Dry powder extinguisher automatic quality inspection instrument based on multi-parameter joint inspection

Abstract

The invention relates to the technical field of fire extinguisher quality inspection, in particular to a dry powder fire extinguisher automatic quality inspection instrument based on multi-parameter joint inspection, which comprises a multi-source sensing unit for synchronously acquiring pressure, weight change, sealing attenuation and powder dispersion characteristic data; the central control unit is used for calculating a defect coupling coefficient psi through an improved coupling analysis model; the dynamic judgment unit is used for generating quality grade and defect positioning information; the data tracing unit is used for associating the detection data with the product identifier; the system further comprises an environment compensation unit and a self-calibration module. Through multi-parameter fusion analysis, dynamic adaptive correction and intelligent defect diagnosis, comprehensive and accurate automatic quality inspection is realized, the detection efficiency and accuracy are improved, quality tracing and production process optimization are supported, and the method is suitable for a large-scale quality inspection scene of the dry powder extinguisher.

Description

Technical Field

[0001] This invention relates to the field of fire extinguisher quality inspection technology, specifically to an automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection. Background Technology

[0002] Dry powder fire extinguishers are key equipment in the field of fire safety, and their quality directly affects the effectiveness of fire fighting and the safety of people and property. Therefore, rigorous and comprehensive quality testing is essential. However, current quality inspection technology for dry powder fire extinguishers still has many limitations and cannot meet the needs of modern production and safety management. The specific background is as follows: Current quality inspection methods often rely on a single parameter as the core criterion for judgment, such as only testing the static pressure value or weight of a fire extinguisher, ignoring the correlation and comprehensive impact between parameters. For example, some fire extinguishers may meet the static pressure standard, but have powder clumping, which cannot be effectively dispersed during actual spraying, resulting in a decrease in fire extinguishing ability; or they may meet the weight standard but have insufficient sealing, leading to pressure leakage after long-term storage and failure to function properly in critical moments. This "single parameter qualification equals overall qualification" model cannot fully reflect the actual performance of fire extinguishers and poses serious safety hazards. In traditional quality inspection processes, steps such as pressure reading, weight measurement, and sealing checks rely heavily on manual operation. This is not only labor-intensive and inefficient, but also prone to inconsistent test results due to human factors (such as reading errors and non-standard operation). For example, it is difficult to quantify the degree of agglomeration when manually visually inspecting powder; when manually recording data, errors and omissions are easy to occur, making quality traceability difficult; at the same time, the judgment standards of different inspectors are subjective and different, making it difficult to form a unified quality assessment system, which affects the impartiality and authority of quality inspection. Therefore, to address the above issues, an automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection is proposed. Summary of the Invention

[0003] The purpose of this invention is to provide an automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: An automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection includes: Multi-source sensing unit, synchronously collects pressure data, weight change, sealing attenuation factor and powder dispersion characteristics data of the fire extinguisher under test; The central control unit executes a multi-parameter fusion analysis algorithm and processes the collected data by calculating the defect coupling coefficient. The defect coupling coefficient is equal to the adaptive weighting parameter α multiplied by the pressure change rate to the power of β, plus the adaptive weighting parameter γ multiplied by the weight loss multiplied by 1, plus the correction factor κ multiplied by the natural logarithm of the sealing attenuation factor. The dynamic judgment unit generates quality level and defect location information based on the defect coupling coefficient. The data traceability unit links testing data with product identification and supports reverse tracing.

[0005] As a preferred method, powder dispersion characteristic data are obtained through the following means: After applying standard vibration excitation, multispectral imaging is used to capture powder sedimentation image sequences, and the agglomeration density index and suspension dispersion are calculated. The adaptive weight parameter α is equal to the negative agglomeration density index multiplied by the natural constant e multiplied by the agglomeration influence coefficient raised to the power of the basic weight parameter α0, and the adaptive weight parameter β is equal to the basic weight parameter β0 plus the dispersion gain factor multiplied by the suspension dispersion. Secondary vibration detection is activated when the agglomeration density index is greater than 2.5.

[0006] As a preferred solution, the dynamic decision unit performs the following: Construct a defect coupling matrix. When pressure anomalies and powder settling anomalies occur concurrently, trigger the insufficient purity judgment branch of the driving gas and output the fault location coordinates. The defect location coordinates are taken as the location of the maximum value of the product of the partial derivative of the defect coupling coefficient with respect to the grid's horizontal axis and the partial derivative of the defect coupling coefficient with respect to the grid's vertical axis in the fire extinguisher surface grid.

[0007] As a preferred solution, the sealing data detection adopts a dual-mode mechanism: Static pressure attenuation rate measurement and dynamic microflow sensing are performed in parallel. When the difference between the two exceeds 15 percent of the sealing attenuation factor, infrared thermal imaging is activated to assist in diagnosis and update the sealing attenuation factor.

[0008] As a preferred option, the central control unit implements: Deep learning-based anomaly detection using an improved loss function: The training loss value is equal to one-half of the number of training samples multiplied by the weighted sum of all samples. The loss of each sample includes the dynamic balance weight ω1 multiplied by the square of the Euclidean distance between the predicted defect coupling coefficient and the real defect coupling coefficient, plus the dynamic balance weight ω2 multiplied by the cluster density index minus the positive part of the threshold 2.0; where the sum of the dynamic balance weights ω1 and ω2 is 1.

[0009] As a preferred option, an environmental compensation unit is also included to compensate for the pressure readings by temperature and humidity. The compensated pressure value is equal to the original pressure value multiplied by the ratio of the standard temperature to the current temperature, then multiplied by 1, plus the first humidity coefficient of 0.0023 multiplied by the relative humidity, divided by 1, plus the second humidity coefficient of 0.0018 multiplied by the relative humidity.

[0010] As a preferred solution, the data traceability unit performs the following: When the similarity of the defect patterns of a batch of products is equal to one-half of the number of products in the batch multiplied by the sum of the squared Euclidean distances between the defect coupling coefficients of each sample and the average defect coupling coefficient, and is greater than the similarity threshold of 0.35, the production process parameters are automatically associated and traceability suggestions are generated.

[0011] As a preferred solution, an integrated self-calibration module is used to periodically generate a sensor health index through comparison with a standard. The health index is equal to 1 minus the absolute deviation between the sensor measurement and the standard reference value, divided by the standard reference value, multiplied by the negative calibration period days of the natural constant e, and divided by the 30th power. An alert is triggered when the health index is less than 0.85.

[0012] As can be seen from the technical solution provided by the present invention above, the automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection provided by the present invention has the following beneficial effects: Multi-parameter collaborative detection: Simultaneously collect key parameters such as dynamic pressure changes, weight loss, sealing performance degradation, and powder dispersion characteristics to construct a multi-dimensional quality assessment system covering fire extinguisher driving capability, filling volume, storage reliability, and fire extinguishing effectiveness. This avoids overlooking comprehensive performance defects due to the compliance of a single parameter (such as normal pressure but insufficient actual fire extinguishing capability caused by powder agglomeration). Parameter correlation quantitative analysis: By using an improved coupling analysis model, independent parameters are transformed into comprehensive defect indicators, accurately capturing the intrinsic correlation between parameters (such as the synergistic effect of pressure change rate and sealing performance decay), upgrading quality assessment from qualitative description to quantitative calculation, and significantly reducing the risk of misjudgment; Dynamic adaptive correction: The model weights are optimized in real time based on powder characteristics. External factors such as temperature and humidity are eliminated through environmental compensation. The performance drift of the equipment during long-term operation is corrected through a self-calibration mechanism to ensure that the testing standards are consistent for different environments and different batches of products, thereby improving the stability and reliability of the results. The entire process is unmanned: from data collection and analysis to quality level determination, the entire testing process does not require human intervention, which greatly reduces the testing time of a single product, significantly improves batch testing capabilities, and meets the quality inspection needs of large-scale production. Intelligent Defect Diagnosis and Localization: By constructing a defect coupling matrix and deep learning algorithms, it automatically identifies single or compound defects (such as insufficient purity of driving gas, powder agglomeration, sealing failure, etc.) and accurately locates the fault location through spatial gradient analysis. This solves the problem that traditional manual methods cannot quickly determine the type and specific location of defects, thus improving the efficiency of fault handling. This invention, through technological innovations such as multi-parameter collaborative detection, intelligent analysis, and full-process traceability, not only meets the basic requirements for automated quality inspection of dry powder fire extinguishers, but also constructs a closed-loop quality control system of "detection-analysis-traceability-optimization," providing comprehensive protection for product quality and safety, and promoting the transformation of the dry powder fire extinguisher quality inspection field from the traditional manual mode to intelligent, precise, and efficient. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the overall structure of an automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection, according to the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0015] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific embodiments.

