An after-sales service management system based on intelligent doors and windows

Abstract

This invention discloses an after-sales service management system based on intelligent doors and windows, belonging to the field of door and window service technology. It acquires motor winding temperature rise and shell temperature drop data through a data acquisition module, calculates an acceleration coefficient through a dust accumulation prediction module, and jointly determines the dust accumulation risk. After determining severe dust accumulation and obtaining user authorization, the after-sales maintenance module uploads an evidence package to the cloud, automatically generating and dispatching proactive maintenance work orders. This invention achieves embedded lightweight diagnostics by synchronously acquiring motor winding temperature rise and shell temperature drop data, using low-frequency sampling and integer operations. It also employs a physical consistency logic that combines temperature rise acceleration and temperature drop acceleration to eliminate mechanical overload and environmental interference. This proactively identifies the risk of dust accumulation in the air duct during the user's insidious incubation period, accurately triggering after-sales maintenance and effectively preventing motor burnout accidents.

Description

Technical Field

[0001] This invention relates to the field of door and window service technology, specifically to an after-sales service management system based on intelligent doors and windows. Background Technology

[0002] As an integrated hardware and software IoT product, smart doors and windows not only involve physical faults in mechanical structure, motors, and sensors during daily use, but also frequently encounter software-level problems such as network configuration, App linkage, and scene automation failures. Traditional after-sales methods, such as manual telephone registration and paper work orders, cannot accurately record equipment logs and user operation trajectories, nor can they achieve remote diagnosis and firmware upgrades. This results in high on-site visit rates, long resolution cycles, and poor customer experience. A dedicated after-sales service management system can link the unique device ID with user files, installation information, historical faults, and remote operation and maintenance records, allowing customer service and technical personnel to grasp the true status of the equipment in real time.

[0003] However, the heat dissipation efficiency of motors used for opening and closing doors and windows decreases when the air ducts become clogged with dust. Long-term high-temperature operation leads to thermal aging of the winding insulation, which can easily cause inter-turn short circuits. However, users are unaware of this at first until the windows suddenly become stuck when opening or closing. This turns what could have been solved with a simple dust cleaning and maintenance into a repair accident that requires motor replacement. Existing after-sales services cannot provide services that proactively predict dust accumulation and blockage in the motor air ducts. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an after-sales service management system based on intelligent doors and windows. This system solves the problem that existing after-sales services lack the ability to proactively predict dust accumulation in motor ducts, leading to situations where users initially do not notice the problem but experience insulation aging and inter-turn short circuits due to prolonged overheating, ultimately escalating from simple dust removal to motor replacement.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an after-sales service management system based on intelligent doors and windows, comprising the following specific modules: a data acquisition module: used to collect winding temperature rise data during motor operation and shell temperature drop data after motor shutdown, and to perform data cleaning processing on the winding temperature rise data and shell temperature drop data; a duct dust accumulation prediction module: used to calculate the temperature rise acceleration coefficient based on the winding temperature rise data, calculate the temperature drop acceleration coefficient based on the shell temperature drop data, perform comprehensive analysis and standardization processing on the temperature rise acceleration coefficient and temperature drop acceleration coefficient to obtain a dust accumulation prediction coefficient, and predict whether serious dust accumulation will occur in the motor duct based on the dust accumulation prediction coefficient; and an after-sales maintenance module: used to upload a standardized fault evidence package to the cloud after-sales platform after the duct dust accumulation prediction module determines that serious dust accumulation exists and obtains prior authorization from the user, so that the cloud platform generates an active maintenance work order and pushes it to the after-sales engineer terminal.

[0006] Furthermore, the method for predicting whether the motor duct has serious dust accumulation based on the dust accumulation prediction coefficient is as follows: a dust accumulation prediction threshold is preset and assigned the value 2X. The dust accumulation prediction coefficient is compared with the dust accumulation prediction threshold. If the dust accumulation prediction coefficient is equal to the dust accumulation prediction threshold, it indicates that serious dust accumulation has occurred; if the dust accumulation prediction coefficient is not equal to the dust accumulation prediction threshold, it indicates that serious dust accumulation has not occurred.

[0007] Furthermore, the specific method for obtaining the dust accumulation prediction coefficient is as follows: if the winding accelerates heating, the heating acceleration coefficient is assigned a value of X; otherwise, it is assigned a value of Y, and X is not equal to Y; if the shell accelerates cooling, the cooling acceleration coefficient is assigned a value of X; otherwise, it is assigned a value of Z, and Z is not equal to X; the heating acceleration coefficient and the cooling acceleration coefficient are added together to calculate the dust accumulation prediction coefficient.

