An unmanned workshop automation equipment upgrade management system and method based on the Internet of Things

Abstract

The present invention discloses an unmanned workshop automation equipment upgrade management system and method based on the Internet of Things, which relates to the technical field of equipment upgrade management, monitors multiple data of automation equipment to analyze the operating status of automation equipment, and after the automation equipment completes processing products, calculates the warning coefficient by combining the influencing factor with the operating status, and judges whether it is necessary to roll back the current system of the automation equipment based on the warning coefficient, and regularly obtains the average value of the warning coefficient of all automation equipment. When there is a system upgrade task for the automation equipment, the average value of the warning coefficient is combined with the work task of the automation equipment the next day to judge whether a system upgrade is needed. After the automation equipment completes the system upgrade, the management system can comprehensively analyze multiple factors to judge whether a system rollback is needed, which not only effectively avoids the decline of the processing accuracy and processing efficiency of the automation equipment, but also ensures the stability and safety of the operation of the automation equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment upgrade management, and in particular to an unmanned workshop automation equipment upgrade management system and method based on the Internet of Things. Background Art

[0002] As the manufacturing industry is accelerating towards digitalization, intelligence and unmanned operations, unmanned workshops, as an important form of intelligent manufacturing, have achieved a high degree of integration in the production process through the collaborative work of fully automated equipment and information systems.

[0003] The prior art has the following defects:

[0004] Since unmanned workshops use automated equipment, generally speaking, in order to improve the operating stability and intelligence of automated equipment, the unmanned workshop management platform will automatically upgrade the system for automated equipment on a regular basis. However, in actual applications, the system upgrade of automated equipment may be reversed (for example, the stability of automated equipment is reduced after the system upgrade). If the negative impact caused by the system upgrade is relatively large, the device can be rolled back after an early warning through the threshold warning method. If the negative impact caused by the system upgrade is relatively small, but there are a large number of them, since the existing management system has no early warning management for such problems, this may lead to a decrease in the overall processing accuracy or efficiency of the automated equipment, and long-term accumulation will cause mechanical fatigue or damage inside the automated equipment, reducing the stability and safety of the automated equipment operation.

[0005] Based on this, the present invention proposes an unmanned workshop automation equipment upgrade management system and method based on the Internet of Things. After the automation equipment completes the system upgrade, it can comprehensively analyze multiple factors to determine whether system rollback is needed. This not only effectively avoids the decline in processing accuracy and processing efficiency of the automation equipment, but also ensures the stability and safety of the operation of the automation equipment. Summary of the invention

[0006] The purpose of the present invention is to provide an unmanned workshop automation equipment upgrade management system and method based on the Internet of Things to solve the shortcomings of the background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: an unmanned workshop automation equipment upgrade management method based on the Internet of Things, the management method comprising the following steps:

[0008] The management system connects to the database of the unmanned workshop through the API interface and obtains all the automation equipment information in the unmanned workshop based on the database;

[0009] When there is an automation device that has completed a system upgrade, the management system monitors multiple data of the automation device to analyze the operating status of the automation device within a period of time after the automation device system upgrade, and after the automation device completes processing the product, analyzes the processing information of the product to generate an impact factor for the automation device;

[0010] The influencing factors are combined with the operating status to calculate the warning coefficient, and based on the warning coefficient, it is determined whether the current system of the automation equipment needs to be rolled back;

[0011] Regularly obtain the average value of the warning coefficient of all automation equipment. When there is a system upgrade task for the automation equipment, combine the average value of the warning coefficient with the work task of the automation equipment the next day to determine whether the system upgrade is needed;

[0012] All automation devices are sorted according to the average value of the warning coefficient to generate a management list, which is sent to the management personnel. The management personnel select the management order of the automation devices based on the management list.

[0013] In a preferred embodiment, the impact factor is combined with the operating status to calculate the warning coefficient, and judging whether the current system of the automation equipment needs to be rolled back based on the warning coefficient includes the following steps:

[0014] Obtain the influencing factor and operation index of the automation equipment, and calculate the warning coefficient by combining the influencing factor with the operation index. The expression is: , where is the warning factor, is the impact factor, is the operation index;

[0015] Compare the warning coefficient with the preset warning threshold, which is used to determine whether the current system of the automation device needs to be rolled back;

[0016] If the warning coefficient is less than or equal to the warning threshold, it is determined that there is no need to roll back the current system of the automation device. If the warning coefficient is greater than the warning threshold, it is determined that the current system of the automation device needs to be rolled back.

