Agricultural product online and offline traceability marketing system and method

Abstract

The invention relates to the technical field of product marketing, in particular to an agricultural product online and offline traceability marketing system and method, and the system comprises a quality monitoring unit which is used for monitoring the environment data of a target agricultural product in real time, and determining the quality attenuation coefficient of the target agricultural product according to the environment data; the product traceability unit is used for generating a traceability code according to the quality attenuation coefficient and the spatio-temporal information of the target agricultural product; and the code scanning and recording unit is used for printing the traceability code on a package of a target agricultural product. The environment data of the target agricultural product is monitored in real time and the traceability code is generated, a consumer can obtain detailed information of the agricultural product by scanning the code, the detailed information comprises the production environment, quality control, attenuation condition and the like, and the transparent traceability information enhances the trust of the consumer on the product quality and improves the product quality. Especially under the background of increasing attention to food safety and agricultural product sources.

Description

Technical Field

[0001] The present invention relates to the technical field of product marketing, and particularly relates to an online and offline traceability marketing system and method for agricultural products. Background Art

[0002] Traditional systems often lack real-time and transparent traceability functions for agricultural products. Consumers have insufficient understanding of information such as the production environment, quality control, and decay situation of agricultural products, and cannot obtain detailed information through scanning codes or other means. This makes it difficult for consumers to make more rational purchase decisions and may result in a relatively low level of trust in product quality. Moreover, traditional systems usually promote products to all consumers in a single way, lacking real-time monitoring and analysis of consumer behavior. Traditional systems often cannot make dynamic adjustments and precise segmentation based on consumers' purchase intentions and historical behaviors, resulting in non-personalized marketing strategies and even potentially missing high-intent consumers. Additionally, traditional systems usually rely on experience or approximate sales data for inventory management and sales forecasting, lacking accurate prediction of consumer purchase behaviors and purchase probabilities. This makes it difficult for merchants to accurately predict which products will sell out quickly and which products have low demand, easily leading to inventory backlogs and increasing the risk of unsold or expired products. And in traditional systems, marketing strategies are often relatively extensive and cannot formulate personalized recommendation or preferential strategies according to each consumer's interests, needs, and purchase intentions. Lack of personalized marketing not only reduces consumers' sense of participation but may also lead to a decline in the purchase conversion rate. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the above-mentioned disadvantages of the prior art and provide an online and offline traceability marketing system and method for agricultural products.

[0004] The technical solution adopted to solve the above technical problem is: An online and offline traceability marketing system for agricultural products, comprising:

[0005] A quality monitoring unit, which is used to monitor the environmental data of the target agricultural product in real time and determine the quality decay coefficient of the target agricultural product according to the environmental data;

[0006] A product traceability unit, which is used to generate a traceability code according to the quality decay coefficient and the spatio-temporal information of the target agricultural product;

[0007] A code scanning record unit, which is used to print the traceability code on the packaging of the target agricultural product, deploy the traceability code to an online page, and obtain the code scanning record of consumers according to the packaging of the target agricultural product and the online page;

[0008] A purchase probability unit, which is used to predict the purchase probability of the consumer according to the scanning records of the consumer and the purchase conversion rate corresponding to each scanning;

[0009] An intention classification unit, which is used to classify the intentions of all consumers according to the purchase probability to obtain a first consumer set and a second consumer set;

[0010] A product marketing unit, which is used to implement a price penetration strategy for the first consumer set and implement an agricultural product value education strategy for the second consumer set.

[0011] Preferably, determining the quality decay coefficient of the target agricultural product according to the environmental data includes:

[0012] Calculating the dynamic spoilage rate of the target agricultural product according to the environmental data;

[0013] Determining the quality decay coefficient of the target agricultural product according to the dynamic spoilage rate of the target agricultural product.

