Petrochemical warehouse management tracing method and system based on supply chain

Abstract

The application discloses a petrochemical warehouse management tracing method and system based on a supply chain, relates to the field of petrochemical warehouse management, and comprises the following steps: acquiring geographic position information of a current warehouse base, combining a freight route and a base map to construct a plane information map, and marking video collection basic information and sensor collection basic information in the plane information map; constructing an anomaly recognition network, acquiring video collection data and sensor data, and obtaining storage data by adopting the anomaly recognition network for anomaly recognition; based on the storage data, establishing a block chain topological relationship in the plane information map to form a supply chain tracing system; acquiring current tracing requirements, and generating a dynamic tracing map by tracing in the supply chain tracing system according to the tracing requirements. The application adopts the block chain to empower the supply chain, improves the transparency in the management process, and enhances the tracing function to optimize the petrochemical warehouse management.

Description

Technical Field

[0001] This invention relates to the field of petrochemical storage management, and in particular to a petrochemical storage management traceability method and system based on the supply chain. Background Technology

[0002] Petrochemical storage management refers to the comprehensive management activities of planning, organizing, directing, coordinating, and controlling the storage, safekeeping, loading, unloading, and transportation of petroleum and its chemical products. Petrochemical products are flammable, explosive, toxic, and hazardous, and are diverse with complex storage conditions. Therefore, petrochemical storage management must not only ensure 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, and environmental monitoring during daily storage, petrochemical storage management covers the entire lifecycle of storage and circulation control from raw materials to finished products. Furthermore, its management also involves the maintenance of storage equipment, the planning of storage space, and the coordination of logistics and transportation, making it a crucial support for petrochemical enterprises to ensure stable production and sales operations.

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

[0004] To address these issues, there is an urgent need for an efficient, secure, and transparent supply chain-based traceability method and system for petrochemical storage management. Summary of the Invention

[0005] To address the aforementioned issues, this application proposes a petrochemical storage management traceability method and system based on the supply chain.

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

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

[0008] S2. Construct an anomaly detection network to acquire video capture data and sensor data, and use the anomaly detection network to perform anomaly detection to obtain stored data.

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

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

[0011] Preferably, in S1, the current geographical location information of the storage base is obtained, and a planar information map is constructed by combining the freight route and the base map. The specific content of marking the basic information collected by video and the basic information collected by sensors on the planar information map includes:

[0012] The basic information for video acquisition includes video acquisition locations and the actual distance between video acquisition locations;

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

[0014] Obtain the current geographical location information and freight routes of the warehousing base, and establish a freight route network with the warehousing base as the origin;

[0015] Video capture points are marked in the freight route network, and the distances to relevant video capture points at the current location are recorded;

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

[0017] Mark the video acquisition points in the base route, storage unit, and production unit, and record the distance to the relevant video acquisition points at the current location;

[0018] The application devices are added as filler elements to the corresponding positions in the storage and production units to generate the base network;

[0019] Label the filling elements according to the sensor mounting location and match the sensor category information.

[0020] Preferably, the specific content of constructing the anomaly detection network is as follows:

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

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

[0023] Extract the freight route network from the planar information map;

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

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

[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 start and end points, combine several distance segments to generate corresponding freight route plans, and record the standard time range of the freight route plans.

[0028] By covering different freight routes on the freight route network, a freight anomaly identification network is obtained.

[0029] Preferably, the specific content of constructing the base anomaly detection network includes:

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

[0031] Based on the destinations within the base, the base routes are planned to form a standard route network;

[0032] The base network is overlaid with a standard travel route network for coverage.

[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] The installation sites of different sensors are analyzed, and sensor connection lines are formed on the base network to perform secondary overlay coverage on the base network;

[0035] By analyzing the influence relationships of different sensors and configuring the influence nodes in the sensor association connection lines, a primary sensor anomaly identification network is obtained.

[0036] A dynamic network is obtained by recording and displaying changes in sensor data in a sensor anomaly identification network based on time, and then inserting image data into the dynamic network in the form of time nodes.

[0037] Abnormal features are obtained by extracting features from image data, and the time nodes corresponding to the abnormal features are identified.

[0038] Reverse-time extraction of sensor data from dynamic networks generates several abnormal reference trend time periods;

[0039] By marking the abnormal reference trend time period in the dynamic network, the base anomaly identification network is obtained.