[0017] In a preferred embodiment, monitoring multiple data of the automation equipment to analyze the operating status of the automation equipment includes the following steps:

[0018] Obtain resource utilization efficiency index, software and hardware error rate, and command response delay amplitude of automation equipment;

[0019] The resource utilization efficiency index, software and hardware error rate, and command response delay amplitude are calculated comprehensively to obtain the operation index, which is expressed as follows: , where is the running index, is the resource utilization efficiency index, is the software and hardware error rate, is the command response delay amplitude, , , are the adjustment coefficients of resource utilization efficiency index, software and hardware error rate, and instruction response delay amplitude, respectively, and , , Both are greater than 0.

[0020] In a preferred embodiment, after the automated equipment completes processing of the product, analyzing the processing information of the product to generate an impact factor for the automated equipment includes the following steps:

[0021] After the automated equipment completes product processing, the quantity deviation and defective rate of the product are obtained, and the quantity deviation and defective rate are normalized so that the value range of the quantity deviation and the defective rate is mapped to [0,1], and the normalized value of the quantity deviation and the normalized value of the defective rate are obtained. The normalized value of the quantity deviation and the normalized value of the defective rate are summed to obtain the influencing factor.

[0022] In a preferred embodiment, the average value of the warning coefficient of all automation equipment is obtained regularly. When the automation equipment has a system upgrade task, the average value of the warning coefficient is combined with the work task of the automation equipment the next day to determine whether the system upgrade is required, including the following steps:

[0023] Regularly obtain the average value of the warning coefficient of all automation equipment, the expression is:

[0024] , where is the mean value of the warning coefficient, Get the number for the warning factor of the automation equipment, For the The warning coefficient value obtained this time;

[0025] The overall coefficient is obtained by weighting the mean value of the warning coefficient and the task urgency index. The expression is:

[0026] , where is the overall coefficient, is the mean value of the warning coefficient, is the task urgency index;

[0027] If the overall coefficient is greater than the preset coefficient threshold, it is determined that a system upgrade is not required; if the overall coefficient is less than or equal to the preset coefficient threshold, it is determined that a system upgrade is required.

[0028] In a preferred embodiment, the logic for obtaining the task urgency index is: obtaining the product processing urgency index and product cost unit price of the automation equipment one day later, normalizing the product processing urgency index and product cost unit price, and then summing the normalized product processing urgency index and product cost unit price to obtain the task urgency index.

[0029] In a preferred embodiment, the calculation logic of the command response delay amplitude is: obtain the number of control instructions received by the automation device during the monitoring period, record the time it takes for the automation device to respond to each control instruction, calculate the mean response time and the standard deviation of the response time, and the expression is: , where is the mean response time, is the standard deviation of response time, The number of control instructions received by the automation device, For automation equipment to respond to the The duration of a control instruction is calculated by dividing the mean response duration by the standard deviation of the response duration to obtain the instruction response delay amplitude, which is expressed as: The starting time point of the monitoring time period is the time point when the automation device receives the first control instruction.

[0030] In a preferred embodiment, the calculation logic of the quantity deviation is: obtaining the actual product production quantity of the automation equipment within the monitoring time period, and subtracting the actual product production quantity from the expected product production quantity to obtain the quantity deviation;

[0031] The calculation logic of the defective rate is as follows: the actual product production quantity of the automated equipment within the monitoring period is obtained, and all the products produced are tested to obtain the number of unqualified products, and the number of unqualified products is divided by the actual product production quantity to obtain the defective rate.

[0032] In a preferred embodiment, the calculation logic of the resource utilization efficiency index is: obtain the actual amount of resources used and the maximum amount of resources available during the operation of the automation equipment, and obtain the real-time resource utilization rate of the automation equipment by dividing the actual amount of resources used by the maximum amount of resources available. The calculation expression of the resource utilization efficiency index is: , where is the resource utilization efficiency index, For real-time resource utilization, is the standard resource utilization;

[0033] The calculation expression for the software and hardware error rate is: obtain the number of hardware failure events and software crashes during the operation of the automation equipment, sum the number of hardware failure events and software crashes to obtain the number of errors, and divide the number of errors by the total number of operations of the automation equipment to obtain the software and hardware error rate.

