Environmental monitoring method, system and equipment for honey sterile processing room and medium

Abstract

The invention relates to an artificial intelligence technology, and discloses an environment monitoring method for a honey sterile processing room, which comprises the following steps: carrying out variance decomposition analysis on air suspended particle data and air microorganism data of the honey sterile processing room obtained from a preset sampling point to obtain air quality data; performing air cleanliness analysis according to the air quality data by using an air cleanliness analysis model to obtain an air cleanliness value, acquiring ultraviolet radiation intensity and disinfection duration of ultraviolet disinfection, calculating a sterility coefficient according to the ultraviolet radiation intensity, the disinfection duration and the air cleanliness value, and calculating the sterility coefficient according to the sterility coefficient. If the sterility coefficient is larger than or equal to a preset sterility coefficient threshold value, it is judged that the honey sterility processing chamber meets the sterility requirement, otherwise, it is judged that the honey sterility processing chamber does not meet the sterility requirement, and warning information is sent out. The invention further provides an environment monitoring system for the honey sterile processing room, electronic equipment and a storage medium. According to the invention, the aseptic condition of the honey aseptic processing environment can be reflected in time.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an environmental monitoring method, system, equipment and medium for a honey aseptic processing room. Background Art

[0002] In the production process of honey, ensuring its sterility is crucial to ensure the quality and safety of the product. With the increasing attention of consumers to food quality and safety, and the increasingly stringent national supervision of food safety, the aseptic processing technology of honey has become an important direction for the development of the industry.

[0003] Traditional honey processing environmental monitoring methods often rely on manual sampling and laboratory testing, which is not only time-consuming and labor-intensive, but also has the problem of delayed detection, making it difficult to timely discover and control pollution sources. In addition, due to the complexity and variability of the honey processing environment, traditional monitoring methods often fail to fully and accurately reflect environmental conditions. Summary of the invention

[0004] The invention provides an environmental monitoring method, system, equipment and medium for a honey aseptic processing room, the main purpose of which is to solve the problem that the existing aseptic monitoring method for the honey processing environment cannot reflect the aseptic status in real time.

[0005] To achieve the above object, the present invention provides an environmental monitoring method for a honey aseptic processing room, comprising:

[0006] Acquire the air suspended particle data of the honey aseptic processing room in real time according to a plurality of preset sampling points, and acquire the air microorganism data of the honey aseptic processing room in real time according to the plurality of sampling points;

[0007] Performing variance decomposition analysis on the airborne particle data and the air microorganism data to obtain air quality data;

[0008] Using a pre-trained air cleanliness analysis model to perform air cleanliness analysis on the honey aseptic processing room according to the air quality data to obtain an air cleanliness value;

[0009] Obtaining the ultraviolet radiation intensity and disinfection time of the honey sterile processing room for ultraviolet disinfection, and calculating the sterility coefficient according to the ultraviolet radiation intensity, the disinfection time and the air cleanliness value;

[0010] If the sterility coefficient is greater than or equal to the preset sterility coefficient threshold, it is determined that the honey aseptic processing room meets the sterility requirements, and the steps of acquiring the air suspended particle data of the honey aseptic processing room in real time according to the preset multiple sampling points and acquiring the air microorganism data of the honey aseptic processing room in real time according to the multiple sampling points are re-executed;

[0011] If the sterility coefficient is less than a preset sterility coefficient threshold, it is determined that the honey aseptic processing room does not meet the sterility requirements and a warning message is issued.

[0012] Optionally, the real-time acquisition of airborne particle data in the honey aseptic processing room according to a plurality of preset sampling points includes:

[0013] Obtaining a second mass of a filter membrane of a first mass disposed on a sampler at each sampling point after inhaling a preset volume of air;

[0014] The suspended particle concentration at each sampling point is calculated according to the first mass, the second mass and the preset volume using the following formula:

[0015]

[0016] Where P is the suspended particle concentration at any sampling point, W 2 is the second mass, W 1 is the first mass, and V is the preset volume;

[0017] The suspended particle concentration of each sampling point is obtained to obtain the air suspended particle data.

[0018] Optionally, performing variance decomposition analysis on the airborne particle data and the air microorganism data to obtain air quality data includes:

[0019] Normalizing the airborne particle data to obtain normalized suspended particle data, and normalizing the airborne microorganism data to obtain normalized suspended particle data;

[0020] Calculating the variance of the normalized suspended particle data and the normalized microorganism data to obtain the variance of the suspended particle data and the variance of the microorganism data;

[0021] Merging the normalized suspended particle data and the normalized suspended particle data to obtain merged data;

[0022] Calculating the variance of the combined data to obtain a total variance;

[0023] Calculate the contribution ratio according to the total variance, the suspended particle data variance and the microbial data variance;

[0024] The airborne particle data and the air microorganism data are weightedly fused according to the contribution ratio to obtain air quality data.

