An import coal cross-border transportation management method and system based on big data

Abstract

This invention belongs to the field of coal transportation management technology, specifically relating to a method and system for cross-border transportation management of imported coal based on big data. This invention comprehensively evaluates each exit station according to scheduling rules, determines the exit stations that meet the conditions, and selects the station with the best overall conditions from a global perspective, thereby improving resource allocation efficiency and scheduling accuracy. Simultaneously, this invention also obtains historical data of exit stations and analyzes it according to anomaly detection rules to identify abnormal exit stations and output early warning signals, thereby effectively avoiding the risk of transportation disruptions and ensuring the stability of the supply chain.

Description

Technical Field

[0001] This invention belongs to the field of coal transportation management technology, specifically relating to a method and system for cross-border transportation management of imported coal based on big data. Background Technology

[0002] In the energy sector, coal, as a basic energy source, involves various modes of transportation, including sea and land transport, in its international trade logistics. It also involves multiple complex nodes such as port clearance, warehousing, and distribution. Therefore, efficient and reliable management and scheduling of the entire transportation chain, especially of export stations that undertake the functions of coal storage and distribution, is of great significance for improving overall transportation efficiency and reducing operating costs.

[0003] Imported coal comes from a wide range of sources, and coal from different producing areas and mining regions exhibits significant differences in key quality indicators such as calorific value, sulfur content, volatile matter, and ash content. Downstream receiving enterprises, such as coal-fired power plants, coking plants, and chemical plants, often need to scientifically blend coal of different qualities to achieve the optimal coal-to-furnace ratio in order to meet environmental emission standards, boiler combustion stability, and production process requirements. This blending requirement necessitates coordinating the acquisition of coal of different qualities from multiple export sites and achieving precise mixing during transportation or transshipment.

[0004] In the process of cross-border transportation management and scheduling of imported coal, existing technologies usually adopt a passive response approach, that is, the site is replaced or repaired only after the equipment at the export station fails or a clear operational abnormality occurs. This approach cannot make forward-looking risk predictions and health status assessments based on operational data, which can easily lead to sudden interruptions in transportation plans and trigger chain delays, affecting the stability and predictability of the supply chain.

[0005] To address the aforementioned issues, this invention provides a method and system for managing cross-border transportation of imported coal based on big data. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for cross-border transportation management of imported coal based on big data, so as to solve the problems of delayed judgment of anomalies at export stations and unreasonable diversion plans in the existing technology.

[0007] The technical solution adopted in this invention is as follows: A method for cross-border transportation management of imported coal based on big data, comprising the following steps: A big data-based method for managing cross-border transportation of imported coal includes the following steps: Real-time monitoring of changes in current coal reserves at each export station and changes in demand from each receiving enterprise is used to identify and update trigger conditions. When an update trigger condition is identified, the exit stations that meet the condition are determined based on parameter information and according to preset scheduling rules. A scheduling plan is generated for the exit stations that meet the condition to achieve dynamic updating of the scheduling plan. The preset scheduling rule is as follows: normalize the values ​​of each item in the parameter information to form the data to be evaluated, and convert the data to be evaluated into scheduling score values ​​based on the preset scoring system. The update trigger condition is as follows: real-time monitoring of changes in coal storage at each export station and changes in demand at each receiving enterprise. When any of the above-mentioned changes in demand reaches a preset threshold, this change is used as the update trigger condition.

[0008] Preferably, the method further includes: The system acquires historical data of each exit station and analyzes the historical data according to preset anomaly judgment rules to identify abnormal exit stations; when an abnormal exit station is identified, an early warning signal is output.

[0009] Preferably, the preset anomaly determination rules include: Obtain historical verification point data and current response time data for each exit station; calculate the time difference between the current response time and the historical verification node offset time contained in the historical verification point data; when the time difference is negative, mark the exit station as an abnormal exit station.

