Commodity traceability management system based on big data

Abstract

The invention discloses a commodity traceablility management system based on big data, and relates to the technical field of commodity traceablility, a user collection unit is used for collecting traceablility records of a user in recent half a year, observation habit analysis of the user is carried out from the traceablility records, and record information and corresponding reading times, reading duration and key reading segments thereof are obtained; then, the data analysis unit can automatically determine concerned record information and record information with key reading segments according to the reading times and reading duration of the record information, and can automatically obtain content consistent with the concerned record information in a tracing segment by means of a processor when a user performs real-time tracing; at the same time, obtaining a corresponding traceability segment with a key reading segment, and marking the content as a display segment; therefore, reasonable analysis can be carried out according to the habits of the user, all contents looked up by the user inertially are intelligently displayed when the user traces, and the method is simple, effective and easy to use.

Description

Technical Field

[0001] The present invention belongs to the technical field of commodity traceability, and specifically is a commodity traceability management system based on big data. Background Art

[0002] The patent with publication number CN111080311A discloses a commodity traceability method and device based on blockchain, the method comprising: registering the device information of each commodity traceability device during the operation of the commodity on a consortium chain server or a public chain server, the device information including device identification, device category and device location; obtaining commodity traceability information through the commodity traceability device; when the commodity traceability information contains privacy information, uploading the commodity traceability information to the consortium chain server; when the commodity traceability information does not contain privacy information, uploading the commodity traceability information to the public chain server; wherein the consortium chain server is for authorized devices to query, and the public chain server is for all devices to query. This method can ensure that the privacy information of the commodity is not leaked when tracing the commodity information.

[0003] However, when it comes to the information being traced, each user has different viewing focuses and different content, so each user needs to select data every time they check it. This is not smart enough for user experience, so a new technical solution is needed. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; To this end, the present invention proposes a commodity traceability management system based on big data, comprising: The user collection unit is used to collect the traceability records of users in the past six months. The past six months refers to the time from the present six months ago. The traceability records are collected in the following ways: Get all the record segments, which include record information, including text header, photo name and video name; Then, for the recorded information of the video name, all the viewing ratios in the user's traceability records in the past six months are automatically obtained. The viewing ratios include the start viewing ratio, which represents the ratio of the video start node to the total duration, and the end viewing ratio, which represents the ratio of the video end node to the total duration. The inertia ratio segment is obtained according to the duration of each viewing ratio segment, and then the inertia ratio segments with overlapping areas are integrated to obtain the key viewing segment; For the recorded information of non-video content, the reading times and reading duration will be automatically obtained, so as to obtain the recorded information and its corresponding reading times, reading duration and key reading segments; Then the data analysis unit automatically determines the record information of interest and the record information with the focused reading segments according to the reading times and reading duration of the record information, and transmits it to the processor; The processor is used to automatically obtain the content in the tracing segment that is consistent with the concerned record information when the user performs real-time tracing, and at the same time obtain the corresponding tracing segment with the key reading segment, and mark the above content as a display segment.

[0005] Furthermore, the recording segment is determined as follows: First, all the record segments of the corresponding product at the front end of the current user's traceability node, each of which includes at least one of several recorded text information, videos and photos; Each record segment includes several record segments. Each record segment indicates that the status of the corresponding product has changed and needs to be recorded in the traceability record. The text headers, photo names and video names in the record segments are uniformly marked as record information.

[0006] Furthermore, all recording segments are set according to the same mode. When recording text, the text will include a text header and text content. The text header is the name of the corresponding record, and the text content is a specific description of the text header. The same mode here means that the text headers of all recording segments are consistent. If it is a video or a photo, it will be shot at the same time point or requirement, and the titles and shooting requirements of the video and photo are consistent.

[0007] Furthermore, the specific method of determining the ratio is as follows: When the recorded information is a video name, when the user views the same recorded short video, the full length of the video is marked as the total duration, and then the real-time duration of the video is divided by the total duration to obtain a value marked as the viewing ratio; Then, the viewing ratio segments that the user has viewed completely are obtained. A viewing ratio segment is a viewing ratio from one viewing ratio to another viewing ratio in the video. The former viewing ratio is marked as the start viewing ratio, and the latter viewing ratio is called the end viewing ratio. A complete viewing means that the user has watched all viewing ratio segments completely without fast forwarding or skipping any viewing ratio segment. Get all viewing ratio segments and delete the viewing ratio segments with a duration lower than T1. T1 is the preset duration set by the administrator.

[0008] Furthermore, the specific method of determining the inertia ratio segment according to the viewing ratio segment is: Then the viewing time of all viewing segments is obtained. The viewing time is the total viewing time of the corresponding viewing segment. When it is watched repeatedly, the repeated viewing time will also be counted. The viewing time exceeding T2 is marked as an inertia segment, and T2 is the preset time.

