A method for building a manufacturing unit innovation platform based on digital twin industrial robots

Abstract

The present invention discloses a method for building an innovative platform for a manufacturing unit based on a digital twin industrial robot. The present invention relates to the field of digital twin industrial robots. A method for building an innovative platform for a manufacturing unit based on a digital twin industrial robot comprises the following steps: collecting a set of building parameters, generating a building plan according to the set of building parameters, entering manual intervention, combining the manual intervention with the building plan to generate a building scheme; receiving building construction parameters, comparing the building construction parameters with the building scheme, and obtaining a set of correction data. By collecting building parameters and generating a building scheme in combination with manual intervention, and then comparing the building construction parameters with the building scheme in real time to obtain correction data, the accuracy of the manufacturing unit construction is ensured. This process not only improves the construction efficiency, but also effectively reduces the production risks caused by construction deviations, thereby improving the overall performance of the manufacturing unit.

Description

Technical Field

[0001] The present invention relates to the field of digital twin industrial robots, and specifically to a method for building an innovative platform for a manufacturing unit based on digital twin industrial robots. Background Art

[0002] With the advent of the intelligent manufacturing era, digital twin technology has become a key force driving industrial transformation and upgrading. Digital twin industrial robots, one of this technology's core applications, achieve precise monitoring and efficient management of the robot's operating status by establishing a real-time mapping relationship between the virtual model and the physical robot. This technology not only improves production efficiency and product quality, but also significantly reduces operating costs and safety risks, providing strong support for the intelligent and automated development of the manufacturing industry.

[0003] In existing technologies, the construction of manufacturing cells typically relies on traditional engineering design and manual debugging methods. This makes errors prone to occur during the construction process and difficult to detect and correct in a timely manner. Furthermore, existing technologies for predicting robot failures primarily rely on regular maintenance and sensor testing.

[0004] Regarding the shortcomings of existing technologies in manufacturing unit construction and robot fault prediction, we can find the following major problems: First, the manual control of manufacturing unit construction has poor error correction effect, lacks accurate data support and real-time feedback mechanism, resulting in difficulty in ensuring construction accuracy; second, fault prediction is insufficient and cannot be automatically adjusted. Existing technologies mainly rely on regular maintenance and sensor detection, and can only provide fault warnings based on real-time data, and do not have automatic adjustment capabilities. Summary of the Invention

[0005] The present application provides a method for building an innovative manufacturing unit platform based on digital twin industrial robots, which is used to solve the technical problems of poor error effect, insufficient fault prediction and inability to automatically adjust the existing manually controlled manufacturing unit construction.

[0006] In view of the above problems, the present application provides a method for building an innovative manufacturing unit platform based on digital twin industrial robots, comprising the following steps: collecting a set of building parameters, generating a building plan based on the set of building parameters, entering manual intervention, and combining the manual intervention with the building plan to generate a building solution; receiving building construction parameters, comparing the construction parameters with the building solution, and obtaining a correction data set; collecting a set of sensor data during the operation of the robot, analyzing the sensor data set to obtain robot operation status prediction data; collecting product quality inspection data, analyzing and establishing a relationship model between the quality inspection data and the robot operation status prediction data, and inputting the operation status prediction data according to the model to obtain failure rate prediction data; when the predicted failure rate reaches a set value, using a random environment to generate an adjustment plan, simulating and predicting the adjustment plan, and obtaining the optimal solution.

[0007] In summary, the present invention mainly has the following beneficial effects:

[0008] 1. This invention ensures the accuracy of manufacturing unit construction by collecting construction parameters and combining them with manual intervention to generate a construction plan. The system then compares the construction parameters with the construction plan in real time to obtain correction data. This process not only improves construction efficiency but also effectively reduces production risks caused by construction deviations, thereby improving the overall performance of the manufacturing unit.

