Linkage control method and system based on water level sensor assembly detection

Abstract

The present invention discloses a linkage control method and system based on assembly detection of a water level sensor, and relates to the technical field related to assembly linkage control. The method comprises: obtaining a predetermined assembly scheme based on water level sensor type matching; performing parameter configuration and automatic loading in sequence according to operating parameters and material types; placing the water level sensor to be assembled on the workbench of the assembly equipment, and starting the image detection station to perform solder joint quality detection on the water level sensor; if the solder joint quality detection result is qualified, performing a small cover installation operation; performing a capping height detection on the water level sensor, and if the expected height index is met, marking it as a qualified product, and clamping the water level sensor to the conveyor belt of the subsequent operation process through a blanking clamp. The method solves the technical problems existing in the prior art that the assembly detection process is inefficient and the assembly accuracy is difficult to ensure, resulting in poor quality and production efficiency of the water level sensor, and achieves the technical effect of improving the assembly efficiency, assembly accuracy and quality stability of the sensor.

Description

Technical Field

[0001] The present application relates to the technical field related to assembly linkage control, and specifically to a linkage control method and system based on water level sensor assembly detection. Background Art

[0002] In modern industrial production, water level sensors are widely used in liquid level monitoring, liquid control and other fields as key components. Their performance and reliability directly affect the operating stability of downstream equipment. The manufacturing of water level sensors involves multiple complex processes, including assembly, welding, testing and other links, which have extremely high requirements for accuracy and consistency. Traditional assembly methods mainly rely on manual operation or semi-automatic equipment. Although they can complete basic tasks, they have problems of low efficiency and unstable assembly quality. Manual operation is limited by technical level and fatigue, which can easily lead to misassembly or error accumulation; and although semi-automatic equipment has a certain production capacity, it lacks linkage control between processes and is difficult to meet the needs of large-scale, high-precision production. Solder point quality inspection and gland height inspection are two important quality control links. Solder point quality directly affects the electrical performance and long-term stability of the sensor, while gland height determines whether the overall assembly of the sensor meets the design specifications. Traditional quality inspection methods usually use offline sampling or simple visual comparison, which has low detection efficiency and the risk of missed detection, resulting in defective products flowing into downstream processes, further increasing rework and repair costs.

[0003] Therefore, in the current relevant technologies, there are technical problems such as low efficiency of assembly and inspection process and difficulty in ensuring assembly accuracy, which lead to poor quality and production efficiency of water level sensors. Summary of the invention

[0004] The present application provides a linkage control method and system based on water level sensor assembly detection, and adopts technical means such as assembly equipment automation, image detection of solder joint quality, and cover height detection to solve the technical problems in the prior art that the assembly detection process is inefficient and the assembly accuracy is difficult to ensure, resulting in poor quality and production efficiency of the water level sensor, thereby achieving the technical effect of improving the sensor assembly efficiency, assembly accuracy and quality stability.

[0005] The present application provides a linkage control method based on water level sensor assembly detection, the method comprising: obtaining a predetermined assembly scheme based on water level sensor type matching, wherein the predetermined assembly scheme comprises operating parameters, material type and assembly process; initializing the assembly equipment, performing parameter configuration and automatic loading in sequence according to the operating parameters and material type, and starting the equipment after confirmation; placing the water level sensor to be assembled on the workbench of the assembly equipment, installing springs and bolting the water level sensor in sequence according to the assembly process, and after the operation is completed, starting the image detection station to perform solder joint quality detection on the water level sensor to obtain solder joint quality detection results; if the solder joint quality detection result is qualified, installing a small cover on the water level sensor, and after the operation is completed, the water level sensor flows into the height detection station; at the height detection station, performing a capping height detection on the water level sensor, if the expected height index is met, marking the water level sensor as a qualified product, and clamping the water level sensor into the conveyor belt of the subsequent operation process through a blanking clamp.

[0006] In a possible implementation, parameter configuration and automatic loading are performed in sequence according to the operating parameters and material types, and the equipment is started after confirmation, and the following processing is also performed: parameter configuration of the assembly equipment is performed according to the operating parameters, wherein the operating parameters at least include operating mode, machine type setting, function setting, servo parameters and process parameters; multiple materials are configured according to the material type, and the multiple materials are poured into corresponding loading hoppers in sequence for automatic loading, wherein the material types include springs, bolts and small covers, and the loading hopper includes a silo and a vibration plate; after confirming that the operating parameter configuration is correct and the loading hopper meets the predetermined loading constraints, the assembly equipment is started, wherein the predetermined loading constraints include a first loading constraint and a second loading constraint.

[0007] In a possible implementation, the predetermined loading constraint includes a first loading constraint and a second loading constraint, and the following processing is also performed: the first loading constraint is that the material capacity in the silo is less than a first capacity ratio, and the second loading constraint is that the material capacity in the vibration plate is less than a second capacity ratio, wherein the first capacity ratio is two-thirds and the second capacity ratio is one-third.

[0008] In a possible implementation, an image detection station is started to perform solder joint quality detection on the water level sensor to obtain a solder joint quality detection result, and the following processing is also performed: the CCD image sensor of the image detection station is activated to collect the solder point image of the water level sensor to obtain a solder point image set; the solder point image set is input into a defect recognition channel to output a plurality of solder point defect sets; based on the plurality of solder point defect sets, solder point defect impact analysis and defect distribution risk prediction are performed to obtain a defect impact coefficient and a distribution risk coefficient; if the defect impact coefficient is less than the expected impact coefficient and the distribution risk coefficient is less than the expected risk coefficient, the solder joint quality detection result is qualified, otherwise the solder joint quality detection result is unqualified.

