Petrochemical warehouse management tracing method and system based on supply chain

Abstract

The invention discloses a petrochemical warehouse management tracing method and system based on a supply chain, and relates to the field of petrochemical warehouse management.The petrochemical warehouse management tracing method comprises the steps that geographic position information of a current warehouse base is obtained, and a plane information graph is constructed by combining a freight route and a base map; marking the video acquisition basic information and the sensor acquisition basic information in a plane information graph; constructing an exception recognition network, obtaining video acquisition data and sensor data, and performing exception recognition by adopting the exception recognition network to obtain storage data; establishing a block chain topological relation in the plane information graph based on the stored data to form a supply chain tracing system; and obtaining a current tracing demand, and performing tracing in the supply chain tracing system according to the tracing demand to generate a dynamic tracing chart. According to the invention, the blockchain is adopted to enable the supply chain, the transparency in the management process is improved, and the traceability function is enhanced to optimize petrochemical warehouse management.

Description

Technical Field

[0001] The present invention relates to the field of petrochemical storage management, and in particular to a petrochemical storage management tracing method and system based on a supply chain. Background Art

[0002] Petrochemical storage management refers to the comprehensive management activities of planning, organizing, directing, coordinating, and controlling the storage, custody, loading and unloading, and transportation of petroleum and its chemical products. Petrochemical products are flammable, explosive, toxic, and harmful, and there are many product types and complex storage conditions. Therefore, petrochemical storage management must not only ensure the accurate quantity and stable quality of products, but also guarantee the safety of storage facilities and personnel. From quality inspection of crude oil entering the warehouse, to precise measurement of refined oil products leaving the warehouse, to environmental monitoring during daily storage, petrochemical storage management covers the storage and flow control of the entire life cycle from raw materials to finished products. At the same time, its management also involves the maintenance of storage equipment, the planning of storage space, the connection of logistics and transportation, and other aspects. It is an important support for petrochemical enterprises to ensure the stable operation of production and sales.

[0003] The petrochemical industry's supply chain system, from crude oil extraction and refining to product distribution and end-consumer consumption, involves numerous links and participants, presenting a series of complex challenges. Improper operation or environmental factors at any stage in the production, transportation, and storage of petrochemical products can lead to quality changes or safety incidents. For example, improper temperature and humidity control during storage can cause oil quality degradation, or illegal operations during loading and unloading can cause fires, explosions, and other accidents. However, due to a lack of effective supervision and traceability measures, it is difficult to quickly identify the responsible parties and the root causes of the problems after an accident occurs. Petrochemical storage management traceability is crucial for resolving these supply chain issues and ensuring the healthy development of the industry.

[0004] In order to solve these problems, there is an urgent need for an efficient, safe and transparent supply chain-based petrochemical storage management traceability method and system. Summary of the Invention

[0005] To solve the above problems, this application proposes a petrochemical storage management traceability method and system based on supply chain.

[0006] A petrochemical storage management traceability method based on a supply chain includes the following steps:

[0007] S1. Obtain the geographic location information of the current storage base, combine the freight route and base map to build a planar information map, and mark the basic information collected by video and sensors on the planar information map;

[0008] S2. Build an anomaly recognition network, obtain video acquisition data and sensor data, and use the anomaly recognition network to perform anomaly recognition to obtain stored data;

[0009] S3. Establishing a blockchain topology relationship in a flat information graph based on stored data and basic information to form a supply chain traceability system;

[0010] S4. Obtain the current traceability requirements, and generate a dynamic traceability map based on the traceability requirements in the supply chain traceability system.

[0011] Preferably, in S1, the geographical location information of the current storage base is obtained, and a plane information map is constructed in combination with the freight route and the base map. The specific contents of marking the basic information collected by the video and the basic information collected by the sensor in the plane information map include:

[0012] The basic information of video acquisition includes video acquisition sites and actual distances between video acquisition sites;

[0013] The basic information collected by the sensor includes sensor category information and sensor installation location;

[0014] Obtain the current storage base's geographic location information and freight routes, and establish a freight route network with the storage base as the origin;

[0015] Mark the video collection points in the freight route network and record the distance to the video collection points related to the current point;

[0016] Obtain a base map and analyze the base map to obtain base routes, storage units, and production units;

[0017] Mark the video collection points in the base route, storage unit and production unit, and record the distance to the video collection point related to the current point;

[0018] Add application equipment as filling elements to the corresponding positions of storage units and production units to generate a base network;

[0019] Mark the filling elements according to the sensor installation location and match the sensor category information.