[0009] 2. This invention collects and analyzes sensor data during robot operation, establishes a relationship model between quality inspection data and the robot's operating status, and thus predicts the failure rate. When the predicted failure rate reaches a set threshold, an adjustment plan is automatically generated and evaluated to determine the optimal solution. This method effectively prevents robot failures, ensures stable operation of the production line, and improves production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a logic diagram of a method for building an innovative manufacturing unit platform based on digital twin industrial robots in the present invention. DETAILED DESCRIPTION

[0011] This application provides a method for building an innovative manufacturing unit platform based on digital twin industrial robots, which is used to solve the technical problems of poor error effect, insufficient fault prediction and inability to automatically adjust the existing manually controlled manufacturing unit construction.

[0012] After introducing the basic principles of the present application, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the sake of ease of description, only the parts related to the present application, not all, are shown in the accompanying drawings.

[0013] Example 1

[0014] A method for building a manufacturing unit innovation platform based on a digital twin industrial robot includes the following steps:

[0015] S100: Collecting a set of construction parameters, generating a construction plan according to the set of construction parameters, entering manual intervention, and combining the manual intervention with the construction plan to generate a construction solution;

[0016] This includes the site's size, shape, floor material, and load-bearing capacity, all of which directly impact the layout and movement paths of the industrial robots. For example, if the site is small and has limited load-bearing capacity, it's necessary to select a smaller, lighter industrial robot and plan its movement path appropriately.

[0017] Among them, S100 also includes:

[0018] S111: Collect site environment model parameters as first construction parameters;

[0019] S112: Collect device model parameters as second building parameters;

[0020] S113: Collect the equipment's operating radius as the third setup parameter;

[0021] S114: Collecting other component model parameters as the fourth building parameters;

[0022] S115: Taking the first construction parameter, the second construction parameter, the third construction parameter, and the fourth construction parameter as the construction parameter set.

[0023] These parameters include, but are not limited to, the industrial robot's model, size, weight, and functional characteristics. Whether the industrial robot is capable of multiple functions, such as grasping, handling, and processing, as well as how and how efficiently these functions are implemented, should be considered. The collection device's operating radius refers to the maximum range the industrial robot can cover under normal operating conditions. This parameter is crucial in determining the number and layout of industrial robots. If the equipment's operating radius is too small, the number of industrial robots will need to be increased to ensure efficient operation of the entire manufacturing cell.

[0024] Other component model parameters include those of the various sensors, controllers, actuators, and other components used with the industrial robot. Understanding the sensitivity, accuracy, and response time of the sensors is crucial to ensure they accurately reflect the operating status of the industrial robot.

[0025] Among them, S100 also includes:

[0026] S121: parsing the set of building parameters;

[0027] S122: Entering manual intervention, analyzing the required parts of the manual intervention, randomly generating multiple sets of random plans, simulating and running the random plans, and outputting the random plans that have passed the simulation as optional plans;

[0028] S123: re-enter the manual intervention, analyze the manual intervention and adjust the optional plan, output it after adjustment, and repeat this step until the manual intervention confirms any plan, and output the plan as the construction plan.

[0029] The collected set of building parameters is parsed and processed to extract key information. Exemplarily, based on the parsed set of building parameters, a computer algorithm is used to randomly generate multiple sets of building plans. These plans will cover different industrial robot layouts, movement paths, and component configurations. These plans are simulated and run, and plans that can successfully pass the simulation test are screened out as optional plans. After obtaining the optional plan, manual intervention will be entered again to adjust and optimize the plan. This includes adjusting the number and layout of industrial robots according to actual needs, optimizing movement paths, replacing or upgrading components, etc. After adjustment, a new plan will be output, and this step will be repeated until manual intervention confirms that any plan is the final building plan.

[0030] S200: receiving construction parameters, comparing the construction parameters with the construction plan, and obtaining a correction data set;

[0031] In a digital twin industrial robot, consistency between the actual construction and the pre-set construction plan must be ensured so that subsequent simulations can be performed using the digital twin system. Real-time parameters of the construction process are first received, and then these parameters are carefully compared with the pre-set construction plan to identify and correct any deviations.

