A washable mattress and a pollution warning system

Abstract

This application discloses a washable mattress and a pollution early warning system. The pollution early warning system includes a control host, a data transmission module, and a data acquisition module. The control host contains a main control chip, a wireless communication module, and an audible and visual alarm module. The data acquisition module is a flexible sensor unit, which includes an optical bacteria detection sensor, a capacitive humidity detection sensor, and a gas resistance detection sensor. These three sensors collect data on bacteria, humidity, and odor, respectively. The collected data is fed back to the main control chip via the data transmission module. The main control chip monitors the collected data in real time, makes early warning judgments, and feeds the results back to the control host to control the audible and visual alarm module for reminders. A washable mattress is also proposed, comprising a mattress body and a protective cover, which together form a cavity for housing the flexible sensor unit. The washable mattress, used in conjunction with the pollution early warning system, monitors the mattress's pollution status in real time and reminds users to replace the mattress.

Description

Technical Field

[0001] This application relates to the fields of medical device and hospital infection control technology, specifically to a washable mattress and a contamination early warning system. Background Technology

[0002] According to data from the "China Hospital Infection Management Development Report (2025)," medical mattresses are a key component of hospital infection control in China.

[0003] While traditional clinical mattresses can be washed as a whole, the washing and drying time for a single mattress is long, resulting in low efficiency. Furthermore, they lack real-time contamination monitoring capabilities and rely on manual experience for judgment, which can easily lead to missed diagnoses and cross-infection. At the same time, the high operating costs resulting from manual inspections and excessive cleaning increase the economic burden.

[0004] Current integrated smart mattresses have built-in sensors, which cannot withstand high-temperature and high-pressure cleaning. They can only be wiped and disinfected in seconds, which poses a risk of incomplete disinfection. In addition, the cost per mattress and maintenance costs are high, making it difficult to popularize them in hospitals. Although they have monitoring functions, they are mostly used to monitor vital signs and are not designed for mattress contamination indicators, which cannot meet the precise needs of hospital infection control. Summary of the Invention

[0005] This application addresses the problems of low efficiency in existing mattress cleaning and drying systems, high cost of smart mattresses, and inability to thoroughly clean mattresses. It proposes a washable mattress and pollution warning system, including a control host, a data transmission module, and a data acquisition module. The control host contains a main control chip, a wireless communication module, and an audible and visual alarm module. The data acquisition module is a flexible sensor unit, including an optical bacteria detection sensor, a capacitive humidity detection sensor, and a gas resistance detection sensor. These three sensors collect data on bacteria, humidity, and odor, respectively. The data acquisition module feeds the collected data back to the main control chip in real time via the data transmission module. The main control chip monitors, processes, stores, analyzes, and makes warning judgments on the collected data in real time, and feeds the processing results back to the control host, which then controls the audible and visual alarm module to issue an alert.

[0006] The technical solution of this application adopts an overall design of "physical separation of monitoring and cleaning". Through the coordinated cooperation of the mattress, pollution early warning system and mobile terminal and a scientific sharing and scheduling mechanism, it can achieve easy cleaning, real-time monitoring and early warning, and low cost. It effectively solves the problem that the sensors built into current integrated smart mattresses cannot be removed, and current smart mattresses can only monitor vital signs but cannot monitor the pollution indicators of the mattress.

[0007] Furthermore, pre-set warning values ​​for bacterial concentration, humidity, or odor concentration are installed in the control host. When the indicators collected by the control host exceed the warning value for 30 consecutive seconds, the warning judgment triggers the sound and light alarm module to issue a reminder, and simultaneously triggers the sound and light alarm module and the mobile terminal to issue a warning.

[0008] Furthermore, the data collected by each sensor in the acquisition module is preprocessed using a moving average filtering algorithm. The data collected over 30 seconds is averaged to obtain the average value for each sensor over 30 seconds. The main control chip then uses a calibration algorithm to convert each filtered average value into a corresponding physical quantity indicator. The physical quantity indicators include the voltage signal V output by the optical bacteria detection sensor, the capacitance signal C output by the capacitive humidity detection sensor, and the resistance signals R1, R2, and R3 output by the gas resistance detection sensor. Among them, the gas resistance detection sensor is a gas sensor array composed of three independent gas-sensitive resistor units. The three gas-sensitive resistor units are arranged in parallel in the same detection chamber, and each independently outputs resistance signals R1, R2, and R3. The three gas-sensitive resistor units integrated in the same detection chamber respond to the target odor gas and output independent resistance signals R1, R2, and R3. The accuracy and anti-interference ability of odor detection are improved through multi-signal feature fusion.

[0009] Furthermore, when the main control chip converts each filtered average value into its corresponding physical quantity, it converts the filtered voltage signal into bacterial C based on the optical detection principle. bac The formula used is: C bac =K2× V- b2; K2 is the calibration coefficient, K2=3.2, b2 is the intercept, b2=0.5; The filtered capacitance signal is converted to humidity (RH) using the following formula: RH = K1× C- b1; K1 is the calibration coefficient, K1=2.5, b1 is the intercept, b1=20; The gas resistance sensor contains three gas resistors. The resistance signals of these three gas resistors, R1, R2, and R3, are collected, and a comprehensive feature value F is calculated using a feature fusion algorithm. R0 is the reference resistance value of the flexible sensor in a clean air environment; Then, the odor concentration C is converted using a secondary calibration curve. odor =a1×F 2 +b3×F+c1, where a1, b3, and c1 are all quadratic fitting coefficients, a1=0.0012, b3=0.035, and c1=0.002.

