Sofa antibacterial performance detection method, system and equipment and storage medium

Abstract

The invention discloses a sofa antibacterial performance detection method, system and equipment and a storage medium, and particularly relates to the field of household product detection. The method comprises the following steps: acquiring a cloth sample of a sofa to be detected, dividing the cloth sample into a plurality of detection areas, and determining environmental parameters of each detection area; based on the use intensity coefficient and the pollution source type of each detection area, determining a pollution source concentration coefficient corresponding to each detection area; according to the pollution source concentration coefficient of each detection area, the spraying amount of the variable pollution source and the spraying amount of the stable pollution source, generating a pollution source mixture corresponding to each detection area, spraying each pollution source mixture to the corresponding detection area, and standing for a preset time in an environment corresponding to the environmental parameter of each detection area; spraying a detection solution containing a color indicator to each detection area, and evaluating the antibacterial property of the sofa to be detected according to the color change degree of the detection solution corresponding to each detection area. The detection accuracy of the antibacterial performance of the sofa is improved.

Description

Technical Field

[0001] This application relates to the field of household product testing, specifically to a method, system, device, and storage medium for testing the antibacterial performance of a sofa. Background Technology

[0002] As people's living standards improve and their health awareness increases, the antibacterial properties of home furnishings are receiving more and more attention. As one of the most frequently used pieces of furniture in the home, sofa fabrics are constantly exposed to complex pollutants, which can easily lead to bacterial growth and affect the health of the home environment.

[0003] Currently, the antibacterial performance of sofas is mainly tested using the laboratory standard bacterial culture method. This involves inoculating a specific concentration of standard bacterial strains onto a sofa fabric sample, culturing it for a specific time under constant temperature and humidity, and then evaluating the antibacterial effect using the counting method or the inhibition zone method.

[0004] However, the laboratory standard bacterial culture method differs significantly from the actual home use environment and cannot truly reflect the antibacterial performance of sofas under the combined effects of multiple pollutants. This leads to a discrepancy between the test results and the actual usage effect, resulting in low accuracy in testing the antibacterial performance of sofas. Summary of the Invention

[0005] This application provides a method, system, device, and storage medium for testing the antibacterial properties of sofas, which improves the accuracy of testing the antibacterial properties of sofas.

[0006] The first aspect of this application provides a method for testing the antibacterial performance of a sofa, specifically comprising: obtaining a fabric sample of the sofa to be tested; dividing the fabric sample into multiple testing areas, each testing area corresponding to a different usage intensity coefficient and a type of contaminant; determining environmental parameters for each testing area based on the usage intensity coefficient of each testing area; determining a contaminant concentration coefficient for each testing area based on the usage intensity coefficient and the type of contaminant; generating a contaminant mixture corresponding to each testing area based on the contaminant concentration coefficient of each testing area, the amount of variable contaminant sprayed, and the amount of stable contaminant sprayed; spraying each contaminant mixture onto the corresponding testing area; and allowing it to stand for a preset time under the environmental parameters corresponding to each testing area, wherein the contaminant sample of each testing area includes variable contaminants and stable contaminants; spraying a test solution containing a colorimetric indicator onto each testing area; and evaluating the antibacterial performance of the sofa to be tested based on the degree of color change of the test solution corresponding to each testing area.

[0007] By adopting the above technical solution, the fabric sample is divided into multiple testing areas and different usage intensity coefficients and types of pollution sources are set. This reflects that there are significant differences in the usage pressure and pollution types experienced by different parts of the sofa during actual use. Environmental parameters are determined based on the usage intensity coefficient, so that the testing environment can simulate the actual usage state. Furthermore, the ratio and amount of variable and stable pollution sources are controlled by the pollution source concentration coefficient, and the samples are left to stand under specific environmental parameters. This makes the pollution level of the testing area highly consistent with the actual usage environment. The antibacterial effect is evaluated by combining the color change of the color indicator, so that the test results can truly reflect the antibacterial performance of the sofa in the actual usage environment, thereby significantly improving the accuracy of antibacterial performance testing.

[0008] Optionally, dividing the fabric sample into multiple testing areas, each corresponding to a different usage intensity coefficient and type of pollution source, specifically includes: collecting usage environment information of the sofa to be tested; determining the usage scenario based on the usage environment information; dividing the fabric sample into multiple testing areas according to the functional structure of the sofa to be tested; obtaining the usage duration, usage intensity, usage frequency, and type of pollution source corresponding to each testing area under the usage scenario from a pre-established usage scenario database; and using the product of the usage duration, usage intensity, and usage frequency corresponding to each testing area as the usage intensity coefficient of the corresponding testing area.

[0009] By adopting the above technical solution, the usage environment information is collected to determine the usage scenario, providing a practical application background for setting the detection conditions. The detection area is divided according to the functional structure of the sofa, ensuring the correspondence between the detection area and the actual usage part. Key parameters such as usage duration, usage intensity, usage frequency and pollution source type of each detection area are obtained from the usage scenario database. Through multiplication operation, the multidimensional usage characteristics are transformed into usage intensity coefficients, so that the usage differences of the detection areas can be accurately quantified, thereby realizing a zoning detection scheme based on the actual usage status.

[0010] Optionally, determining the pollution source concentration coefficient corresponding to each detection area based on the usage intensity coefficient and pollution source type of each detection area specifically includes: multiplying the usage intensity coefficient corresponding to each detection area by a preset coefficient to obtain the basic concentration coefficient of each detection area; marking the pollution source types present in each detection area, and determining the corresponding types of pollution sources for each detection area based on the pollution source types of each detection area; measuring the average residence time of each type of pollution source in each detection area, and dividing the average residence time by the standard residence time to obtain the residence time coefficient of each type of pollution source; weighted summing of the residence time coefficients of each type of pollution source in each detection area to obtain the pollution source retention coefficient of each detection area; and multiplying the basic concentration coefficient of each detection area by the pollution source retention coefficient to obtain the pollution source concentration coefficient of each detection area.

[0011] By adopting the above technical solution, the basic concentration coefficient is obtained by multiplying the intensity coefficient by the preset coefficient, which quantifies the basic pollution level of the detection area. After marking and measuring the average residence time of various pollution sources, the residence time coefficient is obtained by comparing it with the standard residence time, which reflects the cumulative characteristics of pollution sources in actual use. The residence time coefficients of various pollution sources are weighted and summed to obtain the pollution source retention coefficient, which is then multiplied by the basic concentration coefficient to calculate the pollution source concentration coefficient. This allows the pollution level of the detection area to simultaneously reflect the combined effects of the intensity of use and the retention of pollution sources.

[0012] Optionally, a pollution source mixture corresponding to each detection area is generated based on the pollution source concentration coefficient, the spraying amount of variable pollution sources, and the spraying amount of stable pollution sources for each detection area. The pollution source mixtures are then sprayed onto the corresponding detection areas. Specifically, this includes: dividing preset pollution source samples for each detection area into variable and stable pollution sources according to their stability; multiplying the pollution source concentration coefficient and the standard amount of variable pollution sources for each detection area, and then multiplying by the gain coefficient to obtain the spraying amount of variable pollution sources for each detection area; multiplying the pollution source concentration coefficient and the standard amount of stable pollution sources for each detection area to obtain the spraying amount of stable pollution sources for each detection area; and mixing the preset pollution source samples with the spraying amounts of variable and stable pollution sources for each detection area to obtain a pollution source mixture.

[0013] By employing the above technical solution, pollution source samples are classified according to their stability, reflecting the differentiated characteristics of different types of pollution sources. For volatile pollution sources, due to their high reactivity, a gain coefficient is introduced for dosage compensation, while for stable pollution sources, the dosage is directly determined by multiplying the pollution source concentration coefficient. The spraying dosage for both types of pollution sources is correlated with the pollution source concentration coefficient, ensuring the correspondence between the pollution level and the usage scenario. Finally, the resulting pollution source mixture can realistically simulate the complex pollution state in actual use environments, providing reliable testing conditions for subsequent antibacterial performance testing.

