A door and window frame quality detection data processing method and system

Abstract

The application relates to the technical field of doors and windows, and provides a door and window frame quality detection data processing method and system, which comprises the following steps: acquiring original size data of a to-be-tested door and window frame and structure information of the to-be-tested door and window frame; acquiring measurement data of a preset reference object on a bearing device of the to-be-tested door and window frame by a measurement device and environmental data of an environment where the measurement device is located; determining a sensor drift compensation value according to the measurement data of the preset reference object and the environmental data, correcting the original size data according to the sensor drift compensation value, and obtaining corrected size data of the to-be-tested door and window frame; determining whether the to-be-tested door and window frame has an assembly risk according to the structure information of the to-be-tested door and window frame and the corrected size data of the to-be-tested door and window frame; and when the to-be-tested door and window frame has the assembly risk, generating a production intervention suggestion of the to-be-tested door and window frame according to the assembly risk of the to-be-tested door and window frame. The scheme can improve the accuracy of window frame quality detection data.

Description

Technical Field

[0001] This application relates to the field of door and window technology, and in particular to a method and system for processing quality inspection data of door and window frames. Background Technology

[0002] The production process of doors and windows is complex, involving multiple stages and generating a large amount of quality data. Traditional quality management methods struggle to comprehensively track the product manufacturing process and adjust and optimize production based on real-time quality feedback. Particularly in automated quality inspection, although equipment can collect precise dimensional information, the long-term, subtle drift of the measuring equipment, and the resulting quality problems that only manifest after prolonged use, are difficult for existing systems to effectively identify and resolve. This makes it difficult for factories to accurately trace the true source of customer complaints and hinders continuous improvement of the production process.

[0003] In a typical automated quality inspection station, the core equipment is a laser sensor probe used for dimensional measurement. This probe is mounted on a support structure that holds the fixed sensor and emits a laser beam to receive the reflected signal, thereby accurately acquiring the dimensional data of the door and window frame profiles passing through the inspection area. The raw measurement signals collected by the sensor are transmitted to an industrial control computer that processes the signals. The computer uses preset parsing rules to convert the raw data into usable dimensional information and compares it with upper and lower threshold values ​​for determining whether the dimensions are acceptable.

[0004] However, even well-designed and maintained systems can face challenges during long-term operation. For example, after laser sensor probes used for dimensional measurements have been running continuously in a production workshop for several months, their internal optical components may experience extremely slow and imperceptible mechanical shifts due to the cumulative effects of minute vibrations and daily temperature fluctuations in the environment. This shift is not a sudden equipment failure, but rather a gradual change in the equipment's condition under normal wear and environmental stress. This physical phenomenon is characterized by its extremely slow change process, making it difficult to detect during routine inspections or short-term monitoring, thus leading to lower accuracy in window frame quality inspection data. Summary of the Invention

[0005] This application provides a method and system for processing quality inspection data of door and window frames, which can improve the accuracy of window frame quality inspection data.

[0006] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for processing quality inspection data of door and window frames is provided, specifically including: acquiring the original dimensional data and structural information of the door and window frame to be tested; the structural information includes the component types of each part of the door and window frame to be tested; acquiring the measurement data of a preset reference object on the load-bearing device of the door and window frame to be tested, as well as the environmental data of the environment in which the measuring device is located; determining the sensor drift compensation value based on the measurement data of the preset reference object and the environmental data, and correcting the original dimensional data based on the sensor drift compensation value to obtain the corrected dimensional data of the door and window frame to be tested; determining whether there is an assembly risk in the door and window frame to be tested based on the structural information and the corrected dimensional data of the door and window frame to be tested; when there is an assembly risk in the door and window frame to be tested, generating production intervention suggestions for the door and window frame to be tested based on the assembly risk.

[0007] Through this technical solution, this application can effectively identify and compensate for sensor drift of measuring equipment, thereby obtaining more accurate door and window frame size data, and judging assembly risks based on this data, thereby generating production intervention suggestions, solving potential quality problems caused by sensor drift from the source, and realizing precise control and traceability of door and window frame production quality.

[0008] Furthermore, the sensor drift compensation value is determined based on the measurement data of the preset reference object and the environmental data. Specifically, this includes: acquiring the set measurement data of the preset reference object; using the difference between the set measurement data of the preset reference object and the measurement data of the preset reference object as the measurement compensation value; determining the environmental compensation value based on the environmental data; and using the sum of the measurement compensation value and the environmental compensation value as the sensor drift compensation value.

[0009] Through this technical solution, this application can refine the sensor drift compensation value into measurement compensation value and environmental compensation value, making the compensation process more refined and able to more accurately reflect and correct the sensor drift caused by both measurement error and environmental factors, thereby improving the accuracy of the corrected dimensional data.

[0010] Based on the above, this application further proposes that the environmental data includes the vibration intensity value and temperature value of the measuring equipment, and that an environmental compensation value is determined based on the environmental data, specifically including: obtaining a first preset correspondence; the first preset correspondence includes a one-to-one correspondence between multiple vibration intensity value ranges and multiple first compensation values; using the first compensation value corresponding to the vibration intensity value range in which the vibration intensity value of the measuring equipment is located in the first preset correspondence as the vibration compensation value; determining a temperature compensation value based on the temperature value of the measuring equipment; and using the sum of the vibration compensation value and the temperature compensation value as the environmental compensation value.

[0011] Through this technical solution, the environmental compensation value can be further decomposed into vibration compensation value and temperature compensation value, and a preset correspondence is introduced to determine the vibration compensation value, making the calculation of environmental compensation more specific and operable, and can specifically solve the measurement error caused by vibration and temperature changes, thereby further improving the accuracy of compensation.

[0012] More specifically, in some implementation schemes, the temperature compensation value is determined based on the temperature value of the measuring device, which includes: obtaining a preset temperature compensation coefficient; and using the product of the preset temperature compensation coefficient and the temperature value of the measuring device as the temperature compensation value.

[0013] This technical solution enables the direct calculation of temperature compensation value by multiplying a preset temperature compensation coefficient by the temperature value, simplifying the determination process of temperature compensation, improving calculation efficiency, and ensuring the effectiveness of compensation.

[0014] As an optional approach, after determining the environmental compensation value, the method further includes: obtaining a preset temperature compensation value threshold and a preset vibration compensation value threshold; determining an environmental adjustment strategy for the measuring device based on the temperature compensation value and the preset temperature compensation value threshold, as well as the vibration compensation value and the preset vibration compensation value threshold; the environmental adjustment strategy is used to adjust the environment in which the measuring device is located.

[0015] Through this technical solution, this application can proactively generate an environmental adjustment strategy based on the comparison of temperature compensation value and vibration compensation value with their respective thresholds. This allows for timely intervention in the environment when the measuring equipment faces potential drift risks, fundamentally reducing the impact of environmental factors on measurement accuracy and preventing sensor drift.

[0016] Based on the above, this application further proposes an environmental adjustment strategy for the measuring equipment, which is determined according to the temperature compensation value and a preset temperature compensation value threshold, as well as the vibration compensation value and a preset vibration compensation value threshold. Specifically, this includes: determining whether the temperature compensation value is greater than the preset temperature compensation value threshold; if so, determining the environmental adjustment strategy for the measuring equipment to be to be to be to install a heat insulation device around the measuring equipment and to reacquire the original dimensional data of the door and window frame to be tested and the measurement data of the preset reference object on the load-bearing device of the door and window frame to be tested; determining whether the vibration compensation value is greater than the preset vibration compensation value threshold; if so, determining the environmental adjustment strategy for the measuring equipment to be to be to be to install the measuring equipment on a vibration damping device and to reacquire the original dimensional data of the door and window frame to be tested and the measurement data of the preset reference object on the load-bearing device of the door and window frame to be tested.