[0034] An unmanned workshop automation equipment upgrade management system based on the Internet of Things, including a data acquisition module, an analysis module, a judgment module, an upgrade decision module, and a management module;

[0035] Data acquisition module: connects to the database of the unmanned workshop through the API interface, obtains all the automation equipment information in the unmanned workshop based on the database, and sends the automation equipment information to the analysis module and the management module;

[0036] Analysis module: When there is an automation device that completes the system upgrade, within a period of time after the automation device system upgrade, multiple data of the automation device are monitored to analyze the operating status of the automation device. After the automation device completes the product processing, the product processing information is analyzed to generate an impact factor for the automation device. The operating status and impact factor are sent to the judgment module.

[0037] Judgment module: The influencing factor is combined with the operating status to calculate the warning coefficient. Based on the warning coefficient, it is determined whether the current system of the automation equipment needs to be rolled back. The warning coefficient is sent to the upgrade decision module.

[0038] Upgrade decision module: Regularly obtain the average value of the warning coefficient of all automation equipment. When there is a system upgrade task for the automation equipment, the average value of the warning coefficient is combined with the work task of the automation equipment the next day to determine whether the system needs to be upgraded;

[0039] Management module: sort all automation equipment according to the average value of the warning coefficient, generate a management list, send the management list to the management personnel, and the management personnel select the management order of the automation equipment according to the management list.

[0040] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0041] 1. The present invention analyzes the operating status of the automation equipment by monitoring multiple data of the automation equipment within a period of time after the automation equipment system is upgraded, and after the automation equipment completes processing the product, analyzes the processing information of the product to generate an impact factor for the automation equipment, combines the impact factor with the operating status to calculate the warning coefficient, and judges whether it is necessary to roll back the current system of the automation equipment based on the warning coefficient, and regularly obtains the average value of the warning coefficient of all automation equipment. When the automation equipment has a system upgrade task, the average value of the warning coefficient is combined with the work task of the automation equipment the next day to judge whether a system upgrade is required. After the automation equipment completes the system upgrade, the management system can comprehensively analyze multiple factors to determine whether a system rollback is required, which not only effectively avoids the decline in the processing accuracy and processing efficiency of the automation equipment, but also ensures the stability and safety of the operation of the automation equipment.

[0042] 2. When there is a system upgrade task for the automation equipment, the present invention combines the warning coefficient mean with the work task of the automation equipment the next day to determine whether the system upgrade is needed, and sorts all the automation equipment according to the warning coefficient mean, generates a management list, and sends the management list to the management personnel, who select the management order of the automation equipment according to the management list. This is conducive to improving the management efficiency of unmanned workshops. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0044] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Example 1: Please refer to Figure 1 As shown, the present embodiment provides an unmanned workshop automation equipment upgrade management method based on the Internet of Things, and the management method includes the following steps:

[0047] The management system connects to the database of the unmanned workshop through the API interface, and obtains all the information of the automated equipment in the unmanned workshop based on the database. When an automated device completes the system upgrade, the management system monitors multiple data of the automated equipment to analyze the operating status of the automated equipment within a period of time after the system upgrade of the automated equipment. After the automated equipment completes the product processing, the management system analyzes the processing information of the product to generate an impact factor for the automated equipment, and calculates the impact factor in combination with the operating status to obtain the warning coefficient. Based on the warning coefficient, it is determined whether the current system of the automated equipment needs to be rolled back. The average warning coefficient of all automated equipment is obtained regularly. When the automated equipment has a system upgrade task, the average warning coefficient is combined with the work task of the automated equipment the next day to determine whether a system upgrade is needed, and all automated equipment is sorted according to the average warning coefficient to generate a management list, which is sent to the management personnel. The management personnel select the management order of the automated equipment based on the management list.

[0048] This application analyzes the operating status of the automation equipment by monitoring multiple data of the automation equipment within a period of time after the automation equipment system is upgraded, and after the automation equipment completes processing the product, analyzes the processing information of the product to generate an impact factor for the automation equipment, combines the impact factor with the operating status to calculate the warning coefficient, and judges whether it is necessary to roll back the current system of the automation equipment based on the warning coefficient. The average value of the warning coefficient of all automation equipment is regularly obtained. When the automation equipment has a system upgrade task, the average value of the warning coefficient is combined with the work task of the automation equipment the next day to judge whether a system upgrade is needed. After the automation equipment completes the system upgrade, the management system can comprehensively analyze multiple factors to determine whether a system rollback is needed, which not only effectively avoids the decline in the processing accuracy and processing efficiency of the automation equipment, but also ensures the stability and safety of the operation of the automation equipment.

