Quality inspection system for air conditioner component production qualification

Abstract

The invention relates to the technical field of product quality inspection, and discloses an air conditioner component production qualification quality inspection system. Comprising a central processing unit, and a material warehousing and inspection module, a production process monitoring module, a high-precision detection and test module, a data analysis and intelligent diagnosis module, an unqualified product treatment and reinspection module and a quality tracing and feedback optimization module which are connected with the central processing unit. An automatic and intelligent quality inspection means is adopted to replace traditional manual sampling quality inspection, so that the quality inspection speed and accuracy are remarkably improved, quality problems in the production process can be found in time, the root causes and occurrence links of the problems can be quickly positioned, unqualified products are reduced, and the production efficiency is improved. And through the quality tracing and feedback optimization module, the production information of each product can be traced, unqualified products are prevented from flowing into the market, and the production cost and the rejection rate of enterprises are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of product quality inspection, and in particular to a quality inspection system for production of air-conditioning components. Background Art

[0002] As an important part of the home appliance industry, the air-conditioning industry has developed rapidly in recent years. With the intensification of market competition, consumers' requirements for the quality of air conditioners are getting higher and higher. In order to meet market demand, companies need to continuously improve product quality and production efficiency, and product quality inspection is an important means to achieve this goal.

[0003] In the production process of air-conditioning components, manual sampling quality inspection is currently mostly carried out, which is time-consuming and labor-intensive. Due to the interference of human factors, the actual detection speed is slow, and quality inspection is prone to errors, resulting in a low pass rate. In addition, it is difficult to promptly discover which quality inspection link has quality problems with unqualified products, resulting in a large output of unqualified products, which in turn leads to a high scrap rate and increases the production costs of the enterprise. For this reason, we propose a qualified quality inspection system for the production of air-conditioning components. Summary of the invention

[0004] In order to solve the technical problems existing in the background technology, the present invention provides a belt conveyor.

[0005] The invention proposes a qualified quality inspection system for air-conditioning components production, comprising a central processing unit and a material storage and inspection module connected to the central processing unit: responsible for receiving, registering and preliminary quality inspection of raw materials;

[0006] Production process monitoring module: responsible for real-time monitoring of various parameters in the production process and conducting preliminary tests on components;

[0007] High-precision inspection and testing module: receives semi-finished products output by the automated production line, performs multiple inspection tasks, and feeds back the inspection results to the central processor in real time;

[0008] Data analysis and intelligent diagnosis module: Receives test data from the high-precision detection module, conducts in-depth analysis of the test data, determines whether the tested product is qualified, and automatically generates a product analysis report;

[0009] Defective product processing and re-inspection module: Receive defective products, and determine whether the defective products need to be reworked, repaired, downgraded or directly scrapped based on the product analysis report generated by the data analysis and intelligent diagnosis module, and re-inspect the reworked products;

[0010] Quality traceability and feedback optimization module: responsible for recording data from each quality inspection link, providing detailed traceability paths for quality issues, and converting analysis results into specific improvement measures.

[0011] As a further optimized solution of the present invention, the material storage and inspection module includes

[0012] Barcode / RFID scanning unit: used for automatic identification of raw materials when they enter the warehouse, and recording material batch, model, and supplier information;

[0013] Physical properties testing unit: used to evaluate the basic physical properties of raw materials to ensure that they meet production requirements;

[0014] Chemical composition analysis unit: Detects whether the material contains excessive harmful substances through spectral analysis technology.

[0015] As a further optimized solution of the present invention, the production process monitoring module includes

[0016] Sensor network: It has a variety of environmental parameter monitoring sensors to monitor production environment parameters in real time;

[0017] Machine vision inspection unit: detects defects in the production process, such as cracks, deformation, missing parts, etc., through high-definition cameras and AI algorithms;

[0018] Process parameter monitoring unit: monitors and records the parameters of key process steps, such as injection pressure, welding temperature, etc., to ensure process consistency.

[0019] As a further optimized solution of the present invention, the high-precision detection and testing module includes

[0020] Performance simulation test unit: simulates extreme working environment to test the weather resistance and stability of components;

[0021] Electrical performance test unit: responsible for conducting electrical performance tests on components to ensure that the electrical characteristics of components meet the design specifications;

[0022] Refrigeration / heating efficiency test unit: responsible for testing the refrigeration efficiency of refrigeration components and the heating efficiency of heating components.