[0010] Furthermore, the main control chip will convert the humidity (RH) and odor (C) values. odor Bacteria C bacIt compares the value with the preset threshold in real time, and sets a 30-second continuous exceedance judgment. The main control chip accumulates the exceedance collection points through its storage function. When a certain indicator exceeds the preset threshold for 30 consecutive seconds, an early warning is triggered, and the control host starts the sound and light alarm module. The duration of the sound and light alarm module is then transmitted to the mobile terminal via the wireless communication module.

[0011] This application also proposes a method for sharing a pollution early warning system, in which each control host, each data transmission module, and each acquisition module are respectively configured to form a complete early warning system. Each early warning system is matched with a unique ID or QR code, and each early warning system is dynamically matched with a mattress. Multiple early warning systems are matched with a mobile terminal. The mobile terminal displays the status of each early warning system in real time, including whether it is idle, occupied, being disinfected, or malfunctioning. Each early warning system is monitored through the mobile terminal, and the mobile terminal simultaneously transmits information to the management system.

[0012] This application also proposes a washable mattress, including a mattress body, a protective cover on the mattress body, at least two parallel folds on the mattress body, a waterproof zipper on the protective cover, and the protective cover and the mattress body forming a cavity for placing a collection module, which collects real-time data on bacteria, humidity and odor on the mattress body.

[0013] Furthermore, the mattress body consists of three layers: upper, middle, and lower. Each layer has raised points, and the raised points of adjacent layers are staggered. Each raised point has a through-hole drainage hole. The inner surface of the protective cover has fasteners for fixing the flexible sensor unit, and the flexible sensor unit is detachably connected to the fasteners. The raised points allow for air circulation, and the through-hole drainage hole allows for rapid water flow.

[0014] Furthermore, the raised points on each layer of the mattress body are arranged in an equilateral triangular array, with each raised point located at the center of the equilateral triangle formed by three raised points in adjacent layers. The diameter of the air fibers at the creases on the mattress body is smaller than that at the non-crease areas. This smaller diameter facilitates folding the mattress body.

[0015] Furthermore, the creases on the mattress body are evenly spaced at 1 / 3 of its unfolded length. This makes it easy to fit into hospital transport equipment when folded.

[0016] The beneficial effects of this application are: 1. This invention is a mattress and system that combines real-time contamination detection with washable disinfection capabilities. The mattress body has a 3D mesh structure with raised and through-hole drainage design, allowing for direct 85°C high-temperature and high-pressure washing and disinfectant soaking. The contamination warning system can be quickly disinfected by wiping with alcohol. 2. This invention utilizes three sensors in a flexible sensor unit to monitor and issue early warnings for three core types of hospital-acquired infections: bacteria, humidity, and odor. This dual local and remote alert system reduces reliance on manual intervention and the risk of missed diagnoses. 3. This invention completely separates the electronic components from the mattress body, i.e., the monitoring and cleaning processes are physically separated, thus solving the problem of integrated mattresses being unable to be thoroughly cleaned. 4. It also supports multiple beds for sharing and reuse, reducing operation and maintenance costs and keeping maintenance costs low. Attached Figure Description

[0017] Figure 1 A schematic diagram of the main body of the mattress in this application; Figure 2 A schematic diagram of the protective jacket used in this application; Figure 3 This application includes a partial schematic diagram of the three raised points of the mattress body; Figure 4 A schematic diagram of the pollution early warning system in this application; Figure 5 Module diagram of the pollution early warning system in this application; Explanation of reference numerals in the attached diagram: 100-mattress body, 200-protective cover, 210-waterproof zipper, 330-control host, 320-data transmission module, 310-acquisition module, 340-main control chip, 332-wireless communication module, 331-audio-visual alarm module. Detailed Implementation

[0018] The specific embodiments of this application are described in detail below with reference to the accompanying drawings.

[0019] Example 1

[0020] refer to Figure 1 A washable mattress includes a mattress body 100, a protective cover 200 covering the mattress body 100, two parallel folds on the mattress body 100, and a waterproof zipper 210 on the protective cover 200. The protective cover 200 and the mattress body 100 form a cavity for housing a collection module 310, the cavity having a thickness of 5-8 mm. The collection module 310 collects real-time data on bacteria, humidity, and odor on the mattress body 100.

[0021] The mattress body is divided into three layers: upper, middle, and lower. Each layer is made of medical-grade polyolefin elastomer TPE, such as BASF 1185A type. It is integrally molded with air fiber into a 3D three-dimensional mesh structure without closed interlayer, which has excellent elasticity and recovery, resistance to high and low temperatures and chemical corrosion. It can withstand high temperature cleaning of 85℃ and disinfection with 75% alcohol. Water flow and disinfectant can quickly penetrate to achieve thorough disinfection.

[0022] like Figure 1 and Figure 3 As shown, each layer of the mattress body 100 has raised points; the raised points on each layer are arranged in an equilateral triangular array and are staggered. The raised points of adjacent layers are staggered and not aligned vertically. Each raised point is located at the center of the equilateral triangle formed by three raised points of adjacent layers, forming a honeycomb three-dimensional support structure, which provides uniform support, more even pressure distribution, and improved stability. The raised points are 5-8mm high and spaced 15-20mm apart. Each protruding point on each layer has a through-hole 110; the diameter of the drainage hole 110 is 2-4mm, and the spacing is 15mm. The through-hole design ensures that water flows through quickly. The drainage holes 110 on the mattress body 100 are arranged in an equilateral triangle array to achieve optimal drainage efficiency, which can increase the water flow rate during washing by more than 60%.

