Dairy product quality data processing method and system based on traceability information

Abstract

The present invention discloses a method and system for processing dairy product quality data based on traceability information, the method comprising: obtaining traceability information and sensor information of multiple links corresponding to target dairy products; based on a neural network algorithm, identifying predicted risk links with quality risks in the multiple links according to the traceability information; screening out high-risk links and corresponding risk types from all predicted risk links according to the sensor information and a preset information verification algorithm; generating link monitoring instructions corresponding to the high-risk links according to the high-risk links and the corresponding risk types to send to the corresponding device end. It can be seen that the present invention can effectively improve the timeliness and control effect of dairy product quality risk management and control, improve the safety quality of dairy products, and reduce dairy product safety accidents.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for processing dairy product quality data based on traceability information. Background Art

[0002] The manufacturing of dairy products requires multiple links, and its quality control also requires companies to strictly monitor and manage each link. With the expansion of the dairy market, the quality and safety of dairy products are increasingly attracting public attention. When controlling the quality of dairy products, the existing technology generally uses the detection of dairy products to achieve it, and does not make full use of sensor information and traceability information from multiple links to combine algorithm models to predict quality risks. Therefore, its quality control measures are generally lagging and the effect is poor. 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 dairy product quality data processing method and system based on traceability information, which can effectively improve the timeliness and control effect of dairy product quality risk management, improve the safety quality of dairy products, and reduce dairy product safety accidents.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a method for processing dairy product quality data based on traceability information, the method comprising:

[0005] Obtain traceability information and sensor information of multiple links corresponding to the target dairy products;

[0006] Based on the neural network algorithm, according to the traceability information, identifying the predicted risk link with quality risk in the multiple links;

[0007] According to the sensor information and a preset information verification algorithm, high-risk links and corresponding risk types are screened out from all the predicted risk links;

[0008] According to the high-risk links and the corresponding risk types, link monitoring instructions corresponding to the high-risk links are generated and sent to the corresponding device ends.

[0009] As an optional embodiment, in the first aspect of the present invention, the link is a breeding link, a processing link, a distribution link or a sales link.

[0010] As an optional implementation, in the first aspect of the present invention, the traceability information includes at least one of label information, link equipment identification, link personnel information, link equipment location and link time.

[0011] As an optional embodiment, in the first aspect of the present invention, the sensor information includes at least one of image information, sound information, video information and infrared ranging information acquired by a sensor arranged on the production line.

[0012] As an optional implementation, in the first aspect of the present invention, the neural network algorithm is used to identify the predicted risk link with quality risk in the multiple links according to the traceability information, including:

[0013] For each of the links, determine the two adjacent links before and after the link in the preset link sequence;

[0014] Inputting the traceability information of the link and the two adjacent links into the trained risk prediction neural network model to obtain the risk prediction value and risk type corresponding to the link;

[0015] According to the risk prediction values, all the links are sorted from large to small to obtain a first link sequence;

[0016] All the links of the first number in the first link sequence and whose risk prediction values ​​are greater than a first threshold are screened out to obtain a plurality of predicted risk links.

[0017] As an optional embodiment, in the first aspect of the present invention, the risk prediction neural network model is a neural network model of a CNN structure, which is obtained by training a training data set including training traceability information of multiple adjacent links and corresponding risk annotations. During the training, the model parameters are optimized until convergence through a preset loss function and gradient descent algorithm.

[0018] As an optional implementation, in the first aspect of the present invention, the high-risk links and corresponding risk types are screened out from all the predicted risk links according to the sensor information and a preset information verification algorithm, including:

[0019] For each of the risk prediction links, the sensor information corresponding to the risk prediction link is input into a trained irregular operation prediction neural network to obtain a predicted probability value of the existence of irregular operations corresponding to the risk prediction link; the irregular operation prediction neural network is trained by a training data set including a plurality of training sensor information and corresponding standard operation annotations;

[0020] Calculate a first difference between the predicted probability value and a second threshold;

[0021] Calculate the absolute value of the difference between 1 and the calculated ratio; the calculated ratio is the ratio of the first difference to the risk prediction value;

[0022] Determine whether the absolute value of the difference is less than a preset third threshold value to determine whether the sensor abnormality of the risk prediction link is close to the traceability risk situation;

[0023] When the absolute value of the difference is less than the third threshold, the predicted risk link and the corresponding risk type are determined to be a high-risk link and the corresponding risk type.