[0025] Optionally, the normalizing the airborne particle data to obtain normalized airborne particle data includes:

[0026] The airborne particle data is normalized using the following formula:

[0027]

[0028] Among them, Z i is the ith data in the normalized suspended particle data, x i is the i-th data in the airborne particle data, is the average value of the airborne particle data, and N is the number of samples of the airborne particle data.

[0029] Optionally, the using of a pre-trained air cleanliness analysis model to perform air cleanliness analysis on the honey aseptic processing room according to the air quality data to obtain an air cleanliness value comprises:

[0030] Extracting features from the air quality data to obtain air quality features;

[0031] Using the convolution layer of the air cleanliness analysis model to extract local features of the air quality features, to obtain convolution features;

[0032] Using the pooling layer of the air cleanliness analysis model to perform an average pooling operation on the convolutional features to obtain pooled features;

[0033] Using the activation layer of the air cleanliness analysis model to introduce nonlinearity into the pooling feature, so as to obtain an activation feature;

[0034] The air cleanliness value is calculated based on the activation features using the fully connected layer of the air cleanliness analysis model using preset weights and bias items.

[0035] Optionally, extracting features from the air quality data to obtain air quality features includes:

[0036] Calculating a covariance matrix of the air quality data;

[0037] Performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors;

[0038] Select a preset number of eigenvectors according to the size of the eigenvalue;

[0039] Combine the selected eigenvectors into a matrix to obtain the eigenmatrix;

[0040] The feature matrix is ​​subjected to dimensionality reduction processing to obtain air quality features.

[0041] Optionally, the calculating of the sterility coefficient according to the ultraviolet radiation intensity, the disinfection time and the air cleanliness value includes:

[0042] The sterility coefficient was calculated using the following formula:

[0043] C=α*Y+β*U*T

[0044] Among them, C is the sterility coefficient, Y is the air cleanliness value, α is the cleanliness value adjustment parameter, U is the ultraviolet radiation intensity, T is the disinfection time, and β is the ultraviolet adjustment parameter.

[0045] In order to solve the above problems, the present invention also provides an environmental monitoring system for a honey aseptic processing room, the system comprising:

[0046] A data acquisition module, used to acquire the air suspended particle data of the honey aseptic processing room in real time according to a plurality of preset sampling points, and to acquire the air microorganism data of the honey aseptic processing room in real time according to the plurality of sampling points;

[0047] A data analysis module, used for performing variance decomposition analysis on the airborne particle data and the air microorganism data to obtain air quality data;

[0048] A cleanliness analysis module, used to perform air cleanliness analysis on the honey aseptic processing room according to the air quality data using a pre-trained air cleanliness analysis model to obtain an air cleanliness value;

[0049] A data calculation module, used for obtaining the ultraviolet radiation intensity and disinfection time of the honey aseptic processing room for ultraviolet disinfection, and calculating the sterility coefficient according to the ultraviolet radiation intensity, the disinfection time and the air cleanliness value;

[0050] The sterility judgment module determines that the honey aseptic processing room meets the sterility requirements if the sterility coefficient is greater than or equal to a preset sterility coefficient threshold value, and re-executes the above-mentioned steps of acquiring the air suspended particle data of the honey aseptic processing room in real time according to the preset multiple sampling points, and acquiring the air microorganism data of the honey aseptic processing room in real time according to the multiple sampling points. If the sterility coefficient is less than the preset sterility coefficient threshold value, it is determined that the honey aseptic processing room does not meet the sterility requirements, and a warning message is issued.

[0051] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0052] at least one processor;

[0053] and, a memory communicatively coupled to the at least one processor;

[0054] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned environmental monitoring method for the honey aseptic processing room.

[0055] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned environmental monitoring method for a honey sterile processing room.