[0009] In a possible implementation, a denoising convergence model is used to perform denoising processing on the real-time event load data and extract key feature words, and the following processing is also performed: a defect recognition channel is constructed based on a convolutional neural network, wherein the defect recognition channel includes multiple convergence defect recognition branches; defect recognition accuracy is obtained, and the number of calling branches is set according to the recognition accuracy, wherein the number of calling branches is positively correlated with the defect recognition accuracy; random screening is performed within the multiple convergence defect recognition branches according to the number of calling branches to determine multiple matching convergence defect recognition branches; the solder joint image sets are respectively input into the multiple matching convergence defect recognition branches, and the multiple solder joint defect sets are output, wherein the solder joint defect set is the mode of the outputs of the multiple matching convergence defect recognition branches.

[0010] In a possible implementation, defect distribution risk prediction is performed, and the following processing is also performed: taking the water level sensor type as a constraint, querying the detection logs of similar sensors, obtaining multiple sample solder joint defect distributions, similarity clustering the multiple sample solder joint defect distributions, and determining multiple similar solder joint defect distribution sets; respectively counting the failure proportions of the corresponding water level sensors in the multiple similar solder joint defect distribution sets after a preset operation cycle, setting them as risk distribution coefficients, and obtaining multiple risk distribution coefficients; determining multiple solder joint defect distribution feature thresholds based on the multiple similar solder joint defect distribution sets, and constructing a risk predictor based on the mapping of the multiple solder joint defect distribution feature thresholds and multiple risk distribution coefficients; constructing a solder joint defect distribution based on the multiple solder joint defect sets, inputting the solder joint defect distribution into the risk predictor for matching, and outputting a risk distribution coefficient.

[0011] In a possible implementation, the linkage control method based on the water level sensor assembly detection also performs the following processing: if the solder joint quality inspection result is unqualified, the water level sensor is marked as a defective product, and the water level sensor is clamped to a defective box by a blanking clamp.

[0012] In a possible implementation, the water level sensor is subjected to a capping height detection, and if the expected height index is met, the water level sensor is marked as a qualified product, and the following processing is performed: at the height detection station, the capping height of the water level sensor is detected and obtained; if the capping height is less than or equal to a predetermined height index, the water level sensor is marked as a qualified product; if the capping height is greater than the predetermined height index, the water level sensor is marked as a defective product, and the water level sensor is clamped to a defective box by a blanking clamp.

[0013] The present application also provides a linkage control system based on water level sensor assembly detection, including: a predetermined assembly scheme acquisition module, used to obtain a predetermined assembly scheme based on water level sensor type matching, wherein the predetermined assembly scheme includes operating parameters, material types and assembly processes; an equipment initialization module, used to initialize the assembly equipment, perform parameter configuration and automatic material loading in sequence according to the operating parameters and material types, and start the equipment after confirmation; a solder joint quality detection module, used to place the water level sensor to be assembled on the workbench of the assembly equipment, and assemble the water level sensor in sequence according to the assembly process. Spring and bolting operations, and after the operations are completed, start the image detection station to perform solder joint quality inspection on the water level sensor to obtain the solder joint quality inspection results; a small cover installation operation module, used to install a small cover on the water level sensor if the solder joint quality inspection results are qualified, and after the operation is completed, the water level sensor will flow into the height inspection station; a capping height detection module, used to perform capping height inspection on the water level sensor at the height detection station, if the expected height index is met, mark the water level sensor as a qualified product, and clamp the water level sensor to the conveyor belt of the subsequent operation process through the blanking clamp.

[0014] Through the linkage control method and system based on water level sensor assembly detection proposed in this application, a predetermined assembly plan is obtained based on water level sensor type matching; parameter configuration and automatic loading are performed in sequence according to operating parameters and material types; the water level sensor to be assembled is placed on the workbench of the assembly equipment, and the image detection station is started to perform solder joint quality inspection on the water level sensor; if the solder joint quality inspection result is qualified, the small cover is installed; the water level sensor is tested for the pressure cap height, and if it meets the expected height index, it is marked as a qualified product, and the water level sensor is clamped to the conveyor belt of the subsequent operation process through the blanking clamp. The technical problems existing in the prior art, such as low efficiency of the assembly inspection process and difficulty in ensuring assembly accuracy, which lead to poor quality and production efficiency of the water level sensor, are solved, and the technical effect of improving the assembly efficiency, assembly accuracy and quality stability of the sensor is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0016] Figure 1 A schematic flow chart of a linkage control method based on water level sensor assembly detection provided in an embodiment of the present application;

[0017] Figure 2 A schematic diagram of the structure of a linkage control system based on water level sensor assembly detection provided in an embodiment of the present application.

[0018] Explanation of the reference numerals: predetermined assembly plan acquisition module 10 , equipment initialization module 20 , solder joint quality detection module 30 , small cover installation operation module 40 , capping height detection module 50 . DETAILED DESCRIPTION

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0020] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0021] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0022] The embodiment of the present application provides a linkage control method based on water level sensor assembly detection, such as Figure 1 As shown, the method includes:

[0023] Step S100, obtaining a predetermined assembly solution based on the water level sensor type matching, wherein the predetermined assembly solution includes operating parameters, material type and assembly process.

[0024] Furthermore, step S100 also includes step S110, configuring parameters of the assembly equipment according to the operating parameters, wherein the operating parameters at least include operating mode, machine type setting, function setting, servo parameters and process parameters; step S120, configuring multiple materials according to the material type, and pouring the multiple materials into corresponding loading hoppers in sequence for automatic loading, wherein the material types include springs, bolts and small covers, and the loading hopper includes a silo and a vibration plate; step S130, after confirming that the operating parameter configuration is correct and the loading hopper meets the predetermined loading constraints, starting the assembly equipment, wherein the predetermined loading constraints include a first loading constraint and a second loading constraint.