[0014] Optionally, determining the environmental parameters of each detection area based on the usage intensity coefficient of each detection area specifically includes: setting standard room temperature and standard humidity of each detection area as basic environmental parameters; setting standard pressure value, standard action duration, and standard interval duration of each detection area as basic pressure parameters; dividing the usage intensity coefficient of each detection area by the usage intensity benchmark value to obtain the correction coefficient of each detection area; multiplying the correction coefficient of each detection area by the standard pressure value, standard action duration, and standard interval duration of the corresponding detection area to obtain the actual pressure value, actual action duration, and actual interval duration of each detection area; and integrating the basic environmental parameters, actual pressure value, actual action duration, and actual interval duration of each detection area into the environmental parameters of each detection area.

[0015] By adopting the above technical solution, a baseline environmental condition for the detection environment is established based on the standard room temperature and humidity setting. A baseline pressure parameter system is constructed by combining the standard pressure value, the duration of action, and the interval duration. The correction coefficient is calculated by using the ratio of the intensity coefficient to the baseline value, and then multiplied by the baseline pressure parameter to obtain the actual pressure parameter. Finally, the environmental parameters are integrated to form the environmental parameters, so that the environmental conditions of the detection area can accurately reproduce the periodic change characteristics of pressure action under different usage intensities.

[0016] Optionally, the step of spraying a test solution containing a colorimetric indicator onto each of the test areas and evaluating the antibacterial performance of the sofa under test based on the degree of color change of the test solution corresponding to each of the test areas specifically includes: recording the initial color of the test solution in each test area; recording the degree of color change of the test solution in each test area at a first time point, a second time point, and a third time point, where the first time point corresponds to the bacterial adaptation period, the second time point corresponds to the bacterial exponential growth period, and the third time point corresponds to the bacterial stationary period; determining the antibacterial activity value of each test area based on the degree of color change of the test solution in each test area at the first time point, the second time point, and the third time point; and weighted summing the antibacterial activity values ​​of each test area to obtain the antibacterial performance index of the sofa under test, whereby the antibacterial performance index reflects the antibacterial performance.

[0017] By adopting the above technical solution, the initial color of the test solution is recorded as the evaluation benchmark. The degree of color change is recorded at three key stages of bacterial growth. The first time point reflects the initial inhibitory effect of the antibacterial material on bacteria, the second time point reflects the inhibitory effect of the antibacterial material during the rapid bacterial reproduction stage, and the third time point shows the long-term antibacterial ability. The antibacterial activity value is calculated from the data at these three time points, and the antibacterial performance index is determined by combining the weight of each test area, thus realizing the dynamic quantitative evaluation of the antibacterial performance of the sofa.

[0018] Optionally, determining the antibacterial activity value of each detection area based on the degree of color change of the detection solution at the first time point, the second time point, and the third time point specifically includes: establishing a standard color comparison card, the standard color comparison card including different degrees of color change and corresponding antibacterial activity values; matching the degree of color change of the detection solution at the first time point, the second time point, and the third time point of each detection area with the standard comparison card to obtain a first antibacterial activity value, a second antibacterial activity value, and a third antibacterial activity value for each detection area; calculating a first growth rate of the second antibacterial activity value to the first antibacterial activity value for each detection area, and a second growth rate of the third antibacterial activity value to the second antibacterial activity value for each detection area; if both the first growth rate and the second growth rate of each detection area are positive, then increasing the third antibacterial activity value by a preset percentage to obtain the antibacterial activity value of each detection area; if either the first growth rate or the second growth rate of each detection area is negative, then decreasing the third antibacterial activity value by a preset percentage to obtain the antibacterial activity value of each detection area.

[0019] By adopting the above technical solution, a standard color comparison card was established to achieve quantitative characterization of antibacterial activity value. The corresponding antibacterial activity value was obtained by the degree of color change at three time points. The growth rate between adjacent time points was calculated to reflect the changing trend of antibacterial effect. A positive growth rate indicates that the antibacterial effect is continuously enhanced, while a negative growth rate indicates that the antibacterial effect is decaying. Based on this, the third antibacterial activity value was dynamically adjusted so that the final antibacterial activity value can accurately reflect the continuous characteristics of antibacterial performance.

[0020] A second aspect of this application provides a sofa antibacterial performance testing system, the system comprising: The area division module is used to acquire fabric samples of the sofa to be tested and divide the fabric samples into multiple testing areas, each of which corresponds to a different usage intensity coefficient and pollution source type. The parameter optimization module is used to determine the environmental parameters of each detection area based on the usage intensity coefficient of each detection area. The concentration calibration module is used to determine the pollution source concentration coefficient corresponding to each of the detection areas based on the usage intensity coefficient and pollution source type of each detection area; The spraying module is used to generate a pollution source mixture corresponding to each of the detection areas based on the pollution source concentration coefficient, the spraying amount of volatile pollution sources, and the spraying amount of stable pollution sources in each detection area. The pollution source mixture is then sprayed onto the corresponding detection area and left to stand for a preset time under the environmental parameters corresponding to each detection area. The pollution source samples in each detection area include volatile pollution sources and stable pollution sources. The performance testing module is used to spray a test solution containing a color indicator onto each of the test areas, and to evaluate the antibacterial performance of the sofa under test based on the degree of color change of the test solution corresponding to each of the test areas.

[0021] By adopting the above technical solution, the fabric sample is divided into multiple testing areas and different usage intensity coefficients and types of pollution sources are set. This reflects that there are significant differences in the usage pressure and pollution types experienced by different parts of the sofa during actual use. Environmental parameters are determined based on the usage intensity coefficient, so that the testing environment can simulate the actual usage state. Furthermore, the ratio and amount of variable and stable pollution sources are controlled by the pollution source concentration coefficient, and the samples are left to stand under specific environmental parameters. This makes the pollution level of the testing area highly consistent with the actual usage environment. The antibacterial effect is evaluated by combining the color change of the color indicator, so that the test results can truly reflect the antibacterial performance of the sofa in the actual usage environment, thereby significantly improving the accuracy of antibacterial performance testing.

[0022] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any of the foregoing.

[0023] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any of the preceding descriptions. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the architecture of a sofa antibacterial performance testing system disclosed in an embodiment of this application; Figure 2 This is a schematic flowchart of a method for testing the antibacterial performance of a sofa, as disclosed in an embodiment of this application. Figure 3 yes Figure 2A flowchart illustrating a sub-step of step S101; Figure 4 yes Figure 2 A flowchart illustrating a sub-step of step S103; Figure 5 This is a flowchart illustrating a method for generating a mixture of pollutants. Figure 6 yes Figure 2 A flowchart illustrating a sub-step of step S102; Figure 7 yes Figure 2 A flowchart illustrating a sub-step of step S105; Figure 8 yes Figure 7 A flowchart illustrating a sub-step of step S603; Figure 9 This is a schematic diagram of a sofa antibacterial performance testing system provided in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0025] Explanation of reference numerals in the attached diagram: 21. Region division module; 22. Parameter optimization module; 23. Concentration calibration module; 24. Spraying treatment module; 25. Performance testing module; 26. Scheduling execution module; 901. Processor; 902. Communication bus; 903. User interface; 904. Network interface; 905. Memory. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0027] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0028] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0029] Figure 1 An exemplary system architecture 10 for a sofa antibacterial performance testing system is shown.

[0030] like Figure 1 As shown, system architecture 10 may include electronic device 11, network 12, and detection unit 13. Network 12 serves as a medium for providing a communication link between electronic device 11 and detection unit 13. Network 12 may include various connection types, such as wired or wireless communication links or industrial buses.

[0031] Electronic device 11 is a control terminal device with data processing capabilities, and can be a professional testing device with a display screen and control interface, such as an industrial control computer or a professional analyzer. Electronic device 11 is equipped with testing and control software, including a scenario analysis module for determining the usage scenario, an intensity assessment module for calculating the usage intensity coefficient, a pollution source ratio module for calculating the pollution source concentration coefficient, and a data analysis module for evaluating antibacterial activity values. Electronic device 11 sends testing commands to testing unit 13 via network 12 and receives and analyzes the testing data fed back by testing unit 13.

[0032] The detection unit 13 is the execution terminal device that performs specific detection operations, including a sampling module for acquiring fabric samples, an environmental parameter control module for controlling the detection environment, a spraying module for preparing and spraying pollution sources, and an image acquisition module for acquiring the degree of color change. The detection unit 13 executes the corresponding detection operations according to the instructions sent by the electronic device 11, and feeds back the acquired detection data to the electronic device 11 in real time via the network 12.

