accurate calibration of the life status of the transmission module. The technical solution of this application effectively monitors equipment failures and life conditions, saving material losses and production line shutdowns caused by failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings described herein are used to provide a further understanding of the embodiments of the present application, constitute a part of the present application, and do not constitute a limitation on the embodiments of the present application. In the drawings:

[0036] Figure 1 A flow chart of an industrial Internet of Things intelligent manufacturing information perception cloud management method provided for an exemplary embodiment of the present application.

[0037] Figure 2 A disassembly diagram of Example 2 of an industrial Internet of Things intelligent manufacturing information-aware cloud management method provided as an exemplary embodiment of the present application.

[0038] In the figure:

[0039] 101. Transmission module A; 102. Transmission module B; 103. Transmission module C; 104. Transmission module D; 105. Transmission module E. DETAILED DESCRIPTION

[0040] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0041] In the prior art, for old equipment and equipment with inaccurate electronicization, it is impossible to accurately correspond the transmission mode of different transmission modules and the usage status when put into production, and it is also impossible to accurately know the wear status of different mechanical structures in the transmission modules under different conditions.

[0042] The specific application scenario of this application is the transmission module life monitoring data processing.

[0043] The technical concept of the present application is that, since different transmission modules have transmission waiting time and working periods in production, the general statistical usage time is inaccurate, which may affect the early retirement of equipment or the delay of production due to the failure to update the mechanical structure of the equipment in time. The present application conceives to collect data through sensors and subdivide the life status of different internal mechanical structures of the transmission module according to the life model to achieve overall life supervision.

[0044] The industrial Internet of Things intelligent manufacturing information perception cloud management method and system provided in this application are intended to solve the above technical problems of the prior art.

[0045] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0046] The first embodiment of the present invention provides an industrial Internet of Things intelligent manufacturing information perception cloud management system, including a transmission module, a mechanical mechanism, a sensor and an analysis module; the transmission module is a structure on a mechanical structure for realizing mechanical structure action; wherein the sensor is used to collect and send relevant data of the mechanical mechanism to the analysis module; the analysis module is used to collect the transmission form and path information of the corresponding transmission module for analyzing the working time of the transmission module; the analysis module is also used to calculate the transmission waiting time of the transmission module in production based on the transmission form of the transmission module, and at the same time, based on the process path analysis of the transmission waiting time of the transmission module, the rest frequency, rest duration and rest cycle of a single production of the transmission module are counted; the process path refers to the transmission path range required for the transmission module to complete the manufacturing action; and the analysis module pre-stores a service life model of the corresponding transmission module. When the analysis module obtains the relevant data of the mechanical mechanism, the relevant data of the mechanical mechanism, the working time, transmission waiting time, rest frequency, rest duration and rest cycle of the transmission module are input into the service life model, and the life status of the transmission module is analyzed based on the service life model.

[0047] The transmission module in this embodiment includes a transmission mechanism such as a transmission track, a robotic arm, etc., whose specific structure can be observed by various cameras. The transmission mechanism is used to transport materials and process partial or whole active parts of materials.

[0048] Most equipment is specified to have a maximum service life, that is, the entire equipment itself has a common lifespan, but in actual applications, due to differences in the equipment application environment, working objects, etc., its service life is different, and it is impossible to use the entire equipment to its maximum service life. For example, the vulnerable structures in the equipment, such as the transmission structure, are particularly prone to wear and tear, which is also an important cause of equipment aging. Therefore, regular inspections and replacements are required. In the prior art, general equipment lines are repaired and replaced by shutdown inspections, regular inspections, or fault repairs. However, these methods do not have a unified quantitative standard or time, and are all set manually. This results in the inability to accurately control the life detection of the equipment. As a result, some equipment is not repaired in time, which affects the use of the equipment itself and even the operation and collaboration of the entire assembly line. Therefore, this embodiment is based on the defects of the prior art. By using the life model in conjunction with the sensor setting, and by analyzing the life data of the transmission module in the equipment that is most prone to wear, the short board principle is used to realize the life monitoring and data collection of non-universal and obsolete equipment, so as to timely and accurately grasp and understand the service life of the equipment, and respond quickly to actions, so as to solve equipment problems as soon as possible outside the working stage, accurately calibrate the life status of the equipment and its internal transmission module, ensure the normal operation of the equipment, avoid losses caused by failures, and maximize the avoidance of production line shutdowns caused by downtime.