[0024] As an optional implementation, in the first aspect of the present invention, generating a link monitoring instruction corresponding to the high-risk link according to the high-risk link and the corresponding risk type to send to the corresponding device end includes:

[0025] For each of the high-risk links, determine the link monitoring device corresponding to the high-risk link;

[0026] Generate text notification information corresponding to the high-risk link according to the preset text template and the risk type corresponding to the high-risk link;

[0027] According to the preset relationship between the risk type and the monitoring instruction, generating the link monitoring instruction corresponding to the risk type;

[0028] The link monitoring instruction and the text notification information are sent to the link monitoring device.

[0029] A second aspect of an embodiment of the present invention discloses a dairy product quality data processing system based on traceability information, the system comprising:

[0030] An acquisition module, used to obtain traceability information and sensor information of multiple links corresponding to the target dairy product;

[0031] An identification module, configured to identify, based on a neural network algorithm and according to the traceability information, a predicted risk link with quality risk among the multiple links;

[0032] A screening module, used to screen out high-risk links and corresponding risk types from all the predicted risk links according to the sensor information and a preset information verification algorithm;

[0033] The determination module is used to generate a link monitoring instruction corresponding to the high-risk link according to the high-risk link and the corresponding risk type, and send it to the corresponding device end.

[0034] As an optional embodiment, in the second aspect of the present invention, the link is a breeding link, a processing link, a distribution link or a sales link.

[0035] As an optional implementation, in the second aspect of the present invention, the traceability information includes at least one of label information, link equipment identification, link personnel information, link equipment location and link time.

[0036] As an optional implementation, in the second aspect of the present invention, the sensor information includes at least one of image information, sound information, video information and infrared ranging information acquired by a sensor arranged on the production line.

[0037] As an optional implementation, in the second aspect of the present invention, the identification module is based on a neural network algorithm, and according to the traceability information, a specific method for identifying a predicted risk link with quality risk in the multiple links includes:

[0038] For each of the links, determine the two adjacent links before and after the link in the preset link sequence;

[0039] Inputting the traceability information of the link and the two adjacent links into the trained risk prediction neural network model to obtain the risk prediction value and risk type corresponding to the link;

[0040] According to the risk prediction values, all the links are sorted from large to small to obtain a first link sequence;

[0041] All the links of the first number in the first link sequence and whose risk prediction values ​​are greater than a first threshold are screened out to obtain a plurality of predicted risk links.

[0042] As an optional embodiment, in the second aspect of the present invention, the risk prediction neural network model is a neural network model of a CNN structure, which is obtained by training a training data set including training traceability information of multiple adjacent links and corresponding risk annotations. During the training, the model parameters are optimized until convergence through a preset loss function and gradient descent algorithm.

[0043] As an optional implementation, in the second aspect of the present invention, the screening module screens out high-risk links and corresponding risk types from all the predicted risk links according to the sensor information and the preset information verification algorithm, including:

[0044] For each of the risk prediction links, the sensor information corresponding to the risk prediction link is input into a trained irregular operation prediction neural network to obtain a predicted probability value of the existence of irregular operations corresponding to the risk prediction link; the irregular operation prediction neural network is trained by a training data set including a plurality of training sensor information and corresponding standard operation annotations;

[0045] Calculate a first difference between the predicted probability value and a second threshold;

[0046] Calculate the absolute value of the difference between 1 and the calculated ratio; the calculated ratio is the ratio of the first difference to the risk prediction value;

[0047] Determine whether the absolute value of the difference is less than a preset third threshold value to determine whether the sensor abnormality of the risk prediction link is close to the traceability risk situation;

[0048] When the absolute value of the difference is less than the third threshold, the predicted risk link and the corresponding risk type are determined to be a high-risk link and the corresponding risk type.

