A mattress quality detection data processing method and system

Abstract

The application relates to the technical field of data processing, and provides a mattress quality detection data processing method and system. The method is applied to a mattress quality detection data processing system, and comprises the following steps: acquiring current quality index collection information and current heartbeat information of a quality index collection device; for each quality index collection device in a plurality of quality index collection devices, determining a current timestamp deviation rate of the quality index collection device according to the current heartbeat information and a current reference timestamp of the quality index collection device; adjusting the collection timestamp of quality index data in the current quality index collection information of the quality index collection device according to the current timestamp deviation rate of the quality index collection device, to obtain a target collection timestamp of the quality index data; and performing quality detection on a mattress according to the quality index data of a plurality of quality index collection devices corresponding to the same target collection timestamp. The accuracy of mattress quality detection data processing is improved.

Description

Technical Field

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

[0002] In modern industrial production, especially in smart manufacturing environments like automated mattress production lines, precise quality control is crucial, typically requiring the integration of data from multiple sensors across various stages of production. However, even advanced automated systems can encounter subtle technical challenges that can affect the accuracy of quality assessments. One such challenge arises when the internal timing mechanisms of the underlying hardware components responsible for data acquisition exhibit small but persistent deviations. While these deviations may seem small individually, they accumulate over time, causing a systematic timing misalignment in the data streams from different sensors. This timing inconsistency can severely impact the effectiveness of automated quality inspection systems, leading to misinterpretations of production data and potentially allowing defective products to pass inspection undetected.

[0003] On a modern smart mattress production line, the core of the quality control system lies in a sophisticated automated data processing framework designed to integrate data streams from multiple key inspection points throughout the production process in real time. For example, in the crucial stage of mattress manufacturing, pressure sensors on the rolling press, adhesive strength testers, and material elasticity probes continuously collect data. To ensure this data can be accurately used for quality assessment and anomaly identification, the system operates a complex multi-sensor data time synchronization logic. This logic is meticulously designed to efficiently align all incoming data streams, regardless of their physical origin from different sensors or processing units. This precise time alignment mechanism is crucial, guaranteeing that at a specific moment, pressure readings, adhesive strength measurements, and material elasticity values ​​for the same physical area of ​​the mattress can be correctly correlated. This fundamental capability provides a solid foundation for subsequent quality judgment and anomaly detection, enabling the system to build a coherent view of the mattress's quality attributes as it progresses on the production line.

[0004] However, even in highly standardized industrial environments, subtle variables can still emerge. For example, during routine maintenance, an industrial-grade data acquisition card responsible for collecting pressure data from a rolling mill might need to be replaced. Although the replacement card may be identical in model and specifications, it could come from a different production batch. In the manufacturing process of electronic components, even under strict quality control, minute differences in material properties or manufacturing processes can cause slight fluctuations in the performance of individual components. For instance, the quartz crystal oscillator, which serves as the internal time reference of the data acquisition card, might have an extremely small deviation from its design specification in its inherent resonant frequency. This deviation is typically on the order of parts per million and is usually within the component's acceptable tolerance, so it won't immediately trigger any fault alarms. However, it is precisely this tiny frequency difference that causes a persistent and stable small time deviation between the internal clock and the system's master clock when the replaced card is used for independent timing. Over time, this seemingly insignificant frequency offset accumulates into a measurable, albeit small, difference in timestamps, resulting in lower accuracy in mattress quality inspection data processing. Summary of the Invention

[0005] This application provides a method and system for processing mattress quality testing data, which can improve the accuracy of mattress quality testing data processing.

[0006] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for processing mattress quality testing data is provided. This method is applied to a mattress quality testing data processing system and includes: for each of multiple quality indicator acquisition devices used in mattress testing, acquiring current quality indicator acquisition information and current heart rate information of the acquisition device; the current quality indicator acquisition information includes quality indicator data and a timestamp of the acquisition of the quality indicator data, and the current heart rate information includes the original heart rate timestamp; determining a current reference timestamp based on the current heart rate information of the multiple quality indicator acquisition devices; for each of the multiple quality indicator acquisition devices, determining the current timestamp deviation rate of the quality indicator acquisition device based on the current heart rate information and the current reference timestamp; adjusting the acquisition timestamp of the quality indicator data in the current quality indicator acquisition information of the quality indicator acquisition device according to the current timestamp deviation rate to obtain a target acquisition timestamp for the quality indicator data; and performing quality testing on the mattress based on the quality indicator data from the multiple quality indicator acquisition devices corresponding to the same target acquisition timestamp.

[0007] Furthermore, the current baseline timestamp is determined based on the current heartbeat information of multiple quality indicator acquisition devices, including: for each of the multiple quality indicator acquisition devices, the original heartbeat timestamp in the current heartbeat information of the quality indicator acquisition device is corrected to obtain the target heartbeat timestamp; the median of the target heartbeat timestamps corresponding to the multiple quality indicator acquisition devices is used as the current baseline timestamp.

[0008] More specifically, in some implementation schemes, the original heartbeat timestamp in the current heartbeat information of the quality indicator acquisition device is modified to obtain the target heartbeat timestamp, including: obtaining the current network transmission delay duration of the quality indicator acquisition device; the current network transmission delay duration is the delay duration between the quality indicator acquisition device and the mattress quality testing data processing system; and adding the current network transmission delay duration to the original heartbeat timestamp in the current heartbeat information to obtain the target heartbeat timestamp.

[0009] Based on the above, this application further proposes determining the current timestamp deviation rate of the quality indicator acquisition device according to the current heartbeat information and the current reference timestamp of the quality indicator acquisition device, including: acquiring multiple historical heartbeat information of the quality indicator acquisition device within a historical time period and multiple historical reference timestamps corresponding one-to-one with the multiple historical heartbeat information within the historical time period; the last heartbeat information among the multiple historical heartbeat information is the current heartbeat information, the historical heartbeat information includes the original heartbeat timestamp, and the duration of the historical time period is a preset duration; for each historical heartbeat information among the multiple historical heartbeat information, determining the timestamp difference between the original heartbeat timestamp in the historical heartbeat information and the corresponding historical reference timestamp; and determining the current timestamp deviation rate of the quality indicator acquisition device based on the multiple timestamp differences.

[0010] Preferably, determining the current timestamp deviation rate of the quality indicator acquisition device based on multiple timestamp differences includes: generating a timestamp difference time change curve based on multiple timestamp differences and the historical reference timestamp corresponding to each timestamp difference; determining a linear fitting line of the timestamp difference time change curve based on the timestamp difference time change curve; and using the slope of the linear fitting line as the current timestamp deviation rate of the quality indicator acquisition device.

[0011] In one implementation, adjusting the collection timestamp of the quality indicator data in the current quality indicator collection information of the quality indicator collection device according to the current timestamp deviation rate of the quality indicator collection device to obtain the target collection timestamp of the quality indicator data includes: determining a target duration; the target duration is the duration between a first moment and a second moment, where the first moment is the moment when the mattress quality testing data processing system receives the current quality indicator collection information from the quality indicator collection device, and the second moment is the moment when the mattress quality testing data processing system determines the current timestamp deviation rate of the quality indicator collection device; adjusting the collection timestamp of the quality indicator data in the current quality indicator collection information of the quality indicator collection device according to the target duration and the current timestamp deviation rate of the quality indicator collection device to obtain the target collection timestamp of the quality indicator data.

