Polishing digital intelligent management system, method, device and computer storage medium

Abstract

The present invention discloses a digital intelligent management system, method, device and computer storage medium for polishing, comprising an identification module for identifying and associating product information of a product to be processed and machine information of a processing machine; a data acquisition module for acquiring processing parameters of the processing machine; a data statistics module for statistics of production parameters of the processing machine; an abnormality warning module for performing a comprehensive analysis on the processing parameters and / or production parameters, and generating abnormal information carrying product information and / or machine information when an abnormality occurs; a data calculation module for performing a comprehensive calculation on the processing parameters and / or production parameters according to preset rules to obtain processing / production information; and a visual dashboard for displaying the product information, machine information, processing parameters, production parameters, abnormal information and processing / production information in real time, so as to quickly and accurately grasp the processing / production situation and locate abnormal points.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management of polishing devices, and in particular to a digital intelligent management system, method, device and computer storage medium for polishing. Background Art

[0002] In the 3C industry, the grinding process is a non-standard processing technology, and there are many problems in its production management process: First, the processing parameters and production parameters are manually counted, which is not only inaccurate, but also requires a lot of manpower and material resources to do the statistical work; second, the production parameters and processing parameters are mostly debugged and configured by technicians based on experience, and the parameter specification and optimization are not accurate; third, the failure rate of robots in the grinding processing workshop is high, and the tracing of failures and abnormalities requires a large number of technicians and a lot of time (at least 51 hours), which is inefficient, and during the tracing process, the faulty and abnormal processing machines will continue to produce a large number of defective products or even scrapped products, increasing the production cost of the enterprise. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a digital intelligent management system, method, device and computer storage medium for polishing to solve the problem of increased defective and scrap rates in the prior art due to irregular management and long tracing time for failures and abnormalities in the polishing process.

[0004] To achieve the above object, the first aspect of the present invention provides a polishing digital intelligent management system, comprising:

[0005] An identification module, used to identify product information of a product to be processed and associate the product information with machine information of a processing machine;

[0006] A data acquisition module, used to acquire processing parameters of a corresponding processing machine, wherein the processing parameters carry product information of the currently processed product and machine information of the corresponding processing machine;

[0007] A data statistics module, used to count the production parameters of the processing machine, wherein the production parameters carry the machine information of the corresponding processing machine;

[0008] An abnormality warning module is used to conduct a comprehensive analysis of the processing parameters and / or production parameters, and generate abnormal information carrying product information and / or machine information when the processing parameters and / or production parameters are abnormal;

[0009] A data calculation module, used to perform comprehensive calculation on the processing parameters and / or production parameters according to preset rules to obtain processing / production information of the corresponding processing machine and / or processing workshop; and

[0010] The visual dashboard is used to display the product information, machine information, processing parameters, production parameters, abnormal information and processing / production information in real time.

[0011] Furthermore, the processing parameters at least include grinding pressure, air source pressure, vacuum negative pressure, water flow pressure and water flow;

[0012] The data acquisition module comprises:

[0013] A pressure sensor is installed at the grinding head position of the processing machine, and is used to detect in real time the grinding pressure exerted by the grinding head on the product to be processed when the grinding head is in motion;

[0014] An air pressure sensor is installed in the air source channel of the processing machine and is used to detect the air pressure of the air source when the manipulator of the processing machine moves in real time;

[0015] A negative pressure sensor is installed in the negative pressure channel of the processing machine, and is used to detect in real time the vacuum negative pressure when the robot arm of the processing machine absorbs the product to be processed; and

[0016] The water flow sensor is installed in the water flow channel of the processing machine and is used for real-time detection of the water flow pressure and water flow acting on the grinding position.

[0017] Furthermore, the production parameters include at least the consumption of consumables, the number of consumables processing times, the actual output of the cycle, and the number of good products;

[0018] The data statistics module includes:

[0019] The consumables statistics submodule is used to count the number of consumables consumed by the processing machine during the planned production cycle to obtain the consumables consumption and to count the number of processing times of each consumable to obtain the number of consumables processing times of the current consumable;

[0020] The production volume statistics submodule is used to count the total amount of products processed by the processing machine within the planned production cycle to obtain the actual production volume of the cycle; and

[0021] The good product statistics submodule is used to count the number of good products processed by the processing machine within the planned production cycle based on the recognition results of the processed products by the back-end recognition device.

[0022] Furthermore, the abnormal warning module includes:

[0023] an alarm submodule, for matching the processing parameter with a corresponding first preset threshold value, and generating an alarm instruction when the processing parameter fails to match the first preset threshold value;

[0024] an automatic warning submodule, configured to calculate the production parameter, match the calculation result with the corresponding second preset threshold, and generate a warning instruction when the calculation result fails to match the second preset threshold; and

[0025] The abnormal information generation submodule is used to generate corresponding abnormal information according to the alarm instruction and / or early warning instruction.

[0026] Furthermore, the processing / production information includes at least productivity, yield rate and comprehensive utilization rate;

[0027] The data calculation module includes:

[0028] A productivity calculation submodule, used to calculate the productivity of the processing machine and / or the processing workshop in the planned production cycle according to the actual cycle output and the planned cycle output of the processing machine and / or the processing workshop in the planned production cycle, wherein the actual cycle output of the processing workshop in the planned production cycle is calculated based on the actual cycle output of all processing machines in the processing workshop in the planned production cycle;

[0029] A yield rate calculation submodule, used to calculate the yield rate of the processing machine and / or processing workshop according to the number of good products of the processing machine and / or processing workshop in the planned production cycle and the actual output of the cycle; and

[0030] The comprehensive utilization rate calculation submodule is used to calculate the comprehensive utilization rate of the processing machine and / or processing workshop according to the planned working time, actual working time, productivity and yield rate of the processing machine and / or processing workshop during the planned production cycle.