[0040] Preferably, the specific content of the stored data is obtained by acquiring video capture data and sensor data, and using an anomaly recognition network to perform anomaly recognition;

[0041] Real-time information on the current movement of freight vehicles is obtained, and several freight prediction schemes are derived by predicting their freight routes and arrival times when they enter the first monitoring distance segment.

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

[0043] The freight time is marked if it is outside the standard time range of the distance segment, and the video data collected within the range is extracted to obtain the stored data.

[0044] Upon arrival at the base, input the current base travel route into the base anomaly detection network to determine if there is an anomaly in the travel route.

[0045] If everything is normal, the data will be directly aggregated to obtain the secondary storage data.

[0046] If an anomaly is detected, the abnormal route will be marked and an alert will be issued for the corresponding route. At the same time, video images will be captured and the data will be aggregated to obtain secondary storage data.

[0047] The system acquires real-time sensor data and image data from storage and production units, extracts features from the image data to obtain real-time image features, and issues a warning for the corresponding unit if the real-time image features are abnormal. It also collects current sensor data and image data to obtain three sets of storage data.

[0048] If the real-time image features are not abnormal features, the detection continues, and the trend simulation of different sensor data is performed according to the time period to obtain the time period to be predicted.

[0049] The fitted values ​​are obtained by fitting the time period of the trend to be predicted with several abnormal reference trend time periods;

[0050] There is a preset range of fitted values. When the fitted value is within the range, the corresponding unit will issue an early warning and collect sensor data and image data during the time period to obtain three storage data.

[0051] The stored data is obtained by summarizing the primary, secondary, and tertiary storage data.

[0052] Preferably, the specific content of establishing a supply chain traceability system based on stored data and building a blockchain topology in a planar information graph is as follows:

[0053] The freight routes, base routes, and base units are each divided into data blocks;

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

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

[0056] Preferably, the specific content of obtaining current traceability needs and generating dynamic traceability maps in the supply chain traceability system based on these needs is as follows:

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

[0058] Based on the timeline, traceability results are obtained by tracing data generated in freight routes, base routes, storage units, and production units within the anomaly identification network.

[0059] Based on the tracing results, a dynamic tracing network is generated. Using image data containing abnormal features as cutting points, the dynamic tracing network is cut to generate several dynamic tracing maps.

[0060] A petrochemical warehousing management and traceability system based on the supply chain includes:

[0061] Planar Information Unit: Obtain the current geographical location information of the warehousing base, combine it with freight routes and base maps to construct a planar information map, and mark the basic information collected by video and sensors on the planar information map;

[0062] Anomaly Detection Unit: Constructs an anomaly detection network, acquires video capture data and sensor data, and uses the anomaly detection network to perform anomaly detection to obtain stored data;

[0063] Supply chain building blocks: Based on stored data, a blockchain topology relationship is established in a planar information graph to form a supply chain traceability system;

[0064] Dynamic traceability unit: Obtains current traceability needs and generates dynamic traceability maps in the supply chain traceability system based on these needs.

[0065] An electronic device is characterized by comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when calling the computer program in the memory, implements the content of a petrochemical storage management traceability method based on the supply chain.

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

[0067] In summary, the petrochemical storage management traceability method and system based on the supply chain of the present invention, compared with traditional technologies, pre-processes the collected data and marks and predicts abnormal situations, empowers the supply chain with blockchain, improves the transparency of supply chain management and enhances the security of petrochemical storage management, and optimizes the blockchain data.

[0068] The technical method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating the steps of a petrochemical warehousing management traceability method based on the supply chain according to the present invention.

[0070] Figure 2 This is a block diagram of a petrochemical warehousing management and traceability system based on the supply chain, according to the present invention. Detailed Implementation

[0071] The technical method of the present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps 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 and is in no way intended to limit the scope of this application and its application or use.

[0073] Techniques, systems, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the instruction manual.

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

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

[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 current geographical location information of the storage base, combine the freight route and the base map to construct a planar information map, and mark the basic information collected by video and sensors on the planar information map;

[0078] Furthermore, S1 obtains the current geographical location information of the storage base, and constructs a planar information map by combining the freight route and the base map. The specific content of marking the basic information collected by video and sensors on the planar information map includes:

[0079] The basic information for video acquisition includes video acquisition locations and the actual distance between video acquisition locations;

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

[0081] Obtain the current geographical location information and freight routes of the warehousing base, and establish a freight route network with the warehousing base as the origin;

[0082] Video capture points are marked in the freight route network, and the distances to relevant video capture points at the current location are recorded;

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

[0084] Mark the video acquisition points in the base route, storage unit, and production unit, and record the distance to the relevant video acquisition points at the current location;

[0085] The application devices are added as filler elements to the corresponding positions in the storage and production units to generate the base network;

[0086] Label the filling elements according to the sensor mounting location and match the sensor category information.