[0012] As an optional solution, the collection timestamp of the quality indicator data in the current quality indicator collection information of the quality indicator collection device is adjusted according to the target duration and the current timestamp deviation rate of the quality indicator collection device to obtain the target collection timestamp of the quality indicator data. This includes: using the product of the target duration and the current timestamp deviation rate of the quality indicator collection device as the timestamp adjustment duration; determining the target collection timestamp of the quality indicator data based on the collection timestamp of the quality indicator data and the timestamp adjustment duration, and the duration between the target collection timestamp and the collection timestamp is the timestamp adjustment duration.

[0013] To enhance functionality, the mattress is tested for quality based on quality index data from multiple quality index collection devices corresponding to the same target collection timestamp. This includes: performing preset data processing operations on multiple quality index data corresponding to the same target timestamp to obtain comprehensive quality data; and identifying the mattress as abnormal if the comprehensive quality data falls outside the corresponding preset comprehensive quality data range.

[0014] To improve the solution, after determining that the quality label of the mattress is abnormal, the method also includes: generating target information; the target information includes the batch information of the mattress, the quality label of the mattress, and multiple quality index data corresponding to the same target timestamp; sending the target information to the alarm device and making the alarm device display the target information.

[0015] Secondly, this application also discloses a mattress quality testing data processing system, comprising: an acquisition device and a processing device; the acquisition device is used to acquire current quality indicator acquisition information and current heartbeat information of each of a plurality of quality indicator acquisition devices for a mattress; the current quality indicator acquisition information includes quality indicator data and acquisition timestamps of the quality indicator data, and the current heartbeat information includes original heartbeat timestamps and device identifiers of the quality indicator acquisition devices; the processing device is used to determine a current reference timestamp based on the current heartbeat information of the plurality of quality indicator acquisition devices; the processing device is used to determine the current timestamp deviation rate of each of the plurality of quality indicator acquisition devices based on the current heartbeat information and the current reference timestamp; the processing device is used to adjust the acquisition timestamps of the quality indicator data in the current quality indicator acquisition information of the quality indicator acquisition devices according to the current timestamp deviation rate of the quality indicator acquisition devices to obtain a target acquisition timestamp of the quality indicator data; the processing device is used to perform quality testing on the mattress based on the quality indicator data of the plurality of quality indicator acquisition devices corresponding to the same target acquisition timestamp.

[0016] Beneficial Effects: The mattress quality testing data processing method disclosed in this application acquires current quality indicator collection information and heartbeat information from multiple quality indicator collection devices for the mattress. First, the method determines a unified current reference timestamp based on the current heartbeat information from multiple quality indicator collection devices, providing a common time reference for all devices. Next, for each quality indicator collection device, based on its current heartbeat information and current reference timestamp, the current timestamp deviation rate of that device is accurately calculated, thereby quantifying the systematic and cumulative deviation between its internal timing mechanism and the system reference time. Subsequently, using the calculated timestamp deviation rate, the original collection timestamps of the quality indicator data in the current quality indicator collection information of the quality indicator collection devices are adjusted to obtain a corrected target collection timestamp. Finally, based on the quality indicator data from multiple quality indicator collection devices corresponding to the same target collection timestamp, accurate quality testing of the mattress is performed.

[0017] Through the above technical solution, this application effectively solves the problem in existing technologies where the accumulation of small but continuous deviations in the internal timing mechanism of quality indicator acquisition equipment leads to systematic time misalignment in data streams from different sensors, thus affecting the effectiveness of automated quality inspection systems. This method, by introducing a calculation and adjustment mechanism for the timestamp deviation rate, can accurately compensate for the systematic drift of the device clock, ensuring high temporal synchronization of data from different sensors. This overcomes the limitations of existing technologies, which can only compensate for random, small-range time jitter but cannot handle systematic, cumulative time drift. Therefore, this application significantly improves the accuracy of multi-sensor data integration, enabling the quality inspection system to more reliably identify complex time-related quality anomalies, avoiding misjudgments or omissions caused by time misalignment, thereby improving the overall accuracy and reliability of mattress quality inspection. Attached Figure Description

[0018] Figure 1 A flowchart illustrating a mattress quality testing data processing method provided in this application; Figure 2 A flowchart illustrating another mattress quality testing data processing method provided in this application; Figure 3 A flowchart illustrating another mattress quality testing data processing method provided in this application; Figure 4 This is a schematic diagram of the architecture of a mattress quality testing data processing system provided in this application. Detailed Implementation

[0019] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] Traditional mattress quality inspection systems face a significant challenge when processing data from multiple sensors. When the timing mechanisms within the quality indicator acquisition devices exhibit small but persistent deviations, these deviations accumulate over time, leading to systematic time misalignments between data streams from different sensors. This temporal inconsistency severely impacts the accuracy of automated quality inspection, potentially causing the system to misjudge product quality or even allowing defective products to pass inspection undetected, resulting in low accuracy in mattress quality inspection data processing.

[0022] In this regard, such as Figure 1 As shown, this application proposes a method for processing mattress quality inspection data. The method is applied to a mattress quality inspection data processing system and includes: S101. For each of the multiple quality indicator acquisition devices in the mattress, acquire the current quality indicator acquisition information and current heart rate information of the quality indicator acquisition device.

[0023] The current quality indicator collection information includes quality indicator data and the collection timestamp of the quality indicator data, and the current heartbeat information includes the original heartbeat timestamp.

[0024] S102. Determine the current baseline timestamp based on the current heartbeat information of multiple quality indicator acquisition devices.

[0025] S103. For each of the multiple quality indicator acquisition devices, determine the current timestamp deviation rate of the quality indicator acquisition device based on the current heartbeat information and the current reference timestamp of the quality indicator acquisition device.

[0026] S104. Adjust the collection timestamp of the quality indicator data in the current quality indicator collection information of the quality indicator collection device according to the current timestamp deviation rate of the quality indicator collection device to obtain the target collection timestamp of the quality indicator data.

[0027] S105. Perform quality inspection on the mattress based on the quality index data of multiple quality index collection devices corresponding to the same target collection timestamp.

[0028] This application aims to provide a mattress quality inspection data processing method that can effectively solve the aforementioned problem of accumulated timestamp deviation. By introducing a mechanism for determining and adjusting the timestamp deviation rate, this application can accurately synchronize data from different quality indicator acquisition devices, thereby significantly improving the accuracy and reliability of mattress quality inspection and ensuring accurate identification of product defects even in the event of minor deviations in device timing.

[0029] To better understand the technical solution proposed in this application, some key terms and implementation environments involved will be explained first.

[0030] "Quality indicator acquisition equipment" refers to sensors or data acquisition units used to collect various quality indicator data during the mattress production process. For example, on a mattress production line, this may include pressure sensors for the rolling press, adhesive strength testers, and material elasticity probes. These devices monitor various physical and chemical properties of the mattress in real time during the production process.

[0031] "Quality indicator collection information" refers to the raw data collected by quality indicator collection equipment. Its core components are "quality indicator data" and "collection timestamp". Quality indicator data are the actual measured values, such as pressure values, strength values, or elasticity values; the collection timestamp is the time information recorded internally by the equipment when the data was collected.

[0032] "Heartbeat information" refers to a signal periodically sent by the quality indicator acquisition equipment to the mattress quality testing data processing system, indicating that the equipment is in normal working condition. The heartbeat information includes a "raw heartbeat timestamp," which records the internal time of the equipment when the heartbeat signal was emitted.