[0023] As a further optimized solution of the present invention, the data analysis and intelligent diagnosis module includes

[0024] Big data analysis unit: Use data mining technology to analyze the root causes of quality problems, predict potential defects, and optimize production processes;

[0025] Data receiving and analysis diagnosis unit: Receives test data from high-precision detection modules, uses machine learning algorithms to conduct in-depth analysis and comparison of test data, and determines and distinguishes qualified and unqualified products;

[0026] Report generation and feedback unit: Automatically generate product analysis reports, including pass / fail judgment results, problem descriptions and improvement suggestions, and feed them back to the production department.

[0027] As a further optimized solution of the present invention, after receiving the test data from the high-precision detection module, the data receiving and analysis diagnosis unit preprocesses the test data, and extracts key features that can reflect product quality and performance from the preprocessed test data, and inputs the extracted features into the trained machine learning model for predictive classification, and determines whether the product corresponding to the test data is qualified or unqualified based on the output of the model.

[0028] As a further optimized solution of the present invention, the defective product processing and re-inspection module includes

[0029] Defective product isolation unit: According to the diagnosis results of the data analysis and intelligent diagnosis module, the defective products on the production line are sorted and isolated to the designated area;

[0030] Rework / scrap decision-making unit: It has three standards: rework and repair, downgrade and direct scrapping. It receives product analysis reports of non-conforming products and issues instructions for rework and repair / downgrade / direct scrapping based on the reasons for non-conformity in the product analysis reports.

[0031] Re-inspection unit: re-test the reworked products.

[0032] As a further optimized solution of the present invention, the quality traceability and feedback optimization module includes

[0033] Production record unit: records the data and information of each production link to ensure that the production information of each product can be traced;

[0034] Quality traceability unit: When unqualified products are found during quality inspection, the production records are automatically queried to quickly locate the root cause and occurrence of the problem, and provide a detailed traceability path;

[0035] Feedback and optimization unit: Generates specific improvement measures based on quality traceability results and product analysis reports, and feeds these measures back to the central processor for optimization.

[0036] The invention has the following beneficial effects:

[0037] (I) This system adopts automated and intelligent quality inspection methods to replace traditional manual sampling quality inspection, thereby significantly improving the speed and accuracy of quality inspection. Through high-precision detection and testing modules, it can perform multiple inspection tasks on semi-finished products and feed back the inspection results to the central processor in real time to ensure that each product undergoes strict quality control;

[0038] (II) This system can timely discover quality problems in the production process and quickly locate the root causes and occurrence links of the problems, thereby reducing the production of defective products. Through the quality traceability and feedback optimization module, the production information of each product can be traced, avoiding the flow of defective products into the market, reducing the production cost and scrap rate of the enterprise, and the data analysis and intelligent diagnosis module uses data mining technology to analyze the root causes of quality problems and predict potential defects, providing a scientific basis for optimizing the production process;

[0039] (III) The system can generate specific improvement measures based on the quality traceability results and product analysis reports, and feed them back to the central processor for optimization, thereby continuously improving product quality and production efficiency, allowing enterprises to produce high-quality products more efficiently and occupy a larger share in the market.

[0040] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a principle block diagram of the air-conditioning component production qualified quality inspection system provided by the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure more clear, the technical solution of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings of the embodiments of the present disclosure.

[0043] See also Figure 1 , an air-conditioning component production qualified quality inspection system, including a central processing unit and a material storage and inspection module connected to the central processing unit, a production process monitoring module, a high-precision detection and testing module, a data analysis and intelligent diagnosis module, a non-conforming product processing and re-inspection module, and a quality tracing and feedback optimization module;

[0044] The material warehousing and inspection module is responsible for the receipt, registration and preliminary quality inspection of raw materials;

[0045] The production process monitoring module is responsible for real-time monitoring of various parameters in the production process and conducting preliminary tests on components;

[0046] The high-precision inspection and testing module receives semi-finished products output by the automated production line, performs multiple inspection tasks, and feeds back the inspection results to the central processor in real time;

[0047] The data analysis and intelligent diagnosis module receives the test data from the high-precision detection module, performs in-depth analysis on the test data, determines whether the tested product is qualified, and automatically generates a product analysis report;

[0048] The defective product processing and re-inspection module receives defective products and determines whether the defective products need to be reworked, repaired, downgraded or directly scrapped based on the product analysis report generated by the data analysis and intelligent diagnosis module, and re-inspects the reworked products.