[0017] Through this technical solution, this application can provide specific environmental adjustment strategies, such as setting up heat insulation devices or vibration reduction devices and triggering remeasurement, so that environmental adjustment has clear guidance, can specifically solve measurement errors caused by temperature or vibration, ensure the stability of the measurement environment, and further improve the reliability of measurement data.

[0018] Based on the above, this application further proposes a method for determining whether there is an assembly risk in the door and window frame under test based on the structural information and the corrected dimension data of the door and window frame under test. Specifically, this includes: identifying the first and second components that cooperate with each other in the structural information of the door and window frame under test; determining whether the corrected dimension of the first component is less than a second value and whether the corrected dimension of the second component is greater than a third value; the second value is the sum of the minimum set dimension of the first component and the preset dimension tolerance of the first component, and the third value is the difference between the maximum set dimension of the second component and the preset dimension tolerance of the second component; if both are true, it is determined that there is an assembly risk in the first and second components of the door and window frame under test; otherwise, it is determined that there is no assembly risk in the first and second components of the door and window frame under test.

[0019] Through this technical solution, this application can accurately determine whether there is an assembly risk in the door and window frame by comparing the corrected dimensions of the mating parts with the preset tolerance range, thereby discovering potential assembly problems in the early stage of production, avoiding subsequent quality hazards caused by dimensional deviations, and improving the qualification rate of product assembly.

[0020] Based on the above, this application further proposes to generate production intervention suggestions for the door and window frame under test based on the assembly risks present in the frame. Specifically, this includes: obtaining multiple subsequent production processes of the door and window frame under test; for each of the multiple production processes, obtaining a second preset correspondence relationship of the production processes; the second preset correspondence relationship includes a one-to-one correspondence relationship between multiple assembly risks and multiple production intervention suggestions; and using the production intervention suggestions corresponding to the assembly risks present in the door and window frame under test in the second preset correspondence relationship as the production intervention suggestions for the door and window frame under test in the production processes.

[0021] Through this technical solution, this application can generate specific production intervention suggestions for different production processes and assembly risks, making the intervention measures more targeted and effective, and guiding production personnel to adjust production parameters or processes in a timely manner, thereby reducing scrap rate and improving production efficiency.

[0022] Based on the above, this application further proposes that, after generating production intervention suggestions for the door and window frame under test based on the assembly risks present in the frame under test, the method further includes: determining whether the number of assembly risks present in the door and window frame under test is greater than a preset threshold; if so, generating and sending alarm information to the user's user equipment; the alarm information is used to instruct the user to inspect the production equipment of the door and window frame under test.

[0023] Through this technical solution, this application can promptly issue alarm information by judging whether the number of assembly risks exceeds the threshold, instructing users to inspect and repair production equipment, thereby intervening before the problem escalates, avoiding the occurrence of batch quality problems, and ensuring the normal operation of production equipment and the stability of product quality.

[0024] Secondly, this application also discloses a data processing system for quality inspection of door and window frames, specifically including: an acquisition device and a processing device; the acquisition device is used to acquire the original dimensional data and structural information of the door and window frame to be tested; the structural information includes the component types of each component of the door and window frame to be tested; the processing device is used to acquire the measurement data of a preset reference object on the load-bearing device of the door and window frame to be tested, as well as the environmental data of the environment in which the measuring device is located; the processing device is also used to determine the sensor drift compensation value based on the measurement data of the preset reference object and the environmental data, and to correct the original dimensional data based on the sensor drift compensation value to obtain the corrected dimensional data of the door and window frame to be tested; the processing device is also used to determine whether there is an assembly risk in the door and window frame to be tested based on the structural information and the corrected dimensional data of the door and window frame to be tested; the processing device is also used to generate production intervention suggestions for the door and window frame to be tested based on the assembly risk when there is an assembly risk in the door and window frame to be tested.

[0025] Beneficial Effects: The door and window frame quality inspection data processing method disclosed in this application obtains the original dimensional data and structural information of the door and window frame to be tested, and combines this with the measurement data of a preset reference object on the load-bearing device of the door and window frame to be tested, as well as the environmental data of the environment in which the measuring equipment is located. Based on the measurement data of the preset reference object and the environmental data, a sensor drift compensation value can be determined. This compensation value is used to correct the original dimensional data, thereby obtaining more accurate corrected dimensional data of the door and window frame to be tested. On this basis, this application can determine whether there is an assembly risk based on the structural information and corrected dimensional data of the door and window frame to be tested. When an assembly risk exists, the system can generate corresponding production intervention suggestions according to the risk type.

[0026] This method effectively solves the problem in existing technologies where long-term, subtle sensor drift leads to inaccurate measurement data, resulting in quality issues that only manifest after prolonged use and making it difficult to trace the source. By introducing a sensor drift compensation mechanism, this application can correct systematic deviations in the measuring equipment in real time, ensuring the accuracy of dimensional data and avoiding the limitations of traditional calibration methods that cannot identify and compensate for slowly accumulating nonlinear drift. Furthermore, based on the corrected dimensional data, assembly risk assessment can identify potential assembly problems in advance and generate timely production intervention suggestions, thereby achieving dynamic optimization and quality control of the production process. This not only improves the production quality and pass rate of door and window frames but also provides factories with accurate problem traceability, effectively reducing after-sales costs and customer complaints, and realizing continuous improvement of the production process and intelligent quality management. Attached Figure Description

[0027] Figure 1 A flowchart illustrating the first method for processing quality inspection data of door and window frames provided in this application; Figure 2 A flowchart illustrating the second method for processing quality inspection data of door and window frames provided in this application; Figure 3 A flowchart illustrating the third method for processing quality inspection data of door and window frames provided in this application; Figure 4 This is a schematic diagram of the architecture of a door and window frame quality inspection data processing system provided in this application. Detailed Implementation

[0028] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0029] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0030] Traditional quality management methods for door and window production struggle to comprehensively track the entire production process and adjust and optimize production based on real-time quality feedback. Particularly in automated quality inspection, while equipment can collect precise dimensional information, the subtle long-term drift of measuring devices and the resulting quality issues that only manifest after prolonged use are difficult for existing systems to effectively identify and resolve. This makes it difficult for factories to accurately trace the true source of customer complaints and hinders continuous improvement of production processes.

[0031] In this regard, such as Figure 1 As shown, this application proposes a method for processing quality inspection data of door and window frames, including: S101. Obtain the original dimensional data and structural information of the door and window frame to be tested; the structural information includes the component type of each component of the door and window frame to be tested.

[0032] S102. Obtain the measurement data of the preset reference object on the load-bearing device of the door and window frame to be measured, as well as the environmental data of the environment in which the measuring device is located.

[0033] S103. Determine the sensor drift compensation value based on the measurement data of the preset reference object and the environmental data, and correct the original size data of the door and window frame to be tested based on the sensor drift compensation value to obtain the corrected size data of the door and window frame to be tested.

[0034] S104. Determine whether there is an assembly risk in the door and window frame to be tested based on the structural information and the corrected dimension data of the door and window frame to be tested.

[0035] S105. When there is an assembly risk in the door and window frame to be tested, generate production intervention suggestions for the door and window frame to be tested based on the assembly risk.

[0036] This application introduces a sensor drift compensation mechanism and combines it with structural information of the door and window frames for assembly risk assessment, thereby effectively identifying and resolving hidden quality problems caused by long-term sensor drift in traditional methods. By correcting the original dimensional data and judging assembly risks based on the corrected data, this application can more accurately reflect the actual quality status of the product and generate timely production intervention suggestions, thereby achieving dynamic optimization of the production process and improving quality traceability capabilities.