[0031] Furthermore, it also includes a parameter optimization module, which is used to assign weights to the productivity, yield rate and comprehensive utilization rate respectively, and calculate the production efficiency of each processing machine based on the weights, obtain the processing parameters of the processing machine with the best production efficiency in a preset interval, and configure the obtained processing parameters with the best production efficiency as the processing parameters of all processing machines in the preset interval in the next planned production cycle.

[0032] Furthermore, the parameter optimization module includes:

[0033] The weight assignment submodule is used to obtain the productivity, yield rate and comprehensive utilization rate of each processing machine in the previous planned production cycle, and assign weights to the productivity, yield rate and comprehensive utilization rate according to their importance to the production of the processing machine;

[0034] The production efficiency calculation submodule is used to calculate the production efficiency of each processing machine according to the production rate, yield rate and comprehensive utilization rate and their corresponding weights:

[0035] The parameter extraction submodule is used to sort the production efficiency of all processing machines from large to small, and obtain the processing parameters of the processing machine with the best production efficiency in the previous planned production cycle, or to segment the sorted production efficiency according to a preset interval, and obtain the processing parameters of the processing machine with the best production efficiency in the preset interval in the previous planned production cycle;

[0036] The parameter configuration submodule is used to configure the acquired processing parameters of the processing machine with the best production efficiency as the processing parameters of all processing machines in the next planned production cycle or all processing machines in the corresponding preset interval.

[0037] A second aspect of the present invention provides a grinding device, comprising:

[0038] Processing machines for processing the products to be processed; and

[0039] And a digital intelligent management system for polishing configured on the processing machine, which is used to identify product information of the product to be processed, and associate the product information with the machine information of the corresponding processing machine, and obtain the processing parameters and / or production parameters of the processing machine, and generate corresponding abnormal information and processing / production information based on the processing parameters and / or production parameters, and store and visualize the product information, machine information, processing parameters, production parameters, abnormal information and processing / production information.

[0040] A third aspect of the present invention provides a polishing digital intelligent management method, comprising the following steps:

[0041] Identify the product information of the product to be processed, and associate the identified product information with the machine information of the corresponding processing machine;

[0042] Collecting processing parameters of the processing machine and counting the production parameters of the processing machine, wherein the processing parameters and / or production parameters carry product information of the currently processed product and / or machine information of the corresponding processing machine;

[0043] Comprehensively analyzing the processing parameters and / or production parameters, and generating abnormal information carrying product information and / or machine information when the processing parameters and / or production parameters are abnormal;

[0044] Comprehensively calculate the processing parameters and / or production parameters according to preset rules to obtain processing / production information of the corresponding processing machine and / or processing workshop;

[0045] The product information, machine information, processing parameters, production parameters, abnormal information and processing / production information are stored and visualized.

[0046] A fourth aspect of the present invention provides a computer storage medium having an executable computer program stored thereon, wherein the computer program, when executed by a processor, implements the digital intelligent management method for polishing as described above.

[0047] The present invention can collect or count the processing parameters and production parameters of the processing machine by setting a data acquisition module and a data statistics module, and can obtain real-time processing / production data of the processing machine during the grinding process, and comprehensively analyze the processing / production data through the abnormal warning module. When an abnormality occurs in the processing / production data, abnormal information carrying product information and machine information is generated in time and displayed through a visual dashboard, so that relevant personnel can timely discover the abnormality and quickly locate the position of the abnormal processing machine and the defective or scrapped products caused by the abnormal processing machine, which greatly shortens the abnormality investigation time and reduces the number of defective or scrapped products that continue to be produced by the abnormal investigation device, which is beneficial to reducing cost waste and improving the yield rate; in addition, the processing / production data can be comprehensively calculated by the data calculation module to generate processing / production information carrying machine information in real time, and finally the product information, machine information, processing parameters, production parameters, abnormal information and processing / production information can be viewed in real time through a visual dashboard according to demand, which is beneficial for producers or managers to grasp the processing / production situation in time, so as to adjust the processing / production strategy in time and improve production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a structural block diagram of the polishing digital intelligent management system of Example 1 of the present invention.

[0049] Figure 2 for Figure 1 Structural block diagram of the data acquisition module.

[0050] Figure 3 for Figure 1 Structural block diagram of the data statistics module.

[0051] Figure 4 for Figure 1 Structural block diagram of the abnormal warning module.

[0052] Figure 5 for Figure 1 Structural block diagram of the data calculation module.

[0053] Figure 6 This is a structural block diagram of another embodiment of Embodiment 1.

[0054] Figure 7 This is a structural block diagram of another embodiment of Embodiment 1.

[0055] Figure 8 for Figure 7Structural block diagram of the parameter optimization module.

[0056] Fig. 9 This is a flow chart of the polishing digital intelligent management method according to embodiment 2 of the present invention.

[0057] Fig.10 for Fig. 9 A flowchart of another embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following is further described in detail through specific implementation methods:

[0059] Example 1

[0060] like Figure 1 As shown, it is a structural block diagram of the digital intelligent management system for grinding of this embodiment. Although the digital intelligent management system for grinding of this embodiment is used to illustrate the entire digital intelligent management system through a grinding processing machine, it can be understood that the system of this embodiment is not limited to the management of the processing parameters or production parameters of the grinding processing machine, but can also be used for the management of the processing parameters or production parameters of the processing machine in any other non-standard processing industry. In specific implementation, it is only necessary to replace the data acquisition module 2 according to the specific processing function of the processing machine to collect its processing parameters. The digital intelligent management system for grinding of this embodiment includes an identification module 1, a data acquisition module 2, a data statistics module 3, an abnormal warning module 4, a data calculation module 5 and a visual dashboard 6; by real-time collection or statistics of the processing parameters and / or production parameters of the processing machine during the grinding operation, the processing / production conditions of the processing machine and / or the processing workshop are monitored and managed in real time, so as to quickly locate the abnormal processing machine and abnormal products when an abnormal situation occurs, and then timely change the production plan or strategy to deal with the abnormal situation to ensure the maximization of production efficiency.