[0049] The quality traceability and feedback optimization module is responsible for recording data from each quality inspection link, providing a detailed traceability path for quality issues, and converting analysis results into specific improvement measures.

[0050] In this embodiment, the material storage and inspection module includes a barcode / RFID scanning unit, a physical performance testing unit, and a chemical composition analysis unit.

[0051] The barcode / RFID scanning unit is mainly used for automatic identification of raw materials when they enter the warehouse, and records the material batch, model, and supplier information. The specific work is as follows:

[0052] (1) When the raw materials arrive at the warehouse, the barcode or RFID tag on the raw materials is scanned by a barcode scanning device or RFID reader. These barcodes or RFID tags usually contain key data such as the batch number, model, and supplier information of the material;

[0053] (2) After scanning, the system will automatically enter the identified information into the database to ensure that each batch of raw materials has a unique identity for subsequent tracking and management;

[0054] (3) Automatically check whether the entered information is consistent with the expected supplier, model, etc. If there is any inconsistency, the system will issue an alarm to prompt the staff to check;

[0055] (4) All entered information will be stored in the system database for subsequent query and use in production, quality inspection and other links;

[0056] The physical properties testing unit is mainly used to evaluate the basic physical properties of raw materials to ensure that they meet production requirements. The specific work is as follows:

[0057] (1) Take a certain number of samples from each batch of raw materials and carry out necessary pretreatment, such as cutting and grinding, in order to carry out performance testing;

[0058] (2) Conduct different physical property tests according to the type and purpose of the raw materials. For example, for metal materials, hardness tests and tensile strength tests can be conducted; for plastic materials, toughness tests and impact resistance tests can be conducted;

[0059] (3) After the test is completed, the system will automatically record the test results and conduct a preliminary analysis. If the test results do not meet the preset standards, the system will issue an alarm to prompt the staff to take further action;

[0060] (4) The test data will be fed back to the central processor in real time, and then sent to the production department and suppliers through the central processor and its signal transceiver module (self-contained), so that they can understand the quality status of the raw materials and take necessary improvement measures;

[0061] The chemical composition analysis unit mainly uses spectral analysis technology to detect whether the material contains excessive harmful substances. The specific work is as follows:

[0062] (1) It is also necessary to extract samples from the raw materials and perform appropriate pretreatment for chemical composition analysis;

[0063] (2) Detect the chemical composition of the sample using spectral analysis techniques (such as X-ray fluorescence spectroscopy, inductively coupled plasma mass spectrometry, etc.). These techniques can accurately determine the content of various elements in the sample;

[0064] (3) Compare the test results with the preset standards to determine whether the raw materials contain excessive amounts of harmful substances. If the test results do not meet the standards, the system will immediately issue an alarm and generate a detailed analysis report;

[0065] (4) The test results will be fed back to the central processor in real time, and then sent to the production department and suppliers through the central processor and its signal transceiver module (built-in), so that they can take appropriate measures to solve the problem, including changing suppliers, improving production processes, etc.

[0066] In this embodiment, the production process monitoring module includes a sensor network, a machine vision detection unit, and a process parameter monitoring unit.

