Commodity information splitting and inspection method and system based on the Internet of Things

Abstract

The present invention discloses a commodity information splitting and inspection method and system based on the Internet of Things, the method comprising: obtaining the Internet of Things data corresponding to a plurality of commodities to be inspected; dividing the Internet of Things data corresponding to all the commodities to be inspected into a plurality of commodity data groups according to the data proportions of different trade links in the Internet of Things data; screening out at least one risk data group from the plurality of commodity data groups based on a verification algorithm corresponding to the data proportion characteristics; and verifying each of the Internet of Things data in the risk data group according to the data verification rules to obtain a commodity verification result. It can be seen that the present invention can realize the information inspection of a large number of commodities more efficiently and safely, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a commodity information splitting and inspection method and system based on the Internet of Things. Background Art

[0002] With the increase in trade demand and the development of e-commerce logistics, more and more businesses have begun to introduce Internet of Things technology to manage the transportation or storage of goods. For example, various information about goods during the production and transportation process is obtained through sensors and saved for verification of the integrity and authenticity of the goods. However, in the existing technology, when solving the problem of commodity information verification, it generally only uses preset verification algorithms and verification rules for verification. When faced with the big data scenario requirements of large-scale commodity information verification, the efficiency is too low and the computing cost is high. There is no improvement in the inspection efficiency based on the characteristics of different Internet of Things information in different commodity trade links. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a commodity information splitting and inspection method and system based on the Internet of Things, which can more efficiently and safely realize information inspection of a large number of commodities, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a commodity information splitting and inspection method based on the Internet of Things, the method comprising:

[0005] Obtain IoT data corresponding to multiple commodities to be inspected;

[0006] According to the data proportion of different trade links in the IoT data, the IoT data corresponding to all the commodities to be inspected are divided into multiple commodity data groups;

[0007] Based on a verification algorithm corresponding to the data proportion characteristics, at least one risk data group is screened out from the multiple commodity data groups;

[0008] According to the data verification rules, each IoT data in the risk data group is verified to obtain a commodity verification result.

[0009] As an optional embodiment, in the first aspect of the present invention, the Internet of Things data includes multiple sensor data of the goods to be inspected in multiple trade links; the sensor data is location data, electronic lock data, temperature and humidity data or light reflection data; the trade link is the production link, transportation link, cargo pledge link, storage link or store link.

[0010] As an optional implementation, in the first aspect of the present invention, the IoT data corresponding to all the commodities to be inspected are divided into multiple commodity data groups according to the proportion of data of different trade links in the IoT data, including:

[0011] For each of the commodities to be inspected, the product of the link data of each of the trade links in the IoT data of the commodities to be inspected and the corresponding normalized parameters of the trade links is calculated to obtain the data volume characterization parameters of each of the trade links corresponding to the commodities to be inspected; the normalized parameters of the trade links are obtained by analyzing multiple historical link data;

[0012] Calculate the sum of the data volume characterization parameters of all the trade links corresponding to the commodity to be inspected to obtain the total characterization data volume;

[0013] Calculate the ratio between the data volume characterization parameter of each trade link corresponding to the commodity to be inspected and the total characterization data volume to obtain the data proportion corresponding to each trade link corresponding to the commodity to be inspected;

[0014] According to the data proportion corresponding to all the trade links corresponding to each of the commodities to be inspected, based on a clustering algorithm, the Internet of Things data corresponding to all the commodities to be inspected are divided into multiple commodity data groups.

[0015] As an optional implementation, in the first aspect of the present invention, according to the data proportion corresponding to all the trade links corresponding to each of the commodities to be inspected, based on a clustering algorithm, the IoT data corresponding to all the commodities to be inspected are divided into multiple commodity data groups, including:

[0016] Determine the objective function as follows: the similarity of the link proportion between any two of the commodities to be inspected in each commodity data group is greater than a preset similarity threshold; the link proportion similarity is the similarity between the link proportion information corresponding to the two commodities to be inspected; the link proportion information is the data proportion corresponding to all the trade links corresponding to the commodities to be inspected;

[0017] According to the objective function, based on the K-means clustering algorithm, the Internet of Things data corresponding to all the commodities to be inspected are divided into multiple commodity data groups.

[0018] As an optional implementation, in the first aspect of the present invention, the verification algorithm corresponding to the data proportion characteristics screens out at least one risk data group from the multiple commodity data groups, including:

[0019] For each of the commodity data groups, the sum of the data volume characterization parameters corresponding to all the IoT data of each trade link in the commodity data group is calculated to obtain the link data volume corresponding to each of the trade links in the commodity data group;

[0020] Calculate the sum of the link data volumes corresponding to all the trade links in the commodity data group to obtain the total link data volume of the commodity data group;

[0021] Calculate the ratio of the link data volume corresponding to each of the trade links in the commodity data group to the total link data volume to obtain the link proportion of the data group corresponding to each of the trade links in the commodity data group;

[0022] According to the data group link proportion corresponding to each of the trade links in the commodity data group, a matching target neural network is selected from a plurality of candidate prediction neural networks;

[0023] Inputting the commodity data group into the target neural network to obtain a predicted risk index corresponding to the commodity data group;

[0024] All commodity data groups whose predicted risk index is greater than a preset index threshold are screened out from the multiple commodity data groups to obtain at least one risk data group.