[0032] Among them, S200 also includes:

[0033] S210: Collect construction parameters and generate twin construction progress parameters;

[0034] S220: Compare the twin construction progress parameter with the construction plan to obtain a total deviation value, and compare the total deviation value with a manually set allowable deviation value;

[0035] S230: If the total deviation value is less than or equal to the allowable deviation value, overwrite the twin construction progress parameter to the construction plan, and build a twin model according to the overwritten construction plan; if the total deviation value is greater than the allowable deviation value, output each deviation value as a correction data set;

[0036] The total deviation value is obtained by the following formula:

[0037]

[0038] Among them, S represents the total deviation value, W i It represents the weight value of the i-th deviation item, which is set manually according to the deviation accuracy requirement. Psi represents the actual coordinate value of the i-th deviation item, Pyi represents the coordinate value of the construction plan of the i-th deviation item, and Pi represents the allowed deviation vector of the i-th deviation item.

[0039] The system first collects various parameters from the current construction phase, including but not limited to equipment location, installation angle, and connection status. These parameters together constitute the twin construction progress parameters. The system then compares these collected twin construction progress parameters with the pre-set construction plan. This comparison involves precisely matching each parameter to identify any discrepancies. For each discrepancy, the system calculates a total deviation value. This total deviation value reflects the overall degree of discrepancy between the current construction phase and the construction plan. The system then determines whether the total deviation value exceeds the manually set tolerance. If it does, indicating that the current construction phase is still within an acceptable range, the system overwrites the twin construction progress parameters with the construction plan and continues to build the twin model based on the updated plan. If the tolerance value is exceeded, the system outputs each deviation value as a correction data set for subsequent adjustments. For example, suppose that when building a production line based on digital twin industrial robots, the system detects that the installation position of a particular industrial robot deviates from the actual position in the construction plan. This deviation value is 5 centimeters, while the manually set tolerance value is only 2 centimeters. At this point, the system deems the deviation unacceptable and calculates the corresponding total deviation value. The system then outputs this deviation value as part of the correction data set, prompting the operator to adjust the position of the industrial robot to ensure the accuracy and stability of the entire production line. Through this process, the system can monitor the progress and quality of the construction in real time, ensuring that the final manufacturing unit meets the expected requirements.

[0040] S300: collecting sensor data sets during the operation of the robot, and analyzing the sensor data sets to obtain robot operation state prediction data;

[0041] Among them, S300 also includes:

[0042] S310: Collecting operating time data of the transfer mechanism as first sensor data;

[0043] S320: Collecting the operating time data of the working end as the second sensor data;

[0044] S330: Collecting loss rate data of each component as third sensor data;

[0045] S340: Collect idle time data as fourth sensor data;

[0046] S350: Collect average load data as fifth sensor data;

[0047] S360: Collect operating temperature data as sixth sensor data;

[0048] S370: taking the first sensor data, the second sensor data, the third sensor data, the fourth sensor data, the fifth sensor data, and the sixth sensor data as a sensor data set;

[0049] S380: Analyze the sensor data set to obtain robot operation status prediction data.

[0050] Transfer mechanism runtime data reflects the activity of industrial robots during material transfer or workpiece handling. By continuously monitoring this data, we can understand the robot's workload and potential wear. For example, in an automotive manufacturing plant, industrial robots frequently move parts between production lines. By collecting and analyzing transfer mechanism runtime data, we can predict which robots are at risk of wear due to extended operation, allowing for proactive maintenance.

[0051] The working head is the component of an industrial robot that performs specific tasks. Its runtime data is used to assess the robot's efficiency and lifespan. For example, in the assembly line of precision electronics, the working head of an industrial robot is required to perform precise operations for extended periods of time. By collecting this runtime data, the wear and tear of the working head can be calculated, allowing replacement or maintenance plans to be developed accordingly.

