A food safety risk assessment method and system based on smart agricultural wholesale supervision

Abstract

The present invention discloses a food safety risk assessment method and system based on smart agricultural wholesale supervision, which relates to the field of risk assessment technology. The method formulates a safety assessment project for agricultural wholesale food, and constructs a food safety traceability chain based on the safety assessment project for agricultural wholesale food. The food safety traceability chain includes safety detection parameter types corresponding to food production links and food formation links; a cloud database is set up corresponding to the food safety traceability chain, and a safety egg is set up corresponding to the cloud database; safety detection parameters of food are collected based on the food safety traceability chain, and the safety detection parameters are stored in the cloud database; and the safety risk of food is assessed based on the safety detection parameters of food to obtain an assessment result. The present invention can ensure the subsequent preservation of food safety detection data, prevent malicious behavior from accessing and modifying the food safety detection data, ensure the accuracy of food safety assessment, and has a good food safety management effect.

Description

Technical Field

[0001] The present invention relates to the field of risk assessment technology, and in particular to a food safety risk assessment method and system based on smart agricultural wholesale supervision. Background Art

[0002] With improved living standards and shifting consumer attitudes, consumers are increasingly demanding food safety. They demand not only good taste and nutritious food, but also reliable and safe sources. Therefore, the food safety risk assessment methods for smart agricultural wholesale supervision need to be continuously improved and refined to meet consumers' high food safety expectations. While food safety is a key concern for the food processing industry, it is currently difficult to test and store food safety parameters collected by professional testing agencies during the food processing phase. This poses security issues for data storage, which can easily lead to inaccuracies in subsequent food safety assessments. Summary of the Invention

[0003] The purpose of the present invention is to provide a food safety risk assessment method and system based on smart agricultural wholesale supervision to address the shortcomings of the background technology.

[0004] In order to achieve the above objectives, the present invention provides the following technical solution: a food safety risk assessment method based on smart agricultural wholesale supervision, comprising the following steps:

[0005] Formulate a safety assessment program for agricultural wholesale foods and build a food safety traceability chain based on the safety assessment program for agricultural wholesale foods. The food safety traceability chain includes the types of safety testing parameters corresponding to the food production and food formation stages.

[0006] A cloud database is set up corresponding to the food safety traceability chain, and a security egg is set up corresponding to the cloud database, wherein the security egg includes multiple obfuscation ports and information areas;

[0007] Collect food safety testing parameters based on the food safety traceability chain and store them in the cloud database;

[0008] The food safety risk is assessed based on the food safety testing parameters to obtain the assessment results.

[0009] In a preferred embodiment, the steps of formulating safety assessment items for agricultural batch foods and building a food safety traceability chain based on the safety assessment items for agricultural batch foods include:

[0010] Determine the safety assessment items for agricultural and wholesale food;

[0011] Identify the food production links of agricultural wholesale foods and match them with corresponding safety assessment items;

[0012] Determine the corresponding safety detection parameter type according to the safety assessment project, sort the food production links, and bind the corresponding safety detection parameter types of the food production links to obtain the food safety traceability chain.

[0013] In a preferred embodiment, the steps of setting up a cloud database corresponding to the food safety traceability chain and setting up a safe egg corresponding to the cloud database include:

[0014] Configure a cloud database corresponding to the food safety traceability chain, and divide the cloud database into multiple processing links in the food production process with the same number to obtain multiple sub-data spaces;

[0015] One-to-one correspondence is established between the plurality of sub-data spaces and the plurality of processing links in the food production process, and the plurality of sub-data spaces are sorted according to the corresponding processing links and the corresponding food production links;

[0016] Set up a data port on the cloud database, set up multiple confusing ports corresponding to the cloud database, and set up an information area as a safety egg.

[0017] In a preferred embodiment, the steps of setting a data port on the cloud database, setting a plurality of decoy ports corresponding to the cloud database, and setting an information area as a safe egg include:

[0018] An information area is set outside the cloud database, and obfuscated data is stored in the information area;

[0019] Multiple obfuscation ports in the cloud database are combined in pairs without duplication to obtain multiple obfuscation port groups, communication channels are established between two obfuscation ports in each of the multiple obfuscation port groups, access analysis points are set in the communication channels, and the obfuscation ports are connected to the information area;

[0020] A connection group and a main data port are set in the data port, and the main data port is connected to the cloud database. The connection group is composed of multiple transfer ports, and the number of transfer ports is the same as the number of obfuscation port groups. The transfer ports are matched one-to-one with the obfuscation port groups, and the transfer ports are connected to any obfuscation port in the corresponding obfuscation port group.

