CN114417954B - Information processing method and system for improving food detection effect - Google Patents
Information processing method and system for improving food detection effect Download PDFInfo
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Abstract
The invention provides an information processing method and an information processing system for improving food detection effect, wherein the method comprises the following steps: obtaining a food detection dataset; carrying out normalization pretreatment to obtain standard food detection data information; cluster learning is carried out through a Gaussian mixture model, and a cluster detection data set is obtained; obtaining a marker training detection dataset; training a support vector machine model to obtain a food non-standard detection model; performing quality detection to obtain first food detection data information, and performing standardization processing to obtain first standard food detection data information; and obtaining first output information, wherein the first output information comprises a first food detection classification result which does not reach the standard. The method solves the technical problems that in the prior art, food detection data are redundant and complex, data information processing is long in time consumption, most of energy of detection personnel is consumed, comprehensive analysis capability of detection information is poor, the efficiency of food detection work is reduced by the existing information processing method, and the food detection effect is poor.
Description
Technical Field
The invention relates to the field of food detection, in particular to an information processing method and system for improving food detection effect.
Background
With the gradual improvement of life quality, food safety and food health increasingly attract consumers. Therefore, the food sanitation supervision part should strengthen the management, perfects various systems to restrict the food safety problem, and also needs to strengthen the detection of the food so as to provide guarantee for the food safety. Food inspection and detection are used as food quality supervision means, and food inspection efficiency and accuracy are critical to food inspection work. The food safety detection capability is comprehensively improved, and the accuracy and efficiency of each key link in the detection work are required to be enhanced.
However, in the process of implementing the technical scheme of the embodiment of the present application, it is found that the above technology has at least the following technical problems:
the food detection data are redundant and complicated, the data information processing is long in time consumption, most of the energy of detection personnel is consumed, the comprehensive analysis capability of the detection information is poor, and the existing information processing method reduces the efficiency of food detection work and causes the technical problem of poor food detection effect.
Disclosure of Invention
According to the information processing method and system for improving the food detection effect, the technical problems that in the prior art, food detection data are redundant and complex, data information processing is long in time consumption, most of energy of detection personnel is consumed, comprehensive analysis capability of detection information is poor, the efficiency of food detection work is reduced by the existing information processing method, and the food detection effect is poor are solved. The food which does not reach the standard is rapidly detected through machine learning, and a high-efficiency and accurate data processing method is provided, so that the working efficiency of food detection personnel is improved, and the technical effect of food detection is improved.
In view of the above problems, embodiments of the present application provide an information processing method and system for improving food detection effect.
In a first aspect, an embodiment of the present application provides an information processing method for improving a food detection effect, where the method includes: obtaining a food detection data set through big data, wherein the food detection data set comprises detection data information of various foods; carrying out normalization pretreatment on the food detection data set to obtain standard food detection data information; performing cluster learning on the standard food detection data information through a Gaussian mixture model to obtain a cluster detection data set; performing category marking on the cluster detection data set to obtain a marking training detection data set; performing support vector machine model training by taking the marking training detection data set as input data to obtain a food unqualified detection model; performing quality detection on the first food according to the food detection index set to obtain first food detection data information, and performing standardization processing on the first food detection data information to obtain first standard food detection data information; and inputting the first standard food detection data information into the food non-standard detection model to obtain first output information, wherein the first output information comprises a first non-standard food detection classification result.
On the other hand, the embodiment of the application provides an information processing system for improving food detection effect, wherein the system comprises: a first obtaining unit for obtaining a food detection data set including detection data information of various foods through big data; the second obtaining unit is used for carrying out normalization pretreatment on the food detection data set to obtain standard food detection data information; the third obtaining unit is used for carrying out cluster learning on the standard food detection data information through a Gaussian mixture model to obtain a cluster detection data set; a fourth obtaining unit, configured to perform category labeling on the cluster detection data set to obtain a labeled training detection data set; the fifth obtaining unit is used for carrying out support vector machine model training by taking the marking training detection data set as input data to obtain a food unqualified detection model; the sixth obtaining unit is used for detecting the quality of the first food according to the food detection index set to obtain first food detection data information, and carrying out standardized processing on the first food detection data information to obtain first standard food detection data information; and the seventh obtaining unit is used for inputting the first standard food detection data information into the food non-standard detection model to obtain first output information, wherein the first output information comprises a first non-standard food detection classification result.
