A Smart Campus Shared Laundry Infection Prevention Method and System

Abstract

This invention relates to the field of intelligent laundry equipment, specifically disclosing a method and system for preventing infection during shared laundry services in smart campuses. The invention installs microbial sensors at the drain outlet or inside the washing machine to detect the microbial content in the wash water during the rinsing stage. When the detected value exceeds a preset threshold, a secondary disinfection process is triggered, and disinfection parameters are dynamically adjusted according to the degree of exceedance. The disinfection process execution and microbial detection results are uploaded to the cloud to generate a health report that users can query. Simultaneously, the complete process information of each laundry order is generated into an immutable digital certificate and stored in the blockchain, achieving transparent traceability of hygiene data. During non-use periods, the system automatically initiates drying maintenance, blowing and cleaning the inside of the washing machine to prevent moisture and mold growth. This invention achieves real-time monitoring and precise disinfection of the washing machine's hygiene, solving the trust and traceability problems in shared laundry services.

Description

Technical Field

[0001] This invention belongs to the field of intelligent laundry equipment, specifically a method and system for preventing infection during intelligent shared laundry services on campus. Background Technology

[0002] As frequently used public facilities, shared washing machines on campus directly impact the health and safety of students and faculty. Studies show that clothing may carry various pathogens during washing, such as bacteria, fungi, and viruses. These microorganisms can spread through the washing water inside the machine, causing cross-infection. Traditional washing machines use fixed disinfection programs, performing the same process regardless of the degree of soiling, which fails to guarantee disinfection effectiveness and wastes resources. The inner drum and pipes of the washing machine remain damp after washing, becoming a breeding ground for bacteria and mold, further increasing the risk of cross-infection. Furthermore, users lack trust in the hygiene of shared washing machines, and school logistics departments struggle to obtain accurate hygiene data for effective monitoring.

[0003] Currently, there are no technologies for real-time monitoring of microbial content during the washing process in washing machines. Disinfection effectiveness cannot be quantified and verified, users cannot know the actual hygiene conditions of each wash, and it is difficult to trace the source of problems in the event of a public health emergency. Therefore, how to achieve real-time monitoring, precise disinfection, and traceable management of the hygiene conditions of washing machines has become an urgent technical problem to be solved. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a smart campus shared laundry infection prevention method and system, which solves the problems in the prior art where washing machines cannot monitor the microbial content during the washing process in real time, the disinfection program is fixed and its effectiveness cannot be verified, and hygiene data is difficult to trace.

[0005] This invention provides a method for preventing infection during washing in a smart campus shared laundry facility, comprising the following steps:

[0006] Install microbial sensors at the drain outlet or inner drum of the washing machine to detect the residual microbial content in the wash water during the rinsing stage;

[0007] When the detected value exceeds the preset threshold, a secondary disinfection process is triggered, and the disinfection process parameters are recorded.

[0008] The disinfection process and microbial test results are uploaded to the cloud, and a health report that users can query is generated.

[0009] Preferably, the microbial sensor is based on the ATP fluorescence detection principle, and the microbial content... Calculated using the following formula:

[0010]

[0011] in, The fluorescence intensity detected by the sensor. This is the calibration coefficient.

[0012] Preferably, the disinfection time of the secondary disinfection process Dynamically adjust based on the degree of microbial contamination, satisfying the following relationship:

[0013]

[0014] in, Basic disinfection time, This is an excessive amount. For the preset threshold, This is for adjusting the coefficient.

[0015] Preferably, the following steps are also included:

[0016] Generate an immutable digital certificate containing the complete process information of each laundry order and store it in the blockchain;

[0017] The complete process information includes disinfectant batch, concentration, disinfection temperature, disinfection duration, and final microbial test results.

[0018] Preferably, the disinfection effect evaluation value Calculated using the following formula:

[0019]

[0020] in, This refers to the microbial content before disinfection. This represents the microbial content after disinfection.

[0021] This invention also provides an intelligent campus shared laundry system, comprising:

[0022] The microbial detection unit is installed at the drain outlet or inner drum of the washing machine to detect the microbial content in the wash water during the rinsing stage.

[0023] The control unit is used to trigger a secondary disinfection process when the detection value exceeds the standard.

[0024] The communication unit is used to upload disinfection execution data to the cloud;

[0025] Storage unit, used to generate and store health reports for each order.

[0026] Preferably, the system further includes a blockchain module for encrypting and storing the disinfection process information for each order and generating an immutable digital certificate.