[0061] The identification module 1 can identify product information of the product to be processed, and associate the product information with machine information of a processing machine for processing the product to be processed.

[0062] The identification module 1 is installed at the corresponding position of the processing machine. The product to be processed has a unique product identifier, and the unique product identifier carries the product information of the product to be processed, so that when the product to be processed is transferred to the processing position by the robot, the identification module 1 can accurately read the unique product identifier of the product to be processed and obtain the corresponding product information; and the identification module 1 corresponds to the processing machine one by one, and the machine information of the corresponding processing machine is stored therein. When the identification module 1 reads the unique product identifier and obtains the product information, it automatically associates the product information with the stored machine information. In this embodiment, the product information at least includes the product name, product model and product batch, etc.; the machine information at least includes the machine name, machine number, processing workshop, machine status, online time, process name and process number, etc.

[0063] In the specific implementation of this embodiment, the identification module 1 can use a scanning terminal to realize the reading of the unique identification of the product. The unique identification of the product is a product QR code that has been engraved at the corresponding position of the product to be processed in the previous processing technology of the grinding process. The product information has been written into the product QR code when the product QR code is produced, so that the scanning terminal can directly read the product information carried therein after scanning the product QR code, and associate the product information with the machine information. When the identification module 1 associates the product information with the machine information, it can make the product information and the corresponding machine information have the same encoding by encoding, so that the two are associated. In some other embodiments, the unique identification of the product can also be read by a camera. Similarly, the unique identification of the product can also be implemented by a barcode or an identifiable number.

[0064] The data acquisition module 2 can collect the processing parameters of the corresponding processing machine in real time when processing the product to be processed. The processing parameters carry the product information of the current processed product and the machine information of the corresponding processing machine for processing the product, so that the abnormal product and abnormal processing machine can be quickly located during the subsequent abnormality investigation. In this embodiment, the processing parameters at least include grinding pressure, air source pressure, vacuum negative pressure, water flow pressure and water flow; the data corresponding to the grinding pressure, air source pressure, vacuum negative pressure, water flow pressure and water flow all carry the codes of the related product information and machine information, and are written into the corresponding product QR code, so as to identify and obtain the processing parameters of the processing machine in the subsequent parameter analysis and calculation.

[0065] like Figure 2 As shown, the data acquisition module 2 includes a pressure sensor 21, an air pressure sensor 22, a negative pressure sensor 23 and a water flow sensor 24. Among them:

[0066] The pressure sensor 21 is installed at the grinding head position of the processing machine to detect the grinding pressure of the grinding head on the product to be processed in real time during processing to ensure that the grinding pressure is within an appropriate range.

[0067] The air pressure sensor 22 is installed in the air source channel of the processing machine to detect the air source pressure in the air source channel when the robot of the processing machine is in motion, and indirectly obtain the air source pressure when the robot is in motion to ensure the stable movement of the robot.

[0068] The negative pressure sensor 23 is installed in the negative pressure channel of the processing machine to detect the vacuum negative pressure in the negative pressure channel when the robot of the processing machine takes and puts materials, and indirectly obtains the vacuum negative pressure amount when the robot acts on the product to be processed to adsorb the product to be processed, thereby ensuring the stable adsorption of the product to be processed by the robot.

[0069] The water flow sensor 24 is installed in the water flow channel of the processing machine to indirectly obtain the water flow pressure and water flow rate in the water flow channel when the grinding head is grinding the product to be processed, and to ensure that the grinding position is cooled during the processing to ensure the grinding quality.

[0070] The data statistics module 3 can count the production parameters of the corresponding processing machine when processing the product to be processed in real time, and the production parameters carry the machine information of the corresponding processing machine, so that when the processing / production data statistics are performed later, the processing / production data of a single processing machine can be counted. In this embodiment, the production parameters at least include the consumption of consumables, the number of consumables processing times, the actual output of the cycle, and the number of good products; the data corresponding to the consumption of consumables, the number of consumables processing times, the actual output of the cycle, and the number of good products all carry the codes of the related product information and machine information, and are written into the corresponding product QR code, so as to identify and obtain the production parameters of the processing machine in the subsequent parameter analysis and calculation.

[0071] like Figure 3 As shown, the data statistics module 3 includes a consumables statistics submodule 31, a production volume statistics submodule 32 and a good product statistics submodule 33. Among them:

[0072] The consumables statistics submodule 31 can count the consumables consumption of the processing machine and the number of consumables processing of each consumable. In this embodiment, the consumables are specifically various types of sandpaper. According to the grinding process stage, the sandpaper includes at least coarse sandpaper, medium sandpaper and fine sandpaper. The service life (i.e., the number of processing times) of each sandpaper is set in advance according to the degree of consumption of each grinding. For example, a coarse sandpaper can be used to grind the product to be processed six times, a medium sandpaper can be used to grind four times, and a fine sandpaper can be used to grind three times. During the grinding process, the processing machine can automatically replace the sandpaper that has reached its service life (i.e., the maximum number of processing times). Specifically, during the planned production cycle, the processing machine processes each product to be processed once, and the consumables statistics submodule 31 accumulates the processing times of the sandpaper once to obtain the consumables processing times of the current consumables, until the maximum processing times are reached, the system will automatically determine and control the corresponding sandpaper replacement manipulator to replace the sandpaper that has reached its service life. As the sandpaper replacement manipulator automatically replaces each pair of sandpapers, the consumables statistics submodule 31 accumulates the number of consumables consumed once to obtain the consumables consumption. At the same time, the system controls the consumables statistics submodule 31 to clear the consumables processing times corresponding to the sandpaper, so as to re-count the consumables processing times of the next sandpaper; in addition, when the current planned production cycle ends and enters a new planned production cycle, the statistical value of the consumables consumption will also be cleared to re-count the consumables consumption of the next planned production cycle. In this embodiment, the consumables consumption and the number of consumables processing times have corresponding data identifiers according to the different types of sandpapers.

