Dispensing equipment data management method and device and electronic equipment

Abstract

The invention relates to the technical field of automatic control, and provides a dispensing equipment data management method and device and electronic equipment. According to the method, a real-time kernel and a conventional kernel are arranged in a processor, peripheral data of dispensing equipment are stored in a preset resource space of a Zenoh protocol communication layer, the real-time kernel obtains image data from the preset resource space by using a shared memory mechanism, obtains valve control data and distance data, generates a real-time task based on the obtained data, and sends the real-time task to the processor. And obtaining weight data from the preset resource space by a conventional kernel, then generating a non-real-time task based on the obtained data, and finally executing the generated real-time task and the non-real-time task, so that unified integration of peripheral data of the dispensing equipment is realized by utilizing a Zenoh protocol, and meanwhile, the data processing efficiency is improved by utilizing a real-time kernel. The task competition is avoided while the conventional kernel processes the non-real-time task, and the data management efficiency of the dispensing equipment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic control, and particularly relates to a dispensing equipment data management method, a management device and electronic equipment. BACKGROUND

[0002] The dispensing equipment is widely used in the fields of electronic manufacturing, semiconductor packaging, medical instrument assembly, etc., and needs to integrate various peripherals (such as dispensing valves, sensors, cameras) to realize high-precision dispensing.

[0003] However, the integration efficiency of the dispensing equipment peripheral data in the related art is low, and there are problems such as high data integration complexity and insufficient real-time performance. SUMMARY

[0004] Therefore, the embodiments of the present application provide a dispensing equipment data management method, a management device and electronic equipment to solve the problem of low integration efficiency of dispensing equipment peripheral data in the prior art.

[0005] In a first aspect, the present application provides a dispensing equipment data management method, which is executed by a processor including at least a real-time kernel and a general kernel.

[0006] The method comprises the following steps:

[0007] The real-time kernel acquires subscribed first peripheral data from a preset resource space by using a shared memory mechanism, and the first peripheral data at least includes image data of the dispensing equipment;

[0008] The real-time kernel acquires subscribed second peripheral data from a first topic of the preset resource space, and the second peripheral data at least includes valve control data and distance data of the dispensing equipment, and the first topic at least includes a valve control data topic and a distance data topic;

[0009] The general kernel acquires subscribed third peripheral data from a second topic of the preset resource space, and the third peripheral data at least includes weight data of the dispensing equipment, and the second topic at least includes a weight data topic;

[0010] The real-time kernel generates a real-time task based on the first peripheral data and the second peripheral data, and the real-time task at least includes one of a dynamic dispensing task, a snapshot task and a dynamic distance measurement task;

[0011] The general kernel generates a non-real-time task based on the third peripheral data, and the non-real-time task at least includes a glue weighing task;

[0012] The real-time task and the non-real-time task are executed.

[0013] The preset resource space is a storage space in a Zenoh protocol communication layer.

[0014] In a second aspect, the application provides a dispensing equipment data management device, which comprises a processor including at least a real-time kernel and a general kernel;

[0015] The real-time kernel is configured to obtain subscribed first peripheral device data from a preset resource space by using a shared memory mechanism, the first peripheral device data at least including image data of the dispensing equipment;

[0016] The real-time kernel is further configured to obtain subscribed second peripheral device data from a first topic of the preset resource space, the second peripheral device data at least including valve control data and distance data of the dispensing equipment, the first topic at least including a valve control data topic and a distance data topic;

[0017] The general kernel is configured to obtain subscribed third peripheral device data from a second topic of the preset resource space, the third peripheral device data at least including weight data of the dispensing equipment, the second topic at least including a weight data topic;

[0018] The real-time kernel is further configured to generate real-time tasks based on the first peripheral device data and the second peripheral device data, the real-time tasks at least including at least one of a dynamic dispensing task, a snapshot task and a dynamic distance measurement task;

[0019] The general kernel is further configured to generate non-real-time tasks based on the third peripheral device data, the non-real-time tasks at least including a glue weighing task;

[0020] The real-time kernel and the general kernel are configured to execute the real-time tasks and the non-real-time tasks.

[0021] The preset resource space is a storage space in a Zenoh protocol communication layer.

[0022] In a third aspect, the application provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the steps of the above method.

[0023] Compared with the prior art, the application has the following beneficial effects: in the application, the real-time kernel and the general kernel are arranged in the processor, the peripheral device data of the dispensing equipment is saved in a preset resource space in a Zenoh protocol communication layer, the real-time kernel obtains image data from the preset resource space by using a shared memory mechanism, obtains valve control data and distance data, generates real-time tasks based on the obtained data, the general kernel obtains weight data from the preset resource space, generates non-real-time tasks based on the obtained data, and finally executes the generated real-time tasks and non-real-time tasks, so that the Zenoh protocol is used to realize the unified integration of the peripheral device data of the dispensing equipment, the data processing efficiency is improved by using the real-time kernel, the general kernel processes non-real-time tasks without task competition, and the dispensing equipment data management efficiency is improved. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating a data management method for dispensing equipment provided in an embodiment of this application.

[0026] Figure 2 This is a flowchart illustrating the method for determining first peripheral data provided in an embodiment of this application.

[0027] Figure 3 This is a flowchart illustrating the method for determining second peripheral data and third peripheral data provided in an embodiment of this application.

[0028] Figure 4 This is a flowchart illustrating the method for generating dynamic dispensing tasks based on first peripheral data and second peripheral data provided in this application embodiment.

[0029] Figure 5 This is a flowchart illustrating the method for generating aerial photography tasks based on first peripheral data and second peripheral data provided in this application embodiment.

[0030] Figure 6 This is a flowchart illustrating the method for generating dynamic ranging tasks based on first peripheral data and second peripheral data in a real-time kernel according to an embodiment of this application.

[0031] Figure 7 This is a flowchart illustrating a conventional kernel method for generating glue weighing tasks based on third peripheral data, as provided in an embodiment of this application.