[0023] The mattress body 100 has a textured surface, with raised sections providing support and non-raised sections allowing for breathability. This results in advantages such as quick drying, good breathability, easy cleaning, and a single mattress treatment time of ≤5 minutes. The 3D mesh structure of the mattress body 100 ensures rapid dispersion of contaminants, facilitating localized detection and rapid drying after cleaning. It complies with WS / T 508-2025 "Technical Standard for Washing and Disinfection of Medical Textiles in Medical Institutions".

[0024] The mattress body has two parallel creases. The air fiber diameter at the crease is 0.15mm to improve folding flexibility, while the diameter at the non-crease is 0.2mm to ensure support performance. Its folding angle is 180°.

[0025] Two flexible pre-molded creases are molded at 1 / 3 and 2 / 3 of the unfolded length of the mattress body to achieve three-part folding, which can be folded into a compact form with 1 / 3 of the original volume, and is compatible with hospital general medical fabric transfer facilities; the yarn diameter at the crease is smaller than that at the non-crease, which improves folding performance and maintains the overall structural strength while achieving efficient folding.

[0026] The protective jacket 200 is made of high-density 3D mesh fabric of polyester filament with 0.5%-2% nano-silver antibacterial agent added, combined with a microporous breathable TPU film. It is hot-pressed with a hot melt adhesive film with a melting point of 120℃, and the composite strength is ≥2N / cm. It has antibacterial properties and ensures that it will not delaminate after long-term use. It meets the requirements of GB / T 3923.1-2013 "Textiles - Tensile Properties of Fabrics - Part 1: Determination of Breaking Strength and Elongation at Break - Strip Method". The fabric has an antibacterial rate of not less than 90% against Staphylococcus aureus (ATCC 6538) and Escherichia coli (ATCC 25922), which meets the mandatory requirements of antibacterial performance in GB / T 20944.1—2007 "Textiles - Antibacterial Properties - Part 1: Agar Dispersion Method". The protective jacket 200 has a 2cm wide reinforced edging, which is double-stitched with a 3mm stitch spacing to improve tensile strength, prevent seam unraveling during use, and extend service life.

[0027] The protective jacket 200 is sewn with an IPX7-rated medical-grade stainless steel waterproof zipper 210 that can withstand immersion in 1m water for 30 minutes without leakage. The waterproof rating is IPX7. The waterproof zipper 210 matches the opening length of the protective jacket, and the zipper can be closed to achieve a seal.

[0028] When the protective cover 200 is placed outside the mattress body 100, it forms a 5-8mm thick receiving cavity. The fasteners inside the receiving cavity use Velcro, which is sewn to the protective cover 200 to facilitate the fixing of the acquisition module and to ensure that the acquisition module's acquisition front faces upward.

[0029] When transportation is required, the mattress body 100 is folded and placed inside the corresponding protective cover 200. The waterproof zipper 210 is then zipped up to form a sealed unit. A label indicating contamination and needing disinfection is affixed to the surface of the protective cover. The mattress is then transported to the disinfection center via a medical contamination transport vehicle. The mattress body 100 is washed at high temperature in a medical-grade washing machine and then dried in a medical-grade dryer. The protective cover 200 undergoes separate high-temperature sterilization. After cleaning and disinfection, the total bacterial count is ≤20 CFU / cm². The mattress body 100 is compatible with various sizes of hospital beds: standard single bed (200cm×90cm×10cm), ICU bed (200cm×100cm×12cm), and children's bed (160cm×70cm×8cm). The pre-set folds of different sizes of mattresses are set at equal intervals according to 1 / 3 of the unfolded length to ensure that they can be adapted to general hospital transport equipment after folding.

[0030] Example 1: A washable mattress suitable for a standard 200cm×90cm single hospital bed. The mattress body is made of TPE air fiber, integrally thermoformed at a molding temperature of 200-220℃ and a molding pressure of 0.8-1.0MPa, with a heat preservation and pressure holding time of 5-8 minutes. Before molding, the raw material is dried at 80℃ for 4 hours to remove moisture and avoid air bubble defects. The finished product is 10cm thick, with a 3D raised surface height of 6mm and a center-to-center distance of 18mm between adjacent raised areas. Each raised area has a 3mm diameter through-hole 110. Two pre-set creases are formed at 66.7cm from each end of the mattress along its length. The fiber diameter at the creases is 0.15mm, and the fiber diameter at the non-creases is 0.2mm. It can withstand 1000 repeated folds without breakage or permanent deformation, and its air permeability is 85L / (m²·s) under 200Pa pressure.

[0031] The protective jacket 200 uses a high-density 3D mesh fabric made of polyester filament as the base material, laminated with a layer of microporous breathable TPU film, and adds 1% by mass of nano-silver antibacterial masterbatch. It achieves a 95% inhibition rate against Staphylococcus aureus and Escherichia coli, meeting the requirements of the national standard GB / T 46272-2025 "Intelligent Mattresses". The protective jacket 200 has a medical-grade stainless steel waterproof zipper (210) at the opening, with a waterproof rating of IPX7.