[0020] Preferably, the specific contents of constructing the anomaly recognition network are:

[0021] The anomaly identification network includes a freight anomaly identification network and a base anomaly identification network;

[0022] The steps of constructing the freight anomaly identification network include:

[0023] Extract freight route networks from flat infographics;

[0024] Obtain the video collection locations and the actual distances between the video collection locations, and divide the freight route network into several distance segments based on the video collection locations;

[0025] Record the lengths of several distance segments based on the actual distances between the video acquisition sites;

[0026] Collect distance segment information to obtain the speed limit range of the distance segment, and obtain the standard time range of the distance segment based on the speed limit range and length of the distance segment;

[0027] Define the starting point and end point, combine several distance segments to generate the corresponding freight plan route, and record the standard time range of the freight plan route;

[0028] The different freight plan paths are overlaid on the freight route network to obtain the freight anomaly identification network.

[0029] Preferably, the specific contents of building a base anomaly identification network include:

[0030] Extract the base network from the planar information map;

[0031] Plan base routes according to the destinations in the base to form a standard travel route network;

[0032] Use the standard route network to overlay the base network;

[0033] Obtain past base operation data and analyze the past base operation data to obtain different sensor data and image data in the storage unit and production unit;

[0034] Analyze the installation locations of different sensors and form sensor-related connection lines on the base network to provide secondary overlay coverage for the base network;

[0035] Analyze the influence relationship of different sensors and configure the influencing nodes on the sensor related connection lines to obtain a sensor anomaly recognition network;

[0036] Record the changes in sensor data in the sensor anomaly recognition network based on time and display them to obtain a dynamic network. Insert image data into the dynamic network in the form of time nodes.

[0037] Extract features from image data to obtain abnormal features, and identify the time nodes corresponding to the abnormal features;

[0038] Reverse time extraction of sensor data in dynamic networks to generate several abnormal reference trend time periods;

[0039] The abnormal reference trend time period is marked in the dynamic network to obtain the base anomaly identification network.

[0040] Preferably, the video acquisition data and sensor data are obtained, and the specific content of the stored data obtained by using an anomaly recognition network for anomaly recognition is:

[0041] Obtain the current status of freight vehicles in real time, and predict their freight routes and arrival times from the time they enter the first monitoring distance segment to obtain several freight forecasting plans;

[0042] Record the distance segments during each journey and gradually determine the freight forecast plan and its arrival time;

[0043] Mark the range segments where the freight time is not within the standard time range of the distance segment, and extract the video acquisition data within the range segment to obtain primary storage data;

[0044] After arriving at the base, the current base route is input into the base anomaly recognition network to determine whether there is any abnormality;

[0045] If normal, directly aggregate the data to obtain secondary storage data;

[0046] If there is an abnormality, the abnormal route will be marked as abnormal and an early warning will be issued on the corresponding route. At the same time, the video image will be intercepted and the data will be summarized to obtain secondary storage data;

[0047] Acquire different sensor data and image data in the storage unit and production unit in real time, extract features from the image data to obtain real-time image features, and issue an early warning to the corresponding unit if the real-time image features are abnormal. Collect the current sensor data and image data to obtain three storage data.

[0048] If the real-time image feature is not an abnormal feature, the detection is continued, and trend simulation is performed on different sensor data according to the time period to obtain the trend time period to be predicted;

[0049] Fitting the trend time period to be predicted with several abnormal reference trend time periods to obtain fitting values;

[0050] A fitting value range is preset. When the fitting value is within the fitting value range, a corresponding unit warning is issued and sensor data and image data in the time period are collected to obtain three stored data;

[0051] The first-stored data, the second-stored data, and the third-stored data are aggregated to obtain the stored data.

[0052] Preferably, the specific content of establishing a blockchain topological relationship in a planar information graph based on stored data to form a supply chain traceability system is:

[0053] The freight routes, base routes and base units are formed into data blocks respectively;

[0054] Establish block topology lines based on the influence relationships among freight routes, base routes, storage units, and production units in the planar information map;

[0055] The stored data is recorded in chronological order into data blocks to form a supply chain traceability system.