[0032] Figure 8 This is a system architecture diagram for implementing the dispensing equipment data management method provided in the embodiments of this application.

[0033] Figure 9 This is a data interaction diagram of the dispensing equipment data management method provided in the embodiments of this application.

[0034] Figure 10 This is a schematic diagram of the working process of the dispensing valve adapter.

[0035] Figure 11 This is a schematic diagram of the working process of an infrared rangefinder adapter.

[0036] Figure 12is a workflow schematic diagram of a CCD camera adapter.

[0037] Figure 13 is a workflow schematic diagram of a glue weighing instrument adapter.

[0038] Figure 14 is a flow schematic diagram of a real-time kernel running a dynamic dispensing task.

[0039] Figure 15 is a flow schematic diagram of a real-time kernel running a fly-by task.

[0040] Figure 16 is a flow schematic diagram of a real-time kernel running a dynamic distance measurement task.

[0041] Figure 17 is a flow schematic diagram of a regular kernel running a weighing task.

[0042] Figure 18 is a schematic diagram of a dispensing equipment data management device provided by an embodiment of the present application.

[0043] Figure 19 is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0044] In the following description, for the purpose of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0045] A dispensing equipment data management method and device according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0046] As mentioned above, in the related art, the integration efficiency of the dispensing equipment peripheral data is low, and there are problems such as high data integration complexity and insufficient real-time performance.

[0047] In the related art, the dispensing valve control, the range finder and the weighing instrument usually adopt communication protocols such as RS232, RS422 and RS485. These communication protocols are based on serial communication, and the baud rate is from 9600 to 2 Mbps, which is suitable for low-speed control and sensor data acquisition. The CCD (Charge Coupled Device) camera usually adopts the GigE Vision communication protocol. This communication protocol is based on Gigabit Ethernet and supports high-bandwidth image transmission, and is commonly used for visual guidance and defect detection. In addition, other peripheral devices of the dispensing equipment can also use communication protocols such as CAN (Controller Area Network) bus, Modbus, EtherCAT, and some devices can use a proprietary protocol.

[0048] In the related art, when integrating data of various peripheral devices, a special driver program is usually developed for each peripheral device to manage data through an operating system. For example, RS232 uses POSIX serial API (Application Programming Interface), and GigE uses GVSP (GigE Vision Streaming Protocol) protocol stack, such as the open source project Aravis.

[0049] Alternatively, middleware such as MQTT, DDS or OPC UA can also be used to realize peripheral data interaction. MQTT is commonly used for low-frequency data (such as weighing), and DDS is used for high-bandwidth data (such as images). In the traditional industrial scene, the peripheral data can also be integrated through a PLC (Programmable Logic Controller) through a bus (such as Profibus) to control the dispensing process.

[0050] Further, when processing peripheral data, some data may need to be processed as a real-time task. In the related art, a real-time operating system such as VxWorks, FreeRTOS or RTEMS can be set up for an embedded platform to process high-frequency tasks. Alternatively, a real-time patch such as PREEMPT_RT can also be added to the Linux operating system, which is applied to the x86 platform and supports soft real-time tasks. Some solutions use special hardware such as FPGA (Field Programmable Gate Array) or DSP (Digital Signal Processing) for ultra-low latency tasks.

[0051] The above peripheral data integration method has the following technical problems:

[0052] 1) Isomeric protocol management difficulties: RS232, RS422, RS485 and GigE use different protocol stacks, and need to develop drivers and parsing logic separately. For example, Modbus of RS232 needs special parsing, and GVSP of GigE needs TCP / UDP processing, which increases development and maintenance costs.

[0053] 2) Lack of unified interface: Existing middleware is usually optimized for specific scenarios, making it difficult to manage high-bandwidth data and low-frequency data uniformly. For example, MQTT (Message Queuing Telemetry Transport) is suitable for low-frequency publishing, while DDS (Data Distribution Service) is suitable for high-bandwidth, but it is difficult to coordinate the two.

[0054] 3) Data redundancy copy: Traditional communication methods such as socket and MQTT have multiple copies during data transmission, resulting in increased latency and are not suitable for high-frequency tasks.

[0055] 4) Insufficient real-time performance: On the one hand, Linux PREEMPT_RT provides soft real-time, with scheduling jitter of about 50-100μs, which is difficult to meet the requirements of high-frequency tasks. For example, dynamic dispensing requires less than 10μs jitter to ensure less than 0.1mm accuracy. On the other hand, existing systems are difficult to effectively isolate real-time and non-real-time tasks. For example, in a Linux environment, low-frequency weighing tasks may interfere with high-frequency flying tasks, increasing scheduling overhead.

[0056] 5) Low efficiency of high-bandwidth data processing: On the one hand, GigE camera images transmitted through TCP / UDP need to be copied multiple times, with a delay of about 50-100μs, resulting in large image transmission overhead and potentially affecting the real-time performance of flying. On the other hand, the image processing of CCD flying in a Linux environment is affected by CPU competition, and the delay may be too large.

[0057] 6) Limited dynamic adjustment capability: Most peripheral data is currently handled by the Linux kernel, which has large delay and is difficult to dynamically adjust; at the same time, there are also deficiencies in the noise processing of data.

[0058] 7) Poor scalability and maintainability: including complex adaptation of new peripheral devices, cumbersome middleware configuration, limited monitoring capabilities, etc.

[0059] 8) Cost and performance are difficult to balance: the use of dedicated hardware is high in cost, and part of the dedicated hardware is high in energy consumption and not suitable for long time running. On the other hand, most of the existing solutions run on embedded platforms, and the multi-core performance of traditional processors is not effectively utilized.