[0021] In a preferred embodiment, the steps of establishing a communication channel between two decoy ports of the plurality of decoy port groups, setting an access analysis point in the communication channel, and connecting the decoy port to the information area include:

[0022] Establishing a communication channel between two delusion ports of the plurality of delusion port groups, wherein the communication channel passes through the cloud database and avoids the plurality of sub-data spaces;

[0023] An access analysis point is set in the communication channel, and the access analysis point is connected to the deception port of the communication channel;

[0024] The confusion port leads to the information area.

[0025] In a preferred embodiment, the step of collecting food safety detection parameters based on the food safety traceability chain and storing the safety detection parameters in a cloud database includes:

[0026] According to the food production links in the food safety traceability chain, the corresponding food safety testing parameter types are tested to obtain safety testing parameters;

[0027] The safety detection parameters are stored in the sub-data space corresponding to the food production link.

[0028] In a preferred embodiment, the step of evaluating the food safety risk based on the food safety detection parameters to obtain the evaluation result includes:

[0029] Formulate corresponding preset security parameters for corresponding security detection parameter types;

[0030] Obtain safety detection parameters in the entire food production process and compare them with the corresponding preset safety parameters, and regard safety detection parameters that do not meet the preset safety parameters as abnormal parameters;

[0031] An assessment result is obtained based on the abnormal parameters and the gap values that do not meet the preset safety parameters, wherein the assessment result is a risk assessment table of the abnormal parameters and the corresponding gap values that do not meet the preset safety parameters.

[0032] The present invention also provides a food safety risk assessment system based on smart agricultural wholesale supervision, comprising:

[0033] A development module is used to develop safety assessment projects for agricultural wholesale foods and build a food safety traceability chain based on the safety assessment projects for agricultural wholesale foods. The food safety traceability chain includes the types of safety testing parameters corresponding to the food production link and the food formation link;

[0034] A setting module, connected to the formulation module, is used to set up a cloud database corresponding to the food safety traceability chain, and set up a security egg corresponding to the cloud database, wherein the security egg includes multiple obfuscation ports and an information area;

[0035] A storage module, connected to the setting module, is used to collect food safety testing parameters based on the food safety traceability chain and store the safety testing parameters in a cloud database;

[0036] The evaluation module is connected to the storage module and is used to evaluate the safety risk of the food based on the safety detection parameters of the food to obtain the evaluation results.

[0037] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0038] The present invention enables external access to obtain obfuscated data in the information area through an obfuscated port group when an obfuscated port is selected. The obfuscated port is open in the information area. Even when a data port is selected, the connection group can screen the external access through the characteristic information. The external access that meets the characteristic information is guided to the corresponding obfuscated port through the transfer port that meets the characteristic information, so that it obtains the obfuscated data in the information area. The access screened by the connection group is used to access the data end normally through the main data port. This has a better cloud database security protection function, can ensure the subsequent preservation of food safety detection data, avoid malicious behavior from accessing and modifying food safety detection data, ensure the accuracy of food safety assessment, and has a better food safety management function. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0040] Figure 1 Flow chart of the method of the present invention.

[0041] Figure 2 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0043] Example 1, please refer to Figure 1 As shown, the food safety risk assessment method based on smart agricultural wholesale supervision described in this embodiment includes the following steps:

[0044] S1. Develop a safety assessment program for agricultural wholesale foods and build a food safety traceability chain based on the safety assessment program for agricultural wholesale foods. The food safety traceability chain includes the types of safety testing parameters corresponding to the food production and food formation stages.