[0027] Preferably, the system further includes a drying maintenance unit for activating an internal drying cycle during non-use periods, which introduces dry hot air to blow clean the inside of the washing machine, and the drying time... The following relationship must be satisfied:

[0028]

[0029] in, Minimum drying time, To detect humidity, For target humidity, This is for adjusting the coefficient.

[0030] Preferably, the drying maintenance unit has a built-in temperature control detection module; when the temperature exceeds a critical value... The hot air supply will automatically shut off when the time is right.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] By using microbial sensors to monitor the microbial content in the washing water in real time, the system can quantitatively assess the hygiene of the washing machine, breaking the limitation of traditional washing machines that only execute preset programs and cannot verify the disinfection effect. When the detected value exceeds the standard, the system automatically triggers a secondary disinfection process and dynamically adjusts the disinfection parameters according to the degree of exceedance, achieving precise disinfection, avoiding the waste of resources caused by over-disinfection, and ensuring the hygiene and safety of each wash.

[0033] By generating tamper-proof digital credentials from disinfection process information and storing them in the blockchain, the trust and traceability problems in shared laundry services are solved. Users can view their health reports at any time, which enhances their confidence in using the service and provides school logistics and disease control departments with transparent and verifiable means of health supervision.

[0034] In addition, the drying maintenance is automatically activated during non-use periods. By introducing dry hot air to blow clean the inside of the washing machine, it effectively solves the problem of mold growth in a humid environment and prevents cross-infection from the source.

[0035] This invention upgrades traditional washing machines into intelligent health devices with sensing, decision-making, and execution capabilities, comprehensively improving the hygiene level, intelligence level, and user satisfaction of shared laundry services on campus, and has significant social benefits and commercial promotion value. Attached Figure Description

[0036] Figure 1 This is a flowchart of the intelligent campus shared laundry infection prevention method according to Embodiment 1 of the present invention;

[0037] Figure 2 This is a schematic diagram of data flow in the blockchain evidence storage and traceability system of Embodiment 2 of the present invention;

[0038] Figure 3 This is a block diagram of the overall architecture of the intelligent campus shared laundry system according to Embodiment 4 of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1: As Figure 1 As shown in the figure, this embodiment provides a specific implementation of a smart campus shared laundry infection prevention method.

[0041] 1. Sensor Installation and Detection Principles

[0042] A miniature biosensor is installed at the drain of the washing machine. This sensor is based on the principle of ATP fluorescence detection. ATP (adenosine triphosphate) is present in all living cells and produces fluorescence by reacting with luciferase. The fluorescence intensity is proportional to the microbial content.

[0043] Microbial content Calculated using the following formula:

[0044]

[0045] in, The fluorescence intensity detected by the sensor is expressed in relative optical units (RLU). The calibration coefficient is obtained through standard bacterial culture; for example, using a concentration of... The E. coli bacterial suspension was labeled, and the fluorescence intensity was measured to be [value missing]. ,but .

[0046] 2. Detection timing and threshold setting

[0047] During the final rinse phase of the washing program (specifically, from 30 seconds after the start of the rinse to 10 seconds before the end), the sensor initiates detection; the preset threshold T is set according to hygiene standards and equipment usage, as described in this embodiment. (Corresponding microbial content approximately) ).

[0048] The test results fall into three categories:

[0049] like The hygiene was deemed satisfactory, and the procedure was completed normally.

[0050] like The system was determined to have excessive microbial levels, triggering a secondary disinfection process.

[0051] 3. Dynamic control of secondary disinfection

[0052] When the detected value exceeds the standard, the control unit dynamically adjusts the disinfection time according to the degree of exceedance. The disinfection time t satisfies the following relationship:

[0053]

[0054] in:

[0055] Based on the disinfection time, in this embodiment minute;

[0056] This is an excessive amount. (when hour);

[0057] For the preset threshold, ;

[0058] To adjust the coefficients, they were determined experimentally in this embodiment. .

[0059] For example, if the detection value ,but =300 , =0.6, calculated as follows minute.

[0060] The secondary disinfection process includes:

[0061] Increase the amount of disinfectant used (e.g., from the standard dose of 5 mL to 8 mL).

[0062] Extend the disinfection time (e.g., 11.8 minutes as calculated in the example above);

[0063] Increase the disinfection temperature (e.g., from 40℃ to 50℃).

