CN116205688A

CN116205688A - Fresh product information processing method and device, computer equipment and storage medium

Info

Publication number: CN116205688A
Application number: CN202310217734.4A
Authority: CN
Inventors: 汪钦; 彭利
Original assignee: Shanghai Siwei Technology Co ltd
Current assignee: Shanghai Siwei Technology Co ltd
Priority date: 2023-03-08
Filing date: 2023-03-08
Publication date: 2023-06-02

Abstract

The invention discloses a fresh product information processing method, a fresh product information processing device, computer equipment and a storage medium. The method comprises the following steps: responding to the state inquiry command, and acquiring current induction information corresponding to the fresh product from the wireless cloud sensor network; decoding the current induction information corresponding to the fresh product to obtain freshness induction data corresponding to the fresh product; analyzing the freshness sensing data by adopting a product freshness analysis model to obtain a freshness score corresponding to the fresh product; acquiring a target suggestion corresponding to the fresh product according to the freshness score corresponding to the fresh product; and visually displaying the freshness score and the target suggestion corresponding to the fresh product. The method can quickly identify the corresponding freshness score of the fresh-keeping product, so that the freshness score of the fresh-keeping product can be more intuitively reflected; and according to the freshness score corresponding to the fresh product, acquiring a corresponding target suggestion, thereby being beneficial to improving the user satisfaction and the user viscosity, or delaying the product decay time or improving the product freshness.

Description

Fresh product information processing method and device, computer equipment and storage medium

Technical Field

The present invention relates to the field of information processing technologies, and in particular, to a method and apparatus for processing information of a fresh product, a computer device, and a storage medium.

Background

Along with the development of technology and the improvement of income of people, the continuously improved enrolment coefficient means that people have higher requirements on quality of living standard, and meanwhile, technology-driven fresh logistics enterprises continuously transform and upgrade, and the rapid development of fresh logistics is jointly promoted by consumption structure conversion and logistics technology upgrade. For fresh foods, prepared vegetables, milk, melons, fruits and vegetables and other fresh products, people pursue better foods and lower time cost, and purchasing the fresh products on the network becomes the main stream.

When people purchase fresh products through a network order, the freshness of the products is one of main factors for determining purchase, and the existing fresh logistics can be transmitted conveniently, but the freshness of the fresh products cannot be identified quickly, so that whether users purchase the fresh products or not can be influenced to a certain extent, corresponding fresh-keeping measures cannot be taken timely, the decay time of the products is delayed, and the freshness of the products is influenced.

Disclosure of Invention

The embodiment of the invention provides a fresh product information processing method, a fresh product information processing device, computer equipment and a storage medium, which are used for solving the problem that the freshness of a fresh product cannot be identified by a company in the prior art.

A fresh product information processing method comprises the following steps:

responding to the state inquiry command, and acquiring current induction information corresponding to the fresh product from the wireless cloud sensor network;

decoding the current induction information corresponding to the fresh product to obtain freshness induction data corresponding to the fresh product;

analyzing the freshness sensing data by adopting a product freshness analysis model to obtain a freshness score corresponding to the fresh product;

acquiring a target suggestion corresponding to the fresh product according to the freshness score corresponding to the fresh product;

and visually displaying the freshness score and the target suggestion corresponding to the fresh product.

An information processing apparatus for fresh produce, comprising:

the current induction information acquisition module is used for responding to the state query instruction and acquiring current induction information corresponding to the fresh product from the wireless cloud sensor network;

the current induction information decoding module is used for decoding the current induction information corresponding to the fresh product to obtain freshness induction data corresponding to the fresh product;

The freshness score obtaining module is used for analyzing the freshness induction data by adopting a product freshness analysis model to obtain a freshness score corresponding to the fresh product;

the target suggestion acquisition module is used for acquiring target suggestions corresponding to the fresh products according to the freshness scores corresponding to the fresh products;

and the visual display module is used for visually displaying the freshness score and the target suggestion corresponding to the fresh product.

A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the fresh product information processing method described above when executing the computer program.

A computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described fresh product information processing method.

According to the method, the device, the computer equipment and the storage medium for processing the information of the fresh product, the current sensing information corresponding to the fresh product is acquired from the wireless cloud sensing network in response to the state query instruction, and the instantaneity of the acquisition of the current sensing information is ensured; decoding the current induction information to extract freshness induction data related to freshness from the current induction information so as to achieve pertinence in subsequent analysis and processing; analyzing the freshness sensing data by adopting a pre-trained product freshness analysis model to determine the freshness score corresponding to the fresh product, so as to convert the freshness sensing data into a quantization index for representing the freshness, and enable the freshness to be more intuitively reflected; and according to the corresponding freshness score of the fresh product, obtaining a corresponding target suggestion, and visually displaying the freshness score and the target suggestion, thereby being beneficial to improving the satisfaction degree and the viscosity of the user, or delaying the decay time of the product or improving the freshness degree of the product.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic diagram of a method for processing information of a fresh product according to an embodiment of the invention;

FIG. 2 is a flow chart of a method for processing information of fresh products according to an embodiment of the invention;

FIG. 3 is another flow chart of a method for processing information of fresh products according to an embodiment of the invention;

FIG. 4 is another flow chart of a method for processing information of fresh products according to an embodiment of the invention;

FIG. 5 is another flow chart of a method for processing information of fresh products according to an embodiment of the invention;

FIG. 6 is another flow chart of a method for processing information of fresh products according to an embodiment of the invention;

FIG. 7 is another flow chart of a method for processing information of fresh products according to an embodiment of the invention;

FIG. 8 is another flow chart of a method for processing information of fresh products according to an embodiment of the invention;

FIG. 9 is a schematic diagram of an information processing apparatus for fresh products according to an embodiment of the present invention;

FIG. 10 is a schematic diagram of a computer device in accordance with an embodiment of the invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The fresh product information processing method provided by the embodiment of the invention can be applied to a fresh product information processing system shown in fig. 1, wherein the fresh product information processing system comprises a server, a client in communication with the server and a wireless cloud sensing network, and the wireless cloud sensing network comprises a central wireless cloud sensing network in direct communication with the server and a local wireless cloud sensing network in indirect communication with the server, for example, the local wireless cloud sensing network is in communication with the server through the central wireless cloud sensing network. The client is also called a client, and refers to a program corresponding to the server for providing local service for the client. The client may be installed on, but is not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices. The server may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers. The wireless cloud sensor network is a network which communicates with the sensor through a wireless network.