[0049] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module generates a link monitoring instruction corresponding to the high-risk link according to the high-risk link and the corresponding risk type and sends the instruction to the corresponding device end includes:

[0050] For each of the high-risk links, determine the link monitoring device corresponding to the high-risk link;

[0051] Generate text notification information corresponding to the high-risk link according to the preset text template and the risk type corresponding to the high-risk link;

[0052] According to the preset relationship between the risk type and the monitoring instruction, generating the link monitoring instruction corresponding to the risk type;

[0053] The link monitoring instruction and the text notification information are sent to the link monitoring device.

[0054] The third aspect of the present invention discloses another dairy product quality data processing system based on traceability information, the system comprising:

[0055] A memory storing executable program code;

[0056] a processor coupled to the memory;

[0057] The processor calls the executable program code stored in the memory to execute part or all of the steps in the dairy product quality data processing method based on traceability information disclosed in the first aspect of the present invention.

[0058] 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 dairy product quality data processing method based on traceability information disclosed in the first aspect of the present invention.

[0059] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0060] The present invention can determine the high-risk links and corresponding risk types based on the neural network algorithm and the information verification algorithm through the traceability information and sensor information of multiple links corresponding to the target dairy products, and then generate the link monitoring instructions corresponding to the high-risk links to send to the corresponding device end, thereby effectively improving the timeliness and control effect of dairy product quality risk management, improving the safety quality of dairy products, and reducing dairy product safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] 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.

[0062] Figure 1 It is a flow chart of a method for processing dairy product quality data based on traceability information disclosed in an embodiment of the present invention.

[0063] Figure 2 It is a structural schematic diagram of a dairy product quality data processing system based on traceability information disclosed in an embodiment of the present invention.

[0064] Figure 3 It is a structural schematic diagram of another dairy product quality data processing system based on traceability information disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] 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.

[0066] 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.

[0067] 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.

[0068] The present invention discloses a dairy product quality data processing method and system based on traceability information, which can determine high-risk links and corresponding risk types based on neural network algorithms and information verification algorithms through traceability information and sensor information of multiple links corresponding to target dairy products, and then generate link monitoring instructions corresponding to high-risk links to send to corresponding equipment ends, thereby effectively improving the timeliness and control effect of dairy product quality risk management, improving the safety quality of dairy products, and reducing dairy product safety accidents. The following are detailed descriptions.

[0069] Embodiment 1

[0070] See also Figure 1 , Figure 1 : is a flow chart of a method for processing dairy product quality data based on traceability information disclosed in an embodiment of the present invention. Figure 1 The described dairy product quality data processing method based on traceability information 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 dairy product quality data processing method based on traceability information may include the following operations:

[0071] 101. Obtain traceability information and sensor information of multiple links corresponding to the target dairy products.

[0072] 102. Based on the neural network algorithm, according to the traceability information, the predicted risk links with quality risks in multiple links are identified.

[0073] 103. Based on the sensor information and the preset information verification algorithm, high-risk links and corresponding risk types are screened out from all predicted risk links.

[0074] 104. According to the high-risk links and the corresponding risk types, link monitoring instructions corresponding to the high-risk links are generated and sent to the corresponding device ends.

[0075] It can be seen that the above-mentioned embodiments of the invention can determine the high-risk links and corresponding risk types based on the neural network algorithm and the information verification algorithm through the traceability information and sensor information of multiple links corresponding to the target dairy products, and then generate the link monitoring instructions corresponding to the high-risk links to send to the corresponding device end, thereby effectively improving the timeliness and control effect of dairy product quality risk management, improving the safety quality of dairy products, and reducing dairy product safety accidents.

[0076] As an optional embodiment, in the above steps, the link is a breeding link, a processing link, a distribution link or a sales link.

[0077] It can be seen that through the above optional embodiments, the specific types of links are limited to more comprehensively characterize the whole process information of dairy products, assist in improving the timeliness and control effect of dairy product quality risk management, improve the safety quality of dairy products, and reduce dairy product safety accidents.

[0078] As an optional embodiment, in the above steps, the traceability information includes at least one of label information, link equipment identification, link personnel information, link equipment location and link time.

[0079] It can be seen that through the above optional embodiments, the content of the traceability information is clarified to more comprehensively characterize the traceability information of dairy products in the links, assist in improving the timeliness and control effect of dairy product quality risk management, improve the safety and quality of dairy products, and reduce dairy product safety accidents.

