Quality data acquisition method and system based on cloud edge-end cooperation

Abstract

The application relates to the technical field of data processing, in particular to a quality data acquisition method and system based on cloud-edge-end cooperation, which solves the technical problems of uneven resource utilization of edge nodes and high processing delay of urgent tasks caused by only relying on physical distance scheduling in the prior art. The method comprises the following steps: acquiring production state data in a production process; determining the task urgency of a data processing task according to the production state data; determining a plurality of edge nodes with a physical distance from an end node within a preset range as a candidate node set; for each edge node in the candidate node set, determining a scheduling evaluation value of the edge node for the data processing task according to the physical distance between the edge node and the end node, the current load of the edge node, the average task urgency of the existing tasks on the edge node and the task urgency of the data processing task; and scheduling the data processing task to the edge node with a scheduling evaluation value meeting a preset condition for processing.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for quality data acquisition based on cloud-edge-device collaboration. Background Technology

[0002] The core of a quality management system is to achieve continuous improvement in quality management and enhance an enterprise's product quality assurance capabilities. It has been widely applied in various industries such as automotive, aerospace, electronics, and environmental management, and is an important means for enterprises to adapt to the trend of digital intelligence and enhance their market competitiveness. Currently, quality data acquisition often adopts cloud-edge-device collaborative technology, integrating the large-capacity storage and strong computing power of the cloud platform, the low latency and fast response advantages of the edge device, and the multi-channel rapid acquisition capabilities of high-performance endpoints. This significantly improves the transmission and computing efficiency of industrial data and avoids the data transmission latency problems of traditional centralized computing.

[0003] In the automotive parts manufacturing process, it is necessary to collect and inspect the quality data of intermediate products in each processing step, making the application of a cloud-edge-device collaborative system particularly crucial. In this scenario, end nodes are deployed in areas such as machine tools and measurement stations to collect basic quality data, while edge nodes are responsible for timely data processing and issuing operation instructions such as quality inspection approval and abnormal alarms. Finally, the data is uploaded to the cloud for model learning, and the effective scheduling of edge nodes that collect data from end nodes directly affects the overall system efficiency.

[0004] Current technologies allocate edge nodes solely based on the physical distance between end nodes and edge nodes when collecting data from end nodes, aiming to reduce data transmission latency and improve processing and command issuance speed. However, this approach fails to consider the existing scheduling task load of each edge node and ignores the differences in the urgency of data processing at different pipeline stages. This results in edge nodes not being effectively utilized, some edge nodes potentially becoming idle, while data with high urgency on the pipeline is easily delayed in processing, leading to pipeline congestion and ultimately impacting the overall system efficiency. Summary of the Invention

[0005] To address the technical problems of uneven edge node resource utilization and high-urgency task processing delays caused by relying solely on physical distance scheduling in existing technologies, the present invention aims to provide a quality data acquisition method and system based on cloud-edge-device collaboration. The specific technical solution adopted is as follows: Firstly, a method for quality data acquisition based on cloud-edge-device collaboration is provided, comprising: acquiring production status data in the production process; responding to the completion of quality data acquisition by the end node and generating a data processing task, and determining the task urgency of the data processing task based on the production status data; determining multiple edge nodes whose physical distance from the end node is within a preset range as a candidate node set; for each edge node in the candidate node set, determining the scheduling evaluation value of the edge node for the data processing task based on the physical distance between the edge node and the end node, the current load of the edge node, the average task urgency of existing tasks on the edge node, and the task urgency of the data processing task; and scheduling the data processing task to the edge node whose scheduling evaluation value meets the preset conditions for processing.

[0006] Based on the above technical solution, in the quality data acquisition method based on cloud-edge-device collaboration provided by the present invention, by introducing a task urgency assessment based on production status and comprehensively considering the physical distance of edge nodes, real-time load and their existing task urgency, dynamic intelligent scheduling of quality data processing tasks is realized under the cloud-edge-device collaborative architecture. This effectively solves the system efficiency bottleneck problem of uneven resource utilization of edge nodes and processing delay of high-urgency tasks, and improves the overall response speed and resource utilization of the quality data acquisition and processing system.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, the production status data includes the number of processed parts in each process of the production line, the number of deployed machine tools, the processing time of a single part, and the number of pending tasks received by the edge node and the historical records of processed tasks.