[0033] The following detailed explanation uses the electronic device side as an example.

[0034] This embodiment discloses a method for testing the antibacterial properties of sofas. Figure 2 This is a flowchart illustrating a method for testing the antibacterial properties of a sofa, as disclosed in an embodiment of this application. Figure 2 As shown, the method includes steps S101 to S106: S101: Obtain a fabric sample of the sofa to be tested, and divide the fabric sample into multiple testing areas, each testing area corresponding to a different usage intensity coefficient and type of pollution source.

[0035] In this application embodiment, the sofa to be tested refers to the target sofa that needs to be tested for antibacterial performance, such as a fabric sofa used in the home, a leather sofa in the office, or a synthetic leather sofa in public places.

[0036] Specifically, the electronic device sends a detection command to the detection unit via the network, and the detection unit obtains the fabric sample. Then, the detection unit divides the area according to the sofa structure characteristics and usage habit data transmitted by the electronic device, and transmits the division results to the electronic device. After that, the electronic device determines the usage intensity coefficient of each area according to the pre-stored intensity coefficient correspondence table, and at the same time instructs the detection unit to mark the types of pollution sources that may exist in each area. Finally, after the detection unit completes all markings, it feeds back the complete detection area division data to the electronic device.

[0037] For example, when obtaining fabric samples, the testing unit obtains samples of specified dimensions from different parts of the sofa to be tested; when dividing the testing area, the fabric sample is divided into different functional blocks such as the seat cushion area, armrest area, and backrest area; when determining the strength coefficient, the strength coefficient correspondence table is consulted to find that the value of the seat cushion area with heavy load is 0.8-1.0, and the value of the armrest area with moderate force is 0.5-0.7; when marking the types of pollution sources, human pollution sources (such as sebum and sweat) are marked for areas that come into direct contact with the human body, and food pollution sources (such as coffee and juice) are marked for areas near the coffee table.

[0038] Reference Figure 3 , Figure 3 This is provided by the embodiments of this application. Figure 2 A flowchart illustrating a sub-step of step S101, including steps S201 to S204, is shown below: S201: Collect usage environment information of the sofa to be tested, and determine the usage scenario based on the usage environment information.

[0039] In the embodiments of this application, the usage scenario refers to typical application occasions summarized based on usage environment information, such as home scenarios, office scenarios, public place scenarios, etc.

[0040] Specifically, the electronic device first sends a data acquisition command to the detection unit via the network, and the detection unit then collects the usage environment information of the sofa under test. This usage environment information refers to the characteristic parameters of the environment in which the sofa is located, including temperature, humidity, and lighting, as well as usage information such as location, frequency of use, and users. The detection unit then performs scene feature analysis based on the collected characteristic parameters and usage information. The detection unit transmits the analysis results to the electronic device and determines the usage scene type according to a scene feature correspondence table. Finally, the determined usage scene is fed back to the electronic device. The characteristic parameters refer to the physical characteristics of the environment, including temperature, humidity, and lighting, while the usage information refers to the usage status of the sofa under test, including location, frequency of use, and users.

[0041] For example, when collecting information about the environment, the detection unit collects information such as the temperature of the environment where the sofa to be tested is 25℃, the relative humidity is 60%, the illuminance is 500cd, and the fact that it is located in the family living room, the average daily usage time is 4 hours, and the main users are family members. When determining the usage scenario, the system classifies the characteristics such as moderate temperature and humidity, sufficient lighting, moderate usage time, and main use by a fixed group of people as the family usage scenario according to the scenario feature correspondence table.

[0042] S202: Divide the fabric sample into multiple testing areas according to the functional structure of the sofa to be tested.

[0043] Specifically, the electronic device first sends a detection command to the detection unit via the network, and the detection unit obtains the fabric sample of the sofa to be tested. Then, the detection unit divides the area according to the functional structure information, which includes characteristic data such as the purpose, position, and size of each part of the sofa. Then, the detection unit divides the fabric sample into different detection areas according to the area division correspondence table, which is used to record the detection area division method corresponding to different functional structures. Finally, the division result is fed back to the electronic device.

[0044] For example, when obtaining fabric samples, the testing unit obtains samples of specified dimensions from functional parts of the sofa to be tested, such as the seat cushion, armrests, and backrest. When dividing the area, the fabric samples of the seat cushion are divided into load-bearing and non-load-bearing areas according to the area division correspondence table, the fabric samples of the armrest are divided into contact areas and decorative areas, and the fabric samples of the backrest are divided into stress areas and cushioning areas.

[0045] S203: Obtain the usage duration, usage intensity, usage frequency, and pollution source type for each detection area under the usage scenario from the pre-established usage scenario database.

[0046] Specifically, the electronic device first accesses a usage scenario database, which records usage parameter information for each detection area under different usage scenarios. Then, the electronic device searches and matches data in the database based on the current usage scenario, and extracts the corresponding usage parameters for each detection area from the matched data records. These usage parameters include usage duration, usage intensity, usage frequency, and pollution source type. Finally, the extracted parameter information is used for subsequent processing. The pollution source type refers to different types of pollutants, including those from human bodies, food, and the environment.

[0047] For example, in a home use scenario, the daily usage time of the seat cushion area is 4 hours, the usage intensity is heavy, the usage frequency is daily, and the pollution source is human body; the daily usage time of the armrest area is 2 hours, the usage intensity is moderate, the usage frequency is daily, and the pollution sources are human body and food; the daily usage time of the backrest area is 3 hours, the usage intensity is light, the usage frequency is daily, and the pollution source is human body.

[0048] S204: The product of the usage duration, usage intensity, and usage frequency corresponding to each detection area is used as the usage intensity coefficient of the corresponding detection area.

[0049] Specifically, the electronic device first reads the usage duration, usage intensity, and usage frequency values ​​corresponding to each detection area, and then multiplies these three parameters, where the usage duration is expressed in hours, the usage intensity is expressed in the corresponding level coefficient, and the usage frequency is expressed in the number of times per day. The product result is then used as the usage intensity coefficient for that detection area, and finally the usage intensity coefficients for all detection areas are obtained.

[0050] For example, for the seat cushion area, if the usage time is 4 hours, the usage intensity is heavy (1.0), and the usage frequency is 3 times / day, then its usage intensity coefficient is 4×1.0×3=12; for the armrest area, if the usage time is 2 hours, the usage intensity is moderate (0.8), and the usage frequency is 3 times / day, then its usage intensity coefficient is 2×0.8×3=4.8; for the backrest area, if the usage time is 3 hours, the usage intensity is light (0.6), and the usage frequency is 3 times / day, then its usage intensity coefficient is 3×0.6×3=5.4.

[0051] S102: Determine the environmental parameters of each testing area based on the usage intensity coefficient of each testing area.

[0052] Specifically, the electronic device first obtains the configuration range of basic environmental parameters and actual pressure parameters corresponding to each usage intensity coefficient by querying the environmental parameter configuration table. Then, based on the usage intensity coefficient of each detection area, it selects the corresponding environmental parameter configuration and integrates the basic environmental parameters and actual pressure parameters according to a preset data structure. The data structure organizes the parameters for each detection area into two sub-modules: the basic environmental parameter module includes three parameters—temperature, relative humidity, and illuminance; the actual pressure parameter module includes three parameters—actual pressure value, actual action duration, and actual interval duration. These two modules are linked together through a detection area identifier to form a complete parameter set as the environmental parameters. Finally, the integrated environmental parameters are sent to the detection unit via the network for environmental simulation.

[0053] For example, for the seat cushion area using an intensity coefficient of 12, the basic environmental parameters are configured as follows: temperature 35℃, relative humidity 80%, illuminance 200cd; actual pressure parameters are configured as follows: actual pressure value 120N, actual application time 30 minutes, and actual interval time 10 minutes. For the armrest area using an intensity coefficient of 4.8, the basic environmental parameters are configured as follows: temperature 30℃, relative humidity 70%, illuminance 300cd; actual pressure parameters are configured as follows: actual pressure value 80N, actual application time 20 minutes, and actual interval time 15 minutes. For the backrest area using an intensity coefficient of 5.4, the basic environmental parameters are configured as follows: temperature 28℃, relative humidity 65%, illuminance 400cd; actual pressure parameters are configured as follows: actual pressure value 90N, actual application time 25 minutes, and actual interval time 12 minutes.