[0008] Furthermore, the specific method for obtaining the heating acceleration coefficient is as follows: Calculate the temperature increment sequence between adjacent sampling times based on the winding heating data during a period after motor startup; divide the temperature increment sequence into a front sequence representing the initial startup phase and a back sequence representing the later startup phase, according to time sequence. Let M be the total number of temperature data points in the front sequence during motor operation, and CYI-M be the total number of temperature data points in the back sequence during motor operation, where CYI represents the total number of sampling times for winding heating data. Calculate the average temperature increment of the front sequence and the average temperature increment of the back sequence, respectively. Divide the average temperature increment of the back sequence by the average temperature increment of the front sequence to obtain the heating ratio. Analyze the heating ratio against 1 to set the heating acceleration coefficient.

[0009] Furthermore, the average temperature increment is calculated as follows: For the first segment of the sequence, the temperature increment values ​​between all adjacent sampling times contained in the first segment of the sequence are summed to obtain the total increment of the first segment. The total increment of the first segment is divided by the number of temperature increment values ​​in the first segment of the sequence to obtain the average temperature increment of the first segment of the sequence. For the second segment of the sequence, the temperature increment values ​​between all adjacent sampling times contained in the second segment of the sequence are summed to obtain the total increment of the second segment. The total increment of the second segment is divided by the number of temperature increment values ​​in the second segment of the sequence to obtain the average temperature increment of the second segment of the sequence.

[0010] Furthermore, the method of analyzing based on the heating ratio and 1 is as follows: compare the heating ratio with 1. If the heating ratio is greater than 1, it indicates that the winding is accelerating its heating, and a heating acceleration coefficient is set, with an initial value that is not equal to X. If the heating ratio is less than or equal to 1, it indicates that the winding is not accelerating its heating.

[0011] Furthermore, the specific method for obtaining the cooling acceleration coefficient is as follows: Calculate the temperature decay increment sequence between adjacent sampling times based on the casing cooling data over a period of time after the motor stops running; divide the temperature decay increment sequence into a front sequence representing the initial cooling stage and a back sequence representing the later cooling stage, according to time sequence. Let N be the total number of temperature data points in the front sequence when the motor stops, and CY-N be the total number of temperature data points in the back sequence when the motor stops, where CY represents the total number of casing cooling data sampling times. Calculate the average temperature decay of the front sequence and the average temperature decay of the back sequence, respectively. Divide the average temperature decay of the front sequence by the average temperature decay of the back sequence to obtain the cooling ratio. Analyze the cooling ratio against 1 to set the cooling acceleration coefficient.

[0012] Furthermore, the method of analyzing based on the cooling ratio and 1 is as follows: the cooling ratio is compared with 1. If the cooling ratio is less than 1, it indicates that the shell is cooling down faster, and a cooling acceleration coefficient is set, with an initial value that is not equal to X. If the cooling ratio is greater than or equal to 1, it indicates that the shell is not cooling down faster.

[0013] Furthermore, the average temperature decay is calculated as follows: For the first segment of the sequence, the temperature decay increments between all adjacent sampling times contained in the first segment of the sequence are summed to obtain the total temperature decay of the first segment. The total temperature decay of the first segment is then divided by the number of temperature decay increments in the first segment of the sequence to obtain the average temperature decay of the first segment of the sequence. For the second segment of the sequence, the temperature decay increments between all adjacent sampling times contained in the second segment of the sequence are summed to obtain the total temperature decay of the second segment. The total temperature decay of the second segment is then divided by the number of temperature decay increments in the second segment of the sequence to obtain the average temperature decay of the second segment of the sequence.

[0014] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: 1. By synchronously collecting winding temperature rise data during motor operation and casing temperature drop data after shutdown, the heating acceleration coefficient and cooling acceleration coefficient are extracted respectively, and the degree of dust accumulation is determined together. This invention can proactively identify the risk of dust accumulation and blockage in the air duct during the latent period when only microscopic thermal aging of the winding insulation occurs and the user has no operational awareness. It automatically triggers after-sales maintenance work orders. Compared with the passive repair method after a fault occurs in the existing technology, this invention significantly advances the timing of dust cleaning and maintenance intervention, effectively preventing inter-turn short circuits and motor burnout caused by long-term overheating. It downgrades the maintenance accident that originally required the replacement of the entire motor assembly to a routine dust cleaning and maintenance, significantly reducing after-sales maintenance costs and user downtime losses.