[0033] The "Mattress Quality Inspection Data Processing System" is a central processing unit responsible for receiving, processing, and analyzing data from multiple quality indicator acquisition devices, and ultimately determining the quality of the mattress. This system can be a standalone server, an industrial control computer, or a cloud computing platform.

[0034] "Current reference timestamp" refers to a unified time point calculated by the mattress quality testing data processing system based on the heartbeat information of all quality indicator collection devices, serving as a time synchronization reference.

[0035] "Timestamp deviation rate" refers to the rate at which the internal clock of a single quality indicator acquisition device drifts relative to the current reference timestamp. This rate reflects how fast or slow the device's clock is.

[0036] "Target acquisition timestamp" refers to the final and accurate timestamp of the quality indicator data after adjustment for timestamp deviation rate. It represents the actual acquisition time of the data in the reference time system.

[0037] The core of the mattress quality testing data processing method proposed in this application lies in the precise synchronization and calibration of timestamps from multiple quality indicator acquisition devices.

[0038] Specifically, for each of the multiple quality indicator acquisition devices in a mattress system, the first step is to acquire its current quality indicator acquisition information and current heartbeat information. For example, a pressure sensor acquires mattress pressure data at a certain moment and records the acquisition timestamp of its internal clock. Simultaneously, the pressure sensor periodically sends a heartbeat signal containing a raw heartbeat timestamp. This information can be transmitted to the mattress quality testing data processing system via wired or wireless network. In one implementation, the system can be configured with a data receiving module that continuously listens to the network ports of each quality indicator acquisition device. Once a data packet is received, it parses out the quality indicator acquisition information and heartbeat information. For example, the data packet can be in JSON format, containing fields such as "quality_data", "timestamp", and "heartbeat_timestamp".

[0039] Next, the current reference timestamp is determined based on the current heartbeat information collected from multiple quality indicator collection devices. Since the internal clocks of different devices may deviate, directly using the original timestamps of each device will lead to data misalignment. Therefore, a unified reference time needs to be established. One implementation method is that after the mattress quality inspection data processing system receives the current heartbeat information from all devices, it can simply select the original heartbeat timestamp of one of the devices as the reference timestamp. For example, the system can designate a master device and use its original heartbeat timestamp as the current reference timestamp.

[0040] Then, for each of the multiple quality indicator acquisition devices, the current timestamp deviation rate of that quality indicator acquisition device is determined based on its current heartbeat information and current reference timestamp. This step is crucial to this application, as it aims to quantify the drift rate of each device's clock relative to a reference time. In one implementation, the system can record multiple historical heartbeat messages sent by each device over a past period and their corresponding historical reference timestamps. By calculating the timestamp difference between the original heartbeat timestamp in each historical heartbeat message and the corresponding historical reference timestamp, and analyzing the changing trend of these differences, the timestamp deviation rate can be estimated. For example, the system can maintain a historical data buffer, storing the original timestamps and corresponding reference timestamps of the most recent N heartbeats for each device. By calculating the timestamp differences of these historical data points and simply calculating the average rate of change of these differences, the current timestamp deviation rate can be obtained.

[0041] Subsequently, the acquisition timestamps of the quality indicator data in the current quality indicator acquisition information are adjusted according to the current timestamp deviation rate of the quality indicator acquisition devices to obtain the target acquisition timestamps of the quality indicator data. Once the deviation rate of each device is determined, the original timestamps of the quality indicator data acquired by that device can be corrected to align with the reference time system. In one implementation, the system can calculate the time interval (target duration) between the quality indicator acquisition information being received and the system determining the timestamp deviation rate. Then, this target duration is multiplied by the current timestamp deviation rate to obtain a timestamp adjustment amount. Finally, this adjustment amount is added to or subtracted from the original acquisition timestamp to obtain the target acquisition timestamp. For example, if the deviation rate of a device is 0.001 seconds faster per second, and 10 seconds have elapsed from data reception to the completion of adjustment, then the original acquisition timestamp needs to be subtracted by 0.01 seconds for correction.

[0042] Finally, the mattress is quality inspected based on quality indicator data from multiple quality indicator collection devices corresponding to the same target collection timestamp. After the timestamp adjustment, the quality indicator data from different devices are precisely aligned in time. At this point, the system can perform comprehensive analysis based on this synchronized data to determine the mattress's quality. In one implementation, the system can set a series of quality inspection rules or models. For example, for the same target collection timestamp, the system collects quality indicator data (such as pressure, adhesive strength, elasticity, etc.) from all relevant devices and then inputs this data into a preset quality assessment algorithm. This algorithm can be a rule-based expert system or a machine learning model, used to determine whether this comprehensive data meets preset quality standards. If the data exceeds the normal range, the mattress is deemed to be of abnormal quality.

[0043] The mattress quality testing data processing method proposed in this application effectively solves the problem of data time misalignment caused by the accumulation of internal timing deviations in traditional methods by introducing a mechanism for determining and adjusting the timestamp deviation rate of the quality index acquisition equipment.

[0044] Compared to existing technologies, the advantage of this application lies in its ability to identify and compensate for systematic time deviations caused by internal clock drift, rather than just random jitter. By introducing the concept of timestamp deviation rate, this application enables personalized time calibration for each quality indicator acquisition device, ensuring that all data is analyzed under a unified reference time. This precise time synchronization mechanism allows the quality inspection system to more accurately correlate data from different sensors, identifying quality problems caused by multi-parameter co-occurrence anomalies that may be missed by traditional methods. For example, after the rolling mill pressure data and material elasticity data are calibrated using the method of this application, even if the original acquisition devices have clock deviations, they can be precisely aligned under the same target acquisition timestamp. This allows the quality inspection algorithm to accurately capture the correlation anomalies between the two, effectively preventing the missed detection of defective products on the production line. Therefore, this application significantly improves the accuracy and reliability of quality control in automated mattress production lines.

[0045] like Figure 2 As shown, this application further proposes a method for determining the current reference timestamp based on the current heartbeat information of the aforementioned multiple quality indicator acquisition devices, the specific steps of which include: S201. For each of the multiple quality indicator acquisition devices, correct the original heartbeat timestamp in the current heartbeat information of the quality indicator acquisition device to obtain the target heartbeat timestamp.

[0046] S202. Use the median of the target heartbeat timestamps corresponding to multiple quality indicator acquisition devices as the current baseline timestamp.

[0047] Specifically, correcting the original heartbeat timestamp in the current heartbeat information of the quality indicator acquisition device to obtain the target heartbeat timestamp means adjusting the original heartbeat timestamp sent by the quality indicator acquisition device to eliminate or reduce time errors caused by factors such as network transmission delay or device clock deviation. For example, the current network transmission delay between the quality indicator acquisition device and the mattress quality testing data processing system can be obtained, and this current network transmission delay can be added to the original heartbeat timestamp to obtain a corrected target heartbeat timestamp that is closer to the system's reception time. The original heartbeat timestamp is the local timestamp recorded by the quality indicator acquisition device when sending heartbeat information. The target heartbeat timestamp, after correction, is a unified time reference timestamp that better reflects the arrival or processing of heartbeat information by the system. Using the median of the target heartbeat timestamps corresponding to multiple quality indicator acquisition devices as the current reference timestamp means selecting the median value of these timestamps as the system's current reference timestamp after all quality indicator acquisition devices have provided corrected target heartbeat timestamps. As a statistical measure, the median is more robust to outliers (such as a device experiencing a large delay or clock skew) than the mean, and can provide a more stable and representative reference timestamp.