[0067] The sensor network has a variety of environmental parameter monitoring sensors (temperature sensors, humidity sensors, pressure sensors, etc.). The sensors are placed on the production line to monitor the production environment parameters in real time. The specific work is as follows:

[0068] (1) Install temperature sensors, humidity sensors, and pressure sensors at key locations based on the specific layout and process requirements of the production line, such as key parts of machinery and equipment, material flow channels, and key steps in product processing;

[0069] (2) Sensors collect temperature, humidity, and pressure parameters in the production environment in real time and transmit these data to the data receiving end. The frequency and accuracy of data collection are usually determined according to production requirements and sensor performance;

[0070] (3) The data receiving end processes the received environmental parameter data in real time, including preprocessing steps such as data filtering, denoising, and calibration, and transmits the preprocessed data to the central processing unit, which performs statistical analysis and trend prediction on the data, so that the system can promptly detect abnormal conditions in the production environment, such as excessive temperature and humidity, and take appropriate intervention measures;

[0071] (4) When the central processor detects that the production environment parameters are beyond the preset range, it will immediately send out an alarm signal and notify relevant personnel through sound and light prompts, SMS notifications, etc. Relevant personnel can quickly locate the problem based on the alarm information and take appropriate measures to solve it, so as to ensure the stability of the production process and the consistency of product quality;

[0072] The machine vision inspection unit mainly uses high-definition cameras and AI algorithms to detect defects in the production process, such as cracks, deformation, and missing parts. The specific work is as follows:

[0073] (1) High-definition cameras collect images of products on the production line in real time and transmit these images to the machine vision system. The number and location of cameras are usually determined based on the layout of the production line and the characteristics of the products.

[0074] (2) After receiving the image, the machine vision system will perform real-time image processing, which includes image preprocessing (such as denoising, enhancement, etc.), feature extraction (such as edge detection, texture analysis, etc.) and target recognition (such as product model, defect type, etc.). Through these processing steps, the system can accurately identify defects in the product;

[0075] (3) Use AI algorithms to further analyze and judge the processed images to determine whether the product has defects and the type and extent of the defects. AI algorithms usually learn the characteristics and patterns of defects by training a large amount of sample data, thereby achieving accurate detection and classification of defects;

[0076] (4) The machine vision system will output the test results to the central processor in real time. The central processor will compare the test results with the preset quality standards to determine whether the product is qualified. For unqualified products, the alarm will respond and relevant personnel will be notified by means of sound and light prompts, screen displays, etc.;

[0077] The process parameter monitoring unit is mainly used to monitor and record the parameters of key process steps, such as injection pressure, welding temperature, etc., to ensure process consistency. The specific work is as follows:

[0078] (1) Before production begins, set the parameter ranges for key process steps based on the product’s process requirements and the production line’s capabilities. These parameters may include injection pressure, welding temperature, spray thickness, etc.

[0079] (2) During the production process, various sensors and measuring devices are used to collect parameter values ​​of key process steps in real time, and these data are transmitted to the process parameter monitoring unit;

[0080] (3) After receiving the parameter data, the process parameter monitoring unit will perform real-time processing and analysis, which includes steps such as data verification, filtering, and trend analysis. Through these analyses, abnormal conditions of process parameters can be discovered in a timely manner, such as excessive pressure fluctuations and temperatures exceeding the preset range.

[0081] (4) When the process parameter monitoring unit detects that the process parameters exceed the preset range, it will immediately issue an alarm signal and notify relevant personnel. The relevant personnel can quickly locate the problem based on the alarm information and adjust the corresponding process parameters to restore the stability and consistency of the production process.

[0082] In this embodiment, the high-precision detection and testing module includes a performance simulation test unit, an electrical performance test unit, and a cooling / heating efficiency test unit.

[0083] The performance simulation test unit is mainly used to simulate extreme working environments (high temperature, low temperature, high humidity, etc.) to test the weather resistance and stability of components. The specific work is as follows:

[0084] (1) Through professional environmental simulation equipment, such as high temperature chamber, low temperature chamber, high humidity chamber, etc., various extreme working environments that components may face can be simulated, and these environmental parameters (such as temperature and humidity) can be accurately set and controlled according to actual needs;

[0085] (2) Place the components or complete products to be tested in a simulated environment and connect them to the test system through a dedicated test line or interface. This ensures that the performance data of the components can be accurately obtained during the test.