[0010] Preferably, the preset anomaly determination rules also include: Obtain the verification offset of each exit station; and when the verification offset exceeds the preset verification offset threshold, mark the exit station as an abnormal exit station.

[0011] Preferably, the parameter information includes the transportation distance between each export station and the receiving enterprise, the coal storage capacity of each export station, the demand of each receiving enterprise, and the diversion capacity of each export station.

[0012] Preferably, the exit stations that meet the conditions include: The scheduling scores of each exit station are sorted in descending order to generate a scheduling priority list, and the exit station with the highest ranking in the scheduling priority list is selected as the preferred scheduling station.

[0013] Preferably, the preset scheduling rules include the following filtering conditions: The system determines whether the current coal storage at the export station does not exceed its coal storage capacity and whether the current coal storage at the export station is greater than the demand of the receiving enterprise. Export stations that simultaneously meet the conditions of having current coal storage that does not exceed its coal storage capacity and having current coal storage that is greater than the demand are identified as qualified export stations.

[0014] A big data-based cross-border transportation management system for imported coal includes the following modules: The status monitoring module is used to monitor the changes in the current coal storage at each exit station and the changes in the demand of each receiving enterprise in real time, so as to generate updated trigger conditions. The anomaly monitoring module is used to acquire historical data of each exit station, analyze the historical data according to preset anomaly judgment rules to identify abnormal exit stations, and output an early warning signal when an abnormal exit station is identified. And the exit site scheduling module, which is used to determine the exit sites that meet the conditions based on the parameter information and according to the preset scheduling rules, and generate a scheduling plan for the exit sites that meet the conditions, in response to the update trigger conditions generated by the response status monitoring module.

[0015] Preferably, the preset scheduling rules in the exit station scheduling module include the following filtering conditions: The system determines whether the current coal storage at the export station does not exceed its coal storage capacity and whether the current coal storage at the export station is greater than the demand of the receiving enterprise. Export stations that simultaneously meet the conditions of having current coal storage that does not exceed its coal storage capacity and having current coal storage that is greater than the demand are identified as qualified export stations.

[0016] Preferably, the preset anomaly determination rules include: Obtain historical verification point data and current response time data for each exit station; Calculate the time difference between the current response time and the offset time of the historical verification nodes contained in the historical verification point data; when the time difference is negative, mark the exit site as an abnormal exit site. Beneficial effects 1. This invention comprehensively evaluates each exit station based on scheduling rules to determine the exit stations that meet the conditions. This allows for the selection of the station with the optimal overall conditions from a global perspective, thereby improving resource allocation efficiency and scheduling accuracy. Simultaneously, through a multi-site collaborative scheduling mechanism, it can achieve precise matching and blending of coal from multiple sources based on differences in coal quality at each station and downstream blending requirements, meeting the enterprise's requirements for the stability of coal quality entering the furnace. Furthermore, this invention also identifies abnormal exit stations by acquiring historical data from exit stations and analyzing it according to anomaly judgment rules, and outputs early warning signals, thereby effectively avoiding the risk of transportation disruptions and ensuring the stability of the supply chain.

[0017] 2. This invention monitors the current coal reserves at the export station and the demand changes of the receiving enterprise in real time, and uses these changes as an update trigger to dynamically update the scheduling plan. This allows the scheduling plan to be adjusted according to the actual fluctuations in supply and demand, so as to continuously maintain the balance between supply and demand and effectively avoid resource mismatch caused by information lag. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system module diagram of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0020] Example 1 See Figure 1 This embodiment provides a method for managing cross-border transportation of imported coal based on big data, including the following steps: S1. Perform cross-border information collection; By deploying data acquisition interfaces at each business node, the geographical location information of each export station used for transporting imported coal is obtained in real time or periodically, the current coal storage volume representing the current real-time inventory tonnage of the station, and the coal storage capacity representing the station's maximum designed storage capacity; among which, the geographical location information is preferably latitude and longitude coordinates; By connecting with downstream enterprises' resource planning systems or through electronic order platforms, we can obtain coal demand information from receiving enterprises.