[0048] The proposed solution first corrects the original heartbeat timestamps of each quality indicator acquisition device, effectively compensating for time errors introduced by factors such as network transmission delays or device clock drift. This allows the original heartbeat timestamps from different devices to be compared on a more unified time benchmark after correction. Subsequently, by selecting the median of these corrected target heartbeat timestamps as the current benchmark timestamp, the robustness of the benchmark timestamp is further enhanced. The median effectively avoids the significant impact of timestamps from a single or a few abnormal devices on the overall benchmark timestamp, thus ensuring that the determined current benchmark timestamp is more stable and accurate.

[0049] Through the above technical solution, this application can obtain a more accurate and stable current reference timestamp. This correction and median selection method effectively reduces the impact of network latency and device clock drift on the accuracy of the reference timestamp, improving the system's reliability for time synchronization. Therefore, subsequent adjustments to the timestamp for quality indicator data collection will be more accurate, providing a more reliable time synchronization basis for mattress quality testing, ultimately improving the overall accuracy and reliability of mattress quality testing.

[0050] In some preferred embodiments, it is assumed that the mattress quality testing data processing system is connected to three quality indicator acquisition devices, namely device A, device B, and device C. At a certain moment, devices A, B, and C respectively send their current heartbeat information, which includes the original heartbeat timestamp. Assume the original heartbeat timestamp of device A is T_A_raw, that of device B is T_B_raw, and that of device C is T_C_raw. The mattress quality testing data processing system first obtains the current network transmission delay times between devices A, B, C, and the system, which are D_A, D_B, and D_C, respectively. Then, the system corrects the original heartbeat timestamps of each device to obtain the target heartbeat timestamps: T_A_target = T_A_raw + D_A; T_B_target = T_B_raw + D_B; T_C_target = T_C_raw + D_C. Assume the corrected target heartbeat timestamps are 10:00:01.050, 10:00:01.120, and 10:00:01.100. The system sorts these three target heartbeat timestamps as follows: 10:00:01.050, 10:00:01.100, and 10:00:01.120. Then, the median of the sorted timestamps, 10:00:01.100, is selected as the current base timestamp. In this way, even if a device has significant network latency or a clock deviation, the final determined base timestamp can maintain high accuracy and stability.

[0051] Specifically, in the steps of determining the current reference timestamp, the method of correcting the original heartbeat timestamp in the current heartbeat information of the quality indicator acquisition device to obtain the target heartbeat timestamp can be further explained.

[0052] like Figure 3 As shown, this application further proposes a step to correct the original heartbeat timestamp in the current heartbeat information of the aforementioned quality indicator acquisition device to obtain the target heartbeat timestamp, including: S301. Obtain the current network transmission delay duration of the quality indicator acquisition device.

[0053] The current network transmission latency is the latency between the quality indicator acquisition device and the mattress quality testing data processing system.

[0054] S302. Add the current network transmission delay duration to the original heartbeat timestamp in the current heartbeat information to obtain the target heartbeat timestamp.

[0055] Specifically, the current network transmission delay refers to the time delay experienced when data is transmitted between the quality indicator acquisition device and the mattress quality testing data processing system. This delay can be understood as the time required for a data packet to travel from the quality indicator acquisition device to the mattress quality testing data processing system. In practical applications, the current network transmission delay can be obtained in various ways. For example, the mattress quality testing data processing system can periodically send probe packets to each quality indicator acquisition device and measure the round-trip time (RTT) from sending the probe packet to receiving the response packet from the quality indicator acquisition device. This RTT is then divided by two to obtain the current network transmission delay. Alternatively, this delay can be obtained using other network protocols or specialized delay measurement tools. The original heartbeat timestamp is the time recorded by the internal clock of the quality indicator acquisition device when sending heartbeat information. Because clock drift or initial clock inconsistencies may exist among the various quality indicator acquisition devices, and heartbeat information experiences delays during network transmission, the original heartbeat timestamp cannot completely and accurately reflect the actual time when the heartbeat information arrives at the mattress quality testing data processing system. By adding the current network transmission delay to the original heartbeat timestamp, the original heartbeat timestamp can be compensated for, thus obtaining the target heartbeat timestamp. The target heartbeat timestamp aims to more accurately reflect the logical reception time of heartbeat information on the mattress quality inspection data processing system side, or in other words, it is a timestamp that has been corrected for network delay and is closer to the system's unified time reference.

[0056] The proposed solution corrects the original heartbeat timestamp by acquiring the current network transmission delay between the quality indicator acquisition device and the mattress quality testing data processing system and adding it to the original heartbeat timestamp. This correction works because the original heartbeat timestamp reflects the local clock time of the quality indicator acquisition device. However, network transmission delays exist between different devices and between the device and the system, causing a deviation in the original heartbeat timestamp as perceived by the system. By compensating for this network transmission delay, the original heartbeat timestamp can be "shifted" from the device's local time domain to a time domain closer to the system's receiving time domain, allowing the corrected target heartbeat timestamp to more accurately reflect the actual time of the heartbeat information on the system side. Therefore, after the heartbeat information from multiple quality indicator acquisition devices has undergone its own network transmission delay correction, their target heartbeat timestamps become more comparable, providing a more accurate input for subsequently determining the current reference timestamp using the median, effectively reducing the impact of network delay on time synchronization accuracy.

[0057] Through the above technical solution, this application can effectively eliminate or significantly reduce timestamp deviations caused by network transmission delays. In a mattress quality testing data processing system, multiple quality indicator acquisition devices may be distributed in different locations, and their network transmission delays with the processing system may vary. If these delays are not considered and the original heartbeat timestamps are used directly for time synchronization, inconsistencies will occur in the timestamps of different devices on the system side, thus affecting the accuracy of the reference timestamp. This application introduces the current network transmission delay duration and corrects the original heartbeat timestamp, ensuring that the heartbeat timestamps of each quality indicator acquisition device are uniformly calibrated on the system side, thereby improving the accuracy of the target heartbeat timestamp. As a result, a more accurate and reliable reference time can be obtained when determining the current reference timestamp, laying a solid foundation for subsequent timestamp deviation rate calculations and timestamp adjustments of quality indicator data, ultimately improving the overall accuracy and reliability of mattress quality testing.

[0058] This application further proposes a more robust and accurate method for determining the current timestamp deviation rate of quality indicator acquisition equipment. By introducing historical data for comprehensive analysis, the accuracy and stability of deviation rate calculation are improved.

[0059] The steps described above for determining the current timestamp deviation rate of the quality index acquisition device based on its current heartbeat information and current reference timestamp include: The system acquires multiple historical heartbeat information entries from the quality indicator acquisition device within a historical time period, and multiple historical reference timestamps corresponding one-to-one with these entries within the same historical time period. The last heartbeat entry among these historical heartbeat information entries is the current heartbeat information. The historical heartbeat information includes the original heartbeat timestamp, and the duration of the historical time period is a preset duration. For each historical heartbeat information entry, the system determines the timestamp difference between the original heartbeat timestamp and the corresponding historical reference timestamp. Based on these timestamp differences, the system determines the current timestamp deviation rate of the quality indicator acquisition device.