[0086] (3) In a simulated environment, components are subjected to a series of performance tests, such as temperature resistance, moisture resistance, and corrosion resistance. These tests are designed to evaluate the working performance and stability of components in extreme environments;

[0087] (4) During the test, the system will record the performance data of the components in real time and conduct preliminary analysis and judgment. If the performance data of the components exceeds the preset range or shows abnormality, the system will immediately send an abnormal signal to the central processor;

[0088] The electrical performance test unit is mainly responsible for conducting electrical performance tests on components (resistance, capacitance, insulation resistance) to ensure that the electrical characteristics of the components meet the design specifications. The specific work is as follows:

[0089] (1) Select appropriate test instruments and test methods according to the type and specifications of components. For example, for resistors, a multimeter can be used to measure the resistance value; for capacitors, a capacitance tester can be used to measure the capacitance value;

[0090] (2) Connect the components to be tested to the test instrument and set the corresponding test parameters according to the test requirements, for example, setting the test voltage, test current, etc.;

[0091] (3) Under the control of the test instrument, the electrical performance test of the components is carried out, including resistance measurement, capacitance measurement, insulation resistance measurement, etc. During the test, the instrument will display and record the electrical performance data of the components in real time;

[0092] (4) After the test is completed, the system automatically evaluates and judges the acquired data, compares the test data with the preset electrical performance standards, and sends the comparison results to the data analysis and intelligent diagnosis module to determine whether the components are qualified;

[0093] The cooling / heating efficiency test unit is mainly responsible for testing the cooling efficiency of cooling components and the heating efficiency of heating components. The specific work is as follows:

[0094] (1) According to the test requirements, build professional cooling / heating efficiency test equipment, which usually includes a test room, a control system, and data acquisition. The components or complete products to be tested are placed in the test room. The control system is used to control parameters such as temperature and humidity during the test, and the data acquisition is used to record the test data in real time.

[0095] (2) Install the components or complete products to be tested in the test room and perform necessary debugging and settings to ensure that the test system can accurately obtain the cooling / heating efficiency data of the components;

[0096] (3) Under the control of the control system, the cooling / heating efficiency of the components is tested. During the test, the system will record the test data in real time, including temperature, humidity, cooling / heating power, etc. These data will be used to evaluate the cooling / heating efficiency of the components;

[0097] (4) After the test is completed, the system will automatically evaluate and optimize the acquired data, and transmit the evaluation results to the data analysis and intelligent diagnosis module for comparison to determine whether the components are qualified.

[0098] In this embodiment, the data analysis and intelligent diagnosis module includes

[0099] Big data analysis unit: Use data mining technology to analyze the root causes of quality problems, predict potential defects, and optimize production processes;

[0100] Through association analysis, cluster analysis and other technologies, we can dig out the root causes of quality problems and provide a scientific basis for subsequent improvement measures. We can use prediction models and algorithms to monitor and analyze production data in real time, predict potential defects, and take measures in advance to avoid problems. Based on the results of data analysis, we can continuously improve and optimize the production process to improve production efficiency and quality stability.

[0101] Data receiving and analysis diagnosis unit: Receives test data from high-precision detection modules, uses machine learning algorithms to conduct in-depth analysis and comparison of test data, and determines and distinguishes qualified and unqualified products;

[0102] Receive test data transmitted by high-precision detection and testing modules in real time, and perform pre-processing such as data cleaning and formatting to ensure data accuracy and availability. Then use machine learning algorithms (such as support vector machines, neural networks, etc.) to conduct in-depth analysis and comparison of test data to accurately determine whether the product is qualified. While determining the product's eligibility, it can also detect and diagnose abnormal points or potential problems in the product to provide guidance for subsequent improvements.

[0103] Report generation and feedback unit: automatically generates product analysis reports, including pass / fail results, problem descriptions and improvement suggestions, and feeds back to the production department;

[0104] Automatically generate detailed product analysis reports based on the analysis results of the big data analysis unit and the data receiving, analyzing and diagnosing unit. The report content includes the qualified / unqualified judgment results, problem description, improvement suggestions, etc. Then, the generated report is fed back to the production department in a timely manner, so that the production personnel can timely understand the product quality status and problems in the production process, and take corresponding improvement measures. Through the continuous report generation and feedback mechanism, a closed-loop quality management system is formed to promote the continuous improvement and optimization of the production process;

[0105] After receiving the test data from the high-precision detection module, the data receiving and analyzing diagnosis unit preprocesses the test data, extracts key features that can reflect product quality and performance from the preprocessed test data, inputs the extracted features into the trained machine learning model for prediction and classification, and determines whether the product corresponding to the test data is qualified or unqualified based on the output of the model.