[0049] In a specific application, the analysis module pre-stores a service life model of the corresponding transmission module. When the analysis module obtains the relevant data of the mechanical mechanism, the relevant data of the mechanical mechanism, the working time, rest frequency, rest time and rest cycle of the transmission module are input into the service life model, and the life state of the transmission module is analyzed based on the service life model, specifically:

[0050] The life model generates a corresponding module according to the basic parameters of the transmission module, disassembles the mechanical structure involved in the module and obtains the common life span of the corresponding transmission module, and counts the common life span on each mechanical structure to simulate the use and occupancy of each mechanical structure when the corresponding transmission module is put into production for transmission operation; the common life span is the upper limit of the indicator of the service life of the corresponding transmission module;

[0051] A common lifespan consumed by a mechanical structure in use and a preset coefficient consumed by a mechanical structure in an occupied state, wherein the preset coefficient is a preset coefficient greater than 0 and less than 1 set by manually judging the load and operation proportion of the mechanical structure in different occupancy conditions.

[0052] In a specific application, when the working time of the transmission module is input into the life model, the ratio of the use and occupancy of each mechanical structure in the working time is analyzed, and after conversion, the common life corresponding to each mechanical structure is consumed.

[0053] In a specific application, when the waiting time data is input into the service life model, the common life consumed in the same production phase is offset for all mechanical structures of the service life model according to the rest time and rest cycle.

[0054] In a specific application, when the transmission waiting time data is input into the service life model, the difference between the common life consumption and the common life offset corresponding to the same production stage is multiplied by the failure coefficient based on the rest frequency of a single production. The failure coefficient is converted through the rest frequency, and the conversion method adopts an empirical formula. The empirical formula is used to control the relationship between the rest frequency of a single production and the corresponding failure coefficient of the transmission module. The empirical formula is obtained by artificial calculation based on the long-term operating equipment failure rate provided by the transmission module manufacturer.

[0055] In a specific application, when the sensor is a surveillance camera, the number and position of the transmission module are marked by image recognition, and the working time of the transmission module in the video image is obtained according to the image acquisition period.

[0056] In a specific application, the sensor outputs video image data to a management system, which parses the transmission module in the video image into multiple parts and marks the transmission waiting time and working time corresponding to different image parts. The management system loads the service life model, identifies and allocates mechanical structures corresponding to different parts, and also calculates the values ​​of working time and transmission waiting time on different mechanical structures after parsing the video image.

[0057] In a specific application, when the sensor is a laser sensor, the start and stop periods are analyzed by sending and receiving lasers, and the working time and transmission waiting time of the corresponding transmission module are output.

[0058] The above is the entire content of the cloud management system based on industrial Internet of Things intelligent manufacturing information perception. For better understanding and implementation, the present invention also provides a second embodiment, such as Figure 1 As shown, the second embodiment provides an industrial Internet of Things intelligent manufacturing information perception cloud management method, which can be implemented based on the system described in the first embodiment above, and specifically includes the following steps:

[0059] S1. Acquire a video image and identify a transmission module based on the video image.

[0060] In this step, the video image may be acquired by an image sensor disposed inside or outside the corresponding transmission module. The image sensor may be a high-definition camera, a video recorder, an infrared camera, or the like.

[0061] Based on the image sensor, the target area image can be collected, and after pattern recognition, the transmission module information on one or more production lines can be captured and obtained.

[0062] It should be noted that the transmission module in this embodiment includes a transmission mechanism whose specific structure can be observed by various cameras such as a transmission track and a robotic arm. The transmission mechanism is a partial or complete active part used to transport materials and process materials.

[0063] For example, the transmission module can be a rotating robotic arm that transfers materials from A to B. When it moves in the rotating area, the image sensor such as a high-definition camera can cover its rotating area, so as to obtain its movement form during the entire rotation process to obtain the corresponding transmission data information.