[0015] 2. By employing a low-frequency sampling strategy of once per second in both winding temperature and housing temperature acquisition, and replacing complex floating-point spectrum analysis with integer operations such as arithmetic mean and ratio comparison, the entire diagnostic algorithm can run directly on smart door and window MCUs with Flash resources of less than 64KB and no floating-point arithmetic unit. Low-frequency sampling significantly reduces MCU wake-up power consumption and data storage overhead, while the natural ability of mean calculation to suppress random noise ensures judgment stability. Thus, it achieves highly sensitive early identification of gradual heat dissipation failure such as dust accumulation in air ducts in low-power, low-cost embedded systems, taking into account both engineering practicality and diagnostic accuracy.

[0016] 3. This invention employs a physical consistency joint judgment logic of accelerated heating and accelerated cooling. That is, only when the heat accumulation rate of the motor during operation with a heat source accelerates and the heat dissipation rate during shutdown without a heat source simultaneously decreases is it determined to be dust accumulation and blockage in the air duct. If only the heating is accelerated while the cooling is normal, it is determined to be an increase in mechanical load rather than a heat dissipation failure; if only the cooling is accelerated while the heating is normal, it is determined to be a change in environmental conditions rather than a fault. This cross-verification mechanism effectively eliminates the misleading influence of interference factors such as track jamming, pulley wear, and sudden changes in ambient temperature on a single temperature sensor, ensuring the uniqueness and accuracy of the dust accumulation judgment conclusion, avoiding invalid on-site service due to false alarms, and achieving precise allocation of after-sales resources.

[0017] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0018] Figure 1 This is a flowchart of the after-sales service management method for intelligent doors and windows according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0021] Example 1: like Figure 1 As shown in the figure, this embodiment of the invention provides an after-sales service management system based on intelligent doors and windows, including the following specific modules: Data acquisition module: An NTC thermistor closely attached to the enameled wire of the motor winding is used as the temperature sensor. The NTC thermistor selected is the MF52B series enameled wire type, with a nominal resistance of 10KΩ and an operating temperature range of -40℃ to +125℃. NTC thermistors have the advantages of small size, fast response, and low cost. Their close proximity to the enameled wire allows for direct sensing of heat changes generated by the copper losses in the winding, avoiding measurement lag caused by heat conduction delay from the casing. During motor operation, winding temperature data is collected once per second. Since the more severe the dust accumulation and blockage in the motor's air ducts, the higher the winding temperature, therefore… By using winding temperature data, the severity of dust accumulation and blockage in the motor's air ducts can be reflected. Dust accumulation and blockage in the air ducts increase the thermal resistance for heat dissipation, meaning that the amount of heat dissipated by the motor per unit time during operation decreases. Meanwhile, the heat generated by the copper losses in the windings remains essentially unchanged, leading to a faster rate of net heat accumulation. Consequently, the winding temperature rises at an accelerated rate. This acquisition method can capture the trend of temperature rise slope changes by sampling once per second at a low frequency. Compared with high-frequency continuous sampling schemes, it significantly reduces MCU wake-up power consumption and data storage overhead, while still fully preserving the temperature rise acceleration characteristics caused by dust accumulation in the air ducts. This enables highly sensitive early identification of heat dissipation failures in low-power embedded systems. An NTC thermistor, which is attached to the motor housing or embedded in the inner wall of the motor cavity, is used as the temperature sensing element. The NTC thermistor selected is the TS103H25C3950GAMI-SL200A type epoxy-encapsulated Teflon wire NTC temperature sensor, with a nominal resistance of 10KΩ and an operating temperature range of 0℃ to 180℃. For enclosed tubular motors, a single-bus digital temperature sensor can also be used, which is directly attached to the inside of the metal end cover. The sensor should be placed far away from the concentrated heat generation area of ​​the windings so that it can more sensitively reflect the overall thermal resistance change of the housing to dissipate heat. After the motor stops running, the housing cooling data is continuously collected at a low frequency of once per second. The natural cooling rate of the motor under no heat source input is detected by the housing cooling data, thereby quantifying the degree to which dust accumulation in the air duct blocks the thermal resistance of the housing to dissipate heat to the environment. The winding heating data and the shell cooling data are cleaned to remove redundant values. This data cleaning process eliminates abnormal jump points caused by instantaneous sensor disturbances or ADC quantization noise, making the subsequent mean calculation robust to occasional interference and improving the data quality of the winding heating data and the shell cooling data.