[0106] In this embodiment, the defective product processing and re-inspection module includes

[0107] Defective product isolation unit: According to the diagnosis results of the data analysis and intelligent diagnosis module, the defective products on the production line are sorted and isolated to the designated area;

[0108] On the automated production line, the defective product isolation unit is equipped with advanced sensors and identification systems. When the products on the production line pass through, the quality indicators of the products, such as size, weight, appearance, etc., are automatically detected. Once a product that does not meet the preset standards is found, it will be automatically separated from the production line. The separated defective products will be stored in a designated isolation area, which is equipped with clear signs and protective measures to prevent the defective products from being misused or damaged. At the same time, the system records the detailed information of the defective products, including the reasons for the failure, quantity, storage location, etc., for subsequent tracking and processing;

[0109] Rework / scrap decision-making unit: It has three standards: rework and repair, downgrade and direct scrapping. It receives product analysis reports of non-conforming products and issues instructions for rework and repair / downgrade / direct scrapping based on the reasons for non-conformity in the product analysis reports.

[0110] Analyze the reasons for non-conformity according to the product analysis report and make decisions. If the problem of non-conforming products can be solved by repair and the repaired products can meet the quality requirements, then rework and repair will be chosen. If the repair cost is too high or the expected quality standards cannot be met after repair, then downgrade or direct scrapping will be chosen. After the decision is made, the system will record the handling results of non-conforming products and notify the relevant departments and personnel of the relevant information. For rework and repair of non-conforming products, they will be sent back to the production line for repair. For downgrade or direct scrapping of non-conforming products, they will be handled according to the corresponding processing procedures;

[0111] Re-inspection unit: re-test the reworked products. Before the reworked products are sent back to the production line, the test equipment is checked and calibrated to ensure the accuracy of the test results. At the same time, the corresponding test plan and test standards are automatically formulated according to the characteristics and requirements of the reworked products. The reworked products will undergo the same test process as the initial inspection, including appearance inspection, functional performance testing, etc. After the test is completed, the product will be sent to the data analysis and intelligent diagnosis module to determine whether it is qualified. If the test results meet the preset quality standards, the product will be regarded as qualified and can re-enter the production line for subsequent processing or packaging. If the test results still do not meet the quality standards, the product will be regarded as unqualified and needs to be reworked or scrapped. At the same time, the system will record the results of the re-inspection and related information for subsequent tracking and inquiry.

[0112] In this embodiment, the quality tracing and feedback optimization module includes

[0113] Production record unit: records the data and information of each production link to ensure that the production information of each product can be traced;

[0114] This unit is responsible for recording data and information of each production link, including but not limited to the source of raw materials, production time, production batch, operator, equipment status, etc. This information is the basis for subsequent quality traceability, ensuring that the production information of each product can be accurately traced, so as to quickly locate problems in the production process and reduce the time cost of finding the cause;

[0115] Quality traceability unit: When unqualified products are found during quality inspection, the production records are automatically queried to quickly locate the root cause and occurrence of the problem, and a detailed traceability path is provided. Quality traceability can quickly identify the problem and prevent unqualified products from entering the market. It also helps to continuously improve the production process and enhance product quality.

[0116] Feedback and optimization unit: Generate specific improvement measures based on quality traceability results and product analysis reports, and feed these measures back to the central processor for optimization, converting the results of quality traceability into practical actions, promoting continuous improvement of production processes and continuous improvement of product quality;

[0117] By integrating production records, quality traceability and feedback optimization functions, an efficient, closed-loop quality management system has been formed, which can quickly locate problems in the production process, formulate and implement improvement measures, thereby continuously improving product quality and production efficiency.

[0118] The above description is only an exemplary embodiment of the present invention and is not intended to limit the present invention. The protection scope of the present invention is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the essence and protection scope of the present invention, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present invention.