[0052] Component wear rate data is crucial for assessing a robot's overall health. Regularly monitoring component wear allows for timely detection of potential failures and timely action. For example, in heavy machinery manufacturing, the components of industrial robots often face the challenges of high loads and harsh environments. By collecting component wear rate data, the robot's overall performance can be assessed, allowing for component replacement or upgrades as necessary.

[0053] Idle time data helps understand the non-working status of robots, thereby optimizing production scheduling and improving equipment utilization.

[0054] The average load data reflects the average pressure that an industrial robot bears when performing a task and is an important indicator for evaluating its performance.

[0055] Industrial robots operating in high-temperature environments are susceptible to damage to their electrical components due to overheating. By collecting operating temperature data, overheating issues can be detected and addressed promptly, avoiding potential failures and safety incidents.

[0056] S400: Collect product quality inspection data, analyze and establish a relationship model between the quality inspection data and robot operation status prediction data, and input the operation status prediction data according to the model to obtain failure rate prediction data;

[0057] Collect product quality inspection data and analyze the relationship between this data and robot operation status prediction data to predict future failure rates, provide a basis for preventive maintenance, and support production optimization and maintenance decisions.

[0058] Among them, S400 also includes:

[0059] S411: Obtain product quality inspection data;

[0060] S412: Matching the product quality inspection data with the robot operation status prediction data to obtain a matching data set;

[0061] S413: Analyze the influence probability between each parameter in the robot operation state prediction data and the product quality inspection data according to the matching data set, and use the influence probability as the influence weight of each item;

[0062] S414: Construct a relationship model based on the influence weights.

[0063] Product quality inspection data includes but is not limited to product dimensions, weight, appearance defects, performance parameters, and other aspects. This data reflects the overall quality level of the product. The product quality inspection data is matched with the robot's operating status prediction data to form a matching data set.

[0064] Based on the matching data set, the factory analyzes the probability of each parameter in the robot's operating status prediction data affecting the product quality inspection data, and uses these impact probabilities as the impact weights for each parameter. For example, through data analysis, the factory discovered that when the robot is operating under high load and for long periods of time, the dimensional deviations of the parts produced are large. Therefore, the factory considers the robot's load and operating time as important parameters affecting product quality and assigns them higher impact weights.

[0065] Based on the above analysis, a relationship model between quality inspection data and robot operation status prediction data was constructed. This model can reflect the direct impact of the robot operation status on product quality.

[0066] Among them, S400 also includes:

[0067] S421: Bringing the operating status prediction data into the relational model to obtain target fault prediction parameters;

[0068] S422: Traverse all devices to obtain multiple target fault prediction parameters;

[0069] S423: taking all the target fault prediction parameters as failure rate prediction data;

[0070] The failure rate formula used in the relational model is as follows:

[0071] Fr=k*Tta*Tw b *Wec*Ti d *Loe*Te f

[0072] In this formula, Fr is the failure rate, which represents the probability or frequency of failure of the industrial robot per unit time. k is a constant used to adjust the overall scale of the formula. Tt is the operating time of the transfer mechanism, which represents the cumulative operating time of the mechanism responsible for material transfer or workpiece handling in the industrial robot. Tw is the operating time of the working end, which represents the cumulative operating time of the end performing specific work tasks in the industrial robot. We is the loss rate of each component, which is calculated based on data detected by multiple sensors and is used to represent the degree of wear of each key component of the industrial robot. Ti is the idle time, which represents the cumulative time that the industrial robot is in a non-working state. Lo is the average load, which represents the average load size borne by the industrial robot when performing tasks. Te is the operating temperature, which represents the ambient temperature of the industrial robot when it is working. The indices a, b, c, d, e, and f respectively represent the influence weights of these factors.