[0060] Specifically, a historical time period can be understood as a continuous period of time traced back from the current moment. Its duration is preset to a specific value, such as several minutes, several hours, or longer, to ensure sufficient data to reflect clock drift trends. A longer preset duration allows for more historical data points to calculate the deviation rate, resulting in generally more stable calculations, but also increases data storage and processing overhead. Multiple historical heartbeat messages refer to the set of heartbeat messages periodically sent by the quality indicator acquisition devices within a historical time period, where each historical heartbeat message contains an original heartbeat timestamp. The multiple historical reference timestamps corresponding one-to-one with these historical heartbeat messages refer to the system reference timestamp determined by the mattress quality testing data processing system based on the (corrected) heartbeat information from all quality indicator acquisition devices at that time when each historical heartbeat message is received. In this way, a correspondence between the device's own clock and the system reference clock at different historical moments can be established. The timestamp difference refers to the difference between the original heartbeat timestamp sent by the quality indicator acquisition device at a certain historical moment and the corresponding historical reference timestamp determined by the mattress quality testing data processing system. This difference reflects the deviation of the clock of the quality indicator acquisition device from the system reference clock at that specific moment.

[0061] This application's solution incorporates multiple historical heartbeat data points and corresponding historical reference timestamps within a historical time period, and calculates timestamp differences based on this historical data. This allows for a more comprehensive capture of the long-term drift characteristics of the quality indicator acquisition device's clock. Traditional methods for calculating deviation rates that rely solely on current information are easily affected by instantaneous network latency or device clock jitter, leading to unstable calculation results. This solution, however, effectively smooths out these instantaneous noises by analyzing a series of historical timestamp differences, revealing the true and continuous deviation trend of the device's clock. It is precisely this time-series analysis based on historical data that makes the determined current timestamp deviation rate more representative and accurate, thus providing a more reliable basis for subsequent acquisition timestamp adjustments.

[0062] By utilizing historical data to determine the current timestamp deviation rate of the quality indicator acquisition equipment, the accuracy and stability of the deviation rate calculation can be significantly improved. Compared to calculation methods that rely solely on current data, this solution effectively resists interference from instantaneous network fluctuations and device clock jitter, ensuring that the determined deviation rate better reflects the true drift of the device clock. Consequently, when adjusting the acquisition timestamp of quality indicator data, the accuracy of the target acquisition timestamp is greatly improved, ensuring more precise alignment of data from different quality indicator acquisition devices on the timeline and significantly enhancing the reliability and consistency of mattress quality testing results.

[0063] This application further proposes a more accurate and robust method for determining the current timestamp deviation rate of a quality indicator acquisition device. By performing trend analysis and fitting on historical timestamp differences, noise can be effectively filtered out, revealing the long-term trend of clock deviation, thereby improving the accuracy and stability of deviation rate calculation.

[0064] Specifically, determining the current timestamp deviation rate of the quality indicator acquisition device based on multiple timestamp differences includes the following steps: A timestamp difference time variation curve is generated based on multiple timestamp differences and the historical reference timestamp corresponding to each timestamp difference; a linear fitting line for the timestamp difference time variation curve is determined based on the timestamp difference time variation curve; the slope of the linear fitting line is used as the current timestamp deviation rate of the quality indicator acquisition device.

[0065] The "generating a timestamp difference time variation curve" refers to plotting multiple timestamp differences acquired within a historical time period on the ordinate and the corresponding historical reference timestamp for each timestamp difference on the abscissa in a two-dimensional coordinate system. This creates a scatter plot or curve reflecting the change of timestamp differences over time. The curve visually demonstrates the trend of the deviation between the quality indicator acquisition device's clock and the reference timestamp over time.

[0066] "Determining the linear fitting line of the timestamp difference time variation curve" refers to using statistical or mathematical methods, such as the least squares method, to perform linear regression analysis on the data points on the generated timestamp difference time variation curve to find a straight line that best represents the overall trend of these data points. This linear fitting line can effectively smooth out random noise and short-term fluctuations in the data, thereby revealing the long-term, stable trend of the clock deviation of the quality indicator acquisition equipment.

[0067] "Using the slope of the linear fitting line as the current timestamp deviation rate of the quality index acquisition device" refers to the calculated slope value of the linear fitting line. Since the linear fitting line represents the average trend of the timestamp difference changing over time, its slope directly reflects the amount of change in the timestamp difference per unit time, that is, the drift rate of the quality index acquisition device's clock relative to the reference clock.

[0068] This application's solution effectively addresses the noise and short-term fluctuation issues that may exist in the aforementioned technical foundations by introducing linear fitting of the timestamp difference time variation curve. Specifically, when there is a deviation between the clock of the quality indicator acquisition device and the reference clock, this deviation usually exhibits a relatively stable linear drift trend over time. However, the actual acquired timestamp difference may be affected by factors such as instantaneous network latency and device processing latency, exhibiting a certain degree of randomness. By plotting these discrete timestamp differences and their corresponding historical reference timestamps into a time variation curve, and further performing linear fitting on it, the inherent linear trend of the clock deviation can be extracted from a large number of data points. The linear fitting process is essentially a data smoothing and trend extraction method, which can minimize the impact of random errors, thereby enabling the slope of the determined linear fitting line to more accurately and stably reflect the true deviation rate of the quality indicator acquisition device's clock. It is precisely because of this effective suppression of noise and accurate capture of the trend that subsequent timestamp adjustments based on this deviation rate are more reliable.

[0069] Through the above technical solution, this application can significantly improve the calculation accuracy and stability of the current timestamp deviation rate of the quality indicator acquisition equipment. Compared with direct calculation based on discrete timestamp differences, the linear fitting method can effectively filter out random noise and short-term fluctuations in the data, making the obtained deviation rate more representative of the true drift trend of the device clock. This more accurate deviation rate can ensure more accurate adjustment of the acquisition timestamp of the quality indicator data, thereby achieving a higher degree of synchronization in time for data from different quality indicator acquisition devices. Therefore, in the subsequent mattress quality inspection process, analysis can be based on more accurate and synchronized data, avoiding misjudgments or omissions caused by inconsistent timestamps, ultimately improving the overall accuracy and reliability of mattress quality inspection.

[0070] This application further proposes a step to adjust the acquisition timestamp of the quality indicator data in the current quality indicator acquisition information of the quality indicator acquisition device according to the current timestamp deviation rate of the quality indicator acquisition device, and obtain the target acquisition timestamp of the quality indicator data, including: Determine the target duration; the target duration is the time between the first moment and the second moment. The first moment is the moment when the mattress quality testing data processing system receives the current quality indicator collection information from the quality indicator collection device, and the second moment is the moment when the mattress quality testing data processing system determines the current timestamp deviation rate of the quality indicator collection device. Adjust the collection timestamp of the quality indicator data in the current quality indicator collection information of the quality indicator collection device according to the target duration and the current timestamp deviation rate of the quality indicator collection device to obtain the target collection timestamp of the quality indicator data.

[0071] Specifically, the target duration refers to the time elapsed from the moment the mattress quality testing data processing system receives the current quality indicator data from the quality indicator acquisition device, to the moment the mattress quality testing data processing system determines the current timestamp deviation rate of that quality indicator acquisition device, and so on. This target duration reflects the time delay between the data being sent from the acquisition device to the system and processed. The first moment can be understood as the point in time when the system records the receipt of specific quality indicator data, while the second moment is the point in time when the system completes the calculation or update of the current timestamp deviation rate for that device. By accurately determining this target duration, a crucial time reference can be provided for subsequent timestamp adjustments.