In a third aspect, an embodiment of the present application provides an information processing system for improving a food detection effect, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, where the processor implements the steps of the method according to any one of the first aspects when the processor executes the program.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
due to the adoption of the food detection data set obtained through big data, the food detection data set comprises detection data information of various foods; carrying out normalization pretreatment on the food detection data set to obtain standard food detection data information; performing cluster learning on the standard food detection data information through a Gaussian mixture model to obtain a cluster detection data set; performing category marking on the cluster detection data set to obtain a marking training detection data set; performing support vector machine model training by taking the marking training detection data set as input data to obtain a food unqualified detection model; performing quality detection on the first food according to the food detection index set to obtain first food detection data information, and performing standardization processing on the first food detection data information to obtain first standard food detection data information; the first standard food detection data information is input into the food unqualified detection model to obtain first output information, and the first output information comprises the technical scheme of the first unqualified food detection classification result.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Fig. 1 is a schematic flow chart of an information processing method for improving food detection effect according to an embodiment of the present application;
fig. 2 is a schematic flow chart of obtaining a cluster detection dataset by an information processing method for improving food detection effect according to an embodiment of the present application;
fig. 3 is a schematic flow chart of obtaining a second unqualified food detection classification result according to an information processing method for improving food detection effect in the embodiment of the present application;
fig. 4 is a schematic flow chart of obtaining first food detection data information according to an information processing method for improving food detection effect in an embodiment of the present application;
FIG. 5 is a schematic flow chart of sensitivity analysis of an information processing method for improving food detection effect according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an information processing system for improving food detection effect according to an embodiment of the present application;
Fig. 7 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Reference numerals illustrate: the device comprises a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a seventh obtaining unit 17, an electronic device 300, a memory 301, a processor 302, a communication interface 303, and a bus architecture 304.
Detailed Description
According to the information processing method and system for improving the food detection effect, the technical problems that in the prior art, food detection data are redundant and complex, data information processing is long in time consumption, most of energy of detection personnel is consumed, comprehensive analysis capability of detection information is poor, the efficiency of food detection work is reduced by the existing information processing method, and the food detection effect is poor are solved. The food which does not reach the standard is rapidly detected through machine learning, and a high-efficiency and accurate data processing method is provided, so that the working efficiency of food detection personnel is improved, and the technical effect of food detection is improved.
Summary of the application
With the gradual improvement of life quality, food safety and food health increasingly attract consumers. Therefore, the food sanitation supervision part should strengthen the management, perfects various systems to restrict the food safety problem, and also needs to strengthen the detection of the food so as to provide guarantee for the food safety. Food inspection and detection are used as food quality supervision means, and food inspection efficiency and accuracy are critical to food inspection work. The food safety detection capability is comprehensively improved, and the accuracy and efficiency of each key link in the detection work are required to be enhanced. In the prior art, food detection data are redundant and complicated, data information processing is long in time consumption, most of energy of detection personnel is consumed, comprehensive analysis capability of detection information is poor, and therefore the efficiency of food detection work is reduced by the existing information processing method, and the technical problem of poor food detection effect is caused.
Aiming at the technical problems, the technical scheme provided by the application has the following overall thought:
the embodiment of the application provides an information processing method for improving food detection effect, wherein the method comprises the following steps: obtaining a food detection data set through big data, wherein the food detection data set comprises detection data information of various foods; carrying out normalization pretreatment on the food detection data set to obtain standard food detection data information; performing cluster learning on the standard food detection data information through a Gaussian mixture model to obtain a cluster detection data set; performing category marking on the cluster detection data set to obtain a marking training detection data set; performing support vector machine model training by taking the marking training detection data set as input data to obtain a food unqualified detection model; performing quality detection on the first food according to the food detection index set to obtain first food detection data information, and performing standardization processing on the first food detection data information to obtain first standard food detection data information; and inputting the first standard food detection data information into the food non-standard detection model to obtain first output information, wherein the first output information comprises a first non-standard food detection classification result.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides an information processing method for improving food detection effect, where the method includes:
s100: obtaining a food detection data set through big data, wherein the food detection data set comprises detection data information of various foods;
s200: carrying out normalization pretreatment on the food detection data set to obtain standard food detection data information;
specifically, food detection often performs multiple detections on the same food due to different sampling locations, detection items, and detection subjects, and based on the use of digital office and advanced detection equipment, a large amount of detection data is accumulated and stored in a computer, and some detection subjects such as detection institutions will show the detection results on the internet. The food detection data set can be obtained from big data, in other words the food detection data set can be but not limited to detection data from the internet and the user's own unit or an established data sharing relationship unit. The food detection data set includes detection data information of various types of foods, such as: meats, vegetables, fruits, grains, oils, etc. Because the sizes of detection results of different detection items are different, normalization pretreatment is needed for the food detection data set, dimensional data are converted into dimensionless data, and standardized treatment is carried out on each index to obtain standard food detection data information. The standard food detection information can be obtained to lay a foundation for subsequent information processing.