[0037] The method provided in this application aims to optimize the quality inspection and data processing flow of door and window frames to address potential dimensional deviations and assembly risks during production. Here, "door and window frame under test" refers to the door and window frame product undergoing quality inspection. "Original dimensional data" refers to the dimensional information of the door and window frame directly acquired by the measuring equipment without any correction. "Structural information" describes the composition of the door and window frame, including the "component types of each part," such as connectors and glass channels; this information is crucial for subsequent assembly risk assessment. "Preset reference" refers to a standard part of known dimensions placed on the measuring equipment's support device, used to calibrate and test the accuracy of the measuring equipment. "Environmental data of the measuring equipment's environment" includes external factors that may affect measurement accuracy, such as temperature and vibration. "Sensor drift compensation value" is a correction amount calculated based on the measurement data of the preset reference and environmental data, used to compensate for measurement deviations caused by long-term sensor operation or environmental changes. "Corrected dimensional data" is the door and window frame dimensional data adjusted by the sensor drift compensation value, which is closer to the true dimensions of the door and window frame. "Assembly risks" refer to potential mismatches, stress concentrations, or abnormal gaps that may occur during the assembly of door and window frame components. These problems could lead to functional failures during product use. "Production intervention recommendations" are specific adjustments or improvements proposed to the production process in response to the detected assembly risks.

[0038] The method described in this application first requires obtaining the original dimensional data and structural information of the door / window frame to be tested. The original dimensional data can be obtained in various ways. For example, a laser sensor can be used for non-contact measurement of various dimensions of the door / window frame, or contact measuring tools such as high-precision calipers or coordinate measuring machines can be used. These measuring tools will directly output the geometric parameters of the door / window frame, such as its length, width, and thickness. Structural information can be obtained from the design drawings, BOM (Bill of Materials) documents, or production management systems of the door / window frame. These documents detail the components that make up the door / window frame and the specific type of each component. For example, a door / window frame may include various components such as frame profiles, mullions, glass strips, and sealing strips.

[0039] Next, it is necessary to acquire measurement data from a preset reference object on the load-bearing device of the door / window frame under test, as well as environmental data of the environment in which the measuring equipment is located. The preset reference object can be a standard gauge block or calibration rod with known precise dimensions, which the measuring equipment will periodically measure. For example, it can be set to automatically measure a reference object of a standard length at regular intervals and record its readings. Environmental data can be acquired through various sensors; for example, a temperature sensor can monitor the temperature around the measuring equipment in real time, and a vibration sensor can detect the vibration intensity during equipment operation. This data will be recorded for subsequent analysis.

[0040] Subsequently, the sensor drift compensation value is determined based on the measurement data of the preset reference object and environmental data. The original dimensional data is then corrected based on this compensation value to obtain the corrected dimensional data of the door / window frame under test. For example, if the standard length of the preset reference object is 100mm, and the measuring device measures its length as 100.05mm at a certain moment, a preliminary measurement deviation of 0.05mm can be identified. Simultaneously, environmental data is considered; for example, when the ambient temperature rises, some components of the measuring device may experience thermal expansion, leading to systematic deviations in the measurement results. By determining the sensor drift compensation value based on the measurement data of the preset reference object and environmental data, an accurate sensor drift compensation value can be calculated. This compensation value is then used to correct the original dimensional data, resulting in corrected dimensional data. For example, if the original dimensional data is X and the compensation value is C, then the corrected dimensional data is XC.

[0041] For details on determining the sensor drift compensation value based on the measurement data of the preset reference object and environmental data, please refer to the relevant descriptions in the subsequent sections of the specific implementation of this application.

[0042] Furthermore, based on the structural information and corrected dimensional data of the door and window frame under test, it is determined whether there are any assembly risks. For example, the structural information of the door and window frame indicates which components need to fit together, such as the connection between the frame profile and the mullion profile. By analyzing the corrected dimensional data of these mating components, it can be determined whether they are within the allowable tolerance range. For example, if the corrected dimension of a component is too large, while the corrected dimension of its mating component is too small, it may lead to assembly difficulties or excessive stress after assembly.

[0043] Finally, when there are assembly risks in the door and window frame under test, production intervention suggestions are generated based on these risks. For example, if a connector is found to be too large, potentially making it difficult to insert during assembly, the system can generate suggestions such as "adjust connector processing parameters" or "replace with a connector of the correct size." If a profile is found to be out of tolerance, potentially causing overall frame deformation, suggestions such as "re-cut the profile" or "inspect the cutting equipment" can be provided. These suggestions aim to guide production personnel to take timely measures to prevent defective products from reaching the next stage or the end user.

[0044] The method provided in this application, by introducing a sensor drift compensation mechanism, can effectively solve the measurement inaccuracy problem caused by long-term sensor drift in traditional quality inspection systems. In traditional inspection processes, even if the sensor experiences slow, cumulative drift, its measurement results may still fall within the preset acceptable range, leading to a large number of products on the edge of tolerance being misjudged as acceptable. These products are highly susceptible to functional failure during subsequent assembly and long-term use.

[0045] This application enables the precise calculation of sensor drift compensation values ​​by acquiring measurement data from a preset reference and environmental data. For example, when a laser sensor probe experiences optical component misalignment due to minute vibrations and temperature fluctuations during long-term operation, the measurement data from the preset reference will reflect this deviation. Simultaneously, environmental data, such as vibration intensity and temperature values, provide information about the external factors causing the drift. By combining this information, an accurate compensation value can be determined and used to correct the original dimensional data, resulting in corrected dimensional data that more closely approximates reality.

[0046] Based on the corrected dimensional data, this application further combines the structural information of the door and window frames to assess assembly risks. For example, when the corrected dimensional data of two mating components, such as frame profiles and connectors, indicates a mismatch, the system can promptly identify potential assembly risks. This risk assessment mechanism avoids the limitations of traditional methods that rely solely on simple threshold comparisons using original dimensional data, and can more deeply uncover hidden quality problems.

[0047] Once assembly risks are identified, this application can generate specific production intervention suggestions. For example, if a dimensional deviation of a component causes assembly difficulties, the system can suggest adjusting processing parameters or replacing the component. This proactive intervention mechanism enables the production line to adjust in a timely manner, preventing the continued production of defective products and thus significantly reducing rework and scrap rates.

[0048] Compared to existing technologies, the core innovation of this application lies in its precise compensation for sensor drift and assembly risk assessment based on corrected dimensional data. Traditional calibration procedures typically only correct fixed offsets and cannot effectively address slowly accumulating nonlinear drift. This application ensures the long-term accuracy of measurement data by dynamically calculating and applying compensation values ​​through real-time monitoring of preset reference objects and environmental data. Furthermore, this application combines the corrected dimensional data with the structural information of the door and window frames to conduct a deeper analysis of assembly risks, rather than simply performing a dimensional conformity check. This method can effectively identify and prevent hidden quality problems that are overlooked in traditional inspections but may lead to product failure after long-term use, thereby significantly improving the overall quality and production efficiency of door and window frames.

[0049] In some embodiments of this application, the process of determining the sensor drift compensation value based on measurement data of a preset reference object and environmental data may include the following steps: Specifically, the sensor drift compensation value is determined based on the measurement data of the preset reference object and environmental data, including: Acquire the preset measurement data of the preset reference object; use the difference between the preset measurement data of the preset reference object and the measurement data of the preset reference object as the measurement compensation value; determine the environmental compensation value based on the environmental data; use the sum of the measurement compensation value and the environmental compensation value as the sensor drift compensation value.

[0050] In this context, obtaining the preset measurement data for the reference object refers to the standard size or position data obtained by accurately measuring the preset reference object before the measuring equipment is put into use or during periodic calibration. These preset measurement data are regarded as benchmark values ​​and are used for subsequent comparison with actual measurement data.

[0051] Furthermore, the difference between the preset measurement data and the actual measurement data of the preset reference is used as the measurement compensation value. The purpose is to quantify the systematic or random errors that may exist in the measuring equipment when measuring the preset reference. By comparing the known standard value of the preset reference with the actual measured value, a compensation amount that directly reflects the measurement deviation can be obtained.