[0008] In conjunction with the first aspect above, in one possible implementation, the method for determining the urgency of a data processing task based on production status data specifically includes: determining the relative load ratio of the next production stage to the current production stage, and the processing time of the current and next production stages, based on the production status data of the current and next production stages to which the end node belongs; and determining the urgency of the data processing task based on the relative load ratio, the processing time of the current and next production stages.

[0009] In conjunction with the first aspect above, in one possible implementation, the method for determining the relative load ratio of the next production stage relative to the current production stage based on the production status data of the current production stage and the next production stage to which the end node belongs specifically includes: determining the load ratio of production demand relative to production capacity for the current production stage and the next production stage, respectively, based on the number of processed parts, the number of deployed machine tools, and the rated production capacity of the machine tools; and determining the ratio of the load ratio of the next production stage to the load ratio of the current production stage as the relative load ratio of the next production stage relative to the current production stage.

[0010] In conjunction with the first aspect above, in one possible implementation, the method for determining the scheduling evaluation value of an edge node for a data processing task based on the physical distance between the edge node and the end node, the current load of the edge node, the average task urgency of existing tasks on the edge node, and the task urgency of the data processing task specifically includes: determining a transmission timeliness factor based on the physical distance between the edge node and the end node; the transmission timeliness factor is used to characterize the negative impact of physical distance on data transmission latency; determining a processing timeliness factor based on the current load of the edge node; the processing timeliness factor is used to characterize the negative impact of the current load on task processing waiting time; determining an initial evaluation value of the edge node for the data processing task based on the transmission timeliness factor and the processing timeliness factor; and correcting the initial evaluation value based on the relative magnitude between the task urgency of the data processing task and the average task urgency of existing tasks on the edge node to obtain a scheduling evaluation value.

[0011] In conjunction with the first aspect above, in one possible implementation, the method of correcting the initial evaluation value based on the relative magnitude relationship between the task urgency of the data processing task and the average task urgency of existing tasks on the edge node to obtain the scheduling evaluation value specifically includes: determining the parameter tuning coefficient through the relative magnitude relationship to correct the initial evaluation value to obtain the scheduling evaluation value; increasing the parameter tuning coefficient if the task urgency of the data processing task is higher than the average task urgency of existing tasks; and decreasing the parameter tuning coefficient if the task urgency of the data processing task is lower than the average task urgency of existing tasks.

[0012] In conjunction with the first aspect above, in one possible implementation, the method for obtaining production status data in the production process specifically includes: obtaining production status data through a preset regional status synchronization mechanism; the regional status synchronization mechanism is used to synchronize the running status of the production process and the task execution status of each node in real time.

[0013] In conjunction with the first aspect above, in one possible implementation, the method of scheduling data processing tasks to edge nodes whose scheduling evaluation values ​​meet preset conditions specifically includes: selecting edge nodes whose scheduling evaluation values ​​exceed preset thresholds from the candidate node set to form a preferred node set; and prioritizing scheduling data processing tasks to the edge nodes with the highest scheduling evaluation values ​​in the preferred node set.

[0014] In conjunction with the first aspect mentioned above, in one possible implementation, the method further includes: receiving feedback results from edge nodes after processing data processing tasks; associating the feedback results with the original quality data corresponding to the data processing tasks and uploading them to the cloud platform.

[0015] In a second aspect, a quality data acquisition system based on cloud-edge-device collaboration is provided, comprising: a cloud platform, multiple edge nodes, and multiple end nodes deployed on the production line; the end nodes are used to collect quality data during the production process and generate data processing tasks; the system is configured to perform the method as described in any of the first aspects.

[0016] The present invention has the following beneficial effects: By introducing a task urgency assessment based on production status and comprehensively considering the physical distance of edge nodes, real-time load, and existing task urgency, dynamic intelligent scheduling of quality data processing tasks is achieved under the cloud-edge-device collaborative architecture. This effectively solves the system efficiency bottleneck problems of uneven resource utilization of edge nodes and processing delays of high-urgency tasks, and improves the overall response speed and resource utilization of the quality data acquisition and processing system. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A system architecture diagram of a quality data acquisition system based on cloud-edge-device collaboration is provided in one embodiment of the present invention; Figure 2 A flowchart illustrating a quality data acquisition method based on cloud-edge-device collaboration, provided as an embodiment of the present invention; Figure 3 This is a schematic diagram of the hardware structure of a quality data acquisition device based on cloud-edge-device collaboration, provided as an embodiment of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a cloud-edge-device collaborative quality data acquisition method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The following description, in conjunction with the accompanying drawings, details the specific scheme of a quality data acquisition method and system based on cloud-edge-device collaboration provided by the present invention.