[0056] Preferably, the specific contents of obtaining the current traceability requirements and generating a dynamic traceability graph by tracing in the supply chain traceability system according to the traceability requirements are as follows:

[0057] Obtain current tracing requirements and determine the anomaly identification network based on the current tracing requirements;

[0058] Based on the timeline, the data generated in the freight routes, base routes, storage units and production units in the abnormal identification network are traced back to obtain the traceability results;

[0059] A tracing dynamic network is generated according to the tracing results. The image data containing abnormal features is used as the cutting point to cut the tracing dynamic network and generate several dynamic tracing graphs.

[0060] A petrochemical storage management and traceability system based on the supply chain, including:

[0061] Plane information unit: obtains the geographic location information of the current storage base, combines the freight route and base map to build a plane information map, and marks the basic information collected by video and sensors on the plane information map;

[0062] Abnormal identification unit: Build an abnormal identification network, obtain video acquisition data and sensor data, use the abnormal identification network to perform abnormal identification and obtain stored data;

[0063] Supply chain construction unit: Based on the stored data, a blockchain topology relationship is established in the flat information graph to form a supply chain traceability system;

[0064] Dynamic traceability unit: obtains current traceability requirements, and generates dynamic traceability graphs based on the traceability requirements in the supply chain traceability system.

[0065] An electronic device, characterized in that it includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the content of the petrochemical storage management traceability method based on the supply chain is implemented.

[0066] A storage medium, characterized in that the storage medium stores computer-executable instructions, which, when loaded and executed by a processor, implement the content of a petrochemical storage management traceability method based on a supply chain.

[0067] In summary, compared with traditional technologies, the present invention's supply chain-based petrochemical warehouse management traceability method and system pre-processes the collected data and marks and predicts abnormal situations. It uses blockchain to empower the supply chain, thereby improving the transparency of supply chain management while enhancing the security of petrochemical warehouse management, and optimizing blockchain data.

[0068] The technical method of the present invention is further described in detail below through the accompanying drawings and examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a step diagram of a petrochemical storage management traceability method based on a supply chain according to the present invention;

[0070] Figure 2 This is a module diagram of a petrochemical storage management and traceability system based on the supply chain of the present invention. DETAILED DESCRIPTION

[0071] The technical method of the present invention is further described below through the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and values ​​described in these embodiments do not limit the scope of this application.

[0072] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0073] Technologies, systems, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0074] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0075] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0076] like Figure 1 As shown, a petrochemical storage management traceability method based on the supply chain includes the following steps:

[0077] S1. Obtain the geographic location information of the current storage base, combine the freight route and base map to build a planar information map, and mark the basic information collected by video and sensors on the planar information map;

[0078] Furthermore, S1 obtains the geographical location information of the current storage base, combines the freight route and the base map to construct a plane information map, and marks the basic information collected by the video and the basic information collected by the sensor in the plane information map. The specific contents include:

[0079] The basic information of video acquisition includes video acquisition sites and actual distances between video acquisition sites;

[0080] The basic information collected by the sensor includes sensor category information and sensor installation location;

[0081] Obtain the current storage base's geographic location information and freight routes, and establish a freight route network with the storage base as the origin;

[0082] Mark the video collection points in the freight route network and record the distance to the video collection points related to the current point;

[0083] Obtain a base map and analyze the base map to obtain base routes, storage units, and production units;

[0084] Mark the video collection points in the base route, storage unit and production unit, and record the distance to the video collection point related to the current point;

[0085] Add application equipment as filling elements to the corresponding positions of storage units and production units to generate a base network;

[0086] Mark the filling elements according to the sensor installation location and match the sensor category information.

[0087] S2. Build an anomaly recognition network, obtain video acquisition data and sensor data, and use the anomaly recognition network to perform anomaly recognition to obtain stored data;

[0088] Furthermore, the anomaly identification network includes a freight anomaly identification network and a base anomaly identification network;

[0089] The steps of constructing the freight anomaly identification network include:

[0090] Extract freight route networks from flat infographics;

[0091] Obtain the video collection locations and the actual distances between the video collection locations, and divide the freight route network into several distance segments based on the video collection locations;

[0092] Record the lengths of several distance segments based on the actual distances between the video acquisition sites;

[0093] Collect distance segment information to obtain the speed limit range of the distance segment, and obtain the standard time range of the distance segment based on the speed limit range and length of the distance segment;

[0094] Define the starting point and end point, combine several distance segments to generate the corresponding freight plan route, and record the standard time range of the freight plan route;

[0095] The different freight plan paths are overlaid on the freight route network to obtain the freight anomaly identification network.

