Microbial environment adjusting method, system and equipment for intelligent public toilet and medium

Abstract

The embodiment of the invention discloses a microbial environment adjusting method, system and device for an intelligent public toilet and a medium, and relates to the technical field of data processing.The activity is quantified based on environment data through a microbial activity quantification model established in advance, and the environment data can be standardized through the model, so that the environment data can be standardized, and the environment data can be standardized; according to the method, multi-source data can be subjected to weighted summation in numerical value after standardization processing, the influence of different environmental factors on the microbial activity under the actual condition is matched by adopting a weighted summation mode, and a compactness influence factor is added in the weighted summation, so that the influence of the stacking degree in the intelligent toilet on the microbial activity is represented; according to the method, the quantized value can reflect the real activity of the microorganisms more accurately, and finally, a corresponding adjusting strategy can be generated according to the quantized value of the activity of the microorganisms to improve the microorganism environment, so that the utilization effect of the activity of the microorganisms is improved, and the use effect of the intelligent public toilet is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, system, equipment, and medium for regulating the microbial environment of a smart public toilet. Background Technology

[0002] The widespread application of smart public toilets has led to increased attention being paid to research on microbial decomposition. Since these toilets do not flush away waste with water, but rather decompose it through microorganisms, ensuring a suitable environment for these microorganisms to maintain their activity directly impacts the effectiveness of the smart toilet. However, current technologies do not consider monitoring and improving the microbial environment after adding the degradation substrate and microbial inoculants. Instead, they rely on periodic additions or increasing the addition only when the treatment effect is poorly observed. This results in inefficient utilization of microbial activity and ultimately, poor performance of the smart public toilets. Summary of the Invention

[0003] The main purpose of this application is to provide a method, system, equipment and medium for regulating the microbial environment of smart public toilets, aiming to solve the problem of poor performance of smart public toilets in the prior art.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide a method for regulating the microbial environment of a smart public toilet, comprising the following steps: Acquire microbial environment data for smart public toilets; this data includes temperature, humidity, and oxygen concentration. Microbial environmental data is input into the microbial activity quantification model to obtain the microbial activity quantification value; the microbial activity quantification model is used to standardize the microbial environmental data and output the compaction degree influence factor and the weighted sum of the standardized microbial environmental data; Based on the quantification of microbial activity, regulatory strategies are generated to regulate the microbial environment.

[0005] In one possible implementation of the first aspect, before inputting microbial environmental data into the microbial activity quantification model to obtain the microbial activity quantification value, the method further includes: The microbial environment data of the sample were standardized to obtain the temperature data, humidity data and oxygen concentration data of the first sample. A weighted summation was performed based on the compaction influencing factor, the temperature data of the first sample, the humidity data of the first sample, and the oxygen concentration data of the first sample to establish a quantitative model of microbial activity.

[0006] In one possible implementation of the first aspect, the sample microbial environment data is standardized to obtain first sample temperature data, first sample humidity data, and first sample oxygen concentration data, including: The sample temperature data in the microbial environment data is standardized using the Sigmoid function to obtain the first sample temperature data. Linear normalization was performed on the sample humidity data in the sample microbial environment data to obtain the first sample humidity data; The oxygen concentration data of the first sample was obtained by performing a hyperbolic tangent transform on the oxygen concentration data of the sample microbial environment data.

[0007] In one possible implementation of the first aspect, a weighted summation is performed based on the compaction degree influencing factor, the temperature data of the first sample, the humidity data of the first sample, and the oxygen concentration data of the first sample to establish a microbial activity quantification model, including: The weighted summation result is obtained by weighting and summing the compaction degree influencing factor, the temperature data of the first sample, the humidity data of the first sample, and the oxygen concentration data of the first sample. A microbial activity quantification model is established based on the sum of the temperature and humidity cross-term correction value and the weighted summation result; wherein, the temperature and humidity cross-term correction value is used to correct the coupling effect between sample temperature data and sample humidity data.