[0025] As an optional implementation, in the first aspect of the present invention, the step of selecting a matching target neural network from a plurality of candidate prediction neural networks according to the data group link proportion corresponding to each of the trade links in the commodity data group comprises:

[0026] For each candidate prediction neural network, the data group link ratio corresponding to all the trade links in the training data set corresponding to the prediction neural network is determined as the ratio information corresponding to the prediction neural network; the prediction neural network is trained by a training data set including a plurality of training commodity IoT data and corresponding risk index annotations;

[0027] Determine the data group link proportions corresponding to all the trade links in the commodity data group as the proportion information corresponding to the commodity data group;

[0028] Calculate the information similarity between the proportion information corresponding to the prediction neural network and the proportion information corresponding to the product data group;

[0029] The predicted neural network with the highest information similarity is determined as the matching target neural network.

[0030] As an optional implementation, in the first aspect of the present invention, the verification of each IoT data in the risk data group according to the data verification rule to obtain the commodity verification result includes:

[0031] For each of the risk data groups, all of the trade links are sorted from large to small according to the proportion of the data group link corresponding to each of the trade links corresponding to the risk data group, to obtain a link sequence corresponding to the risk data group;

[0032] According to the link sequence, each IoT data in the risk data group is verified to obtain a verification result corresponding to each IoT data;

[0033] According to the verification result corresponding to the Internet of Things data, the commodity verification result corresponding to the commodity to be inspected is determined.

[0034] As an optional implementation, in the first aspect of the present invention, verifying each IoT data in the risk data group according to the link sequence to obtain a verification result corresponding to each IoT data includes:

[0035] For each IoT data in the risk data group, data verification is performed on the link data corresponding to multiple trade links of the IoT data in sequence according to the order of trade links in the link sequence, and if data verification fails, data verification of the next trade link is stopped, and the trade link with different current data verification is determined as the risk link corresponding to the IoT data, and the risk link is determined as the verification result corresponding to the IoT data;

[0036] When the data verification in all trade links is passed, the verification result corresponding to the IoT data is determined to be passed.

[0037] A second aspect of an embodiment of the present invention discloses a commodity information splitting and inspection system based on the Internet of Things, the system comprising:

[0038] An acquisition module, used to acquire IoT data corresponding to a plurality of commodities to be inspected;

[0039] A grouping module, used for dividing the IoT data corresponding to all the commodities to be inspected into multiple commodity data groups according to the proportion of data of different trade links in the IoT data;

[0040] A screening module, configured to screen out at least one risk data group from the plurality of commodity data groups based on a verification algorithm corresponding to the data proportion characteristics;

[0041] The verification module is used to verify each IoT data in the risk data group according to the data verification rules to obtain a commodity verification result.

[0042] As an optional embodiment, in the second aspect of the present invention, the Internet of Things data includes multiple sensor data of the goods to be inspected in multiple trade links; the sensor data is location data, electronic lock data, temperature and humidity data or light reflection data; the trade link is the production link, transportation link, cargo pledge link, storage link or store link.

[0043] As an optional implementation, in the second aspect of the present invention, the specific manner in which the grouping module divides the IoT data corresponding to all the commodities to be inspected into multiple commodity data groups according to the proportion of data of different trade links in the IoT data includes:

[0044] For each of the commodities to be inspected, the product of the link data of each of the trade links in the IoT data of the commodities to be inspected and the corresponding normalized parameters of the trade links is calculated to obtain the data volume characterization parameters of each of the trade links corresponding to the commodities to be inspected; the normalized parameters of the trade links are obtained by analyzing multiple historical link data;

[0045] Calculate the sum of the data volume characterization parameters of all the trade links corresponding to the commodity to be inspected to obtain the total characterization data volume;

[0046] Calculate the ratio between the data volume characterization parameter of each trade link corresponding to the commodity to be inspected and the total characterization data volume to obtain the data proportion corresponding to each trade link corresponding to the commodity to be inspected;

[0047] According to the data proportion corresponding to all the trade links corresponding to each of the commodities to be inspected, based on a clustering algorithm, the Internet of Things data corresponding to all the commodities to be inspected are divided into multiple commodity data groups.

[0048] As an optional implementation, in the second aspect of the present invention, the grouping module divides the IoT data corresponding to all the commodities to be inspected into multiple commodity data groups based on a clustering algorithm according to the data proportion corresponding to all the trade links corresponding to each of the commodities to be inspected, including:

[0049] Determine the objective function as follows: the similarity of the link proportion between any two of the commodities to be inspected in each commodity data group is greater than a preset similarity threshold; the link proportion similarity is the similarity between the link proportion information corresponding to the two commodities to be inspected; the link proportion information is the data proportion corresponding to all the trade links corresponding to the commodities to be inspected;

[0050] According to the objective function, based on the K-means clustering algorithm, the Internet of Things data corresponding to all the commodities to be inspected are divided into multiple commodity data groups.