[0087] S2. Construct an anomaly detection network to acquire video capture data and sensor data, and use the anomaly detection network to perform anomaly detection to obtain stored data.

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

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

[0090] Extract the freight route network from the planar information map;

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

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

[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 start and end points, combine several distance segments to generate corresponding freight route plans, and record the standard time range of the freight route plans.

[0095] By covering different freight routes on the freight route network, a freight anomaly identification network is obtained.

[0096] Furthermore, the specific content of constructing the base anomaly detection network includes:

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

[0098] Based on the destinations within the base, the base routes are planned to form a standard route network;

[0099] The base network is overlaid with a standard travel route network for coverage.

[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] The installation sites of different sensors are analyzed, and sensor connection lines are formed on the base network to perform secondary overlay coverage on the base network;

[0102] By analyzing the influence relationships of different sensors and configuring the influence nodes in the sensor association connection lines, a primary sensor anomaly identification network is obtained.

[0103] A dynamic network is obtained by recording and displaying changes in sensor data in a sensor anomaly identification network based on time, and then inserting image data into the dynamic network in the form of time nodes.

[0104] Abnormal features are obtained by extracting features from image data, and the time nodes corresponding to the abnormal features are identified.

[0105] Reverse-time extraction of sensor data from dynamic networks generates several abnormal reference trend time periods;

[0106] By marking the abnormal reference trend time period in the dynamic network, the base anomaly identification network is obtained.

[0107] Understandably, dynamic networks should have predictive capabilities. A supply chain built by integrating neural networks and blockchain can cope with emergencies, achieve transparency, and enable traceability.

[0108] Furthermore, video capture data and sensor data are acquired, and anomaly detection networks are used to identify anomalies to obtain the specific content of the stored data;

[0109] Real-time information on the current movement of freight vehicles is obtained, and several freight prediction schemes are derived by predicting their freight routes and arrival times when they enter the first monitoring distance segment.

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

[0111] It is understandable that the range of abnormal fluctuations during the freight process is large. Different time periods and peak traffic flow will have an impact. In extreme cases, the goods may not be delivered. By recording the distance of each journey, we can determine how the goods came about and whether there were any errors or replacements along the way.

[0112] The freight time is marked if it is outside the standard time range of the distance segment, and the video data collected within the range is extracted to obtain the stored data.

[0113] Upon arrival at the base, input the current base travel route into the base anomaly detection network to determine if there is an anomaly in the travel route.

[0114] If everything is normal, the data will be directly aggregated to obtain the secondary storage data.

[0115] If an anomaly is detected, the abnormal route will be marked and an alert will be issued for the corresponding route. At the same time, video images will be captured and the data will be aggregated to obtain secondary storage data.

[0116] The system acquires real-time sensor data and image data from storage and production units, extracts features from the image data to obtain real-time image features, and issues a warning for the corresponding unit if the real-time image features are abnormal. It also collects current sensor data and image data to obtain three sets of storage data.

[0117] Understandably, when abnormal features are detected during video capture, an inspection is necessary regardless of the sensor's capabilities.

[0118] If the real-time image features are not abnormal features, the detection continues, and the trend simulation of different sensor data is performed according to the time period to obtain the time period to be predicted.

[0119] The fitted values ​​are obtained by fitting the time period of the trend to be predicted with several abnormal reference trend time periods;

[0120] There is a preset range of fitted values. When the fitted value is within the range, the corresponding unit will issue an early warning and collect sensor data and image data during the time period to obtain three storage data.

[0121] It is understandable that the trend of sensor data at the same location is fitted. When the trend of the sensor associated with the current location is abnormal, it will also affect the current location sensor. Therefore, an early warning will be issued as soon as the fitted value is within the fitted value range.

[0122] The stored data is obtained by summarizing the primary, secondary, and tertiary storage data.

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

[0124] Furthermore, the specific details of establishing a supply chain traceability system based on stored data and building blockchain topology relationships in a planar information graph are as follows:

[0125] The freight routes, base routes, and base units are each divided into data blocks;

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

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

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

[0129] Furthermore, the specific details of obtaining current traceability requirements and generating dynamic traceability maps within the supply chain traceability system based on these requirements are as follows:

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

[0131] Based on the timeline, traceability results are obtained by tracing data generated in freight routes, base routes, storage units, and production units within the anomaly identification network.