In one embodiment, as shown in fig. 2, a method for processing information of fresh products is provided, and the method is applied to a server for illustration, and includes the following steps:

s201: responding to the state inquiry command, and acquiring current induction information corresponding to the fresh product from the wireless cloud sensor network;

s202: decoding the current induction information corresponding to the fresh product to obtain freshness induction data corresponding to the fresh product;

s203: analyzing the freshness sensing data by adopting a product freshness analysis model to obtain a freshness score corresponding to the fresh product;

s204: acquiring a target suggestion corresponding to the fresh product according to the freshness score corresponding to the fresh product;

s205: and visually displaying the freshness score and the target suggestion corresponding to the fresh product.

The state query instruction is an instruction for querying the current state of the fresh product. As an example, the status query instruction may be an instruction for indicating the freshness of the query fresh product, or an instruction for indicating the freshness of the query fresh product and the environmental status. The current induction information is induction information obtained at the current moment and can be understood as the closest induction information inquired from the wireless cloud sensor network at the current moment.

As an example, in step S201, the server may receive a status query command triggered by the client, parse the status query command, and obtain a product identifier; then, inquiring a wireless cloud sensing network based on the product identifier, and acquiring current induction information corresponding to the fresh product corresponding to the product identifier from the wireless cloud sensing network so as to analyze the freshness of the fresh product based on the current induction information. In this example, the current sensing information is sensing information queried at the current moment, specifically, sensing information formed by encoding based on a target encoding rule in a CSI encoding system, where the target encoding rule is a preset rule for implementing encoding processing.

The freshness sensing data refers to sensing data related to freshness of fresh products.

As an example, in step S202, after acquiring the current sensing information formed by encoding the CSI encoding system, the server may decode the current sensing information by using a target decoding rule corresponding to the target encoding rule to acquire freshness sensing data corresponding to the fresh product, where the target decoding rule is a preset rule for implementing decoding processing. For example, when the target encoding rule means that the product attribute codes, the freshness attribute codes and the environment attribute codes are ordered and encoded according to a specific order, the server can identify the freshness attribute codes from the current sensing information and then decode the encoding values corresponding to the freshness attribute codes to obtain freshness sensing data corresponding to the fresh product when decoding based on the target decoding rule.

Wherein the product freshness analysis model is a model trained in advance for analyzing freshness of fresh products.

As an example, in step S203, after decoding the freshness sensing data according to the current sensing information, the server needs to analyze the freshness sensing data by using a pre-trained product freshness analysis model, and determines the output result of the product freshness analysis model as the freshness score corresponding to the fresh product. In this example, a suitable product freshness analysis model is pre-built, wherein the product freshness analysis model models different freshness indexes of existing sensing information, and combines each freshness index into a quantization index corresponding to the freshness of the fresh product, for example, the product freshness analysis model can be summarized as f: R ⁿ →[0,1]Existing sensing information corresponding to n freshness indexes to be collected is mapped into a percentage index capable of expressing freshness through a function. Understandably, the expression mode of the product freshness analysis model needs to be determined according to the product category of the fresh product, and can be modified appropriately according to the actual situation.

Wherein, the target advice is advice corresponding to the freshness score corresponding to the fresh product.

As an example, in step S204, after obtaining the freshness score corresponding to the fresh product, the server may determine the user role of triggering the status query instruction to determine whether the user role is a buyer role or a seller role; and then according to the freshness score corresponding to the fresh product, determining the diet suggestion corresponding to the buyer role or the fresh-keeping suggestion corresponding to the seller role as the target suggestion corresponding to the fresh product. The buyer role here refers to the role of purchasing fresh products; the vendor role is a role in indicating the sale of fresh products, including but not limited to, during storage and shipping of fresh products. Understandably, for the buyer role, providing corresponding dietary advice based on the corresponding freshness score of the fresh product to improve user satisfaction and user viscosity of the buyer role; and providing corresponding fresh-keeping suggestions based on the corresponding freshness scores of the fresh products aiming at the seller roles so as to delay the decay time of the products or improve the freshness of the products.

As an example, in step S205, the server may further control the client that triggers the status query instruction to visually display the freshness score and the target suggestion corresponding to the fresh product, so that the user that triggers the status query instruction may perform the subsequent operation according to the freshness score and the target suggestion. For example, when the user who triggered the status query instruction is a buyer character, the user may be enabled to determine whether to purchase the fresh product and how to eat the fresh product after purchase based on the freshness score and the target suggestion, which may help to improve user satisfaction and user viscosity. For another example, when the user who triggers the status query instruction is a vendor role, the user may be enabled to perform corresponding freshness measures based on the freshness score and the target suggestion, delay the product decay time or promote the freshness of the product.

In the embodiment, the current induction information corresponding to the fresh product is acquired from the wireless cloud sensing network in response to the state query instruction, so that the instantaneity of the current induction information acquisition is ensured; decoding the current induction information to extract freshness induction data related to freshness from the current induction information so as to achieve pertinence in subsequent analysis and processing; analyzing the freshness sensing data by adopting a pre-trained product freshness analysis model to determine the freshness score corresponding to the fresh product, so as to convert the freshness sensing data into a quantization index for representing the freshness, and enable the freshness to be more intuitively reflected; and according to the corresponding freshness score of the fresh product, obtaining a corresponding target suggestion, and visually displaying the freshness score and the target suggestion, thereby being beneficial to improving the satisfaction degree and the viscosity of the user, or delaying the decay time of the product or improving the freshness degree of the product.