[0051] As an optional implementation, in the second aspect of the present invention, the specific manner in which the screening module screens out at least one risk data group from the multiple commodity data groups based on a verification algorithm corresponding to the data proportion characteristics includes:

[0052] For each of the commodity data groups, the sum of the data volume characterization parameters corresponding to all the IoT data of each trade link in the commodity data group is calculated to obtain the link data volume corresponding to each of the trade links in the commodity data group;

[0053] Calculate the sum of the link data volumes corresponding to all the trade links in the commodity data group to obtain the total link data volume of the commodity data group;

[0054] Calculate the ratio of the link data volume corresponding to each of the trade links in the commodity data group to the total link data volume to obtain the link proportion of the data group corresponding to each of the trade links in the commodity data group;

[0055] According to the data group link proportion corresponding to each of the trade links in the commodity data group, a matching target neural network is selected from a plurality of candidate prediction neural networks;

[0056] Inputting the commodity data group into the target neural network to obtain a predicted risk index corresponding to the commodity data group;

[0057] All commodity data groups whose predicted risk index is greater than a preset index threshold are screened out from the multiple commodity data groups to obtain at least one risk data group.

[0058] As an optional implementation, in the second aspect of the present invention, the specific manner in which the screening module screens out a matching target neural network from a plurality of candidate prediction neural networks according to the data group link proportion corresponding to each of the trade links in the commodity data group includes:

[0059] For each candidate prediction neural network, the data group link ratio corresponding to all the trade links in the training data set corresponding to the prediction neural network is determined as the ratio information corresponding to the prediction neural network; the prediction neural network is trained by a training data set including a plurality of training commodity IoT data and corresponding risk index annotations;

[0060] Determine the data group link proportions corresponding to all the trade links in the commodity data group as the proportion information corresponding to the commodity data group;

[0061] Calculate the information similarity between the proportion information corresponding to the prediction neural network and the proportion information corresponding to the product data group;

[0062] The predicted neural network with the highest information similarity is determined as the matching target neural network.

[0063] As an optional implementation, in the second aspect of the present invention, the verification module verifies each IoT data in the risk data group according to the data verification rule, and the specific manner of obtaining the commodity verification result includes:

[0064] For each of the risk data groups, all of the trade links are sorted from large to small according to the proportion of the data group link corresponding to each of the trade links corresponding to the risk data group, to obtain a link sequence corresponding to the risk data group;

[0065] According to the link sequence, each IoT data in the risk data group is verified to obtain a verification result corresponding to each IoT data;

[0066] According to the verification result corresponding to the Internet of Things data, the commodity verification result corresponding to the commodity to be inspected is determined.

[0067] As an optional implementation, in the second aspect of the present invention, the verification module verifies each IoT data in the risk data group according to the link sequence, and obtains a specific method of the verification result corresponding to each IoT data, including:

[0068] For each IoT data in the risk data group, data verification is performed on the link data corresponding to multiple trade links of the IoT data in sequence according to the order of trade links in the link sequence, and if data verification fails, data verification of the next trade link is stopped, and the trade link with different current data verification is determined as the risk link corresponding to the IoT data, and the risk link is determined as the verification result corresponding to the IoT data;

[0069] When the data verification in all trade links is passed, the verification result corresponding to the IoT data is determined to be passed.

[0070] The third aspect of the present invention discloses another commodity information splitting and inspection system based on the Internet of Things, the system comprising:

[0071] A memory storing executable program code;

[0072] a processor coupled to the memory;

[0073] The processor calls the executable program code stored in the memory to execute part or all of the steps in the commodity information splitting and inspection method based on the Internet of Things disclosed in the first aspect of the present invention.

[0074] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the commodity information splitting and inspection method based on the Internet of Things disclosed in the first aspect of the present invention.

[0075] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0076] The present invention can divide the IoT data corresponding to all commodities to be inspected into multiple commodity data groups based on the data proportions of different links in the IoT data, and then screen out the risk data group based on the verification algorithm to verify the data in the risk data group to obtain the commodity verification result, thereby being able to more efficiently and safely realize the information inspection of a large number of commodities, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0078] Figure 1 The present invention is a flowchart of a method for splitting and inspecting commodity information based on the Internet of Things disclosed in an embodiment of the present invention.

[0079] Figure 2 It is a structural schematic diagram of a commodity information splitting and inspection system based on the Internet of Things disclosed in an embodiment of the present invention.

[0080] Figure 3 It is a structural schematic diagram of another commodity information splitting and inspection system based on the Internet of Things disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0081] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0083] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0084] The present invention discloses a commodity information splitting and inspection method and system based on the Internet of Things, which can divide the Internet of Things data corresponding to all commodities to be inspected into multiple commodity data groups based on the data proportions of different links in the Internet of Things data, and then screen out the risk data group based on the verification algorithm to verify the data in the risk data group to obtain the commodity verification result, thereby being able to more efficiently and safely implement the information inspection of a large number of commodities, reduce the data processing cost of commodity information inspection, and ensure the security and authenticity of commodity services. The following are detailed descriptions.

[0085] Embodiment 1

[0086] See also Figure 1 , Figure 1 This is a flow chart of a commodity information splitting and inspection method based on the Internet of Things disclosed in an embodiment of the present invention. Figure 1 The described method for splitting and verifying commodity information based on the Internet of Things can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the commodity information splitting and inspection method based on the Internet of Things may include the following operations:

[0087] 101. Obtain IoT data corresponding to multiple commodities to be inspected.

[0088] 102. According to the data proportion of different trade links in the IoT data, the IoT data corresponding to all the commodities to be inspected shall be divided into multiple commodity data groups.