[0060] Therefore, the embodiment of the present application provides a dispensing equipment data management method, by setting a real-time kernel and a general kernel in a processor, peripheral data of the dispensing equipment is saved in a preset resource space of a Zenoh protocol communication layer, the real-time kernel acquires image data from the preset resource space by using a shared memory mechanism, acquires valve control data and distance data, generates a real-time task based on the acquired data, the general kernel acquires weight data from the preset resource space, generates a non-real-time task based on the acquired data, and finally executes the generated real-time task and non-real-time task, so as to realize unified integration of peripheral data of the dispensing equipment by using the Zenoh protocol, improve data processing efficiency by using the real-time kernel, avoid task competition while the general kernel processes non-real-time tasks, and improve dispensing equipment data management efficiency.

[0061] Figure 1 is a flowchart of a dispensing equipment data management method provided by the embodiment of the present application. As shown in Figure 1 , the method comprises the following steps:

[0062] In step S101, the real-time kernel acquires subscribed first peripheral data from the preset resource space by using a shared memory mechanism.

[0063] The first peripheral data at least includes image data of the dispensing equipment.

[0064] In step S102, the real-time kernel acquires subscribed second peripheral data from a first topic of the preset resource space.

[0065] The second peripheral data at least includes valve control data and distance data of the dispensing equipment, and the first topic at least includes a valve control data topic and a distance data topic.

[0066] In step S103, the general kernel acquires subscribed third peripheral data from a second topic of the preset resource space.

[0067] The third peripheral data at least includes weight data of the dispensing equipment, and the second topic at least includes a weight data topic.

[0068] In step S104, the real-time kernel generates a real-time task based on the first peripheral data and the second peripheral data.

[0069] The real-time task at least includes one of a dynamic dispensing task, a flying shot task and a dynamic ranging task.

[0070] In step S105, the conventional kernel generates a non-real-time task based on the third peripheral device data.

[0071] The non-real-time task at least includes a glue weighing task.

[0072] In step S106, the real-time task and the non-real-time task are executed.

[0073] In some embodiments of the present application, the method can be executed by a processor. The processor can be a multi-core processor including at least a real-time kernel and a conventional kernel. For example, if the processor is an 8-core x86 processor, cores 0-3 can be conventional kernels, such as Linux kernels, and cores 4-6 can be real-time kernels.

[0074] In some embodiments of the present application, the real-time kernel can be a Xenomai kernel.

[0075] In some embodiments of the present application, the real-time kernel can obtain subscribed first peripheral device data from a preset resource space using a shared memory mechanism. The first peripheral device data at least includes image data of the glue dispensing device.

[0076] The preset resource space can be a storage space in a Zenoh protocol communication layer. Part of the storage space can be identified as a shared memory space, and the real-time kernel can subscribe to the first peripheral device data in the shared memory space. For example, the real-time kernel can subscribe to image data in the shared memory space, and when the image data is published, the real-time kernel can obtain the published data through the shared memory. In an example, the preset resource space can be a Namespace.

[0077] On the other hand, the real-time kernel can also obtain subscribed second peripheral device data from a first topic of the preset resource space, and the second peripheral device data at least includes valve control data and distance data of the glue dispensing device. The first topic at least includes a valve control data topic and a distance data topic.

[0078] That is, the valve control data and the distance data can also be saved to a storage space in a Zenoh protocol communication layer and published to respective corresponding topics, such as a Pub-Sub mechanism, for example, the valve control data is published to a valve control data topic, and the distance data is published to a distance data topic. The real-time kernel subscribes to these topics and obtains the corresponding data after the data of each topic is published. For example, the real-time kernel can obtain valve control data from the valve control data topic and distance data from the distance data topic.

[0079] In some embodiments of the present application, the regular kernel can obtain subscribed third peripheral device data from a second topic of the preset resource space, the third peripheral device data at least including weight data of the dispensing device, and the second topic at least including a weight data topic.

[0080] That is, the weight data can also be saved to a storage space in the Zenoh protocol communication layer and published to a corresponding topic through a mechanism such as Pub-Sub. The real-time kernel subscribes to the topic and obtains corresponding data after the data of the topic is published. For example, the real-time kernel can obtain weight data from the weight data topic.

[0081] The real-time kernel can generate real-time tasks based on the first peripheral device data and the second peripheral device data, and the real-time tasks can include at least one of a dynamic dispensing task, a fly-by task, and a dynamic ranging task. The regular kernel can generate non-real-time tasks based on the third peripheral device data, and the non-real-time tasks at least include a glue weighing task.

[0082] The real-time kernel and the regular kernel can execute the generated tasks. In one example, the real-time kernel and the regular kernel can send the generated tasks to corresponding peripheral devices to control the peripheral devices to work. In another example, the real-time kernel and the regular kernel can also execute the generated tasks by themselves.

[0083] According to the technical scheme provided by the embodiments of the present application, by setting the real-time kernel and the regular kernel in the processor, the peripheral device data of the dispensing device is saved in the preset resource space of the Zenoh protocol communication layer, the real-time kernel obtains image data from the preset resource space using a shared memory mechanism, and obtains valve control data and distance data, and then generates real-time tasks based on the obtained data, and the regular kernel obtains weight data from the preset resource space, and then generates non-real-time tasks based on the obtained data, and finally executes the generated real-time tasks and non-real-time tasks, thereby realizing the unified integration of the peripheral device data of the dispensing device by using the Zenoh protocol, improving the data processing efficiency by using the real-time kernel, avoiding task competition while the regular kernel processes non-real-time tasks, and improving the data management efficiency of the dispensing device.

[0084] In some embodiments of the present application, the peripheral device transmits real-time data collected by it to the gateway device according to different communication protocols, including RS232, RS422, RS485, GigE, etc., and the processor can obtain the data transmitted by the peripheral device from the gateway device through the Zenoh protocol.

[0085] Figure 2 is a flowchart of a method for determining first peripheral device data provided by the embodiments of the present application. As shown in Figure 2 the method includes the following steps:

[0086] In step S201, image raw data of the dispensing equipment is acquired.