[0073] The robot's operating status prediction data is input into the constructed relational model to obtain target failure prediction parameters. These parameters reflect the type and probability of future robot failures. To comprehensively assess the failure risk of the entire production line, it is necessary to traverse all industrial robots and obtain their respective target failure prediction parameters. All target failure prediction parameters are integrated to generate failure rate prediction data. This data provides the factory with a comprehensive failure risk assessment and helps formulate targeted maintenance plans.

[0074] S500: When the predicted failure rate reaches a set value, an adjustment plan is generated using a random environment, and a simulation prediction is performed on the adjustment plan to obtain an optimal solution.

[0075] The system initiates a series of contingency plan generation and evaluation processes to find the optimal solution. If the system predicts that the failure rate of a particular industrial robot or robots will reach or exceed a preset threshold, it automatically initiates the contingency plan generation and evaluation process. Through simulation and prediction, the system finds the optimal adjustment plan to reduce the risk of failure and ensure stable operation of the production line.

[0076] S510: Manually set the failure rate adjustment threshold;

[0077] S520: When the failure rate prediction data is greater than or equal to the failure rate adjustment threshold, an adjustment procedure is started;

[0078] S530: Exclude devices whose failure rate prediction data is greater than or equal to the failure rate adjustment threshold, and add the remaining devices to the reassembly sequence;

[0079] S540: generating a plurality of adjustment plans according to the recombinant sequence;

[0080] S550: Verify the adjustment plan, calculate the corresponding overall failure rate, and obtain overall failure rate data of multiple groups of plans;

[0081] S560: Arrange the overall failure rate data of the solutions and take the lowest value as the optimal solution;

[0082] S570: Add maintenance tags to all devices whose failure rate prediction data is greater than or equal to the failure rate adjustment threshold, and generate a maintenance list;

[0083] S580: Output the optimal solution and the maintenance list as the optimal solution.

[0084] This is the foundation of the entire process, requiring manual effort to set a reasonable failure rate threshold based on the actual production line conditions and the robot's performance characteristics. When the predicted failure rate reaches or exceeds this threshold, the system triggers adjustments. For example, in an automotive manufacturing plant, engineers set the failure rate threshold at 0.05%, based on the acceptable defect rate during production and other possible business requirements. This means that if a robot's predicted failure rate reaches or exceeds 0.05%, the system automatically initiates the contingency plan generation and evaluation process.

[0085] When the system detects that the predicted failure rate of a particular robot or robots reaches or exceeds a set threshold, it automatically initiates an adjustment process and enters the contingency plan generation phase. After initiating the adjustment process, the system first removes the equipment whose predicted failure rate reaches or exceeds the threshold from the current production line, then reorganizes the remaining equipment into a new sequence for subsequent generation of an adjustment plan. For example, in an automobile manufacturing plant, when the system detects that the predicted failure rate of a particular robot exceeds 0.05%, the robot is temporarily removed from the production line. The system then reorganizes the remaining equipment to form a new production line sequence with a lower risk of failure.

[0086] Based on the reorganized equipment sequence, the system automatically generates multiple possible adjustment plans. These plans might include adjusting robot workloads, optimizing production processes, replacing faulty components, and other measures. For example, in an automotive manufacturing plant, the system might generate the following adjustment plans: reducing the workload of robots at high risk of failure and assigning them to other production lines; optimizing production processes to reduce unnecessary robot operations; and replacing components with high failure rates, such as motors or sensors.

[0087] For each generated plan, the system performs simulations and forecasts to assess its effectiveness in actual operation. By calculating the overall failure rate after implementation, the system can compare the pros and cons of different plans. After comparing the failure rate data for all plans, the system selects the plan with the lowest failure rate as the optimal solution. This solution will serve as the basis for subsequent implementation adjustments.