[0021] Furthermore, coal demand information specifically includes the required tonnage, expected delivery time, and specific requirements for coal quality. These specific quality requirements include, but are not limited to, key indicators such as calorific value, sulfur content, and volatile matter.

[0022] S2, Perform data preprocessing; The imported coal cross-border transportation management system performs structured processing on the collected raw data to construct the structured data records required for subsequent steps. Specifically: Based on geographic location information, current coal reserves and coal storage capacity, an export station information set is constructed. This export station information set is a structured database or data table. A unique digital file containing its location, current inventory and maximum capacity is established for each export station. Based on coal demand information, the data is processed according to preset classification standards to generate classification data for receiving enterprises.

[0023] The classification criteria match and summarize enterprise needs based on coal type, generating a list of receiving enterprise information for each type of coal. This allows for direct screening of enterprises with corresponding needs when processing the dispatch of specific types of coal, thereby improving the efficiency of subsequent dispatch matching.

[0024] Types of coal include thermal coal, coking coal, and anthracite.

[0025] S3. Perform abnormal information monitoring; The cross-border transportation management system for imported coal continuously monitors the operational status of each export station to ensure the stability of transportation scheduling. Specifically: The system acquires historical data of each exit station from the exit station information set and analyzes it according to preset anomaly judgment rules to identify abnormal exit stations.

[0026] The preset anomaly detection rules include at least two layers of verification logic, as detailed below: Obtain historical verification point data and current response time data for each exit station. The historical verification point data includes the historical verification node offset time as a benchmark. This historical verification node offset is the lower limit of the benchmark response time calculated based on the long-term stable operation data of the station. Calculate the time difference between the current response time data and the lower limit of the baseline response time. When the time difference is negative, it means that the current response time is abnormally faster than the historical baseline, indicating that there is a risk of data transmission error or process omission. At this time, the exit site is marked as an abnormal exit site.

[0027] Further, the verification offset of each exit station is obtained. Here, the verification offset is a quantitative indicator, and its specific calculation method is as follows: Obtain the absolute value of the difference between the planned shipment volume and the actual shipment volume of the station in one or more past scheduling cycles, and compare the verification offset with the tolerable error range set according to historical experience, that is, compare the verification offset with the preset verification offset threshold. When the verification offset exceeds the preset verification offset threshold, it indicates that the execution capability or the accuracy of data reporting at the site has deviated significantly, and the site is also marked as an abnormal exit site.

[0028] When an abnormal exit site is identified, an early warning signal is immediately output and the site's weight is automatically reduced or placed under observation in subsequent scheduling decisions.

[0029] The preferred method for outputting early warning signals is to send alarm information to the dispatch center console.

[0030] S4. Before making scheduling decisions, perform parameter acquisition; Acquire or calculate a series of quantitative parameters for evaluation and ranking, which are used to comprehensively assess the scheduling suitability of each exit station, specifically including: The transportation distance is calculated based on the geographical location information of each export station and the receiving enterprise. The coal storage capacity of each export station is obtained directly from the export station information collection. Demand intensity, which is a measure of the urgency of a company’s demand, is derived from the rate of change in the demand of the receiving company per unit time or the urgency level marked in its orders. The diversion capacity of each exit station is a dynamic evaluation value. It is calculated by subtracting the throughput capacity currently occupied by the station based on its historical maximum shipment volume per unit time and the current assigned tasks. This yields a value representing the station's current available shipment capacity.

[0031] It should be further explained that the demand intensity is a quantitative indicator that represents the urgency of the receiving enterprise's demand. It can be derived from the rate of change of the enterprise's demand per unit time or the urgency level marked in its orders.

[0032] S5. Execute the core exit site scheduling steps. Based on the data and parameters prepared in the previous steps, execute the preset scheduling rules to generate a scheduling plan.