[0087] In step S202, the image raw data is parsed using a preset image data adapter to obtain first peripheral data.

[0088] In step S203, the first peripheral data is saved to an image data resource space in a preset resource space, and the first peripheral data is published to an image data topic.

[0089] In some embodiments of the present application, the Zenoh protocol can determine the first peripheral data in the following manner: acquiring image raw data of the dispensing equipment from the gateway device, which can be real-time image data transmitted by a CCD industrial camera to the gateway device through a GigE communication protocol. In some embodiments, the real-time image data can also be transmitted in combination with Jumbo Frame and ROI (Region of Interest) cropping to further reduce network overhead of image transmission and processing.

[0090] In some embodiments of the present application, the Zenoh protocol can parse the image raw data using a preset image data adapter to obtain the first peripheral data. Then the first peripheral data is saved to an image data resource space in a preset resource space, and the first peripheral data is published to an image data topic.

[0091] Zenoh-pico technology can be used to develop different adapters for various peripheral devices that use different communication protocols to transmit data, to simplify the communication protocol parsing process. For example, a GigE TCP / IP plug-in can be configured for an industrial CCD camera using Zenoh-pico technology, and then the plug-in is used to parse and acquire image data.

[0092] In some embodiments of the present application, the Zenoh protocol can save the parsed image data to an image data resource space in a preset resource space. In an example, the Zenoh protocol can abstract the image data as a resource namespace / glue / camera / image.

[0093] Figure 3 is a flowchart of a method for determining second peripheral data and third peripheral data provided by an embodiment of the present application. As shown in Figure 3 the method includes the following steps:

[0094] In step S301, valve control data, distance data, and weight data of the dispensing equipment are acquired.

[0095] In step S302, the valve control data and the distance data are parsed using a preset serial communication data adapter to obtain second peripheral data, and the weight data is parsed using the preset serial communication data adapter to obtain third peripheral data.

[0096] The type of the serial communication data adapter matches the peripheral device communication protocol corresponding to the parsed data.

[0097] In step S303, the second peripheral data is saved to a valve control data resource space and a distance data resource space in a preset resource space, the parsed valve control data is published to a valve control data topic, and the parsed distance data is published to a distance data topic.

[0098] In step S304, the third peripheral data is saved to a weight data resource space in the preset resource space, and the parsed weight data is published to a weight data topic.

[0099] In some embodiments of the present application, the Zenoh protocol can determine the second peripheral data and the third peripheral data in the following manner: the valve control data, the distance data, and the weight data are obtained from the gateway device. The valve control data is transmitted to the gateway device by a dispensing valve through an RS232 communication protocol, the distance data is transmitted to the gateway device by an infrared distance meter through an RS422 communication protocol, and the weight data is transmitted to the gateway device by a glue weighing instrument through an RS485 communication protocol.

[0100] The adapters for each device can be developed in advance using the Zenoh-pico technology, such as RS232 adapters, RS422 adapters, RS485 adapters, etc., and then the corresponding adapters are used to parse the data in the gateway device to obtain the parsed data. For example, the valve control data is parsed using an RS232 adapter, the distance data is parsed using an RS422 adapter, and the weight data is parsed using an RS485 adapter.

[0101] The parsed data can be saved to the corresponding storage space in the preset resource space and published to the corresponding topic. For example, the valve control data can be published to the resource namespace / glue / valve / control, the distance data can be published to the resource namespace / glue / distance / value, and the weight data can be published to the resource namespace / glue / weight / value.

[0102] In some embodiments of the present application, the Zenoh parsed data can also be saved to a target database. In an example, the target database can be a RocksDB database.

[0103] Figure 4is a flowchart of a method for generating a dynamic dispensing task by a real-time kernel based on first peripheral data and second peripheral data provided by an embodiment of the present application. As shown in Figure 4 the method comprises the following steps:

[0104] In step S401, the real-time kernel acquires subscribed valve control data from the valve control data topic.

[0105] In step S402, cubic spline interpolation is performed on the acquired valve control data to obtain a dispensing path.

[0106] In step S403, the dispensing path is updated based on image data and distance data to obtain an updated dispensing path.

[0107] In step S404, valve control instructions are generated based on the updated dispensing path, and the valve control instructions are sent to the dispensing valve.

[0108] In some embodiments of the present application, the real-time kernel can subscribe to the valve control data topic and acquire real-time valve control data after the topic publishes data, then perform cubic spline interpolation on the acquired valve control data to obtain a dispensing path.

[0109] wherein the cubic spline interpolation can be P(t) = a0 + a1t + a2t 2 +a3t 3 wherein P(t) is a dispensing path point, a0, a1 and a2 are cubic spline coefficients, which are obtained based on the substrate position determined by the CCD flying shot and the path planning, and t is greater than or equal to 0 and less than or equal to 1.

[0110] In certain embodiments of the present application, the dispensing path can also be updated based on image data and distance data to obtain an updated dispensing path. In an example, the current path can be determined with reference to the image data, and the current height of the dispensing equipment can be determined with reference to the distance data, then the dispensing path is updated in combination with the current path, the current height and the boundary conditions for planning the dispensing path, so that the updated dispensing path meets the conditions of higher accuracy and smoother path.

[0111] The real-time kernel can generate valve control instructions based on the updated dispensing path, and send the valve control instructions to the dispensing valve. At the same time, the real-time kernel can also receive feedback information sent back by the dispensing valve.

[0112] Figure 5 is a flowchart of a method for generating a flying shot task by a real-time kernel based on first peripheral data and second peripheral data provided by an embodiment of the present application. As shown in Figure 5 the method comprises the following steps:

[0113] In step S501, the real-time kernel acquires subscribed image data from an image data topic using a shared memory mechanism.

[0114] In step S502, edge detection is performed on the acquired image data to obtain at least one of path offset information and defect information.

[0115] In step S503, the path offset information is sent to a dynamic dispensing task, and / or the defect information is sent to a target database.