[0022] Dust Accumulation Prediction Module: Based on winding temperature rise data, the module analyzes the winding temperature rise acceleration to obtain a temperature rise acceleration coefficient. Based on casing temperature drop data, it analyzes the casing temperature drop acceleration to obtain a temperature drop acceleration coefficient. A comprehensive analysis of both coefficients is performed, along with standardization to eliminate dimensional differences and convert values ​​of different orders of magnitude to a unified range, resulting in a dust accumulation prediction coefficient. This coefficient is used to predict whether severe dust accumulation will occur in the motor's air duct. If severe dust accumulation is predicted, the module executes the after-sales maintenance procedure and terminates immediately. If no severe dust accumulation is predicted, the module returns to the data acquisition module or terminates immediately.

[0023] After-sales maintenance module: After the dust accumulation prediction module determines that there is serious dust accumulation and obtains prior authorization from the user, it packages the winding temperature rise data, shell temperature drop data, dust accumulation prediction coefficient, equipment unique identifier, and user profile into a standardized fault evidence package and uploads it to the cloud-based after-sales platform. The cloud platform then automatically generates a proactive maintenance work order marked with motor dust removal and pushes it to the after-sales engineer's terminal. This enables proactive predictive maintenance intervention before the user perceives abnormal motor operation. By synchronously packaging and uploading the original monitoring data and judgment conclusions, after-sales engineers can remotely reproduce fault characteristics and confirm diagnostic basis before setting out, reducing on-site inspection time by more than 50%. At the same time, the solidified storage of the evidence package provides a traceable technical archive for subsequent after-sales disputes, effectively avoiding service disputes caused by users questioning the necessity of proactive maintenance.

[0024] Example 2 differs from Example 1 in that: The method for predicting whether serious dust accumulation will occur in the motor duct based on the dust accumulation prediction coefficient is as follows: A preset dust accumulation prediction threshold is set and assigned the value 2X. The dust accumulation prediction coefficient is compared with the dust accumulation prediction threshold. If the dust accumulation prediction coefficient is equal to the dust accumulation prediction threshold, it indicates that serious dust accumulation has occurred; if the dust accumulation prediction coefficient is not equal to the dust accumulation prediction threshold, it indicates that serious dust accumulation has not occurred.

[0025] The specific method for obtaining the ash accumulation prediction coefficient is as follows: If the winding heats up faster, the heating acceleration coefficient is assigned a value of X; otherwise, it is assigned a value of Y, and X is not equal to Y. If the shell cools down faster, the cooling acceleration coefficient is assigned a value of X; otherwise, it is assigned a value of Z, and Z is not equal to X. Where X=1, Y=0, and Z=0. The heating acceleration coefficient and the cooling acceleration coefficient are added together to calculate the dust accumulation prediction coefficient. ;in, Represents the ash accumulation prediction coefficient. Indicates the acceleration factor of heating. This represents the cooling acceleration coefficient. This assignment and addition combination scheme replaces the complex continuous value threshold parameter tuning with extremely simple binary logic. Only three constants, X, Y, and Z, need to be set to cover all diagnostic branches, which greatly reduces the complexity of embedded code and the workload of parameter calibration.

[0026] The specific method for obtaining the heating acceleration coefficient is as follows: For the winding temperature rise data during a period after motor startup, e.g., 30 seconds, calculate the temperature increment sequence between adjacent sampling times. Divide the temperature increment sequence into a first segment representing the initial startup phase and a second segment representing the later startup phase, chronologically. If the sampling time is 30 seconds, let M be the total number of temperature data points in the first segment during motor operation, and CYI-M be the total number of temperature data points in the second segment during motor operation, where CYI represents the total number of sampling times for winding temperature rise data, with a value of 30. Calculate the average temperature increment of the first segment and the average temperature increment of the second segment separately. Divide the average temperature increment of the second segment by the average temperature increment of the first segment to obtain the temperature rise ratio. ,in, Indicates the temperature rise ratio. This represents the mean temperature increment of the latter part of the sequence. This represents the average temperature increment of the preceding sequence, and is not zero. Based on the analysis of the temperature rise ratio and 1, a temperature acceleration coefficient is set. The temperature rise ratio is compared with 1 rather than with a fixed empirical threshold. As long as the heat accumulation rate shows a trend change from slow to fast, the temperature rise ratio is greater than 1. This achieves adaptive judgment across models and seasons, without the need to calibrate thresholds for different product models.