[0096] Furthermore, the specific contents of building a base anomaly identification network include:

[0097] Extract the base network from the planar information map;

[0098] Plan base routes according to the destinations in the base to form a standard travel route network;

[0099] Use the standard route network to overlay the base network;

[0100] Obtain past base operation data and analyze the past base operation data to obtain different sensor data and image data in the storage unit and production unit;

[0101] Analyze the installation locations of different sensors and form sensor-related connection lines on the base network to provide secondary overlay coverage for the base network;

[0102] Analyze the influence relationship of different sensors and configure the influencing nodes on the sensor related connection lines to obtain a sensor anomaly recognition network;

[0103] Record the changes in sensor data in the sensor anomaly recognition network based on time and display them to obtain a dynamic network. Insert image data into the dynamic network in the form of time nodes.

[0104] Extract features from image data to obtain abnormal features, and identify the time nodes corresponding to the abnormal features;

[0105] Reverse time extraction of sensor data in dynamic networks to generate several abnormal reference trend time periods;

[0106] The abnormal reference trend time period is marked in the dynamic network to obtain the base anomaly identification network.

[0107] It is understandable that dynamic networks should have predictive capabilities. The supply chain constructed by integrating neural networks and blockchain can respond to emergencies, achieve openness and transparency, and achieve the purpose of tracing the source.

[0108] Furthermore, the video acquisition data and sensor data are obtained, and the anomaly recognition network is used to perform anomaly recognition to obtain the specific content of the stored data;

[0109] Obtain the current status of freight vehicles in real time, and predict their freight routes and arrival times from the time they enter the first monitoring distance segment to obtain several freight forecasting plans;

[0110] Record the distance segments during each journey and gradually determine the freight forecast plan and its arrival time;

[0111] It is understandable that the abnormal fluctuations generated during the freight process are quite large. The peak traffic flow in different time periods will have an impact. In extreme cases, the freight may not be delivered. By recording the distance segment of each journey, we can determine where the goods came from and whether they were replaced due to errors in the middle of the journey.

[0112] Mark the range segments where the freight time is not within the standard time range of the distance segment, and extract the video acquisition data within the range segment to obtain primary storage data;

[0113] After arriving at the base, the current base route is input into the base anomaly recognition network to determine whether there is any abnormality;

[0114] If normal, directly aggregate the data to obtain secondary storage data;

[0115] If there is an abnormality, the abnormal route will be marked as abnormal and an early warning will be issued on the corresponding route. At the same time, the video image will be intercepted and the data will be summarized to obtain secondary storage data;

[0116] Acquire different sensor data and image data in the storage unit and production unit in real time, extract features from the image data to obtain real-time image features, and issue an early warning to the corresponding unit if the real-time image features are abnormal. Collect the current sensor data and image data to obtain three storage data.

[0117] It is understandable that when abnormal features are generated during video acquisition, inspection is required regardless of the sensor.

[0118] If the real-time image feature is not an abnormal feature, the detection is continued, and trend simulation is performed on different sensor data according to the time period to obtain the trend time period to be predicted;

[0119] Fitting the trend time period to be predicted with several abnormal reference trend time periods to obtain fitting values;

[0120] A fitting value range is preset. When the fitting value is within the fitting value range, a corresponding unit warning is issued and sensor data and image data in the time period are collected to obtain three stored data;

[0121] It can be understood that the sensor data trends at the same location are fitted. When the sensor trend associated with the current location sensor is abnormal, it will also affect the current location sensor. Therefore, once the fitting value is within the fitting value range, a timely warning will be issued.

[0122] The first-stored data, the second-stored data, and the third-stored data are aggregated to obtain the stored data.

[0123] S3. Establishing a blockchain topology relationship in a flat information graph based on stored data and basic information to form a supply chain traceability system;

[0124] Furthermore, the specific contents of establishing a blockchain topological relationship in a flat information graph based on stored data to form a supply chain traceability system are as follows:

[0125] The freight routes, base routes and base units are formed into data blocks respectively;

[0126] Establish block topology lines based on the influence relationships among freight routes, base routes, storage units, and production units in the planar information map;

[0127] The stored data is recorded in chronological order into data blocks to form a supply chain traceability system.

[0128] S4. Obtain the current traceability requirements, and generate a dynamic traceability map based on the traceability requirements in the supply chain traceability system.