[0073] The production volume statistics submodule 32 can count the actual production of the processing machine in the planned production cycle. Specifically, when the product to be processed is transferred from the waiting area to the processing position by the robot, the grinding head completes the processing of the product to be processed and then transfers it from the processing position to the unloading area by the robot, the production volume statistics submodule 32 accumulates the number of processing times of the processing machine to be processed once to obtain the actual production of the cycle, and clears the statistical value of the actual production of the cycle when the current planned production cycle ends and enters a new planned production cycle, so as to re-count the actual production of the cycle of the next planned production cycle.

[0074] The good product statistics submodule 33 can identify the products processed by the processing machine, and count the number of good products processed by each processing machine within the planned production cycle according to the identification results. Specifically, the back end of the processing machine is provided with an identification device, which collects the appearance of the product and identifies the appearance of the product through a trained recognition model, and then determines whether the product is a good product. When the product is a good product, the good product statistics submodule 33 accumulates the number of good products within the technical production cycle to obtain the number of good products. In this embodiment, the recognition model can be implemented by a neural network model, which is trained by a large number of good product images collected, and finally the matching rate between the product to be identified and the good product is calculated. If the matching rate reaches a preset value, the product to be identified is identified as a good product, thereby realizing the statistics of the number of good products.

[0075] The abnormal warning module 4 performs a comprehensive analysis based on the processing parameters collected by the data acquisition module 2 and / or the production parameters counted by the data statistics module 3, and determines whether there are any abnormalities in the processing parameters and / or production parameters of the processing machine in the current planned processing cycle, and generates abnormal information carrying product information and / or machine information when the processing parameters and / or production parameters are abnormal, so as to quickly locate the abnormal machine and facilitate producers and managers to check the abnormalities in time.

[0076] like Figure 4 As shown, the abnormal warning module 4 includes an alarm submodule 41, an automatic warning submodule 42 and an abnormal information generation submodule 43. Among them:

[0077] The alarm submodule 41 can identify the processing parameters collected by the data acquisition module 2, and generate an alarm instruction when the processing parameters are abnormal. Specifically, the alarm submodule 41 stores the first preset threshold corresponding to the processing parameters. The alarm submodule 41 can match the processing parameters collected in real time with the corresponding first preset threshold. If the two do not match successfully, it means that the processing machine is abnormal or faulty, and the alarm instruction is generated. More specifically, the alarm submodule 41 stores the first preset thresholds of grinding pressure, air source pressure, vacuum negative pressure, water flow pressure and water flow respectively. When the real-time grinding pressure, air source pressure, vacuum negative pressure, water flow pressure and water flow exceed the first preset threshold, the alarm instruction is generated to remind relevant personnel that the corresponding processing parameters are abnormal.

[0078] The automatic warning submodule 42 can calculate the production parameters counted by the data statistics module 3, and determine whether the calculation result matches the second preset threshold value, and generate a warning instruction when the calculation result does not match the second preset threshold value. In order to avoid the shutdown of the processing machine due to insufficient consumables or insufficient products to be processed in the waiting area, it is necessary to monitor the remaining consumables of the processing machine and the remaining waiting area, so as to promptly notify the relevant personnel to replenish them when they are insufficient; the remaining consumables are the number of consumables currently remaining in the processing machine, and the remaining waiting area is the number of products to be processed currently remaining in the waiting area of ​​the processing machine. Specifically, the second preset threshold corresponding to the remaining consumables and the remaining waiting area is stored in the automatic warning submodule 42. For the remaining consumables, the automatic warning submodule 42 can calculate the following according to the consumables consumption obtained by the data statistics module 3, the initial consumables of the processing machine at the beginning of the current planned production cycle, and the number of supplemented consumables during the processing of the current planned production cycle (if supplemented multiple times, the number of supplemented consumables is the sum of the number of supplemented consumables for multiple times):

[0079] Consumables remaining = initial consumables quantity + replenished consumables quantity - consumables consumption (1)

[0080] The initial quantity of consumables and the quantity of supplementary consumables can be obtained based on data entered into the system by relevant personnel when starting up or supplementing.

[0081] For the remaining amount in the waiting area, the automatic warning submodule 42 can calculate the actual output of the cycle obtained by the data statistics module 3, the initial waiting amount of the waiting area at the beginning of the current planned production cycle, and the amount of replenishment during the processing of the current planned production cycle (similarly, if replenished multiple times, the amount of replenishment is the sum of the number of products to be processed replenished multiple times):

[0082] The remaining amount in the waiting area = the initial waiting amount + the replenishment amount - the actual output of the cycle (2)

[0083] The initial material waiting quantity and the material supplement quantity are detected based on an infrared device arranged in the material waiting area.

[0084] As a preferred mode of this embodiment, the automatic warning submodule 42 can also automatically warn the processing parameters collected by the data acquisition module 2. Specifically, the automatic warning submodule 42 can also predict the value of the processing parameter at the next moment through a trained prediction model, and generate the warning instruction when the predicted value exceeds the first preset threshold. More specifically, the abnormal historical processing parameters of the processing machine are collected to form an abnormal parameter sequence, and the prediction model is trained using the abnormal parameter sequence to obtain a trained prediction model, so that when the real-time processing parameter sequence is input into the training model, the processing parameters at subsequent moments can be predicted to achieve automatic warning of the processing parameters, reminding relevant personnel to formulate countermeasures in advance to avoid unnecessary losses.