In one embodiment, as shown in fig. 3, a method for processing information of fresh products is provided, and the method is applied to a server for illustration, and includes the following steps:

s301: responding to the state inquiry command, and acquiring current induction information corresponding to the fresh product from the wireless cloud sensor network;

S302: decoding the current induction information corresponding to the fresh product to obtain freshness induction data and environment induction data corresponding to the fresh product;

s303: analyzing the freshness induction data corresponding to the fresh product by adopting a product freshness analysis model to obtain a freshness score corresponding to the fresh product;

s304: acquiring a target suggestion corresponding to the fresh product according to the freshness score corresponding to the fresh product;

s305: acquiring an environment change trend chart corresponding to the fresh product according to the environment induction data corresponding to the fresh product;

s306: and visually displaying the corresponding freshness score, target suggestion and environment change trend graph of the fresh product.

Step S301 is the same as step S201, and is not repeated here.

The environmental sensing data refers to sensing data related to the environment in which the fresh product is located, and as an example, the environmental sensing data includes, but is not limited to, sensing data corresponding to an environmental temperature, an environmental humidity or other environmental indicators.

As an example, in step S302, after acquiring the current sensing information formed by encoding the CSI encoding system, the server may decode the current sensing information by using a target decoding rule corresponding to the target encoding rule, to acquire freshness sensing data and environment sensing data corresponding to the fresh product, where the target decoding rule is a preset rule for implementing the decoding process. For example, when the target encoding rule indicates that the product attribute codes, the freshness attribute codes and the environment attribute codes are ordered and encoded according to a specific order, the server can identify the freshness attribute codes and the environment attribute codes from the current sensing information when decoding based on the target decoding rule, and decode the encoding values corresponding to the freshness attribute codes and the encoding values corresponding to the environment attribute codes respectively to obtain freshness sensing data and environment sensing data corresponding to the fresh product.

Step S303 is the same as step S203, step S304 is the same as step S204, and the description is omitted herein to avoid repetition.

As an example, in step S305, after the server decodes the environmental sensing data according to the current sensing information, a corresponding environmental change trend graph may be formed based on all the environmental sensing data. In this example, the environmental trend graph includes, but is not limited to, a temperature trend graph, a humidity trend graph, and an environmental suitability trend graph. The temperature change trend chart is used for recording the change condition of the environmental temperature of the fresh product in the process from the warehouse to the destination. The humidity change trend chart is used for recording the change condition of the environment humidity of the fresh products in the process from the warehouse to the destination. The environment suitability trend chart is used for recording the change of other factors of the environment of the fresh products from the warehouse to the destination, wherein the other factors include but are not limited to illumination and ventilation.

As an example, in step S306, the server may control not only the client terminal triggering the status query instruction to visually display the freshness score and the target suggestion corresponding to the fresh product, but also the client terminal to display the environment change trend graph corresponding to the fresh product, so that the user triggering the status query instruction may perform subsequent operations according to the freshness score, the target suggestion and the environment change trend graph. For example, when the user who triggered the status query instruction is a buyer character, the user may be enabled to determine whether to purchase the fresh product and how to eat the fresh product after purchase based on the freshness score and the target suggestion, which may help to improve user satisfaction and user viscosity. For another example, when the user who triggers the status query instruction is a vendor role, the user may be enabled to perform corresponding freshness measures based on the freshness score and the target suggestion, delay the product decay time or promote the freshness of the product. For another example, the user may analyze whether the current environment is favorable to delay the product decay time according to the environment change trend graph, so as to perform subsequent operations.

In the embodiment, the current induction information corresponding to the fresh product is acquired from the wireless cloud sensing network in response to the state query instruction, so that the instantaneity of the current induction information acquisition is ensured; decoding the current induction information to extract freshness induction data and environment induction data related to freshness from the current induction information so as to be targeted for subsequent analysis and processing; analyzing the freshness sensing data by adopting a pre-trained product freshness analysis model, determining a freshness score corresponding to the fresh product and obtaining a corresponding target suggestion so as to convert the freshness sensing data into a quantitative index for representing the freshness, so that the freshness of the fresh product is more intuitively reflected, the user satisfaction and the user viscosity are improved based on the target suggestion, or the product decay time is delayed or the product freshness is improved; and according to the environment sensing data, the corresponding environment change trend graph is obtained and visually displayed, so that the environment where the fresh product is positioned can be conveniently and intuitively known, and the freshness of the fresh product can be intuitively analyzed by a user.

In an embodiment, as shown in fig. 4, step S302, namely decoding the current sensing information corresponding to the fresh product to obtain the freshness sensing data and the environment sensing data corresponding to the fresh product, includes:

S401: extracting current induction information corresponding to the fresh product based on the target coding sequence to obtain a plurality of attribute codes and original coding values corresponding to the attribute codes;

s402: and decoding the original coding value corresponding to the attribute code by adopting a target decoding rule corresponding to the attribute code to obtain freshness sensing data and environment sensing data corresponding to the fresh product.

The target coding sequence is a preset sequence for sorting different attribute codes and original coding values thereof.

The attribute codes are preset attributes corresponding to different codes. As an example, attribute codes include, but are not limited to, equipment codes, equipment temperature, real-time acquisition time, storage temperature, departure time, temperature change time, transfer station, destination, source production environment humidity, transmission chain environment humidity, destination environment humidity, picking colony initial values, transport chain colony growth, and destination colonies. The original code value refers to a specific code value corresponding to a certain attribute code recorded in the current sensing information.