[0025] Preferably, a corresponding predetermined assembly scheme is matched according to the type of water level sensor, wherein the predetermined assembly scheme covers a number of specific parameters and operating procedures required in the assembly process of the water level sensor, including operating parameters, material types and assembly procedures. Specifically, operating parameters refer to key control and operating parameters that need to be set during the assembly process of the equipment, including operating mode, machine type setting, function setting, servo parameters, process parameters, servo limit and system setting, etc. Specifically, the operating mode defines the overall operating logic of the equipment, such as automatic mode (fully automatic assembly), semi-automatic mode (some steps require manual assistance) or manual debugging mode (used for equipment maintenance or process adjustment); machine type setting It refers to matching water level sensors of different models or types (such as sensors of different specifications, structures or functions); function settings include whether to enable certain specific function modules, such as automatic spring loading, welding detection or gland height measurement; servo parameters include the speed, torque, acceleration, etc. of the servo motor, which are used to control the motion accuracy and efficiency of the assembly equipment; process parameters involve the specific requirements of each assembly step, such as the time and current settings during welding, the pressure range during spring loading, etc.; servo limits define the motion range of the servo mechanism to avoid exceeding the physical limit or damaging the sensor; system settings include the overall operating logic configuration of the equipment, such as start, stop, alarm and emergency shutdown processes.

[0026] Preferably, the material type refers to the material that matches the specific sensor model, including sensor components (such as springs, bolts, small covers and other parts required for assembly); consumables, including welding materials (such as solder), lubricants and other materials that may be consumed during the assembly process; identification materials, such as barcodes and RFID tags, are used to mark the production batch and quality information of the sensor. The assembly process refers to the process steps pre-defined according to the type of sensor and assembly requirements to ensure that each process is completed efficiently in sequence, including clarifying the order of assembly, such as installing the spring first, then bolting, and finally pressing the cover; determining the equipment stations and operation sequence corresponding to each process, such as at which station the solder joint detection is completed; embedding detection points after key steps, such as solder joint quality detection and pressure cover height detection, to ensure that the process is qualified before entering the next link; when the test result is unqualified, start the automatic rejection process or alarm prompt, and record relevant data for traceability.

[0027] Preferably, a variety of materials are configured according to the material type and poured into the corresponding loading hoppers in sequence. The loading hopper structure includes a silo and a vibration plate. The silo is used to store a large amount of material and transport it to the vibration plate on demand. The vibration plate uses vibration to arrange and transport the material along a specific track to ensure that the material is in the correct position before assembly, that is, the corresponding material is poured into the silo according to the material type, the material is distributed to the assembly position through the vibration plate, and the direction of the material is corrected by a positioning device; before starting the assembly equipment, it is necessary to ensure that all parameter configurations and material supply meet the requirements, especially the predetermined loading constraints, including the first loading constraint and the second loading constraint. Specifically, the first loading constraint refers to the material capacity constraint of the silo, and the second loading constraint refers to the material capacity constraint of the vibration plate; when all operating parameters and loading constraints meet the conditions, start the equipment to perform the assembly operation, that is, according to the set operating mode, the equipment completes the processes of installing springs, bolting, and installing small covers in sequence, thereby realizing a stable and efficient water level sensor assembly process.

[0028] Step S130 also includes that the first loading constraint is that the material capacity in the silo is less than a first capacity ratio, and the second loading constraint is that the material capacity in the vibration plate is less than a second capacity ratio, wherein the first capacity ratio is two-thirds and the second capacity ratio is one-third.

[0029] Preferably, the first feeding constraint sets a dynamic threshold control mechanism for the material capacity of the silo. When the material capacity in the silo is lower than the set first capacity ratio (two-thirds of the silo capacity), it is identified as a state in which additional feeding is required to avoid material supply interruption, that is, when the capacity drops below two-thirds, the feeding action will be triggered to automatically add external materials to the silo. The second feeding constraint monitors the material capacity of the vibration plate. When the material capacity in the vibration plate is lower than the second capacity ratio (one-third of the vibration plate capacity), the material needs to be transported from the silo to the vibration plate to ensure the continuity of the material on the vibration plate feeding track to avoid interruption of the assembly process due to insufficient supply, and achieve dynamic balance of material supply through staged control (silo-vibration plate).

[0030] Step S200, initializing the assembly equipment, performing parameter configuration and automatic material loading in sequence according to the operating parameters and material types, and starting the equipment after confirmation.

[0031] Preferably, equipment initialization is the basic preparation work of the assembly equipment before starting, including operations at the hardware and software levels, including status checks on the core components of the equipment (such as servo motors, sensors, and workstation operating devices) to ensure the normal operation of all hardware, such as detecting whether the limit sensors are effective and checking whether the lubrication and movement status of mechanical parts are good; ensuring that the control system of the equipment communicates normally with the main control software, loading operating parameters, and completing the system configuration before the equipment is started; restoring the mechanical moving parts, conveyor belts, and processing stations to their initial state to prevent the remaining status after the equipment interrupts the last task from affecting the new round of operations. Parameter configuration refers to the detailed configuration of the operating mode and key parameters of the equipment according to the operating parameters and material type, including operating mode setting, servo motor parameter configuration, material parameter matching and process parameter setting. Among them, the operating mode setting refers to selecting automatic mode, semi-automatic mode or manual mode according to the current production needs, such as selecting full-automatic mode in batch production and manual mode in individual debugging; servo motor parameter configuration includes setting the motor speed, acceleration and operating range (limit); material parameter matching is to call the material specification information in the material database to ensure that the size and fixed position of the tooling fixture are consistent with the material type, such as adjusting the clamping force and position of the fixture for water level sensors of different specifications; process parameter setting includes welding stations, setting process parameters such as welding time and current; capping stations, setting target values ​​such as capping height and pressure range. Automatic loading refers to the automatic loading and preparation of materials for the equipment according to the material type and assembly plan, that is, starting the automatic loading device to deliver the water level sensor components (such as springs, small covers, bolts) to the material storage area of ​​each workstation to ensure smooth material transmission without jamming; check whether the quantity and specifications of materials in the material storage area are correct to ensure that there is no shortage of materials or wrong materials; use visual recognition or positioning sensors to correct the material position to ensure that the position of the material in the storage area or on the conveyor belt meets the assembly requirements; after confirming that the configuration (including parameter verification, material detection, and operation test) is accurate, officially start the equipment to enter the assembly operation stage.