[0116] In some embodiments of the present application, the real-time kernel can subscribe to an image data topic, and after the topic publishes data, the real-time kernel acquires real-time image data using a shared memory mechanism, and then generates a fly-shooting task using the acquired image data.

[0117] In some embodiments, the real-time kernel can perform edge detection on the acquired image data to obtain at least one of path offset information and defect information. The edge detection can be I edge =Canny(I raw ,θ1,θ2), where I raw is the original image, I edge is the image after edge detection, Canny() is an edge detection function, and θ1 and θ2 are threshold values. In one example, θ1 can be 100 and θ2 can be 200.

[0118] The detected edge features can be used to determine path offset information of the dispensing path, including horizontal coordinate axis offset and vertical coordinate axis offset in a three-dimensional Cartesian coordinate system. On the other hand, the defect information can also be sent to a target database.

[0119] Figure 6 is a flowchart of a method for generating a dynamic ranging task by a real-time kernel based on first peripheral data and second peripheral data according to an embodiment of the present application. As shown in Figure 6 , the method includes the following steps:

[0120] In step S601, the real-time kernel acquires subscribed position data from a position data topic.

[0121] In step S602, the acquired position data is filtered to obtain an updated height position of the dispensing device.

[0122] In step S603, the updated height position is sent to a dynamic dispensing task.

[0123] In some embodiments of the present application, the real-time kernel can subscribe to a position data topic, and after the topic publishes data, the real-time kernel acquires real-time position data, and then generates a dynamic ranging task using the acquired position data.

[0124] In some embodiments, the real-time kernel can filter the acquired position data. In one example, Kalman filter can be used to process the position data. Kalman filter is a mathematical method for estimating the state of a dynamic system from a series of incomplete and noisy measurements. wherein is the state estimate at time k, is the prediction of the state at time k given the known past states at times k-1, K k is the error covariance matrix, and H is a user-defined matrix.

[0125] The height of the dispensing path, i.e. the value of the vertical coordinate in the three-dimensional Cartesian coordinate system, can be updated based on the result of the Kalman filter.

[0126] Figure 7 is a flowchart of a method for generating a glue weighing task based on third peripheral data by a conventional kernel provided in embodiments of the present application. As shown in Figure 7 the method comprises the following steps:

[0127] In step S701, the conventional kernel acquires subscribed weight data from the weight data topic.

[0128] In step S702, the weight data is sent to a target database and / or displayed.

[0129] In some embodiments of the present application, the conventional kernel can subscribe to the weight data Topic and acquire real-time weight data after the Topic publishes data, and then generate a glue weighing task using the acquired weight data.

[0130] The conventional kernel can send the acquired weight data to a target database for data monitoring, or send the weight data to a user interface for display.

[0131] In some embodiments of the present application, when a new peripheral device is added to the dispensing equipment, the Zenoh protocol can quickly integrate the data of the new peripheral device in the following way: in response to determining to add a target peripheral device, customizing a target data adapter of the target peripheral device, and determining a processing type of the target peripheral device data; wherein the processing type includes a real-time processing type and a non-real-time processing type; in response to determining that the processing type is the real-time processing type, using the target data adapter to parse target peripheral device data of the target peripheral device, and saving the parsed target peripheral device data to a preset resource space; publishing the parsed target peripheral device data to a target data topic, so that a real-time kernel subscribed to the target data topic acquires the target peripheral device data using a shared memory mechanism; in response to determining that the processing type is the non-real-time processing type, using the target data adapter to parse the target peripheral device data of the target peripheral device, and saving the parsed target peripheral device data to the preset resource space; publishing the parsed target peripheral device data to the target data topic, so that a regular kernel subscribed to the target data topic acquires the target peripheral device data.

[0132] Figure 8 is a system architecture diagram for implementing the dispensing equipment data management method provided by the embodiments of the present application. As shown in Figure 8 , the system can include an x86 master, which can include a Xenomai real-time kernel and a Linux non-real-time kernel (i.e., a regular kernel). Further, the x86 master can also be connected to a peripheral layer, which connects various peripheral devices to obtain data of the various peripheral devices. The peripheral devices can include a dispensing valve, an infrared range finder, a CCD camera, and a glue weighing instrument.

[0133] The Zenoh communication layer can obtain data of various peripheral devices from the peripheral layer through a preset adapter, and transmit large-capacity data such as image data of the CCD camera to the Xenomai real-time kernel through a shared memory mechanism, and transmit small-capacity data to the Xenomai real-time kernel or the Linux non-real-time kernel through a Pub-Sub mechanism. The Zenoh communication layer can also transmit various data to a RocksDB database for data stream monitoring.

[0134] The Xenomai real-time kernel can generate dynamic dispensing tasks, fly-shooting tasks, and dynamic ranging tasks based on the obtained data, and the Linux non-real-time kernel can generate glue weighing tasks based on the obtained data.

[0135] Figure 9 is a data interaction diagram of the dispensing equipment data management method provided by the embodiments of the present application. As shown in Figure 9As shown, the dispensing valve data can be abstracted by the Zenoh communication protocol to / glue / valve / control, the infrared distance meter data can be abstracted by the Zenoh communication protocol to / glue / distance / value, the CCD camera data can be abstracted by the Zenoh communication protocol to / glue / camera / image, and the glue weighing instrument data can be abstracted by the Zenoh communication protocol to / glue / weight / value.

[0136] The Xenomai real-time kernel can obtain image data from the Zenoh protocol communication layer through a shared memory mechanism, and can also obtain distance data and valve control data through a shared memory mechanism. Alternatively, the Xenomai real-time kernel can obtain distance data and valve control data from the Zenoh protocol communication layer through a Pub-Sub subscription mechanism. The Linux non-real-time kernel can obtain weight data from the Zenoh protocol communication layer through a Pub-Sub subscription mechanism. The Xenomai real-time kernel can send recorded real-time data to the RocksDB database for monitoring, and the Linux non-real-time kernel can send recorded non-real-time data to the RocksDB database for monitoring.