[0016] like Figure 1 As shown, this embodiment of the invention provides an automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection, comprising: Multi-source sensing unit, synchronously collects pressure data, weight change, sealing attenuation factor and powder dispersion characteristics data of the fire extinguisher under test; The central control unit executes a multi-parameter fusion analysis algorithm and processes the collected data by calculating the defect coupling coefficient. The defect coupling coefficient is equal to the adaptive weighting parameter α multiplied by the pressure change rate to the power of β, plus the adaptive weighting parameter γ multiplied by the weight loss multiplied by 1, plus the correction factor κ multiplied by the natural logarithm of the sealing attenuation factor. The dynamic judgment unit generates quality level and defect location information based on the defect coupling coefficient. The data traceability unit links testing data with product identification and supports reverse tracing.

[0017] In this embodiment, the powder dispersion characteristic data is obtained through the following method: After applying standard vibration excitation, multispectral imaging is used to capture powder sedimentation image sequences, and the agglomeration density index and suspension dispersion are calculated. The adaptive weight parameter α is equal to the negative agglomeration density index multiplied by the natural constant e multiplied by the agglomeration influence coefficient raised to the power of the basic weight parameter α0, and the adaptive weight parameter β is equal to the basic weight parameter β0 plus the dispersion gain factor multiplied by the suspension dispersion. Secondary vibration detection is activated when the agglomeration density index is greater than 2.5; Furthermore, the multi-source sensing unit serves as the "data source" and "sensory nerve ending" of the automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection. By deploying multiple high-precision sensors, it simultaneously captures various physical characteristic parameters of the fire extinguisher, providing comprehensive and accurate raw data support for subsequent multi-parameter fusion analysis. The following will elaborate on this unit from the overall perspective to the details. I. Overall Function Overview: The multi-source sensing unit is mainly responsible for synchronously acquiring and pre-processing the core physical parameters of the fire extinguisher, including key indicators such as dynamic pressure changes, weight loss, sealing performance attenuation, and powder dispersion characteristics. By integrating various types of sensor arrays, signal conditioning modules, and synchronous control circuits, it achieves high-precision and high-timeliness measurement of various parameters, and performs pre-processing such as filtering and calibration on the raw data to eliminate environmental interference and equipment errors. Finally, it forms a standardized dataset that is transmitted to the central control unit, providing a reliable data foundation for quality assessment. II. Submodule Composition and Functions: (a) Pressure dynamic monitoring subunit: Sensor configuration: It adopts a high-precision diffused silicon pressure transmitter with a measurement range covering 0-6MPa (meeting the working pressure range of dry powder fire extinguishers). The sampling frequency is set to 1kHz to ensure the capture of instantaneous pressure changes. The sensor is connected to the fire extinguisher pressure gauge through a dedicated interface and adopts a sealed installation structure to avoid gas leakage during the detection process. Data acquisition and preprocessing: Real-time acquisition of instantaneous pressure values, followed by noise reduction using a second-order Butterworth low-pass filter. The filtering method is as follows: the filtered pressure value at a certain moment is equal to the sum of the original pressure sample values ​​at that moment, the previous moment, and the two moments before that, multiplied by 1 and divided by (1 plus the cosine of twice the cutoff angular frequency, plus the square of the cosine of the cutoff angular frequency). Pressure change rate calculation: Based on the preprocessed pressure data, the first partial derivative of pressure with respect to time (pressure change rate) is calculated using the central difference method. The calculation method is as follows: the pressure change rate is equal to the filtered pressure value at the next time step minus the filtered pressure value at the previous time step, and then divided by twice the sampling period. (ii) Weight change sensing subunit: Weighing system configuration: It adopts a four-cantilever beam load cell group with a range of 0-50kg and an accuracy class of 0.1. The load cells are symmetrically distributed and installed under the fire extinguisher placement platform to achieve uniform weight bearing and measurement; it is equipped with a temperature compensation circuit to eliminate the influence of ambient temperature changes on weight measurement. Weight data acquisition: The weight value of the fire extinguisher is acquired in real time at a sampling frequency of 100Hz. The output signals of the four sensors are processed by a data fusion algorithm to obtain the final weight measurement value. The calculation method is as follows: the fused weight value at a certain moment is equal to the original output value of the four weighing sensors at that moment multiplied by their respective calibration coefficients, the sum of the results, and then divided by 4. Weight change calculation: Based on the weight at the initial detection time, calculate the weight loss at any time. The calculation method is: the weight loss at a certain time is equal to the weight value at the initial time minus the weight value at that time. (III) Sealing Performance Multimode Testing Subunit: Dual-mode sensing configuration: integrates a static pressure holding detection module and a dynamic micro-flow sensing module; the static module uses a high-precision pressure sensor to monitor the pressure decay during the pressure holding process; the dynamic module uses a thermal gas mass flow meter to measure minute leakage flow (resolution up to 0.1 mL / min). Calculation of sealing attenuation factor: Static detection: The initial sealing attenuation factor is calculated by the pressure attenuation rate. The calculation method is as follows: the sealing attenuation factor of static detection is equal to the pressure value at the beginning of the pressure holding minus the pressure value at the end of the pressure holding, divided by the product of the pressure value at the beginning of the pressure holding and the pressure holding time, and then multiplied by 100% / min. Dynamic detection: The sealing attenuation factor is calculated by converting micro-flow rate. The calculation method is as follows: The sealing attenuation factor of dynamic detection is equal to the micro-flow rate measurement value multiplied by the gas density, divided by the product of the internal volume of the fire extinguisher and the pressure value at the beginning of the pressure holding, and then multiplied by 100% / min. Dual-mode data fusion: When the absolute value of the difference between the sealing attenuation factor of static detection and dynamic detection is less than or equal to 0.15 times the average of the two, the average is taken as the final sealing attenuation factor; otherwise, infrared thermal imaging-assisted diagnosis is activated to locate the leak point and correct the sealing attenuation factor. (iv) Powder Characteristic Analysis Subunit: Multispectral imaging system: It consists of four wavelength lasers of 405nm, 532nm, 660nm and 850nm, a high-resolution CCD camera (resolution 2048×1536) and a dark box, used to capture powder sedimentation image sequences, with a sampling frame rate of 25fps; Vibration excitation control: The built-in electromagnetic vibration table can apply standard vibration excitation with a frequency of 50Hz and an amplitude of 0.5mm for 10s to ensure that the powder is in a dynamic dispersion state. The calculation of the block density index (Dc) is as follows: the image sequence is binarized, and the proportion of block regions is statistically analyzed. The calculation method is: the block density index is equal to the sum of the areas of all block regions divided by the area of ​​the total detection area of ​​the image. Suspension dispersion (δ) calculation: Based on the standard deviation of gray values, the uniformity of powder dispersion is measured. The calculation method is: suspension dispersion equals the standard deviation of image gray values ​​divided by the mean of image gray values; Secondary detection trigger: When the calculated agglomeration density index is greater than 2.5, a secondary vibration (frequency 60Hz, amplitude 0.8mm, duration 15s) is automatically triggered, and the image is re-acquired to