[0049] When there is a system upgrade task for the automation equipment, this application will combine the average value of the warning coefficient with the work task of the automation equipment the next day to determine whether the system upgrade is needed, and sort all the automation equipment according to the average value of the warning coefficient to generate a management list, which will be sent to the management personnel. The management personnel will choose the management order of the automation equipment according to the management list. This is conducive to improving the management efficiency of unmanned workshops.

[0050] Embodiment 2: The management system connects to the database of the unmanned workshop through an API interface, and obtains all the automation equipment information in the unmanned workshop based on the database, including the following steps:

[0051] Configure API connection parameters: set the database API interface address, port number, protocol (such as HTTPS), timeout settings, etc.

[0052] Authentication: According to the database security policy, use API Key, OAuth2, JWT Token or username and password to complete authentication to ensure legal access.

[0053] Connection test: Send a test request to the API interface, confirm that the connection is successful, and record the return status.

[0054] Select the data interface: According to the API documentation, determine the specific endpoint used to query device information.

[0055] Construct request parameters:

[0056] Specify the query scope (such as all devices or devices in a specific region).

[0057] Set paging parameters (such as limit and offset) to prevent query timeouts due to excessive data volume.

[0058] Define the return fields (such as device ID, model, version number, running status, etc.).

[0059] Data filtering: If the database supports filtering parameters, you can reduce the amount of data by filtering conditions (such as device type, operating status, etc.) in the API request.

[0060] Classification requirements: Add classification tags based on the purpose of the equipment (such as sensors, robots, robotic arms, etc.) or the location in the factory to facilitate subsequent management.

[0061] Parse response data: Parse the JSON, XML and other format data returned by the database into objects or tables supported by the system.

[0062] Handling exception responses:

[0063] Check the return status code and handle errors such as timeout, insufficient permissions, and unavailable resources.

[0064] Log abnormal data for subsequent investigation.

[0065] Local storage: Save the acquired device information to the management system's database or cache to support offline management and quick query.

[0066] Data synchronization: To avoid data redundancy and conflict, synchronize the addition, deletion and update of device information in the database regularly or in real time.

[0067] Verify completeness: Check whether the required fields of the device information are complete (such as device name, model, unique identifier, etc.).

[0068] Data completion: If some information is missing, it can be supplemented based on other interfaces or local data to ensure the completeness and accuracy of device information.

[0069] Update device status: Update the acquired device information to the device list of the management system, including status changes of newly added devices and invalid devices.

[0070] Generate equipment catalog: Display basic information of equipment in the form of a list, and support classification and viewing by type, status or factory location.

[0071] When there is an automation device that has completed the system upgrade, the management system monitors multiple data of the automation device within a period of time after the automation device system upgrade to analyze the operating status of the automation device, including the following steps:

[0072] Obtain resource utilization efficiency index, software and hardware error rate, and command response delay amplitude of automation equipment;

[0073] The resource utilization efficiency index, software and hardware error rate, and command response delay amplitude are calculated comprehensively to obtain the operation index, which is expressed as follows: , where is the running index, is the resource utilization efficiency index, is the software and hardware error rate, is the command response delay amplitude, , , are the adjustment coefficients of resource utilization efficiency index, software and hardware error rate, and instruction response delay amplitude, respectively, and , , All are greater than 0;

[0074] It can be seen from the calculation expression of the operation index that the smaller the value of the operation index is, the worse the operation stability of the automation equipment is after the automation equipment completes the system upgrade.

[0075] The calculation logic of the command response delay amplitude is: obtain the number of control commands received by the automation equipment during the monitoring period, record the time it takes for the automation equipment to respond to each control command, calculate the mean response time and the standard deviation of the response time, and the expression is:

[0076] , where is the mean response time, is the standard deviation of response time, The number of control instructions received by the automation device, For automation equipment to respond to the The duration of a control instruction is calculated by dividing the mean response duration by the standard deviation of the response duration to obtain the instruction response delay amplitude, which is expressed as: The larger the command response delay amplitude is, the greater the delay in the automation device responding to multiple control commands within the monitoring time period is. In addition, in order to avoid the number of control commands received by the automation device being zero within the monitoring time period, in this application, the starting time point of the monitoring time period is the time point when the automation device receives the first control command, that is, within the monitoring time period, the automation device receives at least one control command.