[0054] Reference Figure 6 , Figure 6 This is provided by the embodiments of this application. Figure 2 A flowchart illustrating a sub-step of step S102, including steps S501 to S505, is as follows: S501: Set the standard room temperature and standard humidity of each test area as the basic environmental parameters.

[0055] Specifically, the electronic device first queries the standard environmental parameter configuration table according to the testing standard specifications. The standard environmental parameter configuration table is used to record the standard room temperature and standard humidity range corresponding to different testing types. Then, according to the number of testing areas, the same basic environmental parameters are configured for each testing area. The configured basic environmental parameters are then sent to the testing unit through the network. Finally, the testing unit initializes the environmental parameters for each testing area.

[0056] For example, a standard room temperature of 23°C and a standard humidity of 55% were set for the seat cushion area, armrest area, and backrest area as basic environmental parameters to ensure that each testing area started testing under the same initial environmental conditions, providing a benchmark for subsequent adjustment of environmental parameters based on the usage intensity coefficient.

[0057] S502: Set the standard pressure value, standard action time, and standard interval time for each detection zone as the basic pressure parameters.

[0058] Specifically, the electronic device first queries the standard pressure parameter configuration table according to the testing standard specifications. The standard pressure parameter configuration table is used to record the standard pressure value, standard action time and standard interval time range corresponding to different testing types. Then, according to the number of testing areas, the same basic pressure parameters are configured for each testing area. The configured basic pressure parameters are then sent to the testing unit through the network. Finally, the testing unit initializes the pressure parameters for each testing area.

[0059] For example, a standard pressure value of 100 Newtons, a standard action time of 5 seconds, and a standard interval of 10 seconds are set for the seat cushion area, armrest area, and backrest area as basic pressure parameters to ensure that each test area starts testing under the same initial pressure conditions, providing a benchmark for subsequent adjustment of pressure parameters based on the strength coefficient.

[0060] S503: Divide the service intensity coefficient of each test area by the service intensity benchmark value to obtain the correction coefficient of each test area.

[0061] Specifically, the electronic equipment first obtains the usage strength benchmark value from the testing standard specifications, and then normalizes the usage strength coefficient of each testing area by dividing it by the benchmark value. The usage strength benchmark value is a pre-set standard reference value, and then the correction coefficient corresponding to each testing area is obtained.

[0062] For example, the strength baseline value is set to 6.0. For the seat cushion area using a strength coefficient of 12.0, the calculated correction factor is 2.0 (12.0 / 6.0=2.0); for the armrest area using a strength coefficient of 4.8, the calculated correction factor is 0.8 (4.8 / 6.0=0.8); and for the backrest area using a strength coefficient of 5.4, the calculated correction factor is 0.9 (5.4 / 6.0=0.9). These correction factors will be used to adjust the environmental and pressure parameters of each testing area.

[0063] S504: Multiply the correction coefficient of each detection area by the standard pressure value, standard action duration and standard interval duration of the corresponding detection area to obtain the actual pressure value, actual action duration and actual interval duration of each detection area.

[0064] Specifically, the electronic device first obtains the correction coefficient and standard pressure parameter corresponding to each detection area, and then multiplies the correction coefficient by the standard pressure value, standard action time and standard interval time respectively. The standard pressure parameter includes the standard pressure value, standard action time and standard interval time. Then, the pressure parameter that should be used in the actual detection of each detection area is obtained. Finally, the actual pressure parameter is sent to the detection unit through the network.

[0065] For example, for the seat cushion area with a correction factor greater than 1, the actual pressure value, actual duration of action, and actual interval duration are calculated to be higher than the standard value; for the armrest area with a correction factor close to 1, the actual pressure value, actual duration of action, and actual interval duration are calculated to be close to the standard value; for the backrest area with a correction factor less than 1, the actual pressure value, actual duration of action, and actual interval duration are calculated to be lower than the standard value.

[0066] S505: Integrate the basic environmental parameters, actual pressure values, actual action duration, and actual interval duration of each detection area into the environmental parameters of each detection area.

[0067] Specifically, the electronic device first collects basic environmental parameters and actual pressure parameters for each detection area, and then integrates these parameters according to a preset data structure. The data structure organizes the parameters for each detection area into two sub-modules: the basic environmental parameter module includes three parameters—temperature, relative humidity, and illuminance—and the actual pressure parameter module includes three parameters—actual pressure value, actual action duration, and actual interval duration. These two modules are linked together through a detection area identifier to form a complete parameter set as the environmental parameters. Then, the complete environmental parameters for each detection area are generated, and finally, the integrated environmental parameters are sent to the detection unit via the network.

[0068] For example, for the seat cushion area, its standard room temperature, standard humidity, and the calculated actual pressure value, actual duration of action, and actual interval duration are integrated into a set of environmental parameters; for the armrest area and backrest area, their basic environmental parameters and corresponding actual pressure parameters are integrated into their respective environmental parameter sets to guide the detection unit in performing the corresponding environmental simulation.

[0069] S103: Based on the usage intensity coefficient and pollution source type of each detection area, determine the pollution source concentration coefficient corresponding to each detection area.

[0070] Specifically, the electronic device first obtains the usage intensity coefficient and pollution source type information of each detection area, then queries the pollution source ratio table to obtain the baseline concentration value corresponding to different pollution source types. The pollution source ratio table is used to record the standard concentration ratio scheme of different pollution source types. Then, the usage intensity coefficient of each detection area is multiplied by the baseline concentration value of the corresponding pollution source to obtain the pollution source concentration coefficient. Finally, the concentration coefficient is used for subsequent pollutant preparation.

[0071] For example, for the seat cushion area with a usage intensity coefficient of 12 and the presence of human body pollution sources (baseline concentration value of 0.2), the pollution source concentration coefficient is calculated to be 2.4; for the armrest area with a usage intensity coefficient of 4.8 and the presence of both human body pollution sources (baseline concentration value of 0.2) and food pollution sources (baseline concentration value of 0.15), the pollution source concentration coefficients are calculated to be 0.96 and 0.72, respectively; for the backrest area with a usage intensity coefficient of 5.4 and the presence of environmental pollution sources (baseline concentration value of 0.1), the pollution source concentration coefficient is calculated to be 0.54.

[0072] Reference Figure 4 , Figure 4 This is provided by the embodiments of this application. Figure 2 A flowchart illustrating a sub-step of step S103, including steps S301 to S305, is as follows: S301: Multiply the usage intensity coefficient corresponding to each detection area by a preset coefficient to obtain the basic concentration coefficient of each detection area.

[0073] Specifically, the electronic equipment first obtains the preset coefficient from the testing standard specifications, and then multiplies the usage intensity coefficient of each testing area by the preset coefficient, where the preset coefficient is a fixed value determined based on empirical data, and then obtains the basic concentration coefficient corresponding to each testing area.

[0074] For example, for the seat cushion area with a strength coefficient of 12, when the preset coefficient is 0.2, the calculated basic concentration coefficient is 2.4; for the armrest area with a strength coefficient of 4.8, the calculated basic concentration coefficient is 0.96; and for the backrest area with a strength coefficient of 5.4, the calculated basic concentration coefficient is 1.08.

[0075] S302: Mark the types of pollution sources present in each detection area, and determine the corresponding types of pollution sources for each detection area based on the types of pollution sources in each detection area.

[0076] Specifically, the electronic device first marks the types of pollution sources that may exist in each detection area based on the usage scenario data. Then, it queries the pollution source correspondence table to obtain the specific component information of each type of pollution source. The pollution source correspondence table is used to record the specific component information under different types of pollution sources. Then, it determines the corresponding types of pollution sources for each detection area based on the pollution source type.

[0077] For example, the seat cushion area is marked as a human body source of pollution, and its specific sources of pollution are identified as sebum (60%) and sweat (40%); the armrest area is marked as both a human body source and a food source of pollution, and its specific sources of pollution are identified as sebum (30%), sweat (20%), coffee (30%), and fruit juice (20%); the backrest area is marked as an environmental source of pollution, and its specific sources of pollution are identified as dust (70%) and stains (30%).

[0078] S303: Measure the average residence time of various pollution sources in each detection area, and divide the average residence time by the standard residence time to obtain the residence time coefficient of various pollution sources.