[0027] The average temperature increment is calculated as follows: For the preceding sequence, sum the temperature increment values ​​between all adjacent sampling times contained in the preceding sequence to obtain the sum of the preceding increments. Divide the sum of the preceding increments by the number of temperature increment values ​​in the preceding sequence to obtain the average temperature increment of the preceding sequence. ; This represents the average temperature increment of the preceding sequence. This indicates the first [number]th ... One temperature data point, This indicates the total number of temperature data points included in the initial sequence during motor operation. Indicates adjacent sampling times and The temperature increase increment between them This indicates the number of temperature increment values ​​in the preceding sequence, i.e., the number of adjacent sampling intervals; For the latter part of the sequence, the temperature increment values ​​between all adjacent sampling times contained in the latter part of the sequence are summed to obtain the total increment of the latter part. The total increment of the latter part of the sequence is divided by the number of temperature increment values ​​in the latter part of the sequence to obtain the average temperature increment of the latter part of the sequence. ; This represents the mean temperature increment of the latter part of the sequence. This indicates the sequence of the last segment during motor operation. One temperature data point, This indicates the total number of temperature data points included in the sequence during motor operation. This indicates the number of temperature increment values ​​in the subsequent sequence. Using an arithmetic mean instead of a weighted average or median allows the calculation process to be completed on a resource-constrained MCU with integer accumulation and shift operations, enabling real-time analysis without the need for a floating-point unit. Furthermore, the mean calculation has a natural suppression effect on randomly distributed white noise in the sampled sequence.

[0028] The method of analysis based on the heating ratio and 1 is as follows: The heating ratio is compared with 1. If the heating ratio is greater than 1, it means that the winding is heating up faster, and a heating acceleration coefficient is set, with the initial value being a constant not equal to X. If the heating ratio is less than or equal to 1, it means that the winding is not heating up faster.

[0029] The specific method for obtaining the cooling acceleration coefficient is as follows: For the casing temperature drop data over a period of time after the motor stops running, e.g., 30 seconds, calculate the temperature decay increment sequence between adjacent sampling times. Divide the temperature decay increment sequence into a first sequence representing the initial cooling stage and a second sequence representing the later cooling stage, chronologically. If the sampling time is 30 seconds, let N be the total number of temperature data points in the first sequence when the motor stops, and CY-N be the total number of temperature data points in the second sequence when the motor stops, where CY represents the total number of casing temperature drop data sampling times, with a value of 30. Calculate the average temperature decay of the first and second sequences separately. Divide the average temperature decay of the first sequence by the average temperature decay of the second sequence to obtain the cooling ratio. ,in, Indicates the cooling ratio. This represents the mean temperature decay of the preceding sequence. This represents the average temperature decay of the subsequent sequence, and is not zero; based on the analysis of the cooling ratio and 1, a cooling acceleration coefficient is set.

[0030] The method of analysis based on the cooling ratio and 1 is as follows: The cooling ratio is compared with 1. If the cooling ratio is less than 1, it means that the shell is cooling down faster, and a cooling acceleration coefficient is set, with the initial value being a constant that is not equal to X. If the cooling ratio is greater than or equal to 1, it means that the shell is not cooling down faster.

[0031] The mean temperature decay is calculated as follows: For the first segment of the sequence, sum the temperature decay increment values ​​between all adjacent sampling times contained in the first segment of the sequence to obtain the total decay of the first segment. Divide the total decay of the first segment by the number of temperature decay increment values ​​in the first segment of the sequence to obtain the average temperature decay of the first segment of the sequence. ;in, This represents the mean temperature decay of the preceding sequence. This indicates the first position in the preceding sequence when the motor stops. One temperature data point, This indicates the total number of temperature data points contained in the preceding sequence when the motor stops. Indicates adjacent sampling times and The temperature decay increment between them This indicates the number of temperature decay increment values ​​in the preceding sequence; For the latter part of the sequence, sum the temperature decay increment values ​​between all adjacent sampling times contained in the latter part of the sequence to obtain the total decay of the latter part. Divide the total decay of the latter part by the number of temperature decay increment values ​​in the latter part of the sequence to obtain the average temperature decay of the latter part of the sequence. ;in, This represents the average temperature decay of the latter part of the sequence. This indicates the sequence number in the later part of the sequence when the motor stops. One temperature data point, This indicates the total number of temperature data points contained in the sequence after the motor stops. This indicates the number of temperature decay increment values ​​in the latter part of the sequence; The calculation of the average temperature decay and the average temperature increment adopt the same operation structure, which allows the two signal processing to reuse the same code logic in the MCU. This enables dual-channel thermodynamic feature extraction with minimal program storage space cost. For smart door and window control boards with Flash resources usually less than 64KB, the space saving brought by code reuse has significant engineering value.