[0022] Please see Figure 1 The diagram illustrates a system architecture of a cloud-edge-device collaborative quality data acquisition system according to an embodiment of the present invention. The cloud-edge-device collaborative quality data acquisition system includes: a cloud platform, multiple edge nodes, and multiple end nodes deployed on the production line.

[0023] End nodes are the fundamental units for system data acquisition. They are implemented through physical devices deployed at key locations on the production line, specifically using high-precision sensors, data acquisition terminals, and other hardware equipment. These devices are widely distributed across machine tools, measurement stations, and other areas, directly contacting intermediate products during the production process to acquire quality-related information. End nodes are primarily responsible for two core tasks: first, collecting quality data from intermediate products during the production process. This is achieved through physical devices such as optical sensors, pressure sensors, and dimensional measurement sensors, collecting key parameters such as the size, accuracy, and integrity of the intermediate products. The data acquisition process responds in real-time to the production process progress, ensuring data timeliness; second, initially processing the acquired raw quality data, transforming it into standardized data processing tasks, and clarifying the corresponding production stage, acquisition time, and other related information for each task.

[0024] Edge nodes are the core execution units for system data processing. They can be physical devices with local data processing capabilities, such as edge servers and industrial control hosts, deployed in local areas of the production workshop to achieve low-latency data processing and rapid response. Edge nodes are primarily responsible for two core tasks: receiving scheduling instructions and acquiring raw quality data transmitted from end nodes; analyzing and processing the data according to preset quality inspection rules; comparing the data's conformity with preset quality standards to determine whether intermediate products meet production requirements (they can call upon a locally stored quality standard database to assist in the judgment during processing); and generating corresponding processing instructions (process flow instructions or exception handling instructions) based on the judgment results. These instructions are fed back to the end nodes to guide them in executing intermediate product transfer, rework, or rejection operations. Simultaneously, the processing results and data processing details are integrated into a feedback result and sent synchronously to the cloud platform, providing raw materials for cloud-based data analysis and model optimization.

[0025] The cloud platform is the core unit for system data storage, analysis, and optimization. It is implemented through physical devices such as cloud server clusters, distributed databases, and cloud computing nodes, possessing large-capacity storage, strong computing power, and remote data interaction capabilities. It is a crucial link in forming a closed-loop optimization system. The cloud platform is primarily responsible for three core tasks: receiving feedback results from edge nodes and raw quality data transmitted from end nodes, associating and storing both to establish a complete database containing dimensions such as quality data, processing results, production stages, and scheduling information, providing data support for subsequent data analysis and traceability; employing pre-set data analysis algorithms and modeling techniques to establish a quality analysis model, uncovering the correlation between quality data and production processes and scheduling strategies, and analyzing key indicators such as the rationality of edge node load allocation and the accuracy of task urgency assessment; and generating targeted optimization instructions based on the model output, including suggestions for adjusting scheduling parameters, optimization schemes for task urgency assessment thresholds, and updates to edge node processing rules, continuously optimizing the system's scheduling efficiency and data processing accuracy.

[0026] Please see Figure 2 The diagram illustrates a flowchart of a cloud-edge-device collaborative quality data acquisition method according to an embodiment of the present invention. This cloud-edge-device collaborative quality data acquisition method includes: S1. Obtain production status data in the production process.

[0027] Production status data includes the number of processed parts, the number of deployed machine tools, and the processing time of a single part for each process in the production line, as well as the number of pending tasks received by edge nodes and the historical records of processed tasks. Specifically, the data collection targets are each process in the production line and edge nodes. The machine tool cluster controller of each process collects the number of processed parts (e.g., process 1 is currently processing 120 crankshafts), the number of deployed machine tools (process 1 deploys 8 machine tools), and the processing time of a single part (process 1's single crankshaft processing time is 3 minutes). The task management module of each edge node collects the number of pending tasks it has received (e.g., edge node 1 currently has 5 pending tasks) and the historical records of processed tasks (edge ​​node 1 processed 30 quality data tasks in the past hour).