In step S401, the current sensing information corresponding to the fresh product is formed by sorting the different attribute codes and the original code values thereof based on the target coding sequence, so that the server may extract the current sensing information corresponding to the fresh product based on the target coding sequence after obtaining the current sensing information corresponding to the fresh product from the wireless cloud sensor network, and obtain the original code values corresponding to the attribute codes. For example, if the current induction information is N-bit code, and the k+1st bit to the N-th bit are used for recording the physical information of the transportation device of the fresh product, and the k+1st bit to the N-th bit are used for recording the freshness induction data and the environment induction data of the fresh product, then the k+1st bit to the N-th bit in the current induction information corresponding to the fresh product are extracted, and a plurality of attribute codes and original code values corresponding to each attribute code are obtained, wherein each attribute code is a 1-bit numerical value, and the corresponding original code value can be a 1-bit numerical value or a multi-bit numerical value.

The target decoding rule corresponding to the attribute codes is a rule for decoding the original code value corresponding to the attribute codes. As an example, the target decoding rule is a rule corresponding to a target encoding rule for implementing an encoding process.

As an example, in step S402, after determining the original encoding value corresponding to each attribute encoding, the server may use the target decoding rule corresponding to the attribute encoding to decode the original encoding value corresponding to the attribute encoding, so as to obtain the freshness sensing data and the environment sensing data corresponding to the fresh product. Understandably, the target decoding rule in the decoding process corresponds to the target encoding rule in the encoding process, the target decoding rule and the target encoding rule are inverse transformation processes, and after the server acquires the current induction information, the server can rapidly decode the corresponding original encoding value by adopting the target decoding rule corresponding to the attribute encoding, thereby being beneficial to improving the efficiency and the accuracy of decoding the original encoding value.

In this embodiment, the current sensing information is extracted based on the target coding sequence, a plurality of attribute codes and original coding values thereof are determined, and then the original coding values thereof are decoded based on the target decoding rule corresponding to each attribute code, so that the efficiency and the accuracy of the freshness sensing data and the environment sensing data obtained by decoding can be ensured.

In an embodiment, as shown in fig. 5, before step S201, that is, before the current sensing information corresponding to the fresh product is obtained from the wireless cloud sensor network in response to the status query command, the fresh product information processing method further includes:

s501: obtaining actual measurement sensing data corresponding to fresh products, wherein the actual measurement sensing data comprises sensor identifications and sensor data corresponding to the sensor identifications;

s502: based on the sensor identification, determining a sensor position and a sensor type corresponding to the sensor identification;

s503: determining attribute codes corresponding to the sensor identifications according to the sensor positions and the sensor types corresponding to the sensor identifications;

s504: coding sensor data corresponding to the sensor identifier by adopting a target coding rule corresponding to the attribute code, and obtaining an original coding value corresponding to the attribute code;

s505: based on the target coding sequence, combining the attribute codes and the original code values to obtain actual measurement induction information corresponding to the fresh products, and storing the actual measurement induction information in the wireless cloud sensor network.

The actually measured sensing data is data which is sensed and uploaded by the sensor in real time. The sensor identification is an identification for uniquely identifying a certain sensor. The sensor data corresponding to the sensor identifier refers to data acquired by the sensor corresponding to the sensor identifier in real time.

As an example, the server may obtain measured sensing data corresponding to the fresh product sent by the sensor in communication therewith. The sensor is an object which can obviously sense and collect the variable change degree, and in order to detect the change condition of the fresh product from picking to each process node of the terminal, the sensor can be arranged in a series of places such as a transportation warehouse, a transfer station, a cold chain transport vehicle, an intelligent terminal and the like, is used for collecting but not limited to the change condition of temperature, humidity and other factors, and the collected sensor data is sent to the server so that the server can analyze the fresh product freshness based on the sensor data. In this example, a star-shaped mesh structure may be formed between the sensors, as shown in fig. 1, where different sensors communicate with a local wireless cloud sensor network or a central wireless cloud sensor network, so as to upload the sensor data collected by the sensors to the wireless cloud sensor network, and then the sensor data is transmitted to the server by the wireless cloud sensor network. In this example, the sensor data is generally transmitted to the local wireless cloud sensor network, and then the local wireless cloud sensor network is transmitted to the central wireless cloud sensor network, and the central wireless cloud sensor network pushes the sensor data to the server, so as to ensure data security and stable transmission.

As an example, in step S502, the server may query a preconfigured sensor information table based on the sensor identification, and determine a sensor location and a sensor type corresponding to the sensor identification. The sensor position refers to the position of the sensor corresponding to the sensor identifier recorded in the sensor information table. The sensor type herein refers to the type of the sensor corresponding to the sensor identification recorded in the sensor information table. For example, in the fresh product transmission process, three environmental humidity sensors are provided for collecting the source producing area humidity, the transmission chain environmental humidity, and the destination environmental humidity, respectively, where the sensor position is any one of the source producing area, the transmission chain, and the destination, and the sensor type is humidity.

As an example, in step S503, after determining the sensor position and the sensor type according to the sensor identifier lookup table, the server may determine the attribute name corresponding to the sensor identifier according to the combination of the sensor position and the sensor type, for example, when the sensor position is the source place and the sensor type is humidity, the attribute name may be determined to be the source place environment humidity; and then, inquiring a corresponding name code comparison table based on the determined attribute names, and acquiring the attribute codes corresponding to the attribute names. The name code comparison table is a data table for reflecting the association relationship between different attribute names and their corresponding attribute codes.

Table-name code comparison table

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The target coding rule corresponding to the attribute codes is a rule for coding the sensor data corresponding to the attribute codes. As an example, the sensor data includes, but is not limited to, freshness sensing data and environmental sensing data.

As an example, in step S504, after determining the attribute code corresponding to a certain sensor identifier, the server may employ a target coding rule corresponding to the attribute code to code the sensor data corresponding to the sensor identifier, so as to obtain an original code value corresponding to the attribute code. In the example, the sensor data is encoded by adopting the target encoding rule corresponding to the attribute encoding, so that the sensor data is converted into the original encoding value of the unified standard, and the information such as time, temperature, humidity, bacterial colony and the like is converted into the standard value, thereby not only ensuring the data security, but also facilitating the standard management.