[0014] Preferably, the calculation formula of the dynamic spoilage rate is as follows:

[0015] k = k0·exp(α(T - T opt ) + β(H - H opt )) 2 + γSigmoid(L - L th ));

[0016] Where, k represents the dynamic spoilage rate of the target agricultural product, k0 represents the basic spoilage rate, that is, the spoilage rate under standard environmental conditions, α, β and γ represent environmental sensitivity coefficients, respectively reflecting the influence of temperature, humidity and light changes on the spoilage rate, T, H and L represent the temperature data, humidity data and light data in the environmental data, T opt , H opt and L th represent the optimal storage conditions of the target agricultural product;

[0017] The calculation formula of the quality decay coefficient is as follows:

[0018]

[0019] Where, Q(t) represents the quality decay coefficient at the current sampling time t, Q0 represents the initial quality coefficient, t now represents the current time, t harvest represents the time when the target agricultural product is picked.

[0020] Preferably, generating a traceability code according to the quality decay coefficient and the spatio-temporal information of the target agricultural product includes:

[0021] Obtain the CRC16 check code of the mass attenuation coefficient, and generate a mass channel code according to the mass attenuation coefficient and the corresponding CRC16 check code;

[0022] Obtain the spatio-temporal information of the target agricultural product to generate a spatio-temporal channel code;

[0023] Perform dual-channel synthesis on the mass channel code and the spatio-temporal channel code to obtain a traceability code.

[0024] Preferably, the calculation formula of the mass channel code is as follows:

[0025]

[0026] where, Code Q represents the mass channel code, and CRC16(Q(t)) represents the CRC16 check code of the mass attenuation coefficient;

[0027] The calculation formula of the spatio-temporal channel code is as follows:

[0028]

[0029] where, Code ST represents the spatio-temporal channel code, Geohash(x,y,9) represents the encoding of geographical coordinates (x,y) as Geohash with a precision of 9 bits, represents the exclusive OR operation in bitwise operations, applied to the mixture of geographical location and timestamp, UnixTime(t now ,ms) represents converting the current time in milliseconds to the Unix time format;

[0030] The calculation formula of the traceability code is as follows: [[ID=3८]]

[0031] Code dual = Code Q || Code ST ;

[0032] where, Code dual represents the traceability code.

[0033] Preferably, predicting the purchase probability of the consumer according to the scanning record of the consumer and the purchase conversion rate corresponding to each scan includes:

[0034] Determine the attenuation rate according to the current time and the scanning time in the scanning record of the consumer;

[0035] Determine the trust value of the consumer according to the purchase conversion rate corresponding to each scan of the consumer and the attenuation rate;

[0036] Determine the social influence of the consumer according to the PageRank algorithm;

[0037] Predict the purchase probability of the consumer according to the quality decay coefficient, the trust value of the consumer, and the social influence of the consumer.

[0038] Preferably, the calculation formula of the decay rate is as follows:

[0039] ω(t) = exp(-λ(t now -t scan(i) ));

[0040] where ω(t) represents the decay rate at the current sampling time, t scan(i) represents the scanning time of the i-th scanning in the scanning record, and λ represents the decay factor;

[0041] The calculation formula of the trust value is as follows:

[0042]

[0043] where Trust(u) represents the trust value of the consumer, n represents the number of scans in the scanning record, and Cr(i) represents the purchase conversion rate of the i-th scan in the scanning record; [[ID=2,9]]

[0044] The calculation formula of the social influence is as follows:

[0045]

[0046] where Social(u) represents the social influence of the consumer, d represents the damping coefficient, N represents the total number of consumers, B u represents the set of friends pointing to consumer u, and L(v) represents the out-degree of consumer u, that is, the number of people he follows;

[0047] The prediction formula of the purchase probability is as follows:

[0048]

[0049] where P buy (u) represents the purchase probability of the consumer, a1, a2, and a3 represent preset weight coefficients, and a1 + a2 + a3 = 1, and σ represents the Sigmoid function, which is used to compress the purchase probability into the [0, 1] interval.