[0028] In some implementations, production status data can be obtained through a pre-defined regional status synchronization mechanism. This mechanism is used to synchronize the operational status of the production process and the task execution status of each node in real time. Specifically, for multiple production lines corresponding to automotive parts such as crankshafts and camshafts, a regional status synchronization mechanism combining heartbeat packet broadcasting and central node distribution is first initiated, while a pre-defined synchronization period (e.g., 5 seconds) is set. End nodes deployed at machine tools, measurement stations, edge nodes in local areas of the workshop, and production line control units of each process send a heartbeat packet containing its current status every period, establishing a real-time data transmission link to ensure that the delay of production status data does not exceed the set period, thus guaranteeing data timeliness.

[0029] S2. In response to the end node completing the quality data collection and generating a data processing task, the task urgency of the data processing task is determined based on the production status data.

[0030] In some implementations, the method of S2 can be specifically implemented through the following S21 to S22, which are explained in detail below: S21. Based on the production status data of the current production stage and the next production stage to which the end node belongs, determine the relative load ratio of the next production stage relative to the current production stage, as well as the processing time of the current production stage and the next production stage.

[0031] In some implementations, the method for determining the relative load ratio may include: for the current production stage and the next production stage (e.g., the current production stage is a crankshaft roughing process, and the next production stage is a crankshaft fine grinding process), determining the load ratio K of production demand relative to production capacity based on the number of parts processed in the production stage p, the number of machine tools deployed c, and the rated production capacity n of the machine tools (characterizing the upper limit of the number of parts that a single machine tool can stably process, determined by the machine tool model, process requirements, etc.), expressed as: The ratio of the load ratio of the next production stage to the load ratio of the current production stage is defined as the relative load ratio of the next production stage relative to the current production stage, such as the load ratio of the i-th production stage. Load ratio compared to the (i+1)th production stage The relative load ratio is This represents the load saturation ratio of the next production stage relative to the current production stage. Specifically, if the current production stage is the last process in the pipeline, the load ratio of the next production stage is preset to 1 to avoid calculation interruption due to the lack of subsequent processes.

[0032] S22. Determine the urgency of the data processing task based on the relative load ratio, the processing time of the current production stage, and the processing time of the next production stage.

[0033] In some implementations, based on the relative load ratio Processing time at the current production stage and the next production stage The processing time is used to determine the urgency J of the data processing task, which is expressed as: In the formula, It is the relative load ratio. The smaller the relative load ratio, the more relaxed the production demand in the next stage is relative to its production capacity (the lower the load). The shorter the waiting time for the next stage after the scheduled task is completed, the higher the value of alleviating the congestion in the current stage. Therefore, it needs to be processed first and is negatively correlated with the urgency of the task. The reciprocal of the relative load ratio is normalized to the [0, 1] interval by normalizing the difference in magnitude through the normalization function norm. It is the sum of the processing time of the current stage and the next stage. The smaller the sum of processing time, the faster the processing rhythm of the two stages, and the higher the efficiency of the flow after the scheduled task is completed. It is negatively correlated with the urgency of the task. By balancing the difference in magnitude through the normalization function norm, the reciprocal of the sum of time is normalized to the interval [0, 1]. Multiplication emphasizes synergy; the result will only increase significantly when both factors are at a high level. If one factor is at a low level, the result will be significantly lowered, thus achieving priority screening while simultaneously satisfying the tasks of ample production capacity and short processing time in the next stage.

[0034] The normalization function norm mentioned in the embodiments of this invention ( Unless otherwise specified, all results are normalized using maximum and minimum values. The maximum and minimum values ​​are preset empirical extreme values ​​derived from a large amount of historical experimental data. If the calculated result exceeds the [0, 1] interval, it is restricted to the [0, 1] range using a truncation function (i.e., if the result is less than 0, it is taken as 0; if it is greater than 1, it is taken as 1) to eliminate the influence of outliers on the result.

[0035] Specifically, if the current production stage is the last production stage, then, in accordance with the description in S21, The value is 1. In addition, since the task in the last stage is not without time consumption after completion, it is still necessary to go through the closing stages such as quality inspection and verification, parts warehousing, and data archiving. Therefore, a fixed processing time for the closing stages can be set based on historical production data, such as 5 minutes (applicable to most closing scenarios of parts production, such as parts cleaning, label pasting, and pre-warehousing verification).

[0036] S3. Select multiple edge nodes whose physical distance from the end node is within a preset range as a candidate node set.

[0037] In some implementations, based on the layout of the production workshop and the performance requirements for low latency data transmission, a physical distance threshold (such as 50 meters) is preset to clarify the screening boundaries of edge nodes and ensure that the edge nodes selected subsequently can meet the basic requirements for the timeliness of data transmission.