[0064] S2. Analyze the transmission module. When the transmission module consists of multiple parts, analyze the transmission waiting time and working time of each part of the transmission module. The transmission waiting time is the time period when the transmission module pauses at a position of the production line and waits for the next material input;

[0065] For the analysis of the transmission module, conventional technical means in the field can be used. For example, by eliminating the jaggies occurring at the inclined edge of the moving object, a high-quality image is obtained, and the image pixels are locally identified according to the structural movement, and the displacement and displacement time of the local part in the video are tracked, and the time is accumulated and recorded in paragraph-style statistical data, wherein the frame selection can be performed by edge endpoint pixels, or a better analysis method can be selected for different types of transmission mechanisms by brightness, color difference, etc. At the same time, a structural verification module can be added, including: a register and a pixel comparison unit, and the pixel comparison unit in the structural inspection analyzes the pixel changes in the selected frame in the two previous and subsequent images and compares them with the content in the selected frame of the original register. The selected frame content data in the register is the expected synthesis of all pixel data of the fine structure in the pixel mode of the same frame selection structure after multiple successful verifications. The successful verification means that the contrast difference is within the allowable system error threshold range. The register is continuously updated, which is equivalent to a training process, so that the transmission mechanism at different stages and angles can be better identified during the movement of the picture.

[0066] S3. Analyze the mechanical structures of the transmission modules corresponding to different parts, and assign the transmission waiting time and working time of the mechanical structures, and repeat step 2 to assign values ​​to different mechanical structures. In this embodiment, the assignment process is to better load different mechanical structures into the service life model so that the model can better fit the actual consumed life time of the mechanical structure.

[0067] Based on this, the service life model is expanded. When the data is loaded into the life model, the corresponding module is generated according to the basic parameters of the corresponding transmission module. The mechanical structure involved in the module is disassembled, and the common life duration is included in each mechanical structure to simulate the use and occupancy of each mechanical structure when the corresponding transmission module is put into production for transmission operation; the common life is the upper limit of the indicator of the service life of the corresponding transmission module; the common life is consumed by the mechanical structure in use, and the common life is consumed by the mechanical structure in occupation. The preset coefficient is a preset coefficient greater than 0 and less than 1 set by manually judging the load and operation proportion of the mechanical structure in different occupancy conditions; when the working time is input, the working time is analyzed The usage and occupancy ratios of various mechanical structures are converted to consume the corresponding common life of each mechanical structure; when the transmission waiting time data is input, the common life consumed in the same production stage is offset for all mechanical structures of the life model based on the rest duration and rest cycle; when the transmission waiting time data is input, the difference between the consumption and offset corresponding to the same production stage is multiplied by the failure coefficient based on the rest frequency of a single production. The failure coefficient is converted through the rest frequency, and the conversion method adopts an empirical formula. The empirical formula is used to control the relationship between the single production rest frequency and the corresponding failure coefficient of the transmission module. The empirical formula is obtained by manual calculation based on the long-term operating equipment failure rate provided by the transmission module manufacturer.

[0068] For better understanding, Figure 2As shown in the figure, the physical mechanical structure is disassembled into transmission module A101, transmission module B102, transmission module C103, transmission module D104, and transmission module E105. The lowest long bar in the figure is the total common life. The corresponding transmission modules in this simulated transmission process are partially consumed by the dotted lines, which is only a part of the common life. However, the uneven life of all transmission modules will affect the overall common life of each mechanical structure. Therefore, the ratio of the use and occupancy of each mechanical structure during the working time is analyzed. Here, according to the short board principle, the most frequently used machine is used. Mechanical structure, for example, transmission module B102 is used for lifting and lowering in this mechanical transmission; while transmission module C103 and transmission module A101 are used for rotation, driving the entire mechanical movement. Therefore, the most frequent mechanical structure consumes its own life and has the greatest impact on the overall common life. Therefore, the coefficient before the mean is set higher, and based on the total proportion of the life parts of each transmission module itself, such as the total life of transmission module C103 is 50000h, and a single transmission is only used for 10min after disassembly, the corresponding preset coefficient set for the transmission module C103 is lower. Transmission module B102 itself has poor flexibility and is a rigid structure, which is prone to loss. Its total life is only 200h, and the single loss is 2min, but the single loss accounts for a high proportion of its total life. Therefore, for the same set of mechanical transmissions, the transmission module C103 has a lower self-loss life, and the transmission module B102 has a higher self-loss life. Therefore, the preset coefficient of transmission module B102 in the preset coefficient that determines the overall common life accounts for a higher proportion. This process is verified through repeated mechanical tests, or reference materials are directly searched.