[0132] Based on the tracing results, a dynamic tracing network is generated. Using image data containing abnormal features as cutting points, the dynamic tracing network is cut to generate several dynamic tracing maps.

[0133] It is understandable that using image data containing abnormal features as cutting points to cut the dynamic traceability network and generate several dynamic traceability maps can make the abnormal features more obvious, allowing staff to check these in a targeted manner during the traceability process.

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

[0135] Planar Information Unit: Obtain the current geographical location information of the warehousing base, combine it with freight routes and base maps to construct a planar information map, and mark the basic information collected by video and sensors on the planar information map;

[0136] Anomaly Detection Unit: Constructs an anomaly detection network, acquires video capture data and sensor data, and uses the anomaly detection network to perform anomaly detection to obtain stored data;

[0137] Supply chain building blocks: Based on stored data, a blockchain topology relationship is established in a planar information graph to form a supply chain traceability system;

[0138] Dynamic traceability unit: Obtains current traceability needs and generates dynamic traceability maps in the supply chain traceability system based on these needs.

[0139] An electronic device is characterized by comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when calling the computer program in the memory, implements the content of a petrochemical storage management traceability method based on the supply chain.

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

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical methods of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical methods of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical methods to deviate from the spirit and scope of the technical methods of the present invention.

Claims

1. A petrochemical warehousing management traceability method based on the supply chain, characterized in that, Includes the following steps: S1. Obtain the current geographical location information of the warehouse base, combine the freight route and the base map to construct a planar information map, and mark the basic information collected by video and sensors on the planar information map; S2. Construct an anomaly detection network to acquire video capture data and sensor data, and use the anomaly detection network to perform anomaly detection to obtain stored data. S3. Establish a blockchain topology relationship based on stored data and basic information in a planar information map to form a supply chain traceability system; S4. Obtain current traceability requirements and generate a dynamic traceability map in the supply chain traceability system based on these requirements. The specific content of constructing the anomaly detection network is as follows: The anomaly detection network includes a freight anomaly detection network and a base anomaly detection network; The steps for constructing the freight anomaly identification network include: Extract the freight route network from the planar information map; Obtain the video capture points and the actual distances between them, and divide the freight route network into several distance segments based on the video capture points; Record the lengths of several distance segments based on the actual distances between video capture points; 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 start and end points, combine several distance segments to generate corresponding freight route plans, and record the standard time range of the freight route plans. By covering different freight routes on the freight route network, a freight anomaly identification network is obtained.

2. The petrochemical warehousing management traceability method based on supply chain as described in claim 1, characterized in that, S1 obtains the current geographical location information of the storage base, and constructs a planar information map by combining the freight route and the base map. The specific content of marking the basic information collected by video and sensors on the planar information map includes: The basic information for video acquisition includes video acquisition locations and the actual distance between video acquisition locations; The basic information collected by the sensor includes sensor category information and sensor installation location; Obtain the current geographical location information and freight routes of the warehousing base, and establish a freight route network with the warehousing base as the origin; Video capture points are marked in the freight route network, and the distances to relevant video capture points at the current location are recorded; Obtain the base map and analyze it to obtain the base route, storage units, and production units; Mark the video acquisition points in the base route, storage unit, and production unit, and record the distance to the relevant video acquisition points at the current location; The application devices are added as filler elements to the corresponding positions in the storage and production units to generate the base network; Label the filling elements according to the sensor mounting location and match the sensor category information.

3. The petrochemical warehousing management traceability method based on supply chain as described in claim 2, characterized in that, The specific content of constructing the base anomaly detection network includes: Extract the base network from the planar information map; Based on the destinations within the base, the base routes are planned to form a standard route network; The base network is overlaid with a standard travel route network for coverage. 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; The installation sites of different sensors are analyzed, and sensor connection lines are formed on the base network to perform secondary overlay coverage on the base network; By analyzing the influence relationships of different sensors and configuring the influence nodes in the sensor association connection lines, a primary sensor anomaly identification network is obtained. A dynamic network is obtained by recording and displaying changes in sensor data in a sensor anomaly identification network based on time, and then inserting image data into the dynamic network in the form of time nodes. Abnormal features are obtained by extracting features from image data, and the time nodes corresponding to the abnormal features are identified. Reverse-time extraction of sensor data from dynamic networks generates several abnormal reference trend time periods; By marking the abnormal reference trend time period in the dynamic network, the base anomaly identification network is obtained.