calculate the powder characteristic parameters. (v) Data synchronization and preprocessing subunit: Time synchronization control: A GPS timing module (time synchronization accuracy ≤1ms) is used to provide a unified timestamp for each sensor unit, ensuring the time consistency of pressure, weight, sealing and powder characteristic data; Data validation and cleaning: Outlier detection is performed by marking a data point as an outlier when the absolute value of the difference between a data point and the mean of the data sequence is greater than 3 times the standard deviation of the data sequence; the marked outliers are repaired using linear interpolation to ensure data continuity. Format conversion and encapsulation: The preprocessed data is encapsulated according to a unified protocol (JSON format), including parameter name, value, unit, timestamp and sensor status code, and transmitted to the central control unit via high-speed Ethernet; III. Key Technical Principles: (I) Principle of Multi-physics Parameter Collaborative Sensing: Based on the correlation of four-dimensional parameters of "pressure-weight-sealing-powder characteristics", a complete physical profile of the quality status of fire extinguishers is constructed through the collaborative work of different types of sensors. The pressure parameter reflects the driving capability, the weight change reflects the filling amount, the sealing reflects the storage reliability, and the powder characteristics reflect the fire extinguishing effectiveness. The four parameters corroborate each other, achieving a breakthrough from single parameter detection to multi-dimensional quality assessment. (II) High-precision sensing and signal conditioning technology: High-precision sensor hardware (such as 0.1-grade load cells and 0.2-grade pressure transmitters) combined with advanced signal conditioning circuits (including filtering, amplification, and temperature compensation) are used to convert weak physical signals into stable electrical signals, ensuring that the measurement error of the original data is ≤0.5%. Through multi-point calibration technology, an accurate mapping relationship between the sensor output and the actual physical quantity is established, eliminating the error of the quality inspection instrument. (III) Multi-source data time synchronization technology: Based on GPS timing and a local high-precision crystal oscillator (frequency stability 1ppm), the time base of each sensor unit is unified; through a trigger synchronization mechanism, the sampling time deviation of different parameters is ensured to be ≤1ms, providing a time-aligned data basis for subsequent multi-parameter fusion analysis and avoiding analysis errors caused by time asynchrony. (iv) Dynamic characteristic capture and analysis technology: For dynamic parameters such as pressure change rate and powder settling process, high-frequency sampling (≥100Hz) combined with dynamic signal analysis algorithm is used to effectively capture the transient change characteristics of the parameters; short-time Fourier transform (STFT) is used to perform time-frequency domain analysis on dynamic signals to extract feature quantities reflecting the internal state of fire extinguisher, providing richer information for defect diagnosis; IV. Module Workflow: (a) Initialization phase: The multi-source sensing unit is powered on and starts up, completing the hardware self-test of each sensor (pressure transmitter, load cell, flow meter, CCD camera, etc.), and the working status of each module is fed back through status indicator lights. Load sensor calibration parameters (such as calibration coefficients for weighing sensors and temperature compensation coefficients for pressure sensors), and initialize configuration parameters such as data acquisition frequency and synchronization timestamp; Initiate the preheating program to ensure the sensor operates at a constant temperature (±0.5℃) to reduce the impact of temperature drift; (II) Calibration Phase: Pressure calibration: Apply pressure at four calibration points (0MPa, 2MPa, 4MPa, and 6MPa) using a standard pressure source, record the sensor output values, and establish a linear calibration equation; Weight calibration: The weighing system is calibrated using standard weights (1kg, 5kg, 10kg, 20kg), and the calibration coefficients of each sensor are calculated. Sealing calibration: The dual-mode detection module is calibrated using standard leakage rate devices (0.1% / min, 0.5% / min, 1.0% / min) to correct measurement deviations; Environmental parameter acquisition: Simultaneously collect current temperature and humidity data to provide basic data for subsequent environmental compensation; (III) Data Collection Phase: The fire extinguisher to be tested is placed on the weighing platform, and the quality inspection instrument automatically identifies the product model and calls up the corresponding test parameters (such as standard weight and rated pressure). The pressure sensor and load cell start continuous sampling to record pressure and weight values ​​in real time. The sealing test module is activated by first performing static pressure holding (5 minutes) and recording the pressure decay simultaneously; then performing dynamic micro-flow detection (2 minutes). Powder characteristic analysis module activated: Apply standard vibration excitation to the fire extinguisher and simultaneously activate the multispectral imaging system to continuously acquire a 10-second sequence of powder sedimentation images; (iv) Data preprocessing stage: The raw pressure and weight data are filtered and outlier repaired to calculate the pressure change rate and weight loss. The sealing test data are fused using a dual-mode method to calculate the final sealing attenuation factor. Analyze the powder settling images, calculate the agglomeration density index and suspension dispersion. If the agglomeration density index is greater than 2.5, initiate secondary vibration detection and recalculate. Add timestamps to all parameters and perform time synchronization verification to ensure data time alignment; (v) Data output stage: All preprocessed parameters (pressure value, pressure change rate, weight loss, sealing attenuation factor, agglomeration density index, suspension dispersion, and environmental parameters temperature and humidity) are encapsulated into a standard data frame. The data frames are transmitted to the central control unit via the Ethernet interface, and the system waits for a data confirmation signal. If reception fails, activate the local caching mechanism to store the data and attempt retransmission to ensure no data loss. (vi) Conclusion: After the detection process is completed, the sensor unit enters a low-power sleep mode, waiting for the next detection command; Automatically saves the raw data and preprocessing results of this test to local storage (capacity ≥ 100,000 records), supporting subsequent data traceability and sensor performance analysis; V. Application Value of the Module: (a) Providing high-quality raw data for quality inspection instruments: Through high-precision sensing, multi-mode detection, and rigorous preprocessing, we ensure that the collected pressure, weight, sealing, and powder characteristics data are accurate, reliable, and synchronized, providing a solid data foundation for the multi-parameter fusion analysis of the central control unit and guaranteeing the accuracy of quality inspection results from the source. (II) Achieving comprehensive quality inspection across multiple dimensions: Breaking through the limitations of traditional fire extinguisher quality inspection that relies on a single parameter (such as only pressure), this system simultaneously collects four core parameters to comprehensively reflect the fire extinguisher's driving capability, filler capacity, storage reliability, and fire extinguishing effectiveness. This upgrades the system from "single-point inspection" to "full-state assessment," enhancing the comprehensiveness of quality inspection. (III) Ensuring the stability and consistency of test results: By employing technologies such as temperature compensation, regular calibration, and data synchronization, the effects of environmental interference, equipment errors, and time deviations on measurement results are effectively eliminated, ensuring the comparability of test data for the same product at different times and under different environments, maintaining consistent testing standards for different products, and enhancing the impartiality of quality inspection. (iv) To provide data support for subsequent analysis and tracing: Recording raw data and preprocessing results throughout the testing process to local storage not only provides data for real-time analysis by the central control unit, but also provides historical data query capabilities for batch analysis by the data traceability unit. This supports inferring production issues from quality inspection results and helps in continuous improvement of product quality.