[0077] The calculation logic of the resource utilization efficiency index is: obtain the actual amount of resources used and the maximum amount of resources available during the operation of the automation equipment, and compare the actual amount of resources used with the maximum amount of resources available to obtain the real-time resource utilization rate of the automation equipment. Too high resource utilization (close to 100%) may indicate system overload, resource shortage, and performance bottleneck. Too low resource utilization (close to 0%) may indicate that the equipment fails to effectively utilize resources and has low efficiency. The calculation expression of the resource utilization efficiency index is: , where is the resource utilization efficiency index, For real-time resource utilization, is the standard resource utilization.

[0078] The calculation expression for the software and hardware error rate is: obtain the number of hardware failure events and software crashes during the operation of the automation equipment, sum the number of hardware failure events and software crashes to get the number of errors, and divide the number of errors by the total number of operations of the automation equipment to get the software and hardware error rate. The larger the software and hardware error rate, the worse the operating stability of the automation equipment.

[0079] After the automated equipment processes the product, the product processing information is analyzed to generate the influencing factors for the automated equipment, including the following steps:

[0080] After the automated equipment completes product processing, the quantity deviation and defective rate of the product are obtained, and the quantity deviation and defective rate are normalized so that the value range of the quantity deviation and the defective rate is mapped to [0,1], and the normalized value of the quantity deviation and the normalized value of the defective rate are obtained. The normalized value of the quantity deviation and the normalized value of the defective rate are summed to obtain the impact factor. The larger the impact factor, the lower the overall production efficiency of the automated equipment after the system upgrade.

[0081] The calculation logic of quantity deviation is as follows: obtain the actual product production quantity of the automation equipment during the monitoring period, subtract the expected product production quantity from the actual product production quantity to obtain the quantity deviation. The larger the quantity deviation, the more the actual product production quantity deviates from the expected quantity, that is, the production efficiency of the automation equipment decreases after the system upgrade.

[0082] The calculation logic of the defective rate is as follows: obtain the actual product production quantity of the automation equipment during the monitoring period, and test all products produced to obtain the number of unqualified products. The number of unqualified products is divided by the actual product production quantity to obtain the defective rate. The larger the defective rate, the worse the operating stability of the surface automation equipment after the system upgrade.

[0083] The impact factor is combined with the operating status to calculate the warning coefficient, and based on the warning coefficient, it is determined whether the current system of the automation equipment needs to be rolled back, including the following steps:

[0084] Obtain the influencing factor and operation index of the automation equipment, and calculate the warning coefficient by combining the influencing factor with the operation index. The expression is: , where is the warning factor, is the impact factor, The larger the warning coefficient is, the faster the overall operation stability and operation efficiency of the automation equipment will decline after the system upgrade;

[0085] The warning coefficient is compared with the preset warning threshold. The warning threshold is used to determine whether the current system of the automation equipment needs to be rolled back. If the warning coefficient is less than or equal to the warning threshold, it is determined that the current system of the automation equipment does not need to be rolled back. If the warning coefficient is greater than the warning threshold, it is determined that the current system of the automation equipment needs to be rolled back.

[0086] The average value of the warning coefficient of all automation equipment is obtained regularly. When the automation equipment has a system upgrade task, the average value of the warning coefficient is combined with the work task of the automation equipment the next day to determine whether the system upgrade is required, including the following steps:

[0087] Regularly obtain the average value of the warning coefficient of all automation equipment, the expression is:

[0088] , where is the mean value of the warning coefficient, Get the number for the warning factor of the automation equipment, For the The warning coefficient values ​​obtained for each time, the larger the average value of the warning coefficient, the worse the stability of the automation equipment after the historical system upgrade;

[0089] Obtain the product processing urgency index and product cost unit price of the automation equipment one day later, normalize the product processing urgency index and product cost unit price, and then sum the normalized product processing urgency index and product cost unit price to obtain the task urgency index;

[0090] The overall coefficient is obtained by weighting the mean value of the warning coefficient and the task urgency index. The expression is:

[0091] , where is the overall coefficient, is the mean value of the warning coefficient, It is the task urgency index. The larger the overall coefficient is, the less the automation equipment needs a system upgrade. If the overall coefficient is greater than the preset coefficient threshold, it is determined that a system upgrade is not required. If the overall coefficient is less than or equal to the preset coefficient threshold, it is determined that a system upgrade is required.