[0008] In one possible implementation of the first aspect, before standardizing the sample microbial environment data to obtain the first sample temperature data, first sample humidity data, and first sample oxygen concentration data, the method further includes: Based on the simulated data changes under ideal conditions, identify anomalous data in the sample microbial environment data; Adjust the weights of each data point when performing a weighted summation based on the abnormal data.

[0009] In one possible implementation of the first aspect, the weights of the data are adjusted when performing a weighted summation based on the abnormal data, including: Based on the abnormal data, the weight of the corresponding data when performing a weighted summation is reduced to zero, and the reduced weight is distributed to other data in the weighted summation according to the previously allocated weight ratio.

[0010] In one possible implementation of the first aspect, acquiring microbial environmental data of the smart public toilet includes: Based on the multi-source sensors installed in the smart public toilet, several detection data were obtained; After removing discrete data from the test data, the average is calculated to obtain the microbial environment data of the smart public toilet.

[0011] Secondly, embodiments of this application provide a microbial environment regulation system for an intelligent public toilet, comprising: The acquisition module is used to acquire microbial environment data of the smart public toilet; the microbial environment data includes temperature data, humidity data, and oxygen concentration data. The quantification module is used to input microbial environmental data into the microbial activity quantification model to obtain the microbial activity quantification value. The microbial activity quantification model is used to standardize the microbial environmental data and output the compaction degree influence factor and the weighted sum of the standardized microbial environmental data. The regulation module is used to generate regulation strategies to regulate the microbial environment based on the quantification value of microbial activity.

[0012] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory, wherein... Memory is used to store computer programs; The processor is used to load and execute a computer program to cause the electronic device to perform the microbial environment regulation method for the smart public toilet provided in any of the first aspects above.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the method for regulating the microbial environment of an intelligent public toilet as provided in any of the first aspects above.

[0014] Compared with the prior art, the beneficial effects of this application are: This application proposes a method, system, device, and medium for regulating the microbial environment of a smart public toilet. The method includes: acquiring microbial environment data of the smart public toilet; wherein the microbial environment data includes temperature data, humidity data, and oxygen concentration data; inputting the microbial environment data into a microbial activity quantification model to obtain a microbial activity quantification value; wherein the microbial activity quantification model is used to standardize the microbial environment data and outputs the result of a weighted sum of the compaction influence factor and the standardized microbial environment data; and generating a regulation strategy to regulate the microbial environment based on the microbial activity quantification value. This application utilizes artificial intelligence to monitor the living environment of microorganisms and assess their activity, avoiding the inaccuracies and poor timeliness of manual inspection. Firstly, a pre-established microbial activity quantification model is used to quantify activity based on environmental data. Since the model can standardize environmental data, multi-source data can be weighted and summed after standardization. This weighted summation method matches the impact of different environmental factors on microbial activity under actual conditions. Furthermore, a compaction degree influence factor is added to the weighted summation to characterize the impact of material stacking degree in the smart toilet on microbial activity, enabling the quantified value to more accurately reflect the true activity of microorganisms. Finally, corresponding adjustment strategies can be generated based on the quantified microbial activity value to improve the microbial environment, thereby enhancing the utilization effect of microbial activity and improving the overall effectiveness of smart public toilets. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application; Figure 2 A schematic flowchart illustrating the microbial environment regulation method for an intelligent public toilet provided in this application embodiment; Figure 3 A schematic diagram of the microbial environment regulation system for an intelligent public toilet provided in an embodiment of this application; The diagram is labeled as follows: 101-Processor, 102-Communication bus, 103-Network interface, 104-User interface, 105-Memory. Detailed Implementation

[0016] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0017] See attached document Figure 1 , attached Figure 1This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. The communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 105 may be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk storage device. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component.

[0018] Those skilled in the art will understand that the appendix Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0019] As attached Figure 1 As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a microbial environment regulation system for intelligent public toilets.

[0020] In the appendix Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in this application can be set in the electronic device. The electronic device calls the intelligent public toilet microbial environment regulation system stored in the memory 105 through the processor 101 and executes the intelligent public toilet microbial environment regulation method provided in the embodiment of this application.