[0052] Furthermore, determining environmental compensation values ​​based on environmental data aims to account for the potential impact of environmental factors (such as temperature, humidity, and vibration) on the measurement results. Environmental data may include, but is not limited to, the temperature, humidity, and vibration intensity of the measuring equipment. These factors can cause sensor performance drift, thus affecting measurement accuracy. By analyzing environmental data, the additional compensation amount caused by environmental changes can be calculated.

[0053] Ultimately, the sum of the measurement compensation value and the environmental compensation value is used as the sensor drift compensation value. The purpose is to comprehensively consider the inherent deviation of the measuring equipment itself as well as the deviation caused by the external environment, so as to obtain a more comprehensive and accurate sensor drift compensation value.

[0054] The proposed solution refines the determination of sensor drift compensation values ​​into a superposition of measurement compensation values ​​and environmental compensation values, thus more comprehensively considering various factors affecting measurement accuracy. Specifically, the measurement compensation value directly reflects the measurement deviation of the measuring equipment on a specific reference object, while the environmental compensation value specifically addresses the impact of environmental changes on sensor performance. This step-by-step calculation and summation method allows the sensor drift compensation value to more accurately reflect the actual drift situation, avoiding the inaccuracies that may result from compensation based on a single factor. It is precisely because of this refined compensation mechanism that subsequent corrections to the original dimensional data are more accurate.

[0055] The above technical solution refines and decomposes the process of determining sensor drift compensation values, making the calculation of compensation values ​​more scientific and accurate. This method can effectively distinguish and quantify drift caused by the measurement equipment's own errors and external environmental factors, thereby improving the accuracy of sensor drift compensation. Consequently, the correction of the original dimensional data becomes more reliable, and the final corrected dimensional data of the door and window frame under test is closer to the actual situation, providing a more solid data foundation for subsequent assembly risk assessment, and thus improving the overall accuracy and reliability of door and window frame quality inspection.

[0056] This application further proposes refining the environmental data to more accurately determine the environmental compensation value.

[0057] Specifically, in the above-mentioned method for processing quality inspection data of door and window frames, environmental data includes the vibration intensity value and temperature value of the measuring equipment. Environmental compensation values ​​are determined based on this data, including: Obtain a first preset correspondence; the first preset correspondence includes a one-to-one correspondence between multiple vibration intensity value ranges and multiple first compensation values; take the first compensation value corresponding to the vibration intensity value range of the measuring device in the first preset correspondence as the vibration compensation value; determine the temperature compensation value according to the temperature value of the measuring device; take the sum of the vibration compensation value and the temperature compensation value as the environmental compensation value.

[0058] Environmental data can be understood as external condition parameters that affect the performance of measuring equipment and the accuracy of measurement results. Specifically, this application uses the vibration intensity value and temperature value of the measuring equipment as key environmental data. The vibration intensity value reflects the degree of mechanical vibration experienced by the measuring equipment during operation, while the temperature value reflects the temperature conditions of the environment in which the measuring equipment is located. Both of these environmental factors can cause sensor drift in the measuring equipment, thereby affecting measurement accuracy.

[0059] To accurately determine the environmental compensation value, a first preset correspondence is required. This first preset correspondence is a pre-established mapping relationship that includes a one-to-one correspondence between multiple vibration intensity value ranges and multiple first compensation values. For example, vibration intensity can be divided into multiple ranges such as "low," "medium," and "high," and a corresponding first compensation value can be preset for each range. When the real-time vibration intensity value of the measuring device is obtained, the first compensation value corresponding to the vibration intensity value range in which the measuring device's vibration intensity value falls can be used as the vibration compensation value by consulting this first preset correspondence.

[0060] Simultaneously, a temperature compensation value needs to be determined based on the temperature of the measuring equipment. Temperature changes cause thermal expansion and contraction of the materials in the measuring equipment and may affect the performance of electronic components, thus leading to measurement errors. The temperature compensation value aims to compensate for these errors caused by temperature changes.

[0061] Finally, the sum of the determined vibration compensation value and temperature compensation value is taken as the environmental compensation value. This combination method can comprehensively consider the effects of vibration and temperature on the measuring equipment, thus obtaining a more comprehensive and accurate environmental compensation value.

[0062] Understandably, measurement compensation values ​​are primarily used to quantify the systematic or random errors that may exist in the measuring equipment itself when measuring a preset reference object. The preset reference object has known precise dimensions. By comparing the difference between the actual measurement data of the measuring equipment on the reference object and its preset measurement data, a compensation amount reflecting the inherent bias of the measuring equipment can be directly obtained. This bias may originate from long-term wear of internal components of the sensor, incomplete initial calibration, or minor changes in mechanical structure; these are the sensor's inherent drift characteristics.

[0063] Environmental compensation values ​​are used to address the impact of external environmental factors on measurement accuracy. Environmental parameters such as temperature fluctuations in the production workshop and vibration intensity generated by equipment operation can cause thermal expansion and contraction, slight displacement, or performance drift of internal optical components or electronic parts of the sensor. These are dynamic drifts caused by the sensor's susceptibility to "external" environmental conditions. By acquiring environmental data in real time and determining the corresponding environmental compensation values ​​based on this data, additional measurement errors caused by environmental changes can be effectively offset.

[0064] When the compensation directions of the measured compensation value and the environmental compensation value are consistent (i.e., both are positive or negative), their sum can characterize the joint compensation result of the measured compensation value and the environmental compensation value. Similarly, when the compensation directions of the measured compensation value and the environmental compensation value are inconsistent (i.e., one is positive and the other is negative), their sum can still characterize the joint compensation result of the measured compensation value and the environmental compensation value. The solution in this application refines environmental data into vibration intensity values ​​and temperature values ​​of the measuring equipment, and determines vibration compensation values ​​and temperature compensation values ​​separately, thereby enabling a more comprehensive and accurate quantification of the impact of environmental factors on sensor drift of the measuring equipment. Specifically, the vibration intensity value directly reflects the mechanical shocks or unstable factors that the measuring equipment may experience during operation. These factors may cause minute displacements or deformations of the measuring components, thus generating measurement errors.

[0065] By using a pre-defined first correspondence, vibration intensities of varying degrees can be converted into corresponding vibration compensation values, effectively offsetting errors caused by vibration. Simultaneously, the temperature value of the measuring device reflects thermal effects such as thermal expansion and electronic component performance drift, which can also lead to measurement deviations. By determining a temperature compensation value based on the temperature value, the effects of temperature changes can be corrected. Finally, the vibration compensation value and the temperature compensation value are superimposed to form a comprehensive environmental compensation value, enabling this compensation value to more accurately reflect the overall drift of the measuring device in actual environments. It is precisely this refined consideration and quantitative compensation of environmental factors that makes the sensor drift compensation value more accurate, thus providing a solid foundation for subsequent correction of the original dimensional data.

[0066] Through the above technical solution, this application can significantly improve the accuracy and comprehensiveness of environmental compensation values. By incorporating vibration intensity and temperature values ​​into the environmental data and performing compensation calculations separately, measurement errors caused by these environmental factors can be more effectively offset. Consequently, the accuracy of sensor drift compensation values ​​is improved, allowing the corrected original dimensional data to more accurately reflect the actual dimensions of the door and window frames under test. This more precise dimensional data correction further ensures the reliability of subsequent assembly risk assessment and makes the generated production intervention suggestions more targeted and effective, thereby comprehensively improving the accuracy of door and window frame quality inspection and the control level of the production process.

[0067] Specifically, when determining the temperature compensation value based on the temperature value of the measuring equipment, a direct and effective method can be used.

[0068] According to the above-mentioned method for processing quality inspection data of door and window frames, such as Figure 2As shown, when determining the temperature compensation value based on the temperature value of the measuring equipment, the following steps are included: S201. Obtain the preset temperature compensation coefficient.