As an example, in step S505, the server may sort and combine all attribute codes and their corresponding original code values based on a preset target code rule, obtain actual measurement sensing information corresponding to the fresh product, and store the actual measurement sensing information in the wireless cloud sensor network, so as to determine, when the user triggers the state query instruction, the actual measurement sensing information closest to the current moment as the current sensing information.

In this embodiment, since the tracing of the fresh product is composed of a series of links such as picking, processing, transporting, warehousing, storing and selling, and the like, after the product characteristics of the fresh product and the flow operation of the transporting link are analyzed, each attribute name and attribute code corresponding to the fresh product are determined, so that the sensor data collected by a sensor corresponding to each attribute code provides powerful support for the subsequent fresh product freshness analysis and visualization processing.

In an embodiment, as shown in fig. 6, step S203, that is, analyzing the freshness sensing data by using a product freshness analysis model, obtains a freshness score corresponding to a fresh product, includes:

s601: analyzing the current monitoring image and the original monitoring image corresponding to the fresh product by adopting a color analysis model to obtain a color analysis result corresponding to the fresh product;

s602: analyzing the current detected smell and the original detected smell corresponding to the fresh product by adopting a smell analysis model to obtain a smell analysis result corresponding to the fresh product;

s603: analyzing the current colony information and the original colony information corresponding to the fresh product by adopting a colony analysis model to obtain a colony analysis result corresponding to the fresh product;

S604: and obtaining the freshness score corresponding to the fresh product according to the color analysis result, the smell analysis result and the colony analysis result corresponding to the fresh product.

Wherein the color analysis model is a pre-trained model for analyzing freshness based on color characteristics. The current monitoring image is an image monitored at the current moment. The original monitoring image refers to an image monitored when the fresh product is taken out of the warehouse, and the original monitoring image can be understood as an image shot at the initial moment of fresh product transmission.

In step S601, the server may acquire a current monitoring image and an original monitoring image corresponding to the fresh product acquired by the monitoring camera, and then invoke a pre-trained color analysis model to analyze the current monitoring image and the original monitoring image corresponding to the fresh product, and determine an output result of the color analysis model as a color analysis result corresponding to the fresh product. Generally, the color of the fresh product is related to the freshness thereof, in the training process, a monitoring image corresponding to the initial time to the complete decay time can be continuously acquired, the freshness index at the initial time is determined to be 1, the freshness index at the complete decay time is determined to be 0, and the freshness index corresponding to each acquisition time is determined based on the time difference between the initial time and the complete decay time and the difference between the freshness indexes; then, the freshness mark corresponding to each acquisition time and the corresponding existing monitoring image are used as color training samples, the color training samples are adopted, and a color analysis model is trained, so that the trained color analysis model can analyze the current monitoring image and the original monitoring image, and color analysis results with the numerical value range of 0-1 are obtained. In this example, the color analysis results corresponding to the color feature dimensions may be quickly determined based on a pre-trained color analysis model.

Wherein the odor analysis model is a pre-trained model for analyzing freshness based on odor characteristics. The current detected smell refers to the smell monitored at the current moment. The original detected smell refers to a smell detected when the fresh product is taken out of the warehouse, and the original detected smell can be understood as a smell detected at the initial time of the fresh product transportation. In this example, since the different fresh products have different odors when they decay, for example, oils and fats and fresh products containing more oils, they can generate a rancid odor of aldehydes, ketones, alcohols, acids, etc. when they decay; the high-protein food is easy to be polluted by microorganisms and to be spoiled, and when the high-protein food is rotten, the high-protein food can generate the bad smell such as indole, sulfide, mercaptan, skatole, cadaverine, aldehydes, ketones, bacterial toxins and the like; the food with high carbohydrate can be decomposed under the action of microorganism to generate monosaccharide, disaccharide, organic acid, alcohol and aldehyde substances, and sour or wine smell can be generated; when the aquatic dry goods decay, obvious ammonia smell can be generated, so that the type of gas to be detected can be determined based on the type of the product corresponding to the fresh product, and whether the corresponding fresh product decays or not can be detected based on different gas types.

As an example, in step S602, the server may obtain the current detected smell and the original detected smell corresponding to the fresh product collected by the smell detector, and then call a pre-trained smell analysis model to analyze the current detected smell and the original detected smell corresponding to the fresh product, and determine the output result of the smell analysis model as the smell analysis result corresponding to the fresh product. Generally, the smell of fresh products is related to the freshness thereof, in the training process, the detected smell concentration corresponding to the initial time to the complete decay time can be continuously collected, the freshness index at the initial time is determined to be 1, the freshness index at the complete decay time is determined to be 0, and the freshness index corresponding to each collection time is determined based on the time difference between the initial time and the complete decay time and the difference between the freshness indexes; then, taking the freshness mark corresponding to each acquisition time and the corresponding existing detected smell as a smell training sample, and training a smell analysis model by adopting the smell training sample, so that the trained smell analysis model can analyze the current detected smell and the original detected smell to obtain a smell analysis result with the numerical range of 0-1. In this example, the odor analysis results corresponding to the odor feature dimensions may be quickly determined based on a pre-trained odor analysis model.

Wherein the colony analysis model is a pre-trained model for analyzing freshness based on colony characteristics. The current colony information refers to the colony information monitored at the current moment. The original colony information refers to colony information monitored when the fresh product is taken out of the warehouse, and the original colony information can be understood as colony information detected at the initial moment of fresh product transmission. In this example, since colonies generated when different fresh products decay are different, colony information formed is different, for example, eggs and meats are mainly contaminated with eggs and meats, staphylococcus aureus is contaminated with meats and milk, vibrio parahaemolyticus is contaminated with marine products and shellfish, bacillus cereus is contaminated with residual rice, and thus, the type of colony to be detected can be determined based on the type of product corresponding to the fresh product, so that whether the corresponding fresh product decays or not can be detected based on the colony information of different colony types.