[0032] Example 2

[0033] like Figure 4 and Figure 5 As shown, a pollution early warning system includes a data acquisition module 310, a data transmission module 320, and a control host 330. The data acquisition module 310 is electrically connected to the control host 330 through the data transmission module 320. The data acquisition module 310 monitors the bacterial concentration, humidity, and odor concentration on the surface of the mattress body 100. The data acquisition module 310 is a flexible sensor unit. The back of the flexible sensor unit is attached with Velcro. The flexible sensor unit is placed in the cavity between the mattress body and the protective cover and is detachably fixed with the Velcro backing, so that it does not shift during use.

[0034] The flexible sensor unit uses a polyimide (PI) flexible circuit board with a thickness of 0.8-1.2mm, a rectangular sheet design with a length of 200mm and a width of 15mm. The flexible sensor is encapsulated with medical-grade liquid silicone vacuum potting, with a Shore hardness of 30A, a vacuum degree of -0.09MPa, a curing temperature of 80℃, and a curing time of 30 minutes. After encapsulation, there are no bubbles or cracks, and it has waterproof, antibacterial, and wear-resistant properties. It can withstand 85℃ hot water washing and 75% alcohol wiping disinfection, and can withstand ≥500 wiping cycles without performance degradation.

[0035] The flexible sensor unit includes an optical bacteria detection sensor, a capacitive humidity detection sensor, and a gas resistance detection sensor. The acquisition module continuously collects raw signals from these three types of sensors at a frequency of 10Hz. The flexible sensor unit is placed on the surface of the mattress that comes into contact with the human body, specifically in the center of the mattress. Clinically, the center of the mattress surface in contact with the human body is most prone to contamination; therefore, monitoring this most easily contaminated area is crucial. The optical bacterial detection sensor uses a microfluidic chip optical bacterial sensor, which is a contact detection type. The detection range is limited to the surface in contact with the collection end. The detection wavelength is 600 nm. It is equipped with an LED excitation light source and outputs an absorbance model of 0-5V. The detection limit is 1 CFU / cm². The response time is ≤3 seconds. It can accurately detect common pathogenic bacteria such as Staphylococcus aureus and Escherichia coli. The capacitive humidity sensor uses the SHT30 capacitive humidity sensor, which monitors at close range with an effective monitoring range of about 1-2cm. Humidity detection is affected by environmental factors and angle, resulting in significant local differences. The measurement accuracy is ±2%RH, and the measurement range is 0-100%RH. It quickly captures changes in mattress humidity based on the output I2C digital signal. The gas resistance detection sensor uses a MOS gas sensor array, which has a relatively large detection range for gases. While the odor gas has some diffusion, its concentration distribution is uneven. The MQ-4 / 8 / 135 model is used, which contains three independent gas-sensitive resistor units. These units can identify characteristic gases from bodily fluids such as ammonia and hydrogen sulfide, outputting a resistance signal of 10kΩ-1MΩ to achieve early identification of odor pollution. The odor concentration is calculated by combining the resistance signals R1, R2, and R3 output by the three gas-sensitive resistor units with the reference resistance value R0 of the gas resistance detection sensor in a clean air environment. A feature fusion algorithm is used to calculate the comprehensive feature value F, and a secondary calibration curve is used to convert this into an odor concentration. The detection limits for ammonia and hydrogen sulfide by the gas sensor array are both ≤0.1 mg / m³.

[0036] The control host 330 contains a main control chip 340, an audible and visual alarm module 331, and a wireless communication module 332. The wireless communication module 332 adopts the ESP8266 model and supports the IEEE 802.11 protocol. The audible and visual alarm module 331 adopts an 85dB audible and visual alarm.

[0037] The control host 330 uses an ABS engineering plastic shell with a silver ion antibacterial coating. The silver ion content is 1.5%, achieving an inhibition rate of ≥99% against Staphylococcus aureus, thus reducing bacterial growth on the host surface. The control host 330 measures 120mm × 80mm × 30mm and is fixed to the bedside equipment via a snap-on backplate bracket for easy disassembly and maintenance. The shell of the control host 330 encloses the main control chip 340, the audible and visual alarm module 331, and the wireless communication module 332. The control unit 330 also includes a 2000mAh battery with a voltage of 3.7V. The charging time is ≤2 hours. After a full charge, it can work continuously for ≥72 hours and standby time for ≥168 hours. The battery has four protection functions: overcharge, over-discharge, overcurrent, and short circuit. It supports 5V / 2A Type-C interface charging to meet the clinical needs for 24-hour uninterrupted monitoring.

[0038] The main control panel of the 330 host has a Type-C charging port, an IP54 waterproof power switch, and status indicator lights. The power status is green, the working status is blue, and the alarm status is red. It can intuitively display the operating status of the equipment, making it easy for medical staff to make quick judgments.

[0039] The data transmission module 320 uses a waterproof data transmission cable with oxygen-free copper conductors and a diameter of 0.5mm. 2 The lead wire has an outer sheath made of medical-grade PVC material with a bending radius of ≥10mm, providing excellent bending resistance and biocompatibility. It adopts a magnetic quick-connect design with a insertion and removal life of no less than 1000 cycles and an insertion and removal force of 3-5N to avoid accidental insertion and removal. The early warning system has preset thresholds for bacterial concentration > 5 CFU / cm², humidity > 80%RH, and odor concentration > 0.5 mg / m³. These preset thresholds are derived based on the Class III environmental requirements of GB 15982-2012 "Hospital Disinfection and Hygiene Standards", relevant research data from the 4th edition of "Hospital Infection Control", and the limits for harmful gases in GB / T 18883-2022 "Indoor Air Quality Standards", combined with actual measured data on odor pollution from clinical mattresses.