Claims

1. A quality inspection system for air-conditioning components production, characterized in that: It includes a central processing unit and a Material warehousing and inspection module: responsible for the receipt, registration and preliminary quality inspection of raw materials; Production process monitoring module: responsible for real-time monitoring of various parameters in the production process and conducting preliminary tests on components; High-precision inspection and testing module: receives semi-finished products output by the automated production line, performs multiple inspection tasks, and feeds back the inspection results to the central processor in real time; Data analysis and intelligent diagnosis module: Receives test data from the high-precision detection module, conducts in-depth analysis of the test data, determines whether the tested product is qualified, and automatically generates a product analysis report; Defective product processing and re-inspection module: Receive defective products, and determine whether the defective products need to be reworked, repaired, downgraded or directly scrapped based on the product analysis report generated by the data analysis and intelligent diagnosis module, and re-inspect the reworked products; Quality traceability and feedback optimization module: responsible for recording data from each quality inspection link, providing detailed traceability paths for quality issues, and converting analysis results into specific improvement measures.

2. The air-conditioning component production quality inspection system according to claim 1, characterized in that: The material warehousing and inspection module includes Barcode / RFID scanning unit: used for automatic identification of raw materials when they enter the warehouse, and recording material batch, model, and supplier information; Physical properties testing unit: used to evaluate the basic physical properties of raw materials to ensure that they meet production requirements; Chemical composition analysis unit: Detects whether the material contains excessive harmful substances through spectral analysis technology.

3. The air-conditioning component production qualified inspection system according to claim 1 is characterized in that: The production process monitoring module includes Sensor network: It has a variety of environmental parameter monitoring sensors to monitor production environment parameters in real time; Machine vision inspection unit: detects defects in the production process, such as cracks, deformation, missing parts, etc., through high-definition cameras and AI algorithms; Process parameter monitoring unit: monitors and records the parameters of key process steps.

4. The air-conditioning component production quality inspection system according to claim 1, characterized in that: The high-precision detection and testing module includes Performance simulation test unit: simulates extreme working environment to test the weather resistance and stability of components; Electrical performance test unit: responsible for conducting electrical performance tests on components to ensure that the electrical characteristics of components meet the design specifications; Refrigeration / heating efficiency test unit: responsible for testing the refrigeration efficiency of refrigeration components and the heating efficiency of heating components.

5. The air-conditioning component production qualified inspection system according to claim 1 is characterized in that: The data analysis and intelligent diagnosis module includes Big data analysis unit: Use data mining technology to analyze the root causes of quality problems, predict potential defects, and optimize production processes; Data receiving and analysis diagnosis unit: Receives test data from high-precision detection modules, uses machine learning algorithms to conduct in-depth analysis and comparison of test data, and determines and distinguishes qualified and unqualified products; Report generation and feedback unit: Automatically generate product analysis reports, including pass / fail judgment results, problem descriptions and improvement suggestions, and feed them back to the production department.

6. The air-conditioning component production qualified inspection system according to claim 1 is characterized in that: After receiving the test data from the high-precision detection module, the data receiving and analyzing diagnosis unit preprocesses the test data, extracts key features that can reflect product quality and performance from the preprocessed test data, inputs the extracted features into the trained machine learning model for prediction and classification, and determines whether the product corresponding to the test data is qualified or unqualified based on the output of the model.

7. The air-conditioning component production quality inspection system according to claim 1, characterized in that: The defective product processing and re-inspection module includes Defective product isolation unit: According to the diagnosis results of the data analysis and intelligent diagnosis module, the defective products on the production line are sorted and isolated to the designated area; Rework / scrap decision-making unit: It has three standards: rework and repair, downgrade and direct scrapping. It receives product analysis reports of non-conforming products and issues instructions for rework and repair / downgrade / direct scrapping based on the reasons for non-conformity in the product analysis reports. Re-inspection unit: re-test the reworked products.

8. The air-conditioning component production quality inspection system according to claim 1, characterized in that: The quality traceability and feedback optimization module includes Production record unit: records the data and information of each production link to ensure that the production information of each product can be traced; Quality traceability unit: When unqualified products are found during quality inspection, the production records are automatically queried to quickly locate the root cause and occurrence of the problem, and provide a detailed traceability path; Feedback and optimization unit: Generates specific improvement measures based on quality traceability results and product analysis reports, and feeds these measures back to the central processor for optimization.

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