[0137] Figure 10 is a schematic diagram of the working process of the dispensing valve adapter. As shown in Figure 10 , the working process of the dispensing valve adapter can be: inputting valve control instructions, which can be 8-bit, 9600 baud rate instructions. After initializing the RS232 serial port and configuring 9600 baud rate, the adapter parses Modbus or custom format data, and then publishes to the Zenoh resource / glue / valve / control. In specific implementation, it can be set to poll once every 10ms (milliseconds), publish valve status including open and closed states, and support dynamic dispensing feedback.

[0138] Figure 11 is a schematic diagram of the working process of the infrared distance meter adapter. As shown in Figure 11 , the working process of the infrared distance meter adapter can be: inputting distance data, which can be floating point data. After initializing the RS422 serial port and configuring 115200 baud rate, parse ASCII or binary distance value, and then publish to the Zenoh resource / glue / distance / value.

[0139] Figure 12 is a schematic diagram of the working process of the CCD camera adapter. As shown in Figure 12is a schematic diagram of the workflow of the CCD camera adapter. As shown, the workflow of the CCD camera adapter can be: input image data, initialize the GigE Vision protocol. Next, capture image frames, in an example, the Aravis library can be used to capture image frames to parse the GVSP data stream. Finally, publish the captured image frames to the Zenoh resource / glue / camera / image.

[0140] Figure 13 is a schematic diagram of the workflow of the glue weight indicator adapter. As shown, Figure 13 the workflow of the glue weight indicator adapter can be: input weight data, which can be 16-bit data, with a baud rate of 9600. After initializing the RS485 serial port, configure the Modbus RTU to parse the weight data. Finally, publish the weight data to the Zenoh resource / glue / weight / value.

[0141] Figure 14 is a schematic diagram of the workflow of the real-time kernel running a dynamic dispensing task. As shown, Figure 14 the Xenomai real-time kernel can subscribe to the Zenoh resource / glue / valve / control. After obtaining the valve control data, perform three times of spline interpolation, generate the dispensing path, and send the valve control command. Then, the valve state feedback can be received, and the dispensing path can also be updated in combination with the CCD camera data and the infrared range finder data.

[0142] Figure 15 is a schematic diagram of the workflow of the real-time kernel running a fly-by task. As shown, Figure 15 the Xenomai real-time kernel can subscribe to the Zenoh resource / glue / camera / image through the shared memory mechanism. After obtaining the image data of the CCD camera, perform Canny edge detection, extract the substrate features or defects, and then determine the X-Y (horizontal coordinate axis-vertical coordinate axis) offset of the dispensing device based on the extracted features, send the X-Y offset to the path planner to update the dispensing path, or send the defects to the RocksDB database for recording and monitoring.

[0143] Figure 16 is a schematic diagram of the workflow of the real-time kernel running a dynamic ranging task. As shown, Figure 16 the Xenomai real-time kernel can subscribe to the Zenoh resource / glue / distance / value. After obtaining the distance data, Kalman filtering can be performed to smooth the distance data to obtain the updated vertical coordinate axis value z. Finally, the updated z value can be sent to the path planner to update the dispensing path.

[0144] Figure 17 is a schematic diagram of the workflow of the regular kernel running a weighing task. AsFigure 17 As shown, the Linux non-real-time kernel can subscribe to the Zenoh resource ` / glue / weight / value`. After obtaining the weight data, it parses the weight value and then stores the weight value in the RocksDB database or sends it to the user interface for display.

[0145] The technical solution provided in this application uses the Zenoh protocol to replace independent drivers and middleware, unifying RS232, RS422, RS485, and GigE data into a unified resource namespace, simplifying the development process. Simultaneously, the adapter supports rapid adaptation to new peripherals, reducing maintenance costs. Furthermore, the use of a shared memory mechanism for transmitting large amounts of data achieves zero-copy, enhancing real-time performance and improving dynamic processing capabilities.

[0146] On the other hand, core isolation avoids task contention and fully utilizes x86 multi-core capabilities. The Xenomai real-time kernel is bound to a dedicated kernel, optimizing high-bandwidth processing. Zenoh's automatic discovery and unified namespace simplify configuration, RocksDB provides efficient data logging and optimized debugging, and the Zenoh-pico adapter is modular, supporting rapid expansion.

[0147] Furthermore, the x86 platform is less expensive than dedicated FPGA hardware implementations and offers superior performance compared to embedded real-time operating systems. Moreover, the Zenoh and Xenomai software solutions reduce hardware dependencies and lower power consumption.

[0148] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0149] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0150] Figure 18 This is a schematic diagram of a data management device for a dispensing equipment provided in an embodiment of this application. Figure 18 As shown, the device includes a processor 1801, which includes at least a real-time core 1802 and a regular core 1803.

[0151] The real-time kernel 1802 is configured to use a shared memory mechanism to obtain subscribed first peripheral data from a preset resource space. The first peripheral data includes at least the image data of the dispensing device.

[0152] The real-time kernel 1802 is also configured to obtain subscribed second peripheral data from a first topic in a preset resource space. The second peripheral data includes at least valve control data and distance data of the dispensing device, and the first topic includes at least a valve control data topic and a distance data topic.

[0153] The conventional kernel 1803 is configured to obtain subscribed third peripheral device data from a second topic of a preset resource space, the third peripheral device data at least including weight data of the dispensing equipment, and the second topic at least including a weight data topic.

[0154] The real-time kernel 1802 is further configured to generate a real-time task based on the first peripheral device data and the second peripheral device data, the real-time task at least including one of a dynamic dispensing task, a snapshot task, and a dynamic distance measurement task.

[0155] The conventional kernel 1803 is further configured to generate a non-real-time task based on the third peripheral device data, the non-real-time task at least including a glue weighing task.