[0056] The embodiment of the present invention obtains the air suspended particle data of the honey aseptic processing room in real time according to a plurality of preset sampling points, and obtains the air microorganism data of the honey aseptic processing room in real time according to the plurality of sampling points, performs variance decomposition analysis on the air suspended particle data and the air microorganism data, obtains air quality data, improves the data expression ability, uses a pre-trained air cleanliness analysis model to perform air cleanliness analysis on the honey aseptic processing room according to the air quality data, obtains an air cleanliness value, improves the efficiency of subsequent calculation of the sterility coefficient, and obtains the ultraviolet radiation intensity and sterilization value for ultraviolet disinfection of the honey aseptic processing room. The sterility coefficient is calculated according to the ultraviolet radiation intensity, the disinfection time and the air cleanliness value, so as to improve the efficiency of judging whether the honey sterile processing room meets the sterility standard. If the sterility coefficient is greater than or equal to the preset sterility coefficient threshold, the honey sterile processing room is judged to meet the sterility requirements, and the above-mentioned steps of obtaining the air suspended particle data of the honey sterile processing room in real time according to the preset multiple sampling points and obtaining the air microorganism data of the honey sterile processing room in real time according to the multiple sampling points are re-executed. If the sterility coefficient is less than the preset sterility coefficient threshold, the honey sterile processing room is judged not to meet the sterility requirements and a warning message is issued. Therefore, the environmental monitoring method, system, electronic device and computer-readable storage medium of the honey sterile processing room proposed in the present invention can solve the problem that the existing honey processing environment sterility monitoring method cannot immediately reflect the sterility status. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic flow chart of an environmental monitoring method for a honey aseptic processing room provided by an embodiment of the present invention;

[0058] Figure 2 A schematic diagram of a variance decomposition analysis process provided by an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of a process for extracting features from air quality data provided by an embodiment of the present invention;

[0060] Figure 4 A functional module diagram of an environmental monitoring system for a honey aseptic processing room provided by an embodiment of the present invention;

[0061] Figure 5 A schematic structural diagram of an electronic device for implementing the environmental monitoring method of the honey aseptic processing room provided in one embodiment of the present invention.

[0062] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0063] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0064] The embodiment of the present application provides an environmental monitoring method for a honey aseptic processing room. The execution subject of the environmental monitoring method for the honey aseptic processing room includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the environmental monitoring method for the honey aseptic processing room can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0065] Reference Figure 1 FIG. 1 is a flow chart of an environmental monitoring method for a honey aseptic processing room provided by an embodiment of the present invention. In this embodiment, the environmental monitoring method for a honey aseptic processing room includes:

[0066] S1. Acquire the air suspended particle data of the honey aseptic processing room in real time according to a plurality of preset sampling points, and acquire the air microorganism data of the honey aseptic processing room in real time according to the plurality of sampling points.

[0067] In the embodiment of the present invention, the preset multiple sampling points refer to sampling points set at a certain distance in the honey aseptic processing room. The more sampling points there are, the more accurate the data obtained may be, but the workload and calculation amount will be greater.

[0068] In the embodiment of the present invention, the air-suspended particle data refers to the content data of dust particles in the air in the honey aseptic processing room.

[0069] In the embodiment of the present invention, the real-time acquisition of airborne particle data in the honey aseptic processing room according to the preset multiple sampling points includes:

[0070] Obtaining a second mass of a filter membrane of a first mass disposed on a sampler at each sampling point after inhaling a preset volume of air;

[0071] The suspended particle concentration at each sampling point is calculated according to the first mass, the second mass and the preset volume using the following formula:

[0072]

[0073] Where P is the suspended particle concentration at any sampling point, W 2 is the second mass, W 1 is the first mass, and V is the preset volume;

[0074] The suspended particle concentration of each sampling point is obtained to obtain the air suspended particle data.

[0075] In detail, the sampler is a device for extracting air, and a filter membrane is provided at its air inlet. The filter membrane can be an inorganic filter membrane such as a glass fiber filter membrane or a quartz filter membrane, or an organic filter membrane such as polytetrafluoroethylene, polyvinyl chloride, etc., which is used to capture suspended particles in the air.

[0076] In the embodiment of the present invention, the air microorganism data refers to the data of the microorganism content per unit volume of the honey aseptic processing room.

[0077] In an embodiment of the present invention, the air microorganism data of the honey sterile processing room is obtained in real time according to the multiple sampling points by sucking a certain amount of air to quickly impact the surface of the plate culture medium, then culturing the plate, counting the number of grown colonies, and obtaining the air microorganism content of each sampling point, and integrating the air microorganism content of each sampling point to obtain the air microorganism data.

[0078] In an embodiment of the present invention, by acquiring the air suspended particle data of the honey sterile processing room in real time according to a plurality of preset sampling points, and acquiring the air microorganism data of the honey sterile processing room in real time according to the plurality of sampling points, the efficiency of air cleanliness analysis of the honey sterile processing room is improved.

[0079] S2. Perform variance decomposition analysis on the airborne particle data and the air microorganism data to obtain air quality data.

[0080] In an embodiment of the present invention, the statistical method for determining the contribution of different variables or factors to the total variation of the outcome variable.