[0032] The preferred embodiments disclosed in this invention are merely illustrative examples of feasible implementation methods and are not intended to exhaustively cover all technical details of the invention, nor do they constitute a limitation on the scope of protection of this invention. In practical applications, those skilled in the art can make appropriate adjustments, combinations, or substitutions to the methods or systems described in these embodiments based on specific production conditions, equipment configurations, and process requirements, without departing from the core concept of this invention. For example, the acquisition method, data processing algorithm, control threshold, or specific implementation form of the execution unit can all be reasonably modified according to the actual situation.

[0033] Specifically, at the data acquisition level, although the embodiments of this invention use an NTC thermistor as a temperature sensing element for illustrative purposes, those skilled in the art should understand that any temperature sensing element capable of converting temperature changes into electrical signals—including but not limited to PT100 platinum resistance thermometers, thermocouples, semiconductor integrated temperature sensors, digital temperature sensors, and fiber Bragg grating temperature sensors—can be equivalently replaced according to actual accuracy requirements, cost constraints, and installation space conditions. Similarly, the sampling frequency of once per second described in the embodiments is a preferred solution that balances power consumption and response speed. For different motor thermal time constants or different application scenarios, the sampling frequency can be flexibly adjusted within the range of 0.2 times per second to 10 times per second without affecting the implementation of the core diagnostic logic of this invention.

[0034] At the signal processing and feature extraction level, the algorithms used in the embodiments, such as moving average filtering, arithmetic mean calculation, and ratio comparison, are preferred implementations under embedded low-power constraints. Those skilled in the art can replace the above algorithms with more complex digital signal processing algorithms such as exponentially weighted moving average filtering, median filtering, and Kalman filtering, or upgrade the ratio determination to feature classification determination based on machine learning models, depending on the actual MCU computing resources. As long as the core remains the same—identifying abnormal heat dissipation thermal resistance by comparing the temperature rise dynamics during operation with the temperature drop dynamics after shutdown without a heat source—it should all be considered within the protection scope of this invention.

[0035] At the threshold level, the specific values ​​listed in the embodiments, such as accelerating the determination when the temperature rise ratio is greater than 1, accelerating the determination when the temperature drop ratio is less than 1, and setting the dust accumulation prediction threshold to 2X, are all optimized parameters based on experimental calibration. In actual product deployment, the above thresholds can be appropriately adjusted according to factors such as motor model, climatic characteristics of the operating environment, and the acceptable trade-off between false alarm rate and false negative rate. For example, in high-temperature and high-humidity areas, the determination threshold can be appropriately relaxed to reduce the false alarm rate, while in commercial scenarios with extremely high reliability requirements, the threshold can be tightened to improve early warning sensitivity. Such threshold adjustments based on engineering experience or field calibration do not constitute a substantial improvement to the present invention and are still a natural extension of the technical solution of the present invention.

[0036] At the system architecture level, the data acquisition module, duct dust accumulation prediction module, and after-sales maintenance module described in the embodiments are divisions based on functional logic, and do not limit their physical implementation. Each module can be integrated into the same embedded MCU, or implemented using a layered architecture where data preprocessing is placed on the device side and complex analysis is placed on the gateway or cloud. The work order generation and dispatch functions of the after-sales maintenance module can be handled by an independent after-sales cloud platform, or integrated with the enterprise's existing ERP or CRM system via API. Any module splitting, merging, or cross-device deployment based on the same functional logic should be considered an equivalent implementation of the technical solution disclosed in this invention.