[0050] Preferably, classify all consumers according to the purchase probability to obtain a first consumer set and a second consumer set, including:

[0051] Compare the purchase probability with a preset first intention threshold and the preset second intention threshold;

[0052] If the purchase probability is greater than the preset first intention threshold, add the consumers corresponding to the purchase probability to the empty first consumer set;

[0053] If the purchase probability is less than the preset second intention threshold, add the consumers corresponding to the purchase probability to the empty second consumer set;

[0054] Repeat the above operations until all consumers are processed to obtain the first consumer set and the second consumer set, where the first consumer set represents the set of consumers with high purchase intention, and the second consumer set represents the set of consumers with low purchase intention.

[0055] Preferably, implement a price penetration strategy for the first consumer set and an agricultural product value education strategy for the second consumer set, including:

[0056] Determine a dynamically adjusted price according to the average purchase probability of the consumers in the first consumer set, where the calculation formula for the dynamically adjusted price is as follows:

[0057]

[0058] where p represents the dynamically adjusted price, represents the average purchase probability of the consumers in the first consumer set, p base represents the benchmark price of the target agricultural product;

[0059] Implement a price penetration strategy for the first consumer set according to the dynamically adjusted price;

[0060] Construct agricultural product value education information based on the environmental data and the farming information of the target agricultural product, and implement an agricultural product value education strategy for the second consumer set according to the agricultural product value education information.

[0061] The technical solution adopted to solve the above technical problems is: An online and offline traceability marketing method for agricultural products, which is applicable to the above-mentioned online and offline traceability marketing system for agricultural products, including:

[0062] Real-time monitor the environmental data of the target agricultural product, and determine the quality decay coefficient of the target agricultural product according to the environmental data;

[0063] Generate a traceability code according to the quality decay coefficient and the spatio-temporal information of the target agricultural product;

[0064] Print the traceability code on the packaging of the target agricultural product, deploy the traceability code to the online page, and obtain the scanning records of consumers according to the packaging of the target agricultural product and the online page;

[0065] Predict the purchase probability of the consumer based on the consumer's code scanning records and the purchase conversion rate corresponding to each code scanning.

[0066] Classify the intentions of all consumers according to the purchase probability to obtain a first consumer set and a second consumer set.

[0067] Implement a price penetration strategy for the first consumer set and an agricultural product value education strategy for the second consumer set.

[0068] The beneficial effects of the present invention are as follows: (1) By monitoring the environmental data of the target agricultural product in real time and generating a traceability code, consumers can obtain detailed information about the agricultural product through code scanning, including its production environment, quality control, attenuation situation, etc. The transparent traceability information enhances consumers' trust in product quality, especially in the context of the increasing attention to food safety and the origin of agricultural products. Moreover, by dynamically adjusting according to real-time environmental data and consumers' code scanning records, the purchase probability of consumers can be accurately predicted. Based on this data, merchants can quickly identify and segment consumer groups, and thus implement more precise marketing strategies for different groups. The first consumer set can implement a price penetration strategy, while the second consumer set can improve their awareness and interest in the product through an agricultural product value education strategy. (2) The present invention predicts the purchase probability based on consumers' code scanning records and purchase conversion rates, providing accurate sales predictions for merchants. In this way, merchants can better predict which products will be sold quickly and which products have low demand, thereby optimizing inventory management and reducing the risk of slow-moving and expired products. And by dividing consumers into a first consumer set and a second consumer set according to the purchase probability, different marketing strategies can be adopted. High-intent consumers can be attracted through a price penetration strategy to quickly increase market share; for consumers with lower purchase intentions, the additional value of agricultural products can be shown through educational content to increase their interest in the product, thereby promoting sales. (3) By continuously tracking consumers' code scanning records and the purchase conversion rate after each code scanning, the system can continuously optimize marketing strategies, and the personalized marketing strategies that improve the conversion rate enable the needs of each consumer to be accurately matched, thereby increasing the sales conversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a schematic diagram of the system architecture of the overall system in an embodiment proposed by the present invention;

[0070] Figure 2 It is a schematic diagram of the step flow of the overall method in an embodiment proposed by the present invention.