[0081] In the embodiment of the present invention, refer to Figure 2 As shown, a schematic diagram of a process of variance decomposition analysis is provided in an embodiment of the present invention, wherein the variance decomposition analysis is performed on the airborne particle data and the air microorganism data to obtain air quality data, including:

[0082] S21, normalizing the airborne particle data to obtain normalized suspended particle data, and normalizing the airborne microorganism data to obtain normalized suspended particle data;

[0083] S22, calculating the variance of the normalized suspended particle data and the normalized microbial data to obtain the suspended particle data variance and the microbial data variance;

[0084] S23, merging the normalized suspended particle data and the normalized suspended particle data to obtain merged data;

[0085] S24, calculating the variance of the combined data to obtain a total variance;

[0086] S25, calculating the contribution ratio according to the total variance, the suspended particle data variance and the microbial data variance;

[0087] S26. Perform weighted fusion on the airborne particle data and the air microorganism data according to the contribution ratio to obtain air quality data.

[0088] In detail, the normalization process is a common technique in data preprocessing, which aims to adjust the data to a specific scale range to eliminate the impact of different data dimensions and numerical ranges.

[0089] In the embodiment of the present invention, the normalizing the airborne particle data to obtain normalized suspended particle data includes:

[0090] The airborne particle data is normalized using the following formula:

[0091]

[0092] Among them, Z i is the ith data in the normalized suspended particle data, x i is the i-th data in the airborne particle data, is the average value of the airborne particle data, and N is the number of samples of the airborne particle data.

[0093] In detail, the variance is an important concept in statistics, which is used to measure the fluctuation of a set of data. The larger the variance, the greater the deviation of this set of data from the mean, that is, the greater the fluctuation; the smaller the variance, it means that this set of data is more concentrated, the smaller the deviation of each data from the mean, that is, the smaller the fluctuation.

[0094] In detail, the contribution ratio is calculated based on the total variance, the suspended particle data variance and the microorganism data variance, and the contribution ratio is obtained by calculating the ratio of the suspended particle data variance and the microorganism data variance to the total variance.

[0095] In detail, the weighted fusion refers to statistical analysis of multi-source and multi-dimensional data, and using mathematical methods or practical experience to assign different weights to different data, and finally merging these weighted data into a comprehensive value or result.

[0096] In the embodiment of the present invention, the contribution ratio is used as the weight of the weighted fusion.

[0097] In the embodiment of the present invention, the air quality data is obtained by performing variance decomposition analysis on the airborne particle data and the air microorganism data, thereby improving the information expression capability of the data and improving the efficiency and accuracy of subsequent data analysis.

[0098] S3. Analyze the air cleanliness of the honey aseptic processing room according to the air quality data using a pre-trained air cleanliness analysis model to obtain an air cleanliness value.

[0099] In the embodiment of the present invention, the air cleanliness analysis model is a neural network model, which is a mathematical method that simulates an actual neural network. It is a complex network system formed by a large number of simple processing units (called neurons) that are widely connected to each other. It reflects many basic characteristics of human brain functions and is a highly complex nonlinear dynamic learning system. The neural network model includes but is not limited to a convolutional neural network model and a recurrent neural network model.

[0100] In the embodiment of the present invention, before using the pre-trained air cleanliness analysis model to perform air cleanliness analysis on the honey aseptic processing room according to the air quality data, the method further includes:

[0101] Obtaining historical air quality data of the honey aseptic processing room and its corresponding standard air cleanliness value;

[0102] Extracting features from the historical air quality data to obtain historical air quality features;

[0103] Using a neural network model to analyze the air cleanliness of the honey aseptic processing room according to the historical air quality characteristics to obtain a training cleanliness value;

[0104] Calculating the difference between the training cleanliness value and the standard air cleanliness value to obtain a training difference;

[0105] If the training difference is greater than or equal to a preset difference threshold, then after adjusting the weight and bias item of the neural network model according to the training difference, the step of performing air cleanliness analysis on the honey aseptic processing room according to the historical air quality characteristics using the neural network model is re-executed;

[0106] If the training difference is less than the difference threshold, it is confirmed that the model training is completed and the air cleanliness analysis model is obtained.

[0107] In detail, the weight parameters are important values ​​that connect each neuron, and these weight parameters determine the flow of information in the network and the importance of features. When training a neural network, it is usually the goal to find an optimal set of weight parameters so that the model can accurately predict or classify new data.

[0108] In detail, the bias term is an important parameter in the neural network model. The bias term can increase the expressiveness of the model and make the model more flexible in dealing with complex problems. The size of the bias term determines the degree to which the network generates positive excitation, helping the model to make more precise adjustments to the input data so that the model can better fit the data.