4. The petrochemical warehousing management traceability method based on supply chain as described in claim 3, characterized in that, The specific content of the stored data is obtained by acquiring video capture data and sensor data, and using an anomaly detection network to perform anomaly detection. Real-time information on the current movement of freight vehicles is obtained, and several freight prediction schemes are derived by predicting their freight routes and arrival times when they enter the first monitoring distance segment. Record the distance segments during each journey to gradually determine the freight forecasting plan and its arrival time; The freight time is marked if it is outside the standard time range of the distance segment, and the video data collected within the range is extracted to obtain the stored data. Upon arrival at the base, input the current base travel route into the base anomaly detection network to determine if there is an anomaly in the travel route. If everything is normal, the data will be directly aggregated to obtain the secondary storage data. If an anomaly is detected, the abnormal route will be marked and an alert will be issued for the corresponding route. At the same time, video images will be captured and the data will be aggregated to obtain secondary storage data. The system acquires real-time sensor data and image data from storage and production units, extracts features from the image data to obtain real-time image features, and issues a warning for the corresponding unit if the real-time image features are abnormal. It also collects current sensor data and image data to obtain three sets of storage data. If the real-time image features are not abnormal features, the detection continues, and the trend simulation of different sensor data is performed according to the time period to obtain the time period to be predicted. The fitted values ​​are obtained by fitting the time period of the trend to be predicted with several abnormal reference trend time periods; There is a preset range of fitted values. When the fitted value is within the range, the corresponding unit will issue an early warning and collect sensor data and image data during the time period to obtain three storage data. The stored data is obtained by summarizing the primary, secondary, and tertiary storage data.

5. The petrochemical warehousing management traceability method based on supply chain as described in claim 4, characterized in that, The specific content of establishing a supply chain traceability system based on stored data and building blockchain topology relationships in a planar information map is as follows: The freight routes, base routes, and base units are each divided into data blocks; Establish block topology lines based on the influence relationships between freight routes, base routes, storage units, and production units in the planar information map; The stored data is recorded into data blocks in chronological order to form a supply chain traceability system.

6. The petrochemical warehousing management traceability method based on supply chain as described in claim 5, characterized in that, The specific steps for obtaining current traceability requirements and generating dynamic traceability maps within the supply chain traceability system based on these requirements are as follows: Obtain current traceability requirements and determine the anomaly identification network based on these requirements; Based on the timeline, traceability results are obtained by tracing data generated in freight routes, base routes, storage units, and production units within the anomaly identification network. Based on the tracing results, a dynamic tracing network is generated. Using image data containing abnormal features as cutting points, the dynamic tracing network is cut to generate several dynamic tracing maps.

7. A petrochemical warehousing management and traceability system based on the supply chain, characterized in that, include: Planar Information Unit: Obtain the current geographical location information of the warehousing base, combine it with freight routes and base maps to construct a planar information map, and mark the basic information collected by video and sensors on the planar information map; Anomaly Detection Unit: Constructs an anomaly detection network, acquires video capture data and sensor data, and uses the anomaly detection network to perform anomaly detection to obtain stored data; Supply chain building blocks: Based on stored data, a blockchain topology relationship is established in a planar information graph to form a supply chain traceability system; Dynamic traceability unit: Obtains current traceability needs and generates dynamic traceability maps in the supply chain traceability system based on these needs; The specific content of constructing the anomaly detection network is as follows: The anomaly detection network includes a freight anomaly detection network and a base anomaly detection network; The steps for constructing the freight anomaly identification network include: Extract the freight route network from the planar information map; Obtain the video capture points and the actual distances between them, and divide the freight route network into several distance segments based on the video capture points; Record the lengths of several distance segments based on the actual distances between video capture points; 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 start and end points, combine several distance segments to generate corresponding freight route plans, and record the standard time range of the freight route plans. By covering different freight routes on the freight route network, a freight anomaly identification network is obtained.

8. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor, when calling the computer program in the memory, implements the content of the petrochemical storage management traceability method based on the supply chain as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores computer-executable instructions, which, when loaded and executed by a processor, implement the petrochemical storage management traceability method based on the supply chain as described in any one of claims 1 to 6.

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