[0038] The coordinate distance between the endpoint node and the edge node is calculated. The distance calculation results of all edge nodes are traversed, and edge nodes whose physical distance does not exceed the preset threshold are selected to construct a candidate node set.

[0039] S4. For each edge node in the candidate node set, determine the scheduling evaluation value of the edge node for the data processing task based on the physical distance between the edge node and the end node, the current load of the edge node, the average task urgency of the existing tasks on the edge node, and the task urgency of the data processing task.

[0040] In some implementations, method S4 can be specifically implemented using S41 to S44, which are explained in detail below: S41. Determine the transmission time factor based on the physical distance between the edge node and the end node.

[0041] The transmission timeliness factor is used to characterize the negative impact of physical distance on data transmission latency. In some implementations, the transmission timeliness factor is determined based on the physical distance *l* between the edge node and the end node, combined with the preset physical distance threshold *r* used by the end node in S3 to filter edge nodes. Represented as: In the formula, This represents the relative physical distance. The greater the distance, the longer the transmission delay, and it is negatively correlated with the timeliness of scheduling. It is a normalized representation of the negative value of physical distance, characterizing the degree of negative impact of physical distance on data transmission latency. The larger the value, the smaller the negative impact of physical distance on transmission latency, and the faster the data transmission speed.

[0042] S42. Determine the processing time factor based on the current load of the edge nodes.

[0043] The processing timeliness factor is used to characterize the negative impact of the current load on task processing wait time. In some implementations, the current load w of the edge node can be characterized by the number of existing tasks on the edge node (including the total number of tasks currently queued and being processed), and the processing timeliness factor is determined based on the current load w of the edge node. Represented as: In the formula, the smaller the current load w, the lower the load of the edge node and the shorter the task processing waiting time, which is negatively correlated with the timeliness of scheduling. It is a very small positive number to avoid the denominator being zero, for example, it can be set to 1; It is a negative normalized result of the current load, representing the degree of negative impact of the current load on the task processing wait time. The larger the value, the smaller the negative impact of the current load on the wait time, and the shorter the task processing wait time.

[0044] S43. Determine the initial evaluation value of the edge node for the data processing task based on the transmission time factor and the processing time factor.

[0045] In some implementations, the cooperative nature of multiplication is utilized to calculate the transmission time factor. and processing time factor The product of these terms yields the initial evaluation value S of the edge node for the data processing task, expressed as: This facilitates subsequent filtering while simultaneously ensuring fast transmission and processing, allowing edge nodes to complete tasks as quickly as possible.

[0046] Furthermore, the initial evaluation values ​​of all edge nodes within the candidate node set are traversed to determine the maximum value of the initial evaluation values ​​within the set. This is used to map the initial evaluation values ​​to the interval between 0 and 1, thus obtaining the normalized result. It accurately reflects the relative adaptability of each edge node.

[0047] S44. Based on the relative relationship between the urgency of the data processing task and the average urgency of existing tasks on the edge node, the initial evaluation value is corrected to obtain the scheduling evaluation value.

[0048] In some implementations, parameter tuning coefficients are determined based on relative magnitudes to correct the initial evaluation value and obtain the scheduling evaluation value. If the urgency of the data processing task is higher than the average urgency of existing tasks, the parameter tuning coefficient is increased; if the urgency of the data processing task is lower than the average urgency of existing tasks, the parameter tuning coefficient is decreased. This makes the scheduling evaluation value more closely reflect the actual production requirement of prioritizing urgent tasks with efficient nodes. The specific implementation process is as follows: The average task urgency of existing tasks on the edge nodes is statistically analyzed, and the task urgency J of the data processing task is calculated and compared with the average task urgency of existing tasks. The ratio is used to obtain the relative urgency of the current task and the existing tasks on the edge node. A ratio greater than 1 indicates that the urgency of the data processing task is higher than the average urgency of the existing tasks; a ratio less than 1 indicates that the urgency of the data processing task is lower than the average urgency of the existing tasks.