[0092] All automation devices are sorted according to the average value of the warning coefficient, and a management list is generated. The management list is sent to the management personnel, and the management personnel select the management order of the automation devices according to the management list, including the following steps:

[0093] All automation equipment are sorted from large to small according to the average value of the warning coefficient to generate a management list. The higher the automation equipment is ranked in the management list, the higher the management priority of the automation equipment is. The management list is sent to the management personnel, and the management personnel select the management order of the automation equipment based on the management list (the management content includes analyzing the system upgrade of the automation equipment and maintaining the automation equipment).

[0094] Specifically:

[0095] The larger the average value of the warning coefficient, the more serious the problem of the device, which requires higher priority management. By sorting the average values ​​of the warning coefficients of the devices from large to small, the devices are arranged according to the severity of their average values ​​of the warning coefficients. After the sorting is completed, a management list is generated, which reflects the management priority of the devices.

[0096] Example: Assume there are 5 devices, and the calculated average values ​​of the warning coefficients are: [3.5, 4.2, 2.8, 6.0, 4.7]. Based on these average values ​​of the warning coefficients, the sorted management list is:

[0097] 1st place: Equipment 5 (average warning coefficient 6.0);

[0098] No. 2: Equipment 2 (mean value of warning coefficient 4.7);

[0099] No. 3: Equipment 4 (average warning coefficient 4.2);

[0100] 4th place: Equipment 1 (mean value of warning coefficient 3.5);

[0101] No. 5: Equipment 3 (mean value of warning coefficient: 2.8);

[0102] After the management list is generated, the system sends it to the management personnel (via email, management system interface, SMS, etc.). The management list contains the average value and priority ranking of the warning coefficient of each device, and the management personnel can make decisions based on this list.

[0103] Analyze the system upgrade of automation equipment: According to the average value of the warning coefficient of the equipment, the management personnel will give priority to the system upgrade analysis of the equipment with higher average warning coefficient. This includes:

[0104] Check if the device needs a software update or firmware upgrade.

[0105] Analyze the impact of equipment upgrades on production efficiency, stability, and resource utilization.

[0106] Evaluate the health of the upgraded device to determine if rollback or further adjustments are necessary.

[0107] Maintain equipment: For equipment with high average warning coefficients, managers need to arrange timely maintenance work:

[0108] Maintenance work can include hardware inspection, troubleshooting, equipment calibration, cleaning, lubrication, etc.

[0109] Ensure that the equipment is restored to normal operation and prevent the fault from spreading to other equipment.

[0110] The maintenance content can be notified to the equipment operator or technical support personnel through the system.

[0111] Based on the management list, the manager selects the highest priority equipment for system analysis or maintenance.

[0112] According to the equipment's fault type and warning coefficient average, perform corresponding troubleshooting and evaluate the equipment's upgrade or maintenance needs.

[0113] Handle high-priority equipment: perform system upgrades or repairs to ensure that the equipment is restored and operates stably as soon as possible. For equipment with a low average warning coefficient, managers can choose to delay processing or perform routine inspections. After completing equipment analysis and maintenance, the system should update the operating status of the equipment and recalculate the average warning coefficient of the equipment. Managers can view the maintenance and upgrade records of each device to ensure that management work is effectively implemented. The management system should generate maintenance reports, record maintenance content and upgrade operations, and facilitate subsequent tracking and optimization.

[0114] Through the above steps, the management system can generate a priority management list based on the average value of the warning coefficient of the automation equipment, helping managers to effectively arrange equipment maintenance and system upgrades. The management list automatically generated by the system can help managers focus on the most urgent problem equipment, thereby improving the stability and efficiency of the entire production system.

[0115] Embodiment 3: The unmanned workshop automation equipment upgrade management system based on the Internet of Things described in this embodiment includes a data acquisition module, an analysis module, a judgment module, an upgrade decision module, and a management module;

[0116] Data acquisition module: connects to the database of the unmanned workshop through the API interface, obtains all the automation equipment information in the unmanned workshop based on the database, and sends the automation equipment information to the analysis module and the management module;

[0117] Analysis module: When there is an automation device that completes the system upgrade, within a period of time after the automation device system upgrade, multiple data of the automation device are monitored to analyze the operating status of the automation device. After the automation device completes the product processing, the product processing information is analyzed to generate an impact factor for the automation device. The operating status and impact factor are sent to the judgment module.