[0080] As an optional embodiment, in the above steps, the sensing information includes at least one of image information, sound information, video information and infrared ranging information acquired by a sensor arranged on the production line.

[0081] It can be seen that through the above optional embodiments, the content of the sensor information is clarified to more comprehensively characterize the sensor characteristic information of dairy products in the production process, assist in improving the timeliness and control effect of dairy product quality risk management, improve the safety quality of dairy products, and reduce dairy product safety accidents.

[0082] As an optional embodiment, in the above steps, based on the neural network algorithm, according to the traceability information, the predicted risk link with quality risk in multiple links is identified, including:

[0083] For each link, determine the two adjacent links before and after the link in the preset link sequence;

[0084] Input the traceability information of this link and two adjacent links into the trained risk prediction neural network model to obtain the risk prediction value and risk type corresponding to this link;

[0085] According to the risk prediction value, all links are sorted from large to small to obtain the first link sequence;

[0086] All links that are the first number in the first link sequence and whose risk prediction values ​​are greater than the first threshold are screened out to obtain multiple predicted risk links.

[0087] It can be seen that through the above-mentioned optional embodiments, risk prediction can be carried out based on the traceability information of links and adjacent links through the risk prediction neural network model, and then multiple predicted risk links can be determined based on sorting and screening to facilitate subsequent precise quality control, thereby assisting in improving the timeliness and control effect of dairy product quality risk management, improving the safety quality of dairy products, and reducing dairy product safety accidents.

[0088] As an optional embodiment, in the above steps, the risk prediction neural network model is a neural network model of CNN structure, which is obtained by training a training data set including training traceability information of multiple adjacent links and corresponding risk annotations. During the training, the model parameters are optimized until convergence through a preset loss function and gradient descent algorithm.

[0089] It can be seen that through the above optional embodiments, the type and training details of the risk prediction neural network model are clarified to facilitate subsequent accurate risk prediction and quality control, thereby assisting in improving the timeliness and control effect of dairy product quality risk management, improving the safety quality of dairy products, and reducing dairy product safety accidents.

[0090] As an optional embodiment, in the above steps, based on the sensor information and the preset information verification algorithm, high-risk links and corresponding risk types are screened out from all predicted risk links, including:

[0091] For each predicted risk link, the sensor information corresponding to the predicted risk link is input into the trained irregular operation prediction neural network to obtain a predicted probability value of the existence of irregular operation corresponding to the predicted risk link; optionally, the irregular operation prediction neural network is trained by a training data set including a plurality of training sensor information and corresponding standard operation annotations;

[0092] Calculate a first difference between the predicted probability value and a second threshold value;

[0093] Calculate the absolute value of the difference between 1 and the calculated ratio; optionally, calculate the ratio as the ratio of the first difference to the risk prediction value;

[0094] Determine whether the absolute value of the difference is less than a preset third threshold value to determine whether the sensor abnormality of the predicted risk link is close to the traceability risk situation;

[0095] When the absolute value of the difference is less than the third threshold, the predicted risk link and the corresponding risk type are determined to be a high-risk link and a corresponding risk type.

[0096] It can be seen that through the above-mentioned optional embodiments, the probability of irregular operation in each link can be determined based on the irregular operation prediction algorithm and sensor information, and then based on the preset difference judgment rule, it is determined whether the sensor abnormality of the link is close to the traceability risk situation, and the high-risk links and corresponding risk types are screened out to facilitate subsequent accurate risk prediction and quality control, thereby assisting in improving the timeliness and control effect of dairy product quality risk management, improving the safety and quality of dairy products, and reducing dairy product safety accidents.

[0097] As an optional embodiment, in the above steps, generating a link monitoring instruction corresponding to the high-risk link according to the high-risk link and the corresponding risk type to send to the corresponding device end includes:

[0098] For each high-risk link, determine the link monitoring equipment corresponding to the high-risk link;

[0099] Generate text notification information corresponding to the high-risk link according to the preset text template and the risk type corresponding to the high-risk link;

[0100] Generate monitoring instructions for the corresponding links of risk types according to the preset relationship between risk types and monitoring instructions;

[0101] Send link monitoring instructions and text notification information to the link monitoring device.