[0032] Step S300, place the water level sensor to be assembled on the workbench of the assembly equipment, and install springs and bolts on the water level sensor in sequence according to the assembly process. After the operation is completed, start the image detection station to perform solder joint quality inspection on the water level sensor to obtain the solder joint quality inspection result.

[0033] Preferably, the water level sensor to be assembled is placed on a workbench of an assembly device. Specifically, the water level sensor component to be assembled (such as the outer shell and the internal parts) is placed on the workbench of the assembly equipment. The workbench is equipped with a clamp or a positioning device for accurately fixing the water level sensor, that is, the sensor is automatically transferred to the workbench by a conveyor belt or a robotic arm and positioned; the water level sensor is assembled with a spring, which is a key functional component in the sensor and is used for liquid level measurement or resetting. Specifically, the automated robotic arm grabs the spring and places it accurately, and the servo motor drives the mounting head to complete the compression and fixing operations of the spring to ensure that the spring is correctly embedded in the preset position. A sensor or a visual system is equipped to detect whether the spring is installed in place to avoid deviations in subsequent processes. Control parameters include spring compression force, installation position accuracy, and relaxation state detection after installation. Bolting operation refers to the use of bolts to fix the components of the water level sensor (such as the housing and functional modules). The automatic bolt assembly system grabs the bolts from the silo and quickly delivers them to the installation position. Electric or pneumatic bolting equipment completes the tightening operation. The torque sensor is equipped to detect the installation torque of each bolt in real time to prevent the assembly quality from being affected by over-tightening or over-loosening. The control parameters include the bolt specifications, installation torque, rotation speed and tightening depth.

[0034] Preferably, after completing the mechanical assembly, the sensor is sent to the image detection station to perform quality inspection on the welding part to ensure its electrical performance and assembly stability. Specifically, the image detection station usually uses a high-resolution industrial camera or AI vision system to capture high-definition images of the sensor solder joints. The image processing software analyzes the key quality characteristics of the solder joints (such as surface finish, degree of fusion, and solder joint position deviation), automatically generates solder joint quality inspection results, and determines whether the solder joints are qualified. Among them, the inspection dimensions include the size, shape, surface defects (such as cracks or pores) of the solder joints, and the positional relationship between the solder joints and the surrounding components. The solder joint quality inspection results are usually divided into qualified and unqualified categories, and are marked with a unique identification code for subsequent quality control and problem tracing. By installing springs and bolting operations, the basic structure of the sensor is ensured to be complete and meet the assembly requirements; then the solder joints are inspected with high precision by the image detection station, which improves the reliability and efficiency of quality control, realizes the combination of mechanical operations and intelligent detection, and not only reduces labor costs, but also improves the overall quality of the product.

[0035] Step S300 further includes step S310, activating the CCD image sensor of the image detection station to collect the solder point image of the water level sensor and obtain a solder point image set; step S320, inputting the solder point image set into the defect recognition channel and outputting multiple solder point defect sets; step S330, based on the multiple solder point defect sets, performing solder point defect impact analysis and defect distribution risk prediction to obtain a defect impact coefficient and a distribution risk coefficient; step S340, if the defect impact coefficient is less than the expected impact coefficient and the distribution risk coefficient is less than the expected risk coefficient, then the solder point quality inspection result is qualified, otherwise the solder point quality inspection result is unqualified.

[0036] Preferably, the CCD image sensor is a commonly used acquisition device in industrial image detection, and the detailed image of the solder point of the water level sensor is obtained through high-resolution imaging. Specifically, the CCD image sensor of the image detection station is activated to ensure that it focuses on the solder point area and starts image acquisition. The image detection station is equipped with a multi-angle imaging device to capture a complete view of the solder point, including the front, side and key areas, and the multiple collected solder point images are sorted into a solder point image set; then the solder point image set is input into the defect recognition channel, wherein the defect recognition channel is a recognition model built based on a machine learning algorithm, which is used to automatically identify defects in the solder point image, reduce noise, enhance contrast and extract edges for the solder point image set, improve image quality, and use the algorithm to detect the shape, size, surface texture and other features of the solder point. Identify possible defects, such as cracked solder joints, pores, insufficient or excessive solder, and organize defect information into multiple solder joint defect sets based on defect type and location. Each defect set contains information such as defect type, location, severity, etc.; then perform solder joint defect impact analysis and defect distribution risk prediction based on multiple solder joint defect sets. Solder joint defect impact analysis refers to calculating the defect impact coefficient based on the degree of influence of each defect type on solder joint quality, reflecting the comprehensive influence of defects on solder joint performance. For example, pores may cause unstable electrical connections, and excessive solder may increase mechanical stress; defect distribution risk prediction refers to statistically analyzing the distribution of solder joint defects and calculating the distribution risk coefficient based on the distribution characteristics, reflecting whether the spatial distribution of solder joint defects will cause local weak points. For example, high defect concentration may increase the risk of fracture.

[0037] Preferably, finally, according to the threshold values ​​of the preset influence coefficient and risk coefficient, determine whether the quality of the solder joint is qualified. If the defect influence coefficient is less than the expected influence coefficient, and the distribution risk coefficient is less than the expected risk coefficient, it means that the defect has little effect on the overall performance of the solder joint, and the solder joint quality test result is qualified; if any coefficient exceeds the preset threshold value, it means that there is a large quality risk in the solder joint, and the solder joint quality test result is unqualified; among them, the expected influence coefficient represents the allowable range of the influence of the solder joint defect on the performance, for example, the threshold value of the influence coefficient may be set to 0.2, indicating that the degree of influence shall not exceed 20%; the expected risk coefficient represents the allowable risk range of the defect distribution, for example, the threshold value of the risk coefficient may be set to 0.1, indicating that the risk of concentrated defects shall not exceed 10%. Through the judgment logic of the influence coefficient and the risk coefficient, the automatic detection of the solder joint quality is realized, which solves the problems of strong subjectivity and low efficiency of traditional manual detection, and greatly improves the reliability and accuracy of detection.