[0069] S202. The product of the preset temperature compensation coefficient and the temperature value of the measuring device is used as the temperature compensation value.

[0070] The preset temperature compensation coefficient is a predetermined value that reflects the sensitivity of the measuring device to drift under different temperatures. This coefficient can be obtained through experimental calibration, historical data analysis, or theoretical calculation and stored in the system. The temperature value of the measuring device refers to the actual ambient temperature of the measuring device during dimensional data measurement. This temperature value can be obtained in real time through a temperature sensor integrated inside or near the measuring device. Multiplying the preset temperature compensation coefficient by the temperature value of the measuring device aims to quantify the specific impact of temperature changes on the measurement results, thereby obtaining an accurate temperature compensation value.

[0071] The proposed solution directly and quantitatively reflects the impact of temperature changes on the accuracy of the measuring device by multiplying a preset temperature compensation coefficient by the temperature value of the measuring device. Temperature is one of the key environmental factors affecting the performance of measuring devices; its changes can cause thermal expansion and contraction of internal components, leading to measurement errors. By introducing a preset temperature compensation coefficient and combining it with real-time device temperature values, a linear or nonlinear relationship can be established to estimate the sensor drift caused by temperature. This method makes the determination of temperature compensation values ​​more direct and operable, avoiding complex model building or reliance on large amounts of historical data, thus simplifying compensation calculations.

[0072] The above technical solution provides a simple and efficient way to determine temperature compensation values. This method utilizes the product of a preset temperature compensation coefficient and the real-time temperature value from the measuring device to accurately quantify and compensate for measurement errors caused by temperature. This not only improves the accuracy of sensor drift compensation, thereby enhancing the correction accuracy of the original dimensional data, but also simplifies the calculation process of compensation values, reduces the complexity of system implementation, and facilitates rapid and accurate quality inspection of door and window frames in actual production environments.

[0073] This application further proposes a strategy for proactively adjusting the environment in which the measuring equipment is located after determining the environmental compensation value, so as to optimize the measurement environment from the source and ensure the accuracy and reliability of data processing.

[0074] like Figure 3 As shown, after determining the temperature compensation value and the environmental compensation value, the method also includes: S301. Obtain the preset temperature compensation threshold and the preset vibration compensation threshold.

[0075] S302. Determine the environmental adjustment strategy for the measuring equipment based on the temperature compensation value and the preset temperature compensation value threshold, as well as the vibration compensation value and the preset vibration compensation value threshold; the environmental adjustment strategy is used to adjust the environment in which the measuring equipment is located.

[0076] Specifically, the preset temperature compensation threshold and preset vibration compensation threshold are pre-set critical values ​​used to determine whether intervention is needed in the environment in which the measuring equipment is located. These thresholds can be set based on the performance indicators of the measuring equipment, the quality requirements of door and window frames, and empirical data from the actual production environment. For example, when the temperature compensation value or vibration compensation value exceeds its corresponding preset threshold, it indicates that the current environment's impact on measurement accuracy has reached a level requiring active intervention.

[0077] Environmental adjustment strategies for measuring equipment refer to schemes that optimize and improve the temperature or vibration conditions of the environment in which the measuring equipment operates. This strategy aims to proactively adjust the environment to control the impact of environmental factors on the measuring equipment within an acceptable range, thereby reducing sensor drift, improving the accuracy of raw dimensional data, and reducing the magnitude of subsequent compensation.

[0078] This application's solution introduces preset temperature compensation thresholds and preset vibration compensation thresholds. Based on the comparison between the calculated temperature and vibration compensation values ​​and these thresholds, it proactively determines whether the environment in which the measuring equipment is located needs adjustment. When the impact of environmental factors (such as temperature or vibration) on the measuring equipment reaches or exceeds a preset critical value, an environmental adjustment strategy is triggered. This mechanism allows the system not only to passively compensate for sensor drift but also to proactively intervene in and optimize the measurement environment, thereby reducing the interference of environmental factors on measurement accuracy from the source. By adjusting the environment in which the measuring equipment is located, the degree of sensor drift can be effectively reduced, making subsequent compensation more accurate and effective, thus ensuring the reliability of the corrected dimensional data.

[0079] Through the above technical solution, this application enables real-time monitoring and proactive intervention of the environment in which the measuring equipment is located. Compared to solutions that only perform compensation, this application effectively avoids the problem of excessive compensation values ​​or decreased measurement accuracy caused by continuously adverse environmental conditions. By adjusting the environment of the measuring equipment in a timely manner, the quality of the acquired raw dimensional data can be significantly improved, and the probability and extent of sensor drift can be reduced, thereby ensuring the accuracy and stability of the corrected dimensional data. This provides a more reliable data foundation for subsequent assembly risk assessment and production intervention suggestions, ultimately improving the overall efficiency and reliability of door and window frame quality inspection.

[0080] This application further proposes a method for processing quality inspection data of door and window frames, wherein an environmental adjustment strategy for measuring equipment is determined based on temperature compensation values ​​and preset temperature compensation value thresholds, as well as vibration compensation values ​​and preset vibration compensation value thresholds, including: Determine whether the temperature compensation value is greater than the preset temperature compensation threshold. If so, determine the environmental adjustment strategy for the measuring equipment to be to set up a heat insulation device around the measuring equipment, and reacquire the original size data of the door and window frame to be tested and the measurement data of the preset reference object on the load-bearing device of the door and window frame to be tested. Determine whether the vibration compensation value is greater than the preset vibration compensation threshold. If so, determine the environmental adjustment strategy for the measuring equipment to be to be to set up a vibration damping device, and reacquire the original size data of the door and window frame to be tested and the measurement data of the preset reference object on the load-bearing device of the door and window frame to be tested.

[0081] Specifically, the temperature compensation value refers to the amount of compensation used to correct measurement errors, determined based on the temperature value of the measuring equipment. The preset temperature compensation threshold is a pre-set critical value used to determine whether the influence of the current ambient temperature on the measuring equipment exceeds an acceptable range. When the temperature compensation value exceeds this preset threshold, it indicates that the ambient temperature of the measuring equipment is too high or too low, significantly affecting measurement accuracy. In this case, proactive environmental intervention measures are required.

[0082] Similarly, vibration compensation value refers to the amount of compensation used to correct measurement errors, determined based on the vibration intensity value of the measuring equipment. A preset vibration compensation threshold is a pre-set critical value used to determine whether the impact of current environmental vibration on the measuring equipment exceeds an acceptable range. When the vibration compensation value exceeds this preset threshold, it indicates that the vibration intensity of the environment in which the measuring equipment is located is too high, significantly affecting measurement accuracy. In this case, proactive environmental intervention measures are required. Environmental adjustment strategies refer to specific improvement measures taken to address adverse factors in the environment in which the measuring equipment is located. For example, when the temperature compensation value exceeds the threshold, it can be determined to install a heat insulation device around the measuring equipment to effectively block the influence of external temperature on the measuring equipment, allowing it to operate in a more stable temperature environment.

[0083] Thermal insulation can be achieved using various insulation materials or cooling / heating systems. When vibration compensation values ​​exceed a threshold, it is advisable to mount the measuring equipment on a vibration damping device, such as a damping platform, damping pad, or active damping system, to effectively absorb and isolate external vibrations and ensure the stability of the measuring equipment. After implementing environmental adjustment strategies, to verify the adjustment effects and ensure the accuracy of subsequent measurements, it is necessary to reacquire the original dimensional data of the door / window frame under test and the measurement data of the measuring equipment on the pre-set reference object on the load-bearing device of the door / window frame under test. This step ensures that new measurements are performed in an optimized environment, thereby obtaining more reliable original data and providing a more solid foundation for subsequent corrections and risk assessments.