[0089] 103. Based on a verification algorithm corresponding to the data proportion characteristics, at least one risk data group is screened out from multiple commodity data groups.

[0090] 104. According to the data verification rules, each IoT data in the risk data group is verified to obtain the product verification result.

[0091] It can be seen that the above-mentioned embodiments of the invention can divide the IoT data corresponding to all the commodities to be inspected into multiple commodity data groups based on the data proportions of different links in the IoT data, and then screen out the risk data group based on the verification algorithm to verify the data in the risk data group to obtain the commodity verification result, thereby being able to more efficiently and safely realize the information inspection of a large number of commodities, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services.

[0092] As an optional embodiment, in the above steps, the IoT data includes multiple sensor data of the goods to be inspected in multiple trade links; the sensor data is location data, electronic lock data, temperature and humidity data or light reflection data; the trade link is the production link, transportation link, cargo pledge link, storage link or store link.

[0093] Specifically, the electronic lock data may be switch data or other sensor data acquired by electronic lock sensors in different storage spaces of the commodity to be inspected during transportation or storage.

[0094] It can be seen that through the above optional embodiments, the content of the trade links and sensor information in the IoT data is clarified, which can more comprehensively characterize the IoT information of the goods in the entire trade process, so as to facilitate subsequent data risk screening and data verification, and assist in realizing more efficient and secure information inspection of a large number of goods, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services.

[0095] As an optional embodiment, in the above steps, the IoT data corresponding to all commodities to be inspected are divided into multiple commodity data groups according to the data proportion of different trade links in the IoT data, including:

[0096] For each commodity to be inspected, the product of the link data of each trade link in the IoT data of the commodity to be inspected and the corresponding normalized parameter of the trade link is calculated to obtain the data volume characterization parameter of each trade link corresponding to the commodity to be inspected;

[0097] Calculate the sum of the data volume characterization parameters of all trade links corresponding to the commodity to be inspected to obtain the total characterization data volume;

[0098] Calculate the ratio between the data volume characterization parameter of each trade link corresponding to the commodity to be inspected and the total characterization data volume to obtain the data proportion corresponding to each trade link corresponding to the commodity to be inspected;

[0099] According to the data proportion of all trade links corresponding to each commodity to be inspected, based on the clustering algorithm, the IoT data corresponding to all commodities to be inspected are divided into multiple commodity data groups.

[0100] Optionally, the normalization parameter of the trade link is obtained by analyzing multiple historical link data, and is used to normalize the data of multiple different types of trade links to the same dimension to realize the calculation of data proportion, which can be achieved by using an existing normalization algorithm.

[0101] It can be seen that through the above-mentioned optional embodiments, the data belonging to different trade links in the IoT data of the inspected goods can be calculated and summarized through normalized parameters to calculate the data proportion corresponding to different trade links, and multiple commodity data groups can be divided based on the clustering algorithm to facilitate the subsequent risk data screening and data verification, assist in realizing more efficient and safe information inspection of a large number of commodities, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services.

[0102] As an optional embodiment, in the above steps, based on the data proportion corresponding to all trade links corresponding to each commodity to be inspected, the IoT data corresponding to all commodities to be inspected are divided into multiple commodity data groups based on a clustering algorithm, including:

[0103] Determine the objective function as follows: the similarity of the link proportion between any two commodities to be inspected in each commodity data group is greater than a preset similarity threshold; optionally, the link proportion similarity is the similarity between the link proportion information corresponding to the two commodities to be inspected; the link proportion information is the data proportion corresponding to all trade links corresponding to the commodities to be inspected;

[0104] According to the objective function, based on the K-means clustering algorithm, the IoT data corresponding to all the commodities to be inspected are divided into multiple commodity data groups.

[0105] Optionally, the calculation of similarity in the present invention can be achieved by a vector distance algorithm.

[0106] It can be seen that through the above-mentioned optional embodiments, it is possible to divide multiple commodity data groups based on the K-means clustering algorithm by calculating the similarity of the link proportions between the commodities to be inspected and determining the objective function, so as to facilitate the subsequent risk data screening and data verification, and assist in realizing more efficient and safe information inspection of a large number of commodities, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services.

[0107] As an optional embodiment, in the above steps, based on the verification algorithm corresponding to the data proportion characteristics, at least one risk data group is screened out from multiple commodity data groups, including:

[0108] For each commodity data group, calculate the sum of the data volume characterization parameters corresponding to all IoT data in each trade link in the commodity data group, and obtain the link data volume corresponding to each trade link in the commodity data group;

[0109] Calculate the sum of the link data volumes corresponding to all trade links in the commodity data group to obtain the total link data volume of the commodity data group;

[0110] Calculate the ratio of the link data volume corresponding to each trade link in the commodity data group to the total link data volume, and obtain the link proportion of the data group corresponding to each trade link in the commodity data group;

[0111] According to the proportion of each trade link in the commodity data group, a matching target neural network is selected from multiple candidate prediction neural networks;

[0112] Inputting the commodity data group into the target neural network to obtain a predicted risk index corresponding to the commodity data group;

[0113] All commodity data groups whose predicted risk index is greater than a preset index threshold are screened out from the multiple commodity data groups to obtain at least one risk data group.