[0079] Specifically, the electronic device first measures the average residence time of different pollution sources in each detection area through the detection unit, then obtains the standard residence time from the detection standard specification, where the standard residence time is a pre-set reference value, and then divides the measured average residence time by the corresponding standard residence time to finally obtain the residence time coefficient of various pollution sources.

[0080] For example, for the sebum contamination source in the seat cushion area, the average dwell time was measured to be 120 minutes, and the standard dwell time was 60 minutes, resulting in a dwell time coefficient of 2.0; for the coffee contamination source in the armrest area, the average dwell time was measured to be 45 minutes, and the standard dwell time was 60 minutes, resulting in a dwell time coefficient of 0.75; for the dust contamination source in the backrest area, the average dwell time was measured to be 180 minutes, and the standard dwell time was 60 minutes, resulting in a dwell time coefficient of 3.0.

[0081] S304: The residence time coefficients of various pollution sources in each detection area are weighted and summed to obtain the pollution source retention coefficient of each detection area.

[0082] Specifically, the electronic device first obtains the residence time coefficient and corresponding weight coefficient of different pollution sources in each detection area. Then, it multiplies the residence time coefficient of each pollution source by its weight coefficient, where the weight coefficient is determined according to the proportion of the pollution source in the area. Finally, it sums all the weighted results to obtain the pollution source retention coefficient of the detection area.

[0083] For example, in the seat cushion area, sebum accounts for 60% of the pollution source with a residence time coefficient of 2.0, sweat accounts for 40% with a residence time coefficient of 1.5, and the calculated pollution source retention coefficient is 1.8; in the armrest area, sebum accounts for 30% of the pollution source with a residence time coefficient of 1.8, sweat accounts for 20% with a residence time coefficient of 1.2, coffee accounts for 30% with a residence time coefficient of 0.75, and juice accounts for 20% with a residence time coefficient of 0.9, and the calculated pollution source retention coefficient is 1.17.

[0084] S305: Multiply the basic concentration coefficient of each detection area by the pollution source retention coefficient to obtain the pollution source concentration coefficient of each detection area.

[0085] Specifically, the electronic equipment first obtains the basic concentration coefficient and the pollution source retention coefficient of each detection area, and then multiplies the two coefficients. The basic concentration coefficient reflects the influence of usage intensity, and the pollution source retention coefficient reflects the influence of pollutant accumulation. Then, the pollution source concentration coefficient that takes into account both factors is obtained.

[0086] For example, for the seat cushion area, the basic concentration coefficient is 2.4 and the pollution source retention coefficient is 1.8, so the calculated pollution source concentration coefficient is 2.4 × 1.8 = 4.32; for the armrest area, the basic concentration coefficient is 0.96 and the pollution source retention coefficient is 1.17, so the calculated pollution source concentration coefficient is 0.96 × 1.17 = 1.12; for the backrest area, the basic concentration coefficient is 1.08 and the pollution source retention coefficient is 0.85, so the calculated pollution source concentration coefficient is 1.08 × 0.85 = 0.92.

[0087] S104: Generate a pollution source mixture corresponding to each detection area based on the pollution source concentration coefficient, the amount of spraying of volatile pollution sources, and the amount of spraying of stable pollution sources. Spray each pollution source mixture onto the corresponding detection area and let it stand for a preset time under the environmental parameters corresponding to each detection area. The pollution source samples of each detection area include volatile pollution sources and stable pollution sources.

[0088] Specifically, the electronic equipment first obtains the standard spraying amount of variable and stable pollution sources. Then, it multiplies the pollution source concentration coefficient of each detection area by the standard spraying amount of the two pollution sources to obtain the actual spraying amount. Then, the detection unit mixes the variable and stable pollution sources prepared according to the actual spraying amount to form a pollution source mixture. Finally, the spraying device evenly sprays the mixture onto the corresponding detection area and lets it stand for a predetermined time under the set environmental parameters.

[0089] For example, when obtaining the standard spraying dosage, the baseline preparation dosages for variable and stable pollution sources are determined separately; when calculating the actual spraying dosage, the pollution source concentration coefficient is multiplied by the standard spraying dosages for the two pollution sources respectively; when mixing the formulation, the calculated actual spraying dosages for the two pollution sources are mixed; when spraying and allowing the mixture to stand, the prepared mixture is sprayed onto the detection area and allowed to stand for a preset time under the corresponding environmental parameters.

[0090] Reference Figure 5 , Figure 5 This is a schematic flowchart of a method for generating a mixture of pollutants provided in an embodiment of this application, including steps S401 to S404, as follows: S401: The preset pollution source samples in each detection area are divided into variable pollution sources and stable pollution sources according to their stability.

[0091] Specifically, the electronic device first acquires preset pollution source samples for each detection area, and then consults a pollution source stability comparison table to obtain the stability of various pollution source samples. The pollution source stability comparison table records the stability value corresponding to each type of pollution source sample. Pollution source samples with stability values ​​below the stability threshold are classified as volatile pollution sources, while those with stability values ​​greater than or equal to the stability threshold are classified as stable pollution sources. Volatile pollution sources are pollutants that are prone to physical or chemical changes, including volatile or easily degradable substances such as sweat and fruit juice. Stable pollution sources are pollutants that are not prone to physical or chemical changes, including substances that are not easily deteriorated or changed, such as sebum and dust.

[0092] S402: Multiply the pollution source concentration coefficient and the standard dosage of variable pollution sources in each test area, and then multiply by the gain coefficient to obtain the spraying dosage of variable pollution sources in each test area.

[0093] Specifically, the electronic device first obtains the pollution source concentration coefficient and the standard dosage of variable pollution sources in each detection area. Then, it queries the variable pollution source gain coefficient table to obtain the gain coefficient. The variable pollution source gain coefficient table is used to record the gain coefficient of different variable pollution sources. Then, the pollution source concentration coefficient, standard dosage and gain coefficient are multiplied together to obtain the spraying dosage of variable pollution sources.

[0094] For example, for the seat cushion area, when the pollution source concentration coefficient is 4.32, the standard amount of sweat is 10ml / m², and the gain coefficient is 1.2, the calculated spraying amount of the variable pollution source is 51.84ml / m²; for the armrest area, when the pollution source concentration coefficient is 1.12, the standard amount of juice is 10ml / m², and the gain coefficient is 1.1, the calculated spraying amount of the variable pollution source is 12.32ml / m².

[0095] S403: Multiply the pollution source concentration coefficient of each test area by the standard dosage of stable pollution sources to obtain the spraying dosage of stable pollution sources in each test area.

[0096] Specifically, the electronic device first obtains the pollution source concentration coefficient and the standard dosage of the stable pollution source for each detection area. The standard dosage is a pre-set baseline preparation amount. Then, the pollution source concentration coefficient is multiplied by the standard dosage to obtain the spraying dosage of the stable pollution source.

[0097] For example, for the seat cushion area, when the pollution source concentration coefficient is 4.32 and the standard amount of sebum is 15ml / m², the calculated spraying amount of stable pollution source is 64.8ml / m²; for the armrest area, when the pollution source concentration coefficient is 1.12 and the standard amount of dust is 15ml / m², the calculated spraying amount of stable pollution source is 16.8ml / m².

[0098] S404: Mix the coating amounts of volatile pollution sources and stable pollution sources in each detection area with the preset pollution source samples to obtain a pollution source mixture.

[0099] Specifically, the electronic device first obtains the amount of coating used for both variable and stable pollution sources in each detection area, then sends a preparation instruction to the detection unit. The detection unit then takes preset pollution source samples according to the amount of coating used, and mixes the preset variable and stable pollution sources according to the calculated ratio to obtain a pollution source mixture.

[0100] For example, for the seat cushion area, when the spraying amount of the variable pollutant (sweat) is 51.84 ml / m² and the spraying amount of the stable pollutant (sebum) is 64.8 ml / m², the pollutant mixture is obtained by mixing the preset pollutant samples according to this ratio; for the armrest area, when the spraying amount of the variable pollutant (juice) is 12.32 ml / m² and the spraying amount of the stable pollutant (dust) is 16.8 ml / m², the pollutant mixture is obtained by mixing the preset pollutant samples according to this ratio.

[0101] S105: Spray a test solution containing a colorimetric indicator onto each test area, and evaluate the antibacterial performance of the sofa under test based on the degree of color change of the corresponding test solution in each test area.