[0069] On this basis, the above assigned mechanism can also generate a display screen, corresponding to the operating status and life status of each mechanical structure of the transmission module, using different color markings to display, and directly display it to the user through the cloud, providing a convenient form of human-computer interaction, and making it easy to understand the status of different transmission modules, effectively monitor equipment failures and life span, and save material loss and production line shutdowns caused by failures.

[0070] On this basis, in actual industrial production, the camera collects video images, and through image recognition, obtains the machine's usage, transmission form, working path, etc., generates a three-dimensional view of the abstract transmission module of the machine, and then displays the corresponding operation process on the host, and records the working time and pause time, decomposes the relevant mechanical structure involved in each step of the operation of the transmission module, and the mechanical structure can be automatically synthesized according to the internal structure explosion diagram of the transmission module loaded by the system. Specifically, AI can be used to generate a dynamic display model for the machine, and then the movement of each sub-component of the corresponding mechanical structure is taken into account by simulating and recognizing images. Certain specific operating methods will cause the transmission module to be "newer" as a whole but too "worn" locally, that is, some parts are frequently operated but other parts of the overall machine have only a small amount of wear. In view of this situation, this embodiment 4 can realize the statistics of each position, while the existing technology The technology can only read the actual time and perform difference calculations on the whole to record the corresponding life loss, which is not intuitive enough to display the overall situation. Moreover, for the replaceable consumables involved, it is impossible to evaluate the independent loss status and remaining life of the replaceable consumables in the transmission module. The existing technology only uses direct overall replacement and rough methods to solve the maintenance of the product, which consumes a lot of equipment costs and increases operating costs. However, the present application can also calculate the failure coefficient according to the life model of the overall transmission module for different structural losses. The empirical formula in the life model here is provided by the manufacturer, or a general formula can be given by engineers based on experience, and then the formula is adjusted according to the data content of different transmission modules. After loading it into the life model, it is used to calculate the equipment failure rate, and it can replace equipment components in advance when the remaining life is insufficient, without affecting industrial intelligent production and manufacturing. S4. Load the data into the life model and output the life status of the transmission module.

[0071] After steps S1-S3, the whole machine is inspected in parts through existing monitoring data plus image recognition technology to better diagnose the life status of different parts. For different production line plans, different operating procedures of the transmission mechanism will have differentiated impacts on the transmission mechanism and cause different frequencies of wear on different components of the transmission mechanism. The overall usage status and efficiency are affected by the shortcomings of the transmission mechanism. In the prior art, it is often a rough estimate. First, the overall time estimate is inaccurate, which may easily lead to the early retirement of the equipment. Second, for machinery that has been in production for a long time, it is easy to cause excessive local wear and affect the status. The prior art cannot count the specific situation. However, the present application disassembles the mechanical structure in each transmission module and performs a service life analysis, so that the service life of each transmission module can be accurately obtained based on the short board principle.

[0072] Furthermore, on this basis, this embodiment also includes:

[0073] S5. Send the life status of the transmission module to the cloud management platform. After the cloud management platform obtains the corresponding data, it analyzes the data and executes corresponding actions.

[0074] After the cloud management platform obtains the life status of the corresponding transmission module through data interaction, it can perform different operations according to its specific situation, such as:

[0075] A service life consumption table of the corresponding transmission module can be set on the cloud management platform, and the service life consumption table at least includes the service life, remaining service life, and annual life consumption of the corresponding transmission module.