[0114] It can be seen that through the above optional embodiments, the data proportion characteristics corresponding to the commodity data group can be determined by calculating the data proportion of each trade link in all IoT data in the commodity data group, and the matching target neural network is screened out based on the data proportion characteristics to determine the predicted risk index corresponding to the commodity data group, so as to achieve accurate screening of risk data groups, realize more efficient and secure information inspection of a large number of commodities, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services.

[0115] As an optional embodiment, in the above steps, according to the data group link proportion corresponding to each trade link in the commodity data group, selecting a matching target neural network from multiple candidate prediction neural networks includes:

[0116] For each candidate prediction neural network, the data group link ratio corresponding to all trade links in the training data set corresponding to the prediction neural network is determined as the ratio information corresponding to the prediction neural network; optionally, the prediction neural network is trained by a training data set including a plurality of training commodity IoT data and corresponding risk index annotations;

[0117] Determine the data group link proportions corresponding to all trade links in the commodity data group as the proportion information corresponding to the commodity data group;

[0118] Calculate the information similarity between the proportion information corresponding to the prediction neural network and the proportion information corresponding to the product data group;

[0119] The prediction neural network with the highest information similarity is determined as the matching target neural network.

[0120] Optionally, the prediction neural network or the target neural network can be a neural network with a CNN structure, and is trained with a corresponding training data set based on a gradient descent algorithm and a loss function until convergence.

[0121] It can be seen that through the above-mentioned optional embodiments, by calculating and screening the similarity between the candidate prediction neural network and the data proportion information of the commodity data group, a neural network that better matches the current commodity data group can be determined to predict the risk of the commodity data group, so as to achieve accurate screening of the risk data group, achieve more efficient and safe information verification of a large number of commodities, reduce the data processing cost of commodity information verification, and ensure the safety and authenticity of commodity services.

[0122] As an optional embodiment, in the above steps, each IoT data in the risk data group is verified according to the data verification rules to obtain the commodity verification result, including:

[0123] For each risk data group, all trade links are sorted from large to small according to the proportion of each trade link corresponding to the risk data group, and the link sequence corresponding to the risk data group is obtained;

[0124] According to the link sequence, each IoT data in the risk data group is verified to obtain a verification result corresponding to each IoT data;

[0125] According to the verification result corresponding to the IoT data, the commodity verification result corresponding to the commodity to be inspected is determined.

[0126] It can be seen that through the above optional embodiments, the link sequence can be obtained by sorting the data volume of the total trade links in the risk data group, so as to verify the Internet of Things data in the data group, so as to improve the efficiency and rationality of data verification, realize more efficient and safe information verification of a large number of commodities, reduce the data processing cost of commodity information verification, and ensure the safety and authenticity of commodity services.

[0127] As an optional embodiment, in the above steps, each IoT data in the risk data group is verified according to the link sequence to obtain a verification result corresponding to each IoT data, including:

[0128] For each IoT data in the risk data group, data verification is performed on the link data corresponding to multiple trade links of the IoT data in sequence according to the order of trade links in the link sequence. If data verification fails, data verification of the next trade link is stopped, and the trade link with different current data verification is determined as the risk link corresponding to the IoT data, and the risk link is determined as the verification result corresponding to the IoT data;

[0129] When the data verification in all trade links is passed, the verification result corresponding to the IoT data is determined to be passed.

[0130] It can be seen that through the above-mentioned optional embodiments, the link data of different trade links of the Internet of Things data in the data group can be verified sequentially through the order in the link sequence, so as to improve the efficiency and rationality of data verification, realize more efficient and safe information verification of a large number of commodities, reduce the data processing cost of commodity information verification, and ensure the safety and authenticity of commodity services.

[0131] Embodiment 2

[0132] See also Figure 2 , Figure 2 : is a schematic diagram of a commodity information splitting and inspection system based on the Internet of Things disclosed in an embodiment of the present invention. Figure 2 The described commodity information splitting and inspection system based on the Internet of Things can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the commodity information splitting and inspection system based on the Internet of Things may include:

[0133] The acquisition module 201 is used to acquire IoT data corresponding to a plurality of commodities to be inspected.

[0134] The grouping module 202 is used to divide the IoT data corresponding to all the commodities to be inspected into multiple commodity data groups according to the data proportion of different trade links in the IoT data.

[0135] The screening module 203 is used to screen out at least one risk data group from multiple commodity data groups based on a verification algorithm corresponding to the data proportion characteristics.

[0136] The verification module 204 is used to verify each IoT data in the risk data group according to the data verification rules to obtain a commodity verification result.

[0137] It can be seen that the above-mentioned embodiments of the invention can divide the IoT data corresponding to all the commodities to be inspected into multiple commodity data groups based on the data proportions of different links in the IoT data, and then screen out the risk data group based on the verification algorithm to verify the data in the risk data group to obtain the commodity verification result, thereby being able to more efficiently and safely realize the information inspection of a large number of commodities, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services.

[0138] As an optional embodiment, the Internet of Things data includes multiple sensor data of the goods to be inspected in multiple trade links; the sensor data is location data, electronic lock data, temperature and humidity data or light reflection data; the trade link is the production link, transportation link, cargo pledge link, storage link or store link.

[0139] It can be seen that through the above optional embodiments, the content of the trade links and sensor information in the IoT data is clarified, which can more comprehensively characterize the IoT information of the goods in the entire trade process, so as to facilitate subsequent data risk screening and data verification, and assist in realizing more efficient and secure information inspection of a large number of goods, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services.