S300: performing cluster learning on the standard food detection data information through a Gaussian mixture model to obtain a cluster detection data set;
s400: performing category marking on the cluster detection data set to obtain a marking training detection data set;
specifically, a gaussian mixture model can be considered as a model composed of K single gaussian models, the K sub-models being hidden variables of the mixture model. The Gaussian distribution of the Gaussian mixture model has good mathematical properties and good calculation performance. When the Gaussian mixture model is used for carrying out cluster learning on the standard food detection data information, the accuracy of clustering can be improved, and then a clustering result, namely a clustering detection data set, is obtained. And performing category marking on the clustered detection data set, namely performing marking that detection data accords with the detection standard and does not accord with the detection standard. The markers thus passed train the detection dataset. The clustering learning is carried out through the Gaussian mixture model, and the class marking is carried out according to the clustering result, so that the accuracy of the clustering analysis can be improved, the usability of data can be improved through the label marking, and the subsequent data processing is facilitated.
S500: performing support vector machine model training by taking the marking training detection data set as input data to obtain a food unqualified detection model;
In particular, the support vector machine algorithm may be applied not only to linearly separable data sets, but also to cases where the data sets are not linearly separable. In the machine learning field, a supervised learning model is generally used for pattern recognition, classification and regression analysis, i.e. supervised learning is performed on a labeled training detection data set, and then the food non-standard detection model is constructed according to a trained support vector machine model. The food unqualified detection model is used for distinguishing and classifying food quality detection results, and can accurately identify the unqualified detection results.
S600: performing quality detection on the first food according to the food detection index set to obtain first food detection data information, and performing standardization processing on the first food detection data information to obtain first standard food detection data information;
s700: and inputting the first standard food detection data information into the food non-standard detection model to obtain first output information, wherein the first output information comprises a first non-standard food detection classification result.
Specifically, the food detection index set includes all detection indexes of the existing detection method and detection standard, and the first food is a batch of food to be detected, and may be of the same type or different types, for example: the first food can be Chinese cabbage, or different kinds of food such as Chinese cabbage, green vegetable, tomato, etc. And detecting the quality of the first food according to the food detection index set to obtain first food detection data information, wherein the first food detection data information can be from the detection result of one detection mechanism or the detection result of the cooperation detection of a plurality of detection mechanisms. And carrying out standardization processing on the first food detection data information to obtain first standard food detection data information. Including, but not limited to, normalizing the data for significant digits, normalizing the data, and the like. And obtaining the first standard food detection data information, further judging a food detection result through the food unqualified detection model, and obtaining the first output information, wherein the first output information comprises the first unqualified food detection classification result. Therefore, unqualified foods are detected, and an efficient and accurate data processing method is provided, so that the working efficiency of food detection personnel is improved.
Further, as shown in fig. 2, the step S300 includes:
s310: classifying standard food detection data and unstandard detection data of the standard food detection data information to generate a standard detection database and a unstandard detection database;
s320: clustering the standard detection database and the unstandard detection database through a Gaussian mixture model to obtain a first clustering result;
s330: performing cluster evaluation on the first clustering result based on a linear discriminant analysis algorithm, and determining a first clustering optimal characteristic when the cluster evaluation result meets a preset condition;
s340: and determining the first cluster optimal characteristic as the cluster detection data set.
Further, the embodiment of the application further includes:
s341: inputting the standard food detection data information and the first clustering result into the linear discriminant analysis algorithm to perform optimization target calculation to obtain a detection target function;
s342: evaluating the influence of the cluster number on the first clustering result through the detection objective function, sequentially iterating the clustering result until the detection objective function is converged or meets the minimum value, and determining the optimal cluster number;
S343: and determining the optimal characteristics of the first clusters according to the optimal cluster number.
Specifically, the standard food detection data information comprises the standard food detection data and the unstandard food detection data, the standard food detection data is classified, and the standard food detection database and the unstandard food detection database are generated. And clustering the standard detection database and the unstandard detection database by using a Gaussian mixture model to generate the first clustering result. And evaluating the data of each Gaussian mixture model cluster, namely the first clustering result, by using a linear discriminant analysis algorithm. An objective function is preset as a preset condition for evaluating the influence of the current clustering number on the clustering result.