[0033] Preliminary filtering is performed based on the screening criteria in the preset scheduling rules. The screening criteria are to determine whether the current coal storage of the exit station is greater than its coal storage capacity. If the current coal storage is greater than its coal storage capacity, it indicates that there is a logical error in the data of the station and the exit station should be excluded from the candidate list for this scheduling. If the current coal storage is less than or equal to its coal storage capacity, the next step of evaluation is carried out.

[0034] For the exit sites that pass the screening, a scoring and ranking process is executed: The numerical values ​​in the parameter information, such as transportation distance and demand intensity, are normalized to form the data to be evaluated, which is used to eliminate the impact of differences in units and numerical ranges between different parameters on the evaluation results. Specifically, all parameter values ​​are mapped to a preset, uniform numerical range, such as 0 to 1; Based on a preset scoring system, the data to be evaluated is converted into a scheduling score value. The scoring system is a preset weighted summation calculation rule. This rule assigns a preset weight coefficient to each normalized parameter value and adds up the weighted parameter values ​​to obtain a comprehensive scheduling score value. Sort the scheduling scores of each exit station in descending order to generate a scheduling priority list; select the exit station with the highest ranking in the scheduling priority list as the preferred scheduling station.

[0035] In the process of determining the export station that meets the conditions, if the current coal storage of the preferred dispatch station is greater than or equal to the demand of the receiving enterprise, then the station is determined to be the export station that meets the conditions.

[0036] When a single exit site cannot meet the demand, or when a risk diversification strategy is required, a multi-site collaborative scheduling process is initiated. At this time, the demand of all relevant receiving enterprises is aggregated to obtain the total demand. Based on the diversion capacity or scheduling score of several top-ranked exit sites that meet the conditions in the scheduling priority list, the total demand is allocated to these exit sites proportionally. Based on this allocation result, the diversion ratio of each exit site is calculated, and this diversion ratio is determined as the scheduling allocation ratio.

[0037] Furthermore, it should be noted that the diversion ratio refers to the proportion of the total shipment volume that each station should bear in the total demand, calculated based on the diversion capacity or scheduling score of each exit station in the multi-site collaborative scheduling process.

[0038] Generate a scheduling plan for one or more qualifying export stations. Specifically, the scheduling plan is a specific executable instruction that includes the export station number, the amount of coal allocated to the export station, and a transportation allocation table containing the scheduling allocation ratio. After generating the scheduling plan, the plan is sent to the corresponding export stations and receiving companies to guide the actual cross-border transportation operations.

[0039] The dispatch allocation table is a specific component of the dispatch plan, which clearly lists each exit station and its corresponding dispatch allocation ratio or specific allocation quantity.

[0040] S6. Execute dynamically updated scheduling plans to cope with the dynamically changing transportation environment; Real-time monitoring of changes in coal storage at each export station and changes in demand from each receiving enterprise. When any change is detected to reach a preset threshold, such as when coal storage drops by more than a preset percentage or when a new high-priority order appears, this change is used as an update trigger condition, and the parameter acquisition in step S4 and the export station scheduling in step S5 are automatically re-executed. By re-performing parameter evaluation, scoring and ranking, and allocation ratio calculation, the scheduling order and allocation ratio of export stations are adjusted, and the scheduling plan is dynamically updated to ensure that the scheduling scheme always matches the real-time operating conditions, thereby realizing the management of imported coal transportation and diversion.

[0041] Example 2 See Figure 2 This embodiment provides a big data-based cross-border transportation management system for imported coal, including the following modules: The status monitoring module is configured to monitor in real time changes in the current coal storage at each exit station and changes in the demand of each receiving enterprise. Specifically: This module continuously receives current coal storage data uploaded by level sensors, electronic scales and other equipment from coal storage facilities at each export site through a data interface, and simultaneously receives real-time demand data from the order management system or enterprise resource planning system of each receiving enterprise. When the current coal storage at any exit station changes by a preset range, or when the demand data of any receiving enterprise is updated, the status monitoring module identifies and generates an update trigger condition, and immediately transmits the condition to the exit station scheduling module to start or update the scheduling process.