[0021] See attached document Figure 2 Based on the hardware device described in the foregoing embodiments, embodiments of this application provide a method for regulating the microbial environment of a smart public toilet, comprising the following steps: S10: Acquire microbial environment data of the smart public toilet; the microbial environment data includes temperature data, humidity data and oxygen concentration data.

[0022] In the specific implementation process, the microbial environment data of the smart public toilet is monitored. Environmental factors affecting microbial activity are selected, namely temperature, humidity, and oxygen concentration. This data can be collected by setting up corresponding sensors. In other words, the microbial environment data of the smart public toilet is obtained, including: Based on the multi-source sensors installed in the smart public toilet, several detection data were obtained; After removing discrete data from the test data, the average is calculated to obtain the microbial environment data of the smart public toilet.

[0023] Multi-source sensors are multiple sensors that collect data from different environments. Multiple sensors can be set for each category to comprehensively cover the living environment of microorganisms. The feedback data is then used to remove obvious discrete data and average the effective data. That is, the effective data of all sensors in each category are averaged to obtain data that characterizes the corresponding environmental factors, which further ensures the quality of the initial data and is conducive to improving the level of subsequent processing.

[0024] S20: Input the microbial environment data into the microbial activity quantification model to obtain the microbial activity quantification value; wherein, the microbial activity quantification model is used to standardize the microbial environment data and output the compaction degree influence factor and the weighted sum of the standardized microbial environment data.

[0025] In the specific implementation process, a microbial activity quantification model is used to process the collected environmental data to quantitatively characterize the activity of microorganisms. Microbial environmental data is multi-source and heterogeneous. To enable the combined use of this data, the microbial activity quantification model is used to standardize the data, converting it into a numerical expression that can be directly weighted and summed. This data is then weighted and summed using the microbial activity quantification model, and a compaction degree influence factor is introduced into the weighted summation. The compaction degree influence factor characterizes the impact of the degree of material accumulation on microbial activity; the greater the material accumulation, the higher the compaction degree, and the lower the degradation rate, the greater the impact on microbial activity. The compaction degree influence factor is expressed as a numerical value of compaction degree; for example, the optimal compaction degree is typically 1g ± 0.05g / cm³. 3 The corresponding compaction influence factor is 1±0.05. The weight allocation of the weighted sum is set according to experience, and the sum of all weights is 1. The weight allocation is not fixed. The weight ratio can be appropriately adjusted according to seasonal changes, such as in summer and autumn versus spring and winter, or according to the geographical environment used.

[0026] In one embodiment, before inputting microbial environmental data into a microbial activity quantification model to obtain microbial activity quantification values, the method further includes: The microbial environment data of the sample were standardized to obtain the temperature data, humidity data and oxygen concentration data of the first sample. A weighted summation was performed based on the compaction influencing factor, the temperature data of the first sample, the humidity data of the first sample, and the oxygen concentration data of the first sample to establish a quantitative model of microbial activity.

[0027] In practical implementation, the microbial activity quantification model can be established using sample data. Training with sample data enables the model to standardize the data and then perform a weighted summation of the standardized data and the compaction degree influencing factor. In reality, compaction degree requires collecting relevant data and calculating the weight per unit volume. Therefore, the compaction degree influencing factor used in model training can use measured data or a user-defined value, avoiding cumbersome data collection and calculation processes. Microbial environmental data includes temperature, humidity, and oxygen concentration data. Therefore, the sample microbial environmental data includes sample temperature, humidity, and oxygen concentration data, which, after standardization, correspond to the first sample's temperature, humidity, and oxygen concentration data.

[0028] In one embodiment, the sample microbial environment data is standardized to obtain first sample temperature data, first sample humidity data, and first sample oxygen concentration data, including: The sample temperature data in the microbial environment data is standardized using the Sigmoid function to obtain the first sample temperature data. Linear normalization was performed on the sample humidity data in the sample microbial environment data to obtain the first sample humidity data; The oxygen concentration data of the first sample was obtained by performing a hyperbolic tangent transform on the oxygen concentration data of the sample microbial environment data.