[0038] Step S320 further includes step S321, constructing a defect recognition channel based on a convolutional neural network, wherein the defect recognition channel includes multiple convergent defect recognition branches; step S322, obtaining defect recognition accuracy, and setting the number of calling branches according to the recognition accuracy, wherein the number of calling branches is positively correlated with the defect recognition accuracy; step S323, performing random screening within the multiple convergent defect recognition branches according to the number of calling branches, and determining multiple matching convergent defect recognition branches; step S324, respectively inputting the solder joint image sets into the multiple matching convergent defect recognition branches, and outputting the multiple solder joint defect sets, wherein the solder joint defect sets are the mode of the outputs of the multiple matching convergent defect recognition branches.

[0039] Preferably, the defect recognition channel is a multi-branch processing module designed through a convolutional neural network (CNN) architecture, which is specifically used to analyze solder joint images and identify defects. The channel contains multiple convergent defect recognition branches, each of which is a CNN model with specific training parameters. Specifically, each branch consists of a convolution layer, a pooling layer, an activation layer, and a fully connected layer, which are used to extract features of solder joint images. Different branches may be optimized and trained for different types of defects (such as pores, cracks, excess solder, etc.), and the possible recognition bias of a single model is reduced through the collaborative work of multiple models. The parameters of each branch may be different, such as the convolution kernel size, the number of layers, the activation function, etc., to adapt to different solder joint defect characteristics; according to the defect recognition accuracy requirements of the current task, the number of branches to be called is dynamically adjusted, and the defect recognition accuracy is positively correlated with the number of called branches, that is, when higher recognition accuracy is required, more branches are called for comprehensive analysis. , where the defect recognition accuracy represents the correct rate of defect recognition. The number of branches called is dynamically adjusted according to the recognition accuracy. The more branches there are, the more comprehensive the recognition result is. The random screening mechanism increases the generalization ability of the model and avoids the deviation that may be introduced by the fixed branch call. Finally, the solder joint image set is input into multiple matching convergence defect recognition branches respectively, and each branch processes and outputs the solder joint defect result independently. Specifically, each solder joint image will be processed by convolution layer feature extraction, pooling dimensionality reduction and full connection layer classification. Each branch independently outputs the defect type of the image, such as insufficient solder, pores, cracking, etc., to form multiple solder joint defect sets. The solder joint defect set is the mode of the outputs of multiple matching convergence defect recognition branches. For example, among 8 branches, 6 are identified as pore defects and 2 are identified as insufficient solder, and finally pore defects are output.

[0040] Step S330 further includes step S331, taking the water level sensor type as a constraint, querying the detection log of similar sensors, obtaining multiple sample solder joint defect distributions, similarly clustering the multiple sample solder joint defect distributions, and determining multiple similar solder joint defect distribution sets; step S332, respectively counting the failure proportions of the corresponding water level sensors in the multiple similar solder joint defect distribution sets after running a preset cycle, setting them as risk distribution coefficients, and obtaining multiple risk distribution coefficients; step S333, determining multiple solder joint defect distribution feature thresholds based on the multiple similar solder joint defect distribution sets, and constructing a risk predictor based on the mapping of the multiple solder joint defect distribution feature thresholds and multiple risk distribution coefficients; step S334, constructing a solder joint defect distribution based on the multiple solder joint defect sets, inputting the solder joint defect distribution into the risk predictor for matching, and outputting the risk distribution coefficient.

[0041] Preferably, according to the type of the current water level sensor (such as model, specification, design structure, etc.), the historical detection data of the same type of sensors are screened, and the distribution data of solder joint defects are extracted from the detection log, that is, the distribution of solder joint defects of multiple samples including defect type, location, quantity and severity is obtained, and then the extracted solder joint defect distribution is analyzed by a similar clustering algorithm, and similar distribution patterns are grouped into the same set. Specifically, the characteristics of the solder joint defect distribution are extracted, such as the spatial distribution of defects (such as concentrated at the edge or the middle), the quantity distribution (such as multiple defects concentrated at a certain workstation), etc., and a clustering algorithm (such as K-means, DBSCAN, etc.) is used to cluster the samples according to the distribution characteristics to generate multiple sets of similar defect distributions, each set representing a common defect distribution pattern, such as "center-concentrated type" and "edge-diffused type".

[0042] Preferably, within each defect distribution set, the failure rate of the sensor after a certain period of operation is counted as an indicator for evaluating the risk of the distribution pattern, that is, the risk distribution coefficient; then, the characteristics of each defect distribution set are analyzed, its key indicators are extracted, and the characteristic threshold range is defined, that is, the key dimensions of the defect distribution (such as the number of defects and the location concentration) are statistically analyzed, and the characteristic threshold is defined for each distribution pattern according to the analysis results; finally, the defect distribution characteristic threshold and its corresponding risk distribution coefficient are combined to establish a predictor for predicting the impact of different defect distributions on the sensor operation risk. Specifically, each defect distribution feature is associated with the corresponding risk distribution coefficient to form a mapping relationship between input (feature) and output (risk coefficient). The system uses machine learning models (such as decision trees, random forests or deep learning networks) to establish a prediction model, and uses the data of multiple similar solder joint defect distribution sets to train the predictor to optimize the accuracy of the mapping relationship; constructs the solder joint defect distribution and inputs it into the risk predictor for matching, and outputs its corresponding risk distribution coefficient, that is, according to the current sensor detection data, extracts the solder joint defect characteristics (quantity, location, severity, etc.), constructs the defect distribution, inputs the risk predictor, matches it with the historical data pattern in the training model, and outputs the risk distribution coefficient according to the matching result, indicating the risk level of the current solder joint defect distribution, realizing the intelligence and risk-based defect detection, and greatly improving the efficiency and reliability of automated quality assessment.

[0043] Step S400: If the solder joint quality inspection result is qualified, the water level sensor is covered with a small cover, and after the installation is completed, the water level sensor is placed in a height inspection station.