[0118] Judgment module: The influencing factor is combined with the operating status to calculate the warning coefficient. Based on the warning coefficient, it is determined whether the current system of the automation equipment needs to be rolled back. The warning coefficient is sent to the upgrade decision module.

[0119] Upgrade decision module: Regularly obtain the average value of the warning coefficient of all automation equipment. When there is a system upgrade task for the automation equipment, the average value of the warning coefficient is combined with the work task of the automation equipment the next day to determine whether the system needs to be upgraded;

[0120] Management module: sort all automation equipment according to the average value of the warning coefficient, generate a management list, send the management list to the management personnel, and the management personnel select the management order of the automation equipment according to the management list.

[0121] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0122] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0123] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An unmanned workshop automation equipment upgrade management method based on the Internet of Things, characterized by: The management method comprises the following steps: The management system connects to the database of the unmanned workshop through the API interface and obtains all the automation equipment information in the unmanned workshop based on the database; When there is an automation device that has completed a system upgrade, the management system monitors multiple data of the automation device to analyze the operating status of the automation device within a period of time after the automation device system upgrade, and after the automation device completes processing the product, analyzes the processing information of the product to generate an impact factor for the automation device; The influencing factors are combined with the operating status to calculate the warning coefficient, and based on the warning coefficient, it is determined whether the current system of the automation equipment needs to be rolled back; Regularly obtain the average value of the warning coefficient of all automation equipment. When there is a system upgrade task for the automation equipment, combine the average value of the warning coefficient with the work task of the automation equipment the next day to determine whether the system upgrade is needed; All automation equipment are sorted according to the average value of the warning coefficient, and a management list is generated. The management list is sent to the management personnel, and the management personnel select the management order of the automation equipment according to the management list; Monitoring multiple data of automation equipment to analyze the operating status of automation equipment includes the following steps: Obtain resource utilization efficiency index, software and hardware error rate, and command response delay amplitude of automation equipment; The resource utilization efficiency index, software and hardware error rate, and command response delay amplitude are comprehensively calculated to obtain the operation index, which is expressed as follows: , where is the running index, is the resource utilization efficiency index, is the software and hardware error rate, is the command response delay amplitude, , , are the adjustment coefficients of resource utilization efficiency index, software and hardware error rate, and instruction response delay amplitude, respectively, and , , All are greater than 0; The impact factor is combined with the operating status to calculate the warning coefficient, and based on the warning coefficient, it is determined whether the current system of the automation equipment needs to be rolled back, including the following steps: Obtain the influencing factor and operation index of the automation equipment, and calculate the warning coefficient by combining the influencing factor with the operation index. The expression is: , where is the warning factor, is the impact factor, is the operation index; Compare the warning coefficient with the preset warning threshold, which is used to determine whether the current system of the automation device needs to be rolled back; If the warning coefficient is less than or equal to the warning threshold, it is determined that there is no need to roll back the current system of the automation device. If the warning coefficient is greater than the warning threshold, it is determined that the current system of the automation device needs to be rolled back.

2. According to the method for upgrading and managing unmanned workshop automation equipment based on the Internet of Things according to claim 1, it is characterized in that: After the automated equipment processes the product, the product processing information is analyzed to generate the influencing factors for the automated equipment, including the following steps: After the automated equipment completes product processing, the quantity deviation and defective rate of the product are obtained, and the quantity deviation and defective rate are normalized so that the value range of the quantity deviation and the defective rate is mapped to [0,1], and the normalized value of the quantity deviation and the normalized value of the defective rate are obtained. The normalized value of the quantity deviation and the normalized value of the defective rate are summed to obtain the influencing factor.