[0037] Furthermore, the technical concepts disclosed in this invention possess universal scalability and adaptability. They are not only applicable to the scenario of predicting dust accumulation in the air ducts of intelligent door and window motors described in the embodiments, but can also be applied in similar technical fields or related industrial processes through analogy, transplantation, or improvement. For example, the core diagnostic logic of this invention—identifying heat dissipation path blockage by monitoring the thermodynamic dynamic differences between the equipment under operating loading and shutdown unloading states—can be extended to server fan air duct blockage early warning, electric vehicle charging pile heat dissipation air duct monitoring, industrial inverter radiator dust accumulation diagnosis, and household air conditioner outdoor unit fin dirt blockage prediction, etc., in all electromechanical equipment scenarios where active heat dissipation air ducts exist and dust accumulation will lead to performance degradation. Any technical solution based on the principles, ideas, or framework disclosed in this specification, which transfers the physical consistency method of jointly determining heating acceleration and cooling acceleration to the above-mentioned fields, and makes logically equivalent substitutions, reasonable adjustments to the step sequence, or recombination of module functions, should be considered to fall within the spirit and scope of this invention.

[0038] It should be further clarified that the specific descriptions and drawings in the patent documents are for the purpose of assisting in understanding the present invention only, and their details should not be interpreted as limitations on the claims. The true scope of protection of the present invention covers all equivalent technical solutions that comply with the provisions of the Patent Law under these claims. According to the provisions of the Patent Examination Guidelines regarding the principle of equivalents, any feature that uses substantially the same means to achieve substantially the same function and effect as the technical features recorded in the claims, and that a person skilled in the art could have conceived of without inventive effort at the time of the alleged infringement, constitutes an equivalent technical feature. Therefore, even if a certain embodiment does not fall within the literal scope of the claims in form, as long as its technical essence is the same as or similar to the concept of the present invention.

[0039] All implementation methods that achieve the same or similar functions and effects through reasonable modifications of technical means under the guidance of the concept of this invention fall within the scope of protection sought by this invention. For example, variations such as replacing the "AND" logic judgment of heating acceleration and cooling acceleration with weighted scoring judgment, replacing the comparison of the mean of the preceding and following segments with the slope fitting judgment of the entire curve, and replacing the fixed time sequence boundary with dynamic inflection point detection judgment, as long as they still take thermodynamic two-state comparison as the core concept, should not be excluded from the protection scope of this invention.

[0040] Therefore, the descriptions in this specification are merely illustrative. Any adjustments to implementation methods, equivalent substitutions of technical features, or further applications based on the concept of this invention, as long as they do not depart from the overall technical approach described in this invention, should be included within the scope of protection of this invention. We encourage those skilled in the art to innovate and optimize based on their understanding of the core of this invention and in conjunction with specific practices, so as to jointly promote the progress and development of related technologies. The disclosure of this invention aims to promote technological upgrades in the field of intelligent door and window after-sales service, reduce user costs, extend equipment life, and reduce resource waste through predictive maintenance, which aligns with the industrial direction of green manufacturing and sustainable development.

Claims

1. An after-sales service management system based on intelligent doors and windows, characterized in that: Includes the following specific modules: Data acquisition module: used to collect winding temperature rise data during motor operation and housing temperature drop data after motor stops running, and to perform data cleaning processing on the winding temperature rise data and housing temperature drop data; Dust accumulation prediction module: It is used to calculate the heating acceleration coefficient based on the winding heating data and the cooling acceleration coefficient based on the shell cooling data. It performs comprehensive analysis and standardization on the heating acceleration coefficient and the cooling acceleration coefficient to obtain the dust accumulation prediction coefficient, and predicts whether the motor duct will have serious dust accumulation based on the dust accumulation prediction coefficient. After-sales maintenance module: After the dust accumulation prediction module determines that there is serious dust accumulation and obtains prior authorization from the user, it uploads a standardized fault evidence package to the cloud after-sales platform, and the cloud platform generates an active maintenance work order based on this and pushes it to the after-sales engineer's terminal.

2. The after-sales service management system based on intelligent doors and windows according to claim 1, characterized in that: The method for predicting whether severe dust accumulation occurs in the motor air duct based on the dust accumulation prediction coefficient is as follows: A preset dust accumulation prediction threshold is set and assigned the value 2X. The dust accumulation prediction coefficient is compared with the dust accumulation prediction threshold. If the dust accumulation prediction coefficient is equal to the dust accumulation prediction threshold, it indicates that serious dust accumulation has occurred; if the dust accumulation prediction coefficient is not equal to the dust accumulation prediction threshold, it indicates that serious dust accumulation has not occurred.