[0064] 4. Data Upload and Health Report Generation

[0065] After the disinfection process is completed, the control unit uploads the following data to the cloud via the communication unit:

[0066] Raw detection value ;

[0067] Excess scalar ;

[0068] Disinfection time minute;

[0069] Disinfectant batch (e.g., batch number: D20241001);

[0070] Disinfection temperature (e.g., 50℃);

[0071] Final test results (test value after secondary disinfection) ).

[0072] A health report for this wash cycle is generated by the cloud server, which users can view by scanning the QR code on the washing machine. The health report includes:

[0073] Microbial content before disinfection: ;

[0074] Exceeding standard: 60%;

[0075] Disinfection procedure: Secondary disinfection;

[0076] Disinfection time: 11.8 minutes;

[0077] Microbial content after disinfection: ;

[0078] Disinfection effectiveness assessment: Pass.

[0079] Example 2: As Figure 2 As shown, this embodiment adds a blockchain evidence storage function based on embodiment 1 to solve the trust and traceability issues.

[0080] 1. Digital voucher generation

[0081] After each laundry order is completed, the system will package the following complete process information to generate a digital voucher:

[0082] Order number: 202410150001;

[0083] Washing machine ID: WMS-001;

[0084] Disinfectant batch number: D20241001;

[0085] Disinfectant concentration: 0.5%;

[0086] Disinfection temperature: 50℃;

[0087] Disinfection time: 11.8 minutes;

[0088] Microbial content before disinfection: ;

[0089] Microbial content after disinfection: ;

[0090] Disinfection effectiveness assessment value: 85%.

[0091] Disinfection efficacy evaluation value Calculated using the following formula:

[0092]

[0093] in, This refers to the microbial content before disinfection. This represents the microbial content after disinfection. In this embodiment, , Calculated .

[0094] 2. Blockchain storage

[0095] The system hashes and encrypts the above information to generate a unique digital fingerprint (such as "a3f5c8d2e1b4..."), which is then stored in the blockchain network. Each block contains the hash value of the previous block, forming an immutable chain.

[0096] 3. User Inquiry and Regulatory Verification

[0097] After scanning the QR code, users can view a health report and click "Blockchain Verification" to view the stored information. The system displays:

[0098] Evidence storage time: 2024-10-15 14:23:05;

[0099] Block height: 184523;

[0100] Transaction hash: 0x7a3f5c8d2e1b4...;

[0101] Verification status: Verified and cannot be altered.

[0102] The school's logistics department can use the management backend to collect hygiene data for equipment in a specific area, for example:

[0103] Check the disinfection compliance rate of the washing machine WMS-001 over the past 30 days;

[0104] Export the list of orders exceeding the standard and the corresponding disinfection records;

[0105] Verify whether the batch of disinfectant is compliant.

[0106] Example 3: This example adds a drying maintenance unit to the existing example 1 or 2 to solve the problem of bacteria growth in the damp inner drum after washing.

[0107] 1. Drying timing and start-up conditions

[0108] The drying maintenance unit will automatically start when the following conditions are met:

[0109] The current time is between 23:00 and 5:00 the next day (off-peak usage period);

[0110] The washing machine has not been used for 2 consecutive hours;

[0111] Internal humidity readings .

[0112] 2. Drying time is dynamically adjusted.

[0113] Drying time The system is dynamically adjusted based on the degree of humidity exceeding the standard, while satisfying the following relationship:

[0114]

[0115] in:

[0116] To minimize the drying time, in this embodiment... minute;

[0117] Humidity is detected in real time by a humidity sensor installed in the inner cylinder;

[0118] For the target humidity, in this embodiment ;

[0119] In order to adjust the coefficients, in this embodiment .

[0120] For example, if humidity is detected ,but Calculated minute.

[0121] 3. Drying process and control

[0122] After the drying maintenance unit is started:

[0123] The heater produces dry hot air, with the temperature controlled at 50~60℃;

[0124] Hot air enters through the water inlet pipe and blows through the internal pipes, inner cylinder, and rubber rings;

[0125] Hot, humid air is expelled from the drain pipe;

[0126] The built-in temperature control and detection module monitors the temperature in real time.

[0127] 4. Temperature protection mechanism

[0128] The drying maintenance unit has a built-in temperature control and detection module. When the temperature exceeds the critical value... The hot air supply will automatically shut off when the time is right. The following relationship must be satisfied:

[0129]

[0130] in:

[0131] To ensure a safe upper temperature limit, this embodiment... = 70℃ (to avoid damaging the plastic parts of the washing machine);

[0132] For safety redundancy, in this embodiment ;

[0133] Calculated .