[0072] This application's solution introduces a target duration, allowing timestamp adjustments to fully account for potential further clock drift within the timeframe from data reception to the determination of the deviation rate. Specifically, the clock of the quality indicator acquisition device may exhibit continuous deviation, with its deviation rate remaining relatively stable over a period. When the mattress quality testing data processing system receives quality indicator data, this data carries an acquisition timestamp. However, by the time the system determines the device's current timestamp deviation rate, a certain amount of time may have elapsed. If this timeframe (i.e., the target duration) is not taken into account, and the original acquisition timestamp is directly adjusted using the current timestamp deviation rate, the adjusted timestamp may still exhibit inconsistencies with the actual reference time. By multiplying the target duration by the current timestamp deviation rate, the additional clock drift during this period can be estimated, thereby providing more accurate compensation for the original acquisition timestamp and ensuring that the adjusted target acquisition timestamp more accurately reflects the actual time of data acquisition.

[0073] The above technical solution effectively addresses the problem of insufficient timestamp adjustment accuracy caused by the time difference between data reception and deviation rate determination during mattress quality testing data processing. By introducing a target duration, the timestamp adjustment dynamically adapts to system processing delays, thereby improving the calibration accuracy of quality indicator data timestamps. This ensures more precise temporal alignment of quality indicator data from different acquisition devices, providing a more reliable and consistent data foundation for subsequent mattress quality testing, and ultimately enhancing the overall accuracy and reliability of quality testing.

[0074] This application further proposes a step to adjust the acquisition timestamp of the quality indicator data in the current quality indicator acquisition information of the quality indicator acquisition device according to the target duration and the current timestamp deviation rate of the quality indicator acquisition device, in order to obtain the target acquisition timestamp of the quality indicator data. The steps include: The product of the target duration and the current timestamp deviation rate of the quality indicator acquisition device is used as the timestamp adjustment duration; the target acquisition timestamp of the quality indicator data is determined based on the acquisition timestamp of the quality indicator data and the timestamp adjustment duration, and the duration between the target acquisition timestamp and the acquisition timestamp is the timestamp adjustment duration.

[0075] Specifically, the timestamp adjustment duration refers to the amount of time used to correct the original data acquisition timestamp. It is calculated by multiplying the target duration by the current timestamp deviation rate of the quality indicator acquisition device. The target duration is the time interval between the moment the mattress quality testing data processing system receives the current quality indicator acquisition information from the quality indicator acquisition device (first moment) and the moment the mattress quality testing data processing system determines the current timestamp deviation rate of the quality indicator acquisition device (second moment). The current timestamp deviation rate reflects the drift trend between the quality indicator acquisition device's own clock and the system reference clock. By multiplying these two, the cumulative deviation of the quality indicator acquisition device's local clock relative to the system reference clock within the target duration can be accurately estimated.

[0076] Furthermore, after obtaining the timestamp adjustment duration, the collection timestamps of the quality indicator data are used to calculate the target collection timestamp. The target collection timestamp is a corrected timestamp that is closer to the actual collection time. This adjustment process can be manifested as adding or subtracting the timestamp adjustment duration based on the collection timestamp, depending on the direction of the timestamp deviation rate. For example, if the deviation rate is positive, it indicates that the device clock is too fast, and it may be necessary to subtract the adjustment duration from the collection timestamp; if the deviation rate is negative, it indicates that the device clock is too slow, and it may be necessary to add the adjustment duration to the collection timestamp. Ultimately, the difference between the target collection timestamp and the original collection timestamp is the determined timestamp adjustment duration.

[0077] The proposed solution precisely quantifies the cumulative drift of the device clock relative to the system reference clock from data acquisition to data processing by multiplying the target duration by the current timestamp deviation rate of the quality indicator acquisition device. This precise quantification allows for accurate correction of the original quality indicator data acquisition timestamp. By applying this calculated timestamp adjustment duration to the original acquisition timestamp, time errors introduced by device clock drift can be effectively eliminated, thus unifying data acquired at different times by different quality indicator acquisition devices onto the same time reference. This dynamic adjustment mechanism based on deviation rate and target duration ensures highly synchronized quality indicator data timestamps even when device clocks experience continuous drift.

[0078] The above technical solution provides a more accurate and dynamic timestamp adjustment mechanism. By combining the target duration with the current timestamp deviation rate, the actual clock drift of the device within a specific time period can be accurately calculated, making the adjustment of the timestamp for quality indicator data acquisition more refined and real-time. This significantly improves the synchronization accuracy of data timestamps between different quality indicator acquisition devices, providing a more reliable and consistent data foundation for subsequent mattress quality testing based on the same target acquisition timestamp, and effectively avoiding data misjudgment or analytical bias caused by timestamp inconsistencies.

[0079] Specifically, the steps for quality testing of a mattress based on quality index data from multiple quality index collection devices corresponding to the same target collection timestamp include: Perform preset data processing operations on multiple quality indicator data corresponding to the same target timestamp to obtain comprehensive quality data; if the comprehensive quality data is outside the corresponding preset comprehensive quality data range, determine that the mattress quality is abnormal.

[0080] The preset data processing operation can be understood as the process of integrating, analyzing, and calculating quality indicator data acquired from different quality indicator acquisition devices at the same target acquisition time stamp. Specifically, this operation may include, but is not limited to, calculating the average, weighted average, summation, maximum value, minimum value, standard deviation, or performing other statistical analyses. Its purpose is to transform multiple discrete quality indicator data into a comprehensive indicator that can fully reflect the overall quality of the mattress.

[0081] Comprehensive quality data refers to a single or composite numerical value obtained through the aforementioned preset data processing operations, which represents the overall quality status of the mattress at a specific moment. For example, if the quality indicators of the mattress include pressure distribution, firmness, breathability, etc., then the comprehensive quality data can be a total score after weighted averaging of these indicators, or a multi-dimensional vector.

[0082] A preset quality comprehensive data range refers to a pre-defined set of acceptable quality comprehensive data intervals or thresholds. This range is typically determined based on industry standards, product design specifications, historical data on qualified products, or expert experience. For example, an upper limit and a lower limit can be set; when the quality comprehensive data exceeds this range, the mattress is considered to have a quality problem. Its purpose is to provide an objective standard for judging mattress quality.

[0083] When the overall quality data falls outside the corresponding preset range, it indicates that the mattress's current quality does not meet the preset standards, and the mattress's quality label is determined to be abnormal. This means that the mattress may have manufacturing defects, material problems, or other issues that do not meet quality requirements.

[0084] This application's solution first performs pre-defined data processing on quality indicator data acquired by multiple quality indicator acquisition devices at the same target acquisition time stamp, thereby integrating scattered and potentially heterogeneous raw data into a unified comprehensive quality data. This integration process can more comprehensively and objectively reflect the overall quality status of the mattress, avoiding the bias or errors that may result from judging based on a single indicator or unprocessed raw data. Subsequently, by comparing this comprehensive quality data with a pre-defined comprehensive quality data range, it is possible to quickly and accurately determine whether the mattress meets the established quality standards. When the comprehensive quality data exceeds the pre-defined range, an anomaly indicator is triggered, thereby promptly detecting and identifying mattresses with quality problems.