[0109] In the embodiment of the present invention, the air cleanliness analysis of the honey aseptic processing room is performed according to the air quality data using a pre-trained air cleanliness analysis model to obtain an air cleanliness value, including:

[0110] Extracting features from the air quality data to obtain air quality features;

[0111] Using the convolution layer of the air cleanliness analysis model to extract local features of the air quality features, to obtain convolution features;

[0112] Using the pooling layer of the air cleanliness analysis model to perform an average pooling operation on the convolutional features to obtain pooled features;

[0113] Using the activation layer of the air cleanliness analysis model to introduce nonlinearity into the pooling feature, so as to obtain an activation feature;

[0114] The air cleanliness value is calculated based on the activation features using the fully connected layer of the air cleanliness analysis model using preset weights and bias items.

[0115] In detail, the convolution layer of the air cleanliness analysis model is used to extract local features of the air quality characteristics to obtain convolution features, by moving a preset convolution kernel on the air quality characteristics at certain intervals, calculating the dot product between the convolution kernel and the data covered by each movement, and extracting local features of the air quality characteristics.

[0116] In detail, the pooling layer of the air cleanliness analysis model performs an average pooling operation on the convolution feature to obtain the pooled feature, which is to move a rectangular window on the convolution feature, calculate the average value of the data covered by each movement of the rectangular window, and use the average value as the characteristic value of the covered area data to obtain the pooled feature.

[0117] In detail, the activation layer of the air cleanliness analysis model is used to introduce nonlinearity into the pooling feature to obtain the activation feature, which is to transform the pooling feature using an activation function. Common activation functions include Sigmoid, Softmax, and ReLU.

[0118] In detail, the fully connected layer of the air cleanliness analysis model uses preset weights and bias items to calculate the air cleanliness value according to the activation features, including:

[0119] The air cleanliness value is calculated using the following formula:

[0120] Y=W·X+b

[0121] Wherein, Y is the air cleanliness value, X is the activation feature, W is the weight parameter, and b is the bias term.

[0122] In the embodiment of the present invention, the feature extraction of the air quality data to obtain the air quality feature is performed by extracting the feature of the air quality data through principal component analysis.

[0123] For details, refer to Figure 3 As shown, a schematic diagram of a process of extracting features from air quality data is provided in an embodiment of the present invention. The feature extraction of the air quality data to obtain air quality features includes:

[0124] S31, calculating the covariance matrix of the air quality data;

[0125] S32, performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors;

[0126] S33, selecting a preset number of eigenvectors according to the size of the eigenvalues;

[0127] S34, combining the selected eigenvectors into a matrix to obtain a eigenmatrix;

[0128] S35, performing dimensionality reduction processing on the feature matrix to obtain air quality features.

[0129] In detail, the covariance matrix is ​​a matrix used to describe the covariance relationship between each element in a random vector. It is a square matrix, in which each element represents the covariance between a pair of variables, and the size of the square matrix is ​​related to the number of elements.

[0130] In detail, the eigenvalue decomposition is a mathematical method for decomposing a matrix into eigenvalues ​​and eigenvectors.

[0131] In detail, the dimension reduction process of the feature matrix to obtain the air quality features is to achieve dimension reduction by projecting the feature matrix into a principal component space. The principal component space is a new coordinate system obtained by orthogonal transformation in principal component analysis (PCA), and each coordinate axis of this space is called a principal component.

[0132] In the embodiment of the present invention, the air cleanliness analysis of the honey aseptic processing room is performed according to the air quality data by using a pre-trained air cleanliness analysis model to obtain an air cleanliness value, thereby improving the efficiency of subsequent calculation of the sterility coefficient.

[0133] S4. Obtain the ultraviolet radiation intensity and disinfection duration for ultraviolet disinfection of the honey aseptic processing room, and calculate the sterility coefficient according to the ultraviolet radiation intensity, the disinfection duration and the air cleanliness value.

[0134] In the embodiment of the present invention, since ultraviolet irradiation is required for sterilization in general aseptic production, the intensity of ultraviolet radiation used for disinfection and the duration of disinfection will affect the sterility of the honey aseptic processing room.

[0135] In the embodiment of the present invention, the ultraviolet radiation intensity and the disinfection duration are both pre-set when performing ultraviolet disinfection.

[0136] In the embodiment of the present invention, the calculation of the sterility coefficient according to the ultraviolet radiation intensity, the disinfection time and the air cleanliness value includes:

[0137] The sterility coefficient was calculated using the following formula:

[0138] C=α*Y+β*U*T

[0139] Among them, C is the sterility coefficient, Y is the air cleanliness value, α is the cleanliness value adjustment parameter, U is the ultraviolet radiation intensity, T is the disinfection time, and β is the ultraviolet adjustment parameter.