[0140] As an optional embodiment, the specific manner in which the grouping module divides the IoT data corresponding to all the commodities to be inspected into multiple commodity data groups according to the data proportion of different trade links in the IoT data includes:

[0141] For each commodity to be inspected, the product of the link data of each trade link in the IoT data of the commodity to be inspected and the corresponding normalized parameter of the trade link is calculated to obtain the data volume characterization parameter of each trade link corresponding to the commodity to be inspected; optionally, the normalized parameter of the trade link is obtained by analyzing multiple historical link data;

[0142] Calculate the sum of the data volume characterization parameters of all trade links corresponding to the commodity to be inspected to obtain the total characterization data volume;

[0143] Calculate the ratio between the data volume characterization parameter of each trade link corresponding to the commodity to be inspected and the total characterization data volume to obtain the data proportion corresponding to each trade link corresponding to the commodity to be inspected;

[0144] According to the data proportion of all trade links corresponding to each commodity to be inspected, based on the clustering algorithm, the IoT data corresponding to all commodities to be inspected are divided into multiple commodity data groups.

[0145] It can be seen that through the above-mentioned optional embodiments, the data belonging to different trade links in the IoT data of the inspected goods can be calculated and summarized through normalized parameters to calculate the data proportion corresponding to different trade links, and multiple commodity data groups can be divided based on the clustering algorithm to facilitate the subsequent risk data screening and data verification, assist in realizing more efficient and safe information inspection of a large number of commodities, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services.

[0146] As an optional embodiment, the grouping module divides the IoT data corresponding to all commodities to be inspected into multiple commodity data groups based on a clustering algorithm according to the data proportion corresponding to all trade links corresponding to each commodity to be inspected, including:

[0147] Determine the objective function as follows: the similarity of the link proportion between any two commodities to be inspected in each commodity data group is greater than a preset similarity threshold; optionally, the link proportion similarity is the similarity between the link proportion information corresponding to the two commodities to be inspected; the link proportion information is the data proportion corresponding to all trade links corresponding to the commodities to be inspected;

[0148] According to the objective function, based on the K-means clustering algorithm, the IoT data corresponding to all the commodities to be inspected are divided into multiple commodity data groups.

[0149] It can be seen that through the above-mentioned optional embodiments, it is possible to divide multiple commodity data groups based on the K-means clustering algorithm by calculating the similarity of the link proportions between the commodities to be inspected and determining the objective function, so as to facilitate the subsequent risk data screening and data verification, and assist in realizing more efficient and safe information inspection of a large number of commodities, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services.

[0150] As an optional embodiment, the specific manner in which the screening module screens out at least one risk data group from multiple commodity data groups based on a verification algorithm corresponding to the data proportion characteristics includes:

[0151] For each commodity data group, calculate the sum of the data volume characterization parameters corresponding to all IoT data in each trade link in the commodity data group, and obtain the link data volume corresponding to each trade link in the commodity data group;

[0152] Calculate the sum of the link data volumes corresponding to all trade links in the commodity data group to obtain the total link data volume of the commodity data group;

[0153] Calculate the ratio of the link data volume corresponding to each trade link in the commodity data group to the total link data volume, and obtain the link proportion of the data group corresponding to each trade link in the commodity data group;

[0154] According to the proportion of each trade link in the commodity data group, a matching target neural network is selected from multiple candidate prediction neural networks;

[0155] Inputting the commodity data group into the target neural network to obtain a predicted risk index corresponding to the commodity data group;

[0156] All commodity data groups whose predicted risk index is greater than a preset index threshold are screened out from the multiple commodity data groups to obtain at least one risk data group.

[0157] It can be seen that through the above optional embodiments, the data proportion characteristics corresponding to the commodity data group can be determined by calculating the data proportion of each trade link in all IoT data in the commodity data group, and the matching target neural network is screened out based on the data proportion characteristics to determine the predicted risk index corresponding to the commodity data group, so as to achieve accurate screening of risk data groups, realize more efficient and secure information inspection of a large number of commodities, reduce the data processing cost of commodity information inspection, and ensure the safety and authenticity of commodity services.

[0158] As an optional embodiment, the specific method of the screening module screening out a matching target neural network from a plurality of candidate prediction neural networks according to the data group link proportion corresponding to each trade link in the commodity data group includes:

[0159] For each candidate prediction neural network, the data group link ratio corresponding to all trade links in the training data set corresponding to the prediction neural network is determined as the ratio information corresponding to the prediction neural network; optionally, the prediction neural network is trained by a training data set including a plurality of training commodity IoT data and corresponding risk index annotations;

[0160] Determine the data group link proportions corresponding to all trade links in the commodity data group as the proportion information corresponding to the commodity data group;

[0161] Calculate the information similarity between the proportion information corresponding to the prediction neural network and the proportion information corresponding to the product data group;

[0162] The prediction neural network with the highest information similarity is determined as the matching target neural network.

[0163] It can be seen that through the above-mentioned optional embodiments, by calculating and screening the similarity between the candidate prediction neural network and the data proportion information of the commodity data group, a neural network that better matches the current commodity data group can be determined to predict the risk of the commodity data group, so as to achieve accurate screening of the risk data group, achieve more efficient and safe information verification of a large number of commodities, reduce the data processing cost of commodity information verification, and ensure the safety and authenticity of commodity services.