[0156] The real-time kernel 1802 and the conventional kernel 1803 are configured to execute the real-time task and the non-real-time task.

[0157] The preset resource space is a storage space in a Zenoh protocol communication layer.

[0158] According to the technical scheme provided by the embodiments of the present application, by setting the real-time kernel and the conventional kernel in the processor, the peripheral device data of the dispensing equipment is saved in the preset resource space of the Zenoh protocol communication layer, the real-time kernel obtains the image data from the preset resource space using the shared memory mechanism, and obtains the valve control data and the distance data, and then generates a real-time task based on the obtained data, and the conventional kernel obtains the weight data from the preset resource space, and then generates a non-real-time task based on the obtained data, and finally executes the generated real-time task and non-real-time task, thereby realizing the unified integration of the peripheral device data of the dispensing equipment by using the Zenoh protocol, improving the data processing efficiency by using the real-time kernel, avoiding task competition while the conventional kernel processes the non-real-time task, and improving the data management efficiency of the dispensing equipment.

[0159] In some embodiments, the Zenoh protocol determines the first peripheral device data in the following manner: obtaining image raw data of the dispensing equipment; using a preset image data adapter to parse the image raw data to obtain the first peripheral device data; saving the first peripheral device data to an image data resource space in the preset resource space, and publishing the first peripheral device data to an image data topic.

[0160] In some embodiments, the Zenoh protocol determines the second peripheral data and the third peripheral data in the following manner: obtaining valve control data, distance data, and weight data of the dispensing device; using a preset serial communication data adapter to parse the valve control data and the distance data to obtain the second peripheral data, and using the preset serial communication data adapter to parse the weight data to obtain the third peripheral data; wherein the type of the serial communication data adapter matches the peripheral device communication protocol corresponding to the parsed data; saving the second peripheral data to a valve control data resource space and a distance data resource space in a preset resource space, publishing the parsed valve control data to a valve control data topic, and publishing the parsed distance data to a distance data topic; saving the third peripheral data to a weight data resource space in the preset resource space, and publishing the parsed weight data to a weight data topic.

[0161] In some embodiments, in response to determining that the real-time task is a dynamic dispensing task, the real-time kernel generates the real-time task based on the first peripheral data and the second peripheral data, including: the real-time kernel obtains subscribed valve control data from a valve control data topic; performing three times of spline interpolation on the obtained valve control data to obtain a dispensing path; updating the dispensing path based on the image data and the distance data to obtain an updated dispensing path; generating a valve control instruction based on the updated dispensing path, and sending the valve control instruction to a dispensing valve.

[0162] In some embodiments, in response to determining that the real-time task is a flying task, the real-time kernel generates the real-time task based on the first peripheral data and the second peripheral data, including: the real-time kernel obtains subscribed image data from an image data topic using a shared memory mechanism; performing edge detection on the obtained image data to obtain at least one of path offset information and defect information; sending the path offset information to a dynamic dispensing task, and / or sending the defect information to a target database.

[0163] In some embodiments, in response to determining that the real-time task is a dynamic ranging task, the real-time kernel generates the real-time task based on the first peripheral data and the second peripheral data, including: the real-time kernel obtains subscribed position data from a position data topic; filtering the obtained position data to obtain an updated height position of the dispensing device; sending the updated height position to a dynamic dispensing task.

[0164] In some embodiments, in response to determining that the non-real-time task is a glue weighing task, the regular kernel generates the non-real-time task based on the third peripheral data, including: the regular kernel obtains subscribed weight data from a weight data topic; sending the weight data to a target database, and / or displaying the weight data.

[0165] In some embodiments, the Zenoh protocol further works in the following manner: in response to determining that a target peripheral device is added, customizing a target data adapter of the target peripheral device, and determining a processing type of the target peripheral device data; wherein the processing type includes a real-time processing type and a non-real-time processing type; in response to determining that the processing type is the real-time processing type, using the target data adapter to parse target peripheral device data of the target peripheral device, and saving the parsed target peripheral device data to a preset resource space; publishing the parsed target peripheral device data to a target data topic, so that a real-time kernel subscribed to the target data topic acquires the target peripheral device data using a shared memory mechanism; in response to determining that the processing type is the non-real-time processing type, using the target data adapter to parse target peripheral device data of the target peripheral device, and saving the parsed target peripheral device data to a preset resource space; publishing the parsed target peripheral device data to a target data topic, so that a regular kernel subscribed to the target data topic acquires the target peripheral device data.

[0166] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0167] Figure 19 is a schematic diagram of an electronic device provided by an embodiment of the present application. As shown in Figure 19 The electronic device 19 of this embodiment includes a processor 1901, a memory 1902, and a computer program 1903 stored in the memory 1902 and executable on the processor 1901. The processor 1901 implements the steps in each of the above method embodiments when executing the computer program 1903. Alternatively, the processor 1901 implements the functions of each module / unit in each of the above device embodiments when executing the computer program 1903.

[0168] The electronic device 19 can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The electronic device 19 can include but is not limited to the processor 1901 and the memory 1902. Those skilled in the art can understand that Figure 19 The electronic device 19 is merely an example and does not constitute a limitation on the electronic device 19, and can include more or fewer components or different components than those shown.

[0169] The processor 1901 can be a central processing unit (CPU), or can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc.

[0170] The memory 1902 can be an internal storage unit of the electronic device 19, for example, a hard disk or a memory of the electronic device 19. The memory 1902 can also be an external storage device of the electronic device 19, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 19. The memory 1902 can also include both the internal storage unit and the external storage device of the electronic device 19. The memory 1902 is used to store computer programs and other programs and data required by the electronic device.

[0171] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit or module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0172] The integrated modules / units, if implemented in the form of software functional units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can be executed by a processor to implement the steps of the above-mentioned various method embodiments. The computer program can include computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium, etc.