[0029] In practical applications, the Sigmoid function is a common S-shaped function in biology, also known as an S-shaped growth curve. Due to its monotonically increasing and inversely monotonically increasing properties, the Sigmoid function is often used to map variables to the range of 0-1. Linear normalization is calculated using the maximum and minimum values ​​of the data, as shown in the formula: Y = (X - Xmin) / (Xmax - Xmin) Where Y is the new data after normalization, X is the original data, Xmin is the minimum value of the data, and Xmax is the maximum value of the data. In this way, the original data is mapped to the interval [0,1]. The graph of the hyperbolic tangent function lies between the horizontal lines y=1 and y=-1. When the absolute value of x is large, the graph is close to the line y=1 in the first quadrant and close to the line y=-1 in the third quadrant. Therefore, its range is (-1,1), which maps the sample oxygen concentration data to the interval (-1,1).

[0030] Standardization transforms multi-source heterogeneous data into values ​​that can be weighted and summed, mapping these values ​​to a smaller and more contiguous range, which improves the efficiency of subsequent calculations.

[0031] In one embodiment, a weighted summation is performed based on the compaction degree influence factor, the temperature data of the first sample, the humidity data of the first sample, and the oxygen concentration data of the first sample to establish a microbial activity quantification model, including: The weighted summation result is obtained by weighting and summing the compaction degree influencing factor, the temperature data of the first sample, the humidity data of the first sample, and the oxygen concentration data of the first sample. A microbial activity quantification model is established based on the sum of the temperature and humidity cross-term correction value and the weighted summation result; wherein, the temperature and humidity cross-term correction value is used to correct the coupling effect between sample temperature data and sample humidity data.

[0032] In the specific implementation process, a temperature and humidity cross-term correction value is introduced when establishing a microbial activity quantification model. The cross-term, obtained by multiplying two or more explanatory variables, is used to analyze the interactions between multiple variables. Since considering temperature and humidity data individually can lead to coupling effects such as omitted variable bias or confounding effects, a temperature and humidity cross-term correction value is introduced to eliminate these errors. In other words, when analyzing variable relationships, the product of two variables is added, and an empirical coefficient is set for this product value.

[0033] In one embodiment, before standardizing the sample microbial environment data to obtain the first sample temperature data, the first sample humidity data, and the first sample oxygen concentration data, the method further includes: Based on the simulated data changes under ideal conditions, identify anomalous data in the sample microbial environment data; Adjust the weights of each data point when performing a weighted summation based on the abnormal data.

[0034] In practical implementation, under ideal conditions, there are no data anomalies caused by sensor malfunctions. These data are considered standard data. Before performing weighted summation for quantization, abnormal data is screened. This abnormal data may deviate significantly from normally collected data due to corresponding sensor malfunctions. To minimize the impact of this abnormal data on quantization without interrupting the processing, the weights of the weighted summation need to be adjusted. Specifically, abnormal data is directly excluded. For example, if a temperature sensor malfunctions, the corresponding temperature data is abnormal and is directly excluded during weighted summation. However, to maintain the original model's computational framework and preserve the data structure, the corresponding weight is reduced to zero. This reduced weight is then allocated to other data in the weighted summation. To minimize the impact of environmental factors, this reduced weight is distributed according to the original weight ratio. In other words, the weights of each data point are adjusted during weighted summation based on the abnormal data, including: Based on the abnormal data, the weight of the corresponding data when performing a weighted summation is reduced to zero, and the reduced weight is distributed to other data in the weighted summation according to the previously allocated weight ratio.

[0035] S30: Generate regulatory strategies to regulate the microbial environment based on the quantification value of microbial activity.

[0036] In the specific implementation process, after quantifying the microbial activity, the usage status of the smart toilet has a more concrete representation. Then, based on the reflected microbial activity level, adjustment strategies are generated to adjust the microbial environment accordingly, so that the microbial activity changes in a more optimal direction. For example, the adjustment strategies may include increasing the ambient temperature, requiring auxiliary stirring, or adding substrate bacteria, thereby improving the usage effect of the smart public toilet.