[0085] The abnormal information generation submodule 43 can generate corresponding abnormal information according to the alarm instruction and / or early warning instruction; in this embodiment, the abnormal information includes abnormal parameters, parameter types and abnormal causes, etc.

[0086] The data calculation module 5 performs comprehensive calculations on the processing parameters collected by the data acquisition module 2 and / or the production parameters counted by the data statistics module 3 according to preset rules to obtain processing / production information of the corresponding processing machine and / or processing workshop so as to make statistics on the processing / production situation; in this embodiment, the processing / production information at least includes productivity, yield rate and comprehensive utilization rate.

[0087] like Figure 5 As shown, the data calculation module 5 includes a productivity calculation submodule 51, a yield calculation submodule 52 and a comprehensive utilization calculation submodule 53. Among them:

[0088] The productivity calculation submodule 51 calculates the productivity of the processing machine and / or the processing workshop in the planned production cycle according to the actual cycle output and the planned cycle output of the processing machine and / or the processing workshop in the planned production cycle:

[0089] Productivity = actual output per cycle / planned output per cycle (3)

[0090] Among them, the productivity of the processing machine is calculated based on its actual cycle output and the planned cycle output within the planned production cycle; the productivity of the processing workshop is calculated based on the sum of the actual cycle output of all processing machines in the workshop during the planned production cycle and the planned cycle output of the processing workshop.

[0091] The yield rate calculation submodule 52 calculates the yield rate of the processing machine and / or processing workshop according to the number of good products in the planned production cycle and the actual output of the cycle:

[0092] Yield rate = number of good products / actual output per cycle (4)

[0093] Among them, the yield rate of the processing machine is calculated based on the number of good products in the planned production cycle and the actual output of the cycle; the yield rate of the processing workshop is calculated based on the sum of the number of good products of all processing machines in the workshop during the planned production cycle and the sum of the actual output of all processing machines in the processing workshop during the cycle.

[0094] The comprehensive utilization rate calculation submodule 53 is used to calculate the comprehensive utilization rate of the processing machine and / or processing workshop according to the planned working time, actual working time, productivity and yield rate of the processing machine and / or processing workshop in the planned production cycle:

[0095] Comprehensive utilization rate = availability rate × performance index × quality index (5)

[0096] Among them, the availability of the processing machine and / or processing workshop is calculated based on the actual working time and planned working time corresponding to the planned production cycle, that is: availability = actual working time / planned working time; the performance index of the processing machine and / or processing workshop is calculated based on the actual output, actual working time and productivity of the corresponding period within the planned production cycle, that is: performance index = (actual output of the period / actual working time) / productivity; the quality index of the processing machine and / or processing workshop is the corresponding yield rate within the planned production cycle.

[0097] The visual dashboard 6 can display product information, machine information, processing parameters, production parameters, abnormal information and processing / production information in real time according to the configuration or viewing requirements of the producer or manager. Specifically, the visual dashboard 6 can be based on the configuration requirements of the producer or manager, and can be summarized and displayed according to the product information and machine information, such as displaying the product information or machine information of the batch in the form of a table, displaying the machine information of each processing machine in the form of a block diagram, and distinguishing the machine status of each processing machine (such as running, waiting, and stopping) with different colors, etc. The visual dashboard 6 can also be summarized and displayed according to the processing parameters, production parameters, abnormal information and processing / production information. Since the processing parameters, production parameters and processing / production information carry the corresponding product information and machine information, when configuring the visual dashboard 6, the producer and manager can filter out the data of a certain processing machine, a certain batch, or even a certain product for integration and summary display, and can also filter out all the data of a certain workshop for integration and summary display, so that the producer or manager can grasp the processing / production situation in real time. The visual dashboard 6 can also promptly issue a real-time alarm or early warning for abnormal situations based on the abnormal information, so as to promptly notify on-site technical personnel to conduct investigations when abnormal situations occur, thereby avoiding serious losses.

[0098] like Figure 6 As shown, as a preferred embodiment of the present embodiment, the digital intelligent management system for polishing of the present embodiment further includes a storage module 7. The storage module 7 can store all the above-mentioned product information, machine information, processing parameters, production parameters, abnormal information and processing / production information, so that producers or managers can view historical data and analyze historical data, which is conducive to reasonably formulating and improving processing / production plans to maximize benefits.

[0099] like Figure 7 As shown, as a preferred mode of this embodiment, the digital intelligent management system for polishing of this embodiment also includes a parameter optimization module 8. The parameter optimization module 8 can optimize the processing parameters of each processing machine according to the productivity, yield rate and comprehensive utilization rate calculated by the data calculation module 5 to improve production efficiency. Specifically, the parameter optimization module 8 assigns weights to the productivity, yield rate and comprehensive utilization rate respectively, and calculates the production efficiency of each processing machine according to the weights, obtains the processing parameters of the processing machine with the best production efficiency within a preset interval, and configures the obtained processing parameters with the best production efficiency as the processing parameters of all processing machines within the preset interval of the next planned production cycle.

[0100] like Figure 8 As shown, the parameter optimization module 8 includes: a weight assignment submodule 81, a production efficiency calculation submodule 82, a parameter extraction submodule 83 and a parameter configuration submodule 84. Among them:

[0101] The weight assignment submodule 81 can obtain the productivity, yield rate and comprehensive utilization rate of each processing machine in the previous planned production cycle in the storage module 7, and assign weights to the productivity, yield rate and comprehensive utilization rate according to their importance to the production of the processing machine, and the sum of the weights of the productivity, yield rate and comprehensive utilization rate is one, that is, W1+W2+W3=1, where W1 is the weight of the productivity, W2 is the weight of the yield rate, and W3 is the weight of the comprehensive utilization rate.