As an example, in step S603, the server may obtain the current colony information and the original colony information corresponding to the fresh product collected by the bacterial distribution detection test, call a pre-trained colony analysis model to analyze the current colony information and the original colony information corresponding to the fresh product, and determine the output result of the colony analysis model as the colony analysis result corresponding to the fresh product. Generally, colony information of fresh products is related to the freshness thereof, colony information corresponding to an initial time to a complete decay time can be continuously collected in a training process, a freshness index at the initial time is determined to be 1, a freshness index at the complete decay time is determined to be 0, and the freshness index corresponding to each collection time is determined based on a time difference between the initial time and the complete decay time and a difference between the freshness indexes; then, the freshness mark corresponding to each collection time and the corresponding existing colony information are used as colony training samples, and the colony training samples are adopted to train a colony analysis model, so that the trained colony analysis model can analyze the current colony information and the original colony information, and a colony analysis result with the numerical range of 0-1 is obtained. In this example, colony analysis results corresponding to colony feature dimensions can be quickly determined based on a pre-trained colony analysis model.

As an example, in step S604, after obtaining the color analysis result, the smell analysis result, and the colony analysis result with the data range of 0-1, the server may perform weighted calculation on the color analysis result, the smell analysis result, and the colony analysis result based on a preset weighting algorithm, to obtain the freshness score corresponding to the fresh product, where the freshness score comprehensively considers the freshness degree of the color feature dimension, the smell feature dimension, and the colony feature dimension, and helps to ensure the objectivity and accuracy of the freshness score corresponding to the evaluated fresh product.

In the embodiment, the freshness of the fresh product is estimated from the color feature dimension, the smell feature dimension and the colony feature dimension respectively; and then carrying out weighted calculation on the color analysis result, the smell analysis result and the colony analysis result to obtain the freshness score corresponding to the fresh product so as to ensure the objectivity and the accuracy of the freshness score.

In an embodiment, the current sensing information further includes a current acquisition time and an original acquisition time;

as shown in fig. 7, obtaining a target suggestion corresponding to a fresh product according to a freshness score corresponding to the fresh product includes:

S701: acquiring the residual life cycle corresponding to the fresh product according to the freshness score, the current acquisition time and the original acquisition time corresponding to the fresh product;

s702: and acquiring target suggestions corresponding to the fresh products according to the residual life cycles corresponding to the fresh products.

The current acquisition time refers to the acquisition time closest to the current moment, and is the time of the current induction information acquired latest. The original collection time refers to the corresponding collection time when the fresh product is delivered.

The residual life cycle refers to the time from the current moment to the complete decay of the fresh product, and particularly the residual life cycle of the fresh product under the condition that the current environment is kept unchanged, for example, under the condition that the environment variable is kept unchanged by means of guaranteeing the whole transportation chain process to be closed, low temperature, sterile and the like.

As an example, in step S701, after the server uses the product freshness analysis model to analyze the freshness sensing data and determine the corresponding freshness score, the server may call the preset life cycle analysis logic to analyze the input parameters such as the freshness score, the current collection time, the original collection time, etc. to obtain the remaining life cycle corresponding to the fresh product, so as to determine the remaining time from which the fresh product is completely rotten under the condition that the environmental variable is unchanged. Understandably, the corresponding residual life cycle is determined according to the freshness score, the current collection time and the original collection time corresponding to the fresh product, so that the time of complete decay of the life product distance can be intuitively reflected.

In an example, when a fresh product is in an environment with a constant variable and the life cycle curve corresponding to the fresh product is a linear curve, the collection time interval t1=t1-T0 may be determined according to the current collection time T1 and the original collection time T0, if the freshness score corresponding to the original collection time T0 is 1, the freshness score corresponding to the current collection time T1 is X, and if the fresh product is completely rotten, the freshness score corresponding to the fresh product is 0, and if a linear relationship exists, then

I.e. < ->

Wherein, T2 is the residual life cycle corresponding to fresh products.

In another example, when the current sensing information includes not only the current collection time and the original collection time, but also the environmental sensing data, the server may call a pre-trained life cycle analysis model to analyze and process input parameters such as freshness score, environmental sensing data, current collection time and original collection time, and determine an output result of the life cycle analysis model as a remaining life cycle corresponding to the fresh product. The life cycle analysis model can be a model for training a training sample comprising information such as freshness score, environment sensing data, current acquisition time, original acquisition time and the like by adopting a machine learning algorithm to determine algorithm parameters in the machine learning algorithm.

As an example, in step S702, after obtaining the remaining life cycle corresponding to the fresh product, the server may give a corresponding target suggestion based on the remaining life cycle, so that the user may perform a corresponding operation according to the remaining life cycle corresponding to the fresh product. For example, when the remaining life cycle corresponding to a fresh product is smaller than a preset critical cycle, the remaining life cycle corresponding to the fresh product is determined to be shorter, so that the seller can be prompted to perform prompt operation or upgrade the fresh-keeping measure, or the buyer can be prompted to evaluate whether the fresh product can be consumed in the remaining life cycle based on the remaining life cycle, and then determine whether to purchase the fresh product.

In this embodiment, the corresponding remaining life cycle is determined according to the freshness score, the current collection time and the original collection time corresponding to the fresh product, so as to obtain the target suggestion corresponding to the remaining life cycle, so that the target suggestion is more suitable for the life cycle of the fresh product, and meets the requirements of users. Further, the server may control the client to visually display not only the freshness score and target advice, but also the remaining life cycle to show the freshness of the fresh product from more dimensions.

In an embodiment, as shown in fig. 8, in step S702, a target suggestion corresponding to a fresh product is obtained according to a remaining life cycle corresponding to the fresh product:

s801: acquiring a user role corresponding to the state query instruction;

s802: if the user role is a buyer role, acquiring diet suggestions corresponding to the fresh products based on the residual life cycles corresponding to the fresh products;

s803: and if the user role is a seller role, acquiring a fresh-keeping suggestion corresponding to the fresh product based on the residual life cycle corresponding to the fresh product.