[0045] S2. Setting up a cloud database corresponding to the food safety traceability chain, and setting up a security egg corresponding to the cloud database, wherein the security egg includes multiple obfuscation ports and an information area;

[0046] S3. Collect food safety testing parameters based on the food safety traceability chain and store the safety testing parameters in a cloud database;

[0047] S4. Evaluate the food safety risk based on the food safety testing parameters and obtain an evaluation result;

[0048] As described in the above steps S1-S4, when the external access selects the decoy port, it can obtain the decoy data in the information area through a decoy port group. The decoy port is open in the information area. Even if the data port is selected, the external access can be screened by the characteristic information through the connection group. The external access that meets the characteristic information is guided to the corresponding decoy port through the transfer port that meets the characteristic information, so that it obtains the decoy data in the information area. The access that passes the connection group screening will access the data end normally through the main data port. It can have a better cloud database security protection function, can ensure the subsequent preservation of food safety detection data, avoid malicious behavior to access and modify food safety detection data, ensure the accuracy of food safety assessment, and have a better food safety management function.

[0049] In one embodiment, the step S1 of formulating safety assessment items for agricultural batch food and building a food safety traceability chain based on the safety assessment items for agricultural batch food includes:

[0050] S11. Determine the safety assessment items for agricultural wholesale food;

[0051] S12. Determine the food production links of agricultural wholesale food and match them with corresponding safety assessment items;

[0052] S13. Determine the corresponding safety testing parameter type according to the safety assessment project, sort the food production links, and bind the corresponding safety testing parameter types of the food production links to obtain a food safety traceability chain;

[0053] As described in the above steps S11-S13, the safety assessment items of agricultural bulk food are determined. The safety assessment items of agricultural bulk food here are the detection of agricultural residues in raw materials, vegetables and other agricultural products, and additives in the processing links of food. Then, the food production links are determined, which include the raw materials (agricultural products) link and multiple processing links for processing raw materials into food. Then, corresponding safety assessment items are configured for multiple links, agricultural residue items are detected in the raw materials link, and additive detection items are detected in multiple processing links of subsequent processing of raw materials into food. Then, corresponding safety detection parameter types are determined for corresponding safety assessment items, for example, various types of agricultural drugs acting on raw materials in the agricultural residue detection items, and types of additives in multiple processing links when raw materials are processed into food; the food production links are sorted, and the safety detection parameter types corresponding to the food formation links are bound to obtain a food safety traceability chain.

[0054] In one embodiment, the step S2 of setting up a cloud database corresponding to the food safety traceability chain and setting up a safety egg corresponding to the cloud database includes:

[0055] S21. Configure a cloud database corresponding to the food safety traceability chain, and divide the cloud database into equal parts according to the number of processing links in the food production process to obtain multiple sub-data spaces;

[0056] S22, one-to-one correspondence between the multiple sub-data spaces and the multiple processing links in the food production process, and sorting the multiple sub-data spaces according to the corresponding processing links and the food production links;

[0057] S23. Setting a data port on the cloud database, setting multiple obfuscated ports corresponding to the cloud database, and setting an information area as a safety egg;

[0058] As described in steps S21-S23 above, a cloud database is configured corresponding to the food safety traceability chain. The cloud database is used to store subsequent safety detection parameters in the food safety traceability chain. The cloud database is divided into multiple sub-data spaces according to the same number of multiple processing links in the food production link, and the corresponding processing links store the corresponding safety detection parameters in the corresponding sub-data spaces. The multiple sub-data spaces are matched one-to-one with the multiple processing links in the food production link, and the multiple sub-data spaces are sorted according to the corresponding processing links and the food production links. The sub-data spaces are arranged according to the food safety traceability chain, wherein the food production link in the food safety traceability chain includes all links in the food processing process. Since the cloud database is used to store subsequent safety detection parameters, in order to ensure the security of storage, a data port is set on the cloud database. Here, the data port is used for the cloud database to communicate with the outside world for use by dedicated personnel. The cloud database is provided with multiple obfuscation ports to prevent external network attacks on the database in the cloud database. An information area is set for the cloud database. The information area, the multiple obfuscation ports, and the data port are used as security eggs to optimize the structure of the database and ensure the security of data storage.