[0049] The scheduling evaluation value D is represented as: In the formula, For relative timeliness, it represents the level of timeliness of the selected edge node relative to the best timeliness in the candidate set. The larger the value, the closer the comprehensive timeliness of the edge node is to the best level in the candidate set, and the shorter the waiting time required. It is positively correlated with the scheduling evaluation value D. ε is a very small positive number used to avoid the denominator being zero; for example, it can be 0.01. It represents the relative relationship between the urgency of the current task and the average urgency of existing tasks on the edge node. The larger the value, the more urgent the current task is than the existing tasks on the edge node, and the faster it needs to be processed. It is positively correlated with the scheduling evaluation value D. The two values ​​are multiplied and fused to obtain the scheduling evaluation value D, which is the degree to which the edge node is adapted to the current task. Only when the timeliness of the edge node is good enough and the current task is more urgent than the existing tasks will the scheduling evaluation value of the edge node for the current task increase significantly, which meets the production scheduling requirement of prioritizing the matching of efficient nodes with urgent tasks.

[0050] S5. Schedule the data processing task to the edge node where the scheduling evaluation value meets the preset conditions for processing.

[0051] In some implementations, edge nodes with scheduling evaluation values ​​exceeding a preset threshold are selected from the candidate node set to form a preferred node set. This preset threshold is set based on efficiency calibration results from historical scheduling data and serves to define the minimum standard for edge nodes to adapt to the current task, avoiding the selection of nodes with insufficient adaptability. End nodes send scheduling instructions containing raw quality data via the industrial Ethernet network within the workshop, prioritizing the scheduling of data processing tasks to the edge nodes with the highest scheduling evaluation values ​​in the preferred node set.

[0052] It's important to note that directly selecting the node with the highest scheduling evaluation value creates a single point of dependency. If this node suddenly fails (e.g., due to network interruption or insufficient computing power), the system needs to recalculate the evaluation values ​​of all candidate nodes, leading to task scheduling delays. However, by first constructing a preferred node set, even if the highest-value node fails, the system can quickly select the node with the second-highest evaluation value from the set without retracing all candidate nodes, significantly shortening disaster recovery switchover time and improving the stability of the scheduling process.

[0053] In some implementations, the method further includes: receiving feedback results from edge nodes after processing data processing tasks (including at least quality inspection results (such as crankshaft dimensions being qualified) and task processing time); transmitting the feedback results back to the end nodes through a regional state synchronization mechanism (heartbeat packet broadcast); and then the end nodes associating the feedback results with the original quality data (such as crankshaft diameter, roughness, and other parameters) corresponding to the data processing task as structured data and uploading it to the cloud platform to achieve full-link traceability of production data, providing complete data source support for cloud platform process optimization and model training.

[0054] Based on the above technical solution, by introducing a task urgency assessment based on production status and comprehensively considering the physical distance of edge nodes, real-time load, and existing task urgency, dynamic intelligent scheduling of quality data processing tasks is realized under the cloud-edge-device collaborative architecture. This effectively solves the system efficiency bottleneck problems of uneven resource utilization of edge nodes and processing delay of high-urgency tasks, and improves the overall response speed and resource utilization of the quality data acquisition and processing system.

[0055] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0057] In this embodiment of the invention, the cloud-edge-device collaborative quality data acquisition device can be divided into functional units according to the above method example. For example, each function can be divided into its own functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0058] This invention also provides a hardware structure diagram of a quality data acquisition device based on cloud-edge-device collaboration, see [link / reference]. Figure 3 The cloud-edge-device collaborative quality data acquisition device 300 includes a processor 301, and optionally, a memory 302 connected to the processor 301.

[0059] In the first possible implementation, see Figure 3 The cloud-edge-device collaborative quality data acquisition device 300 also includes a transceiver 303. The processor 301, memory 302, and transceiver 303 are connected via a bus. The transceiver 303 is used to communicate with other devices or communication networks. Optionally, the transceiver 303 may include a transmitter and a receiver. The device in the transceiver 303 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of the present invention. The device in the transceiver 303 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of the present invention.

[0060] Based on the first possible implementation method Figure 3 The structural diagram shown can be used to illustrate the structure of the quality data acquisition device based on cloud-edge-device collaboration involved in the above embodiments.

[0061] in, Figure 3 This can also be illustrated by the system chip in a cloud-edge-device collaborative quality data acquisition device. In this case, the actions performed by the aforementioned cloud-edge-device collaborative quality data acquisition device can be implemented by this system chip. The specific actions performed can be found above and will not be repeated here.

[0062] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings and the disclosure, will understand and implement other variations of the disclosed embodiments in carrying out the claimed invention. In this invention, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several of the functions listed in this invention.