[0076] After the life status of the corresponding transmission module is obtained, the normal service life data of the corresponding transmission module is calculated based on the obtained time period, and the normal service life data at least includes the annual life loss and the remaining service life;

[0077] Extract the data in the life status of the corresponding transmission module, obtain the remaining service life of the corresponding transmission module, and based on the service life data of previous years, obtain the life loss amount of the current year, and make separate comparisons based on the obtained remaining service life of the transmission module and the life loss amount of the transmission module with the annual life loss amount and remaining service life in the normal service life data. Then, when the obtained remaining service life of the transmission module is less than the remaining service life in the normal service life data, and / or the life loss amount of the transmission module is greater than the annual life loss amount in the normal service life data, it means that there are problems in the current use of the transmission module, and its use damage is relatively large. At this time, a warning or alarm instruction can be sent to the corresponding processing end through the cloud management platform, and the corresponding staff of the processing end will perform corresponding maintenance or troubleshooting to troubleshoot manufacturing faults or problems.

[0078] Furthermore, corresponding thresholds can be set in the cloud management platform. When the life status and normal service life data of the above-mentioned transmission module show the above-mentioned situation, the difference can be compared with the corresponding threshold. When it exceeds or is less than the corresponding threshold, the severity level can be further marked based on the difference, such as slight wear and tear level, mild wear and tear level, moderate wear and tear level, severe wear and tear level, etc., so as to send different instructions to the processing end according to the severity level, such as immediate investigation, scheduled inspection, shutdown for maintenance, replacement of transmission mechanism, etc.

[0079] Specifically, take the case where the remaining service life of the transmission module is less than the remaining service life in the normal service life data as an example:

[0080] Assume that the remaining service life of the transmission module is T0, and the remaining service life in the normal service life data is T1, then the difference T between the two is:

[0081] T = T1 - T0;

[0082] Furthermore, we can set the threshold X, calculate the difference of TX, and position the different ranges of the difference as different levels, so that we can classify according to the loss situation. This can not only realize cloud-based manufacturing management, but also adopt different processing solutions according to the loss level, further optimize equipment management, and ensure normal intelligent manufacturing.

[0083] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0084] In addition, each functional module in each embodiment of the present application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0085] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

[0086] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods or systems. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0087] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0088] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

[0089] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims above.

[0090] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. Industrial Internet of Things intelligent manufacturing information perception cloud management system, characterized by: It includes a transmission module, a mechanical mechanism, a sensor and an analysis module; the transmission module is a structure on the mechanical structure used to realize the movement of the mechanical structure; in, The sensor is used to collect and send relevant data of the mechanical mechanism to the analysis module; The analysis module is used to collect the transmission form and path information of the corresponding transmission module for analyzing the working time of the transmission module; The analysis module is also used to calculate the transmission waiting time of the transmission module in production based on the transmission form of the transmission module, and to analyze the transmission waiting time of the transmission module based on the process path to count the rest frequency, rest duration and rest cycle of a single production of the transmission module; the process path refers to the transmission path range required for the transmission module to complete the manufacturing action; as well as, The analysis module pre-stores a service life model of the corresponding transmission module. When the analysis module obtains relevant data of the mechanical mechanism, the relevant data of the mechanical mechanism, the working time of the transmission module, the transmission waiting time, the rest frequency, the rest time and the rest cycle are input into the service life model, and the life status of the transmission module is analyzed based on the service life model.

2. The industrial Internet of Things intelligent manufacturing information perception cloud management system according to claim 1 is characterized in that: The analysis module pre-stores a service life model of the corresponding transmission module. After the analysis module obtains the relevant data of the mechanical mechanism, the relevant data of the mechanical mechanism, the working time, rest frequency, rest time and rest cycle of the transmission module are input into the service life model, and the life state of the transmission module is analyzed based on the service life model, specifically: The life model generates a corresponding module according to the basic parameters of the transmission module, disassembles the mechanical structure involved in the module and obtains the common life span of the corresponding transmission module, and counts the common life span on each mechanical structure to simulate the use and occupancy of each mechanical structure when the corresponding transmission module is put into production for transmission operation; the common life span is the upper limit of the indicator of the service life of the corresponding transmission module; A common lifespan consumed by a mechanical structure in use and a preset coefficient consumed by a mechanical structure in an occupied state, wherein the preset coefficient is a preset coefficient greater than 0 and less than 1 set by manually judging the load and operation proportion of the mechanical structure in different occupancy conditions.

3. The industrial Internet of Things intelligent manufacturing information perception cloud management system according to claim 2 is characterized in that: When the working hours of the transmission module are input into the life model, the ratio of the usage and occupancy of each mechanical structure in the working hours is analyzed, and after conversion, the common life corresponding to each mechanical structure is consumed.