[0059] In one embodiment, the step S23 of setting a data port on the cloud database, setting a plurality of decoy ports corresponding to the cloud database, and setting an information area as a safe egg includes:

[0060] S231. Setting an information area outside the cloud database and storing deceptive data in the information area;

[0061] S232: Combine the multiple obfuscation ports in the cloud database into non-repeated pairs to obtain multiple obfuscation port groups, establish a communication channel between two obfuscation ports in each of the multiple obfuscation port groups, set an access analysis point in the communication channel, and connect the obfuscation port to the information area;

[0062] S233. Setting a connection group and a primary data port in the data port, wherein the primary data port is connected to the cloud database, wherein the connection group is composed of a plurality of transfer ports, the number of transfer ports being the same as the number of decoy port groups, the transfer ports being associated one-to-one with the decoy port groups, and the transfer ports being connected to any decoy port in the corresponding decoy port group;

[0063] In one embodiment, the step S232 of establishing a communication channel between two decoy ports of the plurality of decoy port groups, setting an access analysis point in the communication channel, and connecting the decoy port to the information area includes:

[0064] S2321. Establish a communication channel between two decoy ports of multiple decoy port groups, where the communication channel passes through the cloud database and avoids multiple sub-data spaces.

[0065] S2322. Setting an access analysis point in the communication channel, wherein the access analysis point is connected to the deception port of the communication channel;

[0066] S2323, the obfuscation port leads to the information area;

[0067] As described in the above steps 231-233, the information area is a larger database, and the cloud database is stored in the database. The storage space between the database and the cloud database is used as the information area, and the obfuscation information is stored in the information area. The obfuscation information here is useless and messy information. The multiple obfuscation ports set on the cloud database are combined in pairs without duplication. For example, A1, A2, A3 and A4 represent four obfuscation ports. The non-duplication combination of two pairs can be combined into A1 and A2 and A3 and A4, which means that a obfuscation port can only participate in one combination and no repeated combination can occur. Then, the two obfuscation ports after the combination are used as an obfuscation port group, and a communication channel is established between the two obfuscation ports of the multiple obfuscation port groups. An access analysis point is set in the communication channel, wherein the access analysis point is used to transmit the information to another delusion port of the combination through the transmission channel when an external network invades the delusion port. During the transmission process of the transmission channel, the characteristic information of the external network invasion can be analyzed through the access analysis point. The characteristic information here includes the network address and identity of the external network invasion. Since the access analysis point is connected to the delusion port of the communication channel, the analyzed characteristic information can be provided to the delusion port. The number of transfer ports is the same as the number of delusion port groups. The transfer port is matched one-to-one with the delusion port group, and the transfer port is connected to any delusion port in the corresponding delusion port group. After that, the delusion port can provide the characteristic information to the corresponding transfer port in the connection group. The port can be transferred to store characteristic information. The main data port is close to the cloud database, and the connection group is far away from the database. When there is data access, it is necessary to pass through the connection group first, and then the access after passing the connection group will be formally accessed through the main data port to the cloud database. A communication channel is established between two delusion ports of multiple delusion port groups. The communication channel passes through the cloud database and avoids multiple sub-data spaces. There are intervals of storage space in multiple sub-data spaces. The communication channels of multiple delusion port groups can avoid the sub-data spaces, which can make the external network pass through the cloud database directly through the transmission channel, and cannot obtain the data in the cloud database. The use of the security egg on the cloud database is as follows: due to the existence of multiple delusion ports, external access is easy to select the delusion port. The port greatly improves the security of the cloud database. When the external access selects the decoy port, the characteristic information will be analyzed through the corresponding transmission channel, and then the characteristic information will be provided to the corresponding decoy port through the access analysis point. Then the decoy port will provide the characteristic information to the corresponding transfer port, and the characteristic information will be stored through the transfer port. In this way, when the external access selects the decoy port, it can obtain the decoy data in the information area through a decoy port group. The decoy port is open in the information area. Even if the data port is selected, the external access can be screened by the characteristic information through the connection group. The external access that meets the characteristic information is guided to the corresponding decoy port through the transfer port that meets the characteristic information, so that it can obtain the decoy data in the information area.By allowing the access that passes the connection group screening to access the data end normally through the main data port, it can provide better cloud database security, ensure the subsequent preservation of food safety testing data, prevent malicious behavior from accessing and modifying food safety testing data, ensure the accuracy of food safety assessment, and have a better food safety management function;

[0068] In one embodiment, the step S3 of collecting food safety detection parameters based on the food safety traceability chain and storing the safety detection parameters in a cloud database includes:

[0069] S31. Testing the food safety testing parameter types corresponding to the food production links in the food safety traceability chain to obtain safety testing parameters;

[0070] S32. Storing the safety detection parameters in the sub-data space corresponding to the food production link;

[0071] As described in the above steps S31 and S32, the safety detection parameter types corresponding to the food production links in the food safety traceability chain are detected to obtain safety detection parameters, which are specific food safety component parameters. The safety detection parameters are then stored in the sub-data space corresponding to the food production link. The safety detection parameters in the sub-data space can be protected by the safety egg.