[0071] Reference numerals: 1, quality monitoring unit; 2, product traceability unit; 3, code scanning and recording unit; 4, purchase probability unit; 5, intention classification unit; 6, product marketing unit. Detailed implementation

[0072] Example 1, as Figure 1 shown, an online and offline traceability marketing system for agricultural products proposed by the present invention includes:

[0073] Quality monitoring unit 1, which is used to monitor the environmental data of the target agricultural product in real time and determine the quality decay coefficient of the target agricultural product according to the environmental data;

[0074] Product traceability unit 2, which is used to generate a traceability code according to the quality decay coefficient and the spatio-temporal information of the target agricultural product;

[0075] Code scanning and recording unit 3, which is used to print the traceability code on the packaging of the target agricultural product, deploy the traceability code to the online page, and obtain the code scanning record of the consumer according to the packaging of the target agricultural product and the online page;

[0076] Purchase probability unit 4, which is used to predict the purchase probability of the consumer according to the code scanning record of the consumer and the purchase conversion rate corresponding to each code scanning;

[0077] Intention classification unit 5, which is used to classify the intentions of all consumers according to the purchase probability to obtain a first consumer set and a second consumer set;

[0078] Product marketing unit 6, which is used to implement a price penetration strategy for the first consumer set and implement an agricultural product value education strategy for the second consumer set.

[0079] In the present invention, the mass attenuation coefficient refers to the rate or degree of quality decline of agricultural products during storage and transportation due to environmental changes (such as temperature, humidity, etc.). The mass attenuation coefficient helps monitor and evaluate the change in product quality, so as to give early warnings and ensure that the product still maintains the best quality when sold; the traceability code is a unique identifier generated by technical means (such as two-dimensional code, RFID tag, etc.), which is used to track and record detailed information of the whole process of the product's origin, production, processing, transportation, etc. It allows consumers to understand the detailed background and history of agricultural products by scanning the traceability code; the spatio-temporal information includes the time and space data of agricultural products, such as production time, picking time, transportation route, storage location, etc. These information are important bases for generating the traceability code; the purchase conversion rate refers to the proportion of consumers who actually make purchases among those who scan the traceability code within a specific time period. For example, if 100 consumers scan the code and 10 of them finally purchase the product, then the purchase conversion rate is 10%; the purchase probability is that based on the consumer's code-scanning records and their behaviors (such as the number of code scans, code-scanning time, etc.), the system can predict whether a certain consumer is likely to make a purchase. The purchase probability is a predicted value obtained based on historical data and consumer behavior analysis; the intention classification refers to grouping consumers according to their purchase intentions, usually divided into different consumer sets. Through the purchase probability, consumers can be divided into a high-purchase-intention group (the first consumer set) and a low-purchase-intention group (the second consumer set). This classification helps formulate more precise marketing strategies; the price penetration strategy refers to attracting consumers to purchase products through a lower price; the agricultural product value education strategy aims to educate consumers to understand the value of agricultural products.

[0080] Example 2, an online and offline traceability marketing system for agricultural products proposed by the present invention. Compared with Example 1, this example further includes: determining the mass attenuation coefficient of the target agricultural product according to environmental data, including:

[0081] Calculating the dynamic spoilage rate of the target agricultural product according to environmental data;

[0082] Determining the mass attenuation coefficient of the target agricultural product according to the dynamic spoilage rate of the target agricultural product.

[0083] In this example, the dynamic spoilage rate refers to the speed at which the quality of agricultural products decays over time during storage, transportation or sales due to changes in environmental factors such as microbial activity, temperature, humidity, etc. The spoilage rate is not a fixed value, but changes with environmental conditions (such as temperature, humidity, oxygen concentration, etc.), so it is called the dynamic spoilage rate.