[0040] The mattress data is collected at a frequency of 10Hz, maintaining a value above a preset threshold for 30 consecutive seconds (10Hz x 30 seconds = 300 sampling points). When 300 sampling points continuously exceed the preset threshold, a local 85dB audible and visual alarm and a mobile terminal alert are simultaneously triggered to avoid false alarms caused by fluctuations in a single sampling point and reduce invalid alarms. The main control chip 340 performs real-time monitoring, analysis, storage, filtering, calibration, accumulation, and early warning judgment processing on the data collected by the acquisition module 310.

[0041] The raw signals were preprocessed using a moving average filtering algorithm. The data collected over 30 seconds were averaged to obtain the average value for each sensor over 30 seconds. The calculation formula is as follows: ,in X is the filtered data. i For the i-th collected value, the data window n=5; the filtering algorithm can reduce the signal error rate to below 5%, which meets the requirements of hospital infection monitoring.

[0042] Using calibration algorithms corresponding to bacterial concentration, humidity, and odor concentration, the filtered values ​​are quantified and converted into physical quantity indicators. The bacterial concentration data shows a linear relationship with the voltage value V. A conversion is performed using a calibration curve formula, which is: C bac =K2× V- b2; K2 is the calibration coefficient, K2=3.2, b2 is the intercept, b2=0.5; the formula is: C bac =3.2× V- 0.5; The humidity data shows a linear relationship with the capacitance signal C. A linear calibration formula is used for conversion, which is as follows: RH = K1×C- b1; K1 is the calibration coefficient, K1=2.5, b1 is the intercept, b1=20; the formula is: RH =2.5× C- 20; The odor concentration data shows a linear relationship with the resistance signal R. A computational feature fusion algorithm is used to calculate the comprehensive feature value F. The gas sensor array has three gas-sensitive resistor units with output resistances R1, R2, and R3 respectively. The reference resistance value of the sensor in a clean air environment is R0. The value is then converted using a secondary calibration curve formula, which is: C odor =a1×F 2 +b3×F+c1, where a1, b3, and c1 are all quadratic fitting coefficients, a1=0.0012, b3=0.035, and c1=0.002; the formula is: C odor=0.0012× F 2 +0.035× F +0.002, The main control chip 340 compares the converted bacterial concentration data, humidity data, and odor concentration data with the preset thresholds in real time. To avoid false alarms, a 30-second continuous exceedance judgment logic is set. The counting register of the main control chip 340 accumulates the exceedance sampling points. When a certain indicator exceeds the corresponding preset threshold for 30 consecutive seconds, a total of 300 sampling points are judged as a valid exceedance signal, triggering the early warning mechanism. The false alarm rate is controlled below 5%.

[0043] Upon triggering the warning, the control host 330 sends a response to the audible and visual alarm module 331. The audible and visual alarm module 331 includes a buzzer and a red LED audible and visual alarm. The module immediately emits an audible and visual alarm signal with a frequency of 1Hz, a volume of 85dB, and a duration of ≥3 minutes. The module also emits a buzzing sound and flashes a red alarm light, providing real-time alerts. Simultaneously, the wireless transmission module 332 sends the warning information to the mobile terminal. Data transmission latency is ≤2 seconds, enabling precise remote push notifications. Data transmission complies with mandatory requirements for data encryption and privacy information anonymization, ensuring the security, compliance, and traceability of the monitored data.

[0044] Each early warning system is assigned a unique ID and QR code identifier, which includes full lifecycle information such as device number, specifications, calibration date, and maintenance records. During mattress use, the pollution early warning system collects real-time indicators of bacterial concentration, odor concentration, and other odor levels. The collected data is uploaded to a mobile terminal with a touch screen, allowing medical staff to view the data in real time.

[0045] If any indicator exceeds the preset threshold for 30 consecutive seconds, an alarm will be triggered. Medical staff must arrive at the scene within 5 minutes, disconnect the data transmission line, remove the acquisition module 310 and the control host 330, disinfect the pollution alarm system with alcohol, and return it to the nurse dispatch station. The pollution alarm system will then undergo self-testing and ultraviolet disinfection. The mattress will be folded, placed in a removable protective cover 200, sealed with a waterproof zipper, and transported to the central disinfection supply room for cleaning and disinfection.

[0046] Example 3

[0047] The shared use of the pollution early warning system is based on on-demand configuration, dynamic adjustment, and precise reuse to achieve the sharing of the pollution early warning system and reduce the initial purchase cost per bed.

[0048] First, all beds in the ward were equipped with mattress bodies (100 units) and protective covers (200 units). Mobile terminals and portable storage boxes were installed at the nurses' station. The portable storage boxes contained ultraviolet lamps, 5V charging interfaces, and mounting slots. The ultraviolet lamps were medical-grade disinfection lamps, with disinfection times selectable between 5 and 30 minutes. Secondly, before admission, medical staff can use a mobile terminal or management system to check for available devices, scan the device's QR code to complete the registration, and the system automatically records the collection time and operator. They then take the early warning system to the corresponding bed, fix the data acquisition module 310 to the receiving cavity of the bed's protective cover 200 with Velcro, and connect the data transmission module 320 to the control host 330. After entering the bed number through the control host 330 and completing the binding between the bed and the device code, monitoring is started. The binding information is synchronized to the mobile terminal in real time, and the mobile terminal simultaneously transmits the information to the management system.