Wherein, the buyer role refers to the role of purchasing fresh products. The vendor role is a role in indicating the sale of fresh products, including but not limited to, during storage and shipping of fresh products.

As an example, in step S801, after receiving the status query instruction sent by the client, the server may identify a user role corresponding to the status query instruction, that is, determine whether the user triggering the status query instruction is a buyer role or a seller role, so as to determine a corresponding target suggestion according to the user role.

As an example, in step S802, after determining that the user role is the buyer role, the server may determine that the user who triggers the status query instruction is the purchasing user, and may determine that the purchasing user queries the freshness corresponding to the fresh product, mainly for guaranteeing the eating requirement, and at this time, may query the cloud database or the internet based on the remaining life cycle corresponding to the fresh product to obtain the diet suggestion corresponding to the fresh product, so as to improve the user satisfaction and the user viscosity of the buyer role.

As an example, in step S803, after determining that the user role is the vendor role, the server may determine that the user triggering the status query instruction is the vendor user, that is, the user in the storage and transportation process, and may determine that the vendor user queries the freshness degree corresponding to the fresh product, mainly to see whether the freshness requirement is met, at this time, based on the remaining life cycle corresponding to the fresh product, the freshness suggestion corresponding to the fresh product may be obtained, so as to delay the product decay time or improve the freshness degree of the product.

In the embodiment, according to the corresponding freshness score, the current acquisition time and the original acquisition time of the fresh product, the corresponding residual life cycle is determined, so that the freshness score is converted into the residual life cycle of the time dimension, and the freshness degree of the fresh product is more intuitively reflected; and according to the residual life cycle and the user role corresponding to the fresh product, acquiring the corresponding target suggestion, and visually displaying the residual life cycle and the target suggestion, thereby being beneficial to improving the user satisfaction and the user viscosity, or delaying the product decay time or improving the product freshness.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.

In an embodiment, a fresh product information processing apparatus is provided, and the fresh product information processing apparatus corresponds to the fresh product information processing method in the above embodiment one by one. As shown in fig. 9, the fresh product information processing apparatus includes a current sensing information acquisition module 901, a current sensing information decoding module 902, a freshness score acquisition module 903, a target suggestion acquisition module 904, and a visual display module 905. The functional modules are described in detail as follows:

the current induction information acquisition module 901 is used for responding to the state query instruction and acquiring current induction information corresponding to the fresh product from the wireless cloud sensor network;

the current induction information decoding module 902 is configured to decode current induction information corresponding to a fresh product, and obtain freshness induction data corresponding to the fresh product;

the freshness score obtaining module 903 is configured to analyze the freshness sensing data by using a product freshness analysis model, and obtain a freshness score corresponding to a fresh product;

the target suggestion obtaining module 904 is configured to obtain a target suggestion corresponding to the fresh product according to the freshness score corresponding to the fresh product;

the visual display module 905 is configured to visually display the freshness score and the target suggestion corresponding to the fresh product.

In an embodiment, the current sensing information decoding module 902 is configured to decode current sensing information corresponding to a fresh product, and obtain freshness sensing data and environment sensing data corresponding to the fresh product;

the visual display module 905 is configured to obtain an environmental change trend graph corresponding to the fresh product according to the environmental sensing data corresponding to the fresh product, and visually display a freshness score, a target suggestion, and an environmental change trend graph corresponding to the fresh product.

In one embodiment, the current sense information decoding module 902 includes:

the current induction information extraction unit is used for extracting current induction information corresponding to the fresh product based on the target coding sequence to obtain a plurality of attribute codes and original coding values corresponding to the attribute codes;

and the original coding value decoding unit is used for decoding the original coding value corresponding to the attribute code by adopting a target decoding rule corresponding to the attribute code to obtain freshness sensing data and environment sensing data corresponding to the fresh product.

In one embodiment, the fresh product information processing apparatus further includes:

the actual measurement sensing data acquisition unit is used for acquiring actual measurement sensing data corresponding to the fresh product, wherein the actual measurement sensing data comprises a sensor identifier and sensor data corresponding to the sensor identifier;

A position type determining unit, configured to determine a sensor position and a sensor type corresponding to the sensor identifier based on the sensor identifier;

the attribute code determining unit is used for determining the attribute code corresponding to the sensor identifier according to the sensor position and the sensor type corresponding to the sensor identifier;

the original code value acquisition unit is used for encoding the sensor data corresponding to the sensor identifier by adopting a target encoding rule corresponding to the attribute code to acquire an original code value corresponding to the attribute code;

and the actually-measured induction information storage unit is used for combining the attribute codes and the original code values based on the target coding sequence to obtain actually-measured induction information corresponding to the fresh product, and storing the actually-measured induction information in the wireless cloud sensor network.

In one embodiment, the freshness score acquisition module 903 comprises:

the color analysis result acquisition unit is used for analyzing the current monitoring image and the original monitoring image corresponding to the fresh product by adopting the color analysis model to acquire a color analysis result corresponding to the fresh product;

the odor analysis result acquisition unit is used for analyzing the current detected odor and the original detected odor corresponding to the fresh product by adopting an odor analysis model to acquire an odor analysis result corresponding to the fresh product;

The colony analysis result acquisition unit is used for analyzing the current colony information and the original colony information corresponding to the fresh product by adopting the colony analysis model to acquire a colony analysis result corresponding to the fresh product;

the freshness score obtaining unit is used for obtaining the freshness score corresponding to the fresh product according to the color analysis result, the smell analysis result and the colony analysis result corresponding to the fresh product.

the target suggestion acquisition module 904 includes:

the residual life cycle acquisition unit is used for acquiring the residual life cycle corresponding to the fresh product according to the freshness score, the current acquisition time and the original acquisition time corresponding to the fresh product;

the target suggestion acquisition unit is used for acquiring target suggestions corresponding to the fresh products according to the residual life cycles corresponding to the fresh products.