[0084] In an optional example, the calculation formula of the dynamic spoilage rate is as follows:

[0085] k = k0·exp(α(T - Topt ) + β(H - H opt ) 2 + γSigmoid(L - L th ))

[0086] Where k represents the dynamic spoilage rate of the target agricultural product, k0 represents the basic spoilage rate, i.e., the spoilage rate under standard environmental conditions, α, β, and γ represent environmental sensitivity coefficients, respectively reflecting the impacts of temperature, humidity, and light changes on the spoilage rate, T, H, and L represent the temperature data, humidity data, and light data in the environmental data, T opt , H opt and L th represent the optimal storage conditions of the target agricultural product

[0087] The calculation formula for the mass decay coefficient is as follows

[0088]

[0089] Where Q(t) represents the mass decay coefficient at the current sampling time t, Q0 represents the initial mass coefficient, t now represents the current time, t harvest represents the time when the target agricultural product is picked

[0090] In an optional embodiment, generating a traceability code based on the mass decay coefficient and the spatio-temporal information of the target agricultural product includes

[0091] Obtaining the CRC16 check code of the mass decay coefficient, and generating a mass channel code based on the mass decay coefficient and the corresponding CRC16 check code

[0092] Obtaining the spatio-temporal information of the target agricultural product to generate a spatio-temporal channel code

[0093] Performing dual-channel synthesis on the mass channel code and the spatio-temporal channel code to obtain the traceability code

[0094] It should be noted that CRC (Cyclic Redundancy Check) is a common error detection coding method, usually used to ensure the integrity in data transmission. CRC16 is a variant of CRC, which uses a 16-bit check code; Quality Channel Coding encodes the quality attenuation coefficient and its corresponding CRC16 check code to generate a unique identifier (or code), which can be used to trace the quality information of agricultural products. By combining the quality attenuation coefficient with the CRC16 check code, a unique Quality Channel Coding can be created to identify the quality status of specific agricultural products; Spatiotemporal Channel Coding encodes the spatiotemporal information of the target agricultural product to generate a unique identifier, which contains information about the time and spatial location of the agricultural product at different stages. Spatiotemporal Channel Coding can provide detailed traceability information about the agricultural product, helping users understand the product's transfer path, production background, and historical information.

[0095] In an optional embodiment, the calculation formula for Quality Channel Coding is as follows:

[0096]

[0097] where Code Q represents the Quality Channel Coding, and CRC16(Q(t)) represents the CRC16 check code of the quality attenuation coefficient;

[0098] The calculation formula for Spatiotemporal Channel Coding is as follows:

[0099]

[0100] where Code ST represents the Spatiotemporal Channel Coding, Geohash(x,y,9) represents encoding the geographical coordinates (x,y) into Geohash with a precision of 9 bits, represents the exclusive OR operation in bitwise operations, applied to the mixture of geographical location and timestamp, and UnixTime(t now ,ms) represents converting the current time in milliseconds to the Unix time format;

[0101] The calculation formula for the traceability code is as follows:

[0102] Code dual =Code [[ID=:35]] Q ||Code ST ;

[0103] where Code dual represents the traceability code.

[0104] In an optional embodiment, predicting the purchase probability of consumers based on the consumers' code scanning records and the purchase conversion rate corresponding to each code scan includes:

[0105] Determine the attenuation rate based on the current time and the scanning time in the consumer's scanning record;

[0106] Determine the consumer's trust value based on the purchase conversion rate and the attenuation rate corresponding to each consumer's scan;

[0107] Determine the consumer's social influence according to the PageRank algorithm;

[0108] Predict the consumer's purchase probability based on the quality decay coefficient, the consumer's trust value, and the consumer's social influence.

[0109] It should be noted that the consumer's trust value refers to the degree of trust of the consumer in a certain brand or product; in the social network, the PageRank algorithm can be used to measure the influence of an individual in the social network. By calculating the social connections and influence among consumers, the PageRank algorithm can assign a "social influence score" to each consumer, and this score reflects the potential influence of this consumer on the behavior of others in the social circle. For consumers with greater social influence, the products or brands recommended by them are more likely to affect the purchase decisions of other consumers.