[0049] The early warning system collects raw data on bacterial concentration, humidity, and odor concentration at a frequency of 10Hz. After filtering and calibration, the data is transmitted synchronously to the mobile terminal, and the data retention period is ≥90 days. When an alert is triggered, the local audible and visual alarm module 331 and the mobile terminal issue a notification. Medical staff receive information pushed by the mobile terminal or management system (including bed number, type of contamination, and value exceeding the standard) and arrive at the scene within 5 minutes to handle the situation. After the handling is completed, the handling results (e.g., changing the mattress, local disinfection, etc.) are entered into the mobile terminal, and the system automatically records the handling information, forming a closed-loop management system.

[0050] After the patient is discharged or the early warning system is activated, medical staff first disconnect the data transmission module 320, then remove the acquisition module 310 and the control host 330. The device is then wiped and disinfected with 75% alcohol wipes (disinfection covers all external surfaces, wiping time ≥30 seconds), with a focus on key areas such as the data cable interface and the acquisition end surface. The disinfected early warning system is returned to the nurse dispatch station. Medical staff initiate a self-check of the early warning system through the management software, including sensor accuracy, communication function, and battery power detection. After passing the self-check, the system automatically updates the device status to "idle." The contamination early warning system is then placed in a mobile storage box, and the exterior is disinfected with ultraviolet light, ready for the next use. If the self-test fails, it will be marked as "fault" and a maintenance reminder will be sent to the equipment department. At the same time, information such as the fault type and fault time will be recorded. After the maintenance is completed, the self-test will be performed again. Only after passing the self-test can the "idle" state be restored.

[0051] Example 2: When a patient experiences bodily fluid leakage, if the humidity and odor concentration consistently exceed the standard, an alert is issued. The local system and the nurse dispatch console display the corresponding bed number and the humidity / odor exceeding the standard, requesting immediate action. After on-site confirmation by the nurse, the data transmission module is disconnected, the data acquisition module is removed, the contaminated mattress is folded along the creases, placed in a protective jacket, and transported to the central disinfection center. After alcohol disinfection by the contamination alert system, the mattress is returned to the nurse dispatch console. Example 3: Taking a ward with 50 beds as a benchmark, and combining core parameters such as the ward's infection risk level, bed turnover frequency, and time required for device disinfection and reuse, the number of monitoring devices can be quantitatively calculated to balance the rationality of configuration with the controllability of costs.

[0052] Three months of clinical operation data were collected, covering both off-peak and peak periods, with B beds in the ward. The collected indicators included: a) average length of stay per bed T, in days; b) average daily pollution warning frequency F, in times / day, reflecting the turnover requirements of the equipment; c) time H for disinfection and reuse of a single set of equipment, in minutes / time, including disassembly, disinfection, self-inspection, etc., which determines the maximum number of times the equipment can be reused per day; d) effective working hours per day, with D=720 minutes (corresponding to a 12-hour clinically operable period).

[0053] Baseline configuration calculation method: The first step is to determine the average daily usage time of a single bed (excluding disinfection). Since a single bed needs to continuously occupy the device for T days, the average daily usage time is: t occ =D / T (minutes / bed⋅day); The second step is the average total daily time spent on a single bed's complete occupancy device (including disinfection and reuse). Disinfection is required after each alert, with an average of F disinfection times per day. Therefore, the average total daily disinfection time is F × H minutes. Thus, the average total daily time spent on a single bed is: t bed =t occ+F×H=D / T+F×H (minutes / bed⋅day); The third step is to determine the maximum number of beds a single unit can serve per day; the total daily service time provided by a single unit is D minutes, therefore: N = D / t bed =D / (D / T+F×H)(bed / set) The fourth step is to calculate the number of equipment sets required for the ward. Assuming the total number of beds in the ward is M (beds), then the required number of equipment sets is N. total It can be determined by the ratio of the total number of beds in the ward to the maximum number of beds that a single unit can serve per day, i.e., N. total = M / N.

[0054] By calculating the above four steps, the number of pollution warning system devices required to meet the needs of the ward can be scientifically and rationally determined based on the average length of stay per bed, the average daily frequency of pollution warnings, the time required for disinfection and reuse of a single device, and the effective working hours per day in the clinical operation data, thus providing a quantitative basis for the optimal allocation of medical resources.

[0055] Example verification (data consistent with routine clinical conditions): If the average length of stay per bed T = 6 days, the average daily frequency of contamination warnings F = 3 times / day, and the time for disinfection and reuse of a single set of equipment H = 10 minutes, substituting into the formula: t occ =720 / 6=120 minutes / bed / day; t bed =120 + 3 × 10 = 150 minutes / bed⋅day; N = 720 / 150 = 4.8 beds / set; the baseline configuration number of sets M = 50 / 4.8 ≈ 10.42, rounded up to 11 sets; considering the infection risk level, 8–10 sets can be configured in general wards, and 10–12 sets can be configured in high-infection-risk wards. The result of 11 sets calculated in this case is in line with the range of clinical experience.

[0056] When ≥3 beds trigger an alert simultaneously, the management software automatically issues a scheduling reminder and prioritizes the allocation of devices to high-risk beds, such as ICU and infectious disease beds. For general bed demand, it generates a waiting queue and pushes the estimated waiting time to facilitate medical staff to arrange their work reasonably.