[0102] The production efficiency calculation submodule 82 calculates the production efficiency η of each processing machine in the previous planned production cycle according to the production rate, yield rate and comprehensive utilization rate and the weights assigned to the production rate, yield rate and comprehensive utilization rate by the weight assignment submodule 81:

[0103] η=W1×productivity+W2×yield+W3×comprehensive utilization rate(6)

[0104] The parameter extraction submodule 83 sorts the production efficiency of all processing machines from large to small according to the production efficiency of each processing machine in the previous planned production cycle calculated by the production efficiency calculation submodule 82 to obtain a production efficiency sequence, that is, η1, η2, ..., η n (where n is the number of processing machines), and obtain the processing parameters (including grinding pressure, air source pressure, vacuum negative pressure, water flow pressure and water flow) of the processing machine corresponding to the optimal production efficiency (i.e. η1) in the previous planned production cycle.

[0105] The parameter configuration submodule 84 configures the processing parameters corresponding to the processing machine with the best production efficiency (ie, η1) as the processing parameters of all processing machines in the next planned production cycle, so as to further maximize the benefits.

[0106] In some other embodiments, due to the use time of the processing machines, the older processing machines cannot achieve the production efficiency of the newer processing machines. If the processing parameters of all processing machines are set to the processing parameters corresponding to η1, it may cause the performance of the older processing machines to be unable to meet the processing parameters corresponding to η1. Based on this, the parameter extraction submodule 83 can also divide the sorted production efficiency sequence into a certain number of preset intervals, namely: (where: m is the number of preset intervals, n1+n2+,...,n m =n, n is the number of processing machines), and obtain the optimal production efficiency in each preset interval (i.e. ) The processing parameters of the corresponding processing machine in the previous planned production cycle (including grinding pressure, air source pressure, vacuum negative pressure, water flow pressure and water flow).

[0107] The parameter configuration submodule 84 can also obtain the optimal production efficiency (i.e. ) is configured as the processing parameters of all processing machines in the corresponding preset range of the next planned production cycle, so as to comprehensively consider the processing performance of the processing machine and maximize the benefits under the premise of meeting its performance.

[0108] The digital intelligent management system for polishing of the present embodiment collects or counts the processing parameters and production parameters by setting the data acquisition module 2 and the data statistics module 3, and performs comprehensive calculation on the processing parameters and production parameters by the data calculation module 5, and finally realizes the visualization of the processing parameters and production parameters by configuring the visualization dashboard 6, so as to facilitate the real-time grasp of the processing / production situation; in addition, by setting the abnormal warning module 4, the processing parameters and production parameters in the processing process are monitored in real time, so as to timely alarm or warn when an abnormality occurs, so as to timely detect the abnormality and quickly locate the position of the abnormal processing machine and the defective or scrapped products caused by the abnormal processing machine, which greatly shortens the abnormality investigation time and reduces the number of defective or scrapped products that continue to be produced by the abnormality investigation device, which is conducive to reducing cost waste and improving the yield rate.

[0109] Example 2

[0110] like Fig. 9 The figure shows a flow chart of the digital intelligent management method for polishing of the present embodiment. The digital intelligent management method for polishing of the present embodiment is implemented based on the digital intelligent management system for polishing of the present embodiment 1, and includes an identification module 1, a data acquisition module 2, a data statistics module 3, an abnormal warning module 4, and a data calculation module 5 having the same or similar structure and function as the present embodiment and the present embodiment 1, and optionally includes a storage module 7 and a parameter optimization module 8 having the same or similar structure and function as the present embodiment 1. The digital intelligent management method for polishing of the present embodiment includes the following steps:

[0111] S1: Identify and associate product information and machine information.

[0112] Specifically, an identification module 1 installed at a corresponding position of a processing machine is used to identify a unique product identifier set on a product to be processed. The unique product identifier carries product information of the product to be processed. When the product to be processed is transferred to the processing position by a robot, the identification module 1 can accurately read the unique product identifier of the product to be processed and obtain the corresponding product information. The identification module 1 corresponds to a processing machine one by one, and the corresponding processing machine information is stored therein. When the identification module 1 reads the unique product identifier and obtains the product information, the product information is automatically associated with the stored machine information.

[0113] S2: Collect the processing parameters and production parameters of the processing machine.

[0114] Specifically, a data acquisition module 2 disposed on the processing machine collects the processing parameters of the processing machine, and a data statistics module 3 disposed on the processing machine counts the production parameters of the processing machine, and the processing parameters and / or production parameters carry the product information of the current processed product and / or the machine information of the corresponding processing machine. In this embodiment, the processing parameters at least include grinding pressure, air source pressure, vacuum negative pressure, water flow pressure and water flow; the production parameters at least include consumables consumption, consumables processing times, actual cycle output and number of good products.

[0115] S3: Comprehensively analyze the processing parameters and / or production parameters to obtain abnormal information.

[0116] Specifically, an abnormal warning module 4 is used to perform a comprehensive analysis on the processing parameters and / or production parameters, and determine whether there are any abnormalities in the processing parameters and / or production parameters of the processing machine in the current planned processing cycle, and generate abnormal information carrying product information and / or machine information when abnormalities occur in the processing parameters and / or production parameters.

[0117] S4: Comprehensively calculate the processing parameters and / or production parameters to obtain processing / production information.

[0118] Specifically, a data calculation module 5 performs comprehensive calculation on the processing parameters and / or production parameters according to preset rules to obtain processing / production information of the corresponding processing machine and / or processing workshop so as to make statistics on the processing / production situation; in this embodiment, the processing / production information at least includes productivity, yield rate and comprehensive utilization rate.