4. The industrial Internet of Things intelligent manufacturing information perception cloud management system according to claim 3 is characterized in that: When the transmission waiting time data is input into the service life model, the common life consumed in the same production phase is offset for all mechanical structures of the service life model according to the rest time and rest cycle.

5. The industrial Internet of Things intelligent manufacturing information perception cloud management system according to claim 4 is characterized in that: When the transmission waiting time data is input into the service life model, based on the rest frequency of a single production, the difference between the common life consumption and the common life offset corresponding to the same production stage is multiplied by the failure coefficient. The failure coefficient is converted through the rest frequency. The conversion method adopts an empirical formula. The empirical formula is used to control the relationship between the rest frequency of a single production and the corresponding failure coefficient of the transmission module. The empirical formula is obtained by artificial calculation based on the long-term operating equipment failure rate provided by the transmission module manufacturer.

6. The industrial Internet of Things intelligent manufacturing information perception cloud management system according to claim 5 is characterized in that: When the sensor is a monitoring camera, the number and position of the transmission module are marked by image recognition, and the working time of the transmission module in the video image is obtained according to the image acquisition period.

7. The industrial Internet of Things intelligent manufacturing information perception cloud management system according to claim 6 is characterized in that: The sensor outputs video image data to the management system, which analyzes the transmission module in the video image into multiple parts and marks the transmission waiting time and working time corresponding to different image parts. The management system loads the service life model, identifies and allocates the mechanical structures corresponding to different parts, and also calculates the values ​​of working time and transmission waiting time on different mechanical structures after parsing the video image.

8. The industrial Internet of Things intelligent manufacturing information perception cloud management system according to claim 5 is characterized in that: When the sensor is a laser sensor, the start and stop periods are analyzed by sending and receiving lasers, and the working time and transmission waiting time of the corresponding transmission module are output.

9. The industrial Internet of Things intelligent manufacturing information perception cloud management method is characterized by: The cloud management system based on industrial Internet of Things intelligent manufacturing information perception according to any one of claims 6-7 is implemented, which includes the following steps: Acquire a video image, and identify a transmission module based on the video image; Analyze the transmission module. When the transmission module consists of multiple parts, analyze the transmission waiting time and working time of each part of the transmission module. The transmission waiting time is the time period when the transmission module pauses at a position on the production line and waits for the next material input; Analyze the mechanical structure of the transmission module corresponding to different parts, and assign the transmission waiting time and working time of the mechanical structure; Load the data into the life model and output the life status of the transmission module.

10. The industrial Internet of Things intelligent manufacturing information perception cloud management method according to claim 9 is characterized in that: The lifespan model specifically includes: The life model generates a corresponding module according to the basic parameters of the transmission module, disassembles the mechanical structure involved in the module and obtains the common life span of the corresponding transmission module, and counts the common life span on each mechanical structure to simulate the use and occupancy of each mechanical structure when the corresponding transmission module is put into production for transmission operation; the common life span is the upper limit of the indicator of the service life of the corresponding transmission module; A preset coefficient for consuming a common lifespan of a mechanical structure in use and a common lifespan of a mechanical structure in occupation, wherein the preset coefficient is a preset coefficient greater than 0 and less than 1 set by manually judging the load and operation ratio of the mechanical structure in different occupation conditions; When inputting the working hours, the ratio of the usage and occupancy of each mechanical structure in the working hours is analyzed, and after conversion, the corresponding common life of each mechanical structure is consumed; When inputting the transmission waiting time data, the common life consumed in the same production phase is offset for all mechanical structures of the life model according to the rest time and rest cycle; When inputting the transmission waiting time data, based on the rest frequency of a single production, the difference between the consumption and the offset corresponding to the same production stage is multiplied by the failure coefficient. The failure coefficient is converted through the rest frequency. The conversion method adopts an empirical formula. The empirical formula is used to control the relationship between the rest frequency of a single production and the corresponding failure coefficient of the transmission module. The empirical formula is obtained by manual calculation based on the long-term operating equipment failure rate provided by the transmission module manufacturer.

Images