[0009] Furthermore, the specific method of deleting the inertia ratio segment is as follows: All inertia ratio segments are obtained. When any two inertia ratio segments overlap, the overlapping inertia ratio segments are merged according to the value with the larger ratio at the beginning and the value with the larger ratio at the end of the inertia ratio segment. All the inertia ratio segments with overlapping parts are processed in the same way, the inertia ratio segments are fused and retained, and then the remaining inertia ratio segments are marked as key review segments.

[0010] Furthermore, the user collection unit is used to transmit the reading times, reading duration and key reference segments of all recorded information to the data analysis unit, and the data analysis unit is used to analyze the reading times and reading duration of the recorded information. The specific analysis method is: First, exclude the recorded information whose substantive content is video; Get the reading times and reading duration of all remaining record information, and use the formula to calculate the reading value of the record information. The specific formula is: Reading value = 0.42*reading times + 0.58*reading time; The reading value exceeding the preset value X1 is marked as a record of concern; The data analysis unit is used for transmitting the record information of interest and the record information with the emphasized reading segments to the processor.

[0011] Furthermore, it also includes a barcode reaction detection unit, which is used to automatically generate a traceability signal when any user performs a traceability behavior. The traceability behavior refers to the process of any user scanning a code or performing traceability in other ways. The barcode reaction detection unit is used to transmit the traceability signal to the processor. The barcode reaction unit is also used to automatically obtain all traceability segments that can be obtained while generating the traceability signal. The traceability segment is the small record segment mentioned above; When the processor receives the traceability signal, it will display and analyze the traceability segment. The specific display and analysis methods are as follows: At this time, the content consistent with the concerned record information in the traceability segment will be automatically obtained, and the corresponding traceability segment with the key reading segment will be obtained, and the above content will be marked as the display segment; Get all display segments; The processor is used to drive the slice display unit to partition and display all display segments, so that users can quickly call them.

[0012] Compared with the prior art, the present invention has the following beneficial effects: This application uses a user collection unit to collect the user's traceability records for the past six months, analyzes the user's observation habits from the traceability records, and obtains record information and its corresponding reading times, reading time, and key reading segments; then the data analysis unit automatically determines the record information of interest and the record information with key reading segments based on the reading times and reading time of the record information, and uses a processor to automatically obtain the content consistent with the record information of interest in the traceability segment when the user performs real-time traceability, and at the same time obtains the corresponding traceability segment with the key reading segment, and marks the above content as a display segment; Therefore, the present application can perform reasonable analysis based on user habits and intelligently display all content that users habitually view when they trace the source. The present application is simple, effective, and easy to use. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0014] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0015] See also Figure 1 , this application provides a commodity traceability management system based on big data; As the first embodiment of the present application, it specifically includes: barcode reaction detection unit, user collection unit, data analysis unit, processor and slice display unit; The user collection unit is used to collect the user's traceability records for the past six months. The past six months refers to the time from the present six months ago. The traceability records are collected in the following ways: First, all the record segments of the corresponding product at the front end of the current user's traceability node include: The production and processing section is used to record the production date, production location, source of raw materials and other information of the product; Packaging and storage section, monitoring the packaging process and storage conditions of goods, including key parameters such as temperature and humidity; The channel logistics segment tracks the entire logistics process of goods from the production site to the sales terminal, including information such as transportation routes, transportation time, and transfer nodes.