[0044] Preferably, if the weld quality inspection result is qualified, the water level sensor automatically flows to the next station to install the small cover, wherein the small cover is an important component on the outside of the water level sensor, which is mainly used for sealing protection and fixing functional modules to ensure the integrity of the sensor structure. Specifically, the material conveying system transports the small cover to the cover installation station, ready for assembly, the automatic manipulator grabs the small cover and places it accurately on the top of the sensor, and the assembly is completed by the cover pressing device driven by the servo motor. If the small cover has a snap-on structure, the equipment will complete the accurate fixation of the snap-on. If screws are required, the equipment will perform the tightening operation, and is equipped with a visual system or a pressure sensor to monitor the installation position and assembly pressure of the small cover in real time to ensure that the small cover is in close contact with the sensor body. No misalignment or looseness; after the small cover is assembled, the sensor is transported to the next inspection station for capping height detection to confirm whether the small cover installation meets the design requirements, that is, a conveyor belt or a robotic arm is used to move the sensor from the capping station to the height detection station to ensure a smooth transition and avoid collision or damage. Specifically, height detection usually uses laser ranging sensors, mechanical measuring tools, etc., in a non-contact manner, including measuring the overall height of the small cover after installation, confirming whether it is within the designed tolerance range, and checking whether there is deformation, looseness or out-of-place during the installation process, ensuring high precision, maximizing assembly efficiency and product qualification rate, while reducing the generation of defective products and ensuring the reliability and stability of the sensor.

[0045] Step S400 further includes: if the solder joint quality inspection result is unqualified, marking the water level sensor as a defective product, and clamping the water level sensor to a defective box by a blanking clamp.

[0046] Preferably, if the solder joint quality inspection result is unqualified, that is, the defect influence coefficient is higher than the expected influence coefficient or the distribution risk coefficient is higher than the expected risk coefficient, then the solder joint quality of the water level sensor is determined to be unqualified, and the sensor detected as unqualified is marked to make it clear that it is a defective product. For example, unqualified products are labeled with defective labels or distinguished by color, and automated equipment (unloading clamps) are used to transfer the defective sensor from the inspection station to a dedicated defective product collection box to prevent it from accidentally flowing into the next process, thereby improving the overall quality control level and improving the automation and quality management level of the production line.

[0047] Step S500, at the height detection station, the water level sensor is subjected to a capping height detection. If the expected height index is met, the water level sensor is marked as a qualified product, and the water level sensor is clamped into the conveyor belt of the subsequent operation process by a blanking clamp.

[0048] Preferably, the height detection station is a station specifically used to check whether the height of the small cover after installation meets the design standards. The water level sensor is tested for the cover height to ensure the consistency and accuracy of the assembly. Specifically, a laser rangefinder sensor or a visual inspection system is used to achieve high-precision non-contact height measurement. The water level sensor is accurately positioned at the inspection station through a conveyor belt or a mechanical arm, and starts to measure the actual installation height of the small cover. The data is recorded, and the detected height is compared with the preset target height and the allowable tolerance range. If the height value is within the allowable range, it is marked as a qualified product. If the height exceeds the tolerance range, it is judged as a defective product, the data is recorded, and subsequent processing measures (such as rejection or repair) are taken; after the test result is qualified, the water level sensor is automatically Quality marking, after the qualified products are confirmed by the height detection station, they are safely transferred to the conveyor belt of the next process through a mechanical device (unloading clamp). The unloading clamp is usually a pneumatic or servo-driven mechanical device with adjustable clamping force to adapt to water level sensors of different specifications. The clamping part uses a soft pad or elastic material to avoid damage to the sensor during the clamping process; specifically, the unloading clamp accurately clamps the qualified sensor according to the instructions of the control system and places it at the designated position of the subsequent conveyor belt. The conveyor belt starts and transports the qualified sensor to the next process, such as functional testing or packaging; unqualified products will not enter the unloading clamp process, but will be removed from the production line by a rejection device (such as a diversion mechanism or a rejection slide) and stored in a centralized rework or scrap area. Efficient and safe material flow is achieved, which not only realizes the automated assembly process and improves the overall quality control level of the production line, but also enhances the stability and reliability of water level sensor production.

[0049] Step S500 further includes step S510, detecting and obtaining the pressure cover height of the water level sensor at the height detection station; step S520, if the pressure cover height is less than or equal to a predetermined height index, marking the water level sensor as a qualified product; step S530, if the pressure cover height is greater than the predetermined height index, marking the water level sensor as a defective product, and clamping the water level sensor to a defective box by a blanking clamp.

[0050] Preferably, at the height detection station, the capping height of the water level sensor is detected and obtained, wherein the capping height detection station is used to verify whether the sensor capping installation meets the design requirements, and the capping height refers to the vertical height between the cover of the sensor after installation and the reference datum. Specifically, the sensor is accurately positioned at the height detection station by a conveyor belt or a mechanical arm, and the capping height is measured by a detection device (such as a laser ranging sensor, a displacement sensor), and the measurement result is compared with a predetermined height index in real time. If the detected capping height meets or is lower than the predetermined height index (that is, the height value is within the allowable range), the sensor is determined to be a qualified product, wherein the predetermined height index refers to the capping height standard and the allowable tolerance range set by the equipment, such as 5.0±0.2mm; if the detected capping height exceeds the predetermined height index (that is, deviates from the allowable range), the sensor is determined to be a defective product, and then the blanking clamp automatically transfers it to the designated defective product collection box to prevent it from flowing into the subsequent process. Through real-time detection, precise labeling and automatic sorting, intelligent management of height detection is realized, and detection efficiency and reliability of quality control are improved.

[0051] In the above, refer to Figure 1 The linkage control method based on water level sensor assembly detection according to an embodiment of the present invention is described in detail. Figure 2 A linkage control system based on assembly detection of a water level sensor according to an embodiment of the present invention is described.