[0110] In an optional embodiment, the calculation formula of the attenuation rate is as follows:

[0111] ω(t) = exp(-λ(t now -t scan (i)));

[0112] where ω(t) represents the attenuation rate at the current sampling time, t scan(i) represents the scanning time of the i-th scan in the scanning record, and λ represents the attenuation factor;

[0113] The calculation formula of the trust value is as follows:

[0114]

[0115] where Trust(u) represents the consumer's trust value, n represents the number of scans in the scanning record, and Cr(i) represents the purchase conversion rate of the i-th scan in the scanning record;

[0116] The calculation formula of the social influence is as follows:

[0117]

[0118] where Social(u) represents the consumer's social influence, d represents the damping coefficient, N represents the total number of consumers, B u represents the set of friends pointing to consumer u, and L(v) represents the out-degree of consumer u, that is, the number of people he follows;

[0119] The prediction formula for the purchase probability is as follows:

[0120]

[0121] Where P buy (u) represents the purchase probability of consumers, a1, a2, and a3 represent preset weight coefficients, and a1 + a2 + a3 = 1. σ represents the Sigmoid function, which is used to compress the purchase probability into the interval [0, 1].

[0122] In an optional embodiment, all consumers are classified by intention according to the purchase probability to obtain a first consumer set and a second consumer set, including:

[0123] Compare the purchase probability with a preset first intention threshold and a preset second intention threshold;

[0124] If the purchase probability is greater than the preset first intention threshold, add the consumer corresponding to the purchase probability to the empty first consumer set;

[0125] If the purchase probability is less than the preset second intention threshold, add the consumer corresponding to the purchase probability to the empty second consumer set;

[0126] Repeat the above operations until all consumers are processed to obtain a first consumer set and a second consumer set. Among them, the first consumer set represents the set of consumers with high purchase intention, and the second consumer set represents the set of consumers with low purchase intention.

[0127] It should be noted that the first intention threshold is a preset critical value used to distinguish consumers' purchase intention for products. It represents a standard with a relatively strong purchase intention. If a consumer's purchase probability is greater than this threshold, it is considered that the consumer shows a relatively high purchase intention and belongs to one of the target consumer groups. This threshold is preset based on factors such as historical data, market analysis, and marketing strategies to help the marketing team identify high-potential customer groups; the second intention threshold is another preset critical value used to distinguish consumers with relatively low purchase intention. Consumers with a purchase probability lower than this threshold are considered to have a relatively low purchase intention and are less likely to purchase the product. It also reflects consumers' purchase intention, but usually a relatively low intention.

[0128] In an optional embodiment, a price penetration strategy is implemented for the first consumer set, and an agricultural product value education strategy is implemented for the second consumer set, including:

[0129] Determine the dynamically adjusted price according to the average purchase probability of consumers in the first consumer set. The calculation formula for the dynamically adjusted price is as follows:

[0130]

[0131] Among them, p represents the dynamically adjusted price, represents the average purchase probability of consumers in the first consumer set, p base represents the benchmark price of the target agricultural product;

[0132] Implement a price penetration strategy for the first consumer set according to the dynamically adjusted price;

[0133] Construct agricultural product value education information based on environmental data and farming information of the target agricultural product, and implement an agricultural product value education strategy for the second consumer set according to the agricultural product value education information.

[0134] It should be noted that farming information usually includes various information related to the agricultural production process, such as planting time, weather conditions, fertilization situation, harvesting time, etc. These information are crucial for understanding the production background, quality assurance and supply cycle of agricultural products.

[0135] Example 3, as Figure 2 shown, a method for online and offline traceability marketing of agricultural products proposed by the present invention, which is applicable to the described online and offline traceability marketing system of agricultural products, includes:

[0136] S1. Real-time monitor the environmental data of the target agricultural product, and determine the quality decay coefficient of the target agricultural product according to the environmental data;

[0137] S2. Generate a traceability code according to the quality decay coefficient and the spatio-temporal information of the target agricultural product;

[0138] S3. Print the traceability code on the packaging of the target agricultural product, deploy the traceability code to the online page, and obtain the scanning records of consumers according to the packaging and online page of the target agricultural product;

[0139] S4. Predict the purchase probability of consumers according to the scanning records of consumers and the purchase conversion rate corresponding to each scan;

[0140] S5. Classify the intentions of all consumers according to the purchase probability to obtain a first consumer set and a second consumer set;

[0141] S6. Implement a price penetration strategy for the first consumer set and an agricultural product value education strategy for the second consumer set.