[0119] S5: Data storage and visualization.

[0120] Specifically, a storage module 7 is used to store the product information, machine information, processing parameters, production parameters, abnormal information and processing / production information, and based on a visual dashboard 6, the product information, machine information, processing parameters, production parameters, abnormal information and processing / production information are displayed in real time according to the configuration or viewing requirements of the producer or manager, so that the producer or manager can grasp the processing / production status and abnormal and fault conditions in real time.

[0121] like Fig.10 As shown, in some other embodiments, the polishing digital intelligent management method further includes the following steps after step S5:

[0122] S6: Calculate production efficiency and configure optimal parameters.

[0123] Specifically, a parameter optimization module 8 is used to obtain productivity, yield rate and comprehensive utilization rate and assign weights to them respectively, and then the production efficiency of each processing machine is calculated according to the weights, and the processing parameters of the processing machine with the best production efficiency within a preset interval are obtained, and the obtained processing parameters with the best production efficiency are configured as the processing parameters of all processing machines within the preset interval of the next planned production cycle.

[0124] The digital intelligent management method for polishing of the present embodiment identifies and associates product information and machine information, so that the processing parameters, production parameters, abnormal information and processing / production information obtained through subsequent collection, statistics, analysis and calculation are matched with the product information and machine information. This can achieve accurate positioning of workshops, machines and even products, and accurate viewing of their corresponding data, so that abnormal points can be quickly located and checked when abnormal situations occur, which is conducive to shortening the abnormality troubleshooting cycle and thus reducing unnecessary losses.

[0125] Example 3

[0126] The polishing device of this embodiment is implemented based on the polishing digital intelligent management system of embodiment 1, including an identification module 1, a data acquisition module 2, a data statistics module 3, an abnormal warning module 4, a data calculation module 5 and a visual dashboard 6 with the same or similar structure and function as embodiment 1, and optionally includes a storage module 7 and a parameter optimization module 8 with the same or similar structure and function as embodiment 1. The polishing device of this embodiment includes a processing machine and a polishing digital intelligent management system configured on the processing machine. Among them:

[0127] The processing machine is used to process the product to be processed. In the present embodiment, the processing machine is a grinding machine; in some other embodiments, the processing machine may also be other non-standard processing machines such as cutting machines, turning and milling machines. The digital intelligent management system for grinding is used to identify the product information of the product to be processed, and to associate the product information with the machine information of the corresponding processing machine, and to obtain the processing parameters and / or production parameters of the processing machine, and to generate corresponding abnormal information and processing / production information based on the processing parameters and / or production parameters, and to store and visualize the product information, machine information, processing parameters, production parameters, abnormal information and processing / production information. The specific structure of the digital intelligent management system for grinding is described in Example 1, which will not be repeated in this embodiment.

[0128] The polishing device of this embodiment, by configuring a polishing digital intelligent management system, can collect, count and visualize various data and information of the processing machine during the processing in real time, so that the producer can grasp the processing / production status of the processing machine in time, which is conducive to the reasonable formulation and improvement of production plans, thereby improving production efficiency.

[0129] As another embodiment of the present invention, a computer storage medium is also provided, which stores computer executable instructions. When the computer executable instructions are called and executed by a processor, the computer executable instructions enable the processor to implement the digital intelligent management method for polishing as described in Example 2.

Claims

1. Polishing digital intelligent management system, characterized by: include: An identification module, installed at a corresponding position of the processing machine, for identifying product information of the product to be processed and associating the product information with machine information of the processing machine; A data acquisition module, used to acquire processing parameters of a corresponding processing machine, wherein the processing parameters carry product information of the currently processed product and machine information of the corresponding processing machine; A data statistics module is used to count the production parameters of the processing machine, wherein the production parameters carry the machine information of the corresponding processing machine, and the production parameters at least include the consumption of consumables, the number of consumables processing times, the actual output of the cycle, and the number of good products; An abnormality warning module is used to comprehensively analyze the processing parameters and / or production parameters, and generate abnormal information carrying product information and / or machine information when the processing parameters and / or production parameters are abnormal; the processing / production information at least includes productivity, yield rate and comprehensive utilization rate; A data calculation module, used to perform comprehensive calculation on the processing parameters and / or production parameters according to preset rules to obtain processing / production information of the corresponding processing machine and / or processing workshop; A parameter optimization module, which is used to assign weights to each piece of information in the processing / production information, and calculate the production efficiency of each processing machine according to the weights, and obtain the processing parameters of the processing machine with the best production efficiency in the previous planned production cycle, or segment the sorted production efficiency according to a preset interval, and obtain the processing parameters of the processing machine with the best production efficiency in the preset interval in the previous planned production cycle; configure the obtained processing parameters with the best production efficiency as the processing parameters of all processing machines in the preset interval of the next planned production cycle; and The visual dashboard is used to display the product information, machine information, processing parameters, production parameters, abnormal information and processing / production information in real time.

2. The digital intelligent management system for polishing according to claim 1 is characterized in that: The processing parameters at least include grinding pressure, air source pressure, vacuum negative pressure, water flow pressure and water flow; The data acquisition module comprises: A pressure sensor is installed at the grinding head position of the processing machine, and is used to detect in real time the grinding pressure exerted by the grinding head on the product to be processed when the grinding head is in motion; An air pressure sensor is installed in the air source channel of the processing machine and is used to detect the air pressure of the air source when the manipulator of the processing machine moves in real time; A negative pressure sensor is installed in the negative pressure channel of the processing machine, and is used to detect in real time the vacuum negative pressure when the robot arm of the processing machine absorbs the product to be processed; and The water flow sensor is installed in the water flow channel of the processing machine and is used for real-time detection of the water flow pressure and water flow acting on the grinding position.