[0052] The linkage control system based on the assembly detection of the water level sensor according to the embodiment of the present invention is used to solve the technical problems existing in the prior art that the assembly detection process is inefficient and the assembly accuracy is difficult to ensure, resulting in poor quality and production efficiency of the water level sensor, and achieves the technical effect of improving the assembly efficiency, assembly accuracy and quality stability of the sensor. The linkage control system based on the assembly detection of the water level sensor includes: a predetermined assembly plan acquisition module 10, an equipment initialization module 20, a solder joint quality detection module 30, a small cover installation operation module 40, and a capping height detection module 50.

[0053] A predetermined assembly scheme acquisition module 10 is used to acquire a predetermined assembly scheme based on the water level sensor type matching, wherein the predetermined assembly scheme includes operating parameters, material type and assembly process;

[0054] The equipment initialization module 20 is used to initialize the assembly equipment, perform parameter configuration and automatic material loading in sequence according to the operating parameters and material types, and start the equipment after confirmation;

[0055] The solder joint quality inspection module 30 is used to place the water level sensor to be assembled on the workbench of the assembly equipment, install springs and bolt the water level sensor in sequence according to the assembly process, and after the operation is completed, start the image inspection station to perform solder joint quality inspection on the water level sensor to obtain the solder joint quality inspection result;

[0056] A small cover installation operation module 40 is used to install a small cover on the water level sensor if the welding point quality inspection result is qualified, and after the operation is completed, the water level sensor flows into the height inspection station;

[0057] The capping height detection module 50 is used to detect the capping height of the water level sensor at the height detection station. If the expected height index is met, the water level sensor is marked as a qualified product, and the water level sensor is clamped into the conveyor belt of the subsequent operation process through a blanking clamp.

[0058] The specific configuration of the equipment initialization module 20 will be described in detail below. The equipment initialization module 20 further includes: configuring the parameters of the assembly equipment according to the operating parameters, wherein the operating parameters at least include the operating mode, machine type setting, function setting, servo parameters and process parameters; configuring a plurality of materials according to the material type, and pouring the plurality of materials into the corresponding loading hopper in sequence for automatic loading, wherein the material types include springs, bolts and small covers, and the loading hopper includes a silo and a vibration plate; after confirming that the operating parameter configuration is correct and the loading hopper meets the predetermined loading constraints, starting the assembly equipment, wherein the predetermined loading constraints include the first loading constraint and the second loading constraint.

[0059] The specific configuration of the equipment initialization module 20 will be described in detail below. The equipment initialization module 20 further includes: the first loading constraint is that the material capacity in the silo is less than the first capacity ratio, and the second loading constraint is that the material capacity in the vibration plate is less than the second capacity ratio, wherein the first capacity ratio is two-thirds and the second capacity ratio is one-third.

[0060] The specific configuration of the solder joint quality detection module 30 will be described in detail below. The solder joint quality detection module 30 may further include: activating the CCD image sensor of the image detection station to collect the solder joint image of the water level sensor and obtain a solder joint image set; inputting the solder joint image set into the defect recognition channel and outputting a plurality of solder joint defect sets; based on the plurality of solder joint defect sets, performing solder joint defect impact analysis and defect distribution risk prediction to obtain a defect impact coefficient and a distribution risk coefficient; if the defect impact coefficient is less than the expected impact coefficient and the distribution risk coefficient is less than the expected risk coefficient, the solder joint quality detection result is qualified, otherwise the solder joint quality detection result is unqualified.

[0061] The specific configuration of the solder joint quality detection module 30 will be described in detail below. The solder joint quality detection module 30 may further include: constructing a defect recognition channel based on a convolutional neural network, wherein the defect recognition channel includes multiple convergent defect recognition branches; obtaining defect recognition accuracy, and setting the number of call branches according to the recognition accuracy, wherein the number of call branches is positively correlated with the defect recognition accuracy; performing random screening within the multiple convergent defect recognition branches according to the number of call branches, and determining multiple matching convergent defect recognition branches; inputting the solder joint image set into the multiple matching convergent defect recognition branches respectively, and outputting the multiple solder joint defect sets, wherein the solder joint defect set is the mode of the outputs of the multiple matching convergent defect recognition branches.

[0062] The specific configuration of the solder joint quality detection module 30 will be described in detail below. The solder joint quality detection module 30 may further include: taking the water level sensor type as a constraint, querying the detection log of the same type of sensor, obtaining multiple sample solder joint defect distributions, performing similarity clustering on the multiple sample solder joint defect distributions, and determining multiple sets of similar solder joint defect distributions; respectively counting the failure proportions of the corresponding water level sensors in the multiple sets of similar solder joint defect distributions after running a preset cycle, setting them as risk distribution coefficients, and obtaining multiple risk distribution coefficients; determining multiple solder joint defect distribution feature thresholds according to the multiple sets of similar solder joint defect distributions, and constructing a risk predictor based on the mapping of the multiple solder joint defect distribution feature thresholds and multiple risk distribution coefficients; constructing a solder joint defect distribution based on the multiple solder joint defect sets, inputting the solder joint defect distribution into the risk predictor for matching, and outputting a risk distribution coefficient.

[0063] The specific configuration of the small cover installation module 40 will be described in detail below. The small cover installation module 40 may further include: if the solder joint quality inspection result is unqualified, marking the water level sensor as a defective product, and clamping the water level sensor to a defective box by a blanking clamp.

[0064] The specific configuration of the capping height detection module 50 will be described in detail below. The capping height detection module 50 may further include: at the height detection station, detecting and obtaining the capping height of the water level sensor; if the capping height is less than or equal to a predetermined height index, marking the water level sensor as a qualified product; if the capping height is greater than the predetermined height index, marking the water level sensor as a defective product, and clamping the water level sensor to a defective box by a blanking clamp.