[0084] This application's solution addresses the problem that relying solely on compensation may not be sufficient to handle extreme environmental conditions by introducing a threshold judgment mechanism for environmental compensation values. Specifically, when the temperature compensation value or vibration compensation value of the measuring equipment exceeds a preset threshold, it indicates that the current environment's impact on measurement accuracy has reached a level requiring proactive intervention. At this point, the system no longer relies solely on data compensation but instead determines and implements corresponding environmental adjustment strategies, such as installing heat insulation or vibration reduction devices, to improve the operating environment of the measuring equipment from the source.

[0085] This proactive environmental intervention effectively reduces the impact of environmental factors on the measuring equipment, enabling it to operate under more stable and ideal conditions. Subsequently, by reacquiring the original dimensional data and measurement data from the preset reference object, more accurate and reliable initial measurement data is obtained after environmental optimization. This provides high-quality input for subsequent sensor drift compensation and dimensional correction, further improving the accuracy and reliability of the overall detection method.

[0086] Through the above technical solution, this application enables intelligent monitoring and proactive intervention of the working environment of measuring equipment. Compared to simply performing data compensation, this solution can promptly detect and take physical measures to improve the measurement environment when environmental conditions become severely deteriorated, thereby fundamentally reducing the interference of environmental factors on measurement accuracy. This not only improves the effectiveness of sensor drift compensation, but more importantly, significantly enhances the overall stability and accuracy of door and window frame quality inspection, reduces the risk of misjudgment caused by environmental fluctuations, and ensures the reliability of product quality control.

[0087] Specifically, in some implementations of the above-mentioned method for processing quality inspection data of door and window frames, the step of determining whether there is an assembly risk in the door and window frame under test based on the structural information and corrected dimensional data of the door and window frame under test can be further refined, including: Identify the first and second components that cooperate with each other in the structural information of the door and window frame to be tested; determine whether the corrected dimension of the first component is less than a second value and whether the corrected dimension of the second component is greater than a third value; the second value is the sum of the minimum set dimension of the first component and the preset dimension tolerance of the first component, and the third value is the difference between the maximum set dimension of the second component and the preset dimension tolerance of the second component; if both are true, it is determined that there is an assembly risk between the first and second components of the door and window frame to be tested; otherwise, it is determined that there is no assembly risk between the first and second components of the door and window frame to be tested.

[0088] In the manufacturing process of door and window frames, there are usually multiple interlocking components, such as the door frame and door leaf, glass and profiles, etc. In this application, the first component and the second component refer to these structural components that need to fit tightly during assembly. The corrected dimension of the first component refers to the actual measured dimension of the first component after correction by the sensor drift compensation value, and the corrected dimension of the second component refers to the actual measured dimension of the second component after correction by the sensor drift compensation value.

[0089] Furthermore, the second value is defined as the sum of the minimum set dimension of the first component and the preset dimensional tolerance of the first component. This second value represents the upper limit of the first component's dimensions while meeting assembly requirements. For example, if the minimum set dimension of the first component is L1 and the preset dimensional tolerance is T1, then the second value is L1 + T1.

[0090] Meanwhile, the third value is defined as the difference between the maximum set dimension of the second component and the preset dimensional tolerance of the second component. This third value represents the lower limit of the second component's dimension while meeting assembly requirements. For example, if the maximum set dimension of the second component is L2 and the preset dimensional tolerance is T2, then the third value is L2 - T2.

[0091] Therefore, by comparing the corrected dimension of the first component with the second value, and the corrected dimension of the second component with the third value, it is possible to accurately determine whether there are potential assembly problems between the two mating components. When the corrected dimension of the first component is smaller than the second value, and the corrected dimension of the second component is larger than the third value, it indicates that the first component may be too small and the second component may be too large, and there is a risk of dimensional mismatch between them, thus identifying an assembly risk. Conversely, if the above conditions are not met, it is considered that there is no assembly risk.

[0092] This application's solution compares the corrected dimensions of the first and second components with preset second and third values, enabling precise identification of potential assembly risks in the tested door and window frame from a dimensional matching perspective. Specifically, the second and third values ​​define the acceptable dimensional ranges for the mating components. When the corrected dimension of the first component exceeds its upper limit (i.e., less than the second value), and the corrected dimension of the second component exceeds its lower limit (i.e., greater than the third value), it indicates a potential deviation in the actual dimensions of the two mating components, making it difficult to achieve the expected fitting accuracy or structural stability during actual assembly. This judgment mechanism based on dimensional tolerances and actual measurement data makes the identification of assembly risks more objective and quantifiable, avoiding errors and omissions that may arise from relying solely on experience.

[0093] The above technical solution enables a refined and quantitative assessment of risks associated with door and window frame assembly. Compared to relying solely on experience or simple dimensional comparisons, this solution, by introducing minimum and maximum set dimensions for components and pre-defined dimensional tolerances, combined with corrected actual measurement data, can more accurately identify potential dimensional mismatches. This effectively avoids assembly difficulties, reduced product quality, or increased rework costs caused by dimensional deviations. This precise risk identification capability helps to identify and resolve problems early in production, improving the overall production efficiency and product qualification rate of door and window frames.

[0094] Based on the above-mentioned method for processing quality inspection data of door and window frames, the steps for generating production intervention suggestions for the door and window frame under test after determining that there is an assembly risk include: Obtain multiple subsequent production processes of the door and window frame to be tested; for each of the multiple production processes, obtain the second preset correspondence relationship of the production process; the second preset correspondence relationship includes a one-to-one correspondence relationship between multiple assembly risks and multiple production intervention suggestions; use the production intervention suggestions corresponding to the assembly risks of the door and window frame to be tested in the second preset correspondence relationship as the production intervention suggestions of the door and window frame to be tested in the production process.

[0095] Specifically, "obtaining multiple subsequent production processes of the door / window frame under test" means that after identifying assembly risks in the door / window frame under test, the system will identify and obtain all subsequent production stages that the frame needs to go through from its current state to the final product. For example, these processes may include cutting, welding, grinding, assembly, painting, and packaging. The purpose is to clarify the scope and target stages for intervention recommendations.

[0096] The phrase "obtaining the second preset correspondence for each of the multiple production processes" can be understood as follows: for each identified subsequent production process, the system queries or loads a knowledge base or rule set specifically for that process. This knowledge base, i.e., the second preset correspondence, predefines the mapping relationship between various assembly risks that may occur in that specific process and the corresponding production intervention suggestions. For example, for the "cutting process," if there is an assembly risk of "excessive dimensional deviation," the corresponding production intervention suggestion might be "adjusting the cutting machine parameters" or "replacing the cutting tool." The purpose is to ensure that the generated intervention suggestions are process-specific and operable.

[0097] In practical applications, "using the production intervention suggestions corresponding to the assembly risks of the window / door frame under test in the second preset correspondence as production intervention suggestions for the window / door frame under test in the production process" specifically involves the system searching and matching within the acquired second preset correspondence for a specific production process based on the specific assembly risks existing in the window / door frame under test. Once a production intervention suggestion corresponding to the current assembly risk is found, that suggestion is determined as the production intervention suggestion for that process. For example, if an assembly risk of "excessive bending of window frame profile" is detected, and in the second preset correspondence for the "welding process," this risk corresponds to the suggestion of "adjusting the pressure of the welding fixture," then that suggestion will be generated and sent to the welding process. The purpose is to achieve precise matching between risks and interventions, providing direct and effective production guidance.

[0098] This application's solution addresses the lack of specific and systematic intervention guidance after risk identification in traditional methods by linking assembly risks with subsequent production processes and their corresponding intervention recommendations. First, by acquiring multiple subsequent production processes of the door and window frame under test, the scope and target stages of the intervention recommendations are clarified, enabling intervention measures to be targeted at key nodes in the production process. Second, a second pre-defined correspondence is established for each production process, mapping specific assembly risks one-to-one with feasible production intervention recommendations for that process, ensuring that the generated recommendations are process-specific and actionable. It is precisely this refined mapping mechanism that allows the system to provide accurate and executable production intervention recommendations for the corresponding production processes based on the detected specific assembly risks, thereby effectively guiding the adjustment and optimization of the production line.