[0063] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the invention and its equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A method for quality data collection based on cloud edge-end collaboration, characterized in that, include: Acquire production status data in the production process; In response to the end node completing the quality data collection and generating a data processing task, the urgency of the data processing task is determined based on the production status data. Multiple edge nodes whose physical distance from the end node is within a preset range are identified as a candidate node set; For each edge node in the candidate node set, the scheduling evaluation value of the edge node for the data processing task is determined based on the physical distance between the edge node and the end node, the current load of the edge node, the average task urgency of the existing tasks on the edge node, and the task urgency of the data processing task. The data processing task is scheduled to an edge node whose scheduling evaluation value meets the preset conditions for processing.

2. The mass data acquisition method of claim 1, wherein, The production status data includes the number of processed parts in each process of the production line, the number of deployed machine tools, the processing time of a single part, the number of pending tasks received by the edge node, and the historical records of processed tasks.

3. The mass data acquisition method of claim 2, wherein, Based on the production status data, the urgency of the data processing task is determined, including: Based on the production status data of the current production stage and the next production stage to which the end node belongs, determine the relative load ratio of the next production stage relative to the current production stage, as well as the processing time of the current production stage and the next production stage. The urgency of the data processing task is determined based on the relative load ratio, the processing time of the current production stage, and the processing time of the next production stage.

4. The mass data acquisition method of claim 3, wherein, Based on the production status data of the current and next production stages to which the end node belongs, determine the relative load ratio of the next production stage relative to the current production stage, including: For both the current production stage and the next production stage, the load ratio of production demand to production capacity is determined based on the number of processed parts, the number of machine tools deployed, and the rated production capacity of the machine tools in each production stage. The ratio of the load ratio of the next production stage to the load ratio of the current production stage is determined as the relative load ratio of the next production stage relative to the current production stage.

5. The mass data acquisition method of claim 2, wherein, Based on the physical distance between the edge node and the end node, the current load of the edge node, the average task urgency of existing tasks on the edge node, and the task urgency of the data processing task, the scheduling evaluation value of the edge node for the data processing task is determined, including: A transmission timeliness factor is determined based on the physical distance between edge nodes and end nodes; the transmission timeliness factor is used to characterize the degree of negative impact of the physical distance on data transmission latency; Based on the current load of the edge nodes, a processing time factor is determined; the processing time factor is used to characterize the degree of negative impact of the current load on task processing waiting time. Based on the transmission timeliness factor and the processing timeliness factor, determine the initial evaluation value of the edge node for the data processing task; Based on the relative relationship between the urgency of the data processing task and the average urgency of existing tasks on the edge node, the initial evaluation value is corrected to obtain the scheduling evaluation value.

6. The quality data acquisition method according to claim 5, characterized in that, Based on the relative relationship between the urgency of the data processing task and the average urgency of existing tasks on the edge node, the initial evaluation value is corrected to obtain the scheduling evaluation value, including: The parameter tuning coefficient is determined by the relative size relationship to correct the initial evaluation value and obtain the scheduling evaluation value; if the task urgency of the data processing task is higher than the average task urgency of existing tasks, the parameter tuning coefficient is increased; if the task urgency of the data processing task is lower than the average task urgency of existing tasks, the parameter tuning coefficient is decreased.

7. The quality data acquisition method according to claim 2, characterized in that, Obtain production status data from the production process, including: The production status data is obtained through a preset regional status synchronization mechanism; the regional status synchronization mechanism is used to synchronize the running status of the production process and the task execution status of each node in real time.

8. The quality data acquisition method according to claim 1, characterized in that, Scheduling the data processing task to an edge node whose scheduling evaluation value meets preset conditions includes: From the candidate node set, edge nodes whose scheduling evaluation values ​​exceed a preset threshold are selected to form a preferred node set; The data processing task is preferentially scheduled to the edge node with the highest scheduling evaluation value in the preferred node set.

9. The quality data acquisition method according to claim 1, characterized in that, Also includes: Receive feedback results from edge nodes after processing the data processing task; The feedback result is associated with the original quality data corresponding to the data processing task and uploaded to the cloud platform.

10. A quality data acquisition system based on cloud edge-end cooperation, characterized in that, include: Cloud platform, multiple edge nodes, and multiple end nodes deployed on the production line; The endpoint is used to collect quality data during the production process and generate data processing tasks; The system is configured to perform the method as described in any one of claims 1 to 9.

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