[0016] The terminal sales segment records the sales information of the goods, such as sales time, sales location, purchaser information, etc. Each record segment includes one or more of several recorded text information, videos and photos. Each record segment also includes several record sub-segments. Each record sub-segment represents a state or situation change of the corresponding product in a record segment. For example, in the logistics segment, it enters the transfer station from the starting point, and the transfer station enters the next transfer station or the end point. Each record sub-segment also includes text, video or photos. All record sub-segments are set according to the same mode. For example, when recording text, the text will include a text header and text content. The text header is the name of the corresponding record, and the text content is a detailed description of the text header. The same mode here refers to the fact that the text headers of all record sub-segments are consistent. If it is a video or a photo, it will be shot at the same time point or requirement. The length of the video may vary, but the title of the video is consistent with the shooting requirements, and the photo and video names are consistent. For the convenience of description, the text header, photo name and video name here are uniformly marked as record information. Get the number of times each record information is read, mark it as read times, and get the reading time of the corresponding record information; When the recorded information is a video, when the user views the same recorded short video, the full length of the video is marked as the total duration, and then the real-time duration of the video is divided by the total duration to obtain a value marked as the viewing ratio. The viewing ratio is a node; Then, the viewing ratio segments that the user has viewed completely are obtained. A viewing ratio segment is a viewing ratio from one viewing ratio to another viewing ratio in the video. The former viewing ratio is marked as the start viewing ratio, and the latter viewing ratio is called the end viewing ratio. A complete viewing means that the user has watched all viewing ratio segments completely without fast forwarding or skipping any viewing ratio segment. Get all viewing ratio segments, and delete the viewing ratio segments with a duration lower than T1, where T1 is a preset duration set by the administrator; Then, the viewing time of all viewing segments is obtained. The viewing time is the total viewing time of the corresponding viewing segment, that is, how much time the viewing segment occupies. When it is viewed repeatedly, it will also be counted into the repeated viewing time. The viewing time exceeding T2 is marked as an inertia segment, and T2 is the preset time. Here, an inertia ratio segment deletion is also required. The specific method of deleting the inertia ratio segment is as follows: All inertia ratio segments are obtained. When any two inertia ratio segments overlap, the inertia ratio segments with a larger value at the beginning and at the end will be merged according to the two requirements. All the inertia ratio segments with overlapping parts are processed in the same way, and the inertia ratio segments are fused and retained, and then the remaining inertia ratio segments are marked as key review segments; Get the reading times, reading time and key reading segments of all recorded information. There is no key reading segment for non-video content. The user collection unit is used to transmit the reading times, reading duration and key reference segments of all recorded information to the data analysis unit, and the data analysis unit is used to analyze the reading times and reading duration of the recorded information. The specific analysis method is as follows: First, exclude the recorded information whose substantive content is video; Get the reading times and reading duration of all remaining record information, and use the formula to calculate the reading value of the record information. The specific formula is: Reading value = 0.42*reading times + 0.58*reading time; The reading value exceeding the preset value X1 is marked as a record of concern; The data analysis unit is used to transmit the record information of interest and the record information with the focused reading segments to the processor; It also includes a barcode reaction detection unit, which is used to automatically generate a traceability signal when any user performs a traceability behavior. The traceability behavior refers to the process of any user scanning a code or performing traceability in other ways. The barcode reaction detection unit is used to transmit the traceability signal to the processor. The barcode reaction unit is also used to automatically obtain all traceability segments that can be obtained while generating the traceability signal. The traceability segment is the small record segment mentioned above; When the processor receives the traceability signal, it will display and analyze the traceability segment. The specific display and analysis methods are as follows: At this time, the content consistent with the concerned record information in the traceability segment will be automatically obtained, and the corresponding traceability segment with the key reading segment will be obtained, and the above content will be marked as the display segment; Get all display segments; The processor is used to drive the slice display unit to partition and display all display segments, so that users can quickly call them.

[0017] As the second embodiment of the present application, this embodiment is implemented on the basis of the first embodiment. The difference between this embodiment and the first embodiment is that the method of determining the key review segment in this embodiment is different, which is specifically as follows: When the recorded information is a video, when the user views the same recorded short video, the full length of the video is marked as the total duration, and then the real-time duration of the video is divided by the total duration to obtain a value marked as the viewing ratio. The viewing ratio is a node; Then, the viewing ratio segments that the user has viewed completely are obtained. A viewing ratio segment is a viewing ratio from one viewing ratio to another viewing ratio in the video. The former viewing ratio is marked as the start viewing ratio, and the latter viewing ratio is called the end viewing ratio. A complete viewing means that the user has watched all viewing ratio segments completely without fast forwarding or skipping any viewing ratio segment. Get all viewing ratio segments, and delete the viewing ratio segments with a duration lower than T1, where T1 is a preset duration set by the administrator; Then, the viewing time of all viewing segments is obtained. The viewing time is the total viewing time of the corresponding viewing segment, that is, the actual viewing time of the user watching the viewing segment. Even if it is watched repeatedly, it will be counted. When watching at double speed or non-single speed, it will be calculated according to the actual viewing time. The viewing time exceeding T2 is marked as an inertia segment, and T2 is the preset time. Here, an inertia ratio segment deletion is also required. The specific method of deleting the inertia ratio segment is as follows: All inertia ratio segments are obtained. When any two inertia ratio segments overlap, the inertia ratio segments with a larger value at the beginning and at the end will be merged according to the two requirements. All the inertia ratio segments with overlapping parts are processed in the same way, the inertia ratio segments are fused and retained, and then the remaining inertia ratio segments are marked as key review segments.