[0018] In this embodiment, the dynamic determination unit performs the following: Construct a defect coupling matrix. When pressure anomalies and powder settling anomalies occur concurrently, trigger the insufficient purity judgment branch of the driving gas and output the fault location coordinates. The defect location coordinates are taken as the location of the maximum value of the product of the partial derivative of the defect coupling coefficient with respect to the grid's horizontal axis and the partial derivative of the defect coupling coefficient with respect to the grid's vertical axis in the fire extinguisher surface grid. Furthermore, the dynamic judgment unit is the "decision center" of the automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection. It uses the defect coupling coefficient output by the central control unit. Based on the core criteria, and combined with the original parameters and derived features provided by the multi-source sensing unit, it can accurately classify the quality level of fire extinguishers, intelligently identify the type of defects, and accurately locate the fault, providing final decision support for the output of quality inspection results. The following is a detailed explanation from five aspects: overall function, sub-unit composition, key technical principles, workflow, and application value. I. Overall Function Overview: The core function of the dynamic judgment unit is to transform the abstract data obtained from multi-parameter fusion analysis into concrete quality assessment results, specifically including three dimensions: one is based on the defect coupling coefficient. The system employs three main methods: first, quality grading, which categorizes fire extinguisher quality into four levels: "Excellent," "Good," "Qualified," and "Unqualified" based on preset threshold ranges; second, defect type identification based on multi-parameter correlation models, which accurately identifies single or compound faults such as "abnormal pressure," "powder agglomeration," and "sealing failure" by constructing a defect coupling matrix; and third, defect localization based on gradient analysis, which calculates... The partial derivative product with respect to spatial coordinates pinpoints the specific location of the fault on the surface of the fire extinguisher; at the same time, the unit has adaptive learning capabilities, which can dynamically optimize the judgment threshold and defect identification model based on historical detection data, and continuously improve the judgment accuracy. II. Sub-unit composition and function: (a) Quality grade classification sub-unit: Threshold range setting: Based on industry standards and historical qualified data, a defect coupling coefficient is preset. The fourth threshold: when When it is determined to be "superior grade" (all parameters are in optimal condition); when When it is determined to be "good" (with slight parameter fluctuations but not affecting use); when When the parameters are close to the critical value, it is judged as a "qualified product" (maintenance is required when the parameters are close to the critical value); when The product was deemed "unqualified" (posing significant safety hazards). Threshold dynamic calibration: Thresholds are updated quarterly based on no fewer than 10,000 historical test data points, achieved through the following method: calculating the thresholds for samples of different quality levels. The value distribution is used, and the intersection of the probability density curves of adjacent level distributions is taken as the new threshold to ensure that the recall rate z = 99% and the precision rate of level classification are both high. Quality level output rules: In addition to directly outputting the quality level, the criteria for level determination are generated simultaneously, including "key parameters that lead to the level" (such as "due to the sealing attenuation factor S exceeding the standard"). "Raise to 0.8") and "Parameter optimization suggestions" (such as "It is recommended to supplement the driving gas to reduce..."). The value provides clear guidance for subsequent processing; (ii) Defect type identification subunit: Defect Coupling Matrix Construction: The matrix's row dimension represents the four basic defects: "pressure anomaly, weight loss anomaly, sealing performance degradation anomaly, and powder characteristic anomaly," while the column dimension represents the concurrent combination patterns of each defect, encompassing a total of 15 possible defect types (2). 4 -1, excluding defect-free modes); for example, in the matrix, "pressure abnormality ∩ powder sedimentation abnormality" corresponds to the defect "insufficient driving gas purity", and "sealing performance degradation abnormality ∩ weight loss abnormality" corresponds to the defect "valve leakage". Multi-parameter correlation analysis: When detected When the threshold is exceeded, the raw data from the multi-source sensing unit is retrieved, and the defect type is identified through the following logic chain: First, the pressure change rate is checked. Check if it exceeds ±0.05 MPa,s (normal range); if it does, mark it as "pressure abnormal"; secondly, check the agglomeration density index. If the value is greater than 1.5 (critical value), mark it as "abnormal powder characteristics"; finally, combine this with the sealing attenuation factor. With weight loss The changing trend can be used to determine whether there is a "sealing failure" or "insufficient filling". Prioritization of composite defects: When multiple defects exist at the same time, they are sorted according to the degree of "safety hazard". High-risk defects such as "sealing failure" (which may lead to gas leakage) and "pressure abnormality" (which affects the spraying effect) are marked first, followed by secondary defects such as "powder agglomeration", to ensure that key issues are addressed first during the handling process. (III) Defect Location Subunit: Spatial grid division: The surface of the fire extinguisher is divided into a uniform grid of 100×100 (each grid has a side length of 5mm), and each grid is assigned a unique coordinate. Covering key components such as the cylinder, valves, and nozzles; Gradient calculation and localization: using the formula Loc Calculate the location of the defect, where and These are the defect coupling coefficients. The larger the product of the first-order partial derivatives of the x and y coordinates of the i-th grid, the greater the impact of parameter changes at that grid point. The more significant the impact, the more likely it is to be the location of the defect; for example, when the partial derivative product of the grid at the valve is the largest, the defect is determined to be located on the valve sealing surface; Positioning accuracy verification: By comparing the results of manual disassembly with the positioning coordinates, the positioning algorithm is calibrated regularly to ensure that the positioning error is ≤10mm (i.e., no more than 2 grid spacings). For precision components such as nozzles and pressure gauges, the positioning error is controlled within 5mm by densifying the grid (2mm×2mm); (iv) Adaptive learning sub-unit: Sample library construction: Historical inspection data are classified and stored according to "quality grade - defect type - location result - manual review conclusion" to build a labeled database containing 100,000+ samples, of which "non-conforming products" samples account for no less than 30% to ensure the model's sufficiency in learning defect patterns; Threshold optimization algorithm: A genetic algorithm is used to optimize the quality grade threshold. The fitness function is "consistency between manual review and machine judgment". The threshold parameters are automatically iterated after each batch of testing, so that the accuracy of grade judgment gradually increases with the increase of sample size (target ≥99.5%). Defect Pattern Update: When new defect types emerge (such as unknown sedimentation anomalies caused by novel powders), they are incorporated into the identification system in the following way: extracting the parametric features of the new defects (such as... and (the abnormal ratio), add corresponding rows and columns to the defect coupling matrix, generate new judgment rules and push them to the quality inspection instrument knowledge base; III. Key Technology Principles: (I) Multi-parameter coupled decision-making principle: Based on "defect coupling coefficient" The core logic is "a non-linear comprehensive reflection of pressure, weight, sealing performance, and powder properties," which is achieved through abstract... The value is associated with specific parameter anomaly patterns, enabling a leap from "data fusion" to "decision output"; for example, when The rise is due to When the dominant gas level drops sharply, combined with the fact that the powder dispersion characteristics are normal, it can be determined that there is "insufficient driving gas"; while if... Elevation accompanied by If the pressure increases, it is determined that "powder agglomeration leads to obstructed pressure release"; (II) Spatial gradient positioning principle: Using the defect coupling coefficient Location is achieved through spatial distribution differences: When a fire extinguisher has a defect in a certain part, changes in the parameters of that part (such as a local pressure drop caused by valve leakage) will cause... The partial derivative with respect to the coordinates of the location increases significantly. By calculating the maximum point of the product of the partial derivatives, the location of the defect can be located. This principle is similar to "thermal imagers locate heat sources by temperature gradients", except that the "gradient" here is the gradient of an abstract physical quantity after multi-parameter coupling. (III) Principles of Adaptive Learning Mechanism: Based on the "incremental learning" technique in supervised machine learning, by continuously absorbing new labeled samples, the judgment model can maintain its ability to identify historical defects while also possessing the ability to identify new defects; for example, when a new type of valve leaks... When a change pattern first appears, after manual annotation, the model will adjust the feature weights corresponding to that pattern using the gradient descent algorithm to ensure that it can be automatically identified when encountering the same type of defect again. IV. Module Workflow: (a) Data reception and preprocessing: Receive the defect coupling coefficient Ψ and associated parameter packet (including original and derived data such as P, ΔW, S, Dc, δ, etc.) transmitted by the central control unit, and ensure data integrity through CRC check; The parameter packets are timestamped and checked for outliers. Invalid data caused by transmission errors (such as obvious errors like S being negative) are removed. If the percentage of valid data is less than 90%, the central control unit is requested to retransmit the data. (II) Preliminary assessment of quality grade: Compare the Ψ value with the preset threshold range to preliminarily determine the quality level, and record the basis for the judgment (e.g., "Ψ=0.72 is due to S=0.3 exceeding the standard"). If the initial judgment is "non-conforming product", the defect type identification and location process is automatically triggered; if it is another level, only the level result is output and the data is archived. (III) Defect Type Identification: Call the defect coupling matrix and compare the current parameter anomaly mode (such as "pressure anomaly + powder anomaly") with the standard mode in the matrix. The mode with the highest matching degree is the preliminary defect type. For composite defects (such as simultaneous sealing attenuation and weight loss), the primary and secondary defects are determined by calculating the contribution of each parameter to Ψ (such as the proportion of α(∂P / ∂t)β to Ψ). (iv) Defect location calculation: Load the 3D mesh model of the fire extinguisher and map the spatial distribution data collected by the multi-source sensing unit (such as pressure sensor array data at different locations) to the mesh coordinates; calculate For each grid coordinate partial derivatives and , through formula Determine the coordinates of the defect location; Verify the rationality of the positioning results: If the positioning point is located in a non-physical area of ​​the fire extinguisher (such as a virtual mesh outside the model), recalculate and adjust the mesh mapping relationship; (v) Results Integration and Output: Integrate quality grade, defect type, location coordinates, and judgment confidence level (e.g., "Insufficient purity of driving gas, located at valve interface (x=120mm, y=80mm), confidence level 98.7%) to generate a standardized quality inspection report; The report is simultaneously transmitted to the data traceability unit (associated with product identification) and the quality inspection instrument display terminal (for operators to view), and also stored in the local database for backup; (vi) Adaptive Update: Every day at midnight, the system automatically retrieves the detection data from the previous 24 hours and the results of manual review, and calculates the accuracy rate of grade determination and the correctness rate of defect identification. If the accuracy rate is below 98%, the threshold optimization algorithm and defect mode update process will be initiated. After adjusting the relevant parameters, an update log will be generated for technical personnel to review and confirm.