3. The after-sales service management system based on intelligent doors and windows according to claim 2, characterized in that: The specific method for obtaining the ash accumulation prediction coefficient is as follows: If the winding heats up faster, the heating acceleration coefficient is assigned a value of X; otherwise, it is assigned a value of Y, and X is not equal to Y. If the shell cools down faster, the cooling acceleration coefficient is assigned a value of X; otherwise, it is assigned a value of Z, and Z is not equal to X. The heating acceleration coefficient and the cooling acceleration coefficient are added together to calculate the dust accumulation prediction coefficient.

4. The after-sales service management system based on intelligent doors and windows according to claim 3, characterized in that: The specific method for obtaining the heating acceleration coefficient is as follows: The winding temperature rise data during a period after motor startup is collected, and the temperature increment sequence between adjacent sampling times is calculated. The temperature increment sequence is divided into a first segment representing the initial stage of startup and a second segment representing the later stage of startup, according to the time sequence. Let M be the total number of temperature data points in the first segment sequence during motor operation, and CYI-M be the total number of temperature data points in the second segment sequence during motor operation, where CYI represents the total number of sampling times for winding temperature rise data. The average temperature increment of the first segment sequence and the average temperature increment of the second segment sequence are calculated respectively. The average temperature increment of the second segment sequence is divided by the average temperature increment of the first segment sequence to obtain the temperature rise ratio. Based on the temperature rise ratio and 1, an acceleration coefficient for temperature rise is set.

5. The after-sales service management system based on intelligent doors and windows according to claim 4, characterized in that: The average temperature increment is calculated as follows: For the preceding sequence, sum the temperature increment values ​​between all adjacent sampling times contained in the preceding sequence to obtain the sum of the preceding increments. Divide the sum of the preceding increments by the number of temperature increment values ​​in the preceding sequence to obtain the average temperature increment of the preceding sequence. For the latter part of the sequence, the temperature increment values ​​between all adjacent sampling times contained in the latter part of the sequence are summed to obtain the total increment of the latter part. The total increment of the latter part of the sequence is divided by the number of temperature increment values ​​in the latter part of the sequence to obtain the average temperature increment of the latter part of the sequence.

6. The after-sales service management system based on intelligent doors and windows according to claim 5, characterized in that: The method of analysis based on the heating ratio and 1 is as follows: The heating ratio is compared with 1. If the heating ratio is greater than 1, it indicates that the winding is heating up faster. The heating acceleration coefficient is set and the initial value is a constant that is not equal to X. If the temperature rise ratio is less than or equal to 1, it indicates that the winding has not accelerated its temperature rise.

7. The after-sales service management system based on intelligent doors and windows according to claim 6, characterized in that: The specific method for obtaining the cooling acceleration coefficient is as follows: The casing cooling data during a period after the motor stops running is used to calculate the temperature decay increment sequence between adjacent sampling times. The temperature decay increment sequence is divided into a first segment representing the initial cooling stage and a second segment representing the later cooling stage according to time sequence. Let N be the total number of temperature data points in the first segment when the motor stops, and CY-N be the total number of temperature data points in the second segment when the motor stops, where CY represents the total number of casing cooling data sampling times. The average temperature decay of the first segment and the average temperature decay of the second segment are calculated separately. The average temperature decay of the first segment is divided by the average temperature decay of the second segment to obtain the cooling ratio. The cooling ratio is analyzed and a cooling acceleration coefficient is set based on 1.

8. The after-sales service management system based on intelligent doors and windows according to claim 7, characterized in that: The method of analysis based on the cooling ratio and 1 is as follows: The cooling ratio is compared with 1. If the cooling ratio is less than 1, it means that the shell is cooling down faster. A cooling acceleration coefficient is set and the initial value is a constant that is not equal to X. If the cooling ratio is greater than or equal to 1, it indicates that the shell is not cooling down rapidly.

9. The after-sales service management system based on intelligent doors and windows according to claim 8, characterized in that: The average temperature decay is calculated as follows: For the first segment of the sequence, sum the temperature decay increment values ​​between all adjacent sampling times contained in the first segment of the sequence to obtain the total decay of the first segment. Divide the total decay of the first segment by the number of temperature decay increment values ​​in the first segment of the sequence to obtain the average temperature decay of the first segment of the sequence. For the latter part of the sequence, the temperature decay increment values ​​between all adjacent sampling times contained in the latter part of the sequence are summed to obtain the total decay of the latter part. The total decay of the latter part of the sequence is divided by the number of temperature decay increment values ​​in the latter part of the sequence to obtain the average temperature decay of the latter part of the sequence.

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