[0057] When there is a shortage of equipment in this ward, medical staff can submit a cross-ward allocation request to the hospital's central dispatch platform through the management software. Priority will be given to allocating available equipment from low-risk wards. The hospital's central disinfection supply room will keep 2-3 sets of emergency equipment in reserve to ensure that the allocation response time is ≤30 minutes. Example 4: Initial procurement cost: Shared model of this invention (50 beds): 50 mattresses (400 yuan / mattress) + 50 protective covers (100 yuan / piece) + 11 monitoring devices (1800 yuan / set) = 50×400 + 50×100 + 11×1800 = 20000 + 5000 + 19800 = 44800 yuan (average 896 yuan per bed); The shared model of this invention (20-bed primary care ward): 20 mattresses (400 yuan / mattress) + 20 protective covers (100 yuan / piece) + 5 monitoring devices (1800 yuan / set) = 8000 + 2000 + 9000 = 19000 yuan (950 yuan per bed on average). Traditional washable mattresses (50 mattresses): 50 mattresses × 300 yuan / mattress = 15,000 yuan (300 yuan per mattress on average); (20 mattresses): 20 mattresses × 300 yuan / mattress = 6,000 yuan (300 yuan per mattress on average). Integrated smart mattresses (50 beds): 50 mattresses × 5000 yuan / mattress = 250,000 yuan (average 5000 yuan per mattress); (20 beds): 20 mattresses × 5000 yuan / mattress = 100,000 yuan (average 5000 yuan per mattress).

[0058] Average annual operating costs: The shared model of this invention (50 beds): replacement of protective gowns (50 pieces × 100 yuan / piece × 2 times / year) + equipment maintenance (11 sets × 200 yuan / set / year) + disinfection consumables (50 beds × 50 yuan / bed / year) = 10000 + 2200 + 2500 = 14700 yuan; The shared model of this invention (20 beds): replacement of protective gowns (20 pieces × 100 yuan / piece × 2 times / year) + equipment maintenance (5 sets × 200 yuan / set / year) + disinfection consumables (20 beds × 50 yuan / bed / year) = 4000 + 1000 + 1000 = 6000 yuan; Traditional washable mattresses (50 mattresses): Labor inspection cost (2 people × 3000 yuan / month × 12 months) + excessive cleaning consumables (50 mattresses × 200 yuan / bed / year) = 72000 + 10000 = 82000 yuan; (20 mattresses): Labor inspection cost (1 person × 3000 yuan / month × 12 months) + excessive cleaning consumables (20 mattresses × 200 yuan / bed / year) = 36000 + 4000 = 40000 yuan; Integrated smart mattresses (50 mattresses): Device maintenance (50 sets × 500 yuan / set / year) + replacement for malfunctions (calculated based on a 5% failure rate: 50 × 5% × 5000 yuan / year) = 25000 + 12500 = 37500 yuan; (20 mattresses): Device maintenance (20 sets × 500 yuan / set / year) + replacement for malfunctions (calculated based on a 5% failure rate: 20 × 5% × 5000 yuan / year) = 10000 + 5000 = 15000 yuan.

[0059] Total life cycle cost (calculated over 5 years): The cost of the shared mode of this invention (50 beds) is: 44800 + 14700 × 5 = 44800 + 73500 = 118300 yuan (average price per bed: 2366 yuan). The cost of this invention in the shared mode (20 beds) is: 19000 + 6000 × 5 = 19000 + 30000 = 49000 yuan (average cost of 2450 yuan per bed). Traditional washable mattresses (50 mattresses): 15,000 + 82,000 × 5 = 15,000 + 410,000 = 425,000 yuan (average price per mattress: 8,500 yuan); (20 mattresses): 6,000 + 40,000 × 5 = 6,000 + 200,000 = 206,000 yuan (average price per mattress: 10,300 yuan); Integrated smart mattress (50 beds): 250,000 + 37,500 × 5 = 250,000 + 187,500 = 437,500 yuan (average price per bed: 8,750 yuan); (20 beds): 100,000 + 15,000 × 5 = 100,000 + 75,000 = 175,000 yuan (average price per bed: 8,750 yuan).

[0060] Cost Conclusion: The 5-year lifecycle cost of this invention's shared model is only 22.9% (50 mattresses) and 23.7% (20 mattresses) of traditional washable mattresses, and 27% (50 mattresses) and 28% (20 mattresses) of integrated smart mattresses. The average cost per mattress is less than 2,500 yuan, making it particularly suitable for the cost control needs of primary and secondary hospitals. The initial procurement cost is only 17%-18% of that of integrated mattresses, achieving precise infection control without large upfront investments, significantly reducing the initial investment and long-term operational burden on hospitals, and possessing extremely high cost-effectiveness.

[0061] Example 4

[0062] The pollution early warning system is electrically connected to the mobile terminal and management system. A shared monitoring device scheduling module is added to the existing management system. The shared monitoring device scheduling module includes real-time status monitoring, binding and unbinding, early warning linkage, data statistics, and emergency dispatch. Real-time status monitoring displays the current status of each early warning system. Binding and unbinding can be supported via mobile terminal scanning or manual input of information such as the early warning system and bed location; Early warning linkage: The early warning system synchronously feeds information back to the management system, and the early warning system is linked. Data statistics generate statistical reports based on data such as the usage frequency, turnover efficiency, shortfall rate, idle rate, and failure rate of the early warning system. Emergency allocation will be made to address gaps in the ward's early warning system, involving cross-ward emergency allocation or allocation from emergency reserves. The pollution early warning system transmits information to mobile terminals, which in turn transmit the information to the management system. The management system then monitors and manages the pollution early warning system in real time.