[0019] In this embodiment, the central control unit implements: Deep learning-based anomaly detection using an improved loss function: The training loss value is equal to one-half of the number of training samples multiplied by the weighted sum of all samples. The loss of each sample includes the dynamic balance weight ω1 multiplied by the square of the Euclidean distance between the predicted defect coupling coefficient and the real defect coupling coefficient, plus the dynamic balance weight ω2 multiplied by the cluster density index minus the positive part of the threshold 2.0; where the sum of the dynamic balance weights ω1 and ω2 is 1. The sealing data detection adopts a dual-mode mechanism: Static pressure attenuation rate measurement and dynamic microflow sensing are performed in parallel. When the difference between the two exceeds 15 percent of the sealing attenuation factor, infrared thermal imaging is activated to assist in diagnosis and update the sealing attenuation factor. Furthermore, the central control unit is the "data processing core" and "algorithm hub" of the automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection. It receives raw parameters and pre-processed data transmitted from multi-source sensing units, performs multi-parameter fusion calculations through an improved coupling analysis model, and generates a defect coupling coefficient. It combines deep learning algorithms to achieve anomaly identification and parameter optimization, providing accurate analysis results for the dynamic judgment unit; the following is a detailed explanation of its overall functions and sub-module composition; I. Overall Function Overview: The core function of the central control unit is to perform deep fusion and intelligent analysis of multi-source sensor data, specifically including: receiving multi-dimensional parameters such as pressure, weight, sealing performance, and powder characteristics, and calculating the defect coupling coefficient through an improved coupling analysis model. ;Adaptive weight parameters in the model based on deep learning algorithm optimization With environmental correction factors ); Real-time identification and labeling of abnormal data; Processed data The values ​​and associated parameters are packaged and transmitted to the dynamic judgment unit, and synchronously interact with the data traceability unit to achieve real-time correlation of the detection data; this unit has high computing power (supporting processing per second). Parallel computation of group parameters and adaptive learning capabilities allow for dynamic adjustment of algorithm parameters based on environmental changes and historical data, ensuring the accuracy and robustness of multi-parameter fusion analysis. II. Submodule Composition and Functions: (a) Multi-parameter fusion algorithm execution subunit: Data reception and parsing: Standardized data frames packaged from multi-source sensing units are received via a high-speed data bus (transmission rate ≥ 1Gbps), and the instantaneous pressure value is parsed out. ), weight loss ( ), sealing attenuation factor ( ), agglomeration density index ( ), suspension dispersion ( Environmental parameters (temperature T, humidity H), etc., provide raw inputs for fusion computing; Defect Coupling Coefficient Calculation: Perform the core calculation according to the formula in claim 1, the specific process of which is as follows: Calculate the first partial derivative of pressure with respect to time (rate of change of pressure) and elevate it to... The power of this, multiplied by the adaptive weights Obtain the pressure contribution term; calculate the weight loss. With the natural logarithm (1 plus environmental correction factor) With sealing attenuation factor The product of (the product of) and then multiplied by the adaptive weights. We obtain the weight-sealing coupling term; The sum of the two terms is the defect coupling coefficient. (Dimensionless) reflects the defect risk under the combined effect of multiple parameters; Environmental Correction Factors Dynamic adjustment: Based on real-time collected temperature T and humidity H, corrections are made using the following methods. When the ambient humidity hour Automatically increase by 15% (weighting for enhanced sealing performance degradation); when temperature Appendix Reduce by 10% (to offset the interference of high temperature on sealing tests), ensuring The calculation is unaffected by environmental fluctuations; (II) Adaptive Parameter Optimization Subunit: Weight parameters Dynamic adjustment: combining powder characteristic data ( , The weight parameters are adjusted in real time, specifically as follows: Agglomeration density index The larger the powder (the more severe the agglomeration), the more likely it is to pass through reduce Value (reduce the weight of pressure parameters to avoid misjudgment of pressure due to clumping); Suspension dispersion The higher the concentration (the better the powder dispersibility), the better the dispersion. improve Value (enhances the sensitivity of pressure change rate, more accurately reflecting injection capability); in , As the initial baseline value, , These are the correction coefficients obtained through training with historical data (taken as 0.3 and 0.2 respectively); Parameter convergence verification: every 100 calculations After setting the value, verify the parameter stability in the following way: if the value is calculated 5 times consecutively... Fluctuation range reliability; (III) Deep Learning Anomaly Detection Subunit: Training sample construction: Based on historical inspection data (including 100,000+ qualified samples and 30,000+ defective samples), a training set of "parameter features - defect labels" is constructed. The input features include pressure change rate, weight loss, sealing attenuation factor, powder characteristic parameters, etc., and the output labels are defect types such as "normal", "pressure abnormal", and "sealing failure". Improved Loss Function Optimization: A loss function is used for model training. The calculation logic can be described as follows: the training loss value equals the average of two parts (the sum of samples from 1 to N divided by N). The first part is the weight coefficient of the error term. The second part, multiplied by the square of the difference between the actual and predicted values ​​of the defect coupling coefficient, is the threshold term weighting coefficient. The linearly rectified result is multiplied by the difference between the agglomeration density index and the threshold (2.0) (0 is taken when the difference is negative and the actual difference is taken when it is positive); this function enhances the model's ability to identify powder characteristic anomalies by strengthening the penalty for agglomeration defects. Real-time anomaly detection: This involves real-time calculation of... The values ​​and associated parameters are input into the trained deep learning model (using a 3-layer fully connected neural network). When the abnormal probability output by the model is >95%, it is marked as "high-risk abnormality" and the secondary review process of the dynamic judgment unit is triggered. Two-way data transmission: Real-time connection is established with multi-source sensing units, dynamic judgment units, and data traceability units via industrial Ethernet (communication latency ≤10ms); timestamps and checksums are included when receiving sensor data (to ensure data integrity); analysis results are sent with... Information such as values, parameter weights, and anomaly markers; Local caching mechanism: When communication is interrupted, the calculation results are automatically cached to local solid-state storage (capacity ≥ 512GB). The following strategies are used to manage the cache: storage is sorted by timestamp, and abnormal sample data is retained first; when the cache occupancy rate is > 80%, non-abnormal data older than 72 hours is automatically deleted to ensure storage availability in emergency situations. Data format standardization: All processed data is packaged into a JSON protocol packet in the format of "parameter name-value-unit-confidence level-timestamp", where the confidence level is calculated. The standard deviation of the values ​​(99% confidence level when ≤0.05, decreasing to 80% when >0.1) quantifies the reliability of the analysis results; III. Key Technology Principles: (I) Principle of Multi-parameter Nonlinear Coupling: Based on the physical correlation between "pressure-weight-sealing-powder properties", the mapping relationship between parameters is constructed through nonlinear functions (such as exponential, logarithmic, and power functions), breaking through the limitations of traditional linear weighting. For example, the sealing attenuation factor S is calculated by using the logarithmic term ln(1+κ·S), which amplifies the impact of slight leakage (ensuring that small defects can be identified) and avoids numerical saturation when there is serious leakage (maintaining calculation stability), thus achieving accurate characterization of complex quality states. (II) Adaptive Weight Dynamic Adjustment Principle: By adjusting the weights α and β in real time using powder characteristic parameters (Dc, δ), the essence is to establish a feedback mechanism of "powder physical state - parameter sensitivity": when the powder agglomerates severely, the reliability of the pressure parameter decreases, so α is reduced to reduce its impact; when the powder has good dispersibility, pressure changes can better reflect the true performance, so β ​​is increased to enhance its weight; this dynamic adjustment enables the model to adapt to the internal state of different fire extinguishers and improves the robustness of the analysis. (III) The principle of anomaly detection enhanced by deep learning: By improving the loss function to enhance the learning of key defects (such as powder agglomeration), the problem of low recognition rate of niche defects by traditional algorithms is solved. For example, when the proportion of agglomerated samples in the training set is small, the ω2·ReLU(Dc−τ) term in the loss function will increase the training error of such samples, forcing the model to pay more attention to agglomerated features, and finally improving the recognition accuracy of agglomerated defects to more than 98% (about 85% for traditional algorithms).