[0164] As an optional embodiment, the verification module verifies each IoT data in the risk data group according to the data verification rules, and obtains the specific method of the commodity verification result, including:

[0165] For each risk data group, all trade links are sorted from large to small according to the proportion of each trade link corresponding to the risk data group, and the link sequence corresponding to the risk data group is obtained;

[0166] According to the link sequence, each IoT data in the risk data group is verified to obtain a verification result corresponding to each IoT data;

[0167] According to the verification result corresponding to the IoT data, the commodity verification result corresponding to the commodity to be inspected is determined.

[0168] It can be seen that through the above optional embodiments, the link sequence can be obtained by sorting the data volume of the total trade links in the risk data group, so as to verify the Internet of Things data in the data group, so as to improve the efficiency and rationality of data verification, realize more efficient and safe information verification of a large number of commodities, reduce the data processing cost of commodity information verification, and ensure the safety and authenticity of commodity services.

[0169] As an optional embodiment, the verification module verifies each IoT data in the risk data group according to the link sequence, and obtains the verification result corresponding to each IoT data in a specific manner, including:

[0170] For each IoT data in the risk data group, data verification is performed on the link data corresponding to multiple trade links of the IoT data in sequence according to the order of trade links in the link sequence. If data verification fails, data verification of the next trade link is stopped, and the trade link with different current data verification is determined as the risk link corresponding to the IoT data, and the risk link is determined as the verification result corresponding to the IoT data;

[0171] When the data verification in all trade links is passed, the verification result corresponding to the IoT data is determined to be passed.

[0172] It can be seen that through the above-mentioned optional embodiments, the link data of different trade links of the Internet of Things data in the data group can be verified sequentially through the order in the link sequence, so as to improve the efficiency and rationality of data verification, realize more efficient and safe information verification of a large number of commodities, reduce the data processing cost of commodity information verification, and ensure the safety and authenticity of commodity services.

[0173] Embodiment 3

[0174] See also Figure 3 , Figure 3 This is another commodity information splitting and inspection system based on the Internet of Things disclosed in an embodiment of the present invention. Figure 3 The described commodity information splitting and inspection system based on the Internet of Things is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the commodity information splitting and inspection system based on the Internet of Things may include:

[0175] A memory 301 storing executable program codes;

[0176] a processor 302 coupled to the memory 301;

[0177] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the commodity information splitting and inspection method based on the Internet of Things described in the first embodiment.

[0178] Embodiment 4

[0179] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the commodity information splitting and inspection method based on the Internet of Things described in the first embodiment.

[0180] Embodiment 5

[0181] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the commodity information splitting and inspection method based on the Internet of Things described in Example 1.

[0182] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0183] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0184] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0185] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0186] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0187] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0188] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0189] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0190] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0191] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0192] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0193] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0194] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0195] Finally, it should be noted that the method and system for splitting and inspecting commodity information based on the Internet of Things disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. A commodity information splitting and inspection method based on the Internet of Things, characterized in that: The method comprises: Obtain IoT data corresponding to multiple commodities to be inspected; According to the data proportion of different trade links in the IoT data, the IoT data corresponding to all the commodities to be inspected are divided into multiple commodity data groups, including: For each of the commodities to be inspected, the product of the link data of each of the trade links in the IoT data of the commodities to be inspected and the corresponding normalized parameters of the trade links is calculated to obtain the data volume characterization parameters of each of the trade links corresponding to the commodities to be inspected; the normalized parameters of the trade links are obtained by analyzing multiple historical link data; Calculate the sum of the data volume characterization parameters of all the trade links corresponding to the commodity to be inspected to obtain the total characterization data volume; Calculate the ratio between the data volume characterization parameter of each trade link corresponding to the commodity to be inspected and the total characterization data volume to obtain the data proportion corresponding to each trade link corresponding to the commodity to be inspected; Determine the objective function as follows: the similarity of the link proportion between any two of the commodities to be inspected in each commodity data group is greater than a preset similarity threshold; the link proportion similarity is the similarity between the link proportion information corresponding to the two commodities to be inspected; the link proportion information is the data proportion corresponding to all the trade links corresponding to the commodities to be inspected; According to the objective function, based on the K-means clustering algorithm, the IoT data corresponding to all the commodities to be inspected are divided into multiple commodity data groups; Based on a verification algorithm corresponding to the data proportion characteristics, at least one risk data group is screened out from the multiple commodity data groups, including: For each of the commodity data groups, the sum of the data volume characterization parameters corresponding to all the IoT data of each trade link in the commodity data group is calculated to obtain the link data volume corresponding to each of the trade links in the commodity data group; Calculate the sum of the link data volumes corresponding to all the trade links in the commodity data group to obtain the total link data volume of the commodity data group; Calculate the ratio of the link data volume corresponding to each of the trade links in the commodity data group to the total link data volume to obtain the link proportion of the data group corresponding to each of the trade links in the commodity data group; For each candidate prediction neural network, the data group link ratio corresponding to all the trade links in the training data set corresponding to the prediction neural network is determined as the ratio information corresponding to the prediction neural network; the prediction neural network is trained by a training data set including a plurality of training commodity IoT data and corresponding risk index annotations; Determine the data group link proportions corresponding to all the trade links in the commodity data group as the proportion information corresponding to the commodity data group; Calculate the information similarity between the proportion information corresponding to the prediction neural network and the proportion information corresponding to the product data group; Determine the predicted neural network with the highest information similarity as the matching target neural network; Inputting the commodity data group into the target neural network to obtain a predicted risk index corresponding to the commodity data group; Filter out all commodity data groups whose predicted risk index is greater than a preset index threshold from the multiple commodity data groups to obtain at least one risk data group; According to the data verification rules, each IoT data in the risk data group is verified to obtain a commodity verification result.