[0134] When the temperature sensor detects that the internal temperature reaches 65°C, the heater is immediately turned off, and only the air supply function is kept on until the temperature drops below 50°C before heating is resumed.

[0135] 5. Verification of drying effect

[0136] After the drying process is completed, the system checks the internal humidity again; if If the drying is deemed satisfactory, it is considered acceptable; if it still exceeds the standard, the abnormality is recorded and maintenance personnel are notified for inspection.

[0137] Example 4: Figure 3 As shown, this embodiment provides a complete system integration solution that integrates the technical features of the above embodiments into one.

[0138] 1. System Composition

[0139] The smart campus shared laundry system includes:

[0140] Microbial detection unit: Installed at the drain outlet of the washing machine, it is based on the ATP fluorescence detection principle and is used to detect the microbial content in the wash water during the rinsing stage;

[0141] Control unit: Uses an STM32 microcontroller to receive detection data, determine whether the standard is exceeded, trigger the secondary disinfection process, and dynamically adjust the disinfection time according to the degree of exceeding the standard;

[0142] Communication unit: Employs a 4G or WiFi module to upload disinfection execution data to a cloud server;

[0143] Storage unit: Local storage contains health reports for the 100 most recent orders, while the cloud stores all historical data;

[0144] Blockchain module: Deployed in the cloud, it is used to encrypt and store the disinfection process information of each order, and generate an immutable digital certificate;

[0145] Drying maintenance unit: includes heater, humidity sensor, temperature sensor, and air supply fan, used to start the internal drying cycle during non-use periods;

[0146] Display unit: Installed on the washing machine panel, used to display device status and QR code.

[0147] 2. Complete Workflow

[0148] Daytime work schedule (6:00-23:00):

[0149] Users scan a QR code to place an order and select a laundry program;

[0150] The laundry program is executed, including pre-wash, main wash, and rinse;

[0151] During the final rinsing stage, the microbial detection unit is activated to detect the microbial content C in the wash water;

[0152] like ( The program will terminate normally and a health report will be generated.

[0153] like The control unit calculates the disinfection time. Perform secondary disinfection;

[0154] After disinfection, the microbial content was tested again. ;

[0155] Calculate the disinfection effectiveness evaluation value ;

[0156] Upload the complete process information to the cloud and generate digital credentials to be stored in the blockchain;

[0157] Users can scan a QR code to view their health report and access blockchain-based evidence.

[0158] Nighttime maintenance mode (23:00-5:00 the next day):

[0159] The system monitors the equipment's operating status and internal humidity;

[0160] If it is not used for 2 consecutive hours and the humidity is Initiate drying maintenance;

[0161] Calculate drying time ;

[0162] The drying program is executed, with the temperature controlled at 50~60℃. The hot air will be automatically turned off when the temperature exceeds 65℃.

[0163] After drying, check the humidity. If it still exceeds the standard, record the abnormality.

[0164] 3. Parameter calibration and adjustment

[0165] The parameters in the above embodiments ( , , , , (etc.) can be calibrated and adjusted in the following ways:

[0166] Calibration coefficient Use standard bacterial solution monthly ( The fluorescence intensity was calibrated using E. coli. If the change in fluorescence intensity exceeded ±10%, the readings were recalculated. value.

[0167] Adjustment coefficient By analyzing historical data, the relationship between the amount of contamination exceeding the standard and the required disinfection time is statistically analyzed to optimize the process. Value. In this embodiment The initial value is 0.3, which can be adjusted to 0.25~0.35 based on actual operating data.

[0168] Adjustment coefficient The time required to reach the target humidity under different humidity levels was determined experimentally, and the result was obtained through fitting. Value. In this embodiment The initial value is 0.5, which can be adjusted according to the season (0.6 in humid summer and 0.4 in dry winter).

[0169] Preset threshold Adjustments will be made dynamically based on hygiene standards and user feedback. If user complaints increase, the adjustment may be appropriately reduced. Value (such as from) Down to If frequent secondary disinfection causes resource waste, the level can be appropriately increased. Value (such as from) Rise to ).

[0170] Example 5: This example provides the preferred range and basis for the values ​​of each parameter.