Inputting the standard food detection data information and the first clustering result into a linear discriminant analysis algorithm, and obtaining the detection objective function after optimization objective calculation, namely the preset condition. The number of clusters is determined by the objective function. And sequentially iterating the clustering results, wherein for the clustering cluster number in each iteration, the clustering cluster number is the optimal cluster number when the clustering cluster number changes to the minimum value along with the change of the clustering cluster value. And determining the first clustering optimal characteristics based on the optimal cluster number. The first cluster optimal feature is then determined as the cluster detection dataset. And clustering the standard detection database and the non-standard detection database by using a Gaussian mixture model-linear discriminant analysis (GMM-LDA) clustering feature learning algorithm to obtain optimal features of normal and abnormal data, and taking the optimal features as a clustering detection data set can improve the data quality and value of the detection data set, so that the data processing efficiency of food detection is improved.
Further, as shown in fig. 3, the embodiment of the present application further includes:
s710: performing model verification on the food non-standard detection model, and judging whether parameter verification information of the food non-standard detection model reaches preset iteration times or not;
s720: when the parameter verification information reaches the preset iteration times, obtaining model optimization parameters based on a PSO algorithm;
s730: optimizing the food non-standard detection model through the model optimization parameters to obtain a food optimized non-standard detection model;
s740: and obtaining a second substandard food detection classification result based on the food optimization substandard detection model.
Specifically, based on the marking training detection data set as input data, carrying out support vector machine model training to obtain a food non-standard detection model, judging and analyzing whether the food non-standard detection model is an optimal model, namely, whether model verification is accurately carried out on the classification result of the model. And judging whether the parameter verification information reaches the preset iteration times or not. The preset iteration number is the maximum iteration number. PSO is a population-based stochastic optimization technique algorithm that is commonly used in improving statistical models. In contrast to conventional grid search optimization and genetic algorithm optimization (GA), PSO does not require searching for all points in the grid and has no mutation operation. And when the parameter verification information reaches the preset iteration times, obtaining model optimization parameters based on a PSO algorithm, and optimizing a penalty factor c and a kernel function parameter g of the SVM model based on the PSO algorithm. Optimizing the parameters of the training model to obtain the optimal parameters of the training model, namely the model optimization parameters. And optimizing the food non-standard detection model through the model optimization parameters to obtain a food optimized non-standard detection model, so that food data detection is carried out according to the optimized model, and a second non-standard food detection classification result is obtained. The classification accuracy can be improved and the false alarm rate can be reduced by optimizing the SVM model through the PSO algorithm.
Further, as shown in fig. 4, the step S600 includes:
s610: constructing a food detection index set, wherein the food detection index set comprises physicochemical conventional detection, element detection, food additive detection, pesticide residue detection and toxic substance detection;
s620: performing quality detection on the first food according to the food detection index set to obtain a first food detection result;
s630: obtaining a first weight distribution result, wherein the first weight distribution result is weight information of each index in the food detection index set;
s640: and carrying out weighted calculation on the first food detection result according to the first weight distribution result to obtain first food detection data information.
Further, the embodiment of the application further includes:
s631: determining the detection data quantity corresponding to each index in the food detection index set according to the basic information of the first food;
s632: the ratio of the detected data quantity to the total data quantity corresponding to each index is sequentially obtained, the weight value of each index is distributed according to the ratio result, a first weight distribution result is obtained, and the weight sum of the first weight distribution results is 1.
Specifically, the food detection index set includes, but is not limited to, physicochemical conventional detection, element detection, food additive detection, pesticide residue detection and toxic substance detection. The food detection index set can meet the existing food detection requirements. And determining the detection data amount according to the basic information of the first food. The basic information of the first food includes the shelf life of the food, the type of the food, the form of the food, the packaging mode, etc. In general, the detection index is determined by the detection task and the food property, for example: if the first food is a leek, determining the detected data amount according to a detection method, including but not limited to how much data amount the heavy metal element needs to be detected, how much data amount the pesticide residue needs to be detected, and the like. And further carrying out quality detection on the first food according to the food detection index set to obtain a first food detection result. The data quantity can represent the importance degree of the data, and the weight distribution result of each index is obtained by the ratio of the data quantity corresponding to each index and the total data quantity. This is because if a certain index is harmful to the human body, less detection results may cause inaccurate detection results, and serious food safety accidents may occur, thereby increasing the data size of the index. And the sum of the weights of the first weight distribution results is 1. And carrying out weighted calculation on the first food detection result according to the first weight distribution result, and obtaining comprehensive detection data information of the food, namely the first food detection data information. The comprehensive evaluation capability of the food can be improved through the first food detection data information, rather than the conventional evaluation of the food only by means of partial detection indexes, the method has a remarkable effect on the health care function of the health food and the detection of the nutritional value of the food, and the nutrition and quality of the food can be comprehensively evaluated.