[0102] Specifically, the electronic device first sends a detection command to the detection unit, which then sprays a detection solution containing a colorimetric indicator onto each detection area. Subsequently, the color change degree of each detection area is collected within a preset observation time. The color change degree includes parameters such as chromaticity and saturation. Then, the collected data is compared with a standard evaluation scale. Finally, the antibacterial performance of the sofa under test is evaluated based on the comparison results.

[0103] For example, in the seat cushion area, the color indicator changed from colorless to dark blue (chromaticity value 0.8) after the test solution was sprayed, indicating that the antibacterial performance of this area was poor; in the armrest area, the color indicator changed from colorless to light blue (chromaticity value 0.3) after the test solution was sprayed, indicating that the antibacterial performance of this area was good; and in the backrest area, the color indicator remained basically colorless (chromaticity value 0.1) after the test solution was sprayed, indicating that the antibacterial performance of this area was excellent.

[0104] Reference Figure 7 , Figure 7 This is provided by the embodiments of this application. Figure 2 A flowchart illustrating a sub-step of step S105, including steps S601 to S604, is as follows: S601: Record the initial color of the detection solution in each detection area.

[0105] Specifically, the electronic device first sends a detection command to the detection unit, which then sprays a detection solution onto each detection area. Subsequently, the initial state of each detection area is recorded by an image acquisition device, which acquires color data of the detection area. The acquired color data is then stored in the detection database as the initial color of each detection area, and finally, these initial colors are used as a reference benchmark for subsequent evaluation.

[0106] For example, for the seat cushion area, the initial color was recorded as colorless immediately after the test solution was sprayed (chromaticity value 0.0, saturation 5%, lightness 95%); for the armrest area, the initial color was recorded as colorless immediately after the test solution was sprayed (chromaticity value 0.0, saturation 3%, lightness 96%); and for the backrest area, the initial color was recorded as colorless immediately after the test solution was sprayed (chromaticity value 0.0, saturation 4%, lightness 97%).

[0107] S602: Record the degree of color change of the detection solution in each detection area at the first time point, the second time point and the third time point respectively. The first time point corresponds to the bacterial adaptation period, the second time point corresponds to the bacterial exponential growth period, and the third time point corresponds to the bacterial stationary period.

[0108] Specifically, the electronic device first sends a color change acquisition command to the detection unit via the network, activating the detection unit's image acquisition module to monitor each detection area in real time. At three time points—the bacterial adaptation period, the bacterial exponential growth period, and the bacterial stationary period—the electronic device receives color data from the detection unit. This color data includes three parameters: chromaticity, saturation, and lightness. Based on a preset color change mapping rule, the electronic device converts the color data into corresponding color change values ​​and stores them in the database, forming a record of the color change degree of each detection area at the three time points. The bacterial adaptation period represents the stage where bacteria begin to adapt to the environment, the bacterial exponential growth period represents the stage where bacteria multiply rapidly, and the bacterial stationary period represents the stage where the bacterial population tends to stabilize.

[0109] For example, the electronic device records the color data of the seat cushion area during the bacterial adaptation period (30 minutes) as chroma 30, saturation 15%, and lightness 85%, resulting in a light blue color change with a color change value of 0.8; during the bacterial exponential growth period (60 minutes), the color data is chroma 60, saturation 35%, and lightness 75%, resulting in a medium blue color change with a color change value of 0.6; and during the bacterial stationary period (90 minutes), the color data is chroma 90, saturation 55%, and lightness 65%, resulting in a dark blue color change with a color change value of 0.4. The armrest area records the color data during the bacterial adaptation period as chroma 0, saturation 3%, and lightness 96%, resulting in a colorless color change with a color change value of 1.0; and during both the bacterial exponential growth and stationary periods, the color data is chroma 30, saturation 15%, and lightness 85%, resulting in a light blue color change with a color change value of 0.8. The electronic device stores the color change data at various time points for subsequent antibacterial activity evaluation.

[0110] S603: Determine the antibacterial activity value of each detection area based on the degree of color change of the detection solution at the first, second, and third time points.

[0111] Specifically, the electronic device first collects the degree of color change of the detection solution in each detection area at three preset time points. Then, it consults the color evaluation reference table to obtain the antibacterial activity reference value corresponding to the degree of color change at each time point. The color evaluation reference table records the correspondence between different degrees of color change and the antibacterial activity reference value. Then, the antibacterial activity reference values ​​at the three time points are weighted according to preset weights to calculate the antibacterial activity value of each detection area.

[0112] For example, for the seat cushion area, the solution was light blue at the first time point, and the antibacterial activity reference value was 4.0 (weight 0.2) according to the table; it was medium blue at the second time point, and the antibacterial activity reference value was 3.0 (weight 0.3) according to the table; it was dark blue at the third time point, and the antibacterial activity reference value was 2.0 (weight 0.5) according to the table. The final antibacterial activity value was calculated to be 2.7. For the armrest area, the solution colors at the three time points were colorless, light blue, and light blue, respectively. The antibacterial activity reference values ​​were 5.0, 4.0, and 4.0 according to the table. The final antibacterial activity value was calculated to be 4.3 with the same weight.

[0113] Reference Figure 8 , Figure 8 This is provided by the embodiments of this application. Figure 7 A flowchart illustrating a sub-step of step S603, including steps S701 to S705, is as follows: S701: Establish a standard color comparison card, which includes different degrees of color change and corresponding antibacterial activity values.

[0114] Specifically, the electronic equipment first determines the degree of color change according to the testing standards and specifications, then establishes a correspondence table between the degree of color change and the antibacterial activity value. The correspondence table is used to record the antibacterial activity value corresponding to different degrees of color change. Then, these data are made into a standard color comparison card, and finally, the standard color comparison card is stored in the testing system database.

[0115] For example, the color change of a colorimetric indicator from colorless to dark blue can be divided into five levels: colorless corresponds to an antibacterial activity value of 5.0 (excellent antibacterial performance), light blue corresponds to an antibacterial activity value of 4.0 (good antibacterial performance), medium blue corresponds to an antibacterial activity value of 3.0 (moderate antibacterial performance), dark blue corresponds to an antibacterial activity value of 2.0 (poor antibacterial performance), and dark blue corresponds to an antibacterial activity value of 1.0 (poor antibacterial performance).

[0116] S702: Match the color change of the test solution in each test area at the first, second, and third time points with the standard comparison card to obtain the first antibacterial activity value, the second antibacterial activity value, and the third antibacterial activity value of each test area.

[0117] Specifically, the electronic device first controls the detection unit to collect the degree of color change in each detection area at three preset time points. Then, it automatically matches the collected degree of color change with a standard comparison card, which is stored in the detection system database. Based on the matching results, the antibacterial activity value corresponding to each time point is determined, and finally, the antibacterial activity evaluation results of each detection area at different time points are obtained.

[0118] For example, for the seat cushion area, the color is light blue at the first time point (30 minutes), and the first antibacterial activity value is 4.0; the color is medium blue at the second time point (60 minutes), and the second antibacterial activity value is 3.0; the color is dark blue at the third time point (90 minutes), and the third antibacterial activity value is 2.0. For the armrest area, the colors at the three time points are colorless, light blue, and light blue, respectively, and the corresponding antibacterial activity values ​​are 5.0, 4.0, and 4.0.

[0119] S703: Calculate the first growth rate of the second antibacterial activity value and the first antibacterial activity value of each detection area, and the second growth rate of the third antibacterial activity value and the second antibacterial activity value of each detection area.

[0120] Specifically, the electronic device first acquires the antibacterial activity values ​​of each detection area at three time points, then calculates the difference in antibacterial activity values ​​between adjacent time points. The growth rate is calculated by subtracting the antibacterial activity value of the previous time point from the antibacterial activity value of the later time point and dividing by the antibacterial activity value of the previous time point. Then, the first growth rate and the second growth rate are obtained respectively. Finally, the growth rate data is used to evaluate the dynamic changes in antibacterial performance.

[0121] For example, for the seat cushion area, the first antibacterial activity value is 4.0, the second antibacterial activity value is 3.0, and the third antibacterial activity value is 2.0. The calculated first growth rate is -25% ((3.0-4.0) / 4.0), and the second growth rate is -33.3% ((2.0-3.0) / 3.0). For the armrest area, the first antibacterial activity value is 5.0, the second antibacterial activity value is 4.0, and the third antibacterial activity value is 4.0. The calculated first growth rate is -20% ((4.0-5.0) / 5.0), and the second growth rate is 0% ((4.0-4.0) / 4.0).