[0171] parameter meaning Preferred range The value in this embodiment Basis for value selection Calibration coefficient 10~50 20 Standardized according to bacterial culture Basic disinfection time 5~15 minutes 10 minutes Meets basic sterilization needs Time adjustment factor 0.1~0.5 0.3 Experience points can be optimized based on data. Preset threshold 300~800 500 correspond Minimum drying time 10-20 minutes 15 minutes Ensure basic dehumidification effect Drying adjustment coefficient 0.3~0.7 minutes / % 0.5 Experimental measurement Target humidity 30~50% 40% RH Critical value for inhibiting mold growth Temperature critical value 60~70℃ 65℃ Temperature resistance limit of plastic parts reduced with safety redundancy Safety redundancy 3~10℃ 5℃ Ensure safety and reliability

[0172] Example 6: To verify the technical effect of the present invention, the following comparative experiment was conducted:

[0173] Experimental Design:

[0174] Control group: Ordinary shared washing machines on campus, running a fixed disinfection program (10 minutes each time, 40℃);

[0175] Experimental group: The intelligent washing machine using the technical solution of this invention has real-time microbial detection and dynamic disinfection control;

[0176] Sample size: 100 laundry orders each;

[0177] Testing indicators: microbial content after disinfection, disinfection time, and user satisfaction.

[0178] Experimental results:

[0179] index control group experimental group Improvement effect Average microbial content after disinfection 350 RLU 120 RLU Reduced by 65.7% Disinfection compliance rate (E≥90%) 72% 96% Increased by 24 percentage points Average disinfection time 10 minutes (fixed) 9.2 minutes Save 8% User satisfaction (out of 5) 3.2 points 4.7 points An increase of 46.9% Mold detection rate (after 30 days) 68% 12% 56 percentage points lower

[0180] Experimental results show that the technical solution of the present invention can significantly improve the disinfection effect, save disinfection time, effectively inhibit mold growth, and improve user experience and trust.

[0181] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Any changes, modifications, substitutions and variations made by those skilled in the art to the above embodiments within the scope of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for preventing infection during shared laundry services in smart campuses, characterized in that, Includes the following steps: Install microbial sensors at the drain outlet or inner drum of the washing machine to detect the residual microbial content in the wash water during the rinsing stage; When the detected value exceeds the preset threshold, a secondary disinfection process is triggered, and the disinfection process parameters are recorded. The disinfection process and microbial test results are uploaded to the cloud, and a health report that users can query is generated.

2. The method for preventing infection during shared laundry in a smart campus according to claim 1, characterized in that, The microbial sensor is based on the ATP fluorescence detection principle and measures microbial content. Calculated using the following formula: in, The fluorescence intensity detected by the sensor. This is the calibration coefficient.

3. The method for preventing infection during shared laundry in a smart campus according to claim 1, characterized in that, The disinfection time of the secondary disinfection process Dynamically adjust based on the degree of microbial contamination, satisfying the following relationship: in, Basic disinfection time, This is an excessive amount. For the preset threshold, This is for adjusting the coefficient.

4. The method for preventing infection during shared laundry in a smart campus according to claim 1, characterized in that, It also includes the following steps: Generate an immutable digital certificate containing the complete process information of each laundry order and store it in the blockchain; The complete process information includes disinfectant batch, concentration, disinfection temperature, disinfection duration, and final microbial test results.

5. The method for preventing infection during shared laundry in a smart campus according to claim 4, characterized in that, The disinfection effect evaluation value Calculated using the following formula: in, This refers to the microbial content before disinfection. This represents the microbial content after disinfection.

6. A smart campus shared laundry system, characterized in that, include: The microbial detection unit is installed at the drain outlet or inner drum of the washing machine to detect the microbial content in the wash water during the rinsing stage. The control unit is used to trigger a secondary disinfection process when the detection value exceeds the standard. The communication unit is used to upload disinfection execution data to the cloud; Storage unit, used to generate and store health reports for each order.

7. The intelligent campus shared laundry system according to claim 6, characterized in that, The system also includes a blockchain module, which is used to encrypt and store the disinfection process information of each order and generate an immutable digital certificate.

8. The intelligent campus shared laundry system according to claim 6, characterized in that, The system also includes a drying maintenance unit for activating an internal drying cycle during non-use periods. This cycle involves introducing hot, dry air to clean the inside of the washing machine, and the drying time is [not specified]. The following relationship must be satisfied: in, Minimum drying time, To detect humidity, For target humidity, This is for adjusting the coefficient.

9. The intelligent campus shared laundry system according to claim 8, characterized in that, The drying maintenance unit has a built-in temperature control and detection module; when the temperature exceeds a critical value... The hot air supply will automatically shut off when the time is right.

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