[0072] In one embodiment, the step S4 of evaluating the food safety risk based on the food safety detection parameters to obtain the evaluation result includes:

[0073] S41. Formulate corresponding preset security parameters for the corresponding security detection parameter types;

[0074] S42. Obtain safety detection parameters in the entire food production process and compare them with corresponding preset safety parameters, and identify safety detection parameters that do not meet the preset safety parameters as abnormal parameters;

[0075] S43. Obtaining an assessment result based on the abnormal parameters and the gap values that do not meet the preset safety parameters, wherein the assessment result is a risk assessment table of the abnormal parameters and the corresponding gap values that do not meet the preset safety parameters;

[0076] As described in the above steps S41-S43, corresponding preset safety parameters are formulated for corresponding safety detection parameter types as a standard for abnormality judgment, and the safety detection parameters in the entire food production link are obtained and compared with the corresponding preset safety parameters. The safety detection parameters that do not meet the preset safety parameters are regarded as abnormal parameters, and then the abnormal parameters and the gap values that do not meet the preset safety parameters are evaluated. The evaluation result is a risk assessment table of abnormal parameters and the corresponding gap values that do not meet the preset safety parameters, which can provide staff with information about safety risks in the food production link and has a good food safety risk management function.

[0077] Example 2, please refer to Figure 2 As shown, the food safety risk assessment system based on smart agricultural wholesale supervision described in this embodiment includes:

[0078] A development module is used to develop safety assessment projects for agricultural wholesale foods and build a food safety traceability chain based on the safety assessment projects for agricultural wholesale foods. The food safety traceability chain includes the types of safety testing parameters corresponding to the food production link and the food formation link;

[0079] A setting module, connected to the formulation module, is used to set up a cloud database corresponding to the food safety traceability chain, and set up a security egg corresponding to the cloud database, wherein the security egg includes multiple obfuscation ports and an information area;

[0080] A storage module, connected to the setting module, is used to collect food safety testing parameters based on the food safety traceability chain and store the safety testing parameters in a cloud database;

[0081] The evaluation module is connected to the storage module and is used to evaluate the safety risk of the food based on the safety detection parameters of the food to obtain the evaluation results.

[0082] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A food safety risk assessment method based on smart agricultural wholesale supervision, characterized in that: The following steps are involved: Formulate a safety assessment program for agricultural wholesale foods and build a food safety traceability chain based on the safety assessment program for agricultural wholesale foods. The food safety traceability chain includes the types of safety testing parameters corresponding to the food production and food formation stages. A cloud database is set up corresponding to the food safety traceability chain, and a security egg is set up corresponding to the cloud database, wherein the security egg includes multiple obfuscation ports and information areas; An information area is set outside the cloud database, and obfuscated data is stored in the information area; Multiple obfuscation ports in the cloud database are combined in pairs without duplication to obtain multiple obfuscation port groups, communication channels are established between two obfuscation ports in each of the multiple obfuscation port groups, access analysis points are set in the communication channels, and the obfuscation ports are connected to the information area; A connection group and a main data port are set in the data port, and the main data port is connected to the cloud database. The connection group is composed of multiple transfer ports, and the number of transfer ports is the same as the number of obfuscation port groups. The transfer ports are matched one-to-one with the obfuscation port groups, and the transfer ports are connected to any obfuscation port in the corresponding obfuscation port group. The steps of establishing a communication channel between two decoy ports of the plurality of decoy port groups, setting an access analysis point in the communication channel, and connecting the decoy port to the information area include: Establishing a communication channel between two delusion ports of the plurality of delusion port groups, wherein the communication channel passes through the cloud database and avoids the plurality of sub-data spaces; An access analysis point is set in the communication channel, and the access analysis point is connected to the deception port of the communication channel; The confusion port leads to the information area; Collect food safety testing parameters based on the food safety traceability chain and store them in the cloud database; When an external network selects the decoy port for access, during the transmission of the communication channel, the characteristic information of the external network intrusion can be analyzed through the access analysis point. The characteristic information includes the network address and identity of the external network intrusion. The characteristic information is provided to the corresponding decoy port through the access analysis point. Then the decoy port provides the characteristic information to the corresponding transfer port. The characteristic information is stored through the transfer port. When the external network selects the decoy port, it can obtain the decoy data in the information area through a decoy port group through the cloud database; when the main data port is selected, the characteristic information of the external network is screened through the connection group, and the external network is guided to the corresponding decoy port through the transfer port that meets the characteristic information, and the decoy data in the information area is obtained through the decoy port. The network screened by the connection group can normally access the food safety detection data in the cloud database through the main data port. The food safety risk is assessed based on the food safety testing parameters to obtain the assessment results.