Further, as shown in fig. 5, the embodiment of the present application further includes:
s643: obtaining a first environmental sensitivity factor according to a preset transportation position of the first food;
s644: obtaining a first storage sensitive factor according to the basic information of the first food;
s645: performing sensitivity analysis on the first environmental sensitivity factor and the first storage sensitivity factor to obtain a first sensitivity factor;
s646: and correcting the detection classification result of the second unqualified food according to the first sensitive factor.
Specifically, the preset transportation location of the first food is a sales site of food sold by logistic transportation, for example, navel orange transported from Guangxi to Siam, and Siam is the preset transportation location. The first environmental sensitivity factor is an environmental factor that has a serious influence on the quality of the first food, such as temperature, humidity, altitude, microorganisms in the environment, etc., of the preset transportation location. And obtaining the first storage sensitive factor based on the basic information of the first food, wherein the basic information of the first food comprises the quality guarantee period of the food, the type of the food, the shape of the food, the packaging mode and the like. The first storage sensitivity factor refers to a factor that is detrimental to the storage of the first food product. Such as the degree of maturity of the food during storage, the quality of the food during storage, etc. And carrying out sensitivity analysis by combining the first environmental sensitivity factor and the first storage sensitivity factor, thereby evaluating the transportation and storage processes of the first food to obtain the first sensitivity factor, wherein the first sensitivity factor has direct influence on the quality of the first food and is beneficial to analyzing and explaining the detection result of the food, and therefore, the second substandard food detection classification result is corrected according to the first sensitivity factor. As an example, without limitation: if the first food is seafood, the second unqualified food detection classification result is that the second unqualified food is qualified, but some seafood microorganisms are out of standard in the storage process, the first sensitive factor is large, but the partial result of the spot inspection is that the second unqualified food detection classification result is that the second unqualified food is qualified, and then the second unqualified food detection classification result is corrected according to the first sensitive factor, so that the second unqualified food detection classification result is corrected to be unqualified. The technical effects that the first sensitivity factor is obtained to correct the second unqualified food detection classification result through sensitivity analysis, the food quality is prejudged through the food transportation and storage process, and the accuracy of the detection result is improved are achieved.
In summary, the information processing method and system for improving food detection effect provided by the embodiment of the application have the following technical effects:
1. due to the adoption of the food detection data set obtained through big data, the food detection data set comprises detection data information of various foods; carrying out normalization pretreatment on the food detection data set to obtain standard food detection data information; performing cluster learning on the standard food detection data information through a Gaussian mixture model to obtain a cluster detection data set; performing category marking on the cluster detection data set to obtain a marking training detection data set; performing support vector machine model training by taking the marking training detection data set as input data to obtain a food unqualified detection model; performing quality detection on the first food according to the food detection index set to obtain first food detection data information, and performing standardization processing on the first food detection data information to obtain first standard food detection data information; the first standard food detection data information is input into the food unqualified detection model to obtain first output information, and the first output information comprises a technical scheme of first unqualified food detection classification results.
2. Due to the adoption of the sensitivity analysis method, the first sensitivity factor is obtained to correct the second unqualified food detection classification result, and the food quality is prejudged through the food transportation and storage process, so that the technical effect of improving the accuracy of the detection result is achieved.
Example two
Based on the same inventive concept as the information processing method for improving food detection effect in the foregoing embodiments, as shown in fig. 6, an embodiment of the present application provides an information processing system for improving food detection effect, where the system includes:
a first obtaining unit 11, wherein the first obtaining unit 11 is used for obtaining a food detection data set through big data, and the food detection data set comprises detection data information of various foods;
a second obtaining unit 12, where the second obtaining unit 12 is configured to perform normalization preprocessing on the food detection data set to obtain standard food detection data information;
a third obtaining unit 13, where the third obtaining unit 13 is configured to perform cluster learning on the standard food detection data information through a gaussian mixture model to obtain a cluster detection data set;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to perform category labeling on the cluster detection data set to obtain a labeled training detection data set;
A fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to perform support vector machine model training with the marker training detection data set as input data, to obtain a food non-standard detection model;
a sixth obtaining unit 16, where the sixth obtaining unit 16 is configured to perform quality detection on a first food according to a food detection index set to obtain first food detection data information, and perform normalization processing on the first food detection data information to obtain first standard food detection data information;
a seventh obtaining unit 17, where the seventh obtaining unit 17 is configured to input the first standard food detection data information into the food non-standard detection model, and obtain first output information, where the first output information includes a first non-standard food detection classification result.