[0099] Through the aforementioned technical solution, this application enables precise intervention in quality issues during the production of door and window frames. Compared to basic solutions that only identify assembly risks, this application significantly improves the targeting and effectiveness of production intervention by providing customized production intervention suggestions for each subsequent production process. This not only helps to correct production deviations in a timely manner, reducing rework and scrap rates, but also optimizes the production process, improving overall production efficiency and product quality. Furthermore, this systematic intervention mechanism also provides more refined data support and decision-making basis for production management.

[0100] This application further proposes that, after generating production intervention suggestions for the door and window frame under test based on the assembly risks present in the frame, the method also includes: Determine whether the number of assembly risks in the window and door frame under test exceeds a preset threshold; if so, generate and send an alarm message to the user's device; the alarm message is used to instruct the user to inspect the production equipment of the window and door frame under test.

[0101] Specifically, after generating production intervention recommendations for a single window / door frame under test, the system is configured to count the assembly risks present in that frame. This "number of assembly risks" can refer to the total number of different types of assembly risks detected in a window / door frame, or the total number of window / door frames with assembly risks detected in a batch or over a period of time. The "preset quantity threshold" is a configurable parameter whose value can be set based on the actual production line's quality control requirements, product complexity, and historical data analysis results. For example, the threshold can be set to 3, indicating that if a window / door frame has 3 or more different assembly risks, the problem is considered relatively serious.

[0102] Furthermore, when the number of assembly risks detected in the tested window and door frames exceeds a preset threshold, the system is triggered to generate an "alarm message." This alarm message aims to alert relevant users, indicating a potential deeper production problem. "User equipment" can be any terminal device connected to the production line management system, such as an operator's workstation, a manager's mobile device, or a display screen in the central control room. The alarm message typically includes specific window and door frame batch information, the number of detected risks, and a clear recommendation to "instruct the user to inspect the production equipment for the tested window and door frames." Here, "production equipment" can include various mechanical equipment used for cutting, welding, assembly, drilling, etc., related to window and door frame manufacturing, which may cause persistent assembly problems due to wear, calibration deviations, or malfunctions.

[0103] This application's solution, by introducing a mechanism to assess the number of assembly risks, effectively identifies systemic quality defects caused by problems with the production equipment itself. When the number of assembly risks detected in a single door / window frame or a batch of door / window frames exceeds a preset threshold, this usually indicates that the problem is not merely an occasional individual deviation, but rather a deeper issue such as wear, inaccurate calibration, or malfunction of the production equipment. By promptly generating and sending alarm messages and clearly instructing users to inspect the production equipment for the door / window frame under test, this solution enables production managers to address problems at their source, preventing persistent and widespread quality issues caused by equipment failure. This mechanism elevates quality control from intervention at the single-product level to maintenance at the production equipment level, thereby achieving more fundamental quality improvement.

[0104] Through the above technical solution, this application provides a more comprehensive and proactive quality management strategy. It not only offers specific production intervention suggestions for individual assembly risks, but more importantly, when the number of assembly risks reaches a certain level, it can provide timely warnings and guide users to maintain production equipment, thereby fundamentally eliminating the potential causes leading to a large number of assembly risks. This helps prevent the mass production of defective products, significantly reduces rework and scrap rates, and improves production efficiency and product quality stability. Furthermore, by conducting preventative or timely maintenance on equipment, the service life of equipment can be extended, and unexpected downtime can be reduced, bringing significant economic benefits to enterprises.

[0105] This application also discloses a data processing system for quality inspection of door and window frames, comprising: an acquisition device and a processing device; the acquisition device is used to acquire the original dimensional data and structural information of the door and window frame to be tested; the structural information includes the component types of each component of the door and window frame to be tested; the processing device is used to acquire the measurement data of a preset reference object on the load-bearing device of the door and window frame to be tested, and the environmental data of the environment in which the measuring device is located; the processing device is further used to determine the sensor drift compensation value based on the measurement data of the preset reference object and the environmental data, and to correct the original dimensional data based on the sensor drift compensation value to obtain the corrected dimensional data of the door and window frame to be tested; the processing device is further used to determine whether there is an assembly risk in the door and window frame to be tested based on the structural information and the corrected dimensional data of the door and window frame to be tested; the processing device is further used to generate production intervention suggestions for the door and window frame to be tested based on the assembly risk when there is an assembly risk in the door and window frame to be tested.

[0106] This application provides a door and window frame quality inspection data processing system, which aims to effectively solve the problems of inaccurate dimensional data caused by long-term drift of measuring equipment in traditional door and window production, as well as the resulting assembly risks and quality traceability issues, through integrated hardware and software collaboration. The system acquires the original dimensions and structural information of the door and window frames precisely through an acquisition device, and the processing device intelligently corrects and assesses the risks of the measurement data, ultimately generating instructive production intervention suggestions. This enables comprehensive, real-time monitoring and optimization of the production quality of door and window frames.

[0107] The door and window frame quality inspection data processing system provided in this application is based on the fact that it achieves accurate inspection and intelligent intervention of door and window frame quality through the coordinated work of acquisition and processing devices.

[0108] The acquisition device is configured to acquire the original dimensional data and structural information of the door / window frame under test. Specifically, the acquisition device can include, but is not limited to, various dimensional measurement sensors, such as laser displacement sensors, vision measurement systems, coordinate measuring machines, or high-precision calipers, for non-contact or contact measurements of the geometric dimensions of the door / window frame to obtain its original dimensional data, such as length, width, thickness, and angles. Furthermore, the acquisition device can connect to the factory's production management system, product lifecycle management system, or computer-aided design system via a data interface to acquire the structural information of the door / window frame, such as its component types, material specifications, and design tolerances. This structural information can be read and parsed by the acquisition device in the form of electronic documents, database records, or preset configuration files.

[0109] The processing unit is configured to perform subsequent data processing, analysis, and decision-making functions. Specifically, the processing unit can be one or more computing units, such as industrial control computers, servers, embedded processors, or cloud computing platforms. Specialized software modules or algorithms can run within the processing unit to achieve the following functions: First, the processing device is used to acquire measurement data of a preset reference object on the load-bearing device of the door / window frame under test, as well as environmental data of the environment in which the measuring equipment is located. For example, the processing device can be connected to the measuring equipment to periodically receive measurement readings from the preset reference object. Simultaneously, the processing device can also be connected to environmental sensors, such as temperature sensors, humidity sensors, and vibration sensors, to acquire various parameters of the measuring equipment's operating environment in real time.

[0110] Secondly, the processing device determines the sensor drift compensation value based on the measurement data of a preset reference object and environmental data, and corrects the original dimensional data according to the sensor drift compensation value to obtain the corrected dimensional data of the door and window frame to be measured. The processing device can have a built-in or invoke preset compensation model or algorithm, which can correlate the measurement deviation of the preset reference object with environmental data based on historical data, machine learning, or physical models to calculate an accurate sensor drift compensation value. Subsequently, the processing device applies this compensation value to the original dimensional data collected by the acquisition device to eliminate measurement errors, thereby obtaining more accurate corrected dimensional data.

[0111] Furthermore, the processing device is used to determine whether there are assembly risks in the door and window frame under test based on the structural information and corrected dimensional data of the frame. The processing device can analyze the corrected dimensional data of each component according to the design specifications and assembly requirements of the door and window frame, and determine whether the fit clearances and tolerance matching between them meet the standards. For example, the processing device can identify components that are too large or too small and assess their impact on the overall assembly process and the performance of the final product.