[0140] In detail, the cleanliness value adjustment parameter is a parameter for adjusting the weight ratio of the air cleanliness value in the sterility coefficient. The ultraviolet adjustment parameter is a parameter for adjusting the weight ratio of the ultraviolet radiation intensity and the disinfection time in the sterility coefficient.

[0141] In an embodiment of the present invention, by obtaining the ultraviolet radiation intensity and disinfection time of the honey sterile processing room for ultraviolet disinfection, the sterility coefficient is calculated according to the ultraviolet radiation intensity, the disinfection time and the air cleanliness value, thereby improving the efficiency of judging whether the honey sterile processing room meets the sterility requirements.

[0142] S5. Determine whether the sterility coefficient is greater than or equal to a preset sterility coefficient threshold.

[0143] If the sterility coefficient is greater than or equal to the preset sterility coefficient threshold, execute S6, determine that the honey aseptic processing room meets the sterility requirements, and then re-execute S1, obtain the air suspended particle data of the honey aseptic processing room in real time according to the preset multiple sampling points, and obtain the air microorganism data of the honey aseptic processing room in real time according to the multiple sampling points.

[0144] In the embodiment of the present invention, the sterility coefficient threshold is set according to the actual production requirements of honey aseptic processing.

[0145] In an embodiment of the present invention, when the sterility coefficient is greater than or equal to a preset sterility coefficient threshold, it means that the microbial content and suspended particle content in the honey sterile processing room are lower than the threshold and meet the sterility standards. Real-time monitoring of the honey sterile processing room is achieved by re-executing the steps of obtaining the number of air suspended particles and air microorganism data.

[0146] If the sterility coefficient is less than a preset sterility coefficient threshold, S7 is executed to determine that the honey aseptic processing room does not meet the sterility requirements and issue a warning message.

[0147] In the embodiment of the present invention, when the sterility coefficient is less than the sterility coefficient threshold, it means that the microbial content and the suspended particle content in the honey aseptic processing room exceed the requirements and do not meet the sterility standards.

[0148] In the embodiment of the present invention, when the sterility coefficient is less than a preset sterility coefficient threshold, it is determined that the honey aseptic processing room does not meet the sterility requirements, and a warning message is issued, thereby improving the environmental monitoring efficiency of the honey aseptic processing room.

[0149] like Figure 4 , which is a functional module diagram of an environmental monitoring system for a honey aseptic processing room provided by an embodiment of the present invention.

[0150] The environmental monitoring system 100 for the aseptic honey processing room of the present invention can be installed in an electronic device. According to the functions to be implemented, the environmental monitoring system 100 for the aseptic honey processing room can include a data acquisition module 101, a data analysis module 102, a clean analysis module 103, a data calculation module 104 and a sterility judgment module 105. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0151] In this embodiment, the functions of each module / unit are as follows:

[0152] The data acquisition module 101 is used to acquire the air suspended particle data of the honey aseptic processing room in real time according to a plurality of preset sampling points, and to acquire the air microorganism data of the honey aseptic processing room in real time according to the plurality of sampling points;

[0153] The data analysis module 102 is used to perform variance decomposition analysis on the airborne particle data and the air microorganism data to obtain air quality data;

[0154] The cleanliness analysis module 103 is used to perform air cleanliness analysis on the honey aseptic processing room according to the air quality data using a pre-trained air cleanliness analysis model to obtain an air cleanliness value;

[0155] The data calculation module 104 is used to obtain the ultraviolet radiation intensity and disinfection time of the honey aseptic processing room for ultraviolet disinfection, and calculate the sterility coefficient according to the ultraviolet radiation intensity, the disinfection time and the air cleanliness value;

[0156] The sterility judgment module 105 determines that the honey aseptic processing room meets the sterility requirements if the sterility coefficient is greater than or equal to a preset sterility coefficient threshold, and re-executes the above steps of acquiring the air suspended particle data of the honey aseptic processing room in real time according to the preset multiple sampling points, and acquiring the air microorganism data of the honey aseptic processing room in real time according to the multiple sampling points; if the sterility coefficient is less than the preset sterility coefficient threshold, it is determined that the honey aseptic processing room does not meet the sterility requirements, and a warning message is issued.

[0157] In detail, each module described in the environmental monitoring system 100 of the honey aseptic processing room in the embodiment of the present invention is used in the same manner as described above. Figures 1 to 3 The environmental monitoring method for the aseptic honey processing room described in the present invention is the same technical means and can produce the same technical effects, so I will not go into details here.

[0158] like Figure 5, which is a schematic diagram of the structure of an electronic device of an environmental monitoring method for a honey aseptic processing room provided by an embodiment of the present invention.