[0063] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several modifications and improvements can be made without departing from the inventive concept of this application, and these all fall within the protection scope of this application.

Claims

1. A pollution early warning system, characterized in that: It includes a control host (330), a data transmission module (320) and an acquisition module (310). The control host (330) contains a main control chip (340), a wireless communication module (332) and an audible and visual alarm module (331). The acquisition module (310) is a flexible sensor unit, which includes an optical bacteria detection sensor, a capacitive humidity detection sensor and a gas resistance detection sensor. These three sensors collect data on bacteria, humidity and odor respectively. The acquisition module (310) feeds the collected data back to the main control chip (340) in real time through the data transmission module (320). The main control chip (340) monitors, processes, stores, analyzes and makes early warning judgments on the collected data in real time, and feeds the processing results back to the control host (330) to control the sound and light alarm module (331) to issue a reminder.

2. The pollution early warning system according to claim 1, characterized in that: The control host (330) presets a bacterial concentration warning value, a humidity warning value, or an odor concentration warning value. When the indicators collected by the control host (330) exceed the warning value for 30 consecutive seconds, the warning judgment triggers the sound and light alarm module (331) to issue a reminder, and simultaneously triggers the sound and light alarm module (331) and the mobile terminal to issue a warning.

3. A pollution early warning system according to claim 2, characterized in that: The data of each sensor collected by the acquisition module (310) is preprocessed by the moving average filtering algorithm. The average value of the data collected within 30 seconds is calculated to obtain the average value of each sensor within 30 seconds. The main control chip (340) uses a calibration algorithm to convert each filtered average value into the corresponding physical quantity index. The physical quantity index includes the voltage signal V output by the optical bacteria detection sensor, the capacitance signal C output by the capacitance humidity detection sensor, and the resistance signals R1, R2, and R3 output by the gas resistance detection sensor. Among them, the gas resistance detection sensor is a gas sensor array, which consists of three independent gas-sensitive resistor units. The three gas-sensitive resistor units are arranged in parallel in the same detection chamber, and each independently outputs resistance signals R1, R2, and R3.

4. A pollution early warning system according to claim 3, characterized in that: When the main control chip (340) converts each filtered average value into the corresponding physical quantity index, it converts the filtered voltage signal into bacterial C based on the optical detection principle. bac The formula used is: C bac =K2× V- b2; K2 is the calibration coefficient, K2=3.2, b2 is the intercept, b2=0.5; The filtered capacitance signal is converted to humidity (RH) using the following formula: RH = K1× C- b1; K1 is the calibration coefficient, K1=2.5, b1 is the intercept, b1=20; The gas resistance sensor contains three gas resistors. The resistance signals of these three gas resistors, R1, R2, and R3, are collected, and a comprehensive feature value F is calculated using a feature fusion algorithm. R0 is the reference resistance value of the flexible sensor in a clean air environment; Then, the odor concentration C is converted using a secondary calibration curve. odor =a1×F 2 +b3×F+c1, where a1, b3, and c1 are all quadratic fitting coefficients, a1=0.0012, b3=0.035, and c1=0.

002.

5. A pollution early warning system according to claim 4, characterized in that: The main control chip (340) converts the humidity (RH) and odor (C) values. odor Bacteria C bac The system compares the data with a preset threshold in real time and sets a 30-second continuous exceedance judgment. The system accumulates the exceedance data points through the storage function of the main control chip (340). When a certain indicator exceeds the preset threshold for 30 consecutive seconds, an early warning is triggered. The control host (330) starts the sound and light alarm module (331). The sound and light alarm module (331) continues for a certain duration. The early warning information is fed back to the mobile terminal through the wireless communication module (332).

6. A pollution early warning system according to claims 1-5, wherein a method for sharing the pollution early warning system is proposed, characterized in that: Each control host, each data transmission module, and each acquisition module constitutes a complete early warning system. Each early warning system is matched with a unique ID or QR code. Each early warning system is dynamically matched with the mattress. Multiple early warning systems are matched with one mobile terminal. The mobile terminal displays the status of each early warning system in real time, including whether it is idle, occupied, being disinfected, or malfunctioning. Each early warning system can be monitored through the mobile terminal, and the mobile terminal also transmits information to the management system simultaneously.

7. A washable mattress, characterized in that: The device includes a mattress body (100), a protective cover (200) covering the mattress body (100), at least two parallel creases on the mattress body (100), and a waterproof zipper (210) on the protective cover (200). The protective cover (200) and the mattress body (100) form a cavity for housing a data acquisition module (310), which collects real-time data on bacteria, humidity and odor on the mattress body (100).

8. A washable mattress according to claim 7, characterized in that: The mattress body (100) is divided into three layers: upper, middle and lower. Each layer is provided with a raised point, and the raised points between adjacent layers are staggered. Each raised point has a through-hole drainage hole. The inner surface of the protective cover (200) is provided with a fastener for fixing the acquisition module (310). The acquisition module (310) and the fastener are detachably connected.

9. A washable mattress according to claim 8, characterized in that: The raised points on each layer of the mattress body (100) are arranged in an equilateral triangular array. Each raised point is located at the center of the equilateral triangle formed by three raised points of adjacent layers. The diameter of the air fiber filaments in the creases on the mattress body (100) is smaller than the diameter of the filaments in the non-crease areas.

10. A washable mattress according to claim 8, characterized in that: The creases of the mattress body (100) are set at equal intervals of 1 / 3 of the unfolded length.

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