[0085] The above technical solution enables accurate and comprehensive evaluation of mattress quality. By processing multiple quality indicator data in a unified manner and generating comprehensive quality data, the limitations of judging by a single indicator are effectively avoided, improving the comprehensiveness and accuracy of quality inspection. Simultaneously, by setting a preset comprehensive quality data range and comparing it, the process of judging quality anomalies is standardized and automated, significantly improving the efficiency and reliability of quality inspection. This helps to promptly identify and isolate substandard products, ensuring product quality.

[0086] This application further proposes that after determining that the quality label of the mattress is abnormal, target information is generated and sent to the alarm device to achieve timely notification and handling of abnormal situations.

[0087] After determining that the mattress's quality label is abnormal, the method also includes: Generate target information; the target information includes the batch information of the mattress, the quality identification of the mattress, and multiple quality index data corresponding to the same target timestamp; send the target information to the alarm device and make the alarm device display the target information.

[0088] Specifically, generating target information refers to the automatic collection of important information related to an abnormal mattress after the mattress quality inspection data processing system detects an anomaly. This includes: batch information for tracing the mattress's production batch, facilitating subsequent quality traceability and problem investigation; a quality identifier that clearly indicates the mattress's abnormal quality status, which could be a Boolean value or a specific anomaly code; and multiple quality indicator data corresponding to the same target timestamp, which are the original or processed key data leading to the anomaly judgment. These could include readings from various quality indicator acquisition devices at the time the anomaly occurred, and this data helps in analyzing the specific causes of the anomaly.

[0089] Furthermore, sending target information to alarm devices refers to transmitting the aforementioned generated target information to preset alarm devices via a network or other communication methods. Alarm devices may include, but are not limited to, displays on the production line, operator workstations, mobile terminals (such as mobile phones and tablets), or monitoring systems in the central control room. The purpose is to ensure that abnormal information can be quickly and accurately transmitted to the personnel or systems that need to be aware of it and take action.

[0090] Displaying target information on alarm devices can be understood as the alarm device presenting the target information to the user in an intuitive and easy-to-understand manner after receiving it. For example, the batch number, abnormal status, and relevant quality indicator data of the mattress can be displayed on the screen in the form of text, charts, or color warnings, so that operators can understand the abnormal situation at a glance and make quick judgments and responses based on this information.

[0091] This application's solution, upon determining that a mattress quality issue is abnormal, immediately generates target information containing key traceability information (batch information), specific status information (quality identifier), and diagnostic information (quality indicator data), and proactively sends this information to the alarm device for display. This solves the problem of merely identifying an anomaly without a subsequent effective response. It is precisely this proactive information generation and distribution mechanism that enables timely detection and handling of abnormal situations, avoiding information delays or omissions, and ensuring rapid response and resolution of quality issues.

[0092] Through the above technical solution, this application enables timely early warning and information transmission for mattress quality abnormalities, significantly improving the production line's response speed to quality issues. Specifically, by providing target information including batch information, quality identification, and original quality indicator data, relevant personnel can quickly locate problematic mattresses, trace their production batches, and conduct preliminary fault analysis based on detailed quality indicator data. This effectively shortens the time for problem investigation and resolution, reduces the risk of substandard products entering the market, and improves overall production management efficiency and product quality control levels.

[0093] In some preferred embodiments, assuming the mattress quality inspection data processing system, while inspecting a mattress, finds that the overall quality data obtained after processing the pressure distribution data exceeds a preset acceptable range, the mattress is identified as "abnormal." At this point, the system immediately generates target information, which may include the mattress's batch number "20230815-001," the quality label "abnormal," and the original readings of multiple pressure sensors at the time of inspection that caused the abnormality (e.g., pressure value 150N in the upper left corner, 80N in the upper right corner, 120N in the lower left corner, and 90N in the lower right corner, while the normal range should be between 100N and 110N). Subsequently, this target information is sent to an LED display screen next to the production line and the workshop supervisor's mobile terminal. The LED display screen will prominently display "Batch: 20230815-001, Status: Abnormal, Pressure Data: [150, 80, 120, 90]" in red, and the workshop supervisor's mobile terminal will also receive a push notification displaying the same information. In this way, production line operators and workshop supervisors can be informed of abnormalities immediately and quickly locate the abnormal mattress for isolation or rework based on batch information. At the same time, based on pressure data, they can make a preliminary judgment that the problem may be in the upper left corner of the mattress, thereby guiding subsequent detailed inspection and repair.

[0094] This application also discloses a mattress quality testing data processing system, comprising: an acquisition device and a processing device; the acquisition device is used to acquire current quality indicator acquisition information and current heart rate information of each of a plurality of quality indicator acquisition devices for a mattress; the current quality indicator acquisition information includes quality indicator data and acquisition timestamp of the quality indicator data, and the current heart rate information includes the original heart rate timestamp and the device identifier of the quality indicator acquisition device; the processing device is used to determine a current reference timestamp based on the current heart rate information of the plurality of quality indicator acquisition devices; the processing device is used to determine the current timestamp deviation rate of each of the plurality of quality indicator acquisition devices based on the current heart rate information and the current reference timestamp of the quality indicator acquisition device; the processing device is used to adjust the acquisition timestamp of the quality indicator data in the current quality indicator acquisition information of the quality indicator acquisition device according to the current timestamp deviation rate of the quality indicator acquisition device to obtain a target acquisition timestamp of the quality indicator data; the processing device is used to perform quality testing on the mattress based on the quality indicator data of the plurality of quality indicator acquisition devices corresponding to the same target acquisition timestamp.

[0095] The core steps of the mattress quality inspection data processing method have been described in the above embodiments and will not be repeated here. It should be emphasized that this application further proposes a mattress quality inspection data processing system, which implements the above method through a specific device.

[0096] Specifically, this system includes an acquisition device and a processing device.

[0097] An acquisition device can be understood as a hardware or software module responsible for data communication and information reception with external quality indicator acquisition devices. For example, an acquisition device could be a network interface card working with packet parsing software to monitor network data streams from various quality indicator acquisition devices. Its function is to receive current quality indicator acquisition information and current heartbeat information sent by the quality indicator acquisition devices. In one implementation, the acquisition device can be configured to receive data via the TCP / IP protocol and decapsulate the received data packets to extract quality indicator data, acquisition timestamps, original heartbeat timestamps, and device identifiers of the quality indicator acquisition devices. For example, an acquisition device could be an industrial gateway configured with multiple communication ports, each port connecting to one or a group of quality indicator acquisition devices, receiving data through polling or interrupt mechanisms.

[0098] The device identifier of the quality indicator acquisition device included in the current heartbeat information is a code used to uniquely identify the quality indicator acquisition device sending the heartbeat signal. For example, each quality indicator acquisition device is assigned a unique serial number or MAC address at the factory, and this identifier is sent to the mattress quality inspection data processing system along with the heartbeat information. The purpose is to enable the processing device to distinguish the raw heartbeat timestamps from different devices and to independently calculate and maintain the timestamp deviation rate for each device. In practical applications, the device identifier can be a string pre-configured in the device firmware or an ID dynamically generated when the device first connects to the system.