[0159] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as an environmental monitoring program for a honey aseptic processing room.

[0160] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as an environmental monitoring program for a honey sterile processing room, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device and process data.

[0161] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the environmental monitoring program of the honey sterile processing room, but also can be used to temporarily store data that has been output or is to be output.

[0162] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0163] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in this embodiment, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0164] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0165] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0166] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0167] The environment monitoring program of the honey aseptic processing room stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0168] Acquire the air suspended particle data of the honey aseptic processing room in real time according to a plurality of preset sampling points, and acquire the air microorganism data of the honey aseptic processing room in real time according to the plurality of sampling points;

[0169] Performing variance decomposition analysis on the airborne particle data and the air microorganism data to obtain air quality data;

[0170] Using a pre-trained air cleanliness analysis model to perform air cleanliness analysis on the honey aseptic processing room according to the air quality data to obtain an air cleanliness value;

[0171] Obtaining the ultraviolet radiation intensity and disinfection time of the honey sterile processing room for ultraviolet disinfection, and calculating the sterility coefficient according to the ultraviolet radiation intensity, the disinfection time and the air cleanliness value;

[0172] If the sterility coefficient is greater than or equal to the preset sterility coefficient threshold, it is determined that the honey aseptic processing room meets the sterility requirements, and the steps of acquiring the air suspended particle data of the honey aseptic processing room in real time according to the preset multiple sampling points and acquiring the air microorganism data of the honey aseptic processing room in real time according to the multiple sampling points are re-executed;

[0173] If the sterility coefficient is less than a preset sterility coefficient threshold, it is determined that the honey aseptic processing room does not meet the sterility requirements and a warning message is issued.

[0174] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0175] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0176] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0177] Acquire the air suspended particle data of the honey aseptic processing room in real time according to a plurality of preset sampling points, and acquire the air microorganism data of the honey aseptic processing room in real time according to the plurality of sampling points;

[0178] Performing variance decomposition analysis on the airborne particle data and the air microorganism data to obtain air quality data;

[0179] Using a pre-trained air cleanliness analysis model to perform air cleanliness analysis on the honey aseptic processing room according to the air quality data to obtain an air cleanliness value;

[0180] Obtaining the ultraviolet radiation intensity and disinfection time of the honey sterile processing room for ultraviolet disinfection, and calculating the sterility coefficient according to the ultraviolet radiation intensity, the disinfection time and the air cleanliness value;

[0181] If the sterility coefficient is greater than or equal to the preset sterility coefficient threshold, it is determined that the honey aseptic processing room meets the sterility requirements, and the steps of acquiring the air suspended particle data of the honey aseptic processing room in real time according to the preset multiple sampling points and acquiring the air microorganism data of the honey aseptic processing room in real time according to the multiple sampling points are re-executed;

[0182] If the sterility coefficient is less than a preset sterility coefficient threshold, it is determined that the honey aseptic processing room does not meet the sterility requirements and a warning message is issued.

[0183] In the embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0184] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0185] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0186] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0187] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0188] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0189] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0190] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for monitoring the environment of a honey aseptic processing room, characterized in that: The method comprises: Acquire the air suspended particle data of the honey aseptic processing room in real time according to a plurality of preset sampling points, and acquire the air microorganism data of the honey aseptic processing room in real time according to the plurality of sampling points; Performing variance decomposition analysis on the airborne particle data and the air microorganism data to obtain air quality data; Using a pre-trained air cleanliness analysis model to perform air cleanliness analysis on the honey aseptic processing room according to the air quality data to obtain an air cleanliness value; Obtaining the ultraviolet radiation intensity and disinfection time of the honey sterile processing room for ultraviolet disinfection, and calculating the sterility coefficient according to the ultraviolet radiation intensity, the disinfection time and the air cleanliness value; If the sterility coefficient is greater than or equal to the preset sterility coefficient threshold, it is determined that the honey aseptic processing room meets the sterility requirements, and the steps of acquiring the air suspended particle data of the honey aseptic processing room in real time according to the preset multiple sampling points and acquiring the air microorganism data of the honey aseptic processing room in real time according to the multiple sampling points are re-executed; If the sterility coefficient is less than a preset sterility coefficient threshold, it is determined that the honey aseptic processing room does not meet the sterility requirements and a warning message is issued.

2. The environmental monitoring method for a honey aseptic processing room according to claim 1, characterized in that: The method of obtaining the air suspended particle data of the honey aseptic processing room in real time according to the preset multiple sampling points includes: Obtaining a second mass of a filter membrane of a first mass disposed on a sampler at each sampling point after inhaling a preset volume of air; The suspended particle concentration at each sampling point is calculated according to the first mass, the second mass and the preset volume using the following formula: Wherein, P is the suspended particle concentration at any sampling point, W2 is the second mass, W1 is the first mass, and V is the preset volume; The suspended particle concentration of each sampling point is obtained to obtain the air suspended particle data.