[0112] Finally, the processing unit is used to generate production intervention suggestions for the door and window frame under test when assembly risks exist. Based on these risks, the processing unit can automatically generate corresponding production intervention measures for different types of assembly risks, such as adjusting processing parameters, replacing defective parts, and recalibrating equipment, according to a preset rule base or expert system. These suggestions can be presented to production operators or automated production lines in the form of text, instructions, or a visual interface.

[0113] Compared with existing technologies, the door and window frame quality inspection data processing system provided in this application represents a significant advancement. Traditional quality inspection systems are often limited to simple conformity judgments based on raw dimensional data, lacking dynamic compensation mechanisms for long-term drift of measuring equipment, and failing to deeply analyze the hidden risks that dimensional deviations may pose to actual assembly. This results in a large number of products on the edge of tolerance being misjudged as qualified, with quality problems only revealing themselves after long-term use, making it difficult for factories to trace the source of problems and optimize production processes.

[0114] This application's system achieves a comprehensive upgrade in the processing of quality inspection data for door and window frames by introducing specialized acquisition and processing devices. The acquisition device efficiently and accurately collects multi-source data, while the processing device, through intelligent algorithms, not only precisely compensates for sensor drift to ensure the authenticity of dimensional data, but also proactively identifies potential assembly risks based on the corrected data and structural information. This integrated solution enables the system to prevent quality problems from occurring at the source, rather than passively tracing them afterward. By generating timely production intervention suggestions, this system can guide production lines to make real-time adjustments, significantly improving product quality stability and production efficiency. It effectively resolves the contradiction between "qualified" data and "failure" in actual use in traditional systems, providing strong technical support for intelligent quality management in door and window production.

[0115] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for processing quality inspection data of door and window frames, characterized in that, include: Obtain the original dimensional data and structural information of the door and window frame to be tested; The structural information includes the component types of each part of the door and window frame under test; The measuring equipment acquires measurement data of a preset reference object on the load-bearing device of the door and window frame to be measured, as well as environmental data of the environment in which the measuring equipment is located; The sensor drift compensation value is determined based on the measurement data of the preset reference object and the environmental data, and the original size data is corrected based on the sensor drift compensation value to obtain the corrected size data of the door and window frame to be tested. Determine whether there are assembly risks in the door and window frame under test based on the structural information and corrected dimensional data of the door and window frame under test; When there are assembly risks in the door and window frame to be tested, production intervention suggestions for the door and window frame to be tested are generated based on the assembly risks.

2. The method for processing quality inspection data of door and window frames according to claim 1, characterized in that, Determining sensor drift compensation values ​​based on measurement data from a preset reference object and the environmental data includes: Acquire the preset measurement data of the preset reference object; The difference between the preset measurement data of the preset reference object and the measurement data of the preset reference object is used as the measurement compensation value; Determine the environmental compensation value based on the environmental data; The sum of the measured compensation value and the environmental compensation value is used as the sensor drift compensation value.

3. The method for processing quality inspection data of door and window frames according to claim 2, characterized in that, The environmental data includes the vibration intensity value and temperature value of the measuring equipment. An environmental compensation value is determined based on the environmental data, including: Obtain a first preset correspondence; the first preset correspondence includes a one-to-one correspondence between multiple vibration intensity value ranges and multiple first compensation values; The first compensation value corresponding to the vibration intensity value range of the measuring device in the first preset correspondence is used as the vibration compensation value. Determine the temperature compensation value based on the temperature value from the measuring device; The sum of the vibration compensation value and the temperature compensation value is used as the environmental compensation value.

4. The method for processing quality inspection data of door and window frames according to claim 3, characterized in that, Determining the temperature compensation value based on the temperature value from the measuring device includes: Obtain the preset temperature compensation coefficient; The product of the preset temperature compensation coefficient and the temperature value of the measuring device is used as the temperature compensation value.

5. The method for processing quality inspection data of door and window frames according to claim 3, characterized in that, After determining the environmental compensation value, the method further includes: Obtain the preset temperature compensation threshold and the preset vibration compensation threshold; The environmental adjustment strategy for the measuring equipment is determined based on the temperature compensation value and the preset temperature compensation value threshold, as well as the vibration compensation value and the preset vibration compensation value threshold; the environmental adjustment strategy is used to adjust the environment in which the measuring equipment is located.

6. The method for processing quality inspection data of door and window frames according to claim 5, characterized in that, The environmental adjustment strategy for the measuring equipment is determined based on the temperature compensation value and the preset temperature compensation value threshold, as well as the vibration compensation value and the preset vibration compensation value threshold, including: Determine whether the temperature compensation value is greater than the preset temperature compensation value threshold. If so, determine that the environmental adjustment strategy for the measuring equipment is to set up a heat insulation device around the measuring equipment and reacquire the original size data of the door and window frame to be tested and the measurement data of the preset reference object on the load-bearing device of the door and window frame to be tested. Determine whether the vibration compensation value is greater than the preset vibration compensation value threshold. If so, determine the environmental adjustment strategy for the measuring equipment to be set on the vibration damping device, and reacquire the original size data of the door and window frame to be tested and the measurement data of the preset reference object on the load-bearing device of the door and window frame to be tested.

7. The method for processing quality inspection data of door and window frames according to claim 1, characterized in that, Based on the structural information and corrected dimensional data of the door and window frame to be tested, determine whether there are any assembly risks in the door and window frame to be tested, including: Determine the first and second components that cooperate with each other in the structural information of the door and window frame to be tested; Determine whether the corrected dimension of the first component is less than the second value and whether the corrected dimension of the second component is greater than the third value; the second value is the sum of the minimum set dimension of the first component and the preset dimension tolerance of the first component, and the third value is the difference between the maximum set dimension of the second component and the preset dimension tolerance of the second component. If both are true, it is determined that there is an assembly risk in the first and second components of the door and window frame under test; otherwise, it is determined that there is no assembly risk in the first and second components of the door and window frame under test.

8. A method for processing quality inspection data of door and window frames according to any one of claims 1-7, characterized in that, Based on the assembly risks present in the window and door frames under test, production intervention recommendations for the window and door frames under test are generated, including: Obtain information on multiple subsequent production processes of the door and window frame to be tested; For each of the multiple production processes, a second preset correspondence is obtained; the second preset correspondence includes a one-to-one correspondence between multiple assembly risks and multiple production intervention suggestions. The production intervention suggestions corresponding to the assembly risks of the door and window frames to be tested in the second preset correspondence are used as the production intervention suggestions for the door and window frames to be tested in the production process.

9. A method for processing quality inspection data of door and window frames according to any one of claims 1-7, characterized in that, After generating production intervention recommendations for the door and window frame under test based on the assembly risks present in the frame, the method further includes: Determine whether the number of assembly risks existing in the door and window frame under test is greater than a preset threshold. If so, an alarm message is generated and sent to the user's device; the alarm message is used to instruct the user to inspect the production equipment of the door and window frame under test.

10. A data processing system for quality inspection of door and window frames, characterized in that, include: Acquisition device and processing device; The acquisition device is used to acquire the original size data and structural information of the door and window frame to be tested; The structural information includes the component types of each part of the door and window frame under test; The processing device is used to acquire measurement data of a preset reference object on the load-bearing device of the door and window frame to be measured, as well as environmental data of the environment in which the measuring device is located. The processing device is also used to determine the sensor drift compensation value based on the measurement data of the preset reference object and the environmental data, and to correct the original size data based on the sensor drift compensation value to obtain the corrected size data of the door and window frame to be tested. The processing device is also used to determine whether there is an assembly risk in the door and window frame under test based on the structural information of the door and window frame under test and the corrected dimension data of the door and window frame under test. The processing device is also used to generate production intervention suggestions for the door and window frame under test based on the assembly risks present in the door and window frame under test when there are assembly risks.

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