[0129] Furthermore, the current traceability requirements are obtained, and the specific contents of tracing and generating a dynamic traceability graph in the supply chain traceability system according to the traceability requirements are as follows:

[0130] Obtain current tracing requirements and determine the anomaly identification network based on the current tracing requirements;

[0131] Based on the timeline, the data generated in the freight routes, base routes, storage units and production units in the abnormal identification network are traced back to obtain the traceability results;

[0132] A tracing dynamic network is generated according to the tracing results. The image data containing abnormal features is used as the cutting point to cut the tracing dynamic network and generate several dynamic tracing graphs.

[0133] It can be understood that using image data containing abnormal features as cutting points to cut the tracing dynamic network and generate several dynamic tracing graphs can make the abnormal features more obvious, so that the staff can check them in a targeted manner during the tracing process.

[0134] like Figure 2 As shown in the figure, a petrochemical storage management and traceability system based on the supply chain includes:

[0135] Plane information unit: obtains the geographic location information of the current storage base, combines the freight route and base map to build a plane information map, and marks the basic information collected by video and sensors on the plane information map;

[0136] Abnormal identification unit: Build an abnormal identification network, obtain video acquisition data and sensor data, use the abnormal identification network to perform abnormal identification and obtain stored data;

[0137] Supply chain construction unit: Based on the stored data, a blockchain topology relationship is established in the flat information graph to form a supply chain traceability system;

[0138] Dynamic traceability unit: obtains current traceability requirements, and generates dynamic traceability graphs based on the traceability requirements in the supply chain traceability system.

[0139] An electronic device, characterized in that it includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the content of the petrochemical storage management traceability method based on the supply chain is implemented.

[0140] A storage medium, characterized in that the storage medium stores computer-executable instructions, which, when loaded and executed by a processor, implement the content of a petrochemical storage management traceability method based on a supply chain.

[0141] Finally, it should be noted that 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 they can still modify or replace the technical method of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical method to deviate from the spirit and scope of the technical method of the present invention.

Claims

1. A petrochemical storage management traceability method based on supply chain, characterized in that: The following steps are involved: S1. Obtain the geographic location information of the current storage base, combine the freight route and base map to build a planar information map, and mark the basic information collected by video and sensors on the planar information map; S2. Build an anomaly recognition network, obtain video acquisition data and sensor data, and use the anomaly recognition network to perform anomaly recognition to obtain stored data; S3. Establishing a blockchain topology relationship in a flat information graph based on stored data and basic information to form a supply chain traceability system; S4. Obtain the current traceability requirements, and generate a dynamic traceability map based on the traceability requirements in the supply chain traceability system.

2. The petrochemical storage management traceability method based on supply chain according to claim 1 is characterized in that: S1 obtains the current storage base's geographic location information, combines the freight route and base map to construct a planar information map, and marks the basic information collected by video and sensors on the planar information map. The specific contents include: The basic information of video acquisition includes video acquisition sites and actual distances between video acquisition sites; The basic information collected by the sensor includes sensor category information and sensor installation location; Obtain the current storage base's geographic location information and freight routes, and establish a freight route network with the storage base as the origin; Mark the video collection points in the freight route network and record the distance to the video collection points related to the current point; Obtain a base map and analyze the base map to obtain base routes, storage units, and production units; Mark the video collection points in the base route, storage unit and production unit, and record the distance to the video collection point related to the current point; Add application equipment as filling elements to the corresponding positions of storage units and production units to generate a base network; Mark the filling elements according to the sensor installation location and match the sensor category information.

3. The petrochemical storage management traceability method based on supply chain according to claim 2 is characterized in that: The specific contents of building an anomaly recognition network are: The anomaly identification network includes a freight anomaly identification network and a base anomaly identification network; The steps of constructing the freight anomaly identification network include: Extract freight route networks from flat infographics; Obtain the video collection locations and the actual distances between the video collection locations, and divide the freight route network into several distance segments based on the video collection locations; Record the lengths of several distance segments based on the actual distances between the video acquisition sites; Collect distance segment information to obtain the speed limit range of the distance segment, and obtain the standard time range of the distance segment based on the speed limit range and length of the distance segment; Define the starting point and end point, combine several distance segments to generate the corresponding freight plan route, and record the standard time range of the freight plan route; The different freight plan paths are overlaid on the freight route network to obtain the freight anomaly identification network.