Further, the system includes:
the first generation unit is used for generating a standard detection database and a non-standard detection database by classifying standard detection data and non-standard detection data of the standard food detection data information;
the eighth obtaining unit is used for carrying out clustering operation on the standard detection database and the unstandard detection database through a Gaussian mixture model to obtain a first clustering result;
The first execution unit is used for carrying out clustering evaluation on the first clustering result based on a linear discriminant analysis algorithm, and determining a first clustering optimal characteristic when the clustering evaluation result meets a preset condition;
and the second execution unit is used for determining the first clustering optimal characteristic as the clustering detection data set.
Further, the system includes:
a ninth obtaining unit, configured to input the standard food detection data information and the first clustering result into the linear discriminant analysis algorithm to perform optimization target calculation, so as to obtain a detection target function;
the third execution unit is used for evaluating the influence of the cluster number on the first clustering result through the detection objective function, sequentially iterating the clustering result until the detection objective function has converged or meets the minimum value, and determining the optimal cluster number;
and the fourth execution unit is used for determining the first clustering optimal characteristics according to the optimal cluster number.
Further, the system includes:
the first judging unit is used for carrying out model verification on the food unqualified detection model and judging whether parameter verification information of the food unqualified detection model reaches preset iteration times or not;
A tenth obtaining unit, configured to obtain model optimization parameters based on a PSO algorithm when the parameter verification information reaches the preset iteration number;
an eleventh obtaining unit, configured to optimize the food non-standard detection model through the model optimization parameter, to obtain a food optimized non-standard detection model;
a twelfth obtaining unit, configured to obtain a second substandard food detection classification result based on the food optimization substandard detection model.
Further, the system includes:
the first construction unit is used for constructing a food detection index set, wherein the food detection index set comprises physicochemical conventional detection, element detection, food additive detection, pesticide residue detection and toxic substance detection;
a thirteenth obtaining unit, configured to perform quality detection on the first food according to the food detection index set, to obtain a first food detection result;
a fourteenth obtaining unit, configured to obtain a first weight distribution result, where the first weight distribution result is weight information of each index in the food detection index set;
A fifteenth obtaining unit, configured to perform a weighted calculation on the first food detection result according to the first weight distribution result, to obtain first food detection data information.
Further, the system includes:
the fifth execution unit is used for determining the detection data quantity corresponding to each index in the food detection index set according to the basic information of the first food;
the sixteenth obtaining unit is used for sequentially obtaining the ratio of the detected data quantity and the total data quantity corresponding to each index, distributing the weight value of each index according to the ratio result, and obtaining a first weight distribution result, wherein the weight sum of the first weight distribution result is 1.
Further, the system includes:
a seventeenth obtaining unit, configured to obtain a first environmental sensitivity factor according to a preset transportation position of the first food;
an eighteenth obtaining unit for obtaining a first storage sensitivity factor according to the basic information of the first food;
a nineteenth obtaining unit, configured to perform sensitivity analysis on the first environmental sensitivity factor and the first storage sensitivity factor, to obtain a first sensitivity factor;
And the sixth execution unit is used for correcting the second unqualified food detection classification result according to the first sensitive factor.
Exemplary electronic device
An electronic device of an embodiment of the present application is described below with reference to fig. 7.
Based on the same inventive concept as the information processing method for improving the food detection effect in the foregoing embodiments, the embodiments of the present application further provide an information processing system for improving the food detection effect, including: a processor coupled to a memory for storing a program that, when executed by the processor, causes the system to perform the method of any of the first aspects.
The electronic device 300 includes: a processor 302, a communication interface 303, a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein the communication interface 303, the processor 302 and the memory 301 may be interconnected by a bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry Standard architecture, EISA) bus, among others. The bus architecture 304 may be divided into address buses, data buses, control buses, and the like. For ease of illustration, only one thick line is shown in fig. 7, but not only one bus or one type of bus.
Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of the programs of the present application.
The communication interface 303 uses any transceiver-like means for communicating with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), wired access network, etc.