3. The digital intelligent management system for polishing according to claim 1 is characterized in that: The data statistics module includes: The consumables statistics submodule is used to count the number of consumables consumed by the processing machine during the planned production cycle to obtain the consumables consumption and to count the number of processing times of each consumable to obtain the number of consumables processing times of the current consumable; The production volume statistics submodule is used to count the total amount of products processed by the processing machine within the planned production cycle to obtain the actual production volume of the cycle; and The good product statistics submodule is used to count the number of good products processed by the processing machine within the planned production cycle based on the recognition results of the processed products by the back-end recognition device.

4. The digital intelligent management system for polishing according to claim 1 is characterized in that: The abnormal warning module includes: an alarm submodule, for matching the processing parameter with a corresponding first preset threshold value, and generating an alarm instruction when the processing parameter fails to match the first preset threshold value; an automatic warning submodule, configured to calculate the production parameter, match the calculation result with the corresponding second preset threshold, and generate a warning instruction when the calculation result fails to match the second preset threshold; and The abnormal information generation submodule is used to generate corresponding abnormal information according to the alarm instruction and / or early warning instruction.

5. The digital intelligent management system for polishing according to claim 1 is characterized in that: The data calculation module includes: A productivity calculation submodule, used to calculate the productivity of the processing machine and / or the processing workshop in the planned production cycle according to the actual cycle output and the planned cycle output of the processing machine and / or the processing workshop in the planned production cycle, wherein the actual cycle output of the processing workshop in the planned production cycle is calculated based on the actual cycle output of all processing machines in the processing workshop in the planned production cycle; A yield rate calculation submodule, used to calculate the yield rate of the processing machine and / or processing workshop according to the number of good products of the processing machine and / or processing workshop in the planned production cycle and the actual output of the cycle; and The comprehensive utilization rate calculation submodule is used to calculate the comprehensive utilization rate of the processing machine and / or processing workshop according to the planned working time, actual working time, productivity and yield rate of the processing machine and / or processing workshop during the planned production cycle.

6. The digital intelligent management system for polishing according to claim 1 is characterized in that: The parameter optimization module includes: The weight assignment submodule is used to obtain the productivity, yield rate and comprehensive utilization rate of each processing machine in the previous planned production cycle, and assign weights to the productivity, yield rate and comprehensive utilization rate according to their importance to the production of the processing machine; The production efficiency calculation submodule is used to calculate the production efficiency of each processing machine according to the production rate, yield rate and comprehensive utilization rate and their corresponding weights: A parameter extraction submodule, which is used to sort the production efficiency of all processing machines from large to small, and obtain the processing parameters of the processing machine with the best production efficiency in the previous planned production cycle, or to segment the sorted production efficiency according to a preset interval, and obtain the processing parameters of the processing machine with the best production efficiency in the preset interval in the previous planned production cycle; and The parameter configuration submodule is used to configure the acquired processing parameters of the processing machine with the best production efficiency as the processing parameters of all processing machines in the next planned production cycle or all processing machines in the corresponding preset interval.

7. A grinding device, characterized in that: include: Processing machines, used to process the products to be processed; as well as and a digital intelligent management system for polishing configured on the processing machine, which is used to identify product information of the product to be processed, and associate the product information with the machine information of the corresponding processing machine, and obtain the processing parameters and / or production parameters of the processing machine, and generate corresponding abnormal information and processing / production information based on the processing parameters and / or production parameters, and store and visualize the product information, machine information, processing parameters, production parameters, abnormal information and processing / production information; the digital intelligent management system for polishing is also used to assign weights to each piece of information in the processing / production information, and assign weights to the information according to the weights. Recalculate the production efficiency of each processing machine, obtain the processing parameters of the processing machine with the best production efficiency in the previous planned production cycle, or segment the sorted production efficiency according to a preset interval, and obtain the processing parameters of the processing machine with the best production efficiency in the preset interval in the previous planned production cycle; configure the obtained processing parameters with the best production efficiency as the processing parameters of all processing machines in the preset interval in the next planned production cycle; wherein, the production parameters at least include consumables consumption, number of consumables processing times, actual cycle output and number of good products; the processing / production information at least includes productivity, good product rate and comprehensive utilization rate.

8. A polishing digital intelligent management method, characterized in that: The following steps are involved: Identify the product information of the product to be processed, and associate the identified product information with the machine information of the corresponding processing machine; Collecting the processing parameters of the processing machine and counting the production parameters of the processing machine, wherein the processing parameters and / or production parameters carry the product information of the currently processed product and / or the machine information of the corresponding processing machine; the production parameters at least include the consumption of consumables, the number of consumables processing times, the actual output of the cycle, and the number of good products; Comprehensively analyzing the processing parameters and / or production parameters, and generating abnormal information carrying product information and / or machine information when the processing parameters and / or production parameters are abnormal; Comprehensively calculate the processing parameters and / or production parameters according to preset rules to obtain processing / production information of the corresponding processing machine and / or processing workshop; the processing / production information at least includes productivity, yield rate and comprehensive utilization rate; Assign weights to each piece of information in the processing / production information, and calculate the production efficiency of each processing machine according to the weights, and obtain the processing parameters of the processing machine with the best production efficiency in the previous planned production cycle, or divide the sorted production efficiency into segments according to a preset interval, and obtain the processing parameters of the processing machine with the best production efficiency in the preset interval in the previous planned production cycle; configure the obtained processing parameters with the best production efficiency as the processing parameters of all processing machines in the preset interval of the next planned production cycle; The product information, machine information, processing parameters, production parameters, abnormal information and processing / production information are stored and visualized.

9. A computer storage medium having an executable computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the digital intelligent management method for polishing as described in claim 8.

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