[0088] For all devices whose predicted failure rates meet or exceed the threshold, the system assigns a maintenance tag and generates a maintenance list. This list serves as a reference for subsequent maintenance tasks. For example, in an automotive manufacturing plant, all robots with a failure rate exceeding 0.05% are assigned a maintenance tag and placed on a detailed maintenance list. This list includes each robot's failure type, maintenance recommendations, and estimated maintenance time.

[0089] Finally, the system outputs the optimal solution and maintenance list as the output of the entire process. These results will serve as the basis for subsequent production adjustments and maintenance work.

[0090] Those skilled in the art will understand that the various numerical numbers such as the first and second involved in this application are only for the convenience of description, and are not used to limit the scope of this application, nor do they indicate the order of precedence. "And / or" describes the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one" refers to one or more. At least two refers to two or more. "At least one", "any one" or similar expressions refer to any combination of these items, including any combination of single items (individuals) or plural items (individuals). For example, at least one item (individual, kind) of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0091] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described herein are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0092] The steps of the method or algorithm described in this application can be directly embedded in hardware, software units executed by a processor, or a combination of the two. The software units can be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM or any other form of storage medium in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC, and the ASIC can be arranged in a terminal. Optionally, the processor and the storage medium can also be arranged in different components in the terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are performed on the computer or other programmable device to produce computer-implemented processing, so that the instructions executed on the computer or other programmable device provide for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1A step that specifies a function in one or more boxes.

[0093] Although the present application has been described with reference to specific features and embodiments thereof, it will be apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.

Claims

1. A method for building a manufacturing unit innovation platform based on digital twin industrial robots, characterized in that: The following steps are involved: Collecting a set of building parameters, generating a building plan based on the set of building parameters, entering manual intervention, and combining the manual intervention with the building plan to generate a building solution; Receiving construction parameters, comparing the construction parameters with the construction plan, and obtaining a correction data set further includes: Collect construction parameters and generate twin construction progress parameters; Comparing the twin construction progress parameter with the construction plan to obtain a total deviation value, and comparing the total deviation value with a manually set allowable deviation value; If the total deviation value is less than or equal to the allowable deviation value, the twin construction progress parameter is overwritten into the construction plan, and the twin model is constructed according to the overwritten construction plan; if the total deviation value is greater than the allowable deviation value, each deviation value is output as a correction data set; The total deviation value is obtained by the following formula: Among them, S represents the total deviation value, W i Represents the weight value of the i-th deviation item, which is set manually according to the deviation accuracy requirement. Psi represents the actual coordinate value of the i-th deviation item, Pyi represents the coordinate value of the construction plan of the i-th deviation item, and Pi represents the allowed deviation vector of the i-th deviation item; Collecting a set of sensor data during the operation of the robot, and analyzing the sensor data set to obtain robot operation state prediction data; Collect product quality inspection data, analyze and establish a relationship model between the quality inspection data and robot operation status prediction data, and input the operation status prediction data according to the model to obtain failure rate prediction data; When the predicted failure rate reaches a set value, a random environment is used to generate an adjustment plan, and the adjustment plan is simulated and predicted to obtain the optimal solution.

2. The method for building a manufacturing unit innovation platform based on a digital twin industrial robot according to claim 1 is characterized in that: The acquisition and establishment parameter set further includes: Collect site environment model parameters as the first construction parameters; Collect device model parameters as the second building parameters; The operating radius of the acquisition equipment is used as the third construction parameter; Collect other component model parameters as the fourth building parameter; The first construction parameter, the second construction parameter, the third construction parameter and the fourth construction parameter are taken as the construction parameter set.

3. The method for building a manufacturing unit innovation platform based on a digital twin industrial robot according to claim 2, characterized in that: Generating a construction plan according to the construction parameter set, inputting manual intervention, and combining the manual intervention with the construction plan to generate a construction solution further includes: Parsing the set of building parameters; Entering manual intervention, analyzing the required parts of the manual intervention, randomly generating multiple sets of random plans, simulating and running the random plans, and outputting the random plans that have passed the simulation as optional plans; The manual intervention is input again, the manual intervention is analyzed and the optional plan is adjusted, and the adjusted plan is output, and this step is repeated until the manual intervention confirms any plan, and the plan is output as the construction plan.