[0142] The above detailed description of the embodiments of the present invention is made in conjunction with the accompanying drawings. However, the present invention is not limited thereto. Without departing from the spirit of the present invention, various changes can be made within the scope of knowledge possessed by those skilled in the art.

Claims

1. An online and offline traceability marketing system for agricultural products, characterized by: include: A quality monitoring unit (1), the quality monitoring unit (1) is used to monitor the environmental data of the target agricultural product in real time, and determine the quality attenuation coefficient of the target agricultural product based on the environmental data; A product traceability unit (2), the product traceability unit (2) being used to generate a traceability code according to the mass attenuation coefficient and the spatiotemporal information of the target agricultural product; A code scanning and recording unit (3), the code scanning and recording unit (3) is used to print the traceability code on the packaging of the target agricultural product, deploy the traceability code to an online page, and obtain the consumer's code scanning record based on the packaging of the target agricultural product and the online page; A purchase probability unit (4), the purchase probability unit (4) is used to predict the purchase probability of the consumer based on the consumer's code scanning records and the purchase conversion rate corresponding to each code scanning; An intention classification unit (5), the intention classification unit (5) is used to classify the intentions of all consumers according to the purchase probability to obtain a first consumer set and a second consumer set; A product marketing unit (6) is used to implement a price penetration strategy for the first consumer group and an agricultural product value education strategy for the second consumer group.

2. The online and offline agricultural product traceability marketing system according to claim 1, characterized in that: Determining the mass attenuation coefficient of the target agricultural product according to the environmental data includes: Calculating the dynamic spoilage rate of the target agricultural product according to the environmental data; The mass attenuation coefficient of the target agricultural product is determined according to the dynamic spoilage rate of the target agricultural product.

3. The online and offline traceability marketing system for agricultural products according to claim 2 is characterized in that: The calculation formula of the dynamic corruption rate is as follows: k=k0·exp(α(T-T opt )+β(H-H opt ) 2 +γSigmoid(L-L th )); Among them, k represents the dynamic spoilage rate of the target agricultural product, k0 represents the basic spoilage rate, that is, the spoilage rate under standard environmental conditions, α, β and γ represent the environmental sensitivity coefficients, which reflect the impact of temperature, humidity and light changes on the spoilage rate, T, H and L represent the temperature data, humidity data and light data in the environmental data, T opt 、H opt and L th Indicates the optimal storage conditions for target agricultural products; The calculation formula of the mass attenuation coefficient is as follows: Among them, Q(t) represents the mass attenuation coefficient at the current sampling time t, Q0 represents the initial mass coefficient, t now Indicates the current time, t harvest Indicates the time when the target agricultural products are picked.

4. The online and offline traceability marketing system for agricultural products according to claim 3 is characterized in that: Generating a traceability code according to the mass attenuation coefficient and the spatiotemporal information of the target agricultural product, including: Obtaining a CRC16 check code of the quality attenuation coefficient, and generating a quality channel code according to the quality attenuation coefficient and the corresponding CRC16 check code; Acquiring the spatiotemporal information of the target agricultural product to generate a spatiotemporal channel code; The quality channel coding and the space-time channel coding are dual-channel synthesized to obtain a traceability code.

5. The online and offline agricultural product traceability marketing system according to claim 4 is characterized in that: The calculation formula of the quality channel coding is as follows: Among them, Code Q represents the quality channel coding, CRC16(Q(t)) represents the CRC16 checksum of the quality attenuation coefficient; The calculation formula of the space-time channel coding is as follows: Among them, Code ST Indicates spatiotemporal channel coding. Geohash(x,y,9) indicates that the geographic coordinates (x,y) are encoded as Geohash with a precision of 9 bits. Represents the bitwise exclusive OR operation, applied to a mixture of geographic location and timestamp, UnixTime(t now ,ms) means converting the current time in milliseconds to Unix time format; The calculation formula of the traceability code is as follows: Code dual =Code Q ||Code ST ; Among them, Code dual Indicates the traceability code.