[0042] The anomaly monitoring module is configured to perform health assessments on the operational status of each exit site, promptly detect and issue warnings of potential operational anomalies, specifically: The system acquires and stores historical information from each exit station, including response time of historical dispatch instructions, accuracy and frequency of data reporting, equipment maintenance records, etc.; then, it performs periodic or event-triggered analysis on the historical information according to preset anomaly judgment rules.

[0043] In the specific judgment process, the preset anomaly judgment rules include one or more of the following logics: Obtain historical verification point data and current response time data for a specific exit station, and calculate the time difference between the current response time and the offset time of the historical verification node. If the time difference is negative, meaning the current response speed is significantly faster than the historical average, it may indicate an abnormal situation such as data reporting errors or process skipping. In this case, the module will mark the exit site as an abnormal exit site.

[0044] Among them, the historical verification point data includes the historical verification node offset time of the historical average response time; the current response time data is the actual time taken from receiving the scheduling instruction to starting the shipment.

[0045] In addition, the verification offset of each exit station can be obtained, such as the deviation between the actual shipment volume and the planned scheduling volume. When the verification offset exceeds the preset verification offset threshold, it indicates that there is a problem with the execution accuracy of the station, and the exit station is also marked as an abnormal exit station.

[0046] When any exit station is identified as an abnormal exit station, the module immediately outputs an early warning signal. This early warning signal can be displayed as a highlighted alarm on the system monitoring interface, or it can be notified to relevant management personnel through message queues, emails, SMS messages, etc., so that timely manual verification and intervention can be carried out.

[0047] The exit site scheduling module is configured to determine the optimal exit site and generate a specific scheduling plan after receiving the update trigger condition generated by the status monitoring module. Its workflow is as follows: Obtain the parameter information required for decision-making. The parameter information specifically includes the transportation distance between each export station and each receiving enterprise, the coal storage capacity of each export station, the current demand of each receiving enterprise, and the diversion capacity of each export station.

[0048] Before making a decision, the exit station scheduling module performs a preliminary screening of all exit stations based on the screening criteria in the preset scheduling rules. Specifically: Iterate through all export stations, determine whether their current coal storage does not exceed their coal storage capacity, and at the same time determine whether the current coal storage of the export station is greater than the demand of the target receiving enterprise. Only exit stations that meet both of these conditions will be identified as qualified exit stations and enter the subsequent selection process.

[0049] For all exit stations that pass the initial screening and meet the criteria, the exit station scheduling module will perform scoring and sorting.

[0050] The values ​​of each parameter in the information of each station are normalized to eliminate the influence of different units and dimensions, and to form the data to be evaluated. Based on the preset scoring system, the data to be evaluated is converted into a comprehensive scheduling score value; the exit station scheduling module sorts the scheduling scores of each exit station in descending order, generates a scheduling priority list, and selects the exit station with the highest ranking in the list as the preferred scheduling station.

[0051] After the dispatch sites are determined, this module begins to generate a dispatch plan. If only the preferred dispatch site is determined, the dispatch plan will directly assign that site to meet the corresponding recipient enterprise's needs. If multiple exit sites that meet the conditions are determined after screening or scoring, for example, if they have the same score or all meet the emergency dispatch conditions, this module will execute a multi-site collaborative dispatch process, as follows: The total demand is calculated by summing the demands of all relevant receiving companies. Based on the respective diversion capacity of multiple eligible exit stations, the total demand is allocated proportionally to these exit stations. The scheduling allocation ratio for each exit station is calculated and determined using the above method.

[0052] The final generated scheduling plan will clearly include the specific shipment volume, destination, and shipping time requirements for each scheduled export station, and will be issued to the corresponding export station for execution in the form of instructions.