[0018] Some of the data in the above formula are calculated by removing the dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0019] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A commodity traceability management system based on big data, characterized in that: include: The user collection unit is used to collect the user's traceability records for the past six months. The traceability records are collected in the following ways: Get all the record segments, which include record information, including text header, photo name and video name; Then, for the recorded information of the video name, all the viewing ratios in the user's traceability records in the past six months are automatically obtained. The viewing ratios include the start viewing ratio, which represents the ratio of the video start node to the total duration, and the end viewing ratio, which represents the ratio of the video end node to the total duration. The inertia ratio segment is obtained according to the duration of each viewing ratio segment, and then the inertia ratio segments with overlapping areas are integrated to obtain the key viewing segment; For the recorded information of non-video content, the reading times and reading duration will be automatically obtained, so as to obtain the recorded information and its corresponding reading times, reading duration and key reading segments; Then the data analysis unit automatically determines the record information of interest and the record information with the focused reading segments according to the reading times and reading duration of the record information, and transmits it to the processor; The processor is used to automatically obtain the content in the tracing segment that is consistent with the concerned record information when the user performs real-time tracing, and at the same time obtain the corresponding tracing segment with the key reading segment, and mark the above content as a display segment.

2. According to the big data-based commodity traceability management system of claim 1, it is characterized in that: The record segments are determined as follows: First, all the record segments of the corresponding product at the front end of the current user's traceability node, each of which includes at least one of several recorded text information, videos and photos; Each record segment includes several record segments. Each record segment indicates that the status of the corresponding product has changed and needs to be recorded in the traceability record. The text headers, photo names and video names in the record segments are uniformly marked as record information.

3. A commodity traceability management system based on big data according to claim 2, characterized in that: All recording segments are set up in the same mode. When recording text, the text will include a header and text content. The header is the name of the corresponding record, and the text content is a specific description of the header. The same mode here means that the headers of all recording segments are consistent. If it is a video or photo, it will be shot at the same time point or requirements, and the titles and shooting requirements of the video and photo are consistent.

4. The commodity traceability management system based on big data according to claim 1, characterized in that: Check the specific method of determining the ratio: When the recorded information is a video name, when the user views the same recorded short video, the full length of the video is marked as the total duration, and then the real-time duration of the video is divided by the total duration to obtain a value marked as the viewing ratio; Then, the viewing ratio segments that the user has viewed completely are obtained. A viewing ratio segment is a viewing ratio from one viewing ratio to another viewing ratio in the video. The former viewing ratio is marked as the start viewing ratio, and the latter viewing ratio is called the end viewing ratio. A complete viewing means that the user has watched all viewing ratio segments completely without fast forwarding or skipping any viewing ratio segment. Get all viewing ratio segments and delete the viewing ratio segments with a duration lower than T1. T1 is the preset duration set by the administrator.

5. The commodity traceability management system based on big data according to claim 1, characterized in that: The specific method of determining the inertia ratio segment by viewing the ratio segment is: Then the viewing time of all viewing segments is obtained. The viewing time is the total viewing time of the corresponding viewing segment. When it is watched repeatedly, the repeated viewing time will also be counted. The viewing time exceeding T2 is marked as an inertia segment, and T2 is the preset time.

6. The commodity traceability management system based on big data according to claim 1, characterized in that: The specific method of deleting the inertia ratio segment is: All inertia ratio segments are obtained. When any two inertia ratio segments overlap, the overlapping inertia ratio segments are merged according to the value with the larger ratio at the beginning and the value with the larger ratio at the end of the inertia ratio segment. All the inertia ratio segments with overlapping parts are processed in the same way, the inertia ratio segments are fused and retained, and then the remaining inertia ratio segments are marked as key review segments.

7. The commodity traceability management system based on big data according to claim 1, characterized in that: The user collection unit is used to transmit the reading times, reading duration and key reference segments of all recorded information to the data analysis unit, and the data analysis unit is used to analyze the reading times and reading duration of the recorded information. The specific analysis method is as follows: First, exclude the recorded information whose substantive content is video; Get the reading times and reading duration of all remaining record information, and use the formula to calculate the reading value of the record information. The specific formula is: Reading value = 0.42*reading times + 0.58*reading time; The reading value exceeding the preset value X1 is marked as a record of concern; The data analysis unit is used for transmitting the record information of interest and the record information with the emphasized reading segments to the processor.

8. The commodity traceability management system based on big data according to claim 1, characterized in that: It also includes a barcode reaction detection unit, which is used to automatically generate a traceability signal when any user performs a traceability behavior. The traceability behavior refers to the process of any user scanning a code or performing traceability in other ways. The barcode reaction detection unit is used to transmit the traceability signal to the processor. The barcode reaction unit is also used to automatically obtain all traceability segments that can be obtained while generating the traceability signal. The traceability segment is the small record segment mentioned above; When the processor receives the traceability signal, it will display and analyze the traceability segment. The specific display and analysis methods are as follows: At this time, the content consistent with the concerned record information in the traceability segment will be automatically obtained, and the corresponding traceability segment with the key reading segment will be obtained, and the above content will be marked as the display segment; Get all display segments; The processor is used to drive the slice display unit to partition and display all display segments, so that users can quickly call them.

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