The memory 301 may be, but is not limited to, ROM or other type of static storage device that may store static information and instructions, RAM or other type of dynamic storage device that may store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through bus architecture 304. The memory may also be integrated with the processor.
The memory 301 is used for storing computer-executable instructions for executing the embodiments of the present application, and is controlled by the processor 302 to execute the instructions. The processor 302 is configured to execute computer-executable instructions stored in the memory 301, thereby implementing an information processing method for improving food detection effect according to the above embodiment of the present application.
Alternatively, the computer-executable instructions in the embodiments of the present application may be referred to as application program codes, which are not specifically limited in the embodiments of the present application.
The embodiment of the application provides an information processing method for improving food detection effect, wherein the method comprises the following steps: obtaining a food detection data set through big data, wherein the food detection data set comprises detection data information of various foods; carrying out normalization pretreatment on the food detection data set to obtain standard food detection data information; performing cluster learning on the standard food detection data information through a Gaussian mixture model to obtain a cluster detection data set; performing category marking on the cluster detection data set to obtain a marking training detection data set; performing support vector machine model training by taking the marking training detection data set as input data to obtain a food unqualified detection model; performing quality detection on the first food according to the food detection index set to obtain first food detection data information, and performing standardization processing on the first food detection data information to obtain first standard food detection data information; and inputting the first standard food detection data information into the food non-standard detection model to obtain first output information, wherein the first output information comprises a first non-standard food detection classification result.
Those of ordinary skill in the art will appreciate that: the various numbers of first, second, etc. referred to in this application are merely for convenience of description and are not intended to limit the scope of embodiments of the present application, nor to indicate a sequence. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any one," or the like, refers to any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one of a, b, or c (species ) may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more servers, data centers, etc. that can be integrated with the available medium. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
The various illustrative logical blocks and circuits described in the embodiments of the present application may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the general purpose processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in the embodiments of the present application may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software elements may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. In an example, a storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may reside in a terminal. In the alternative, the processor and the storage medium may reside in different components in a terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the application. Accordingly, the specification and drawings are merely exemplary illustrations of the present application as defined in the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the present application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to include such modifications and variations.
Claims (5)
1. An information processing method for improving food detection effect, which is characterized by comprising the following steps:
obtaining a food detection data set through big data, wherein the food detection data set comprises detection data information of various foods;
carrying out normalization pretreatment on the food detection data set to obtain standard food detection data information;
performing cluster learning on the standard food detection data information through a Gaussian mixture model to obtain a cluster detection data set;
Performing category marking on the cluster detection data set to obtain a marking training detection data set;
performing support vector machine model training by taking the marking training detection data set as input data to obtain a food unqualified detection model;
performing quality detection on the first food according to the food detection index set to obtain first food detection data information, and performing standardization processing on the first food detection data information to obtain first standard food detection data information;
inputting the first standard food detection data information into the food non-standard detection model to obtain first output information, wherein the first output information comprises a first non-standard food detection classification result;
performing model verification on the food non-standard detection model, and judging whether parameter verification information of the food non-standard detection model reaches preset iteration times or not;
when the parameter verification information reaches the preset iteration times, obtaining model optimization parameters based on a PSO algorithm;
optimizing the food non-standard detection model through the model optimization parameters to obtain a food optimized non-standard detection model;
obtaining a second substandard food detection classification result based on the food optimization substandard detection model;
Wherein the obtaining the first food detection data information includes:
constructing a food detection index set, wherein the food detection index set comprises physicochemical conventional detection, element detection, food additive detection, pesticide residue detection and toxic substance detection;
performing quality detection on the first food according to the food detection index set to obtain a first food detection result;
obtaining a first weight distribution result, wherein the first weight distribution result is weight information of each index in the food detection index set;
weighting calculation is carried out on the first food detection result according to the first weight distribution result, and first food detection data information is obtained;
determining the detection data quantity corresponding to each index in the food detection index set according to the basic information of the first food;
sequentially obtaining the ratio of the detected data quantity to the total data quantity corresponding to each index, and distributing the weight value of each index according to the ratio result to obtain a first weight distribution result, wherein the sum of the weights of the first weight distribution results is 1;
wherein the method further comprises:
obtaining a first environmental sensitivity factor according to a preset transportation position of the first food, wherein the first environmental sensitivity factor is an environmental factor which has serious influence on the quality of the first food at the preset transportation position and comprises temperature, humidity, altitude and microorganisms in the environment;
Obtaining a first storage sensitive factor according to the basic information of the first food, wherein the basic information of the first food comprises the quality guarantee period of the food, the type of the food, the shape of the food and the packaging mode, and the first storage sensitive factor refers to factors which are unfavorable for the storage of the first food, including the maturity of the food during storage and the quality of the food during storage;
performing sensitivity analysis on the first environmental sensitivity factor and the first storage sensitivity factor to obtain a first sensitivity factor;
and correcting the detection classification result of the second unqualified food according to the first sensitive factor.