4. The method for building a manufacturing unit innovation platform based on a digital twin industrial robot according to claim 1, characterized in that: The collecting of sensor data sets during the operation of the robot and analyzing the sensor data sets to obtain robot operation state prediction data further includes: collecting operating time data of the transfer mechanism as first sensor data; collecting operating time data of the working end as second sensor data; Collecting loss rate data of each component as third sensor data; Collecting idle time data as fourth sensor data; Collecting average load data as fifth sensor data; collecting operating temperature data as sixth sensor data; taking the first sensor data, the second sensor data, the third sensor data, the fourth sensor data, the fifth sensor data and the sixth sensor data as a sensor data set; Analyze the sensor data set to obtain robot operation status prediction data.

5. The method for building a manufacturing unit innovation platform based on a digital twin industrial robot according to claim 1, characterized in that: The collecting of product quality inspection data and analyzing and establishing a relationship model between the quality inspection data and robot operation status prediction data further includes: Obtain product quality inspection data; Matching the product quality inspection data with the robot operation status prediction data to obtain a matching data set; Analyze the influence probability between each parameter in the robot operation state prediction data and the product quality inspection data according to the matching data set, and use the influence probability as the influence weight of each item; Build a relationship model based on influence weights.

6. The method for building a manufacturing unit innovation platform based on a digital twin industrial robot according to claim 5, characterized in that: The step of inputting the operating status prediction data according to the model to obtain the failure rate prediction data further includes: Bringing the operating status prediction data into the relational model to obtain target fault prediction parameters; Traverse all devices and obtain multiple target fault prediction parameters; using all of the target fault prediction parameters as failure rate prediction data; The failure rate formula used in the relational model is as follows: Fr=k*Tta*Tw b *Lose*Ti d *You*You f In this formula, Fr is the failure rate, which represents the probability or frequency of failure of the industrial robot per unit time. k is a constant used to adjust the overall scale of the formula. Tt is the operating time of the transfer mechanism, which represents the cumulative operating time of the mechanism responsible for material transfer or workpiece handling in the industrial robot. Tw is the operating time of the working end, which represents the cumulative operating time of the end performing specific work tasks in the industrial robot. We is the loss rate of each component, which is calculated based on data detected by multiple sensors and is used to represent the degree of wear of each key component of the industrial robot. Ti is the idle time, which represents the cumulative time that the industrial robot is in a non-working state. Lo is the average load, which represents the average load size borne by the industrial robot when performing tasks. Te is the operating temperature, which represents the ambient temperature of the industrial robot when it is working. The indices a, b, c, d, e, and f respectively represent the influence weights of these factors.

7. The method for building a manufacturing unit innovation platform based on a digital twin industrial robot according to claim 6, characterized in that: When the predicted failure rate reaches a set value, generating an adjustment plan using a random environment, performing simulation prediction on the adjustment plan, and obtaining an optimal solution further includes: Manually set the failure rate adjustment threshold; When the failure rate prediction data is greater than or equal to the failure rate adjustment threshold, the adjustment procedure is started; Excluding devices whose failure rate prediction data is greater than or equal to the failure rate adjustment threshold, and adding the remaining devices to the reorganization sequence; generating a plurality of groups of adjustment plans according to the recombinant sequence; Verify the adjustment plan, calculate the corresponding overall failure rate, and obtain overall failure rate data for multiple groups of plans; Arrange the overall failure rate data of the solutions and take the lowest value as the optimal solution; Adding maintenance tags to all devices whose failure rate prediction data is greater than or equal to the failure rate adjustment threshold, and generating a maintenance list; The optimal solution and the maintenance list are output as the optimal solution.

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