[0020] In this embodiment, the data traceability unit performs the following: When the similarity of the defect patterns of a batch of products is equal to one-half of the number of products in the batch multiplied by the sum of the squared Euclidean distances between the defect coupling coefficients of each sample and the average defect coupling coefficient, and is greater than the similarity threshold of 0.35, the production process parameters are automatically associated and traceability suggestions are generated. Furthermore, the data traceability unit serves as the "information memory center" and "quality traceability bridge" of the automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection. It is responsible for associating and binding the raw data from the multi-source sensing units, the analysis results from the central control unit, and the quality level information from the dynamic judgment unit with the product's unique identifier, constructing a quality archive for the entire lifecycle, and supporting traceability queries from the product to the production process through reverse tracing algorithms, providing data support for quality problem investigation, production process optimization, and responsibility traceability; the following will elaborate on its overall functions and sub-unit composition; I. Overall Function Overview: The core function of the data traceability unit is to achieve full-chain traceability of quality inspection data, specifically including: assigning a unique digital identifier to each fire extinguisher under test (using both QR code and RFID identification), real-time correlation of original parameters such as pressure, weight, sealing performance, and powder characteristics during the testing process, and defect coupling coefficient. This unit incorporates analytical parameters such as adaptive weights, as well as judgment information including quality level, defect type, and location results. A distributed database is constructed to store this related data, supporting multi-dimensional queries by product identifier, inspection time, quality level, and other criteria. When a batch of products exhibits quality anomalies, the problematic batch is identified through defect pattern similarity calculation, and the data is traced back to key process parameters in the production process (such as filling pressure, sealing material batches, etc.), generating a traceability report. This unit features high-capacity storage (supporting ≥10 million product data records), high security (using blockchain technology to ensure data immutability), and fast retrieval capabilities (single data query response time ≤0.5 seconds), providing comprehensive data support for quality control. II. Sub-unit composition and function: (a) Product Identification and Related Sub-units: Identification and coding: A dual identification system of "QR code + RFID" is adopted. The QR code contains basic information such as product model, production batch, and production date. The RFID tag has a unique electronic code built in (supports non-contact reading, with a reading distance of ≥5 meters). Before the test begins, the identification is bound to the fire extinguisher through an automatic coding device (binding success rate ≥99.9%) and uploaded to the central database for record. Real-time data association: Receives various types of data transmitted from multi-source sensing units, central control units, and dynamic judgment units, and associates and stores them according to the format of "product identifier-time stamp-data type-value". For example, the associated data for a fire extinguisher includes: pressure value of 1.2MPa and weight loss of 5g at 09:30:15; and defect coupling coefficient at 09:30:20. The quality grade "Good" and "No Significant Defects" judgment results for 0.45; 09:30:25; Association verification mechanism: The integrity of data association is verified every 5 minutes. If the missing rate of test data for a product is greater than 5% (such as missing sealing parameters), a retransmission request is triggered to ensure that the quality file of each product is complete. For data that cannot be retransmitted, it is marked as "data abnormal" and the missing type is recorded for easy manual review later. (II) Distributed Storage and Retrieval Subunit: Database architecture design: A distributed storage architecture of "local edge nodes + cloud central nodes" is adopted. Local nodes store the detection data for the past 3 months (supporting offline query), while cloud nodes store the full historical data. The database table structure includes a product information table (identifier, model, production information, etc.), a raw parameter table (data from each sensor), and an analysis parameter table (…). The table contains values, weight parameters, etc.), and a judgment result table (quality grade, defect information, etc.). The tables are linked by product identifiers and timestamps to establish an index. Multi-dimensional search function: Supports precise query by product identifier (enter RFID code or scan QR code to obtain full data of the product); query by testing time range (e.g., query all testing data from July 1 to July 10, 2025); filter by quality grade (e.g., filter to get data of all "non-conforming products"); search by defect type (e.g., search all products with "insufficient driving gas purity"); search results can be exported to Excel or PDF format, including data trend charts (e.g., pressure change curve over time). Data compression and archiving: Historical data exceeding one year is stored using lossless compression algorithms (compression rate ≥ 50%) to reduce storage space usage; compressed data is archived quarterly, and archived files are encrypted (AES-256 encryption algorithm) to ensure data security while retaining a fast retrieval interface; (III) Defect Mode Analysis and Source Tracing Subunit: Batch quality assessment: Calculate the defect pattern similarity sim of batch products. The calculation logic is described as follows: the similarity is equal to the sum of the squares of the differences between the defect coupling coefficients of products 1 to n and the average value of the batch, and then divided by the number of products n. When sim > 0.35 (threshold), the batch is judged to have significant quality fluctuations and is marked as a "problem batch". Reverse source analysis: For problematic batches, the production process is traced back through the following steps: First, common defects of the batch of products are extracted (such as abnormal sealing performance degradation in all batches); then, the production information database is linked to extract process parameters such as filling pressure, gasket supplier, and assembly workshop temperature for the batch; finally, by comparing the differences in process parameters between normal batches and problematic batches, key influencing factors are identified (such as differences in sealing performance caused by different gasket batches). Lake Source Report Generation: The traceability report includes three parts: basic information of the problematic batch (product quantity, pass rate, main defect types); defect pattern analysis (sim value calculation process, abnormal behavior of common parameters); and traceability results of the production process (differences in key process parameters, recommended corrective measures). The report supports automatic push to the production management system, with no push delay. minute; (iv) Data Security and Access Control Subunit: Blockchain Evidence Preservation: Key data (such as quality level and defect judgment results) are stored on the blockchain using consortium blockchain technology. Each block contains information such as data hash value, timestamp, and node signature, ensuring that the data cannot be tampered with once stored (100% accuracy in tamper detection). At the same time, the on-chain data supports judicial evidence preservation, providing legal basis for quality disputes. Hierarchical access control: A three-tiered access control system is set up: Level 1 (administrator) can view all data and modify the parameters of the quality inspection instrument; Level 2 (quality inspector) can query test data and export reports but cannot modify them; Level 3 (customer) can only query the quality grade and key parameters of their own products through the product identifier; permission changes require multiple authentications (password + fingerprint + SMS verification) to ensure data access security; Data backup and recovery: We adopt a strategy of "real-time incremental backup + daily full backup". Incremental backup is performed once an hour (backup data size ≤ 10GB), and full backup is automatically performed at 3:00 AM (integrity verification is performed after backup is completed). Backup data is stored in an off-site disaster recovery center. When local data is damaged, it can be restored within 1 hour through backup, with a data recovery success rate of ≥ 99.9%.