[0122] S704: If both the first growth rate and the second growth rate of each detection area are positive, the third antibacterial activity value is increased by a preset percentage to obtain the antibacterial activity value of each detection area.

[0123] Specifically, the electronic device first determines whether the first growth rate and the second growth rate of each detection area are both positive. Then, for the detection areas that meet the conditions, the third antibacterial activity value is multiplied by a preset upward adjustment percentage coefficient, where the upward adjustment percentage coefficient is 1 + preset percentage. Then, the corrected antibacterial activity value is obtained, and finally, the corrected result is used as the final antibacterial activity evaluation value of the detection area.

[0124] For example, for a certain detection area, the first growth rate is 15%, the second growth rate is 10% (both positive values), and the third antibacterial activity value is 4.0. When the preset percentage is 10%, the final antibacterial activity value is calculated to be 4.4 (4.0 × 1.1).

[0125] S705: If there is a negative value in the first growth rate and the second growth rate of each detection area, the third antibacterial activity value is reduced by a preset percentage to obtain the antibacterial activity value of each detection area.

[0126] Specifically, the electronic device first determines whether the first and second growth rates of each detection area are negative. Then, for the detection areas with negative growth rates, its third antibacterial activity value is multiplied by a preset reduction percentage coefficient, where the reduction percentage coefficient is 1 - preset percentage. The corrected antibacterial activity value is then obtained, and the corrected result is used as the final antibacterial activity evaluation value for that detection area.

[0127] For example, for a certain detection area, the first growth rate is -15% (there are negative values), the second growth rate is 10%, and the third antibacterial activity value is 4.0. When the preset percentage is 10%, the final antibacterial activity value is calculated to be 3.6 (4.0 × 0.9).

[0128] S604: The antibacterial activity values ​​of each test area are weighted and summed to obtain the antibacterial performance index of the sofa to be tested. The antibacterial performance index reflects the antibacterial performance.

[0129] Specifically, the electronic device first acquires the antibacterial activity value and corresponding weight coefficient of each detection area. Then, it multiplies the antibacterial activity value of each detection area by its weight coefficient. The weight coefficient is calculated as follows: First, based on a preset frequency coefficient lookup table and importance coefficient lookup table, the frequency coefficient corresponding to the usage frequency and the importance coefficient corresponding to the importance of each detection area are obtained. The frequency coefficient lookup table records the standard frequency coefficients corresponding to different usage frequencies, and the importance coefficient lookup table records the standard importance coefficients corresponding to different importance levels. Then, the arithmetic mean of the frequency coefficients and importance coefficients is calculated to obtain the initial weight. Finally, the initial weight of each detection area is divided by the sum of the initial weights of all detection areas for normalization, resulting in the final weight coefficient. This ensures that the sum of the weight coefficients of all detection areas is 1. Finally, all weighted results are summed to obtain the antibacterial performance index, which reflects the overall antibacterial performance.

[0130] This embodiment also discloses a sofa antibacterial performance testing system. Figure 9 This is a schematic diagram of a sofa antibacterial performance testing system disclosed in an embodiment of this application. The system includes: The area division module 21 is used to acquire a fabric sample of the sofa to be tested and divide the fabric sample into multiple testing areas, each of which corresponds to a different usage intensity coefficient and pollution source type. The parameter optimization module 22 is used to determine the environmental parameters of each detection area based on the usage intensity coefficient of each detection area. Concentration calibration module 23 is used to determine the pollution source concentration coefficient corresponding to each detection area based on the usage intensity coefficient and pollution source type of each detection area; The spraying module 24 is used to generate a pollution source mixture corresponding to each detection area based on the pollution source concentration coefficient, the spraying amount of volatile pollution sources and the spraying amount of stable pollution sources in each detection area, spray each pollution source mixture onto the corresponding detection area, and let it stand for a preset time under the environmental parameters corresponding to each detection area. The pollution source samples in each detection area include volatile pollution sources and stable pollution sources. The performance testing module 25 is used to spray a test solution containing a color indicator onto each of the test areas, and to evaluate the antibacterial performance of the sofa under test based on the degree of color change of the test solution corresponding to each of the test areas.

[0131] Optionally, the area division module 21 is also used to collect the usage environment information of the sofa to be tested, determine the usage scenario based on the usage environment information, divide the fabric sample into multiple testing areas according to the functional structure of the sofa to be tested, obtain the usage duration, usage intensity, usage frequency and pollution source type corresponding to each testing area under the usage scenario from a pre-established usage scenario database, and use the product of the usage duration, usage intensity and usage frequency corresponding to each testing area as the usage intensity coefficient of the corresponding testing area.

[0132] Optionally, the concentration calibration module 23 is further configured to multiply the usage intensity coefficient corresponding to each detection area by a preset coefficient to obtain the basic concentration coefficient of each detection area; mark the types of pollution sources present in each detection area, and determine the types of pollution sources corresponding to each detection area based on the types of pollution sources in each detection area; measure the average residence time of each type of pollution source in each detection area, and divide the average residence time by the standard residence time to obtain the residence time coefficient of each type of pollution source; perform a weighted summation of the residence time coefficients of each type of pollution source in each detection area to obtain the pollution source retention coefficient of each detection area; and multiply the basic concentration coefficient of each detection area by the pollution source retention coefficient to obtain the pollution source concentration coefficient of each detection area.

[0133] Optionally, the spraying module 24 is further configured to: set the standard room temperature and standard humidity of each of the detection areas as basic environmental parameters; set the standard pressure value, standard action duration, and standard interval duration of each of the detection areas as basic pressure parameters; divide the usage intensity coefficient of each detection area by the usage intensity benchmark value to obtain the correction coefficient of each detection area; multiply the correction coefficient of each detection area by the corresponding standard pressure value, standard action duration, and standard interval duration to obtain the actual pressure value, actual action duration, and actual interval duration of each detection area; and integrate the basic environmental parameters, actual pressure value, actual action duration, and actual interval duration of each detection area into the environmental parameters of each detection area.

[0134] Optionally, the parameter optimization module 22 is further configured to set the standard room temperature and standard humidity of each detection area as basic environmental parameters; set the standard pressure value, standard action duration, and standard interval duration of each detection area as basic pressure parameters; divide the usage intensity coefficient of each detection area by the usage intensity benchmark value to obtain the correction coefficient of each detection area; multiply the correction coefficient of each detection area by the standard pressure value, standard action duration, and standard interval duration of the corresponding detection area to obtain the actual pressure value, actual action duration, and actual interval duration of each detection area; and integrate the basic environmental parameters, actual pressure value, actual action duration, and actual interval duration of each detection area into the environmental parameters of each detection area.

[0135] Optionally, the performance testing module 25 is further configured to record the initial color of the test solution in each test area; record the degree of color change of the test solution in each test area at a first time point, a second time point, and a third time point, where the first time point corresponds to the bacterial adaptation period, the second time point corresponds to the bacterial exponential growth period, and the third time point corresponds to the bacterial stationary period; determine the antibacterial activity value of each test area based on the degree of color change of the test solution in each test area at the first time point, the second time point, and the third time point; and obtain the antibacterial performance index of the sofa to be tested by weighted summation of the antibacterial activity values ​​of each test area, wherein the antibacterial performance index reflects the antibacterial performance.

[0136] Optionally, the performance testing module 25 is further configured to establish a standard color comparison card, which includes different color change degrees and corresponding antibacterial activity values; match the color change degree of the test solution in each test area at the first time point, the second time point, and the third time point with the standard comparison card to obtain the first antibacterial activity value, the second antibacterial activity value, and the third antibacterial activity value of each test area; calculate the first growth rate of the second antibacterial activity value to the first antibacterial activity value of each test area, and the second growth rate of the third antibacterial activity value to the second antibacterial activity value of each test area; if both the first growth rate and the second growth rate of each test area are positive, then the third antibacterial activity value is increased by a preset percentage to obtain the antibacterial activity value of each test area; if either the first growth rate or the second growth rate of each test area is negative, then the third antibacterial activity value is decreased by a preset percentage to obtain the antibacterial activity value of each test area.