[0102] It can be seen that through the above-mentioned optional embodiments, based on the correspondence between text templates and instructions, link monitoring instructions and text notification information can be generated and sent to the monitoring equipment corresponding to the link, thereby improving the timeliness and control effect of dairy product quality risk management, improving the safety quality of dairy products, and reducing dairy product safety accidents.

[0103] Embodiment 2

[0104] See also Figure 2 , Figure 2 Schematic diagram of a dairy product quality data processing system based on traceability information disclosed in an embodiment of the present invention. Figure 2 The described dairy product quality data processing system based on traceability information 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 dairy product quality data processing system based on traceability information may include:

[0105] The acquisition module 201 is used to acquire the traceability information and sensor information of multiple links corresponding to the target dairy product.

[0106] The identification module 202 is used to identify the predicted risk links with quality risks among multiple links based on the neural network algorithm and traceability information.

[0107] The screening module 203 is used to screen out high-risk links and corresponding risk types from all predicted risk links based on the sensor information and a preset information verification algorithm.

[0108] The determination module 204 is used to generate a link monitoring instruction corresponding to the high-risk link according to the high-risk link and the corresponding risk type, and send it to the corresponding device end.

[0109] It can be seen that the above-mentioned embodiments of the invention can determine the high-risk links and corresponding risk types based on the neural network algorithm and the information verification algorithm through the traceability information and sensor information of multiple links corresponding to the target dairy products, and then generate the link monitoring instructions corresponding to the high-risk links to send to the corresponding device end, thereby effectively improving the timeliness and control effect of dairy product quality risk management, improving the safety quality of dairy products, and reducing dairy product safety accidents.

[0110] As an optional embodiment, the link is a breeding link, a processing link, a distribution link or a sales link.

[0111] It can be seen that through the above optional embodiments, the specific types of links are limited to more comprehensively characterize the whole process information of dairy products, assist in improving the timeliness and control effect of dairy product quality risk management, improve the safety quality of dairy products, and reduce dairy product safety accidents.

[0112] As an optional embodiment, the traceability information includes at least one of label information, link equipment identification, link personnel information, link equipment location and link time.

[0113] It can be seen that through the above optional embodiments, the content of the traceability information is clarified to more comprehensively characterize the traceability information of dairy products in the links, assist in improving the timeliness and control effect of dairy product quality risk management, improve the safety and quality of dairy products, and reduce dairy product safety accidents.

[0114] As an optional embodiment, the sensing information includes at least one of image information, sound information, video information and infrared ranging information acquired by a sensor arranged on the production line.

[0115] It can be seen that through the above optional embodiments, the content of the sensor information is clarified to more comprehensively characterize the sensor characteristic information of dairy products in the production process, assist in improving the timeliness and control effect of dairy product quality risk management, improve the safety quality of dairy products, and reduce dairy product safety accidents.

[0116] As an optional embodiment, the identification module identifies the specific method of predicting risk links with quality risks in multiple links based on the traceability information based on the neural network algorithm, including:

[0117] For each link, determine the two adjacent links before and after the link in the preset link sequence;

[0118] Input the traceability information of this link and two adjacent links into the trained risk prediction neural network model to obtain the risk prediction value and risk type corresponding to this link;

[0119] According to the risk prediction value, all links are sorted from large to small to obtain the first link sequence;

[0120] All links that are the first number in the first link sequence and whose risk prediction values ​​are greater than the first threshold are screened out to obtain multiple predicted risk links.

[0121] It can be seen that through the above-mentioned optional embodiments, risk prediction can be carried out based on the traceability information of links and adjacent links through the risk prediction neural network model, and then multiple predicted risk links can be determined based on sorting and screening to facilitate subsequent precise quality control, thereby assisting in improving the timeliness and control effect of dairy product quality risk management, improving the safety quality of dairy products, and reducing dairy product safety accidents.

[0122] As an optional embodiment, the risk prediction neural network model is a neural network model with a CNN structure, which is obtained by training a training data set including training traceability information of multiple adjacent links and corresponding risk annotations. During the training, the model parameters are optimized until convergence through a preset loss function and gradient descent algorithm.