[0021] In this embodiment, an environmental compensation unit is also included to compensate for the pressure detection value by temperature and humidity. The compensated pressure value is equal to the original pressure value multiplied by the ratio of the standard temperature to the current temperature, then multiplied by 1, plus the first humidity coefficient of 0.0023 multiplied by the relative humidity, divided by 1, plus the second humidity coefficient of 0.0018 multiplied by the relative humidity; Furthermore, the environmental compensation unit is an "environmental adaptor" for the automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint testing. It collects environmental parameters such as temperature and humidity in real time and uses a scientific compensation algorithm to correct the original detection data from the multi-source sensor units, eliminating the interference of environmental fluctuations on the measurement of key parameters such as pressure and sealing performance. This ensures that the data received by the central control unit accurately reflects the inherent quality characteristics of the fire extinguisher. The following section elaborates on its overall function and sub-module composition: I. Overall Function Overview: The core function of the environmental compensation unit is to achieve environmental adaptive correction of the detection data. Specifically, this includes: real-time acquisition of key parameters such as temperature (T) and relative humidity (H) of the detection environment; temperature and humidity compensation calculations for the original pressure value, weight measurement value, and sealing attenuation factor output by the multi-source sensing unit based on physical laws and empirical models, generating calibrated parameters; synchronous transmission of calibration data to the central control unit, while recording the parameter difference and environmental conditions before and after compensation, providing a basis for compensation algorithm optimization; this unit has a high sampling rate (environmental parameter acquisition frequency ≥10Hz), low latency (compensation calculation time ≤10ms) and adaptive correction capability (the compensation coefficient can be dynamically adjusted according to environmental changes), ensuring that the deviation of the detection data is controlled within ±1% in complex environments such as high temperature, high humidity, and low temperature, providing a stable and reliable input for subsequent multi-parameter fusion analysis; II. Submodule Composition and Functions: (a) Environmental Parameter Acquisition Subunit: Sensor configuration: Integrated high-precision temperature and humidity sensors (temperature measurement range -40℃~85℃, accuracy ±0.1℃; humidity measurement range 0~100%RH, accuracy ±2%RH), installed at three different locations in the testing station (50cm, 100cm, and 150cm from the fire extinguisher), reducing the impact of environmental inhomogeneity through multi-point spatial sampling; also equipped with a barometric pressure sensor (measurement range 80~110kPa, accuracy ±0.1kPa) to assist in correcting pressure detection values ​​under high-altitude or low-pressure environments; Data preprocessing: A weighted average of the temperature and humidity data from the three locations is calculated (closer locations have higher weights, with weighting coefficients of 0.5, 0.3, and 0.2 respectively) to obtain the ambient temperature representing the detection area (unit: K, which needs to be converted to thermodynamic temperature, i.e., K). ) and relative humidity (Unit: %); Outliers (such as temperature rise exceeding 5°C / s) are smoothed using a sliding window averaging method (window size of 5 sampling points) to ensure stable output of environmental parameters; Status monitoring: Real-time monitoring of sensor operating status (such as power supply voltage and communication signal strength). When a sensor fails (e.g., no data updates for 10 consecutive seconds), it automatically switches to a backup sensor (the backup sensor has the same parameters as the main sensor) and triggers a maintenance alarm to ensure the continuity of environmental parameter acquisition. (II) Compensation Algorithm Execution Subunit: Pressure parameter compensation: To address the influence of temperature and humidity on pressure detection, the state equation in claim 6 is used for compensation. The calculation logic can be described as follows: the compensated pressure value equals the original pressure detection value multiplied by the ratio of the standard temperature to the current temperature, then multiplied by (1 plus the humidity coefficient). (product of relative humidity) and (1 plus humidity coefficient) The ratio of the product of temperature and relative humidity; where standard temperature is the standard temperature. Set to 293.15K (20°C), humidity coefficient , (Calibrated through extensive experimental data to ensure humidity compensation error s ≤ 0.02 MPa); Weight parameter compensation: Considering the influence of temperature on the weighing sensor (the sensitivity of the metal strain gauge changes with temperature), a linear compensation formula is adopted: the compensated weight value equals the original measured weight value plus the product of the temperature deviation and the temperature coefficient (i.e., ... ),in The weight temperature coefficient (taken as -0.02 g / °C, determined through calibration experiments in the range of low temperature (-10°C) to high temperature (50°C)). The calibration reference temperature is 20℃. Sealing parameter compensation: At high humidity, sealing materials (such as rubber seals) will expand due to moisture absorption, affecting the sealing performance test, thus affecting the sealing performance attenuation factor. Perform humidity correction: After correction When humidity At that time, enlarge appropriately. Value (compensation for increased leakage due to moisture absorption by the sealing material); when At that time, reduce appropriately The value (the leakage measurement due to the drying of the compensation material is too small); (III) Application and Feedback Subunit of Compensation Values: Compensation data transmission: The calculated compensated pressure ,weight Sealing attenuation factor Parameters including ambient temperature ,humidity The compensation timestamp is sent to the central control unit via the internal data bus (transmission rate 100Mbps) as input data for multi-parameter fusion analysis (replacing the original detection value); Compensation effectiveness evaluation: The compensation effectiveness is evaluated by calculating the stability (standard deviation) of the data before and after compensation. If the standard deviation of the pressure data after compensation is lower than that before compensation... The stability of the compensated parameters meets the requirements (pressure standard deviation). ; Parameter adaptive update: Based on more than 1,000 sets of test data under different environmental conditions each month (covering -10°C to 50°C, 20% to 90% RH), the humidity coefficient is optimized using the least squares method. , and weight temperature coefficient This will continuously reduce the compensation error (Annual target: absolute error of the compensated parameters). ).