[0137] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0138] This embodiment also discloses an electronic device 900, as shown in the reference... Figure 10 The electronic device may include: at least one processor 901, at least one communication bus 902, user interface 903, network interface 904, and at least one memory 905.

[0139] The communication bus 902 is used to enable communication between these components.

[0140] The user interface 903 may include a display screen and a camera. Optional user interfaces may also include standard wired interfaces and wireless interfaces.

[0141] The network interface 904 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0142] The processor 901 may include one or more processing cores. The processor connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory, and by calling data stored in memory. Optionally, the processor may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor.

[0143] The memory 905 may include random access memory (RAM) or read-only memory. Optionally, the memory may include a non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor. As shown in the figure, the memory, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a sofa antibacterial performance testing method.

[0144] exist Figure 10In the electronic device shown, the user interface is mainly used to provide an input interface for the user and to obtain the user input data; while the processor can be used to call an application program stored in the memory for detecting the antibacterial performance of a sofa. When executed by one or more processors, the electronic device performs one or more methods as described in the above embodiments.

[0145] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0146] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0148] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0149] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0150] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0151] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the disclosure in this specification. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for testing the antibacterial properties of a sofa, characterized in that, The method includes: Obtain a fabric sample of the sofa to be tested, and divide the fabric sample into multiple testing areas, each of which corresponds to a different usage intensity coefficient and type of pollution source; The environmental parameters of each detection area are determined based on the usage intensity coefficient of each detection area. Based on the usage intensity coefficient and pollution source type of each detection area, the pollution source concentration coefficient corresponding to each detection area is determined; Based on the pollution source concentration coefficient, the amount of spraying of volatile pollution sources and the amount of spraying of stable pollution sources for each detection area, a pollution source mixture corresponding to each detection area is generated. The pollution source mixtures are sprayed onto the corresponding detection areas and left to stand for a preset time under the environmental parameters corresponding to each detection area. The pollution source samples of each detection area include volatile pollution sources and stable pollution sources. A test solution containing a colorimetric indicator is sprayed onto each of the test areas, and the antibacterial performance of the sofa under test is evaluated based on the degree of color change of the test solution corresponding to each of the test areas.

2. The method according to claim 1, characterized in that, The process of dividing the fabric sample into multiple testing areas, each corresponding to a different usage intensity coefficient and type of pollution source, specifically includes: Collect the usage environment information of the sofa to be tested, and determine the usage scenario based on the usage environment information; The fabric sample is divided into multiple testing areas based on the functional structure of the sofa to be tested. From a pre-established database of usage scenarios, obtain the usage duration, usage intensity, usage frequency, and pollution source types corresponding to each detection area under the usage scenario; The product of the usage duration, usage intensity, and usage frequency corresponding to each detection area is used as the usage intensity coefficient of the corresponding detection area.

3. The method according to claim 1, characterized in that, The determination of the pollution source concentration coefficient for each of the detection areas based on the usage intensity coefficient and pollution source type of each detection area specifically includes: Multiply the usage intensity coefficient corresponding to each of the detection areas by a preset coefficient to obtain the basic concentration coefficient of each of the detection areas; Mark the types of pollution sources present in each of the detection areas, and determine the corresponding types of pollution sources for each of the detection areas based on the types of pollution sources in each detection area; The average residence time of various pollution sources in each of the detection areas is measured, and the average residence time is divided by the standard residence time to obtain the residence time coefficient of each type of pollution source. The residence time coefficients of various pollution sources in each of the detection areas are weighted and summed to obtain the pollution source retention coefficient of each of the detection areas; The pollution source concentration coefficient of each detection area is obtained by multiplying the basic concentration coefficient of each detection area by the pollution source retention coefficient.

4. The method according to claim 1, characterized in that, The step of generating a pollution source mixture corresponding to each of the detection areas based on the pollution source concentration coefficient, the amount of coating used for variable pollution sources, and the amount of coating used for stable pollution sources specifically includes: The pre-defined pollution source samples in each of the detection areas are classified into variable pollution sources and stable pollution sources according to their stability in each of the detection areas. Multiply the pollution source concentration coefficient and the standard dosage of the variable pollution source in each of the test areas, and then multiply by the gain coefficient to obtain the spraying dosage of the variable pollution source in each of the test areas. Multiply the pollution source concentration coefficient of each test area by the standard amount of stable pollution source to obtain the amount of stable pollution source to be sprayed in each test area. The amount of coating for the variable pollution source and the amount of coating for the stable pollution source in each detection area are mixed with the preset pollution source sample to obtain a pollution source mixture.

5. The method according to claim 1, characterized in that, The step of determining the environmental parameters of each detection area based on the usage intensity coefficient of each detection area specifically includes: Set the standard room temperature and standard humidity of each of the aforementioned detection areas as the basic environmental parameters; Set the standard pressure value, standard action duration, and standard interval duration for each of the aforementioned detection zones as the basic pressure parameters; Divide the service intensity coefficient of each test area by the service intensity benchmark value to obtain the correction coefficient of each test area; The correction coefficient for each detection area is multiplied by the standard pressure value, standard action duration, and standard interval duration for each detection area to obtain the actual pressure value, actual action duration, and actual interval duration for each detection area. The basic environmental parameters, actual pressure values, actual action duration, and actual interval duration of each detection area are integrated into the environmental parameters of each detection area.

6. The method according to claim 1, characterized in that, The process of spraying a test solution containing a colorimetric indicator onto each of the aforementioned test areas, and evaluating the antibacterial performance of the sofa under test based on the degree of color change of the test solution corresponding to each of the aforementioned test areas, specifically includes: Record the initial color of the detection solution in each detection area; The degree of color change of the detection solution in each detection area at the first time point, the second time point and the third time point is recorded respectively. The first time point corresponds to the bacterial adaptation period, the second time point corresponds to the bacterial exponential growth period, and the third time point corresponds to the bacterial stationary period. The antibacterial activity value of each detection area is determined based on the degree of color change of the detection solution in each detection area at the first time point, the second time point, and the third time point. The antibacterial activity values ​​of each of the test areas are weighted and summed to obtain the antibacterial performance index of the sofa to be tested, which reflects the antibacterial performance.

7. The method according to claim 6, characterized in that, The step of determining the antibacterial activity value of each detection area based on the degree of color change of the detection solution at the first time point, the second time point, and the third time point specifically includes: Establish a standard color comparison card, which includes different degrees of color change and corresponding antibacterial activity values; The color change of the detection solution in each detection area at the first time point, the second time point, and the third time point is matched with the standard comparison card to obtain the first antibacterial activity value, the second antibacterial activity value, and the third antibacterial activity value of each detection area. Calculate the first growth rate of the second antibacterial activity value and the first antibacterial activity value of each of the detection regions, and the second growth rate of the third antibacterial activity value and the second antibacterial activity value of each of the detection regions; If both the first growth rate and the second growth rate of each detection area are positive, then the third antibacterial activity value is increased by a preset percentage to obtain the antibacterial activity value of each detection area. If there is a negative value between the first growth rate and the second growth rate of each of the detection areas, the third antibacterial activity value is reduced by a preset percentage to obtain the antibacterial activity value of each of the detection areas.

8. A sofa antibacterial performance testing system, characterized in that, The system includes: The area division module is used to acquire fabric samples of the sofa to be tested and divide the fabric samples into multiple testing areas, each of which corresponds to a different usage intensity coefficient and pollution source type. The parameter optimization module is used to determine the environmental parameters of each detection area based on the usage intensity coefficient of each detection area. The concentration calibration module is used to determine the pollution source concentration coefficient corresponding to each of the detection areas based on the usage intensity coefficient and pollution source type of each detection area; The spraying module is used to generate a pollution source mixture corresponding to each of the detection areas based on the pollution source concentration coefficient, the spraying amount of volatile pollution sources, and the spraying amount of stable pollution sources in each detection area. The pollution source mixture is then sprayed onto the corresponding detection area and left to stand for a preset time under the environmental parameters corresponding to each detection area. The pollution source samples in each detection area include volatile pollution sources and stable pollution sources. The performance testing module is used to spray a test solution containing a color indicator onto each of the test areas, and to evaluate the antibacterial performance of the sofa under test based on the degree of color change of the test solution corresponding to each of the test areas.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-7.

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