3. According to the method for upgrading and managing unmanned workshop automation equipment based on the Internet of Things according to claim 2, it is characterized in that: The average value of the warning coefficient of all automation equipment is obtained regularly. When the automation equipment has a system upgrade task, the average value of the warning coefficient is combined with the work task of the automation equipment the next day to determine whether the system upgrade is required, including the following steps: Regularly obtain the average value of the warning coefficient of all automation equipment, the expression is: , where is the mean value of the warning coefficient, Get the number for the warning factor of the automation equipment, For the The warning coefficient value obtained this time; The overall coefficient is obtained by weighting the mean value of the warning coefficient and the task urgency index. The expression is: , where is the overall coefficient, is the mean value of the warning coefficient, is the task urgency index; If the overall coefficient is greater than the preset coefficient threshold, it is determined that a system upgrade is not required; if the overall coefficient is less than or equal to the preset coefficient threshold, it is determined that a system upgrade is required.

4. According to the method for upgrading and managing unmanned workshop automation equipment based on the Internet of Things according to claim 3, it is characterized in that: The logic for obtaining the task urgency index is as follows: obtaining the product processing urgency index and product cost unit price of the automation equipment one day later, normalizing the product processing urgency index and product cost unit price, and then summing the normalized product processing urgency index and product cost unit price to obtain the task urgency index.

5. According to the method for upgrading and managing unmanned workshop automation equipment based on the Internet of Things according to claim 4, it is characterized in that: The calculation logic of the command response delay amplitude is: obtain the number of control commands received by the automation device during the monitoring period, record the time it takes for the automation device to respond to each control command, calculate the mean response time and the standard deviation of the response time, and the expression is: , where is the mean response time, is the standard deviation of response time, The number of control instructions received by the automation device, For automation equipment to respond to the The duration of a control instruction is calculated by dividing the mean response duration by the standard deviation of the response duration to obtain the instruction response delay amplitude, which is expressed as: The starting time point of the monitoring time period is the time point when the automation device receives the first control instruction.

6. The method for upgrading and managing automated equipment in an unmanned workshop based on the Internet of Things according to claim 5 is characterized in that: The calculation logic of the quantity deviation is: obtain the actual product production quantity of the automation equipment within the monitoring time period, and subtract the expected product production quantity from the actual product production quantity to obtain the quantity deviation; The calculation logic of the defective rate is as follows: the actual product production quantity of the automated equipment within the monitoring period is obtained, and all the products produced are tested to obtain the number of unqualified products, and the number of unqualified products is divided by the actual product production quantity to obtain the defective rate.

7. The method for upgrading and managing automated equipment in an unmanned workshop based on the Internet of Things according to claim 6 is characterized in that: The calculation logic of the resource utilization efficiency index is: obtain the actual amount of resources used and the maximum amount of resources available during the operation of the automation equipment, and divide the actual amount of resources used by the maximum amount of resources available to obtain the real-time resource utilization rate of the automation equipment. The calculation expression of the resource utilization efficiency index is: , where is the resource utilization efficiency index, For real-time resource utilization, is the standard resource utilization; The calculation expression for the software and hardware error rate is: obtain the number of hardware failure events and software crashes during the operation of the automation equipment, sum the number of hardware failure events and software crashes to obtain the number of errors, and divide the number of errors by the total number of operations of the automation equipment to obtain the software and hardware error rate.

8. An unmanned workshop automation equipment upgrade management system based on the Internet of Things, used to implement the management method according to any one of claims 1 to 7, characterized in that: It includes data acquisition module, analysis module, judgment module, upgrade decision module and management module; Data acquisition module: connects to the database of the unmanned workshop through the API interface, obtains all the automation equipment information in the unmanned workshop based on the database, and sends the automation equipment information to the analysis module and the management module; Analysis module: When there is an automation device that completes the system upgrade, within a period of time after the automation device system upgrade, multiple data of the automation device are monitored to analyze the operating status of the automation device. After the automation device completes the product processing, the product processing information is analyzed to generate an impact factor for the automation device. The operating status and impact factor are sent to the judgment module. Judgment module: The influencing factor is combined with the operating status to calculate the warning coefficient. Based on the warning coefficient, it is determined whether the current system of the automation equipment needs to be rolled back. The warning coefficient is sent to the upgrade decision module. Upgrade decision module: Regularly obtain the average value of the warning coefficient of all automation equipment. When there is a system upgrade task for the automation equipment, the average value of the warning coefficient is combined with the work task of the automation equipment the next day to determine whether the system needs to be upgraded; Management module: sort all automation equipment according to the average value of the warning coefficient, generate a management list, send the management list to the management personnel, and the management personnel select the management order of the automation equipment according to the management list.

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