In an embodiment, the target advice acquisition unit comprises:

the user role acquisition subunit is used for acquiring the user roles corresponding to the state query instructions;

the dietary advice acquisition subunit is used for acquiring the dietary advice corresponding to the fresh product based on the residual life cycle corresponding to the fresh product if the user role is a buyer role;

And the diet suggestion acquisition subunit is used for acquiring the fresh-keeping suggestion corresponding to the fresh product based on the residual life cycle corresponding to the fresh product if the user role is the seller role.

The specific limitation of the fresh product information processing apparatus may be referred to as limitation of the fresh product information processing method hereinabove, and will not be described herein. The above-described respective modules in the fresh product information processing apparatus may be realized in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 10. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing data adopted or generated in the process of executing the fresh product information processing method. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method for processing information of fresh produce.

In an embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the computer program to implement the method for processing information of fresh products in the above embodiment, such as S201-S205 shown in fig. 2 or S201-S205 shown in fig. 3-8, and is not repeated here. Alternatively, the processor may implement the functions of the modules/units in this embodiment of the fresh product information processing apparatus when executing the computer program, for example, the functions of the current sensing information acquisition module 901, the current sensing information decoding module 902, the freshness score acquisition module 903, the target suggestion acquisition module 904, and the visual display module 905 shown in fig. 9, which are not repeated here.

In an embodiment, a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the method for processing information of fresh products in the above embodiment is implemented, for example, S201-S205 shown in fig. 2, or S201-S205 shown in fig. 8, which are not repeated herein. Alternatively, the computer program when executed by the processor implements the functions of each module/unit in the embodiment of the fresh product information processing apparatus, for example, the functions of the current sensing information acquisition module 901, the current sensing information decoding module 902, the freshness score acquisition module 903, the target suggestion acquisition module 904, and the visual display module 905 shown in fig. 9, which are not repeated here.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.

The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.

Claims

1. A method for processing information of fresh products, comprising:

2. The method of claim 1, wherein decoding the current sensing information corresponding to the fresh product to obtain the freshness sensing data corresponding to the fresh product comprises:

decoding the current induction information corresponding to the fresh product to obtain freshness induction data and environment induction data corresponding to the fresh product;

the visual display of the freshness score and the target suggestion corresponding to the fresh product comprises the following steps:

acquiring an environment change trend graph corresponding to the fresh product according to the environment induction data corresponding to the fresh product;

and visually displaying the freshness score, the target suggestion and the environment change trend graph corresponding to the fresh product.

3. The method for processing information of fresh products according to claim 2, wherein decoding the current sensing information corresponding to the fresh products to obtain freshness sensing data and environment sensing data corresponding to the fresh products comprises:

extracting current induction information corresponding to the fresh product based on a target coding sequence to obtain a plurality of attribute codes and original coding values corresponding to the attribute codes;

and decoding the original coding value corresponding to the attribute code by adopting a target decoding rule corresponding to the attribute code to obtain the freshness sensing data and the environment sensing data corresponding to the fresh product.

4. The method of claim 1, wherein before the responding to the status query command and obtaining the current sensing information corresponding to the fresh product from the wireless cloud sensor network, the method further comprises:

obtaining actual measurement sensing data corresponding to fresh products, wherein the actual measurement sensing data comprises a sensor identifier and sensor data corresponding to the sensor identifier;

determining a sensor position and a sensor type corresponding to the sensor identifier based on the sensor identifier;

Determining an attribute code corresponding to the sensor identifier according to the sensor position and the sensor type corresponding to the sensor identifier;

coding the sensor data corresponding to the sensor identifier by adopting a target coding rule corresponding to the attribute code, and obtaining an original coding value corresponding to the attribute code;

based on a target coding sequence, combining the attribute codes and the original coding values to obtain actual measurement induction information corresponding to the fresh products, and storing the actual measurement induction information in the wireless cloud sensing network.

5. The method of claim 1, wherein the analyzing the freshness sensing data using a product freshness analysis model to obtain a freshness score corresponding to the fresh product comprises:

analyzing the current monitoring image and the original monitoring image corresponding to the fresh product by adopting a color analysis model to obtain a color analysis result corresponding to the fresh product;

analyzing the current detected smell and the original detected smell corresponding to the fresh product by adopting a smell analysis model to obtain a smell analysis result corresponding to the fresh product;

Analyzing the current colony information and the original colony information corresponding to the fresh product by adopting a colony analysis model to obtain a colony analysis result corresponding to the fresh product;

and obtaining the freshness score corresponding to the fresh product according to the color analysis result, the smell analysis result and the colony analysis result corresponding to the fresh product.

6. The method for processing fresh product information according to claim 1, wherein the current sensing information further comprises a current acquisition time and an original acquisition time;

the obtaining the target suggestion corresponding to the fresh product according to the freshness score corresponding to the fresh product comprises the following steps:

obtaining a residual life cycle corresponding to the fresh product according to the freshness score, the current acquisition time and the original acquisition time corresponding to the fresh product;

and acquiring target suggestions corresponding to the fresh products according to the residual life cycles corresponding to the fresh products.

7. The method for processing information of fresh products according to claim 1, wherein the step of obtaining the target advice corresponding to the fresh products according to the remaining life cycle corresponding to the fresh products comprises:

Acquiring a user role corresponding to the state query instruction;

if the user role is a buyer role, acquiring diet suggestions corresponding to the fresh products based on the residual life cycles corresponding to the fresh products;

and if the user role is a seller role, acquiring a fresh-keeping suggestion corresponding to the fresh product based on the residual life cycle corresponding to the fresh product.

8. An information processing apparatus for fresh produce, comprising:

9. Computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the fresh product information processing method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the fresh product information processing method according to any one of claims 1 to 7.