[0099] The processing device can be understood as the core computing unit responsible for performing data analysis, timestamp calibration, and quality inspection logic. For example, the processing device can be a high-performance industrial control computer or server cluster running software programs that implement the methods of this application. The processing device receives information from the acquisition device and performs subsequent calculation tasks based on this information. Specifically, the processing device determines the current reference timestamp based on the current heartbeat information of multiple quality indicator acquisition devices. For example, the processing device can run a time synchronization algorithm that receives the original heartbeat timestamps and device identifiers of all devices and calculates a unified reference time. Subsequently, for each quality indicator acquisition device, the processing device determines the current timestamp deviation rate of that device based on its current heartbeat information and the current reference timestamp. For example, the processing device can maintain a database storing the historical heartbeat data and corresponding reference timestamps of each device and use this historical data to fit the timestamp deviation rate. Finally, the processing device adjusts the acquisition timestamps of the quality indicator data sent by the quality indicator acquisition devices according to the determined timestamp deviation rate to obtain the target acquisition timestamp. For example, the processing device can apply a time correction formula to calculate the original acquisition timestamp with the deviation rate and time interval to eliminate time drift.

[0100] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for processing mattress quality inspection data, characterized in that, The method is applied to a mattress quality inspection data processing system, and includes: For each of the multiple quality indicator acquisition devices for the mattress, the current quality indicator acquisition information and current heart rate information of the quality indicator acquisition device are obtained; the current quality indicator acquisition information includes quality indicator data and the acquisition timestamp of the quality indicator data, and the current heart rate information includes the original heart rate timestamp; The current baseline timestamp is determined based on the current heartbeat information from the multiple quality indicator acquisition devices. For each of the plurality of quality indicator acquisition devices, the current timestamp deviation rate of the quality indicator acquisition device is determined based on the current heartbeat information of the quality indicator acquisition device and the current reference timestamp; The acquisition timestamp of the quality indicator data in the current quality indicator acquisition information of the quality indicator acquisition device is adjusted according to the current timestamp deviation rate of the quality indicator acquisition device to obtain the target acquisition timestamp of the quality indicator data. The mattress is quality tested based on quality index data from multiple quality index collection devices corresponding to the same target collection timestamp.

2. The mattress quality inspection data processing method according to claim 1, characterized in that, The current baseline timestamp is determined based on the current heartbeat information from the multiple quality indicator acquisition devices, including: For each of the multiple quality indicator acquisition devices, the original heartbeat timestamp in the current heartbeat information of the quality indicator acquisition device is corrected to obtain the target heartbeat timestamp; The median of the target heartbeat timestamps corresponding to multiple quality indicator acquisition devices is used as the current baseline timestamp.

3. The mattress quality inspection data processing method according to claim 2, characterized in that, The original heartbeat timestamp in the current heartbeat information of the quality index acquisition device is corrected to obtain the target heartbeat timestamp, including: The current network transmission delay of the quality indicator acquisition device is obtained; the current network transmission delay is the delay between the quality indicator acquisition device and the mattress quality testing data processing system. The target heartbeat timestamp is obtained by adding the current network transmission delay duration to the original heartbeat timestamp in the current heartbeat information.

4. The mattress quality inspection data processing method according to claim 1, characterized in that, Determining the current timestamp deviation rate of the quality indicator acquisition device based on the current heartbeat information of the quality indicator acquisition device and the current reference timestamp includes: The quality indicator acquisition device acquires multiple historical heartbeat information within a historical time period and multiple historical reference timestamps corresponding one-to-one with the multiple historical heartbeat information within the historical time period; the last heartbeat information among the multiple historical heartbeat information is the current heartbeat information, the historical heartbeat information includes the original heartbeat timestamp, and the duration of the historical time period is a preset duration; For each historical heartbeat information in multiple historical heartbeat information, determine the timestamp difference between the original heartbeat timestamp in the historical heartbeat information and the corresponding historical reference timestamp; The current timestamp deviation rate of the quality indicator acquisition device is determined based on multiple timestamp differences.

5. The mattress quality inspection data processing method according to claim 4, characterized in that, Determining the current timestamp deviation rate of the quality indicator acquisition device based on multiple timestamp differences includes: A time change curve for the timestamp difference is generated based on the multiple timestamp differences and the historical reference timestamp corresponding to each timestamp difference; A linear fitting line for the timestamp difference time variation curve is determined based on the timestamp difference time variation curve. The slope of the linear fitting line is used as the current timestamp deviation rate of the quality index acquisition device.

6. The mattress quality inspection data processing method according to claim 1, characterized in that, The acquisition timestamps of the quality indicator data in the current quality indicator acquisition information of the quality indicator acquisition device are adjusted according to the current timestamp deviation rate of the quality indicator acquisition device to obtain the target acquisition timestamp of the quality indicator data, including: Determine the target duration; the target duration is the duration between a first moment and a second moment, the first moment being the moment when the mattress quality testing data processing system receives the current quality indicator collection information from the quality indicator collection device, and the second moment being the moment when the mattress quality testing data processing system determines the current timestamp deviation rate of the quality indicator collection device; The acquisition timestamps of the quality indicator data in the current quality indicator acquisition information of the quality indicator acquisition device are adjusted according to the target duration and the current timestamp deviation rate of the quality indicator acquisition device to obtain the target acquisition timestamp of the quality indicator data.

7. The mattress quality inspection data processing method according to claim 6, characterized in that, The acquisition timestamps of the quality indicator data in the current quality indicator acquisition information of the quality indicator acquisition device are adjusted according to the target duration and the current timestamp deviation rate of the quality indicator acquisition device to obtain the target acquisition timestamp of the quality indicator data, including: The product of the target duration and the current timestamp deviation rate of the quality index acquisition device is used as the timestamp adjustment duration; The target collection timestamp of the quality indicator data is determined based on the collection timestamp of the quality indicator data and the timestamp adjustment duration, and the duration between the target collection timestamp and the collection timestamp is the timestamp adjustment duration.

8. The mattress quality inspection data processing method according to claim 1, characterized in that, The mattress is subjected to quality inspection based on quality index data from multiple quality index acquisition devices corresponding to the same target acquisition timestamp, including: Perform preset data processing operations on multiple quality indicator data corresponding to the same target timestamp to obtain comprehensive quality data; If the overall quality data falls outside the corresponding preset overall quality data range, the quality of the mattress is determined to be abnormal.

9. A mattress quality inspection data processing method according to claim 8, characterized in that, After determining that the quality label of the mattress is abnormal, the method further includes: Generate target information; the target information includes the batch information of the mattress, the quality identifier of the mattress, and multiple quality indicator data corresponding to the same target timestamp; The target information is sent to the alarm device, and the alarm device displays the target information.

10. A mattress quality inspection data processing system, characterized in that, The system includes: Acquisition device and processing device; The acquisition device is used to acquire the current quality indicator acquisition information and current heart rate information of each of the multiple quality indicator acquisition devices for the mattress; the current quality indicator acquisition information includes quality indicator data and the acquisition timestamp of the quality indicator data, and the current heart rate information includes the original heart rate timestamp and the device identifier of the quality indicator acquisition device. The processing device is used to determine the current reference timestamp based on the current heartbeat information of the multiple quality index acquisition devices. The processing device is used to determine the current timestamp deviation rate of each of the plurality of quality indicator acquisition devices based on the current heartbeat information of the quality indicator acquisition device and the current reference timestamp. The processing device is used to adjust the collection timestamp of the quality indicator data in the current quality indicator collection information of the quality indicator collection device according to the current timestamp deviation rate of the quality indicator collection device, so as to obtain the target collection timestamp of the quality indicator data. The processing device is used to perform quality testing on the mattress based on quality index data from multiple quality index acquisition devices corresponding to the same target acquisition timestamp.

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