[0123] It can be seen that through the above optional embodiments, the type and training details of the risk prediction neural network model are clarified to facilitate subsequent accurate risk prediction and quality control, thereby assisting in improving the timeliness and control effect of dairy product quality risk management, improving the safety quality of dairy products, and reducing dairy product safety accidents.

[0124] As an optional embodiment, the screening module screens out high-risk links and corresponding risk types from all predicted risk links according to the sensor information and a preset information verification algorithm, including:

[0125] For each predicted risk link, the sensor information corresponding to the predicted risk link is input into the trained irregular operation prediction neural network to obtain a predicted probability value of the existence of irregular operation corresponding to the predicted risk link; optionally, the irregular operation prediction neural network is trained by a training data set including a plurality of training sensor information and corresponding standard operation annotations;

[0126] Calculate a first difference between the predicted probability value and a second threshold value;

[0127] Calculate the absolute value of the difference between 1 and the calculated ratio; optionally, calculate the ratio as the ratio of the first difference to the risk prediction value;

[0128] Determine whether the absolute value of the difference is less than a preset third threshold value to determine whether the sensor abnormality of the predicted risk link is close to the traceability risk situation;

[0129] When the absolute value of the difference is less than the third threshold, the predicted risk link and the corresponding risk type are determined to be a high-risk link and a corresponding risk type.

[0130] It can be seen that through the above-mentioned optional embodiments, the probability of irregular operation in each link can be determined based on the irregular operation prediction algorithm and sensor information, and then based on the preset difference judgment rule, it is determined whether the sensor abnormality of the link is close to the traceability risk situation, and the high-risk links and corresponding risk types are screened out to facilitate subsequent accurate risk prediction and quality control, thereby assisting in improving the timeliness and control effect of dairy product quality risk management, improving the safety and quality of dairy products, and reducing dairy product safety accidents.

[0131] As an optional embodiment, the specific manner in which the determination module generates a link monitoring instruction corresponding to the high-risk link according to the high-risk link and the corresponding risk type and sends the instruction to the corresponding device end includes:

[0132] For each high-risk link, determine the link monitoring equipment corresponding to the high-risk link;

[0133] Generate text notification information corresponding to the high-risk link according to the preset text template and the risk type corresponding to the high-risk link;

[0134] Generate monitoring instructions for the corresponding links of risk types according to the preset relationship between risk types and monitoring instructions;

[0135] Send link monitoring instructions and text notification information to the link monitoring device.

[0136] It can be seen that through the above-mentioned optional embodiments, based on the correspondence between text templates and instructions, link monitoring instructions and text notification information can be generated and sent to the monitoring equipment corresponding to the link, thereby improving the timeliness and control effect of dairy product quality risk management, improving the safety quality of dairy products, and reducing dairy product safety accidents.

[0137] Embodiment 3

[0138] See also Figure 3 , Figure 3 This is another dairy product quality data processing system based on traceability information disclosed in an embodiment of the present invention. Figure 3 The described dairy product quality data processing system based on traceability information 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 dairy product quality data processing system based on traceability information may include:

[0139] A memory 301 storing executable program codes;

[0140] a processor 302 coupled to the memory 301;

[0141] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the dairy product quality data processing method based on traceability information described in the first embodiment.

[0142] Embodiment 4

[0143] 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 method for processing dairy product quality data based on traceability information described in the first embodiment.

[0144] Embodiment 5

[0145] 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 dairy product quality data processing method based on traceability information described in Example 1.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Finally, it should be noted that the dairy product quality data processing method and system based on traceability information disclosed in the embodiment of the present invention discloses only 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, it should be understood by those skilled in the art 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 various embodiments of the present invention.