2. The food safety risk assessment method based on smart agricultural wholesale supervision according to claim 1 is characterized by: The steps of formulating the agricultural food safety assessment project and building a food safety traceability chain based on the agricultural food safety assessment project include: Determine the safety assessment items for agricultural and wholesale food; Identify the food production links of agricultural wholesale foods and match them with corresponding safety assessment items; Determine the corresponding safety detection parameter type according to the safety assessment project, sort the food production links, and bind the corresponding safety detection parameter types of the food production links to obtain the food safety traceability chain.

3. The food safety risk assessment method based on smart agricultural wholesale supervision according to claim 1 is characterized by: The steps of setting up a cloud database corresponding to the food safety traceability chain and setting up a safety egg corresponding to the cloud database include: Configure a cloud database corresponding to the food safety traceability chain, and divide the cloud database into multiple processing links in the food production process with the same number to obtain multiple sub-data spaces; One-to-one correspondence is established between the plurality of sub-data spaces and the plurality of processing links in the food production process, and the plurality of sub-data spaces are sorted according to the corresponding processing links and the corresponding food production links; Set up a data port on the cloud database, set up multiple confusing ports for the corresponding cloud database, and set up an information area as a safety egg.

4. The food safety risk assessment method based on smart agricultural wholesale supervision according to claim 1 is characterized by: The step of collecting food safety detection parameters based on the food safety traceability chain and storing the safety detection parameters in a cloud database includes: According to the food production links in the food safety traceability chain, the corresponding food safety testing parameter types are tested to obtain safety testing parameters; The safety detection parameters are stored in the sub-data space corresponding to the food production link.

5. The food safety risk assessment method based on smart agricultural wholesale supervision according to claim 1 is characterized by: The step of evaluating the food safety risk based on the food safety detection parameters to obtain the evaluation result includes: Formulate corresponding preset security parameters for corresponding security detection parameter types; Obtain safety detection parameters in the entire food production process and compare them with the corresponding preset safety parameters, and regard safety detection parameters that do not meet the preset safety parameters as abnormal parameters; An assessment result is obtained based on the abnormal parameters and the gap values that do not meet the preset safety parameters, wherein the assessment result is a risk assessment table of the abnormal parameters and the corresponding gap values that do not meet the preset safety parameters.

6. A food safety risk assessment system based on smart agricultural wholesale supervision, used to implement the food safety risk assessment method based on smart agricultural wholesale supervision according to any one of claims 1 to 5, characterized in that: include: A development module is used to develop safety assessment projects for agricultural wholesale foods and build a food safety traceability chain based on the safety assessment projects for agricultural wholesale foods. The food safety traceability chain includes the types of safety testing parameters corresponding to the food production link and the food formation link; A setting module, connected to the formulation module, is used to set up a cloud database corresponding to the food safety traceability chain, and set up a security egg corresponding to the cloud database, wherein the security egg includes multiple obfuscation ports and an information area; A storage module, connected to the setting module, is used to collect food safety testing parameters based on the food safety traceability chain and store the safety testing parameters in a cloud database; The evaluation module is connected to the storage module and is used to evaluate the safety risk of the food based on the safety detection parameters of the food to obtain the evaluation results.

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