6. The online and offline agricultural product traceability marketing system according to claim 5, characterized in that: Predicting the consumer's purchase probability based on the consumer's code scanning records and the purchase conversion rate corresponding to each code scanning includes: Determine the decay rate based on the current time and the scanning time in the scanning record of the consumer; Determine the consumer's trust value based on the purchase conversion rate corresponding to each scan of the code by the consumer and the decay rate; Determining the social influence of the consumer based on the PageRank algorithm; The purchase probability of the consumer is predicted according to the quality attenuation coefficient, the trust value of the consumer, and the social influence of the consumer.

7. The online and offline traceability marketing system for agricultural products according to claim 6, characterized in that: The calculation formula of the decay rate is as follows: ω(t)=exp(-λ(t now -t scan (i))); Among them, ω(t) represents the decay rate at the current sampling time, t scan (i) represents the scanning time of the i-th scanning in the scanning record, and λ represents the attenuation factor; The calculation formula of the trust value is as follows: Where Trust(u) represents the consumer's trust value, n represents the number of scans in the scan record, and Cr(i) represents the purchase conversion rate of the i-th scan in the scan record; The calculation formula for the social influence is as follows: Among them, Social(y) represents the social influence of consumers, d represents the damping coefficient, N represents the total number of consumers, B u represents the set of friends pointing to consumer u, L(v) represents the number of outgoing links of consumer u, that is, the number of people he follows; The prediction formula for the purchase probability is as follows: Among them, P buy (u) represents the consumer's purchase probability, a1, a2, and a3 represent preset weight coefficients, and a1+a2+a3=1. σ represents the Sigmoid function, which is used to compress the purchase probability to the interval [0,1].

8. The online and offline agricultural product traceability marketing system according to claim 7, characterized in that: All consumers are classified according to the purchase probability to obtain a first consumer set and a second consumer set, including: Comparing the purchase probability with a preset first intention threshold and a preset second intention threshold; If the purchase probability is greater than the preset first intention threshold, the consumer corresponding to the purchase probability is added to the empty first consumer set; If the purchase probability is less than the preset second intention threshold, the consumer corresponding to the purchase probability is added to the empty second consumer set; Repeat the above operation until all consumers are processed to obtain a first consumer set and a second consumer set, wherein the first consumer set represents a set of consumers with high purchase intention, and the second consumer set represents a set of consumers with low purchase intention.

9. The online and offline agricultural product traceability marketing system according to claim 8, characterized in that: Executing a price penetration strategy on the first consumer group and executing an agricultural product value education strategy on the second consumer group, including: The dynamically adjusted price is determined according to the average purchase probability of consumers in the first consumer set, wherein the calculation formula of the dynamically adjusted price is as follows: Among them, p represents the dynamic adjustment price, represents the average purchase probability of consumers in the first consumer set, p base Indicates the benchmark price of the target agricultural product; executing a price penetration strategy on the first set of consumers according to the dynamically adjusted price; Agricultural product value education information is constructed based on the environmental data and the farming information of the target agricultural product, and an agricultural product value education strategy is executed on the second consumer set based on the agricultural product value education information.

10. An online and offline traceability marketing method for agricultural products, applicable to an online and offline traceability marketing system for agricultural products according to any one of claim 9, characterized in that: include: monitoring environmental data of target agricultural products in real time, and determining a mass attenuation coefficient of the target agricultural products based on the environmental data; generating a traceability code according to the mass attenuation coefficient and the spatiotemporal information of the target agricultural product; Printing the traceability code on the packaging of the target agricultural product, deploying the traceability code on an online page, and obtaining consumers' code scanning records based on the packaging of the target agricultural product and the online page; Predicting the consumer's purchase probability based on the consumer's code scanning records and the purchase conversion rate corresponding to each code scanning; Classifying the intentions of all consumers according to the purchase probability to obtain a first consumer set and a second consumer set; A price penetration strategy is implemented for the first consumer group, and an agricultural product value education strategy is implemented for the second consumer group.

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