Claims

1. A big data-based import coal cross-border transportation management method, characterized in that, Includes the following steps: Real-time monitoring of changes in current coal reserves at each export station and changes in demand from each receiving enterprise is used to identify and update trigger conditions. When an update trigger condition is identified, the exit stations that meet the condition are determined based on parameter information and according to preset scheduling rules. A scheduling plan is generated for the exit stations that meet the condition to achieve dynamic updating of the scheduling plan. The preset scheduling rule is as follows: normalize the values ​​of each item in the parameter information to form the data to be evaluated, and convert the data to be evaluated into scheduling score values ​​based on the preset scoring system. The update trigger condition is as follows: real-time monitoring of changes in coal storage at each export station and changes in demand at each receiving enterprise. When any of the above-mentioned changes in demand reaches a preset threshold, this change is used as the update trigger condition.

2. The method according to claim 1, wherein, The method further includes: The system acquires historical data of each exit station and analyzes the historical data according to preset anomaly judgment rules to identify abnormal exit stations; when an abnormal exit station is identified, an early warning signal is output.

3. The method according to claim 2, wherein, The preset anomaly detection rules include: Obtain historical verification point data and current response time data for each exit station; calculate the time difference between the current response time and the historical verification node offset time contained in the historical verification point data; when the time difference is negative, mark the exit station as an abnormal exit station.

4. The method according to claim 3, wherein, The preset anomaly detection rules also include: Obtain the verification offset of each exit station; and when the verification offset exceeds the preset verification offset threshold, mark the exit station as an abnormal exit station.

5. The method of claim 1, wherein, The parameter information includes the transportation distance between each export station and the receiving enterprise, the coal storage capacity of each export station, the demand of each receiving enterprise, and the diversion capacity of each export station.

6. The method according to claim 5, wherein, The exit stations that meet the criteria include: The scheduling scores of each exit station are sorted in descending order to generate a scheduling priority list, and the exit station with the highest ranking in the scheduling priority list is selected as the preferred scheduling station.

7. The method according to claim 1, wherein, The preset scheduling rules include the following filtering criteria: The system determines whether the current coal storage at the export station does not exceed its coal storage capacity and whether the current coal storage at the export station is greater than the demand of the receiving enterprise. Export stations that simultaneously meet the conditions of having current coal storage that does not exceed its coal storage capacity and having current coal storage that is greater than the demand are identified as qualified export stations.

8. A big data based import coal cross-border transportation management system, characterized in that, Includes the following modules: The status monitoring module is used to monitor the changes in the current coal storage at each exit station and the changes in the demand of each receiving enterprise in real time, so as to generate updated trigger conditions. The anomaly monitoring module is used to acquire historical data of each exit station, analyze the historical data according to preset anomaly judgment rules to identify abnormal exit stations, and output an early warning signal when an abnormal exit station is identified. And the exit site scheduling module, which is used to determine the exit sites that meet the conditions based on the parameter information and according to the preset scheduling rules, and generate a scheduling plan for the exit sites that meet the conditions, in response to the update trigger conditions generated by the response status monitoring module.

9. The import coal cross-border transportation management system based on big data according to claim 8, characterized in that, The preset scheduling rules in the exit site scheduling module include the following filtering conditions: The system determines whether the current coal storage at the export station does not exceed its coal storage capacity and whether the current coal storage at the export station is greater than the demand of the receiving enterprise. Export stations that simultaneously meet the conditions of having current coal storage that does not exceed its coal storage capacity and having current coal storage that is greater than the demand are identified as qualified export stations.

10. The cross-border transportation management system for imported coal based on big data according to claim 8, characterized in that, The preset anomaly detection rules include: Obtain historical verification point data and current response time data for each exit station; Calculate the time difference between the current response time and the offset time of the historical verification nodes contained in the historical verification point data; when the time difference is negative, mark the exit site as an abnormal exit site.

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