Claims

1. A method for processing dairy product quality data based on traceability information, characterized in that: The method comprises: Obtain traceability information and sensor information of multiple links corresponding to the target dairy products; Based on the neural network algorithm, according to the traceability information, the predicted risk link with quality risk in the multiple links is identified, including: For each of the links, determine the two adjacent links before and after the link in the preset link sequence; Inputting the traceability information of the link and the two adjacent links into the trained risk prediction neural network model to obtain the risk prediction value and risk type corresponding to the link; According to the risk prediction values, all the links are sorted from large to small to obtain a first link sequence; Screening out all the links of the first number in the first link sequence and whose risk prediction values ​​are greater than a first threshold, to obtain a plurality of predicted risk links; According to the sensor information and a preset information verification algorithm, high-risk links and corresponding risk types are screened out from all the predicted risk links; According to the high-risk links and the corresponding risk types, link monitoring instructions corresponding to the high-risk links are generated and sent to the corresponding device ends.

2. The method for processing dairy product quality data based on traceability information according to claim 1, characterized in that: The links mentioned are breeding link, processing link, distribution link or sales link.

3. The method for processing dairy product quality data based on traceability information according to claim 1, characterized in that: The traceability information includes at least one of label information, link equipment identification, link personnel information, link equipment location and link time.

4. The method for processing dairy product quality data based on traceability information according to claim 1, characterized in that: The sensor information includes at least one of image information, sound information, video information and infrared ranging information acquired by a sensor arranged on the production line.

5. The method for processing dairy product quality data based on traceability information according to claim 1, characterized in that: The risk prediction neural network model is a neural network model with a CNN structure, which is obtained by training a training data set including training traceability information of multiple adjacent links and corresponding risk annotations. During the training, the model parameters are optimized until convergence through a preset loss function and gradient descent algorithm.

6. The method for processing dairy product quality data based on traceability information according to claim 1, characterized in that: The step of screening out high-risk links and corresponding risk types from all the predicted risk links based on the sensor information and a preset information verification algorithm includes: For each of the risk prediction links, the sensor information corresponding to the risk prediction link is input into a trained irregular operation prediction neural network to obtain a predicted probability value of the existence of irregular operations corresponding to the risk prediction link; the irregular operation prediction neural network is trained by a training data set including a plurality of training sensor information and corresponding standard operation annotations; Calculate a first difference between the predicted probability value and a second threshold; Calculate the absolute value of the difference between 1 and the calculated ratio; the calculated ratio is the ratio of the first difference to the risk prediction value; Determine whether the absolute value of the difference is less than a preset third threshold value to determine whether the sensor abnormality of the risk prediction link is close to the traceability risk situation; When the absolute value of the difference is less than the third threshold, the predicted risk link and the corresponding risk type are determined to be a high-risk link and the corresponding risk type.

7. The method for processing dairy product quality data based on traceability information according to claim 6, characterized in that: The step of generating a link monitoring instruction corresponding to the high-risk link according to the high-risk link and the corresponding risk type to send the instruction to the corresponding device end includes: For each of the high-risk links, determine the link monitoring device corresponding to the high-risk link; Generate text notification information corresponding to the high-risk link according to the preset text template and the risk type corresponding to the high-risk link; According to the preset relationship between the risk type and the monitoring instruction, generating the link monitoring instruction corresponding to the risk type; The link monitoring instruction and the text notification information are sent to the link monitoring device.

8. A dairy product quality data processing system based on traceability information, characterized in that: The system comprises: An acquisition module, used to obtain traceability information and sensor information of multiple links corresponding to the target dairy product; An identification module is used to identify, based on a neural network algorithm and according to the traceability information, a predicted risk link with quality risk among the multiple links, including: For each of the links, determine the two adjacent links before and after the link in the preset link sequence; Inputting the traceability information of the link and the two adjacent links into the trained risk prediction neural network model to obtain the risk prediction value and risk type corresponding to the link; According to the risk prediction values, all the links are sorted from large to small to obtain a first link sequence; Screening out all the links of the first number in the first link sequence and whose risk prediction values ​​are greater than a first threshold, to obtain a plurality of predicted risk links; A screening module, used to screen out high-risk links and corresponding risk types from all the predicted risk links according to the sensor information and a preset information verification algorithm; The determination module is used to generate a link monitoring instruction corresponding to the high-risk link according to the high-risk link and the corresponding risk type, and send it to the corresponding device end.

9. A dairy product quality data processing system based on traceability information, 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 dairy product quality data processing method based on traceability information as described in any one of claims 1-7.

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