2. The method for separating and inspecting commodity information based on the Internet of Things according to claim 1, characterized in that: The Internet of Things data includes multiple sensor data of the commodity to be inspected in multiple trade links; the sensor data is location data, electronic lock data, temperature and humidity data or light reflection data; the trade link is the production link, transportation link, cargo pledge link, storage link or store link.

3. The commodity information splitting and inspection method based on the Internet of Things according to claim 1 is characterized in that: The step of verifying each IoT data in the risk data group according to the data verification rules to obtain a commodity verification result includes: For each of the risk data groups, all of the trade links are sorted from large to small according to the proportion of the data group link corresponding to each of the trade links corresponding to the risk data group, to obtain a link sequence corresponding to the risk data group; According to the link sequence, each IoT data in the risk data group is verified to obtain a verification result corresponding to each IoT data; According to the verification result corresponding to the Internet of Things data, the commodity verification result corresponding to the commodity to be inspected is determined.

4. The method for separating and inspecting commodity information based on the Internet of Things according to claim 3, characterized in that: According to the link sequence, each IoT data in the risk data group is verified to obtain a verification result corresponding to each IoT data, including: For each IoT data in the risk data group, data verification is performed on the link data corresponding to multiple trade links of the IoT data in sequence according to the order of trade links in the link sequence, and if data verification fails, data verification of the next trade link is stopped, and the trade link with different current data verification is determined as the risk link corresponding to the IoT data, and the risk link is determined as the verification result corresponding to the IoT data; When the data verification in all trade links is passed, the verification result corresponding to the IoT data is determined to be passed.

5. A commodity information splitting and inspection system based on the Internet of Things, characterized in that: The system comprises: An acquisition module, used to acquire IoT data corresponding to a plurality of commodities to be inspected; The grouping module is used to divide the IoT data corresponding to all the commodities to be inspected into multiple commodity data groups according to the proportion of data of different trade links in the IoT data, including: For each of the commodities to be inspected, the product of the link data of each of the trade links in the IoT data of the commodities to be inspected and the corresponding normalized parameters of the trade links is calculated to obtain the data volume characterization parameters of each of the trade links corresponding to the commodities to be inspected; the normalized parameters of the trade links are obtained by analyzing multiple historical link data; Calculate the sum of the data volume characterization parameters of all the trade links corresponding to the commodity to be inspected to obtain the total characterization data volume; Calculate the ratio between the data volume characterization parameter of each trade link corresponding to the commodity to be inspected and the total characterization data volume to obtain the data proportion corresponding to each trade link corresponding to the commodity to be inspected; Determine the objective function as follows: the similarity of the link proportion between any two of the commodities to be inspected in each commodity data group is greater than a preset similarity threshold; the link proportion similarity is the similarity between the link proportion information corresponding to the two commodities to be inspected; the link proportion information is the data proportion corresponding to all the trade links corresponding to the commodities to be inspected; According to the objective function, based on the K-means clustering algorithm, the IoT data corresponding to all the commodities to be inspected are divided into multiple commodity data groups; A screening module, used to screen out at least one risk data group from the multiple commodity data groups based on a verification algorithm corresponding to the data proportion characteristics, including: For each of the commodity data groups, the sum of the data volume characterization parameters corresponding to all the IoT data of each trade link in the commodity data group is calculated to obtain the link data volume corresponding to each of the trade links in the commodity data group; Calculate the sum of the link data volumes corresponding to all the trade links in the commodity data group to obtain the total link data volume of the commodity data group; Calculate the ratio of the link data volume corresponding to each of the trade links in the commodity data group to the total link data volume to obtain the link proportion of the data group corresponding to each of the trade links in the commodity data group; For each candidate prediction neural network, the data group link ratio corresponding to all the trade links in the training data set corresponding to the prediction neural network is determined as the ratio information corresponding to the prediction neural network; the prediction neural network is trained by a training data set including a plurality of training commodity IoT data and corresponding risk index annotations; Determine the data group link proportions corresponding to all the trade links in the commodity data group as the proportion information corresponding to the commodity data group; Calculate the information similarity between the proportion information corresponding to the prediction neural network and the proportion information corresponding to the product data group; Determine the predicted neural network with the highest information similarity as the matching target neural network; Inputting the commodity data group into the target neural network to obtain a predicted risk index corresponding to the commodity data group; Filter out all commodity data groups whose predicted risk index is greater than a preset index threshold from the multiple commodity data groups to obtain at least one risk data group; The verification module is used to verify each IoT data in the risk data group according to the data verification rules to obtain a commodity verification result.

6. A commodity information splitting and inspection system based on the Internet of Things, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the commodity information splitting and inspection method based on the Internet of Things as described in any one of claims 1 to 4.

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