[0065] The linkage control system based on water level sensor assembly detection provided in the embodiment of the present invention can execute the linkage control method based on water level sensor assembly detection provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0066] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0067] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A linkage control method based on water level sensor assembly detection, characterized in that: include: Acquire a predetermined assembly scheme based on the water level sensor type matching, wherein the predetermined assembly scheme includes operating parameters, material type and assembly process; Initialize the assembly equipment, perform parameter configuration and automatic loading in sequence according to the operating parameters and material types, and start the equipment after confirmation; The water level sensor to be assembled is placed on the workbench of the assembly equipment, and the spring and bolt operations are sequentially performed on the water level sensor according to the assembly process. After the operations are completed, the image detection station is started to perform solder joint quality inspection on the water level sensor to obtain the solder joint quality inspection result; If the welding point quality inspection result is qualified, the water level sensor is installed with a small cover, and after the operation is completed, the water level sensor flows into the height inspection station; At the height detection station, the water level sensor is tested for the capping height. If the expected height index is met, the water level sensor is marked as a qualified product, and the water level sensor is clamped into the conveyor belt of the subsequent operation process by a blanking clamp; The image detection station is started to perform solder joint quality detection on the water level sensor to obtain solder joint quality detection results, including: Activate the CCD image sensor of the image detection station to collect the solder point image of the water level sensor to obtain a solder point image set; Inputting the solder joint image set into a defect recognition channel and outputting a plurality of solder joint defect sets; Based on the multiple solder joint defect sets, solder joint defect impact analysis and defect distribution risk prediction are performed to obtain defect impact coefficients and distribution risk coefficients, wherein the solder joint defect impact analysis refers to calculating the defect impact coefficient according to the degree of influence of each defect type on solder joint quality; If the defect influence coefficient is less than the expected influence coefficient and the distribution risk coefficient is less than the expected risk coefficient, the solder joint quality test result is qualified, otherwise the solder joint quality test result is unqualified; Among them, defect distribution risk prediction is carried out, including: Taking the water level sensor type as a constraint, querying the detection logs of similar sensors, obtaining multiple sample solder joint defect distributions, performing similarity clustering on the multiple sample solder joint defect distributions, and determining multiple similar solder joint defect distribution sets; The failure proportions of the corresponding water level sensors in the plurality of similar solder joint defect distribution sets after the preset operation period are counted respectively, and set as distribution risk coefficients, to obtain a plurality of distribution risk coefficients; Determine a plurality of weld spot defect distribution feature thresholds according to the plurality of similar weld spot defect distribution sets, and construct a risk predictor based on the plurality of weld spot defect distribution feature thresholds and a plurality of distribution risk coefficient mappings; Constructing a solder joint defect distribution based on the multiple solder joint defect sets, inputting the solder joint defect distribution into the risk predictor for matching, and outputting a distribution risk coefficient; The solder joint image set is input into a defect recognition channel, and multiple solder joint defect sets are output, including: Constructing a defect recognition channel based on a convolutional neural network, wherein the defect recognition channel includes a plurality of convergent defect recognition branches; Obtaining defect recognition accuracy, and setting the number of call branches according to the recognition accuracy, wherein the number of call branches is positively correlated with the defect recognition accuracy; Randomly screening the multiple convergence defect identification branches according to the number of calling branches to determine multiple matching convergence defect identification branches; The solder joint image sets are respectively input into the multiple matching convergence defect recognition branches, and the multiple solder joint defect sets are output, wherein the solder joint defect set is the mode of the outputs of the multiple matching convergence defect recognition branches.

2. The linkage control method based on water level sensor assembly detection according to claim 1 is characterized in that: Perform parameter configuration and automatic loading in sequence according to the operating parameters and material types, and start the equipment after confirmation, including: Parameter configuration of the assembly equipment is performed according to the operating parameters, wherein the operating parameters at least include operating mode, machine type setting, function setting, servo parameters and process parameters; According to the material type, a plurality of materials are configured, and the plurality of materials are sequentially poured into the corresponding loading hopper for automatic loading, wherein the material type includes a spring, a bolt and a small cover, and the loading hopper includes a silo and a vibration plate; After confirming that the operating parameter configuration is correct and the loading hopper meets the predetermined loading constraints, the assembly equipment is started, wherein the predetermined loading constraints include a first loading constraint and a second loading constraint, the first loading constraint being that the material capacity in the silo is less than a first capacity ratio, the second loading constraint being that the material capacity in the vibration plate is less than a second capacity ratio, and the first capacity ratio is two-thirds, and the second capacity ratio is one-third.

3. The linkage control method based on water level sensor assembly detection according to claim 1 is characterized in that: If the solder joint quality inspection result is unqualified, the water level sensor is marked as a defective product, and the water level sensor is clamped into a defective box by a blanking clamp.

4. The linkage control method based on water level sensor assembly detection according to claim 1 is characterized in that: The water level sensor is tested for the cover height. If the expected height index is met, the water level sensor is marked as a qualified product, including: At the height detection station, detecting and obtaining the gland height of the water level sensor; If the gland height is less than or equal to the predetermined height index, the water level sensor is marked as a qualified product; If the pressure cover height is greater than a predetermined height index, the water level sensor is marked as a defective product, and the water level sensor is clamped into a defective box by a blanking clamp.

5. The linkage control system based on water level sensor assembly detection is characterized in that: The system is used to implement the linkage control method based on water level sensor assembly detection according to any one of claims 1 to 4, and the system comprises: A predetermined assembly scheme acquisition module, used to acquire a predetermined assembly scheme based on the water level sensor type matching, wherein the predetermined assembly scheme includes operating parameters, material type and assembly process; The equipment initialization module is used to initialize the assembly equipment, perform parameter configuration and automatic material loading in sequence according to the operating parameters and material types, and start the equipment after confirmation; A solder joint quality inspection module is used to place the water level sensor to be assembled on the workbench of the assembly equipment, install springs and bolt the water level sensor in sequence according to the assembly process, and after the operation is completed, start the image inspection station to perform solder joint quality inspection on the water level sensor to obtain the solder joint quality inspection result; A small cover installation operation module is used to install a small cover on the water level sensor if the welding point quality inspection result is qualified, and after the operation is completed, the water level sensor flows into the height detection station; The capping height detection module is used to detect the capping height of the water level sensor at the height detection station. If the expected height index is met, the water level sensor is marked as a qualified product, and the water level sensor is clamped into the conveyor belt of the subsequent operation process through a blanking clamp.

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