[0037] In this embodiment, artificial intelligence is used to monitor the living environment of microorganisms and assess their activity, avoiding the inaccuracies and poor timeliness of manual inspection. First, a pre-established microbial activity quantification model is used to quantify activity based on environmental data. Since the model can standardize environmental data, multi-source data can be weighted and summed after standardization. The weighted summation method matches the impact of different environmental factors on microbial activity under actual conditions. Furthermore, a compaction degree influence factor is added to the weighted summation to characterize the impact of the degree of material stacking in the smart toilet on microbial activity, so that the quantified value can more accurately reflect the true activity of microorganisms. Finally, corresponding adjustment strategies can be generated based on the quantified microbial activity value to improve the microbial environment, thereby enhancing the utilization effect of microbial activity and improving the use effect of smart public toilets.

[0038] See attached document Figure 3Based on the same inventive concept as in the foregoing embodiments, this application also provides a microbial environment regulation system for an intelligent public toilet, comprising: The acquisition module is used to acquire microbial environment data of the smart public toilet; the microbial environment data includes temperature data, humidity data, and oxygen concentration data. The quantification module is used to input microbial environmental data into the microbial activity quantification model to obtain the microbial activity quantification value. The microbial activity quantification model is used to standardize the microbial environmental data and output the compaction degree influence factor and the weighted sum of the standardized microbial environmental data. The regulation module is used to generate regulation strategies to regulate the microbial environment based on the quantification value of microbial activity.

[0039] Those skilled in the art should understand that the division of the various modules in the embodiments is merely a logical functional division. In actual applications, they can be fully or partially integrated into one or more actual carriers. These modules can be implemented entirely in software through processing unit calls, entirely in hardware, or a combination of software and hardware. It should be noted that each module in the microbial environment regulation system of the intelligent public toilet in this embodiment corresponds one-to-one with each step in the microbial environment regulation method of the intelligent public toilet in the aforementioned embodiments. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned microbial environment regulation method of the intelligent public toilet, which will not be repeated here.

[0040] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the microbial environment regulation method for intelligent public toilets as provided in the embodiments of this application.

[0041] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide an electronic device, including a processor and a memory, wherein, Memory is used to store computer programs; The processor is used to load and execute computer programs to cause electronic devices to perform the microbial environment regulation method for smart public toilets provided in the embodiments of this application.

[0042] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0043] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0044] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0045] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0046] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0047] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0048] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0049] In summary, the embodiments of this application provide a method, system, device, and medium for regulating the microbial environment of a smart public toilet. The method includes: acquiring microbial environment data of the smart public toilet; wherein the microbial environment data includes temperature data, humidity data, and oxygen concentration data; inputting the microbial environment data into a microbial activity quantification model to obtain a microbial activity quantification value; wherein the microbial activity quantification model is used to standardize the microbial environment data and output the result of a weighted sum of the compaction influence factor and the standardized microbial environment data; and generating a regulation strategy to regulate the microbial environment based on the microbial activity quantification value. This application utilizes artificial intelligence to monitor the living environment of microorganisms and assess their activity, avoiding the inaccuracies and poor timeliness of manual inspection. Firstly, a pre-established microbial activity quantification model is used to quantify activity based on environmental data. Since the model can standardize environmental data, multi-source data can be weighted and summed after standardization. This weighted summation method matches the impact of different environmental factors on microbial activity under actual conditions. Furthermore, a compaction degree influence factor is added to the weighted summation to characterize the impact of material stacking degree in the smart toilet on microbial activity, enabling the quantified value to more accurately reflect the true activity of microorganisms. Finally, corresponding adjustment strategies can be generated based on the quantified microbial activity value to improve the microbial environment, thereby enhancing the utilization effect of microbial activity and improving the overall effectiveness of smart public toilets.