2. The method of claim 1, wherein the performing cluster learning on the standard food detection data information by a gaussian mixture model to obtain a cluster detection data set comprises:
classifying standard food detection data and unstandard detection data of the standard food detection data information to generate a standard detection database and a unstandard detection database;
clustering the standard detection database and the unstandard detection database through a Gaussian mixture model to obtain a first clustering result;
Performing cluster evaluation on the first clustering result based on a linear discriminant analysis algorithm, and determining a first clustering optimal characteristic when the cluster evaluation result meets a preset condition;
and determining the first cluster optimal characteristic as the cluster detection data set.
3. The method according to claim 2, wherein the method comprises:
inputting the standard food detection data information and the first clustering result into the linear discriminant analysis algorithm to perform optimization target calculation to obtain a detection target function;
evaluating the influence of the cluster number on the first clustering result through the detection objective function, sequentially iterating the clustering result until the detection objective function is converged or meets the minimum value, and determining the optimal cluster number;
and determining the optimal characteristics of the first clusters according to the optimal cluster number.
4. An information processing system for enhancing food detection, the system comprising:
a first obtaining unit for obtaining a food detection data set including detection data information of various foods through big data;
the second obtaining unit is used for carrying out normalization pretreatment on the food detection data set to obtain standard food detection data information;
The third obtaining unit is used for carrying out cluster learning on the standard food detection data information through a Gaussian mixture model to obtain a cluster detection data set;
a fourth obtaining unit, configured to perform category labeling on the cluster detection data set to obtain a labeled training detection data set;
the fifth obtaining unit is used for carrying out support vector machine model training by taking the marking training detection data set as input data to obtain a food unqualified detection model;
the sixth obtaining unit is used for detecting the quality of the first food according to the food detection index set to obtain first food detection data information, and carrying out standardized processing on the first food detection data information to obtain first standard food detection data information;
a seventh obtaining unit, configured to input the first standard food detection data information into the food non-standard detection model, to obtain first output information, where the first output information includes a first non-standard food detection classification result;
the first judging unit is used for carrying out model verification on the food unqualified detection model and judging whether parameter verification information of the food unqualified detection model reaches preset iteration times or not;
A tenth obtaining unit, configured to obtain model optimization parameters based on a PSO algorithm when the parameter verification information reaches the preset iteration number;
an eleventh obtaining unit, configured to optimize the food non-standard detection model through the model optimization parameter, to obtain a food optimized non-standard detection model;
a twelfth obtaining unit, configured to obtain a second substandard food detection classification result based on the food optimization substandard detection model;
the first construction unit is used for constructing a food detection index set, wherein the food detection index set comprises physicochemical conventional detection, element detection, food additive detection, pesticide residue detection and toxic substance detection;
a thirteenth obtaining unit, configured to perform quality detection on the first food according to the food detection index set, to obtain a first food detection result;
a fourteenth obtaining unit, configured to obtain a first weight distribution result, where the first weight distribution result is weight information of each index in the food detection index set;
A fifteenth obtaining unit, configured to perform a weighted calculation on the first food detection result according to the first weight distribution result, to obtain first food detection data information;
the fifth execution unit is used for determining the detection data quantity corresponding to each index in the food detection index set according to the basic information of the first food;
a sixteenth obtaining unit, configured to sequentially obtain a ratio of the detected data amount to the total data amount corresponding to each index, and allocate weight values of each index according to a ratio result, to obtain a first weight allocation result, where a weight sum of the first weight allocation results is 1;
a seventeenth obtaining unit, configured to obtain a first environmental sensitivity factor according to a preset transportation position of the first food;
an eighteenth obtaining unit for obtaining a first storage sensitivity factor according to the basic information of the first food;
a nineteenth obtaining unit, configured to perform sensitivity analysis on the first environmental sensitivity factor and the first storage sensitivity factor, to obtain a first sensitivity factor;
And the sixth execution unit is used for correcting the second unqualified food detection classification result according to the first sensitive factor.
5. An information processing system for enhancing food detection effects, comprising: a processor coupled to a memory for storing a program which, when executed by the processor, causes the system to perform the method of any one of claims 1 to 3.
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