[0173] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A data management method for a dispensing apparatus, characterized by, The method is executed by a processor, the processor comprising at least a real-time kernel and a general kernel; The method comprises: The real-time kernel acquires subscribed first peripheral data from a preset resource space by a shared memory mechanism, the first peripheral data comprising at least image data of the dispensing equipment; The real-time kernel acquires subscribed second peripheral data from a first topic of the preset resource space, the second peripheral data comprising at least valve control data and distance data of the dispensing equipment, the first topic comprising at least a valve control data topic and a distance data topic; The general kernel acquires subscribed third peripheral data from a second topic of the preset resource space, the third peripheral data comprising at least weight data of the dispensing equipment, the second topic comprising at least a weight data topic; The real-time kernel generates real-time tasks based on the first peripheral data and the second peripheral data, the real-time tasks comprising at least one of a dynamic dispensing task, a snapshot task and a dynamic distance measurement task; The general kernel generates non-real-time tasks based on the third peripheral data, the non-real-time tasks comprising at least a glue weighing task; The real-time tasks and non-real-time tasks are executed; The preset resource space is a storage space in a Zenoh protocol communication layer.

2. The method of claim 1, wherein, The Zenoh protocol determines the first peripheral data in the following manner: Image raw data of the dispensing equipment is acquired; The image raw data is parsed using a preset image data adapter to obtain the first peripheral data; The first peripheral data is saved to an image data resource space in the preset resource space, and the first peripheral data is published to an image data topic.

3. The method of claim 1, wherein, The Zenoh protocol determines the second peripheral data and the third peripheral data in the following manner: Valve control data, distance data and weight data of the dispensing equipment are acquired; The valve control data and distance data are parsed using a preset serial communication data adapter to obtain the second peripheral data, and the weight data is parsed using a preset serial communication data adapter to obtain the third peripheral data; the type of the serial communication data adapter matches the communication protocol of the peripheral equipment corresponding to the parsed data; The second peripheral data is saved to a valve control data resource space and a distance data resource space in the preset resource space, the parsed valve control data is published to a valve control data topic, and the parsed distance data is published to a distance data topic; The third peripheral data is saved to a weight data resource space in the preset resource space, and the parsed weight data is published to a weight data topic.

4. The method of claim 1, wherein, In response to determining that the real-time task is a dynamic dispensing task, the real-time kernel generates real-time tasks based on the first peripheral data and the second peripheral data, comprising: The real-time kernel acquires subscribed valve control data from the valve control data topic; Three times of spline interpolation are performed on the acquired valve control data to obtain a dispensing path; The dispensing path is updated based on the image data and the distance data to obtain an updated dispensing path; A valve control instruction is generated based on the updated dispensing path, and the valve control instruction is sent to a dispensing valve.

5. The method of claim 1, wherein, In response to determining that the real-time task is a flying task, the real-time kernel generates a real-time task based on the first peripheral data and the second peripheral data, including: The real-time kernel acquires subscribed image data from the image data topic using a shared memory mechanism; Edge detection is performed on the acquired image data to obtain at least one of path offset information and defect information; The path offset information is sent to a dynamic dispensing task, and / or the defect information is sent to a target database.

6. The method of claim 1, wherein, In response to determining that the real-time task is a dynamic ranging task, the real-time kernel generates a real-time task based on the first peripheral data and the second peripheral data, including: The real-time kernel acquires subscribed position data from the position data topic; Filtering is performed on the acquired position data to obtain an updated height position of the dispensing device; The updated height position is sent to a dynamic dispensing task.

7. The method of claim 1, wherein, In response to determining that the non-real-time task is a glue weighing task, the regular kernel generates a non-real-time task based on the third peripheral data, including: The regular kernel acquires subscribed weight data from the weight data topic; The weight data is sent to a target database, and / or the weight data is displayed.

8. The method according to any one of claims 1 to 7, characterized in that, The Zenoh protocol also works in the following way: In response to determining that a target peripheral device is added, a target data adapter of the target peripheral device is customized, and the processing type of the target peripheral device data is determined; wherein the processing type includes a real-time processing type and a non-real-time processing type; In response to determining that the processing type is a real-time processing type, the target data adapter is used to parse the target peripheral data of the target peripheral device, and the parsed target peripheral data is saved to a preset resource space; The parsed target peripheral data is published to a target data topic, so that the real-time kernel subscribed to the target data topic acquires the target peripheral data using a shared memory mechanism; In response to determining that the processing type is a non-real-time processing type, the target data adapter is used to parse the target peripheral data of the target peripheral device, and the parsed target peripheral data is saved to a preset resource space; The parsed target peripheral data is published to a target data topic, so that the regular kernel subscribed to the target data topic acquires the target peripheral data.

9. A data management device for a dispensing apparatus, the device comprising: The device includes a processor, which includes at least a real-time kernel and a regular kernel; The real-time kernel is configured to acquire subscribed first peripheral data from a preset resource space using a shared memory mechanism, the first peripheral data including at least image data of the dispensing device; The real-time kernel is also configured to acquire subscribed second peripheral data from a first topic of the preset resource space, the second peripheral data including at least valve control data and distance data of the dispensing device, the first topic including at least a valve control data topic and a distance data topic; The regular kernel is configured to acquire subscribed third peripheral data from a second topic of the preset resource space, the third peripheral data including at least weight data of the dispensing device, the second topic including at least a weight data topic; The real-time kernel is further configured to generate a real-time task based on the first peripheral data and the second peripheral data, the real-time task comprising at least one of a dynamic dispensing task, a fly-by task, and a dynamic ranging task; The general kernel is further configured to generate a non-real-time task based on the third peripheral data, the non-real-time task comprising at least a glue weighing task; The real-time kernel and the general kernel are configured to execute the real-time task and the non-real-time task. The preset resource space is a storage space in a Zenoh protocol communication layer.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the method in any one of claims 1 to 8.

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