3. The environmental monitoring method for the honey aseptic processing room according to claim 2, characterized in that: The performing variance decomposition analysis on the airborne particle data and the air microorganism data to obtain air quality data includes: Normalizing the airborne particle data to obtain normalized suspended particle data, and normalizing the airborne microorganism data to obtain normalized suspended particle data; Calculating the variance of the normalized suspended particle data and the normalized microorganism data to obtain the variance of the suspended particle data and the variance of the microorganism data; Merging the normalized suspended particle data and the normalized suspended particle data to obtain merged data; Calculating the variance of the combined data to obtain a total variance; Calculate the contribution ratio according to the total variance, the suspended particle data variance and the microbial data variance; The airborne particle data and the air microorganism data are weightedly fused according to the contribution ratio to obtain air quality data.

4. The method for monitoring the environment of a honey aseptic processing room according to claim 3, characterized in that: The step of normalizing the airborne particle data to obtain normalized suspended particle data includes: The airborne particle data is normalized using the following formula: Among them, Z i is the ith data in the normalized suspended particle data, x i is the i-th data in the airborne particle data, is the average value of the airborne particle data, and N is the number of samples of the airborne particle data.

5. The environmental monitoring method for a honey aseptic processing room according to claim 1, characterized in that: The method of using the pre-trained air cleanliness analysis model to perform air cleanliness analysis on the honey aseptic processing room according to the air quality data to obtain an air cleanliness value includes: Extracting features from the air quality data to obtain air quality features; Using the convolution layer of the air cleanliness analysis model to extract local features of the air quality features, to obtain convolution features; Using the pooling layer of the air cleanliness analysis model to perform an average pooling operation on the convolutional features to obtain pooled features; Using the activation layer of the air cleanliness analysis model to introduce nonlinearity into the pooling feature, so as to obtain an activation feature; The air cleanliness value is calculated based on the activation features using the fully connected layer of the air cleanliness analysis model using preset weights and bias items.

6. The method for monitoring the environment of a honey aseptic processing room according to claim 5, characterized in that: The extracting features of the air quality data to obtain air quality features includes: Calculating a covariance matrix of the air quality data; Performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors; Select a preset number of eigenvectors according to the size of the eigenvalue; Combine the selected eigenvectors into a matrix to obtain the eigenmatrix; The feature matrix is ​​subjected to dimensionality reduction processing to obtain air quality features.

7. The method for monitoring the environment of a honey aseptic processing room according to claim 5, characterized in that: The calculating of the sterility coefficient according to the ultraviolet radiation intensity, the disinfection time and the air cleanliness value comprises: The sterility coefficient was calculated using the following formula: C=α*Y+β*U*T Among them, C is the sterility coefficient, Y is the air cleanliness value, α is the cleanliness value adjustment parameter, U is the ultraviolet radiation intensity, T is the disinfection time, and β is the ultraviolet adjustment parameter.

8. An environmental monitoring system for a honey aseptic processing room, characterized in that: The system comprises: A data acquisition module, used to acquire the air suspended particle data of the honey aseptic processing room in real time according to a plurality of preset sampling points, and to acquire the air microorganism data of the honey aseptic processing room in real time according to the plurality of sampling points; A data analysis module, used for performing variance decomposition analysis on the airborne particle data and the air microorganism data to obtain air quality data; A cleanliness analysis module, used to perform air cleanliness analysis on the honey aseptic processing room according to the air quality data using a pre-trained air cleanliness analysis model to obtain an air cleanliness value; A data calculation module, used for obtaining the ultraviolet radiation intensity and disinfection time of the honey aseptic processing room for ultraviolet disinfection, and calculating the sterility coefficient according to the ultraviolet radiation intensity, the disinfection time and the air cleanliness value; The sterility judgment module determines that the honey aseptic processing room meets the sterility requirements if the sterility coefficient is greater than or equal to a preset sterility coefficient threshold value, and re-executes the above-mentioned steps of acquiring the air suspended particle data of the honey aseptic processing room in real time according to the preset multiple sampling points, and acquiring the air microorganism data of the honey aseptic processing room in real time according to the multiple sampling points. If the sterility coefficient is less than the preset sterility coefficient threshold value, it is determined that the honey aseptic processing room does not meet the sterility requirements, and a warning message is issued.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the environmental monitoring method for the honey aseptic processing room as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the environmental monitoring method for a honey aseptic processing room according to any one of claims 1 to 7 is implemented.

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