[0050] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for regulating the microbial environment of an intelligent public toilet, characterized in that, Includes the following steps: Acquire microbial environment data of smart public toilets; wherein, the microbial environment data includes temperature data, humidity data, and oxygen concentration data; The microbial environment data is input into the microbial activity quantification model to obtain the microbial activity quantification value; wherein, the microbial activity quantification model is used to standardize the microbial environment data and output the compaction degree influence factor and the weighted sum of the standardized microbial environment data; Based on the quantified microbial activity value, a regulatory strategy is generated to regulate the microbial environment.

2. The method for regulating the microbial environment of an intelligent public toilet according to claim 1, characterized in that, Before inputting the microbial environment data into the microbial activity quantification model to obtain the microbial activity quantification value, the method further includes: The microbial environment data of the sample were standardized to obtain the temperature data, humidity data and oxygen concentration data of the first sample. The microbial activity quantification model is established by weighting and summing the compaction influencing factor, the temperature data of the first sample, the humidity data of the first sample, and the oxygen concentration data of the first sample.

3. The method for regulating the microbial environment of an intelligent public toilet according to claim 2, characterized in that, The process of standardizing the sample microbial environment data to obtain the first sample temperature data, the first sample humidity data, and the first sample oxygen concentration data includes: The sample temperature data in the sample microbial environment data is standardized using the Sigmoid function to obtain the first sample temperature data. The sample humidity data in the sample microbial environment data is linearly normalized to obtain the first sample humidity data; The oxygen concentration data of the sample in the microbial environment data is subjected to hyperbolic tangent transformation to obtain the oxygen concentration data of the first sample.

4. The method for regulating the microbial environment of an intelligent public toilet according to claim 3, characterized in that, The step of establishing the microbial activity quantification model by weighted summation based on the compaction degree influencing factor, the temperature data of the first sample, the humidity data of the first sample, and the oxygen concentration data of the first sample includes: The weighted summation result is obtained by weighting and summing the compaction degree influencing factor, the temperature data of the first sample, the humidity data of the first sample, and the oxygen concentration data of the first sample. The microbial activity quantification model is established by summing the temperature and humidity cross-term correction value with the weighted summation result; wherein the temperature and humidity cross-term correction value is used to correct the coupling effect between the sample temperature data and the sample humidity data.

5. The method for regulating the microbial environment of an intelligent public toilet according to claim 2, characterized in that, Before standardizing the sample microbial environment data to obtain the first sample temperature data, first sample humidity data, and first sample oxygen concentration data, the method further includes: Based on the simulated data changes under ideal conditions, abnormal data in the sample microbial environment data are identified; Based on the abnormal data, adjust the weights of each data point when performing a weighted summation.

6. The method for regulating the microbial environment of an intelligent public toilet according to claim 5, characterized in that, The step of adjusting the weights of each data point during weighted summation based on the abnormal data includes: Based on the abnormal data, the weight of the corresponding data when performing a weighted summation is reduced to zero, and the reduced weight is distributed to other data undergoing weighted summation according to the previously allocated weight ratio.

7. The method for regulating the microbial environment of an intelligent public toilet according to claim 1, characterized in that, The acquisition of microbial environment data for smart public toilets includes: Based on the multi-source sensors installed in the smart public toilet, several detection data were obtained; The microbial environment data of the smart public toilet is obtained by averaging the discrete data after removing the discrete data from the detection data.

8. A microbial environment regulation system for an intelligent public toilet, characterized in that, include: The acquisition module is used to acquire microbial environment data of the smart public toilet; wherein, the microbial environment data includes temperature data, humidity data, and oxygen concentration data; The quantification module is used to input the microbial environment data into the microbial activity quantification model to obtain the microbial activity quantification value; wherein, the microbial activity quantification model is used to standardize the microbial environment data and output the compaction degree influence factor and the weighted sum of the standardized microbial environment data; The regulation module is used to generate a regulation strategy to regulate the microbial environment based on the quantified value of microbial activity.

9. An electronic device, characterized in that, Including processor and memory, among which, The memory is used to store computer programs; The processor is used to load and execute the computer program to cause the electronic device to perform the microbial environment regulation method for the smart public toilet as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the method for regulating the microbial environment of the intelligent public toilet as described in any one of claims 1-7.

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