[0022] In this embodiment, the automated quality inspection instrument integrates a self-calibration module, which periodically generates a sensor health index by comparing with a standard instrument. The health index is equal to 1 minus the absolute deviation between the sensor measurement and the standard reference value, divided by the standard reference value, multiplied by the negative calibration period days of the natural constant e, and divided by the 30th power. An alert is triggered when the health index is less than 0.85; Furthermore, the self-calibration module acts as the "precision guardian" of the automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint testing. It dynamically evaluates the performance status of each sensor and core component by periodically comparing it with a standard instrument, generating a health index and automatically correcting deviations. This ensures the measurement accuracy and stability of the quality inspection instrument during long-term operation, providing a fundamental guarantee for the reliability of multi-source sensor data. The following section elaborates on its overall function and sub-unit composition: I. Overall Function Overview: The core function of the self-calibration module is to achieve automated accuracy calibration and performance monitoring of key components of the quality inspection instrument. Specifically, it includes: starting the calibration process according to a preset cycle (usually 24 hours) or triggering conditions (such as abnormal sensor health index); calling a standard (such as a high-precision pressure standard source, standard weight, etc.) to compare parameters of core components such as pressure sensors, weighing sensors, and sealing detection modules; evaluating the performance degradation of each component through the health index calculation formula, triggering an early warning and generating a calibration report when the health index is lower than the threshold (0.85); automatically updating the sensor calibration coefficient to eliminate measurement errors caused by quality inspection instrument drift; this module has fully automated operation (no manual intervention required), high-precision calibration (error ≤0.2% after calibration), and adaptive cycle adjustment capability (shortening or extending the calibration interval according to the health status), ensuring that the quality inspection instrument maintains stable detection accuracy during long-term operation and reducing manual calibration costs and downtime; II. Sub-unit composition and function: (a) Standard Instrument Management Subunit: Standard configuration and maintenance: Equipped with a complete set of Level 1 standard equipment, including: Pressure standard source: Measurement range 0-6MPa, accuracy ±0.01%FS (full scale), supports 0.1MPa step output; Standard weight set: measuring range 100g-20kg, accuracy class F1 (error ≤0.1mg); Leakage test device for airtightness: leakage rate 0.01-10 mL / min, accuracy ±5%; Temperature and humidity standard chamber: Temperature control range -10℃~50℃ (accuracy ±0.1℃), humidity control range 30%~80%RH (accuracy ±1%RH); The standard instrument is sent to the metrology institution for verification periodically (every 12 months). It can only be used after it has passed the verification to ensure the authority of the calibration benchmark. Standard status monitoring: Real-time monitoring of the standard's operating status, including output stability (e.g., 30-minute drift of the pressure standard source ≤ 0.005MPa), power supply voltage (220V±5%), and communication status (communication delay with the self-calibration module ≤ 50ms); when the standard malfunctions (e.g., output exceeds tolerance), the calibration process is immediately paused and a "standard fault" alarm is triggered to avoid incorrect calibration; (II) Sensor Health Assessment Subunit: Health index calculation: The health index is calculated by comparing sensor measurements with standard reference values ​​according to a formula. The logic, described in words, is as follows: The sensor health index equals 1 minus the absolute value of the difference between the sensor measurement and the standard reference value, divided by the standard reference value, then multiplied by the negative calibration period of the natural constant, divided by 30 (i.e., ... ;in, The sensor measurement value. The reference value output by the standard is... The calibration period (days) is used to correct for performance degradation caused by long-term use (the longer the period, the greater the correction weight). Multi-dimensional assessment: Besides the pressure sensor, similar logic is used to calculate the health index for other core components (such as the load cell and sealing detection module). :) Load cell: ,in This is the weighing value. Standard weight mass; Sealing module: ,in To measure the leakage rate, The set value for the standard leakage device; Threshold determination: set health index) threshold 0.85, when the health index of any component... When this occurs, it is determined to be "performance degradation," triggering a calibration warning; when If the result is deemed "seriously inaccurate," the testing process is immediately suspended, and calibration is forcibly initiated. (III) Automatic calibration execution subunit: Calibration parameter calculation: When the health index fails to meet the standard, the sensor calibration coefficient is calculated, and the deviation is eliminated through linear correction; for example, the calibration coefficient of a pressure sensor. Calculated according to the following logic: (corrected measurement value) ), ensure calibration (Meets the first-level accuracy requirement); Step-by-step calibration process: Pressure sensor calibration: The standard outputs four calibration points: 0MPa, 2MPa, 4MPa, and 6MPa. The sensor collects and records these points sequentially, calculates the calibration coefficient for each point, and uses piecewise linear interpolation to generate a full-range calibration curve. Weighing sensor calibration: Load standard weights of 1kg, 5kg, 10kg, and 20kg sequentially, and calculate the weight calibration coefficient. This covers the typical range of fire extinguisher weight testing; Sealing module calibration: The standard leakage device is set with three leakage rates of 0.196 / min, 0.5% / min, and 1.0% / min to correct the measurement deviation of the sealing attenuation factor and ensure that the error after calibration is ≤0.05% / min; Calibration validity verification: After calibration, randomly select a non-calibration point (e.g., pressure 3MPa) for verification. If the deviation between the corrected measured value and the standard value is ≤0.02MPa, the calibration is considered valid; otherwise, repeat the calibration (up to 3 times). If it still fails, trigger the "sensor replacement" alarm. (iv) Calibration Data Management Subunit: Calibration record storage: Detailed records of key information for each calibration, including calibration time, standard number, calibrated component, health index before and after calibration, calibration coefficient, verification results, etc., forming a "calibration archive" that supports querying by component type and time range (query response time ≤ 1 second). Trend Analysis and Cycle Adjustment: By analyzing historical data of health indices (such as the past 30 days) The curve of change indicates the rate of sensor performance degradation. If the rate of decline of a sensor's health index is >0.01 / day (normal rate ≤0.005 / day), its calibration cycle is automatically shortened (e.g., adjusted from 24 hours to 12 hours) to prevent accuracy loss in advance. If the rate of decline is <0.003 / day, the cycle is extended (e.g., adjusted to 48 hours) to reduce unnecessary calibration time. Data synchronization and traceability: The calibration records are synchronized to the data traceability unit and linked to the product testing data. When there is a dispute over the testing data of a certain batch of products, the data credibility can be verified by querying the calibration records of the same period (such as "the pressure sensor was calibrated and qualified during this period, with a deviation of 0.005MPa"), providing a calibration basis for quality traceability.

[0023] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection, characterized in that: include: Multi-source sensing unit, synchronously collects pressure data, weight change, sealing attenuation factor and powder dispersion characteristics data of the fire extinguisher under test; The central control unit executes a multi-parameter fusion analysis algorithm and processes the collected data by calculating the defect coupling coefficient. The defect coupling coefficient is equal to the adaptive weighting parameter α multiplied by the pressure change rate to the power of β, plus the adaptive weighting parameter γ multiplied by the weight loss amount multiplied by 1 plus the correction factor κ multiplied by the natural logarithm of the sealing attenuation factor. The dynamic judgment unit generates quality level and defect location information based on the defect coupling coefficient. The data traceability unit links testing data with product identification and supports reverse tracing.

2. The automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection according to claim 1, characterized in that: The powder dispersion characteristic data were obtained through the following methods: After applying standard vibration excitation, a powder settling image sequence is captured using multispectral imaging, and the agglomeration density index and suspension dispersion are calculated. The adaptive weight parameter α is equal to the base weight parameter α0 multiplied by the negative agglomeration density index of the natural constant e multiplied by the agglomeration influence coefficient raised to the power of the base weight parameter α0. The adaptive weight parameter β is equal to the base weight parameter β0 plus the dispersion gain factor multiplied by the suspension dispersion. Secondary vibration detection is activated when the agglomeration density index is greater than 2.

5.

3. The automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection according to claim 1, characterized in that: The dynamic determination unit performs the following: Construct a defect coupling matrix. When pressure anomalies and powder settling anomalies occur concurrently, trigger the insufficient purity judgment branch of the driving gas and output the fault location coordinates. The defect location coordinates are taken as the location of the maximum value of the product of the partial derivative of the defect coupling coefficient with respect to the horizontal coordinate of the grid on the surface of the fire extinguisher and the partial derivative of the defect coupling coefficient with respect to the vertical coordinate of the grid.

4. The automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection according to claim 1, characterized in that: The sealing data detection adopts a dual-mode mechanism: Static pressure attenuation rate measurement and dynamic microflow sensing are performed in parallel. When the difference between the two exceeds 15 percent of the sealing attenuation factor, infrared thermal imaging is activated to assist in diagnosis and update the sealing attenuation factor.

5. The automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection according to claim 1, characterized in that: The central control unit implements: Deep learning-based anomaly detection using an improved loss function: The training loss value is equal to one-half of the number of training samples multiplied by the weighted sum of all samples. The loss of each sample includes the dynamic balance weight ω1 multiplied by the square of the Euclidean distance between the predicted defect coupling coefficient and the actual defect coupling coefficient, plus the dynamic balance weight ω2 multiplied by the cluster density index minus the positive value of the threshold 2.0; wherein, the sum of the dynamic balance weights ω1 and ω2 is 1.

6. The automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection according to claim 1, characterized in that: It also includes an environmental compensation unit to compensate for pressure readings by temperature and humidity. The compensated pressure value is equal to the original pressure value multiplied by the ratio of the standard temperature to the current temperature, then multiplied by 1, plus the first humidity coefficient of 0.0023 multiplied by the relative humidity, divided by 1, plus the second humidity coefficient of 0.0018 multiplied by the relative humidity.

7. The automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection according to claim 1, characterized in that: The data traceability unit performs the following: When the similarity of the defect patterns of a batch of products is equal to one-half of the number of products in the batch multiplied by the sum of the squared Euclidean distances between the defect coupling coefficients of each sample and the average defect coupling coefficient, and is greater than the similarity threshold of 0.35, the production process parameters are automatically associated and traceability suggestions are generated.

8. The automated quality inspection instrument for dry powder fire extinguishers based on multi-parameter joint inspection according to claim 1, characterized in that: An integrated self-calibration module periodically generates a sensor health index through comparison with a standard. The health index is equal to 1 minus the absolute deviation between the sensor measurement and the standard reference value, divided by the standard reference value, multiplied by the negative calibration period days of the natural constant e, and divided by the 30th power. An alert is triggered when the health index is less than 0.85.

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