4. The petrochemical storage management traceability method based on supply chain according to claim 3 is characterized in that: The specific contents of building a base anomaly identification network include: Extract the base network from the planar information map; Plan base routes according to the destinations in the base to form a standard travel route network; Use the standard route network to overlay the base network; Obtain past base operation data and analyze the past base operation data to obtain different sensor data and image data in the storage unit and production unit; Analyze the installation locations of different sensors and form sensor-related connection lines on the base network to provide secondary overlay coverage for the base network; Analyze the influence relationship of different sensors and configure the influencing nodes on the sensor related connection lines to obtain a sensor anomaly recognition network; Record the changes in sensor data in the sensor anomaly recognition network based on time and display them to obtain a dynamic network. Insert image data into the dynamic network in the form of time nodes. Extract features from image data to obtain abnormal features, and identify the time nodes corresponding to the abnormal features; Reverse time extraction of sensor data in dynamic networks to generate several abnormal reference trend time periods; The abnormal reference trend time period is marked in the dynamic network to obtain the base anomaly identification network.

5. The petrochemical storage management traceability method based on supply chain according to claim 4 is characterized in that: Obtain video acquisition data and sensor data, use anomaly recognition network to perform anomaly recognition and obtain the specific content of the stored data; Obtain the current status of freight vehicles in real time, and predict their freight routes and arrival times from the time they enter the first monitoring distance segment to obtain several freight forecasting plans; Record the distance segments during each journey and gradually determine the freight forecast plan and its arrival time; Mark the range segments where the freight time is not within the standard time range of the distance segment, and extract the video acquisition data within the range segment to obtain primary storage data; After arriving at the base, the current base route is input into the base anomaly recognition network to determine whether there is any abnormality; If normal, directly aggregate the data to obtain secondary storage data; If there is an abnormality, the abnormal route will be marked as abnormal and an early warning will be issued on the corresponding route. At the same time, the video image will be intercepted and the data will be summarized to obtain secondary storage data; Acquire different sensor data and image data in the storage unit and production unit in real time, extract features from the image data to obtain real-time image features, and issue an early warning to the corresponding unit if the real-time image features are abnormal. Collect the current sensor data and image data to obtain three storage data. If the real-time image feature is not an abnormal feature, the detection is continued, and trend simulation is performed on different sensor data according to the time period to obtain the trend time period to be predicted; Fitting the trend time period to be predicted with several abnormal reference trend time periods to obtain fitting values; A fitting value range is preset. When the fitting value is within the fitting value range, a corresponding unit warning is issued and sensor data and image data in the time period are collected to obtain three stored data; The first-stored data, the second-stored data, and the third-stored data are aggregated to obtain the stored data.

6. A petrochemical storage management traceability method based on supply chain according to claim 5, characterized in that: The specific contents of establishing a supply chain traceability system based on the blockchain topology relationship in the flat information graph based on the stored data are as follows: The freight routes, base routes and base units are formed into data blocks respectively; Establish block topology lines based on the influence relationships among freight routes, base routes, storage units, and production units in the planar information map; The stored data is recorded in chronological order into data blocks to form a supply chain traceability system.

7. The petrochemical storage management traceability method based on supply chain according to claim 6 is characterized in that: Obtain the current traceability requirements and generate a dynamic traceability graph based on the traceability requirements in the supply chain traceability system. The specific contents are as follows: Obtain current tracing requirements and determine the anomaly identification network based on the current tracing requirements; Based on the timeline, the data generated in the freight routes, base routes, storage units and production units in the abnormal identification network are traced back to obtain the traceability results; A tracing dynamic network is generated according to the tracing results. The image data containing abnormal features is used as the cutting point to cut the tracing dynamic network and generate several dynamic tracing graphs.

8. A petrochemical storage management and traceability system based on the supply chain, characterized in that: include: Plane information unit: obtains the geographic location information of the current storage base, combines the freight route and base map to build a plane information map, and marks the basic information collected by video and sensors on the plane information map; Abnormal identification unit: Build an abnormal identification network, obtain video acquisition data and sensor data, use the abnormal identification network to perform abnormal identification and obtain stored data; Supply chain construction unit: Based on the stored data, a blockchain topology relationship is established in the flat information graph to form a supply chain traceability system; Dynamic traceability unit: obtains current traceability requirements, and generates dynamic traceability graphs based on the traceability requirements in the supply chain traceability system.

9. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the content of the petrochemical storage management traceability method based on the supply chain as claimed in any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium stores computer-executable instructions, which, when loaded and executed by a processor, implement the content of the supply chain-based petrochemical storage management traceability method as described in any one of claims 1 to 7.

Images